Assessing snow extent data sets over North America to inform and improve trace gas retrievals from solar backscatter

NASA Astrophysics Data System (ADS)

Cooper, Matthew J.; Martin, Randall V.; Lyapustin, Alexei I.; McLinden, Chris A.

2018-05-01

Accurate representation of surface reflectivity is essential to tropospheric trace gas retrievals from solar backscatter observations. Surface snow cover presents a significant challenge due to its variability and thus snow-covered scenes are often omitted from retrieval data sets; however, the high reflectance of snow is potentially advantageous for trace gas retrievals. We first examine the implications of surface snow on retrievals from the upcoming TEMPO geostationary instrument for North America. We use a radiative transfer model to examine how an increase in surface reflectivity due to snow cover changes the sensitivity of satellite retrievals to NO2 in the lower troposphere. We find that a substantial fraction (> 50 %) of the TEMPO field of regard can be snow covered in January and that the average sensitivity to the tropospheric NO2 column substantially increases (doubles) when the surface is snow covered.We then evaluate seven existing satellite-derived or reanalysis snow extent products against ground station observations over North America to assess their capability of informing surface conditions for TEMPO retrievals. The Interactive Multisensor Snow and Ice Mapping System (IMS) had the best agreement with ground observations (accuracy of 93 %, precision of 87 %, recall of 83 %). Multiangle Implementation of Atmospheric Correction (MAIAC) retrievals of MODIS-observed radiances had high precision (90 % for Aqua and Terra), but underestimated the presence of snow (recall of 74 % for Aqua, 75 % for Terra). MAIAC generally outperforms the standard MODIS products (precision of 51 %, recall of 43 % for Aqua; precision of 69 %, recall of 45 % for Terra). The Near-real-time Ice and Snow Extent (NISE) product had good precision (83 %) but missed a significant number of snow-covered pixels (recall of 45 %). The Canadian Meteorological Centre (CMC) Daily Snow Depth Analysis Data set had strong performance metrics (accuracy of 91 %, precision of 79 %, recall of 82 %). We use the Fscore, which balances precision and recall, to determine overall product performance (F = 85 %, 82 (82) %, 81 %, 58 %, 46 (54) % for IMS, MAIAC Aqua (Terra), CMC, NISE, MODIS Aqua (Terra), respectively) for providing snow cover information for TEMPO retrievals from solar backscatter observations. We find that using IMS to identify snow cover and enable inclusion of snow-covered scenes in clear-sky conditions across North America in January can increase both the number of observations by a factor of 2.1 and the average sensitivity to the tropospheric NO2 column by a factor of 2.7.

A microwave backscattering model for precipitation

NASA Astrophysics Data System (ADS)

Ermis, Seda

A geophysical microwave backscattering model for space borne and ground-based remote sensing of precipitation is developed and used to analyze backscattering measurements from rain and snow type precipitation. Vector Radiative Transfer (VRT) equations for a multilayered inhomogeneous medium are applied to the precipitation region for calculation of backscattered intensity. Numerical solution of the VRT equation for multiple layers is provided by the matrix doubling method to take into account close range interactions between particles. In previous studies, the VRT model was used to calculate backscattering from a rain column on a sea surface. In the model, Mie scattering theory for closely spaced scatterers was used to determine the phase matrix for each sublayer characterized by a set of parameters. The scatterers i.e. rain drops within the sublayers were modelled as spheres with complex permittivities. The rain layer was bounded by rough boundaries; the interface between the cloud and the rain column as well as the interface between the sea surface and the rain were all analyzed by using the integral equation model (IEM). Therefore, the phase matrix for the entire rain column was generated by the combination of surface and volume scattering. Besides Mie scattering, in this study, we use T-matrix approach to examine the effect of the shape to the backscattered intensities since larger raindrops are most likely oblique in shape. Analyses show that the effect of obliquity of raindrops to the backscattered wave is related with size of the scatterers and operated frequency. For the ground-based measurement system, the VRT model is applied to simulate the precipitation column on horizontal direction. Therefore, the backscattered reflectivities for each unit range of volume are calculated from the backscattering radar cross sections by considering radar range and effective illuminated area of the radar beam. The volume scattering phase matrices for each range interval are calculated by Mie scattering theory. VRT equations are solved by matrix doubling method to compute phase matrix for entire radar beam. Model results are validated with measured data by X-band dual polarization Phase Tilt Weather Radar (PTWR) for snow, rain, wet hail type precipitation. The geophysical parameters given the best fit with measured reflectivities are used in previous models i.e. Rayleigh Approximation and Mie scattering and compared with the VRT model. Results show that reflectivities calculated by VRT models are differed up to 10 dB from the Rayleigh approximation model and up to 5 dB from the Mie Scattering theory due to both multiple scattering and attenuation losses for the rain rates as high as 80 mm/h.

Remote sensing of Earth terrain

NASA Technical Reports Server (NTRS)

Kong, Jin AU

1987-01-01

Earth terrain covers were modeled as random media characterized by different dielectric constants and correlation functions. In order to model sea ice with brine inclusions and vegetation with row structures, the random medium is assumed to be anisotropic. A three layer model is used to simulate a vegetation field or a snow covered ice field with the top layer being snow or leaves, the middle layer being ice or trunks, and the bottom layer being sea water or ground. The strong fluctuation theory with the distorted Born approximation is applied to the solution of the radar backscattering coefficients.

New Perspectives on Blowing Snow Transport, Sublimation, and Layer Thermodynamic Structure over Antarctica

NASA Technical Reports Server (NTRS)

Palm, Steve; Kayetha, Vinay; Yang, Yuekui; Pauly, Rebecca M.

2017-01-01

Blowing snow over Antarctica is a widespread and frequent event. Satellite remote sensing using lidar has shown that blowing snow occurs over 70% of the time over large areas of Antarctica in winter. The transport and sublimation of blowing snow are important terms in the ice sheet mass balance equation and the latter is also an important part of the hydrological cycle. Until now the only way to estimate the magnitude of these processes was through model parameterization. We present a technique that uses direct satellite observations of blowing snow and model (MERRA-2) temperature and humidity fields to compute both transport and sublimation of blowing snow over Antarctica for the period 2006 to 2016. The results show a larger annual continent-wide integrated sublimation than current published estimates and a significant transport of snow from continent to ocean. The talk will also include the lidar backscatter structure of blowing snow layers that often reach heights of 200 to 300 m as well as the first dropsonde measurements of temperature, moisture and wind through blowing snow layers.

Summer snowmelt patterns in the South Shetlands using TerraSAR-X imagery

NASA Astrophysics Data System (ADS)

Mora, C.; Jimenez, J. J.; Catalao Fernades, J.; Ferreira, A.; David, A.; Ramos, M.; Vieira, G.

2014-12-01

Snow plays an important role in controlling ground thermal regime and thus influencing permafrost distribution in the lower areas of the South Shetlands archipelago, where late lying snowpatches protect the soil from summer warming. However, summer snow distribution is complex in the mountainous environments of the Maritime Antarctica and it is very difficult to obtain accurate mapping products of snow cover extent and also to monitor snowmelt. Field observations of snow cover in the region are currently based on: i) thickness data from a very scarce network of meteorological stations, ii) temperature poles allowing to estimate snow thickness, iii) and time-lapse cameras allowing for assessing snow distribution over relatively small areas. The high cloudiness of the Maritime Antarctic environment limits good mapping results from the analysis of optical remote sensing imagery such as Landsat, QuickBird or GeoEye. Therefore, microwave sensors provide the best imagery, since they are not influenced by cloudiness and are sensitive to wet-snow, typical of the melting season. We have acquired TerraSAR-X scenes for Deception and Livingston Islands for January-March 2014 in spotlight (HH, VV and HH/VV) and stripmap modes (HH) and analyse the radar backscattering for determining the differences between wet-snow, dry-snow and bare soil aiming at developing snow melt pattern maps. For ground truthing, snowpits were dug in order to characterize snow stratigraphy, grain size, grain type and snow density and to evaluate its effects on radar backscattering. Time-lapse cameras allow to identify snow patch boundaries in the field and ground surface temperatures obtained with minloggers, together with air temperatures, allow to identify the presence of snow cover in the ground. The current research is conducted in the framework of the project PERMANTAR-3 (Permafrost monitoring and modelling in Antarctic Peninsula - PTDC/AAG-GLO/3908/2012 of the FCT and PROPOLAR).

Using SAR satellite data time series for regional glacier mapping

NASA Astrophysics Data System (ADS)

Winsvold, Solveig H.; Kääb, Andreas; Nuth, Christopher; Andreassen, Liss M.; van Pelt, Ward J. J.; Schellenberger, Thomas

2018-03-01

With dense SAR satellite data time series it is possible to map surface and subsurface glacier properties that vary in time. On Sentinel-1A and RADARSAT-2 backscatter time series images over mainland Norway and Svalbard, we outline how to map glaciers using descriptive methods. We present five application scenarios. The first shows potential for tracking transient snow lines with SAR backscatter time series and correlates with both optical satellite images (Sentinel-2A and Landsat 8) and equilibrium line altitudes derived from in situ surface mass balance data. In the second application scenario, time series representation of glacier facies corresponding to SAR glacier zones shows potential for a more accurate delineation of the zones and how they change in time. The third application scenario investigates the firn evolution using dense SAR backscatter time series together with a coupled energy balance and multilayer firn model. We find strong correlation between backscatter signals with both the modeled firn air content and modeled wetness in the firn. In the fourth application scenario, we highlight how winter rain events can be detected in SAR time series, revealing important information about the area extent of internal accumulation. In the last application scenario, averaged summer SAR images were found to have potential in assisting the process of mapping glaciers outlines, especially in the presence of seasonal snow. Altogether we present examples of how to map glaciers and to further understand glaciological processes using the existing and future massive amount of multi-sensor time series data.

Evaluation of single-band snow-patch mapping using high-resolution microwave remote sensing: an application in the maritime Antarctic

NASA Astrophysics Data System (ADS)

Mora, Carla; Jiménez, Juan Javier; Pina, Pedro; Catalão, João; Vieira, Gonçalo

2017-01-01

The mountainous and ice-free terrains of the maritime Antarctic generate complex mosaics of snow patches, ranging from tens to hundreds of metres. These can only be accurately mapped using high-resolution remote sensing. In this paper we evaluate the application of radar scenes from TerraSAR-X in High Resolution SpotLight mode for mapping snow patches at a test area on Fildes Peninsula (King George Island, South Shetlands). Snow-patch mapping and characterization of snow stratigraphy were conducted at the time of image acquisition on 12 and 13 January 2012. Snow was wet in all studied snow patches, with coarse-grain and rounded crystals showing advanced melting and with frequent ice layers in the snow pack. Two TerraSAR-X scenes in HH and VV polarization modes were analysed, with the former showing the best results when discriminating between wet snow, lake water and bare soil. However, significant overlap in the backscattering signal was found. Average wet-snow backscattering was -18.0 dB in HH mode, with water showing -21.1 dB and bare soil showing -11.9 dB. Single-band pixel-based and object-oriented image classification methods were used to assess the classification potential of TerraSAR-X SpotLight imagery. The best results were obtained with an object-oriented approach using a watershed segmentation with a support vector machine (SVM) classifier, with an overall accuracy of 92 % and Kappa of 0.88. The main limitation was the west to north-west facing snow patches, which showed significant error, an issue related to artefacts from the geometry of satellite imagery acquisition. The results show that TerraSAR-X in SpotLight mode provides high-quality imagery for mapping wet snow and snowmelt in the maritime Antarctic. The classification procedure that we propose is a simple method and a first step to an implementation in operational mode if a good digital elevation model is available.

Fraser, Colorado

NASA Technical Reports Server (NTRS)

2003-01-01

This sequence of three images in northern Colorado was taken by NASA's Airborne Synthetic Aperture Radar (AirSar) for the joint NASA-National Oceanic and Atmospheric Administration Cold Land Processes Experiment. The images were produced from data acquired on February 19, 21 and 23, 2002 (top to bottom), and demonstrate the effects of snow on the radar backscatter at different frequencies. The images are centered at 40 degrees north latitude and 106 degrees west longitude, 12 kilometers (7.5 miles) west of the town of Fraser. The colors red, green and blue indicate the relative total power of the radar backscatter at P-, L-, and C-bands, respectively.

The top image was acquired before snowfall; the middle image was acquired the morning after the snow. When the snow melted, the most prominent changes were visible and can be seen in the bottom image. In this image, melting snow allows less of the radar signal to backscatter and some features appear darker.

The Cold Land Processes Experiment is a multi-year experiment to study how snow processes work and how snow-covered areas affect weather and climate. Fraser, Colo., is one of three study areas in northern Colorado and southern Wyoming providing ideal natural laboratories for snow research.

AirSar flies aboard a NASA DC-8 based at NASA's Dryden Flight Research Center, Edwards, Calif. Built, operated and managed by NASA's Jet Propulsion Laboratory, Pasadena, Calif., AirSar is part of NASA's Earth Science Enterprise program. JPL is a division of the California Institute of Technology in Pasadena.

Relating C-band Microwave and Optical Satellite Observations as A Function of Snow Thickness on First-Year Sea Ice during the Winter to Summer Transition

NASA Astrophysics Data System (ADS)

Zheng, J.; Yackel, J.

2015-12-01

The Arctic sea ice and its snow cover have a direct impact on both the Arctic and global climate system through their ability to moderate heat exchange across the ocean-sea ice-atmosphere (OSA) interface. Snow cover plays a key role in the OSA interface radiation and energy exchange, as it controls the growth and decay of first-year sea ice (FYI). However, meteoric accumulation and redistribution of snow on FYI is highly stochastic over space and time, which makes it poorly understood. Previous studies have estimated local-scale snow thickness distributions using in-situ technique and modelling but it is spatially limited and challenging due to logistic difficulties. Moreover, snow albedo is also critical for determining the surface energy balance of the OSA during the critical summer ablation season. Even then, due to persistent and widespread cloud cover in the Arctic at various spatio-temporal scales, it is difficult and unreliable to remotely measure albedo of snow cover on FYI in the optical spectrum. Previous studies demonstrate that only large-scale sea ice albedo was successfully estimated using optical-satellite sensors. However, space-borne microwave sensors, with their capability of all-weather and 24-hour imaging, can provide enhanced information about snow cover on FYI. Daily spaceborne C-band scatterometer data (ASCAT) and MODIS data are used to investigate the the seasonal co-evolution of the microwave backscatter coefficient and optical albedo as a function of snow thickness on smooth FYI. The research focuses on snow-covered FYI near Cambridge Bay, Nunavut (Fig.1) during the winter to advanced-melt period (April-June, 2014). The ACSAT time series (Fig.2) show distinct increase in scattering at melt onset indicating the first occurrence of melt water in the snow cover. The corresponding albedo exhibits no decrease at this stage. We show how the standard deviation of ASCAT backscatter on FYI during winter can be used as a proxy for surface roughness and subsequent snow thickness (ie. Rougher surfaces acquire thicker snow covers) and then how this surface manifests into statistically distinguishable surface melt pond fractions which largely governs the optical derived albedo. Such relationships are useful for modelling the subsequent summer melt pond fraction and albedo from winter snow cover.

Effects of changing environmental conditions on synthetic aperture radar backscattering coefficient, scattering mechanisms, and class separability in a forest area

NASA Astrophysics Data System (ADS)

Mahdavi, Sahel; Maghsoudi, Yasser; Amani, Meisam

2017-07-01

Environmental conditions have considerable effects on synthetic aperture radar (SAR) imagery. Therefore, assessing these effects is important for obtaining accurate and reliable results. In this study, three series of RADARSAT-2 SAR images were evaluated. In each of these series, the sensor configuration was fixed, but the environmental conditions differed. The effects of variable environmental conditions were also investigated on co- and cross-polarized backscattering coefficients, Freeman-Durden scattering contributions, and the pedestal height in different classes of a forest area in Ottawa, Ontario. It was observed that the backscattering coefficient of wet snow was up to 2 dB more than that of dry snow. The absence of snow also caused a decrease of up to 3 dB in the surface scattering of ground and up to 5 dB in that of trees. In addition, the backscatter coefficients of ground vegetation, hardwood species, and softwood species were more similar at temperatures below 0°C than those at temperatures above 0°C. Moreover, the pedestal height was generally greater at temperatures above 0°C than at temperatures below 0°C. Finally, the highest class separability was observed when the temperature was at or above 0°C and there was no snow on the ground or trees.

Investigation of radar backscattering from second-year sea ice

NASA Technical Reports Server (NTRS)

Lei, Guang-Tsai; Moore, Richard K.; Gogineni, S. P.

1988-01-01

The scattering properties of second-year ice were studied in an experiment at Mould Bay in April 1983. Radar backscattering measurements were made at frequencies of 5.2, 9.6, 13.6, and 16.6 GHz for vertical polarization, horizontal polarization and cross polarizations, with incidence angles ranging from 15 to 70 deg. The results indicate that the second-year ice scattering characteristics were different from first-year ice and also different from multiyear ice. The fading properties of radar signals were studied and compared with experimental data. The influence of snow cover on sea ice can be evaluated by accounting for the increase in the number of independent samples from snow volume with respect to that for bare ice surface. A technique for calculating the snow depth was established by this principle and a reasonable agreement has been observed. It appears that this is a usable way to measure depth in snow or other snow-like media using radar.

C-Band Backscatter Measurements of Winter Sea-Ice in the Weddell Sea, Antarctica

NASA Technical Reports Server (NTRS)

Drinkwater, M. R.; Hosseinmostafa, R.; Gogineni, P.

1995-01-01

During the 1992 Winter Weddell Gyre Study, a C-band scatterometer was used from the German ice-breaker R/V Polarstern to obtain detailed shipborne measurement scans of Antarctic sea-ice. The frequency-modulated continuous-wave (FM-CW) radar operated at 4-3 GHz and acquired like- (VV) and cross polarization (HV) data at a variety of incidence angles (10-75 deg). Calibrated backscatter data were recorded for several ice types as the icebreaker crossed the Weddell Sea and detailed measurements were made of corresponding snow and sea-ice characteristics at each measurement site, together with meteorological information, radiation budget and oceanographic data. The primary scattering contributions under cold winter conditions arise from the air/snow and snow/ice interfaces. Observations indicate so e similarities with Arctic sea-ice scattering signatures, although the main difference is generally lower mean backscattering coefficients in the Weddell Sea. This is due to the younger mean ice age and thickness, and correspondingly higher mean salinities. In particular, smooth white ice found in 1992 in divergent areas within the Weddell Gyre ice pack was generally extremely smooth and undeformed. Comparisons of field scatterometer data with calibrated 20-26 deg incidence ERS-1 radar image data show close correspondence, and indicate that rough Antarctic first-year and older second-year ice forms do not produce as distinctively different scattering signatures as observed in the Arctic. Thick deformed first-year and second-year ice on the other hand are clearly discriminated from younger undeformed ice. thereby allowing successful separation of thick and thin ice. Time-series data also indicate that C-band is sensitive to changes in snow and ice conditions resulting from atmospheric and oceanographic forcing and the local heat flux environment. Variations of several dB in 45 deg incidence backscatter occur in response to a combination of thermally-regulated parameters including sea-ice brine volume, snow and ice complex dielectric properties, and snow physical properties.

Snow Cover Mapping and Ice Avalanche Monitoring from the Satellite Data of the Sentinels

NASA Astrophysics Data System (ADS)

Wang, S.; Yang, B.; Zhou, Y.; Wang, F.; Zhang, R.; Zhao, Q.

2018-04-01

In order to monitor ice avalanches efficiently under disaster emergency conditions, a snow cover mapping method based on the satellite data of the Sentinels is proposed, in which the coherence and backscattering coefficient image of Synthetic Aperture Radar (SAR) data (Sentinel-1) is combined with the atmospheric correction result of multispectral data (Sentinel-2). The coherence image of the Sentinel-1 data could be segmented by a certain threshold to map snow cover, with the water bodies extracted from the backscattering coefficient image and removed from the coherence segment result. A snow confidence map from Sentinel-2 was used to map the snow cover, in which the confidence values of the snow cover were relatively high. The method can make full use of the acquired SAR image and multispectral image under emergency conditions, and the application potential of Sentinel data in the field of snow cover mapping is exploited. The monitoring frequency can be ensured because the areas obscured by thick clouds are remedied in the monitoring results. The Kappa coefficient of the monitoring results is 0.946, and the data processing time is less than 2 h, which meet the requirements of disaster emergency monitoring.

Wet Snow Mapping in Southern Ontario with Sentinel-1A Observations

NASA Astrophysics Data System (ADS)

Chen, H.; Kelly, R. E. J.

2017-12-01

Wet snow is defined as snow with liquid water present in an ice-water mix. It is can be an indicator for the onset of the snowmelt period. Knowledge about the extent of wet snow area can be of great importance for the monitoring of seasonal snowmelt runoff with climate-induced changes in snowmelt duration having implications for operational hydrological and ecological applications. Spaceborne microwave remote sensing has been used to observe seasonal snow under all-weather conditions. Active microwave observations of snow at C-band are sensitive to wet snow due to the high dielectric contrast with non-wet snow surfaces and synthetic aperture radar (SAR) is now openly available to identify and map the wet snow areas globally at relatively fine spatial resolutions ( 100m). In this study, a semi-automated workflow is developed from the change detection method of Nagler et al. (2016) using multi-temporal Sentinel-1A (S1A) dual-polarization observations of Southern Ontario. Weather station data and visible-infrared satellite observations are used to refine the wet snow area estimates. Wet snow information from National Operational Hydrologic Remote Sensing Center (NOHRSC) is used to compare with the S1A estimates. A time series of wet snow maps shows the variations in backscatter from wet snow on a pixel basis. Different land cover types in Southern Ontario are assessed with respect to their impacts on wet snow estimates. While forests and complex land surfaces can impact the ability to map wet snow, the approach taken is robust and illustrates the strong sensitivity of the approach to wet snow backscattering characteristics. The results indicate the feasibility of the change detection method on non-mountainous large areas and address the usefulness of Sentinel-1A data for wet snow mapping.

Probing insect backscatter cross section and melanization using kHz optical remote detection system

NASA Astrophysics Data System (ADS)

Gebru, Alem; Brydegaard, Mikkel; Rohwer, Erich; Neethling, Pieter

2017-01-01

A kHz optical remote sensing system is implemented to determine insect melanization features. This is done by measuring the backscatter signal in the visible and near-infrared (VIS-NIR) and short-wave infrared (SWIR) in situ. It is shown that backscatter cross section in the SWIR is insensitive to melanization and absolute melanization can be derived from the ratio of backscatter cross section of different bands (SWIR/VIS-NIR). We have shown that reflectance from insect is stronger in the SWIR as compared to NIR and VIS. This reveals that melanization plays a big role to determine backscatter cross section. One can use this feature as a tool to improve insect species and age classification. To support the findings, we illustrated melanization feature using three different insects [dead, dried specimens of snow white moth (Spilosoma genus), fox moth (Macrothylacia), and leather beetle (Odontotaenius genus)]. It is shown that reflectance from the leather beetle in the VIS and NIR is more affected by melanization as compared with snow white moth.

1D-Var multilayer assimilation of X-band SAR data into a detailed snowpack model

NASA Astrophysics Data System (ADS)

Phan, X. V.; Ferro-Famil, L.; Gay, M.; Durand, Y.; Dumont, M.; Morin, S.; Allain, S.; D'Urso, G.; Girard, A.

2014-10-01

The structure and physical properties of a snowpack and their temporal evolution may be simulated using meteorological data and a snow metamorphism model. Such an approach may meet limitations related to potential divergences and accumulated errors, to a limited spatial resolution, to wind or topography-induced local modulations of the physical properties of a snow cover, etc. Exogenous data are then required in order to constrain the simulator and improve its performance over time. Synthetic-aperture radars (SARs) and, in particular, recent sensors provide reflectivity maps of snow-covered environments with high temporal and spatial resolutions. The radiometric properties of a snowpack measured at sufficiently high carrier frequencies are known to be tightly related to some of its main physical parameters, like its depth, snow grain size and density. SAR acquisitions may then be used, together with an electromagnetic backscattering model (EBM) able to simulate the reflectivity of a snowpack from a set of physical descriptors, in order to constrain a physical snowpack model. In this study, we introduce a variational data assimilation scheme coupling TerraSAR-X radiometric data into the snowpack evolution model Crocus. The physical properties of a snowpack, such as snow density and optical diameter of each layer, are simulated by Crocus, fed by the local reanalysis of meteorological data (SAFRAN) at a French Alpine location. These snowpack properties are used as inputs of an EBM based on dense media radiative transfer (DMRT) theory, which simulates the total backscattering coefficient of a dry snow medium at X and higher frequency bands. After evaluating the sensitivity of the EBM to snowpack parameters, a 1D-Var data assimilation scheme is implemented in order to minimize the discrepancies between EBM simulations and observations obtained from TerraSAR-X acquisitions by modifying the physical parameters of the Crocus-simulated snowpack. The algorithm then re-initializes Crocus with the modified snowpack physical parameters, allowing it to continue the simulation of snowpack evolution, with adjustments based on remote sensing information. This method is evaluated using multi-temporal TerraSAR-X images acquired over the specific site of the Argentière glacier (Mont-Blanc massif, French Alps) to constrain the evolution of Crocus. Results indicate that X-band SAR data can be taken into account to modify the evolution of snowpack simulated by Crocus.

Blowing Snow Sublimation and Transport over Antarctica from 11 Years of CALIPSO Observations

NASA Technical Reports Server (NTRS)

Palm, Stephen P.; Kayetha, Vinay; Yang, Yuekui; Pauly, Rebecca

2017-01-01

Blowing snow processes commonly occur over the earth's ice sheets when the 10 mile wind speed exceeds a threshold value. These processes play a key role in the sublimation and redistribution of snow thereby influencing the surface mass balance. Prior field studies and modeling results have shown the importance of blowing snow sublimation and transport on the surface mass budget and hydrological cycle of high-latitude regions. For the first time, we present continent-wide estimates of blowing snow sublimation and transport over Antarctica for the period 2006-2016 based on direct observation of blowing snow events. We use an improved version of the blowing snow detection algorithm developed for previous work that uses atmospheric backscatter measurements obtained from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) lidar aboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite. The blowing snow events identified by CALIPSO and meteorological fields from MERRA-2 are used to compute the blowing snow sublimation and transport rates. Our results show that maximum sublimation occurs along and slightly inland of the coastline. This is contrary to the observed maximum blowing snow frequency which occurs over the interior. The associated temperature and moisture reanalysis fields likely contribute to the spatial distribution of the maximum sublimation values. However, the spatial pattern of the sublimation rate over Antarctica is consistent with modeling studies and precipitation estimates. Overall, our results show that the 2006-2016 Antarctica average integrated blowing snow sublimation is about 393 +/- 196 Gt yr(exp -1), which is considerably larger than previous model-derived estimates. We find maximum blowing snow transport amount of 5 Mt km-1 yr(exp -1) over parts of East Antarctica and estimate that the average snow transport from continent to ocean is about 3.7 Gt yr(exp -1). These continent-wide estimates are the first of their kind and can be used to help model and constrain the surface mass budget over Antarctica.

Microwave Observations of Snow-Covered Freshwater Lake Ice obtained during the Great Lakes Winter EXperiment (GLAWEX), 2017

NASA Astrophysics Data System (ADS)

Gunn, G. E.; Hall, D. K.; Nghiem, S. V.

2017-12-01

Studies observing lake ice using active microwave acquisitions suggest that the dominant scattering mechanism in ice is caused by double-bounce of the signal off vertical tubular bubble inclusions. Recent polarimetric SAR observations and target decomposition algorithms indicate single-bounce interactions may be the dominant source of returns, and in the absence of field observations, has been hypothesized to be the result of roughness at the ice-water interface on the order of incident wavelengths. This study presents in-situ physical observations of snow-covered lake ice in western Michigan and Wisconsin acquired during the Great Lakes Winter EXperiment in 2017 (GLAWEX'17). In conjunction with NASA's SnowEx airborne snow campaign in Colorado (http://snow.nasa.gov), C- (Sentinel-1, RADARSAT-2) and X-band (TerraSAR-X) synthetic aperture radar (SAR) observations were acquired coincidently to surface physical snow and ice observations. Small/large scale roughness features at the ice-water interface are quantified through auger transects and used as an input variable in lake ice backscatter models to assess the relative contributions from different scattering mechanisms.

Radar backscattering from snow facies of the Greenland ice sheet: Results from the AIRSAR 1991 campaign

NASA Technical Reports Server (NTRS)

Rignot, Eric; Jezek, K.; Vanzyl, J. J.; Drinkwater, Mark R.; Lou, Y. L.

1993-01-01

In June 1991, the NASA/JPL airborne SAR (AIRSAR) acquired C- (lambda = 5.6cm), L- (lambda = 24cm), and P- (lambda = 68m) band polarimetric SAR data over the Greenland ice sheet. These data are processed using version 3.55 of the AIRSAR processor which provides radiometrically and polarimetrically calibrated images. The internal calibration of the AIRSAR data is cross-checked using the radar response from corner reflectors deployed prior to flight in one of the scenes. In addition, a quantitative assessment of the noise power level at various frequencies and polarizations is made in all the scenes. Synoptic SAR data corresponding to a swath width of about 12 by 50 km in length (compared to the standard 12 x 12 km size of high-resolution scenes) are also processed and calibrated to study transitions in radar backscatter as a function of snow facies at selected frequencies and polarizations. The snow facies on the Greenland ice sheet are traditionally categorized based on differences in melting regime during the summer months. The interior of Greenland corresponds to the dry snow zone where terrain elevation is the highest and no snow melt occurs. The lowest elevation boundary of the dry snow zone is known traditionally as the dry snow line. Beneath it is the percolation zone where melting occurs in the summer and water percolates through the snow freezing at depth to form massive ice lenses and ice pipes. At the downslope margin of this zone is the wet snow line. Below it, the wet snow zone corresponds to the lowest elevations where snow remains at the end of the summer. Ablation produces enough meltwater to create areas of snow saturated with water, together with ponds and lakes. The lowest altitude zone of ablation sees enough summer melt to remove all traces of seasonal snow accumulation, such that the surface comprises bare glacier ice.

Spectral Profiler Probe for In Situ Snow Grain Size and Composition Stratigraphy

NASA Technical Reports Server (NTRS)

Berisford, Daniel F.; Molotch, Noah P.; Painter, Thomas

2012-01-01

An ultimate goal of the climate change, snow science, and hydrology communities is to measure snow water equivalent (SWE) from satellite measurements. Seasonal SWE is highly sensitive to climate change and provides fresh water for much of the world population. Snowmelt from mountainous regions represents the dominant water source for 60 million people in the United States and over one billion people globally. Determination of snow grain sizes comprising mountain snowpack is critical for predicting snow meltwater runoff, understanding physical properties and radiation balance, and providing necessary input for interpreting satellite measurements. Both microwave emission and radar backscatter from the snow are dominated by the snow grain size stratigraphy. As a result, retrieval algorithms for measuring snow water equivalents from orbiting satellites is largely hindered by inadequate knowledge of grain size.

Simulation of multistatic and backscattering cross sections for airborne radar

NASA Astrophysics Data System (ADS)

Biggs, Albert W.

1986-07-01

In order to determine susceptibilities of airborne radar to electronic countermeasures and electronic counter-countermeasures simulations of multistatic and backscattering cross sections were developed as digital modules in the form of algorithms. Cross section algorithms are described for prolate (cigar shape) and oblate (disk shape) spheroids. Backscattering cross section algorithms are also described for different categories of terrain. Backscattering cross section computer programs were written for terrain categorized as vegetation, sea ice, glacial ice, geological (rocks, sand, hills, etc.), oceans, man-made structures, and water bodies. PROGRAM SIGTERRA is a file for backscattering cross section modules of terrain (TERRA) such as vegetation (AGCROP), oceans (OCEAN), Arctic sea ice (SEAICE), glacial snow (GLASNO), geological structures (GEOL), man-made structures (MAMMAD), or water bodies (WATER). AGCROP describes agricultural crops, trees or forests, prairies or grassland, and shrubs or bush cover. OCEAN has the SLAR or SAR looking downwind, upwind, and crosswind at the ocean surface. SEAICE looks at winter ice and old or polar ice. GLASNO is divided into a glacial ice and snow or snowfields. MANMAD includes buildings, houses, roads, railroad tracks, airfields and hangars, telephone and power lines, barges, trucks, trains, and automobiles. WATER has lakes, rivers, canals, and swamps. PROGRAM SIGAIR is a similar file for airborne targets such as prolate and oblate spheroids.

A Model with Ellipsoidal Scatterers for Polarimetric Remote Sensing of Anisotropic Layered Media

NASA Technical Reports Server (NTRS)

Nghiem, S. V.; Kwok, R.; Kong, J. A.; Shin, R. T.

1993-01-01

This paper presents a model with ellipsoidal scatterers for applications to polarimetric remote sensing of anisotropic layered media at microwave frequencies. The physical configuration includes an isotropic layer covering an anisotropic layer above a homogeneous half space. The isotropic layer consists of randomly oriented spheroids. The anisotropic layer contains ellipsoidal scatterers with a preferential vertical alignment and random azimuthal orientations. Effective permittivities of the scattering media are calculated with the strong fluctuation theory extended to account for the nonspherical shapes and the scatterer orientation distributions. On the basis of the analytic wave theory, dyadic Green's functions for layered media are used to derive polarimetric backscattering coefficients under the distorted Born approximation. The ellipsoidal shape of the scatterers gives rise to nonzero cross-polarized returns from the untilted anisotropic medium in the first-order approximation. Effects of rough interfaces are estimated by an incoherent addition method. Theoretical results and experimental data are matched at 9 GHz for thick first-year sea ice with a bare surface and with a snow cover at Point Barrow, Alaska. The model is then used to study the sensitivity of polarimetric backscattering coefficients with respect to correlation lengths representing the geometry of brine inclusions. Polarimetric signatures of bare and snow-covered sea ice are also simulated based on the model to investigate effects of different scattering mechanisms.

TOMS UV Algorithm: Problems and Enhancements. 2

NASA Technical Reports Server (NTRS)

Krotkov, Nickolay; Herman, Jay; Bhartia, P. K.; Seftor, Colin; Arola, Antti; Kaurola, Jussi; Kroskinen, Lasse; Kalliskota, S.; Taalas, Petteri; Geogdzhaev, I.

2002-01-01

Satellite instruments provide global maps of surface ultraviolet (UV) irradiance by combining backscattered radiance measurements with radiative transfer models. The models are limited by uncertainties in input parameters of the atmosphere and the surface. We evaluate the effects of possible enhancements of the current Total Ozone Mapping Spectrometer (TOMS) surface UV irradiance algorithm focusing on effects of diurnal variation of cloudiness and improved treatment of snow/ice. The emphasis is on comparison between the results of the current (version 1) TOMS UV algorithm and each of the changes proposed. We evaluate different approaches for improved treatment of pixel average cloud attenuation, with and without snow/ice on the ground. In addition to treating clouds based only on the measurements at the local time of the TOMS observations, the results from other satellites and weather assimilation models can be used to estimate attenuation of the incident UV irradiance throughout the day. A new method is proposed to obtain a more realistic treatment of snow covered terrain. The method is based on a statistical relation between UV reflectivity and snow depth. The new method reduced the bias between the TOMS UV estimations and ground-based UV measurements for snow periods. The improved (version 2) algorithm will be applied to re-process the existing TOMS UV data record (since 1978) and to the future satellite sensors (e.g., Quik/TOMS, GOME, OMI on EOS/Aura and Triana/EPIC).

Regional Glacier Mapping by Combination of Dense Optical and SAR Satellite Image Time-Series

NASA Astrophysics Data System (ADS)

Winsvold, S. H.; Kääb, A.; Andreassen, L. M.; Nuth, C.; Schellenberger, T.; van Pelt, W.

2016-12-01

Near-future dense time series from both SAR (Sentinel-1A and B) and optical satellite sensors (Landsat 8, Sentinel-2A and B) will promote new multisensory time series applications for glacier mapping. We assess such combinations of optical and SAR data among others by 1) using SAR data to supplement optical time series that suffer from heavy cloud cover (chronological gap-filling), 2) merging the two data types based on stack statistics (Std.dev, Mean, Max. etc.), or 3) better explaining glacier facies patterns in SAR data using optical satellite images. As one example, summer SAR backscatter time series have been largely unexplored and even neglected in many glaciological studies due to the high content of liquid melt water on the ice surface and its intrusion in the upper part of the snow and firn. This water content causes strong specular scattering and absorption of the radar signal, and little energy is scattered back to the SAR sensor. We find in many scenes of a Sentinel-1 time series a significant temporal backscatter difference between the glacier ice surface and the seasonal snow as it melts up glacier. Even though both surfaces have typically wet conditions, we suggest that the backscatter difference is due to different roughness lengths of the two surfaces. Higher backscatter is found on the ice surface in the ablation area compared to the firn/seasonal snow surface. We find and present also other backscatter patterns in the Sentinel-1 time series related to glacier facies and weather events. For the Ny Ålesund area, Svalbard we use Radarsat-2 time series to explore the glacier backscatter conditions in a > 5 year period, discussing distinct temporal signals from among others refreezing of the firn in late autumn, or temporal lakes. All these examples are analyzed using the above 3 methods. By this multi-temporal and multi-sensor approach we also explore and describe the possible connection between combined SAR/optical time series and surface mass balance.

Wide-area mapping of snow water equivalent by Sentinel-1&2 data

NASA Astrophysics Data System (ADS)

Conde, Vasco; Nico, Giovanni; Catalao, Joao; Kontu, Anna; Gritsevich, Maria

2017-04-01

The mapping of snow physical properties over large mountain areas of remote areas is an important topic in both climatological studies and hydrological models where the effects of snow melting are modeled and used to forecast extreme flood events. Usually, these models are run using in-situ measurements of snow which are expensive and statistically not representative of the spatial distribution of snow properties due to slope orientation of terrain, local terrain morphology and height as well as vegetation cover. In this work we investigate the use of data acquired by Sentinel-1 and 2 missions using a C-band SAR and multispectral sensor, respectively. The Sentinel-1 SAR data are processed to estimate the Snow Water Equivalent (SWE) using both the radar amplitude and the output of the SAR interferometry processing. Both approaches need in-situ data to process SAR data and calibrate SWE estimates. The use of SAR amplitude to estimate the SWE is well established and the basic idea is that the radar signal backscattered by snow is related to the SWE so, after modeling the relationship between these two quantities at the site of in-situ measurements this relationship can be used to map the SWE at all site where the SAR amplitude information is available. The physical principle used by SAR interferometry is that of phase delay due to propagation in a non-dispersive medium. This implies that the snow is supposed to be dry in order to allow the propagation of the SAR signal. Sentinel-2 images have been used to get land-use maps and identify areas covered by vegetation. Finland has been chosen as a study region with in-situ measurements acquired thanks to the availability of rich database of in-situ measurements of SWE. Sentinel data used in this work have been acquired starting from November 2015. Publication supported by FCT- project UID/GEO/50019/2013 - Instituto Dom Luiz.

Potential Elevation Biases for Laser Altimeters from Subsurface Scattered Photons: Laboratory and Model Exploration of Green Light Scattering in Snow

NASA Astrophysics Data System (ADS)

Greeley, A.; Neumann, T.; Markus, T.; Kurtz, N. T.; Cook, W. B.

2015-12-01

Existing visible light laser altimeters such as MABEL (Multiple Altimeter Beam Experimental Lidar) - a single photon counting simulator for ATLAS (Advanced Topographic Laser Altimeter System) on NASA's upcoming ICESat-2 mission - and ATM (Airborne Topographic Mapper) on NASA's Operation IceBridge mission provide scientists a view of Earth's ice sheets, glaciers, and sea ice with unprecedented detail. Precise calibration of these instruments is needed to understand rapidly changing parameters like sea ice freeboard and to measure optical properties of surfaces like snow covered ice sheets using subsurface scattered photons. Photons travelling into snow, ice, or water before scattering back to the altimeter receiving system (subsurface photons) travel farther and longer than photons scattering off the surface only, causing a bias in the measured elevation. We seek to identify subsurface photons in a laboratory setting using a flight-tested laser altimeter (MABEL) and to quantify their effect on surface elevation estimates for laser altimeter systems. We also compare these estimates with previous laboratory measurements of green laser light transmission through snow, as well as Monte Carlo simulations of backscattered photons from snow.

An Inexpensive, Implantable Electronic Sensor for Autonomous Measurement of Snow Pack Parameters

NASA Astrophysics Data System (ADS)

De Roo, R. D.; Haengel, E.; Rogacki, S.

2015-12-01

Snow accumulations on the ground are an important source of water in many parts of the world. Mapping the accumulation, usually represented as the snow water equivalent (SWE), is valuable for water resource management. The longest record of regional and global maps of SWE are from orbiting microwave radiometers, which do not directly measure SWE but rather measure the scatter darkening from the snow pack. Robustly linking the scatter darkening to SWE eludes us to this day, in part because the snow pack is highly variable in both time and space. The data needed is currently collected by hand in "snow pits," and the labor-intensive process limits the size of the data sets that can be obtained. In particular, time series measurements are only a one or two samples per day at best, and come at the expense of spatial sampling. We report on the development of a low-power wireless device that can be embedded within a snow pack to report on some of the critical parameters needed to understand scatter darkening. The device autonomously logs temperature, the microwave dielectric constant and infrared backscatter local to the device. The microwave dielectric constant reveals the snow density and the presence of liquid water, while the infrared backscatter measurement, together with the density measurement, reveals a characteristic grain size of the snow pack. The devices are made to be inexpensive (less than $200 in parts each) and easily replicated, so that many can be deployed to monitor variations vertically and horizontally in the snow pack. The low-power operation is important both for longevity of observations as well as insuring minimal anomalous metamorphism of the snow pack. The hardware required for the microwave measurement is intended for wireless communications, and this feature will soon be implemented for near real-time monitoring of snow conditions. We will report on the design, construction and initial deployment of about 30 of these devices in northern lower Michigan, and, data permitting, on the measurements that these novel devices have acquired.

Snowfall retrieval at X, Ka and W bands: consistency of backscattering and microphysical properties using BAECC ground-based measurements

NASA Astrophysics Data System (ADS)

Tecla Falconi, Marta; von Lerber, Annakaisa; Ori, Davide; Silvio Marzano, Frank; Moisseev, Dmitri

2018-05-01

Radar-based snowfall intensity retrieval is investigated at centimeter and millimeter wavelengths using co-located ground-based multi-frequency radar and video-disdrometer observations. Using data from four snowfall events, recorded during the Biogenic Aerosols Effects on Clouds and Climate (BAECC) campaign in Finland, measurements of liquid-water-equivalent snowfall rate S are correlated to radar equivalent reflectivity factors Ze, measured by the Atmospheric Radiation Measurement (ARM) cloud radars operating at X, Ka and W frequency bands. From these combined observations, power-law Ze-S relationships are derived for all three frequencies considering the influence of riming. Using microwave radiometer observations of liquid water path, the measured precipitation is divided into lightly, moderately and heavily rimed snow. Interestingly lightly rimed snow events show a spectrally distinct signature of Ze-S with respect to moderately or heavily rimed snow cases. In order to understand the connection between snowflake microphysical and multi-frequency backscattering properties, numerical simulations are performed by using the particle size distribution provided by the in situ video disdrometer and retrieved ice particle masses. The latter are carried out by using both the T-matrix method (TMM) applied to soft-spheroid particle models with different aspect ratios and exploiting a pre-computed discrete dipole approximation (DDA) database for rimed aggregates. Based on the presented results, it is concluded that the soft-spheroid approximation can be adopted to explain the observed multi-frequency Ze-S relations if a proper spheroid aspect ratio is selected. The latter may depend on the degree of riming in snowfall. A further analysis of the backscattering simulations reveals that TMM cross sections are higher than the DDA ones for small ice particles, but lower for larger particles. The differences of computed cross sections for larger and smaller particles are compensating for each other. This may explain why the soft-spheroid approximation is satisfactory for radar reflectivity simulations under study.

Correlation studies of passive and active microwave data in the marginal ice zone

NASA Technical Reports Server (NTRS)

Comiso, J. C.

1991-01-01

The microwave radiative and backscatter characteristics of sea ice in an Arctic marginal ice zone have been studied using near-simultaneous passive and active synthetic aperture radar microwave data. Intermediate-resolution multichannel passive microwave data were registered and analyzed. Passive and active microwave data generally complement each other as the two sensors are especially sensitive to different physical properties of the sea ice. In the inner pack, undeformed first-year ice is observed to have low backscatter values but high brightness temperatures while multiyear ice has generally high backscatter values and low brightness temperatures. However, in the marginal ice zone, the signature and backscatter for multiyear ice are considerably different and closer to those of first-year ice. Some floes identified by photography as snow-covered thick ice have backscatter similar to that of new ice or open water while brash ice has backscatter similar to or higher than that of ridged ice.

Active remote sensing of snow using NMM3D/DMRT and comparison with CLPX II airborne data

USGS Publications Warehouse

Xu, X.; Liang, D.; Tsang, L.; Andreadis, K.M.; Josberger, E.G.; Lettenmaier, D.P.; Cline, D.W.; Yueh, S.H.

2010-01-01

We applied the Numerical Maxwell Model of three-dimensional simulations (NMM3D) in the Dense Media Radiative Theory (DMRT) to calculate backscattering coefficients. The particles' positions are computer-generated and the subsequent Foldy-Lax equations solved numerically. The phase matrix in NMM3D has significant cross-polarization, particularly when the particles are densely packed. The NMM3D model is combined with DMRT in calculating the microwave scattering by dry snow. The NMM3D/DMRT equations are solved by an iterative solution up to the second order in the case of small to moderate optical thickness. The numerical results of NMM3D/DMRT are illustrated and compared with QCA/DMRT. The QCA/DMRT and NMM3D/DMRT results are also applied to compare with data from two specific datasets from the second Cold Land Processes Experiment (CLPX II) in Alaska and Colorado. The data are obtained at the Ku-band (13.95 GHz) observations using airborne imaging polarimetric scatterometer (POLSCAT). It is shown that the model predictions agree with the field measurements for both co-polarization and cross-polarization. For the Alaska region, the average snow depth and snow density are used as the inputs for DMRT. The grain size, selected from within the range of the ground measurements, is used as a best-fit parameter within the range. For the Colorado region, we use the Variable Infiltration Capacity Model (VIC) to obtain the input snow profiles for NMM3D/DMRT. ?? 2010 IEEE.

Spatial variability and trends of seasonal snowmelt processes over Antarctic sea ice observed by satellite scatterometers

NASA Astrophysics Data System (ADS)

Arndt, S.; Haas, C.

2017-12-01

Snow is one of the key drivers determining the seasonal energy and mass budgets of sea ice in the Southern Ocean. Here, we analyze radar backscatter time series from the European Remote Sensing Satellites (ERS)-1 and-2 scatterometers, from the Quick Scatterometer (QSCAT), and from the Advanced Scatterometer (ASCAT) in order to observe the regional and inter-annual variability of Antarctic snowmelt processes from 1992 to 2014. On perennial ice, seasonal backscatter changes show two different snowmelt stages: A weak backscatter rise indicating the initial warming and metamorphosis of the snowpack (pre-melt), followed by a rapid rise indicating the onset of internal snowmelt and thaw-freeze cycles (snowmelt). In contrast, similar seasonal backscatter cycles are absent on seasonal ice, preventing the periodic retrieval of spring/summer transitions. This may be due to the dominance of ice bottom melt over snowmelt, leading to flooding and ice disintegration before strong snowmelt sets in. Resulting snowmelt onset dates on perennial sea ice show the expected latitudinal gradient from early melt onsets (mid-November) in the northern Weddell Sea towards late (end-December) or even absent snowmelt conditions further south. This result is likely related to seasonal variations in solar shortwave radiation (absorption). In addition, observations with different microwave frequencies allow to detect changing snow properties at different depths. We show that short wavelengths of passive microwave observations indicate earlier pre-melt and snowmelt onset dates than longer wavelength scatterometer observations, in response to earlier warming of upper snow layers compared to lower snow layers. Similarly, pre-melt and snowmelt onset dates retrieved from Ku-Band radars were earlier by an average of 11 and 23 days, respectively, than those retrieved from C-Band. This time difference was used to correct melt onset dates retrieved from Ku-Band to compile a consistent time series from 1992 to 2014. The subsequent regression analysis showed that no significant temporal trend in the retrieved snowmelt onset dates can be observed, but strong inter-annual variability. This absence of any notable changes in snowmelt behavior is in line with the small observed temporal changes of the Antarctic sea ice cover and atmospheric warming

Measured backscatter and attenuation properties, including polarization effects, of various dispersions at 0.9 micron

NASA Technical Reports Server (NTRS)

Kohl, R. H.; Flaherty, M. I.; Partin, R. L.

1977-01-01

The optical properties of a wide variety of atmospheric dispersions were studied using a 0.9-micron lidar system which included a GaAs laser stack transmitter emitting a horizontally polarized beam of 4 milliradians vertical divergence and 1.5 milliradians horizontal divergence. A principal means for assessing optical properties was the polarization ratio, that is, the backscattered radiation power perpendicular to the transmitter beam divided by the backscattered radiation power parallel to the beam polarization. The ratio of the backscattered fraction to the attenuation coefficient was also determined. Data on the dispersion properties of black carbon smoke, road dust, fog, fair-weather cumulus clouds, snow and rain were obtained; the adverse effects of sunlight-induced background noise on the readings is also discussed.

Scattering of electromagnetic waves from a half-space of randomly distributed discrete scatterers and polarized backscattering ratio law

NASA Technical Reports Server (NTRS)

Zhu, P. Y.

1991-01-01

The effective-medium approximation is applied to investigate scattering from a half-space of randomly and densely distributed discrete scatterers. Starting from vector wave equations, an approximation, called effective-medium Born approximation, a particular way, treating Green's functions, and special coordinates, of which the origin is set at the field point, are used to calculate the bistatic- and back-scatterings. An analytic solution of backscattering with closed form is obtained and it shows a depolarization effect. The theoretical results are in good agreement with the experimental measurements in the cases of snow, multi- and first-year sea-ice. The root product ratio of polarization to depolarization in backscattering is equal to 8; this result constitutes a law about polarized scattering phenomena in the nature.

Balloon-borne match measurements of mid-latitude cirrus clouds

NASA Astrophysics Data System (ADS)

Cirisan, A.; Luo, B. P.; Engel, I.; Wienhold, F. G.; Krieger, U. K.; Weers, U.; Romanens, G.; Levrat, G.; Jeannet, P.; Ruffieux, D.; Philipona, R.; Calpini, B.; Spichtinger, P.; Peter, T.

2013-10-01

Observations of persistent high supersaturations with respect to ice inside cirrus clouds are challenging our understanding of cloud microphysics and of climate feedback processes in the upper troposphere. Single measurements of a cloudy air mass provide only a snapshot from which the persistence of ice supersaturation cannot be judged. We introduce here the "cirrus match technique" to obtain information of the evolution of clouds and their saturation ratio. The aim of these coordinated balloon soundings is to analyze the same air mass twice. To this end the standard radiosonde equipment is complemented by a frost point hygrometer "SnowWhite" and a particle backscatter detector "COBALD" (Compact Optical Backscatter Aerosol Detector). Extensive trajectory calculations based on regional weather model COSMO forecasts are performed for flight planning and COSMO analyses are used as basis for comprehensive microphysical box modeling (with grid scale 2 km and 7 km, respectively). Here we present the results of matching a cirrus cloud to within 2-15 km, realized on 8 June 2010 over Payerne, Switzerland, and a location 120 km downstream close to Zurich. A thick cirrus was detected over both measurement sites. We show that in order to quantitatively reproduce the measured particle backscatter ratios, the small-scale temperature fluctuations not resolved by COSMO must be superimposed on the trajectories. The stochastic nature of the fluctuations is captured by ensemble calculations. Possibilities for further improvements in the agreement with the measured backscatter data are investigated by assuming a very slow mass accommodation of water on ice, the presence of heterogeneous ice nuclei, or a wide span of (spheroidal) particle shapes. However, the resulting improvements from microphysical refinements are moderate and comparable in magnitude with changes caused by assuming different regimes of temperature fluctuations for clear sky or cloudy sky conditions, highlighting the importance of a proper treatment of subscale fluctuations. The model yields good agreement with the measured backscatter over both sites and reproduces the measured saturation ratios with respect to ice over Payerne. Conversely, the 30% in-cloud supersaturation measured in a massive, 4-km thick cloud layer over Zurich cannot be reproduced, irrespective of the choice of meteorological or microphysical model parameters. The measured supersaturation can only be explained by either resorting to an unknown physical process, which prevents the ice particles from consuming the excess humidity, or - much more likely - by a measurement error, such as a contamination of the sensor housing of the SnowWhite hygrometer by a precipitation drop from a mixed phase cloud just below the cirrus layer or from some very slight rain in the boundary layer. This uncertainty calls for in-flight checks or calibrations of hygrometers under the extreme humidity conditions in the upper troposphere.

The 20-22 January 2007 Snow Events over Canada: Microphysical Properties

NASA Technical Reports Server (NTRS)

Tao. W.K.; Shi, J.J.; Matsui, T.; Hao, A.; Lang, S.; Peters-Lidard, C.; Skofronick-Jackson, G.; Petersen, W.; Cifelli, R.; Rutledge, S.

2009-01-01

One of the grand challenges of the Global Precipitation Measurement (GPM) mission is to improve precipitation measurements in mid- and high-latitudes during cold seasons through the use of high-frequency passive microwave radiometry. Toward this end, the Weather Research and Forecasting (WRF) model with the Goddard microphysics scheme is coupled with a Satellite Data Simulation Unit (WRF-SDSU) that has been developed to facilitate over-land snowfall retrieval algorithms by providing a virtual cloud library and microwave brightness temperature (Tb) measurements consistent with the GPM Microwave Imager (GMI). This study tested the Goddard cloud microphysics scheme in WRF for snowstorm events (January 20-22, 2007) that took place over the Canadian CloudSAT/CALIPSO Validation Project (C3VP) ground site (Centre for Atmospheric Research Experiments - CARE) in Ontario, Canada. In this paper, the performance of the Goddard cloud microphysics scheme both with 2ice (ice and snow) and 3ice (ice, snow and graupel) as well as other WRF microphysics schemes will be presented. The results are compared with data from the Environment Canada (EC) King Radar, an operational C-band radar located near the CARE site. In addition, the WRF model output is used to drive the Goddard SDSU to calculate radiances and backscattering signals consistent with direct satellite observations for evaluating the model results.

Polarimetric scattering from layered media with multiple species of scatterers

NASA Technical Reports Server (NTRS)

Nghiem, S. V.; Kwok, R.; Yueh, S. H.; Kong, J. A.; Hsu, C. C.; Tassoudji, M. A.; Shin, R. T.

1995-01-01

Geophysical media are usually heterogeneous and contain multiple species of scatterers. In this paper a model is presented to calculate effective permittivities and polarimetric backscattering coefficients of multispecies-layered media. The same physical description is consistently used in the derivation of both permittivities and scattering coefficients. The strong permittivity fluctuation theory is extended to account for the multiple species of scatterers with a general ellipsoidal shape whose orientations are randomly distributed. Under the distorted Born approximation, polarimetric scattering coefficients are obtained. These calculations are applicable to the special cases of spheroidal and spherical scatterers. The model is used to study effects of scatterer shapes and multispecies mixtures on polarimetric signatures of heterogeneous media. The multispecies model accounts for moisture content in scattering media such as snowpack in an ice sheet. The results indicate a high sensitivity of backscatter to moisture with a stronger dependence for drier snow and ice grain size is important to the backscatter. For frost-covered saline ice, model results for bare ice are compared with measured data at C band and then the frost flower formation is simulated with a layer of fanlike ice crystals including brine infiltration over a rough interface. The results with the frost cover suggest a significant increase in scattering coefficients and a polarimetric signature closer to isotropic characteristics compared to the thin saline ice case.

The Impact of Microstructure on an Accurate Snow Scattering Parameterization at Microwave Wavelengths

NASA Astrophysics Data System (ADS)

Honeyager, Ryan

High frequency microwave instruments are increasingly used to observe ice clouds and snow. These instruments are significantly more sensitive than conventional precipitation radar. This is ideal for analyzing ice-bearing clouds, for ice particles are tenuously distributed and have effective densities that are far less than liquid water. However, at shorter wavelengths, the electromagnetic response of ice particles is no longer solely dependent on particle mass. The shape of the ice particles also plays a significant role. Thus, in order to understand the observations of high frequency microwave radars and radiometers, it is essential to model the scattering properties of snowflakes correctly. Several research groups have proposed detailed models of snow aggregation. These particle models are coupled with computer codes that determine the particles' electromagnetic properties. However, there is a discrepancy between the particle model outputs and the requirements of the electromagnetic models. Snowflakes have countless variations in structure, but we also know that physically similar snowflakes scatter light in much the same manner. Structurally exact electromagnetic models, such as the discrete dipole approximation (DDA), require a high degree of structural resolution. Such methods are slow, spending considerable time processing redundant (i.e. useless) information. Conversely, when using techniques that incorporate too little structural information, the resultant radiative properties are not physically realistic. Then, we ask the question, what features are most important in determining scattering? This dissertation develops a general technique that can quickly parameterize the important structural aspects that determine the scattering of many diverse snowflake morphologies. A Voronoi bounding neighbor algorithm is first employed to decompose aggregates into well-defined interior and surface regions. The sensitivity of scattering to interior randomization is then examined. The loss of interior structure is found to have a negligible impact on scattering cross sections, and backscatter is lowered by approximately five percent. This establishes that detailed knowledge of interior structure is not necessary when modeling scattering behavior, and it also provides support for using an effective medium approximation to describe the interiors of snow aggregates. The Voronoi diagram-based technique enables the almost trivial determination of the effective density of this medium. A bounding neighbor algorithm is then used to establish a greatly improved approximation of scattering by equivalent spheroids. This algorithm is then used to posit a Voronoi diagram-based definition of effective density approach, which is used in concert with the T-matrix method to determine single-scattering cross sections. The resulting backscatters are found to reasonably match those of the DDA over frequencies from 10.65 to 183.31 GHz and particle sizes from a few hundred micrometers to nine millimeters in length. Integrated error in backscatter versus DDA is found to be within 25% at 94 GHz. Errors in scattering cross-sections and asymmetry parameters are likewise small. The observed cross-sectional errors are much smaller than the differences observed among different particle models. This represents a significant improvement over established techniques, and it demonstrates that the radiative properties of dense aggregate snowflakes may be adequately represented by equal-mass homogeneous spheroids. The present results can be used to supplement retrieval algorithms used by CloudSat, EarthCARE, Galileo, GPM and SWACR radars. The ability to predict the full range of scattering properties is potentially also useful for other particle regimes where a compact particle approximation is applicable.

A laboratory investigation into microwave backscattering from sea ice. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Bredow, Jonathan W.

1989-01-01

The sources of scattering of artificial sea ice were determined, backscatter measurements semi-quantitatively were compared with theoretical predictions, and inexpensive polarimetric radars were developed for sea ice backscatter studies. A brief review of the dielectric properties of sea ice and of commonly used surface and volume scattering theories is presented. A description is provided of the backscatter measurements performed and experimental techniques used. The development of inexpensive short-range polarimetric radars is discussed. The steps taken to add polarimetric capability to a simple FM-W radar are considered as are sample polarimetric phase measurements of the radar. Ice surface characterization data and techniques are discussed, including computation of surface rms height and correlation length and air bubble distribution statistics. A method is also presented of estimating the standard deviation of rms height and correlation length for cases of few data points. Comparisons were made of backscatter measurements and theory. It was determined that backscatter from an extremely smooth saline ice surface at C band cannot be attributed only to surface scatter. It was found that snow cover had a significant influence on backscatter from extremely smooth saline ice at C band.

Polarimetric Signatures of Sea Ice. Part 1; Theoretical Model

NASA Technical Reports Server (NTRS)

Nghiem, S. V.; Kwok, R.; Yueh, S. H.; Drinkwater, M. R.

1995-01-01

Physical, structural, and electromagnetic properties and interrelating processes in sea ice are used to develop a composite model for polarimetric backscattering signatures of sea ice. Physical properties of sea ice constituents such as ice, brine, air, and salt are presented in terms of their effects on electromagnetic wave interactions. Sea ice structure and geometry of scatterers are related to wave propagation, attenuation, and scattering. Temperature and salinity, which are determining factors for the thermodynamic phase distribution in sea ice, are consistently used to derive both effective permittivities and polarimetric scattering coefficients. Polarimetric signatures of sea ice depend on crystal sizes and brine volumes, which are affected by ice growth rates. Desalination by brine expulsion, drainage, or other mechanisms modifies wave penetration and scattering. Sea ice signatures are further complicated by surface conditions such as rough interfaces, hummocks, snow cover, brine skim, or slush layer. Based on the same set of geophysical parameters characterizing sea ice, a composite model is developed to calculate effective permittivities and backscattering covariance matrices at microwave frequencies for interpretation of sea ice polarimetric signatures.

Balloon-borne match measurements of midlatitude cirrus clouds

NASA Astrophysics Data System (ADS)

Cirisan, A.; Luo, B. P.; Engel, I.; Wienhold, F. G.; Sprenger, M.; Krieger, U. K.; Weers, U.; Romanens, G.; Levrat, G.; Jeannet, P.; Ruffieux, D.; Philipona, R.; Calpini, B.; Spichtinger, P.; Peter, T.

2014-07-01

Observations of high supersaturations with respect to ice inside cirrus clouds with high ice water content (> 0.01 g kg-1) and high crystal number densities (> 1 cm-3) are challenging our understanding of cloud microphysics and of climate feedback processes in the upper troposphere. However, single measurements of a cloudy air mass provide only a snapshot from which the persistence of ice supersaturation cannot be judged. We introduce here the "cirrus match technique" to obtain information about the evolution of clouds and their saturation ratio. The aim of these coordinated balloon soundings is to analyze the same air mass twice. To this end the standard radiosonde equipment is complemented by a frost point hygrometer, "SnowWhite", and a particle backscatter detector, "COBALD" (Compact Optical Backscatter AerosoL Detector). Extensive trajectory calculations based on regional weather model COSMO (Consortium for Small-Scale Modeling) forecasts are performed for flight planning, and COSMO analyses are used as a basis for comprehensive microphysical box modeling (with grid scale of 2 and 7 km, respectively). Here we present the results of matching a cirrus cloud to within 2-15 km, realized on 8 June 2010 over Payerne, Switzerland, and a location 120 km downstream close to Zurich. A thick cirrus cloud was detected over both measurement sites. We show that in order to quantitatively reproduce the measured particle backscatter ratios, the small-scale temperature fluctuations not resolved by COSMO must be superimposed on the trajectories. The stochastic nature of the fluctuations is captured by ensemble calculations. Possibilities for further improvements in the agreement with the measured backscatter data are investigated by assuming a very slow mass accommodation of water on ice, the presence of heterogeneous ice nuclei, or a wide span of (spheroidal) particle shapes. However, the resulting improvements from these microphysical refinements are moderate and comparable in magnitude with changes caused by assuming different regimes of temperature fluctuations for clear-sky or cloudy-sky conditions, highlighting the importance of proper treatment of subscale fluctuations. The model yields good agreement with the measured backscatter over both sites and reproduces the measured saturation ratios with respect to ice over Payerne. Conversely, the 30% in-cloud supersaturation measured in a massive 4 km thick cloud layer over Zurich cannot be reproduced, irrespective of the choice of meteorological or microphysical model parameters. The measured supersaturation can only be explained by either resorting to an unknown physical process, which prevents the ice particles from consuming the excess humidity, or - much more likely - by a measurement error, such as a contamination of the sensor housing of the SnowWhite hygrometer by a precipitation drop from a mixed-phase cloud just below the cirrus layer or from some very slight rain in the boundary layer. This uncertainty calls for in-flight checks or calibrations of hygrometers under the special humidity conditions in the upper troposphere.

Satellite Detection of Smoke Aerosols Over a Snow/Ice Surface by TOMS

NASA Technical Reports Server (NTRS)

Hsu, N. Christina; Herman, Jay R.; Gleason, J. F.; Torres, O.; Seftor, C. J.

1998-01-01

The use of TOMS (Total Ozone Mapping Spectrometer) satellite data demonstrates the recently developed technique of using satellite UV radiance measurements to detect absorbing tropospheric aerosols is effective over snow/ice surfaces. Instead of the traditional single wavelength (visible or infrared) method of measuring tropospheric aerosols, this method takes advantage of the wavelength dependent reduction in the backscattered radiance due to the presence of absorbing aerosols over snow/ice surfaces. An example of the resulting aerosol distribution derived from TOMS data is shown for an August 1998 event in which smoke generated by Canadian forest fires drifts over and across Greenland. As the smoke plume moved over Greenland, the TOMS observed 380 nm reflectivity over the snow/ice surface dropped drastically from 90-100% down to 30-40%. To study the effects of this smoke plume in both the UV and visible regions of the spectrum, we compared a smoke-laden spectrum taken over Greenland by the high spectral resolution (300 to 800 nm) GOME instrument with one that is aerosol-free. We also discuss the results of modeling the darkening effects of various types of absorbing aerosols over snow/ice surfaces using a radiative transfer code. Finally, we investigated the history of such events by looking at the nearly twenty year record of TOMS aerosol index measurements and found that there is a large interannual variability in the amount of smoke aerosols observed over Greenland. This information will be available for studies of radiation and transport properties in the Arctic.

Scattering properties of natural snow and frost - Comparison with icy satellite photometry

NASA Technical Reports Server (NTRS)

Verbiscer, Anne J.; Veverka, Joseph

1990-01-01

The Hapke (1986) equation is presently fit to ascertain the single-scattering albedo of the icy satellites of Uranus and Neptune and the one-term Henyey-Greenstein particle-phase function g for each of the Middleton and Mungall (1952) goniophotometric data samples. There emerge both very high single-scattering albedos and strongly forward-scattering particle phase functions; while these are in keeping with Mie theory-based theoretical considerations, they contrast with the observed backscattering behavior of icy satellites. It is suggested the icy satellite frost grains are aggregated into particles of complex texture, which produce the unusual backscattering behavior.

Airborne Spectral Measurements of Surface-Atmosphere Anisotropy for Arctic Sea Ice and Tundra

NASA Technical Reports Server (NTRS)

Arnold, G. Thomas; Tsay, Si-Chee; King, Michael D.; Li, Jason Y.; Soulen, Peter F.

1999-01-01

Angular distributions of spectral reflectance for four common arctic surfaces: snow-covered sea ice, melt-season sea ice, snow-covered tundra, and tundra shortly after snowmelt were measured using an aircraft based, high angular resolution (1-degree) multispectral radiometer. Results indicate bidirectional reflectance is higher for snow-covered sea ice than melt-season sea ice at all wavelengths between 0.47 and 2.3 pm, with the difference increasing with wavelength. Bidirectional reflectance of snow-covered tundra is higher than for snow-free tundra for measurements less than 1.64 pm, with the difference decreasing with wavelength. Bidirectional reflectance patterns of all measured surfaces show maximum reflectance in the forward scattering direction of the principal plane, with identifiable specular reflection for the melt-season sea ice and snow-free tundra cases. The snow-free tundra had the most significant backscatter, and the melt-season sea ice the least. For sea ice, bidirectional reflectance changes due to snowmelt were more significant than differences among the different types of melt-season sea ice. Also the spectral-hemispherical (plane) albedo of each measured arctic surface was computed. Comparing measured nadir reflectance to albedo for sea ice and snow-covered tundra shows albedo underestimated 5-40%, with the largest bias at wavelengths beyond 1 pm. For snow-free tundra, nadir reflectance underestimates plane albedo by about 30-50%.

Monitoring Snow on ice as Critical Habitat for Ringed Seals

NASA Astrophysics Data System (ADS)

Kelly, B. P.; Moran, J.; Douglas, D. C.; Nghiem, S. V.

2007-12-01

Ringed seals are the primary prey of polar bears, and they are found in all seasonally ice covered seas of the northern hemisphere as well as in several freshwater lakes. The presence of snow covered sea ice is essential for successful ringed seal reproduction. Ringed seals abrade holes in the ice allowing them to surface and breathe under the snow cover. Where snow accumulates to sufficient depths, ringed seals excavate subnivean lairs above breathing holes. They rest, give birth, and nurse their young in those lairs. Temperatures within the lairs remain within a few degrees of freezing, well within the zone of thermal neutrality for newborn ringed seals, even at ambient temperatures of -30° C. High rates of seal mortality have been recorded when early snow melt caused lairs to collapse exposing newborn seals to predators and to subsequent extreme cold events. As melt onset dates come earlier in the Arctic Ocean, ringed seal populations (and the polar bears that depend upon them) will be increasingly challenged. We determined dates of lair abandonment by ringed seals fitted with radio transmitters in the Beaufort Sea (n = 60). We compared abandonment dates to melt onset dates measured in the field, as well as estimated dates derived from active (Ku-band backscatter) and passive (SSM/I) microwave satellite imagery. Date of snow melt significantly improved models of environmental influences on the timing of lair abandonment. We used an algorithm based on multi-channel means and variances of passive microwave data to detect melt onset dates. Those melt onset dates predicted the date of lair abandonment ± 3 days (r 2 = 0.982, p = 0.001). The predictive power of passive microwave proxies combined with their historical record suggest they could serve to monitor critical changes to ringed seal habitat.

Snow depth of the Weddell and Bellingshausen sea ice covers from IceBridge surveys in 2010 and 2011: An examination

NASA Astrophysics Data System (ADS)

Kwok, R.; Maksym, T.

2014-07-01

We examine the snow radar data from the Weddell and Bellingshausen Seas acquired by eight IceBridge (OIB) flightlines in October of 2010 and 2011. In snow depth retrieval, the sidelobes from the stronger scattering snow-ice (s-i) interfaces could be misidentified as returns from the weaker air-snow (a-s) interfaces. In this paper, we first introduce a retrieval procedure that accounts for the structure of the radar system impulse response followed by a survey of the snow depths in the Weddell and Bellingshausen Seas. Limitations and potential biases in our approach are discussed. Differences between snow depth estimates from a repeat survey of one Weddell Sea track separated by 12 days, without accounting for variability due to ice motion, is -0.7 ± 13.6 cm. Average snow depth is thicker in coastal northwestern Weddell and thins toward Cape Norvegia, a decrease of >30 cm. In the Bellingshausen, the thickest snow is found nearshore in both Octobers and is thickest next to the Abbot Ice Shelf. Snow depth is linearly related to freeboard when freeboards are low but diverge as the freeboard increases especially in the thicker/rougher ice of the western Weddell. We find correlations of 0.71-0.84 between snow depth and surface roughness suggesting preferential accumulation over deformed ice. Retrievals also seem to be related to radar backscatter through surface roughness. Snow depths reported here, generally higher than those from in situ records, suggest dissimilarities in sample populations. Implications of these differences on Antarctic sea ice thickness are discussed.

a New High-Resolution Elevation Model of Greenland Derived from Tandem-X

NASA Astrophysics Data System (ADS)

Wessel, B.; Bertram, A.; Gruber, A.; Bemm, S.; Dech, S.

2016-06-01

In this paper we present for the first time the new digital elevation model (DEM) for Greenland produced by the TanDEM-X (TerraSAR add-on for digital elevation measurement) mission. The new, full coverage DEM of Greenland has a resolution of 0.4 arc seconds corresponding to 12 m. It is composed of more than 7.000 interferometric synthetic aperture radar (InSAR) DEM scenes. X-Band SAR penetrates the snow and ice pack by several meters depending on the structures within the snow, the acquisition parameters, and the dielectricity constant of the medium. Hence, the resulting SAR measurements do not represent the surface but the elevation of the mean phase center of the backscattered signal. Special adaptations on the nominal TanDEM-X DEM generation are conducted to maintain these characteristics and not to raise or even deform the DEM to surface reference data. For the block adjustment, only on the outer coastal regions ICESat (Ice, Cloud, and land Elevation Satellite) elevations as ground control points (GCPs) are used where mostly rock and surface scattering predominates. Comparisons with ICESat data and snow facies are performed. In the inner ice and snow pack, the final X-Band InSAR DEM of Greenland lies up to 10 m below the ICESat measurements. At the outer coastal regions it corresponds well with the GCPs. The resulting DEM is outstanding due to its resolution, accuracy and full coverage. It provides a high resolution dataset as basis for research on climate change in the arctic.

A novel multi-temporal approach to wet snow retrieval with Sentinel-1 images (Conference Presentation)

NASA Astrophysics Data System (ADS)

Marin, Carlo; Callegari, Mattia; Notarnicola, Claudia

2016-10-01

Snow is one of the most relevant natural water resources present in nature. It stores water in winter and releases it in spring during the melting season. Monitoring snow cover and its variability is thus of great importance for a proactive management of water-resources. Of particular interest is the identification of snowmelt processes, which could significantly support water administration, flood prediction and prevention. In the past years, remote sensing has demonstrated to be an essential tool for providing accurate inputs to hydrological models concerning the spatial and temporal variability of snow. Even though the analysis of snow pack can be conducted in the visible, near-infrared and short-wave infrared spectrum, the presence of clouds during the melting season, which may be pervasive in some parts of the World (e.g., polar regions), renders impossible the regular acquisition of information needed for the operational purposes. Therefore, the use of the microwave sensors, which signal can penetrate the clouds, can be an asset for the detection of snow proprieties. In particular, the SAR images have demonstrated to be effective and robust measurements to identify the wet snow. Among the several methods presented in the literature, the best results in wet snow mapping have been achieved by the bi-temporal change detection approach proposed by Nagler and Rott [1], or its slight improvements presented afterwards (e.g., [2]). Nonetheless, with the introduction of the Sentinel-1 by ESA, which provides free-of-charge SAR images every 6 days over the same geographical area with a resolution of 20m, the scientists have the opportunity to better investigate and improve the state-of-the-art methods for wet snow detection. In this work, we propose a novel method based on a supervised learning approach able to exploit both the experience of the state-of-the-art algorithms and the high multi-temporal information provided by the Sentinel-1 data. In detail, this is done by training the proposed method with examples extracted by [1] and refine this information by deriving additional training for the complex cases where the state-of-the-art algorithm fails. In addition, the multi-temporal information is fully exploited by modelling it as a series of statistical moments. Indeed, with a proper time sampling, statistical moments can describe the shape of the probability density function (pdf) of the backscattering time series ([3-4]). Given the description of the shape of the multi-temporal VV and VH backscattering pdfs, it is not necessary to explicitly identify which time instants in the time series are to be assigned to the reference image as done in the bi-temporal approach. This information is implicit in the shape of the pdf and it is used in the training procedure for solving the wet snow detection problem based on the available training samples. The proposed approach is designed to work in an alpine environment and it is validated considering ground truth measurements provided by automatic weather stations that record snow depth and snow temperature over 10 sites deployed in the South Tyrol region in northern Italy. References: [1] Nagler, T.; Rott, H., "Retrieval of wet snow by means of multitemporal SAR data," in Geoscience and Remote Sensing, IEEE Transactions on , vol.38, no.2, pp.754-765, Mar 2000. [2] Storvold, R., Malnes, E., and Lauknes, I., "Using ENVISAT ASAR wideswath data to retrieve snow covered area in mountainous regions", EARSeL eProceedings 4, 2/2006 [3] Inglada, J and Mercier, G., "A New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Multiscale Change Analysis," in IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 5, pp. 1432-1445, May 2007. [4] Bujor, F., Trouve, E., Valet, L., Nicolas J. M., and Rudant, J. P., "Application of log-cumulants to the detection of spatiotemporal discontinuities in multitemporal SAR images," in IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 10, pp. 2073-2084, Oct. 2004.

Subsurface Scattered Photons: Friend or Foe? Improving visible light laser altimeter elevation estimates, and measuring surface properties using subsurface scattered photons

NASA Astrophysics Data System (ADS)

Greeley, A.; Kurtz, N. T.; Neumann, T.; Cook, W. B.; Markus, T.

2016-12-01

Photon counting laser altimeters such as MABEL (Multiple Altimeter Beam Experimental Lidar) - a single photon counting simulator for ATLAS (Advanced Topographical Laser Altimeter System) - use individual photons with visible wavelengths to measure their range to target surfaces. ATLAS, the sole instrument on NASA's upcoming ICESat-2 mission, will provide scientists a view of Earth's ice sheets, glaciers, and sea ice with unprecedented detail. Precise calibration of these instruments is needed to understand rapidly changing parameters such as sea ice freeboard, and to measure optical properties of surfaces like snow covered ice sheets using subsurface scattered photons. Photons that travel through snow, ice, or water before scattering back to an altimeter receiving system travel farther than photons taking the shortest path between the observatory and the target of interest. These delayed photons produce a negative elevation bias relative to photons scattered directly off these surfaces. We use laboratory measurements of snow surfaces using a flight-tested laser altimeter (MABEL), and Monte Carlo simulations of backscattered photons from snow to estimate elevation biases from subsurface scattered photons. We also use these techniques to demonstrate the ability to retrieve snow surface properties like snow grain size.

European In-Situ Snow Measurements: Practices and Purposes.

PubMed

Pirazzini, Roberta; Leppänen, Leena; Picard, Ghislain; Lopez-Moreno, Juan Ignacio; Marty, Christoph; Macelloni, Giovanni; Kontu, Anna; von Lerber, Annakaisa; Tanis, Cemal Melih; Schneebeli, Martin; de Rosnay, Patricia; Arslan, Ali Nadir

2018-06-22

In-situ snow measurements conducted by European institutions for operational, research, and energy business applications were surveyed in the framework of the European Cooperation in Science and Technology (COST) Action ES1404, called "A European network for a harmonised monitoring of snow for the benefit of climate change scenarios, hydrology, and numerical weather prediction". Here we present the results of this survey, which was answered by 125 participants from 99 operational and research institutions, belonging to 38 European countries. The typologies of environments where the snow measurements are performed range from mountain to low elevated plains, including forests, bogs, tundra, urban areas, glaciers, lake ice, and sea ice. Of the respondents, 93% measure snow macrophysical parameters, such as snow presence, snow depth (HS), snow water equivalent (SWE), and snow density. These describe the bulk characteristics of the whole snowpack or of a snow layer, and they are the primary snow properties that are needed for most operational applications (such as hydrological monitoring, avalanche forecast, and weather forecast). In most cases, these measurements are done with manual methods, although for snow presence, HS, and SWE, automatized methods are also applied by some respondents. Parameters characterizing precipitating and suspended snow (such as the height of new snow, precipitation intensity, flux of drifting/blowing snow, and particle size distribution), some of which are crucial for the operational services, are measured by 74% of the respondents. Parameters characterizing the snow microstructural properties (such as the snow grain size and shape, and specific surface area), the snow electromagnetic properties (such as albedo, brightness temperature, and backscatter), and the snow composition (such as impurities and isotopes) are measured by 41%, 26%, and 13% of the respondents, respectively, mostly for research applications. The results of this survey are discussed from the perspective of the need of enhancing the efficiency and coverage of the in-situ observational network applying automatic and cheap measurement methods. Moreover, recommendations for the enhancement and harmonization of the observational network and measurement practices are provided.

Snow cover data records from satellite and conventional measurements

NASA Astrophysics Data System (ADS)

Derksen, C.; Brown, R.; Wang, L.

2008-12-01

A major goal of snow-related research in the Climate Research Division of Environment Canada is the development of consistent snow cover information from satellite and in situ data sources for climate monitoring and model evaluation. This work involves new satellite algorithm development for reliable mapping of snow water equivalent (SWE), snow cover extent (SCE) and snow cover onset and melt dates, evaluation of existing snow cover products such as the NOAA weekly data set with in situ and satellite data, and the reconstruction and reanalysis of snow cover information from the application of physical snow models, geostatistics and data assimilation methods. In the context of the International Polar Year, a major effort is being made to develop and evaluate snow cover information over the Arctic region with a particular focus on the dynamic spring melt period where positive feedbacks to the climate system are more pronounced. Assessment of the NOAA daily and weekly SCE products with MODIS and QuikSCAT derived datasets identified a systematic late bias of 2-3 weeks in snow-off dates over northern Canada. This bias was not observed over northern Eurasia which suggests that regional differences in variables such as lake fraction and cloud cover are systematically influencing the accuracy of the NOAA product over northern Canada. Considerable progress has been made in deriving passive microwave derived SWE information over sub- Arctic regions of North America where pre-existing algorithms were unable to account for the influence of forest cover and lake ice. Previous uncertainties in retrieving SWE across the boreal forest have been resolved with the combination of 18.7 and 10.7 GHz measurements from the Advanced Microwave Scanning Radiometer (AMSR-E; 2002-present). Full time series development (1978-onwards) remains problematic, however, because 10.7 GHz measurements are not available from the Special Sensor Microwave/Imager (1987-present). Satellite measurements coupled with lake ice model simulations have illustrated frequency dependent, seasonally evolving relationships between brightness temperature and lake fraction across tundra regions. A potential solution based on the temporal evolution of 37 GHz AMSR-E measurements shows some promise as this was found to be significantly correlated with field measurements of tundra SWE, and to be relatively insensitive to lake fraction. New pan-Arctic (N 60°N) snowmelt onset and end date records (2000-2006) were produced from enhanced resolution (4.45 km) QuikSCAT (QSCAT) Ku-band backscatter measurements. The goal is to merge this with melt onset information from other components of the cryosphere (e.g. glaciers, ice caps, ice sheets, lake ice, sea ice) to provide an integrated circumpolar melt onset and duration dataset for climate monitoring and research on cryosphere-climate links and feedbacks. A major challenge is expanding the relatively short time period of Ku-band satellite measurements with historical C-band data (i.e. from ERS-1). Geostatistical methods and snow cover modeling were used to develop a 10-km gridded SWE dataset over Quebec from 1970-2005 for climate studies and evaluation of the performance of the Canadian Regional Climate Model.

Quantifying the accuracy of snow water equivalent estimates using broadband radar signal phase

NASA Astrophysics Data System (ADS)

Deeb, E. J.; Marshall, H. P.; Lamie, N. J.; Arcone, S. A.

2014-12-01

Radar wave velocity in dry snow depends solely on density. Consequently, ground-based pulsed systems can be used to accurately measure snow depth and snow water equivalent (SWE) using signal travel-time, along with manual depth-probing for signal velocity calibration. Travel-time measurements require a large bandwidth pulse not possible in airborne/space-borne platforms. In addition, radar backscatter from snow cover is sensitive to grain size and to a lesser extent roughness of layers at current/proposed satellite-based frequencies (~ 8 - 18 GHz), complicating inversion for SWE. Therefore, accurate retrievals of SWE still require local calibration due to this sensitivity to microstructure and layering. Conversely, satellite radar interferometry, which senses the difference in signal phase between acquisitions, has shown a potential relationship with SWE at lower frequencies (~ 1 - 5 GHz) because the phase of the snow-refracted signal is sensitive to depth and dielectric properties of the snowpack, as opposed to its microstructure and stratigraphy. We have constructed a lab-based, experimental test bed to quantify the change in radar phase over a wide range of frequencies for varying depths of dry quartz sand, a material dielectrically similar to dry snow. We use a laboratory grade Vector Network Analyzer (0.01 - 25.6 GHz) and a pair of antennae mounted on a trolley over the test bed to measure amplitude and phase repeatedly/accurately at many frequencies. Using ground-based LiDAR instrumentation, we collect a coordinated high-resolution digital surface model (DSM) of the test bed and subsequent depth surfaces with which to compare the radar record of changes in phase. Our plans to transition this methodology to a field deployment during winter 2014-2015 using precision pan/tilt instrumentation will also be presented, as well as applications to airborne and space-borne platforms toward the estimation of SWE at high spatial resolution (on the order of meters) over large regions (> 100 square kilometers).

Technique to separate lidar signal and sunlight.

PubMed

Sun, Wenbo; Hu, Yongxiang; MacDonnell, David G; Weimer, Carl; Baize, Rosemary R

2016-06-13

Sunlight contamination dominates the backscatter noise in space-based lidar measurements during daytime. The background scattered sunlight is highly variable and dependent upon the surface and atmospheric albedo. The scattered sunlight contribution to noise increases over land and snow surfaces where surface albedos are high and thus overwhelm lidar backscatter from optically thin atmospheric constituents like aerosols and thin clouds. In this work, we developed a novel lidar remote sensing concept that potentially can eliminate sunlight induced noise. The new lidar concept requires: (1) a transmitted laser light that carries orbital angular momentum (OAM); and (2) a photon sieve (PS) diffractive filter that separates scattered sunlight from laser light backscattered from the atmosphere, ocean and solid surfaces. The method is based on numerical modeling of the focusing of Laguerre-Gaussian (LG) laser beam and plane-wave light by a PS. The model results show that after passing through a PS, laser light that carries the OAM is focused on a ring (called "focal ring" here) on the focal plane of the PS filter, very little energy arrives at the center of the focal plane. However, scattered sunlight, as a plane wave without the OAM, focuses at the center of the focal plane and thus can be effectively blocked or ducted out. We also find that the radius of the "focal ring" increases with the increase of azimuthal mode (L) of LG laser light, thus increasing L can more effectively separate the lidar signal away from the sunlight noise.

North Slope of Alaska Snow Intensive Operational Period Field Campaign Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Verlinde, Johannes; Bartholomew, Mary Jane; Cherry, Jessica

The campaign was motivated by the need to improve the quantification of measurements of ice-phase precipitation in the Arctic and was by the acquisition and deployment of the new X- and Ka/W-band radars. These radars opened up an opportunity for the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility to obtain spatial estimates of snowfall rates using the polarimetric X-band measurements and dual-frequency measurements (using different combinations of the three wavelengths). However, calculations of X- and Ka-band radar back-scattering of ice crystal aggregates with their complex structure suggest that the commonly used T-matrix approach (Matrosov etmore » al. 2007) for modeling the radar back-scattering underestimates the reflectivity by several decibels, with errors increasing with increasing radar frequency (Botta et al. 2010, 2011). Moreover, the X-band polarimetric measurements and the Ka/W-band measurements are sensitive to the assumed shape of the snow (Botta et al. 2011). One of the five ARM two-dimensional video disdrometers (manufactured by Joanneum Research) were deployed in Barrow at the ARM North Slope of Alaska (NSA) site from 1 October, 2011 to 31 May, 2012 in an attempt to use the instrument in a novel way. The instrument was originally designed to measure the drop size distribution of rain but it seemed worthwhile to explore its capability to quantify ice precipitation particle size and shape distributions in the cold north for scattering calculations and precipitation estimations. Furthermore, this deployment gave us an opportunity to see how reliable it could be in arctic conditions.« less

Analysis of ERS 1 synthetic aperture radar data of frozen lakes in northern Montana and implications for climate studies

USGS Publications Warehouse

Hall, Dorothy K.; Fagre, Daniel B.; Klasner, Fritz; Linebaugh, Gregg; Liston, Glen E.

1994-01-01

Lakes that freeze each winter are good indicators of regional climate change if key parameters, such as freeze-up and breakup date and maximum ice thickness, are measured over a decade-scale time frame. Synthetic aperture radar (SAR) satellite data have proven to be especially useful for measurement of climatologically significant parameters characteristic of frozen lakes. In this paper, five lakes in Glacier National Park, Montana, have been studied both in the field and using Earth Remote-Sensing Satellite (ERS) 1 SAR data during the 1992-1993 winter. The lakes are characterized by clear ice, sometimes with tubular or rounded bubbles, and often with a layer of snow ice on top of the clear ice. They are also often snow covered. Freeze-up is detected quite easily using ERS 1 SAR data as soon as a thin layer of ice forms. The effect of snow ice on the backscatter is thought to be significant but is, as yet, undetermined. On the five lakes studied, relative backscatter was found to increase with ice thickness until a maximum was reached in February. Breakup, an often ill-defined occurrence, is difficult to detect because surface water causes the SAR signal to be absorbed, thus masking the ice below. Comparison of the bubble structure of thaw lakes in northern Alaska with lakes in northern Montana has shown that the ice structure is quite different, and this difference may contribute to differential SAR signature evolution in the lakes of the two areas.

Freeze/thaw conditions at periglacial landforms in Kapp Linné, Svalbard, investigated using field observations, in situ, and radar satellite monitoring

NASA Astrophysics Data System (ADS)

Eckerstorfer, M.; Malnes, E.; Christiansen, H. H.

2017-09-01

In periglacial landscapes, snow dynamics and microtopography have profound implications of freeze-thaw conditions and thermal regime of the ground. We mapped periglacial landforms at Kapp Linné, central Svalbard, where we chose six widespread landforms (solifluction sheet, nivation hollow, palsa and peat in beach ridge depressions, raised marine beach ridge, and exposed bedrock ridge) as study sites. At these six landforms, we studied ground thermal conditions, freeze-thaw cycles, and snow dynamics using a combination of in situ monitoring and C-band radar satellite data in the period 2005-2012. Based on these physical parameters, the six studied landforms can be classified into raised, dry landforms with minor ground ice content and a thin, discontinuous snow cover and into wet landforms with high ice content located in the topographical depressions in-between with medium to thick snow cover. This results in a differential snow-melting period inferred from the C-band radar satellite data, causing the interseasonal and interlandform variability in the onset of ground surface thawing once the ground becomes snow free. Therefore, variability also exists in the period of thawed ground surface conditions. However, the length of the season with thawed ground surface conditions does not determine the mean annual ground surface temperature, it only correlates well with the active layer depths. From the C-band radar satellite data series, measured relative backscatter trends hint toward a decrease in snow cover through time and a more frequent presence of ice layers from mid-winter rain on snow events at Kapp Linné, Svalbard.

Improving the Representation of Snow Crystal Properties with a Single-Moment Mircophysics Scheme

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Petersen, Walter A.; Case, Jonathan L.; Demek, Scott R.

2010-01-01

Single-moment microphysics schemes are utilized in an increasing number of applications and are widely available within numerical modeling packages, often executed in near real-time to aid in the issuance of weather forecasts and advisories. In order to simulate cloud microphysical and precipitation processes, a number of assumptions are made within these schemes. Snow crystals are often assumed to be spherical and of uniform density, and their size distribution intercept may be fixed to simplify calculation of the remaining parameters. Recently, the Canadian CloudSat/CALIPSO Validation Project (C3VP) provided aircraft observations of snow crystal size distributions and environmental state variables, sampling widespread snowfall associated with a passing extratropical cyclone on 22 January 2007. Aircraft instrumentation was supplemented by comparable surface estimations and sampling by two radars: the C-band, dual-polarimetric radar in King City, Ontario and the NASA CloudSat 94 GHz Cloud Profiling Radar. As radar systems respond to both hydrometeor mass and size distribution, they provide value when assessing the accuracy of cloud characteristics as simulated by a forecast model. However, simulation of the 94 GHz radar signal requires special attention, as radar backscatter is sensitive to the assumed crystal shape. Observations obtained during the 22 January 2007 event are used to validate assumptions of density and size distribution within the NASA Goddard six-class single-moment microphysics scheme. Two high resolution forecasts are performed on a 9-3-1 km grid, with C3VP-based alternative parameterizations incorporated and examined for improvement. In order to apply the CloudSat 94 GHz radar to model validation, the single scattering characteristics of various crystal types are used and demonstrate that the assumption of Mie spheres is insufficient for representing CloudSat reflectivity derived from winter precipitation. Furthermore, snow density and size distribution characteristics are allowed to vary with height, based upon direct aircraft estimates obtained from C3VP data. These combinations improve the representation of modeled clouds versus their radar-observed counterparts, based on profiles and vertical distributions of reflectivity. These meteorological events are commonplace within the mid-latitude cold season and present a challenge to operational forecasters. This study focuses on one event, likely representative of others during the winter season, and aims to improve the representation of snow for use in future operational forecasts.

Evolution of microwave sea ice signatures during early summer and midsummer in the marginal ice zone

NASA Technical Reports Server (NTRS)

Onstott, R. G.; Grenfell, T. C.; Matzler, C.; Luther, C. A.; Svendsen, E. A.

1987-01-01

Emissivities at frequencies from 5 to 94 GHz and backscatter at frequencies from 1 to 17 GHz were measured from sea ice in Fram Strait during the marginal Ice Zone Experiment in June and July of 1983 and 1984. The ice observed was primarily multiyear; the remainder, first-year ice, was often deformed. Results from this active and passive microwave study include the description of the evolution of the sea ice during early summer and midsummer; the absorption properties of summer snow; the interrelationship between ice thickness and the state and thickness of snow; and the modulation of the microwave signature, especially at the highest frequencies, by the freezing of the upper few centimeters of the ice.

Backscatter from ice growing on shallow tundra lakes near Barrow, Alaska, winter 1991-1992

NASA Technical Reports Server (NTRS)

Jeffries, M. O.; Wakabayashi, H.; Weeks, W. F.; Morris, K.

1993-01-01

The timing of freeze-up and break-up of Arctic lake ice is a potentially useful environmental indicator that could be monitored using SAR. In order to do this, it is important to understand how the properties and structure of the ice during its growth and decay affect radar backscatter and thus lake ice SAR signatures. The availability of radiometrically and geometrically calibrated digital SAR data time series from the Alaska SAR Facility has made it possible for the first time to quantify lake ice backscatter intensity (sigma(sup o)) variations. This has been done for ice growing on shallow tundra lakes near Barrow, NW Alaska, from initial growth in September 1991 until thawing and decay in June 1992. Field and laboratory observations and measurements of the lake ice were made in late April 1992. The field investigations of the coastal lakes near Barrow confirmed previous findings that, (1) ice frozen to the lake bottom had a dark signature in SAR images, indicating weak backscatter, while, (2) ice that was floating had a bright signature, indicating strong backscatter. At all sites, regardless of whether the ice was grounded or floating, there was a layer of clear, inclusion-free ice overlaying a layer of ice with dense concentrations of vertically oriented tubular bubbles. At some sites, there was a third layer of porous, snow-ice overlaying the clear ice.

Scattering from randomly oriented scatterers of arbitrary shape in the low-frequency limit with application to vegetation

NASA Technical Reports Server (NTRS)

Karam, M. A.; Fung, A. K.

1983-01-01

A general theory of intensity scattering from small particles of arbitrary shape has been developed based on the radiative transfer theory. Upon permitting the particles to orient in accordance with any prescribed distribution, scattering models can be derived. By making an appropriate choice of the particle size, the scattering model may be used to estimate scattering from media such as snow, vegetation and sea ice. For the purpose of illustration only comparisons with measurements from a vegetated medium are shown. The difference in scattering between elliptic- and circular-shaped leaves is demonstrated. In the low-frequency limit, the major factors on backscattering from vegetation are found to be the depth of the vegetation layer and the orientation distribution of the leaves. The shape of the leaf is of secondary importance.

Scattering from randomly oriented scatterers of arbitrary shape in the low-frequency limit with application to vegetation

NASA Technical Reports Server (NTRS)

Karam, M. A.; Fung, A. K.

1984-01-01

A general theory of intensity scattering from small particles of arbitrary shape was developed based on the radiative transfer theory. Upon permitting the particles to orient in accordance with any prescribed distribution, scattering models can be derived. By making an appropriate choice of the particle size, the scattering model may be used to estimate scattering from media such as snow, vegetation and sea ice. For the purpose of illustration only comparisons with measurements from a vegetated medium are shown. The difference in scattering between elliptic and circular shaped leaves is demonstrated. In the low frequency limit, the major factors on backscattering from vegetation are found to be the depth of the vegetation layer and the orientation distribution of the leaves. The shape of the leaf is of secondary importance.

Observation of melt onset on multiyear Arctic sea ice using the ERS 1 synthetic aperture radar

NASA Technical Reports Server (NTRS)

Winebrenner, D. P.; Nelson, E. D.; Colony, R.; West, R. D.

1994-01-01

We present nearly coincident observations of backscattering from the Earth Remote-Sensing Satellite (ERS) 1 synthetic aperture radar (SAR) and of near-surface temperature from six drifting buoys in the Beaufort Sea, showing that the onset of melting in snow on multiyear sea ice is clearly detectable in the SAR data. Melt onset is marked by a clean, steep decrease in the backscattering cross section of multiyear ice at 5.3 GHz and VV polarization. We investigate the scattering physics responsible for the signature change and find that the cross section decrease is due solely to the appearance of liquid water in the snow cover overlying the ice. A thin layer of moist snow is sufficient to cause the observed decrease. We present a prototype algorithm to estimate the date of melt onset using the ERS 1 SAR and apply the algorithm first to the SAR data for which we have corresponding buoy temperatures. The melt onset dates estimated by the SAR algorithm agree with those obtained independently from the temperature data to within 4 days or less, with the exception of one case in which temperatures oscillated about 0 C for several weeks. Lastly, we apply the algorithm to the entire ERS 1 SAR data record acquired by the Alaska SAR Facility for the Beaufort Sea north of 73 deg N during the spring of 1992, to produce a map of the dates of melt onset over an area roughly 1000 km on a side. The progression of melt onset is primarily poleward but shows a weak meridional dependence at latitudes of approximately 76 deg-77 deg N. Melting begins in the southern part of the study region on June 13 and by June 20 has progressed to the northermost part of the region.

Empirical Retrieval of Surface Melt Magnitude from Coupled MODIS Optical and Thermal Measurements over the Greenland Ice Sheet during the 2001 Ablation Season

PubMed Central

Lampkin, Derrick; Peng, Rui

2008-01-01

Accelerated ice flow near the equilibrium line of west-central Greenland Ice Sheet (GIS) has been attributed to an increase in infiltrated surface melt water as a response to climate warming. The assessment of surface melting events must be more than the detection of melt onset or extent. Retrieval of surface melt magnitude is necessary to improve understanding of ice sheet flow and surface melt coupling. In this paper, we report on a new technique to quantify the magnitude of surface melt. Cloud-free dates of June 10, July 5, 7, 9, and 11, 2001 Moderate Resolution Imaging Spectroradiometer (MODIS) daily reflectance Band 5 (1.230-1.250μm) and surface temperature images rescaled to 1km over western Greenland were used in the retrieval algorithm. An optical-thermal feature space partitioned as a function of melt magnitude was derived using a one-dimensional thermal snowmelt model (SNTHERM89). SNTHERM89 was forced by hourly meteorological data from the Greenland Climate Network (GC-Net) at reference sites spanning dry snow, percolation, and wet snow zones in the Jakobshavn drainage basin in western GIS. Melt magnitude or effective melt (E-melt) was derived for satellite composite periods covering May, June, and July displaying low fractions (0-1%) at elevations greater than 2500m and fractions at or greater than 15% at elevations lower than 1000m assessed for only the upper 5 cm of the snow surface. Validation of E-melt involved comparison of intensity to dry and wet zones determined from QSCAT backscatter. Higher intensities (> 8%) were distributed in wet snow zones, while lower intensities were grouped in dry zones at a first order accuracy of ∼ ±2%. PMID:27873793

Empirical Retrieval of Surface Melt Magnitude from Coupled MODIS Optical and Thermal Measurements over the Greenland Ice Sheet during the 2001 Ablation Season.

PubMed

Lampkin, Derrick; Peng, Rui

2008-08-22

Accelerated ice flow near the equilibrium line of west-central Greenland Ice Sheet (GIS) has been attributed to an increase in infiltrated surface melt water as a response to climate warming. The assessment of surface melting events must be more than the detection of melt onset or extent. Retrieval of surface melt magnitude is necessary to improve understanding of ice sheet flow and surface melt coupling. In this paper, we report on a new technique to quantify the magnitude of surface melt. Cloud-free dates of June 10, July 5, 7, 9, and 11, 2001 Moderate Resolution Imaging Spectroradiometer (MODIS) daily reflectance Band 5 (1.230-1.250μm) and surface temperature images rescaled to 1km over western Greenland were used in the retrieval algorithm. An optical-thermal feature space partitioned as a function of melt magnitude was derived using a one-dimensional thermal snowmelt model (SNTHERM89). SNTHERM89 was forced by hourly meteorological data from the Greenland Climate Network (GC-Net) at reference sites spanning dry snow, percolation, and wet snow zones in the Jakobshavn drainage basin in western GIS. Melt magnitude or effective melt (E-melt) was derived for satellite composite periods covering May, June, and July displaying low fractions (0-1%) at elevations greater than 2500m and fractions at or greater than 15% at elevations lower than 1000m assessed for only the upper 5 cm of the snow surface. Validation of E-melt involved comparison of intensity to dry and wet zones determined from QSCAT backscatter. Higher intensities (> 8%) were distributed in wet snow zones, while lower intensities were grouped in dry zones at a first order accuracy of ~ ±2%.

A Meteorological Supersite for Aviation and Cold Weather Applications

NASA Astrophysics Data System (ADS)

Gultepe, Ismail; Agelin-Chaab, M.; Komar, J.; Elfstrom, G.; Boudala, F.; Zhou, B.

2018-05-01

The goal of this study is to better understand atmospheric boundary layer processes and parameters, and to evaluate physical processes for aviation applications using data from a supersite observing site. Various meteorological sensors, including a weather and environmental unmanned aerial vehicle (WE-UAV), and a fog and snow tower (FSOS) observations are part of the project. The PanAm University of Ontario Institute of Technology (UOIT) Meteorological Supersite (PUMS) observations are being collected from April 2015 to date. The FSOS tower gathers observations related to rain, snow, fog, and visibility, aerosols, solar radiation, and wind and turbulence, as well as surface and sky temperature. The FSOSs are located at three locations at about 450-800 m away from the PUMS supersite. The WE-UAV measurements representing aerosol, wind speed and direction, as well as temperature (T) and relative humidity (RH) are provided during clear weather conditions. Other measurements at the PUMS site include cloud backscattering profiles from CL51 ceilometer, MWR observations of liquid water content (LWC), T, and RH, and Microwave Rain Radar (MRR) reflectivity profile, as well as the present weather type, snow water depth, icing rate, 3D-ultrasonic wind and turbulence, and conventional meteorological observations from compact weather stations, e.g., WXTs. The results based on important weather event studies, representing fog, snow, rain, blowing snow, wind gust, planetary boundary layer (PBL) wind research for UAV, and icing conditions are given. The microphysical parameterizations and analysis processes for each event are provided, but the results should not be generalized for all weather events and be used cautiously. Results suggested that integrated observing systems based on data from a supersite as well as satellite sites can provide better information applicable to aviation meteorology, including PBL weather research, validation of numerical weather model predictions, and remote-sensing retrievals. Overall, the results from the five cases are provided and challenges related to observations applicable to aviation meteorology are discussed.

Radar measurements of melt zones on the Greenland Ice Sheet

NASA Technical Reports Server (NTRS)

Jezek, Kenneth C.; Gogineni, Prasad; Shanableh, M.

1994-01-01

Surface-based microwave radar measurements were performed at a location on the western flank of the Greenland Ice Sheet. Here, firn metamorphasis is dominated by seasonal melt, which leads to marked contrasts in the vertical structure of winter and summer firn. This snow regime is also one of the brightest radar targets on Earth with an average backscatter coefficient of 0 dB at 5.3 GHz and an incidence angle of 25 deg. By combining detailed observations of firn physical properties with ranging radar measurements we find that the glaciological mechanism associated with this strong electromagnetic response is summer ice lens formation within the previous winter's snow pack. This observation has important implications for monitoring and understanding changes in ice sheet volume using spaceborne microwave sensors.

Active microwave investigation of snowpacks: Experimental documentation, Colorado 1979-1980

NASA Technical Reports Server (NTRS)

Stiles, W. H.; Ulaby, F. T.; Aslam, A.; Abdelrazik, M.

1981-01-01

During the winter of 1979-1980, the University of Kansas Microwave Active Spectrometer systems measured the backscattering properties of snowpacks under varying conditions at four test sites in Colorado. In addition to the radar data over 1-35 GHz, ground-truth measurements of the atmospheric, snow, and soil characteristics were obtained for each radar data set. The test sites, data acquisition procedures, and data that were acquired in this experiment are presented and described.

Various remote sensing approaches to understanding roughness in the marginal ice zone

NASA Astrophysics Data System (ADS)

Gupta, Mukesh

Multi-platform based measurement approaches to understanding complex marginal ice zone (MIZ) are suggested in this paper. Physical roughness measurements using ship- and helicopter-based laser systems combined with ship-based active microwave backscattering (C-band polarimetric coherences) and dual-polarized passive microwave emission (polarization ratio, PR and spectral gradient ratios, GR at 37 and 89 GHz) are presented to study diverse sea ice types found in the MIZ. Autocorrelation functions are investigated for different sea ice roughness types. Small-scale roughness classes were discriminated using data from a ship-based laser profiler. The polarimetric coherence parameter ρHHVH , is not found to exhibit any observable sensitivity to the surface roughness for all incidence angles. Rubble-ridges, pancake ice, snow-covered frost flowers, and dense frost flowers exhibit separable signatures using GR-H and GR-V at >70° incidence angles. This paper diagnosed changes in sea ice roughness on a spatial scale of ∼0.1-4000 m and on a temporal scale of ∼1-240 days (ice freeze-up to summer melt). The coupling of MIZ wave roughness and aerodynamic roughness in conjunction with microwave emission and backscattering are future avenues of research. Additionally, the integration of various datasets into thermodynamic evolution model of sea ice will open pathways to successful development of inversion models of MIZ behavior.

A Distributed Snow Evolution Modeling System (SnowModel)

NASA Astrophysics Data System (ADS)

Liston, G. E.; Elder, K.

2004-12-01

A spatially distributed snow-evolution modeling system (SnowModel) has been specifically designed to be applicable over a wide range of snow landscapes, climates, and conditions. To reach this goal, SnowModel is composed of four sub-models: MicroMet defines the meteorological forcing conditions, EnBal calculates surface energy exchanges, SnowMass simulates snow depth and water-equivalent evolution, and SnowTran-3D accounts for snow redistribution by wind. While other distributed snow models exist, SnowModel is unique in that it includes a well-tested blowing-snow sub-model (SnowTran-3D) for application in windy arctic, alpine, and prairie environments where snowdrifts are common. These environments comprise 68% of the seasonally snow-covered Northern Hemisphere land surface. SnowModel also accounts for snow processes occurring in forested environments (e.g., canopy interception related processes). SnowModel is designed to simulate snow-related physical processes occurring at spatial scales of 5-m and greater, and temporal scales of 1-hour and greater. These include: accumulation from precipitation; wind redistribution and sublimation; loading, unloading, and sublimation within forest canopies; snow-density evolution; and snowpack ripening and melt. To enhance its wide applicability, SnowModel includes the physical calculations required to simulate snow evolution within each of the global snow classes defined by Sturm et al. (1995), e.g., tundra, taiga, alpine, prairie, maritime, and ephemeral snow covers. The three, 25-km by 25-km, Cold Land Processes Experiment (CLPX) mesoscale study areas (MSAs: Fraser, North Park, and Rabbit Ears) are used as SnowModel simulation examples to highlight model strengths, weaknesses, and features in forested, semi-forested, alpine, and shrubland environments.

Multi-scale responses of scattering layers to environmental variability in Monterey Bay, California

NASA Astrophysics Data System (ADS)

Urmy, Samuel S.; Horne, John K.

2016-07-01

A 38 kHz upward-facing echosounder was deployed on the seafloor at a depth of 875 m in Monterey Bay, CA, USA (36° 42.748‧N, 122° 11.214‧W) from 27 February 2009 to 18 August 2010. This 18-month record of acoustic backscatter was compared to oceanographic time series from a nearby data buoy to investigate the responses of animals in sound-scattering layers to oceanic variability at seasonal and sub-seasonal time scales. Pelagic animals, as measured by acoustic backscatter, moved higher in the water column and decreased in abundance during spring upwelling, attributed to avoidance of a shoaling oxycline and advection offshore. Seasonal changes were most evident in a non-migrating scattering layer near 500 m depth that disappeared in spring and reappeared in summer, building to a seasonal maximum in fall. At sub-seasonal time scales, similar responses were observed after individual upwelling events, though they were much weaker than the seasonal relationship. Correlations of acoustic backscatter with oceanographic variability also differed with depth. Backscatter in the upper water column decreased immediately following upwelling, then increased approximately 20 days later. Similar correlations existed deeper in the water column, but at increasing lags, suggesting that near-surface productivity propagated down the water column at 10-15 m d-1, consistent with sinking speeds of marine snow measured in Monterey Bay. Sub-seasonal variability in backscatter was best correlated with sea-surface height, suggesting that passive physical transport was most important at these time scales.

Multiple Volume Scattering in Random Media and Periodic Structures with Applications in Microwave Remote Sensing and Wave Functional Materials

NASA Astrophysics Data System (ADS)

Tan, Shurun

The objective of my research is two-fold: to study wave scattering phenomena in dense volumetric random media and in periodic wave functional materials. For the first part, the goal is to use the microwave remote sensing technique to monitor water resources and global climate change. Towards this goal, I study the microwave scattering behavior of snow and ice sheet. For snowpack scattering, I have extended the traditional dense media radiative transfer (DMRT) approach to include cyclical corrections that give rise to backscattering enhancements, enabling the theory to model combined active and passive observations of snowpack using the same set of physical parameters. Besides DMRT, a fully coherent approach is also developed by solving Maxwell's equations directly over the entire snowpack including a bottom half space. This revolutionary new approach produces consistent scattering and emission results, and demonstrates backscattering enhancements and coherent layer effects. The birefringence in anisotropic snow layers is also analyzed by numerically solving Maxwell's equation directly. The effects of rapid density fluctuations in polar ice sheet emission in the 0.5˜2.0 GHz spectrum are examined using both fully coherent and partially coherent layered media emission theories that agree with each other and distinct from incoherent approaches. For the second part, the goal is to develop integral equation based methods to solve wave scattering in periodic structures such as photonic crystals and metamaterials that can be used for broadband simulations. Set upon the concept of modal expansion of the periodic Green's function, we have developed the method of broadband Green's function with low wavenumber extraction (BBGFL), where a low wavenumber component is extracted and results a non-singular and fast-converging remaining part with simple wavenumber dependence. We've applied the technique to simulate band diagrams and modal solutions of periodic structures, and to construct broadband Green's functions including periodic scatterers.

User's guide for the Solar Backscattered Ultraviolet (SBUV) and the Total Ozone Mapping Spectrometer (TOMS) RUT-S and RUT-T data sets: October 31, 1978 to November 1, 1980

NASA Technical Reports Server (NTRS)

Fleig, A. J.; Heath, D. F.; Klenk, K. F.; Oslik, N.; Lee, K. D.; Park, H.; Bhartia, P. K.; Gordon, D.

1983-01-01

Raw data from the Solar Backscattered Ultrviolet/Total Ozone Mapping Spectrometer (SBUV/TOMS) Nimbus 7 operation are available on computer tape. These data are contained on two separate sets of RUTs (Raw Units Tapes) for SBUV and TOMS, labelled RUT-S and RUT-T respectively. The RUT-S and RUT-T tapes contain uncalibrated radiance and irradiance data, housekeeping data, wavelength and electronic calibration data, instrument field-of-view location and solar ephemeris information. These tapes also contain colocated cloud, terrain pressure and snow/ice thickness data, each derived from an independent source. The "RUT User's Guide" describes the SBUV and TOMS experiments, the instrument calibration and performance, operating schedules, and data coverage, and provides an assessment of RUT-S and -T data quality. It also provides detailed information on the data available on the computer tapes.

A distributed snow-evolution modeling system (SnowModel)

Treesearch

Glen E. Liston; Kelly Elder

2006-01-01

SnowModel is a spatially distributed snow-evolution modeling system designed for application in landscapes, climates, and conditions where snow occurs. It is an aggregation of four submodels: MicroMet defines meteorological forcing conditions, EnBal calculates surface energy exchanges, SnowPack simulates snow depth and water-equivalent evolution, and SnowTran-3D...

Determination of Spatial Distribution of Air Pollution by Dye Laser Measurement of Differential Absorption of Elastic Backscatter

NASA Technical Reports Server (NTRS)

Ahmed, S. A.; Gergely, J. S.

1973-01-01

This paper presents the results of an analytical study of a lidar system which uses tunable organic dye lasers to accurately determine spatial distribution of molecular air pollutants. Also described will be experimental work to date on simultaneous multiwavelength output dye laser sources for this system. Basically the scheme determines the concentration of air pollutants by measuring the differential absorption of an (at least) two wavelength lidar signal elastically backscattered by the atmosphere. Only relative measurements of the backscattered intensity at each of the two wavelengths, one on and one off the resonance absorption of the pollutant in question, are required. The various parameters of the scheme are examined and the component elements required for a system of this type discussed, with emphasis on the dye laser source. Potential advantages of simultaneous multiwavelength outputs are described. The use of correlation spectroscopy in this context is examined. Comparisons are also made for the use of infrared probing wavelengths and sources instead of dye lasers. Estimates of the sensitivity and accuracy of a practical dye laser system of this type, made for specific pollutants, snow it to have inherent advantages over other schemes for determining pollutant spatial distribution.

A Laboratory Study of the Effect of Frost Flowers on C Band Radar Backscatter from Sea Ice

NASA Technical Reports Server (NTRS)

Nghiem, S. V.; Martin, S.; Perovich, D. K.; Kwok, R.; Drucker, R.; Gow, A. J.

1997-01-01

C band images of Arctic sea ice taken by the ERS 1 synthetic aperture radar show transitory regions of enhanced radar backscatter from young sea ice. Published field observations associate this increase with frost flower growth and the capture of blowing snow by the flowers. To investigate the first part of this phenomenon, we carried out a laboratory experiment on the response of C band radar backscatter to frost flowers growing on the surface of newly formed saline ice. The experiment took place in a 5 m by 7 m by 1.2 m deep saline water pool located in a two-story indoor refrigerated facility at the Cold Regions Research and Engineering Laboratory. Sodium chloride ice was grown in this pool at an air temperature of -28 C. The frost flowers first appeared on the ice surface as dendrites and then changed to needles as the ice sheet grew thicker and the surface temperatures became colder. The frost flowers reached to a height of 10-15 mm, and beneath each cluster of frost flowers a slush layer formed to a thickness of approximately 4 mm. Far-field radar measurements of the backscatter from the ice were made at incident angles from 20 to 40 deg and at approximately 6-hour intervals throughout the 3-day period of the experiment. A backscatter minimum occurred early in the flower growth at the time coincident with an abrupt doubling in the ice surface salinity. Once the full flower coverage was achieved, we removed first the crystal flowers and then the slush layer from the ice surface. The results for these cases show that the crystals have little impact on the backscatter, while the underlying slush patches yield a backscatter increase of 3-5 dB over that of bare ice. The laboratory results suggest that this relative backscatter increase of approximately 5 dB can be used as an index to mark the full area coverage of frost flowers.

A Laboratory Study of the Effect of Frost Flowers on C Band Radar Backscatter from Sea Ice

NASA Technical Reports Server (NTRS)

Nghiem, S. V.; Martin, S.; Perovich, D. K.; Kwok, R.; Drucker, R.; Gow, A. J.

1997-01-01

C band images of Arctic sea ice taken by the ERS 1 synthetic aperture radar show transitory regions of enhanced radar backscatter from young sea ice. Published field observations associate this increase with frost flower growth and the capture of blowing snow by the flowers. To investigate the first part of this phenomenon, we carried out a laboratory experiment on the response of C band radar backscatter to frost flowers growing on the surface of newly formed saline ice. The experiment took place in a 5 m by 7 m by 1.2 m deep saline water pool located in a two-story indoor refrigerated facility at the Cold Regions Research and Engineering Laboratory. Sodium chloride ice was grown in this pool at an air temperature of -28 C. The frost flowers first appeared on the ice surface as dendrites and then changed to needles as the ice sheet grew thicker and the surface temperatures became colder. The frost flowers reached to a height of 10-15 mm, and beneath each cluster of frost flowers a slush layer formed to a thickness of approximately 4 mm. Far-field radar measurements of the backscatter from the ice were made at incident angles from 20 C to 40 C and at approximately 6-hour intervals throughout the 3-day period of the experiment. A backscatter minimum occurred early in the flower growth at the time coincident with an abrupt doubling in the ice surface salinity. Once the full flower coverage was achieved, we removed first the crystal flowers and then the slush layer from the ice surface. The results for these cases show that the crystals have little impact on the backscatter, while the underlying slush patches yield a backscatter increase of 3-5 dB over that o f bare ice. The laboratory results suggest that this relative backscatter increase of approximately 5 dB can be used as an index to mark the full areal coverage of frost flowers.

Improving snow density estimation for mapping SWE with Lidar snow depth: assessment of uncertainty in modeled density and field sampling strategies in NASA SnowEx

NASA Astrophysics Data System (ADS)

Raleigh, M. S.; Smyth, E.; Small, E. E.

2017-12-01

The spatial distribution of snow water equivalent (SWE) is not sufficiently monitored with either remotely sensed or ground-based observations for water resources management. Recent applications of airborne Lidar have yielded basin-wide mapping of SWE when combined with a snow density model. However, in the absence of snow density observations, the uncertainty in these SWE maps is dominated by uncertainty in modeled snow density rather than in Lidar measurement of snow depth. Available observations tend to have a bias in physiographic regime (e.g., flat open areas) and are often insufficient in number to support testing of models across a range of conditions. Thus, there is a need for targeted sampling strategies and controlled model experiments to understand where and why different snow density models diverge. This will enable identification of robust model structures that represent dominant processes controlling snow densification, in support of basin-scale estimation of SWE with remotely-sensed snow depth datasets. The NASA SnowEx mission is a unique opportunity to evaluate sampling strategies of snow density and to quantify and reduce uncertainty in modeled snow density. In this presentation, we present initial field data analyses and modeling results over the Colorado SnowEx domain in the 2016-2017 winter campaign. We detail a framework for spatially mapping the uncertainty in snowpack density, as represented across multiple models. Leveraging the modular SUMMA model, we construct a series of physically-based models to assess systematically the importance of specific process representations to snow density estimates. We will show how models and snow pit observations characterize snow density variations with forest cover in the SnowEx domains. Finally, we will use the spatial maps of density uncertainty to evaluate the selected locations of snow pits, thereby assessing the adequacy of the sampling strategy for targeting uncertainty in modeled snow density.

Monitoring of environmental conditions in taiga forests using ERS-1 SAR

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rignot, E.; Way, J.B.; McDonald, K.

1994-08-01

Synthetic-aperture radar images of forest site near Manley Hot Springs (64[degree]N, 151[degree]W), Alaska, were collected between August 1991 and December 1991, day and night, every 3 days, at C-band frequency ([lambda] = 5.7 cm), vertical receive and transmit polarization, by the European Space Agency First Remote Sensing Satellite, ERS-1. During the same period, air and soil temperatures and dielectric and gravimetric moisture properties of the forest canopy and forest floor were monitored in three forest stands dominated, respectively, by black spruce (Picea mariana), white spruce (Picea glauca), and balsam poplar (Populus balsamifera). The calibrated ERS-1 radar backscatter values, [sigma][degree], ofmore » the forest stands are shown to exhibit a pronounced temporal pattern, with little separability between tree species. The largest change in [sigma][degree], a 3-dB decrease almost independent of tree species, is observed in early winter when the soil and vegetation freeze. In the summer, temporal fluctuations in [sigma][degree] are about 1--2 dB in magnitude, depending on tree species. Diurnal variations in [sigma][degree] are as large as 2 dB during fall freeze-up, and less than 1 dB in summer and winter. These temporal variations in radar backscatter from the forest are interpreted using the MIMICS radar backscatter model and the in situ surface observations as due to changes in the dielectric properties of the forest floor and forest canopy induced by precipitation (summer), drought (fall), and freezing (fall-winter) events. In winter, [sigma][degree] increases across the entire landscape, probably because of volume scattering from large depth hoar ice crystals forming in the snow pack.« less

Monitoring environmental state of Alaskan forests with AIRSAR

NASA Technical Reports Server (NTRS)

Mcdonald, Kyle C.; Way, Jobea; Rignot, Eric; Williams, Cindy; Viereck, Les; Adams, Phylis

1992-01-01

During March 1988 and May 1991, the JPL airborne synthetic aperture radar, AIRSAR, collected sets of multi-temporal imagery of the Bonanza Creek Experimental Forest near Fairbanks, Alaska. These data sets consist of series of multi-polarized images collected at P-, L-, and C-bands each over a period of a few days. The AIRSAR campaigns were complemented with extensive ground measurements that included observations of both static canopy characteristics such as forest architecture as well as properties that vary on short term time scales such as canopy dielectric conditions. Observations exist for several stands of deciduous and coniferous species including white spruce (Picea glauca), black spruce (Picea mariana), and balsam poplar (Populus balsamifera). Although the duration of each campaign was fairly short, significant changes in environmental conditions caused notable variations in the physiological state of the canopies. During the 1988 campaign, environmental conditions ranged from unseasonably warm to more normal subfreezing temperatures. This permitted AIRSAR observations of frozen and thawed canopy states. During May 1991, ice jams that occurred along the river caused many stands to flood while the subsequent clearing of the river then allowed the waters to recede, leaving a snow covered ground surface. This allowed observations of several stands during both flooded and nonflooded conditions. Furthermore, the local weather varied from clear sunny days to heavy overcast days with some occurrence of rain. Measurements of leaf water potential indicated that this caused significant variations in canopy water status, allowing SAR observations of water stressed and unstressed trees. Mean backscatter from several stands is examined for the various canopy physiological states. The changes in canopy backscatter that occur as a function of environmental and physiological state are analyzed. Preliminary results of a backscatter signature modeling analysis are presented. The implications of using SAR to monitor canopy phenological state are addressed.

Prediction of Backscatter and Emissivity of Snow at Millimeter Wavelengths.

DTIC Science & Technology

1980-01-01

LIST OF FIGURES Figure Title Page C-I General Area Map 204 C-2 Location of Alpena National Weather 205 Service Station C-3 Photographs of Site E 206 C-4...February 5, 6, 7, 1979 were ob- tained from National Weather Service, Alpena Station and included in Tables C-9 through C-12. Figure C-2 shows the...relative position * 1 of the sites and the Alpena National Weather Service Station. 203 I 0 1 s LAKE HURON ROGERS CITY 20 0 5 10 MILES 0 C HEBOYGAN NORTH

Broad perspectives in radar for ocean measurements

NASA Technical Reports Server (NTRS)

Jain, A.

1978-01-01

The various active radar implementation options available for the measurement functions of interest for the SEASAT follow-on missions were evaluated. These functions include surface feature imaging, surface pressure and vertical profile, atmospheric sounding, surface backscatter and wind speed determination, surface current location, wavelength spectra, sea surface topography, and ice/snow thickness. Some concepts for the Synthetic Aperture Imaging Radar were examined that may be useful in the design and selection of the implementation options for these missions. The applicability of these instruments for the VOIR mission was also kept under consideration.

Snow specific surface area simulation using the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS)

NASA Astrophysics Data System (ADS)

Roy, A.; Royer, A.; Montpetit, B.; Bartlett, P. A.; Langlois, A.

2012-12-01

Snow grain size is a key parameter for modeling microwave snow emission properties and the surface energy balance because of its influence on the snow albedo, thermal conductivity and diffusivity. A model of the specific surface area (SSA) of snow was implemented in the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS) version 3.4. This offline multilayer model (CLASS-SSA) simulates the decrease of SSA based on snow age, snow temperature and the temperature gradient under dry snow conditions, whereas it considers the liquid water content for wet snow metamorphism. We compare the model with ground-based measurements from several sites (alpine, Arctic and sub-Arctic) with different types of snow. The model provides simulated SSA in good agreement with measurements with an overall point-to-point comparison RMSE of 8.1 m2 kg-1, and a RMSE of 4.9 m2 kg-1 for the snowpack average SSA. The model, however, is limited under wet conditions due to the single-layer nature of the CLASS model, leading to a single liquid water content value for the whole snowpack. The SSA simulations are of great interest for satellite passive microwave brightness temperature assimilations, snow mass balance retrievals and surface energy balance calculations with associated climate feedbacks.

When Models and Observations Collide: Journeying towards an Integrated Snow Depth Product

NASA Astrophysics Data System (ADS)

Webster, M.; Petty, A.; Boisvert, L.; Markus, T.; Kurtz, N. T.; Kwok, R.; Perovich, D. K.

2017-12-01

Knowledge of snow depth is essential for assessing changes in sea ice mass balance due to snow's insulating and reflective properties. In remote sensing applications, the accuracy of sea ice thickness retrievals from altimetry crucially depends on snow depth. Despite the need for snow depth data, we currently lack continuous observations that capture the basin-scale snow depth distribution and its seasonal evolution. Recent in situ and remote sensing observations are sparse in space and time, and contain uncertainties, caveats, and/or biases that often require careful interpretation. Likewise, using model output for remote sensing applications is limited due to uncertainties in atmospheric forcing and different treatments of snow processes. Here, we summarize our efforts in bringing observational and model data together to develop an approach for an integrated snow depth product. We start with a snow budget model and incrementally incorporate snow processes to determine the effects on snow depth and to assess model sensitivity. We discuss lessons learned in model-observation integration and ideas for potential improvements to the treatment of snow in models.

The Impact of Detailed Snow Physics on the Simulation of Snow Cover and Subsurface Thermodynamics at Continental Scales

NASA Technical Reports Server (NTRS)

Stieglitz, Marc; Ducharne, Agnes; Koster, Randy; Suarez, Max; Busalacchi, Antonio J. (Technical Monitor)

2000-01-01

The three-layer snow model is coupled to the global catchment-based Land Surface Model (LSM) of the NASA Seasonal to Interannual Prediction Project (NSIPP) project, and the combined models are used to simulate the growth and ablation of snow cover over the North American continent for the period 1987-1988. The various snow processes included in the three-layer model, such as snow melting and re-freezing, dynamic changes in snow density, and snow insulating properties, are shown (through a comparison with the corresponding simulation using a much simpler snow model) to lead to an improved simulation of ground thermodynamics on the continental scale.

Improvement of a snow albedo parameterization in the Snow-Atmosphere-Soil Transfer model: evaluation of impacts of aerosol on seasonal snow cover

NASA Astrophysics Data System (ADS)

Zhong, Efang; Li, Qian; Sun, Shufen; Chen, Wen; Chen, Shangfeng; Nath, Debashis

2017-11-01

The presence of light-absorbing aerosols (LAA) in snow profoundly influence the surface energy balance and water budget. However, most snow-process schemes in land-surface and climate models currently do not take this into consideration. To better represent the snow process and to evaluate the impacts of LAA on snow, this study presents an improved snow albedo parameterization in the Snow-Atmosphere-Soil Transfer (SAST) model, which includes the impacts of LAA on snow. Specifically, the Snow, Ice and Aerosol Radiation (SNICAR) model is incorporated into the SAST model with an LAA mass stratigraphy scheme. The new coupled model is validated against in-situ measurements at the Swamp Angel Study Plot (SASP), Colorado, USA. Results show that the snow albedo and snow depth are better reproduced than those in the original SAST, particularly during the period of snow ablation. Furthermore, the impacts of LAA on snow are estimated in the coupled model through case comparisons of the snowpack, with or without LAA. The LAA particles directly absorb extra solar radiation, which accelerates the growth rate of the snow grain size. Meanwhile, these larger snow particles favor more radiative absorption. The average total radiative forcing of the LAA at the SASP is 47.5 W m-2. This extra radiative absorption enhances the snowmelt rate. As a result, the peak runoff time and "snow all gone" day have shifted 18 and 19.5 days earlier, respectively, which could further impose substantial impacts on the hydrologic cycle and atmospheric processes.

Evaluating the Performance of the Goddard Multi-Scale Modeling Framework against GPM, TRMM and CloudSat/CALIPSO Products

NASA Astrophysics Data System (ADS)

Chern, J. D.; Tao, W. K.; Lang, S. E.; Matsui, T.; Mohr, K. I.

2014-12-01

Four six-month (March-August 2014) experiments with the Goddard Multi-scale Modeling Framework (MMF) were performed to study the impacts of different Goddard one-moment bulk microphysical schemes and large-scale forcings on the performance of the MMF. Recently a new Goddard one-moment bulk microphysics with four-ice classes (cloud ice, snow, graupel, and frozen drops/hail) has been developed based on cloud-resolving model simulations with large-scale forcings from field campaign observations. The new scheme has been successfully implemented to the MMF and two MMF experiments were carried out with this new scheme and the old three-ice classes (cloud ice, snow graupel) scheme. The MMF has global coverage and can rigorously evaluate microphysics performance for different cloud regimes. The results show MMF with the new scheme outperformed the old one. The MMF simulations are also strongly affected by the interaction between large-scale and cloud-scale processes. Two MMF sensitivity experiments with and without nudging large-scale forcings to those of ERA-Interim reanalysis were carried out to study the impacts of large-scale forcings. The model simulated mean and variability of surface precipitation, cloud types, cloud properties such as cloud amount, hydrometeors vertical profiles, and cloud water contents, etc. in different geographic locations and climate regimes are evaluated against GPM, TRMM, CloudSat/CALIPSO satellite observations. The Goddard MMF has also been coupled with the Goddard Satellite Data Simulation Unit (G-SDSU), a system with multi-satellite, multi-sensor, and multi-spectrum satellite simulators. The statistics of MMF simulated radiances and backscattering can be directly compared with satellite observations to assess the strengths and/or deficiencies of MMF simulations and provide guidance on how to improve the MMF and microphysics.

Improving Snow Modeling by Assimilating Observational Data Collected by Citizen Scientists

NASA Astrophysics Data System (ADS)

Crumley, R. L.; Hill, D. F.; Arendt, A. A.; Wikstrom Jones, K.; Wolken, G. J.; Setiawan, L.

2017-12-01

Modeling seasonal snow pack in alpine environments includes a multiplicity of challenges caused by a lack of spatially extensive and temporally continuous observational datasets. This is partially due to the difficulty of collecting measurements in harsh, remote environments where extreme gradients in topography exist, accompanied by large model domains and inclement weather. Engaging snow enthusiasts, snow professionals, and community members to participate in the process of data collection may address some of these challenges. In this study, we use SnowModel to estimate seasonal snow water equivalence (SWE) in the Thompson Pass region of Alaska while incorporating snow depth measurements collected by citizen scientists. We develop a modeling approach to assimilate hundreds of snow depth measurements from participants in the Community Snow Observations (CSO) project (www.communitysnowobs.org). The CSO project includes a mobile application where participants record and submit geo-located snow depth measurements while working and recreating in the study area. These snow depth measurements are randomly located within the model grid at irregular time intervals over the span of four months in the 2017 water year. This snow depth observation dataset is converted into a SWE dataset by employing an empirically-based, bulk density and SWE estimation method. We then assimilate this data using SnowAssim, a sub-model within SnowModel, to constrain the SWE output by the observed data. Multiple model runs are designed to represent an array of output scenarios during the assimilation process. An effort to present model output uncertainties is included, as well as quantification of the pre- and post-assimilation divergence in modeled SWE. Early results reveal pre-assimilation SWE estimations are consistently greater than the post-assimilation estimations, and the magnitude of divergence increases throughout the snow pack evolution period. This research has implications beyond the Alaskan context because it increases our ability to constrain snow modeling outputs by making use of snow measurements collected by non-expert, citizen scientists.

The Snowcloud System: Architecture and Algorithms for Snow Hydrology Studies

NASA Astrophysics Data System (ADS)

Skalka, C.; Brown, I.; Frolik, J.

2013-12-01

Snowcloud is an embedded data collection system for snow hydrology field research campaigns conducted in harsh climates and remote areas. The system combines distributed wireless sensor network technology and computational techniques to provide data at lower cost and higher spatio-temporal resolution than ground-based systems using traditional methods. Snowcloud has seen multiple Winter deployments in settings ranging from high desert to arctic, resulting in over a dozen node-years of practical experience. The Snowcloud system architecture consists of multiple TinyOS mesh-networked sensor stations collecting environmental data above and, in some deployments, below the snowpack. Monitored data modalities include snow depth, ground and air temperature, PAR and leaf-area index (LAI), and soil moisture. To enable power cycling and control of multiple sensors a custom power and sensor conditioning board was developed. The electronics and structural systems for individual stations have been designed and tested (in the lab and in situ) for ease of assembly and robustness to harsh winter conditions. Battery systems and solar chargers enable seasonal operation even under low/no light arctic conditions. Station costs range between 500 and 1000 depending on the instrumentation suite. For remote field locations, a custom designed hand-held device and data retrieval protocol serves as the primary data collection method. We are also developing and testing a Gateway device that will report data in near-real-time (NRT) over a cellular connection. Data is made available to users via web interfaces that also provide basic data analysis and visualization tools. For applications to snow hydrology studies, the better spatiotemporal resolution of snowpack data provided by Snowcloud is beneficial in several aspects. It provides insight into snowpack evolution, and allows us to investigate differences across different spatial and temporal scales in deployment areas. It enables the relation of local variations in accumulation to environmental parameters such as slope, micro-topography and vegetation cover. The relative importance of such factors is likely to change over the snow season, for example as deposition fills depressions reducing surface roughness due to micro-topography and low lying vegetation. Snowcloud has also been deployed to support the acquisition of satellite imagery from Radarsat-2 and TanDEM-X missions. In capturing spatial variability Snowcloud provides better validation data than single node systems. An important use case of Snowcloud is its application to modeling snow-water equivalent SWE evolution, and more accurate areal average SWE estimation in locations with a typical paucity of snowpack data. We are especially interested in applying machine learning techniques to find relations between average SWE in remote areas and regional infrastructure data (e.g. regional snow pillows). These techniques leverage a combination of data provided by Snowcloud systems and periodic manual snow courses in short field campaigns. Furthermore, using machine learning we can explore the importance of snowpack variables for synthetic aperture radar backscatter at co- and cross-polarizations to better understand scattering processes and unify models of scattering with in situ observations at high resolution.

A conceptual snow model with an analytic resolution of the heat and phase change equations

NASA Astrophysics Data System (ADS)

Riboust, Philippe; Le Moine, Nicolas; Thirel, Guillaume; Ribstein, Pierre

2017-04-01

Compared to degree-day snow models, physically-based snow models resolve more processes in an attempt to achieve a better representation of reality. Often these physically-based models resolve the heat transport equations in snow using a vertical discretization of the snowpack. The snowpack is decomposed into several layers in which the mechanical and thermal states of the snow are calculated. A higher number of layers in the snowpack allow for better accuracy but it also tends to increase the computational costs. In order to develop a snow model that estimates the temperature profile of snow with a lower computational cost, we used an analytical decomposition of the vertical profile using eigenfunctions (i.e. trigonometric functions adapted to the specific boundary conditions). The mass transfer of snow melt has also been estimated using an analytical conceptualization of runoff fingering and matrix flow. As external meteorological forcing, the model uses solar and atmospheric radiation, air temperature, atmospheric humidity and precipitations. It has been tested and calibrated at point scale at two different stations in the Alps: Col de Porte (France, 1325 m) and Weissfluhjoch (Switzerland, 2540 m). A sensitivity analysis of model parameters and model inputs will be presented together with a comparison with measured snow surface temperature, SWE, snow depth, temperature profile and snow melt data. The snow model is created in order to be ultimately coupled with hydrological models for rainfall-runoff modeling in mountainous areas. We hope to create a model faster than physically-based models but capable to estimate more physical processes than degree-day snow models. This should help to build a more reliable snow model capable of being easily calibrated by remote sensing and in situ observation or to assimilate these data for forecasting purposes.

A Comprehensive Snow Density Model for Integrating Lidar-Derived Snow Depth Data into Spatial Snow Modeling

NASA Astrophysics Data System (ADS)

Marks, D. G.; Kormos, P.; Johnson, M.; Bormann, K. J.; Hedrick, A. R.; Havens, S.; Robertson, M.; Painter, T. H.

2017-12-01

Lidar-derived snow depths when combined with modeled or estimated snow density can provide reliable estimates of the distribution of SWE over large mountain areas. Application of this approach is transforming western snow hydrology. We present a comprehensive approach toward modeling bulk snow density that is reliable over a vast range of weather and snow conditions. The method is applied and evaluated over mountainous regions of California, Idaho, Oregon and Colorado in the western US. Simulated and measured snow density are compared at fourteen validation sites across the western US where measurements of snow mass (SWE) and depth are co-located. Fitting statistics for ten sites from three mountain catchments (two in Idaho, one in California) show an average Nash-Sutcliff model efficiency coefficient of 0.83, and mean bias of 4 kg m-3. Results illustrate issues associated with monitoring snow depth and SWE and show the effectiveness of the model, with a small mean bias across a range of snow and climate conditions in the west.

Examining Scattering Mechanisms within Bubbled Freshwater Lake Ice using a Time-Series of RADARSAT-2 (C-band) and UW-Scat (X-, Ku-band) Polarimetric Observations

NASA Astrophysics Data System (ADS)

Gunn, Grant; Duguay, Claude; Atwood, Don

2017-04-01

This study identifies the dominant scattering mechanism for C-, X- and Ku-band for bubbled freshwater lake ice in the Hudson Bay Lowlands near Churchill, Canada, using a winter time series of fully polarimetric ground-based (X- and Ku-band, UW-Scat) scatterometer and spaceborne (C-band) synthetic aperture radar (SAR, Radarsat-2) observations collected coincidentally to in-situ snow and ice measurements. Scatterometer observations identify two dominant backscatter sources from the ice cover: the snow-ice, and ice-water interface. Using in-situ measurements as ground-truth, a winter time series of scatterometer and satellite acquisitions show increases in backscatter from the ice-water interface prior to the timing of tubular bubble development in the ice cover. This timing indicates that scattering in the ice is independent of double-bounce scatter caused by tubular bubble inclusions. Concurrently, the co-polarized phase difference of interactions at the ice-water interface from both scatterometer and SAR observations are centred at 0° throughout the time series, indicating a scattering regime other than double bounce. A Yamaguchi three-component decomposition of SAR observations is presented for C-band acquisitions indicating a dominant single-bounce scattering mechanism regime, which is hypothesized to be a result of an ice-water interface that presents a rough surface or a surface composed of preferentially oriented facets. This study is the first to present a winter time series of coincident ground-based and spaceborne fully polarimetric active microwave observations for bubbled freshwater lake ice.

Low Latency DESDynI Data Products for Disaster Response, Resource Management and Other Applications

NASA Technical Reports Server (NTRS)

Doubleday, Joshua R.; Chien, Steve A.; Lou, Yunling

2011-01-01

We are developing onboard processor technology targeted at the L-band SAR instrument onboard the planned DESDynI mission to enable formation of SAR images onboard opening possibilities for near-real-time data products to augment full data streams. Several image processing and/or interpretation techniques are being explored as possible direct-broadcast products for use by agencies in need of low-latency data, responsible for disaster mitigation and assessment, resource management, agricultural development, shipping, etc. Data collected through UAVSAR (L-band) serves as surrogate to the future DESDynI instrument. We have explored surface water extent as a tool for flooding response, and disturbance images on polarimetric backscatter of repeat pass imagery potentially useful for structural collapse (earthquake), mud/land/debris-slides etc. We have also explored building vegetation and snow/ice classifiers, via support vector machines utilizing quad-pol backscatter, cross-pol phase, and a number of derivatives (radar vegetation index, dielectric estimates, etc.). We share our qualitative and quantitative results thus far.

Close packing effects on clean and dirty snow albedo and associated climatic implications

NASA Astrophysics Data System (ADS)

He, C.; Liou, K. N.; Takano, Y.

2017-12-01

Previous modeling of snow albedo, a key climate feedback parameter, follows the independent scattering approximation (ISA) such that snow grains are considered as a number of separate units with distances longer than wavelengths. Here we develop a new snow albedo model for widely observed close-packed snow grains internally mixed with black carbon (BC) and demonstrate that albedo simulations match closer to observations. Close packing results in a stronger light absorption for clean and BC-contaminated snow. Compared with ISA, close packing reduces pure snow albedos by up to 0.05, whereas it enhances BC-induced snow albedo reduction and associated surface radiative forcing by up to 15% (20%) for fresh (old) snow, with larger enhancements for stronger structure packing. Finally, our results suggest that BC-snow albedo forcing and snow albedo feedback (climate sensitivity) are underestimated in previous modeling studies, making snow close packing consideration a necessity in climate modeling and analysis.

Close packing effects on clean and dirty snow albedo and associated climatic implications

NASA Astrophysics Data System (ADS)

He, Cenlin; Takano, Yoshi; Liou, Kuo-Nan

2017-04-01

Previous modeling of snow albedo, a key climate feedback parameter, follows the independent scattering approximation (ISA) such that snow grains are considered as a number of separate units with distances longer than wavelengths. Here we develop a new snow albedo model for widely observed close-packed snow grains internally mixed with black carbon (BC) and demonstrate that albedo simulations match closer to observations. Close packing results in a stronger light absorption for clean and BC-contaminated snow. Compared with ISA, close packing reduces pure snow albedos by up to 0.05, whereas it enhances BC-induced snow albedo reduction and associated surface radiative forcing by up to 15% (20%) for fresh (old) snow, with larger enhancements for stronger structure packing. Finally, our results suggest that BC-snow albedo forcing and snow albedo feedback (climate sensitivity) are underestimated in previous modeling studies, making snow close packing consideration a necessity in climate modeling and analysis.

Simulating Snow in Canadian Boreal Environments with CLASS for ESM-SnowMIP

NASA Astrophysics Data System (ADS)

Wang, L.; Bartlett, P. A.; Derksen, C.; Ireson, A. M.; Essery, R.

2017-12-01

The ability of land surface schemes to provide realistic simulations of snow cover is necessary for accurate representation of energy and water balances in climate models. Historically, this has been particularly challenging in boreal forests, where poor treatment of both snow masking by forests and vegetation-snow interaction has resulted in biases in simulated albedo and snowpack properties, with subsequent effects on both regional temperatures and the snow albedo feedback in coupled simulations. The SnowMIP (Snow Model Intercomparison Project) series of experiments or `MIPs' was initiated in order to provide assessments of the performance of various snow- and land-surface-models at selected locations, in order to understand the primary factors affecting model performance. Here we present preliminary results of simulations conducted for the third such MIP, ESM-SnowMIP (Earth System Model - Snow Model Intercomparison Project), using the Canadian Land Surface Scheme (CLASS) at boreal forest sites in central Saskatchewan. We assess the ability of our latest model version (CLASS 3.6.2) to simulate observed snowpack properties (snow water equivalent, density and depth) and above-canopy albedo over 13 winters. We also examine the sensitivity of these simulations to climate forcing at local and regional scales.

Atmospheric Backscatter Model Development for CO Sub 2 Wavelengths

NASA Technical Reports Server (NTRS)

Deepak, A.; Kent, G.; Yue, G. K.

1982-01-01

The results of investigations into the problems of modeling atmospheric backscatter from aerosols, in the lowest 20 km of the atmosphere, at CO2 wavelengths are presented, along with a summary of the relevant aerosol characteristics and their variability, and a discussion of the measurement techniques and errors involved. The different methods of calculating the aerosol backscattering function, both from measured aerosol characteristics and from optical measurements made at other wavelengths, are discussed in detail, and limits are placed on the accuracy of these methods. The effects of changing atmospheric humidity and temperature on the backscatter are analyzed and related to the actual atmosphere. Finally, the results of modeling CO2 backscatter in the atmosphere are presented and the variation with height and geographic location discussed, and limits placed on the magnitude of the backscattering function. Conclusions regarding modeling techniques and modeled atmospheric backscatter values are presented in tabular form.

Factors Impacting Spatial Patterns of Snow Distribution in a Small Catchment near Nome, AK

NASA Astrophysics Data System (ADS)

Chen, M.; Wilson, C. J.; Charsley-Groffman, L.; Busey, R.; Bolton, W. R.

2017-12-01

Snow cover plays an important role in the climate, hydrology and ecological systems of the Arctic due to its influence on the water balance, thermal regimes, vegetation and carbon flux. Thus, snow depth and coverage have been key components in all the earth system models but are often poorly represented for arctic regions, where fine scale snow distribution data is sparse. The snow data currently used in the models is at coarse resolution, which in turn leads to high uncertainty in model predictions. Through the DOE Office of Science Next Generation Ecosystem Experiment, NGEE-Arctic, high resolution snow distribution data is being developed and applied in catchment scale models to ultimately improve representation of snow and its interactions with other model components in the earth system models . To improve these models, it is important to identify key factors that control snow distribution and quantify the impacts of those factors on snow distribution. In this study, two intensive snow depth surveys (1 to 10 meters scale) were conducted for a 2.3 km2 catchment on the Teller road, near Nome, AK in the winter of 2016 and 2017. We used a statistical model to quantify the impacts of vegetation types, macro-topography, micro-topography, and meteorological parameters on measured snow depth. The results show that snow spatial distribution was similar between 2016 and 2017, snow depth was spatially auto correlated over small distance (2-5 meters), but not spatially auto correlated over larger distance (more than 2-5 meters). The coefficients of variation of snow depth was above 0.3 for all the snow survey transects (500-800 meters long). Variation of snow depth is governed by vegetation height, aspect, slope, surface curvature, elevation and wind speed and direction. We expect that this empirical statistical model can be used to estimate end of winter snow depth for the whole watershed and will further develop the model using data from other arctic regions to estimate seasonally dynamic snow coverage and properties for use in catchment scale to pan-Arctic models.

Assessing the benefit of snow data assimilation for runoff modeling in Alpine catchments

NASA Astrophysics Data System (ADS)

Griessinger, Nena; Seibert, Jan; Magnusson, Jan; Jonas, Tobias

2016-09-01

In Alpine catchments, snowmelt is often a major contribution to runoff. Therefore, modeling snow processes is important when concerned with flood or drought forecasting, reservoir operation and inland waterway management. In this study, we address the question of how sensitive hydrological models are to the representation of snow cover dynamics and whether the performance of a hydrological model can be enhanced by integrating data from a dedicated external snow monitoring system. As a framework for our tests we have used the hydrological model HBV (Hydrologiska Byråns Vattenbalansavdelning) in the version HBV-light, which has been applied in many hydrological studies and is also in use for operational purposes. While HBV originally follows a temperature-index approach with time-invariant calibrated degree-day factors to represent snowmelt, in this study the HBV model was modified to use snowmelt time series from an external and spatially distributed snow model as model input. The external snow model integrates three-dimensional sequential assimilation of snow monitoring data with a snowmelt model, which is also based on the temperature-index approach but uses a time-variant degree-day factor. The following three variations of this external snow model were applied: (a) the full model with assimilation of observational snow data from a dense monitoring network, (b) the same snow model but with data assimilation switched off and (c) a downgraded version of the same snow model representing snowmelt with a time-invariant degree-day factor. Model runs were conducted for 20 catchments at different elevations within Switzerland for 15 years. Our results show that at low and mid-elevations the performance of the runoff simulations did not vary considerably with the snow model version chosen. At higher elevations, however, best performance in terms of simulated runoff was obtained when using the snowmelt time series from the snow model, which utilized data assimilation. This was especially true for snow-rich years. These findings suggest that with increasing elevation and the correspondingly increased contribution of snowmelt to runoff, the accurate estimation of snow water equivalent (SWE) and snowmelt rates has gained importance.

Radar backscatter measurements from Arctic sea ice during the fall freeze-up

NASA Technical Reports Server (NTRS)

Beaven, S.; Gogineni, S. P.; Shanableh, M.; Gow, A.; Tucker, W.; Jezek, K.

1993-01-01

Radar backscatter measurements from sea ice during the fall freeze-up were performed by the United States Coast Guard Icebreaker Polar Star as a part of the International Arctic Ocean Expedition (IAOE'91) from Aug. to Sep. 1991. The U.S. portion of the experiment took place on board the Polar Star and was referred to as TRAPOLEX '91 (Transpolar expedition) by some investigators. Before prematurely aborting its mission because of mechanical failure of her port shaft, the Polar Star reached 84 deg 57 min N latitude at 35 deg E longitude. The ship was in the ice (greater than 50 percent coverage) from 14 Aug. until 3 Sep. and was operational for all but 6 days due to two instances of mechanical problems with the port shaft. The second was fatal to the ship's participation in the expedition. During the expedition, radar backscatter was measured at C-band under a variety of conditions. These included measurements from young ice types as well as from multiyear and first-/second-year sea ice during the fall freeze-up. The sea ice types were determined by measurement of the ice properties at several of the stations and by visual inspection on others. Radar backscatter measurements were performed over a large portion of the ship's transit into the Arctic ice pack. These were accompanied by in situ sea ice property characterization by the U.S. Army Cold Regions Research and Engineering Laboratory (CRREL) at several stations and, when snow was present, its properties were documented by The Microwave Group, Ottawa River (MWG).

Spring floods prediction with the use of optical satellite data in Québec

NASA Astrophysics Data System (ADS)

Roy, A.; Royer, A.; Turcotte, R.

2009-04-01

The Centre d'expertise hydrique du Québec (CEHQ) operates a distributed hydrological model, which integrates a snow model, for the management of dams in the south of Québec. It appears that the estimation of the water quantity of snowmelt in spring remains a variable with a large uncertainty and induces generally to an important error in stream flow simulation. Therefore, the National snow and ice center (NSIDC) produces, from MODIS (Moderate Resolution Imaging Spectroradiometer) data, continuous and homogeneous spatial snow cover (snow/swow-free) data on the whole territory, but with a cloud contamination. This research aims to improve the prediction of spring floods and the estimation of the rate of discharge by integrating snow cover data in the CEHQ's snow model. The study is done on two watersheds: du Nord river watershed (45,8°N) and Aux Écorces river watershed (47,9°N). The snow model used in the study (SPH-AV) is an implementation developed by the CEHQ of the snowmelt model of HYDROLTEL in is hydrological forecast system to simulate the melted water. The melted water estimated is then used as input in the empirical hydrological model MOHYSE to simulate stream flow. MODIS data are considered valid only when the cloud cover on each pixel of the watersheds is less then 30%. A pixel by pixel correction is applied to the snow pack when there is a difference between satellite snow cover and modeled snow cover. In the case of model shows to much snow, a factor is applied on temperatures by iterative process (starting from the last valid MODIS data) to melt the snow. In the opposite case, the snow quantity added to the last valid MODIS data is found by iterative process so that the pixel's snow water equivalent is equal to the nonzero neighbor minimum value. The study shows, through the simulations done on the two watersheds, the interest of the use of snow/snow-free product for the operational update of snow water equivalent with the objective to improve spring snowmelt stream flow simulations. The binary aspect (snow/snowfree) of the data is however a limit. Alternatives are discussed (passive microwave data). Keywords : satellite snow cover data, MODIS, satellite data integration, snow model, hydrological model, stream flow simulation, flood.

Estimation of Snow Particle Model Suitable for a Complex and Forested Terrain: Lessons from SnowEx

NASA Astrophysics Data System (ADS)

Gatebe, C. K.; Li, W.; Stamnes, K. H.; Poudyal, R.; Fan, Y.; Chen, N.

2017-12-01

SnowEx 2017 obtained consistent and coordinated ground and airborne remote sensing measurements over Grand Mesa in Colorado, which feature sufficient forested stands to have a range of density and height (and other forest conditions); a range of snow depth/snow water equivalent (SWE) conditions; sufficiently flat snow-covered terrain of a size comparable to airborne instrument swath widths. The Cloud Absorption Radiometer (CAR) data from SnowEx are unique and can be used to assess the accuracy of Bidirectional Reflectance-Distribution Functions (BRDFs) calculated by different snow models. These measurements provide multiple angle and multiple wavelength data needed for accurate surface BRDF characterization. Such data cannot easily be obtained by current satellite remote sensors. Compared to ground-based snow field measurements, CAR measurements minimize the effect of self-shading, and are adaptable to a wide variety of field conditions. We plan to use the CAR measurements as the validation data source for our snow modeling effort. By comparing calculated BRDF results from different snow models to CAR measurements, we can determine which model best explains the snow BRDFs, and is therefore most suitable for application to satellite remote sensing of snow parameters and surface energy budget calculations.

Testing a blowing snow model against distributed snow measurements at Upper Sheep Creek, Idaho, United States of America

Treesearch

Rajiv Prasad; David G. Tarboton; Glen E. Liston; Charles H. Luce; Mark S. Seyfried

2001-01-01

In this paper a physically based snow transport model (SnowTran-3D) was used to simulate snow drifting over a 30 m grid and was compared to detailed snow water equivalence (SWE) surveys on three dates within a small 0.25 km2 subwatershed, Upper Sheep Creek. Two precipitation scenarios and two vegetation scenarios were used to carry out four snow transport model runs in...

Snow hydrology in a general circulation model

NASA Technical Reports Server (NTRS)

Marshall, Susan; Roads, John O.; Glatzmaier, Gary

1994-01-01

A snow hydrology has been implemented in an atmospheric general circulation model (GCM). The snow hydrology consists of parameterizations of snowfall and snow cover fraction, a prognostic calculation of snow temperature, and a model of the snow mass and hydrologic budgets. Previously, only snow albedo had been included by a specified snow line. A 3-year GCM simulation with this now more complete surface hydrology is compared to a previous GCM control run with the specified snow line, as well as with observations. In particular, the authors discuss comparisons of the atmospheric and surface hydrologic budgets and the surface energy budget for U.S. and Canadian areas. The new snow hydrology changes the annual cycle of the surface moisture and energy budgets in the model. There is a noticeable shift in the runoff maximum from winter in the control run to spring in the snow hydrology run. A substantial amount of GCM winter precipitation is now stored in the seasonal snowpack. Snow cover also acts as an important insulating layer between the atmosphere and the ground. Wintertime soil temperatures are much higher in the snow hydrology experiment than in the control experiment. Seasonal snow cover is important for dampening large fluctuations in GCM continental skin temperature during the Northern Hemisphere winter. Snow depths and snow extent show good agreement with observations over North America. The geographic distribution of maximum depths is not as well simulated by the model due, in part, to the coarse resolution of the model. The patterns of runoff are qualitatively and quantitatively similar to observed patterns of streamflow averaged over the continental United States. The seasonal cycles of precipitation and evaporation are also reasonably well simulated by the model, although their magnitudes are larger than is observed. This is due, in part, to a cold bias in this model, which results in a dry model atmosphere and enhances the hydrologic cycle everywhere.

Remote sensing of the atmosphere from environmental satellites

NASA Technical Reports Server (NTRS)

Allison, L. J.; Wexler, R.; Laughlin, C. R.; Bandeen, W. R.

1977-01-01

Various applications of satellite remote sensing of the earth are reviewed, including (1) the use of meteorological satellites to obtain photographic and radiometric data for determining weather conditions; (2) determination of the earth radiation budget from measurements of reflected solar radiation and emitted long wave terrestrial radiation; (3) the use of microwave imagery for measuring ice and snow cover; (4) LANDSAT visual and near infrared observation of floods and crop growth; and (5) the use of the Nimbus 4 backscatter ultraviolet instrument to measure total ozone and vertical ozone distribution. Plans for future activities are also discussed.

Comparison of Modeled Backscatter using Measured Aerosol Microphysics with Focused CW Lidar Data over Pacific

NASA Technical Reports Server (NTRS)

Srivastava, Vandana; Clarke, Antony D.; Jarzembski, Maurice A.; Rothermel, Jeffry

1997-01-01

During NASA's GLObal Backscatter Experiment (GLOBE) II flight mission over the Pacific Ocean in May-June 1990, extensive aerosol backscatter data sets from two continuous wave, focused CO2 Doppler lidars and an aerosol microphysics data set from a laser optical particle counter (LOPC) were obtained. Changes in aerosol loading in various air masses with associated changes in chemical composition, from sulfuric acid and sulfates to dustlike crustal material, significantly affected aerosol backscatter, causing variation of about 3 to 4 orders of magnitude. Some of the significant backscatter features encountered in different air masses were the low backscatter in subtropical air with even lower values in the tropics near the Intertropical Convergence Zone (ITCZ), highly variable backscatter in the ITCZ, mid-tropospheric aerosol backscatter background mode, and high backscatter in an Asian dust plume off the Japanese coast. Differences in aerosol composition and backscatter for northern and southern hemisphere also were observed. Using the LOPC measurements of physical and chemical aerosol properties, we determined the complex refractive index from three different aerosol mixture models to calculate backscatter. These values provided a well-defined envelope of modeled backscatter for various atmospheric conditions, giving good agreement with the lidar data over a horizontal sampling of approximately 18000 km in the mid-troposphere.

Evaluation of the Snow Simulations from the Community Land Model, Version 4 (CLM4)

NASA Technical Reports Server (NTRS)

Toure, Ally M.; Rodell, Matthew; Yang, Zong-Liang; Beaudoing, Hiroko; Kim, Edward; Zhang, Yongfei; Kwon, Yonghwan

2015-01-01

This paper evaluates the simulation of snow by the Community Land Model, version 4 (CLM4), the land model component of the Community Earth System Model, version 1.0.4 (CESM1.0.4). CLM4 was run in an offline mode forced with the corrected land-only replay of the Modern-Era Retrospective Analysis for Research and Applications (MERRA-Land) and the output was evaluated for the period from January 2001 to January 2011 over the Northern Hemisphere poleward of 30 deg N. Simulated snow-cover fraction (SCF), snow depth, and snow water equivalent (SWE) were compared against a set of observations including the Moderate Resolution Imaging Spectroradiometer (MODIS) SCF, the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover, the Canadian Meteorological Centre (CMC) daily snow analysis products, snow depth from the National Weather Service Cooperative Observer (COOP) program, and Snowpack Telemetry (SNOTEL) SWE observations. CLM4 SCF was converted into snow-cover extent (SCE) to compare with MODIS SCE. It showed good agreement, with a correlation coefficient of 0.91 and an average bias of -1.54 x 10(exp 2) sq km. Overall, CLM4 agreed well with IMS snow cover, with the percentage of correctly modeled snow-no snow being 94%. CLM4 snow depth and SWE agreed reasonably well with the CMC product, with the average bias (RMSE) of snow depth and SWE being 0.044m (0.19 m) and -0.010m (0.04 m), respectively. CLM4 underestimated SNOTEL SWE and COOP snow depth. This study demonstrates the need to improve the CLM4 snow estimates and constitutes a benchmark against which improvement of the model through data assimilation can be measured.

The DMRT-ML Model: Numerical Simulations of the Microwave Emission of Snowpacks Based on the Dense Media Radiative Transfer Theory

NASA Technical Reports Server (NTRS)

Picard, Ghislain; Brucker, Ludovic; Roy, Alexandre; DuPont, FLorent; Champollion, Nicolas; Morin, Samuel

2014-01-01

Microwave radiometer observations have been used to retrieve snow depth and snow water equivalent on both land and sea ice, snow accumulation on ice sheets, melt events, snow temperature, and snow grain size. Modeling the microwave emission from snow and ice physical properties is crucial to improve the quality of these retrievals. It also is crucial to improve our understanding of the radiative transfer processes within the snow cover, and the snow properties most relevant in microwave remote sensing. Our objective is to present a recent microwave emission model and its validation. The model is named DMRT-ML (DMRT Multi-Layer).

Coupling of snow and permafrost processes using the Basic Modeling Interface (BMI)

NASA Astrophysics Data System (ADS)

Wang, K.; Overeem, I.; Jafarov, E. E.; Piper, M.; Stewart, S.; Clow, G. D.; Schaefer, K. M.

2017-12-01

We developed a permafrost modeling tool based by implementing the Kudryavtsev empirical permafrost active layer depth model (the so-called "Ku" component). The model is specifically set up to have a basic model interface (BMI), which enhances the potential coupling to other earth surface processes model components. This model is accessible through the Web Modeling Tool in Community Surface Dynamics Modeling System (CSDMS). The Kudryavtsev model has been applied for entire Alaska to model permafrost distribution at high spatial resolution and model predictions have been verified by Circumpolar Active Layer Monitoring (CALM) in-situ observations. The Ku component uses monthly meteorological forcing, including air temperature, snow depth, and snow density, and predicts active layer thickness (ALT) and temperature on the top of permafrost (TTOP), which are important factors in snow-hydrological processes. BMI provides an easy approach to couple the models with each other. Here, we provide a case of coupling the Ku component to snow process components, including the Snow-Degree-Day (SDD) method and Snow-Energy-Balance (SEB) method, which are existing components in the hydrological model TOPOFLOW. The work flow is (1) get variables from meteorology component, set the values to snow process component, and advance the snow process component, (2) get variables from meteorology and snow component, provide these to the Ku component and advance, (3) get variables from snow process component, set the values to meteorology component, and advance the meteorology component. The next phase is to couple the permafrost component with fully BMI-compliant TOPOFLOW hydrological model, which could provide a useful tool to investigate the permafrost hydrological effect.

Simulating polarized light scattering in terrestrial snow based on bicontinuous random medium and Monte Carlo ray tracing

NASA Astrophysics Data System (ADS)

Xiong, Chuan; Shi, Jiancheng

2014-01-01

To date, the light scattering models of snow consider very little about the real snow microstructures. The ideal spherical or other single shaped particle assumptions in previous snow light scattering models can cause error in light scattering modeling of snow and further cause errors in remote sensing inversion algorithms. This paper tries to build up a snow polarized reflectance model based on bicontinuous medium, with which the real snow microstructure is considered. The accurate specific surface area of bicontinuous medium can be analytically derived. The polarized Monte Carlo ray tracing technique is applied to the computer generated bicontinuous medium. With proper algorithms, the snow surface albedo, bidirectional reflectance distribution function (BRDF) and polarized BRDF can be simulated. The validation of model predicted spectral albedo and bidirectional reflectance factor (BRF) using experiment data shows good results. The relationship between snow surface albedo and snow specific surface area (SSA) were predicted, and this relationship can be used for future improvement of snow specific surface area (SSA) inversion algorithms. The model predicted polarized reflectance is validated and proved accurate, which can be further applied in polarized remote sensing.

Coupling of a Simple 3-Layer Snow Model to GISS GCM

NASA Astrophysics Data System (ADS)

Aleinov, I.

2001-12-01

Appropriate simulation of the snow cover dynamics is an important issue for the General Circulation Models (GCMs). The presence of snow has a significant impact on ground albedo and on heat and moisture balance. A 3-layer snow model similar to the one proposed by Lynch-Stieglitz was developed with the purpose of using it inside the GCM developed in the NASA Goddard Institute for Space Studies (GISS). The water transport between the layers is modeled explicitly while the heat balance is computed implicitly between the snow layers and semi-implicitly on the surface. The processes of melting and refreezing and compactification of layers under the gravitational force are modeled appropriately. It was noticed that implicit computation of the heat transport can cause a significant under- or over-estimation of the incoming heat flux when the temperature of the upper snow layer is equal to 0 C. This may lead in particular to delayed snow melting in spring. To remedy this problem a special flux-control algorithm was added to the model, which checks computed flux for possible errors and if such are detected the heat transport is recomputed again with the appropriate corrections. The model was tested off-line with Sleepers River forcing data and exhibited a good agreement between simulated and observed quantities for snow depth, snow density and snow temperature. The model was then incorporated into the GISS GCM. Inside the GCM the model is driven completely by the data simulated by other parts of the GCM. The screening effect of the vegetation is introduced by means of masking depth. For a thin snowpack a fractional cover is implemented so that the total thickness of the the snow is never less then 10 cm (rather, the areal fraction of the snow cover decreases when it melts). The model was tested with 6 year long GCM speed-up runs. It proved to be stable and produced reasonable results for the global snow cover. In comparison to the old GISS GCM snow model (which was incorporating the snow into the first soil layer) the new snow model has better insulating properties, thus preventing the ground from overcooling in winter. It also provides better simulation for water retention and release by the snow which results in more physical ground water runoff.

SNOWMIP2: An evaluation of forest snow process simulations

Treesearch

Richard Essery; Nick Rutter; John Pomeroy; Robert Baxter; Manfred Stahli; David Gustafsson; Alan Barr; Paul Bartlett; Kelly Elder

2009-01-01

Models of terrestrial snow cover, or snow modules within land surface models, are used in many meteorological, hydrological, and ecological applications. Such models were developed first, and have achieved their greatest sophistication, for snow in open areas; however, huge tracts of the Northern Hemisphere both have seasonal snow cover and are forested (Fig. 1)....

National Snow Analyses - NOHRSC - The ultimate source for snow information

Science.gov Websites

Equivalent Thumbnail image of Modeled Snow Water Equivalent Animate: Season --- Two weeks --- One Day Snow Depth Thumbnail image of Modeled Snow Depth Animate: Season --- Two weeks --- One Day Average Snowpack Temp Thumbnail image of Modeled Average Snowpack Temp Animate: Season --- Two weeks --- One Day SWE

Snow Water Equivalent Retrieval By Markov Chain Monte Carlo Based on Memls and Hut Snow Emission Model

NASA Astrophysics Data System (ADS)

Pan, J.; Durand, M. T.; Vanderjagt, B. J.

2014-12-01

The Markov chain Monte Carlo (MCMC) method had been proved to be successful in snow water equivalent retrieval based on synthetic point-scale passive microwave brightness temperature (TB) observations. This method needs only general prior information about distribution of snow parameters, and could estimate layered snow properties, including the thickness, temperature, density and snow grain size (or exponential correlation length) of each layer. In this study, the multi-layer HUT (Helsinki University of Technology) model and the MEMLS (Microwave Emission Model of Layered Snowpacks) will be used as observation models to assimilate the observed TB into snow parameter prediction. Previous studies had shown that the multi-layer HUT model tends to underestimate TB at 37 GHz for deep snow, while the MEMLS does not show sensitivity of model bias to snow depth. Therefore, results using HUT model and MEMLS will be compared to see how the observation model will influence the retrieval of snow parameters. The radiometric measurements at 10.65, 18.7, 36.5 and 90 GHz at Sodankyla, Finland will be used as MCMC input, and the statistics of all snow property measurement will be used to calculate the prior information. 43 dry snowpits with complete measurements of all snow parameters will be used for validation. The entire dataset are from NorSREx (Nordic Snow Radar Experiment) experiments carried out by Juha Lemmetyinen, Anna Kontu and Jouni Pulliainen in FMI in 2009-2011 winters, and continued two more winters from 2011 to Spring of 2013. Besides the snow thickness and snow density that are directly related to snow water equivalent, other parameters will be compared with observations, too. For thin snow, the previous studies showed that influence of underlying soil is considerable, especially when the soil is half frozen with part of unfrozen liquid water and part of ice. Therefore, this study will also try to employ a simple frozen soil permittivity model to improve the performance of retrieval. The behavior of the Markov chain in soil parameters will be studied.

Evaluation of the SMAP model calculated snow albedo at the SIGMA-A site, northwest Greenland, during the 2012 record surface melt event

NASA Astrophysics Data System (ADS)

Niwano, M.; Aoki, T.; Matoba, S.; Yamaguchi, S.; Tanikawa, T.; Kuchiki, K.; Motoyama, H.

2015-12-01

The snow and ice on the Greenland ice sheet (GrIS) experienced the extreme surface melt around 12 July, 2012. In order to understand the snow-atmosphere interaction during the period, we applied a physical snowpack model SMAP to the GrIS snowpack. In the SMAP model, the snow albedo is calculated by the PBSAM component explicitly considering effects of snow grain size and light-absorbing snow impurities such as black carbon and dust. Temporal evolution of snow grain size is calculated internally in the SMAP model, whereas mass concentrations of snow impurities are externally given from observations. In the PBSAM, the (shortwave) snow albedo is calculated from a weighted summation of visible albedo (primarily affected by snow impurities) and near-infrared albedo (mainly controlled by snow grain size). The weights for these albedos are the visible and near-infrared fractions of the downward shortwave radiant flux. The SMAP model forced by meteorological data obtained from an automated weather station at SIGMA-A site, northwest GrIS during 30 June to 14 July, 2012　(IOP) was evaluated in terms of surface (optically equivalent) snow grain size and snow albedo. Snow grain size simulated by the model was compared against that retrieved from in-situ spectral albedo measurements. Although the RMSE and ME were reasonable (0.21 mm and 0.17 mm, respectively), the small snow grain size associated with the surface hoar could not be simulated by the SMAP model. As for snow albedo, simulation results agreed well with observations throughout the IOP (RMSE was 0.022 and ME was 0.008). Under cloudy-sky conditions, the SMAP model reproduced observed rapid increase in the snow albedo. When cloud cover is present the near-infrared fraction of the downward shortwave radiant flux is decreased, while it is increased under clear-sky conditions. Therefore, the above mentioned performance of the SMAP model can be attributed to the PBSAM component driven by the observed near-infrared and visible fractions of the downward shortwave radiant flux. This result suggests that it is necessary for snowpack models to consider changes in the visible and near-infrared fractions of the downward shortwave radiant flux caused by the presence of cloud cover to reproduce realistic temporal changes in the snow albedo and consequently the surface energy balance.

A model for the spatial distribution of snow water equivalent parameterized from the spatial variability of precipitation

NASA Astrophysics Data System (ADS)

Skaugen, Thomas; Weltzien, Ingunn H.

2016-09-01

Snow is an important and complicated element in hydrological modelling. The traditional catchment hydrological model with its many free calibration parameters, also in snow sub-models, is not a well-suited tool for predicting conditions for which it has not been calibrated. Such conditions include prediction in ungauged basins and assessing hydrological effects of climate change. In this study, a new model for the spatial distribution of snow water equivalent (SWE), parameterized solely from observed spatial variability of precipitation, is compared with the current snow distribution model used in the operational flood forecasting models in Norway. The former model uses a dynamic gamma distribution and is called Snow Distribution_Gamma, (SD_G), whereas the latter model has a fixed, calibrated coefficient of variation, which parameterizes a log-normal model for snow distribution and is called Snow Distribution_Log-Normal (SD_LN). The two models are implemented in the parameter parsimonious rainfall-runoff model Distance Distribution Dynamics (DDD), and their capability for predicting runoff, SWE and snow-covered area (SCA) is tested and compared for 71 Norwegian catchments. The calibration period is 1985-2000 and validation period is 2000-2014. Results show that SDG better simulates SCA when compared with MODIS satellite-derived snow cover. In addition, SWE is simulated more realistically in that seasonal snow is melted out and the building up of "snow towers" and giving spurious positive trends in SWE, typical for SD_LN, is prevented. The precision of runoff simulations using SDG is slightly inferior, with a reduction in Nash-Sutcliffe and Kling-Gupta efficiency criterion of 0.01, but it is shown that the high precision in runoff prediction using SD_LN is accompanied with erroneous simulations of SWE.

Domain-averaged snow depth over complex terrain from flat field measurements

NASA Astrophysics Data System (ADS)

Helbig, Nora; van Herwijnen, Alec

2017-04-01

Snow depth is an important parameter for a variety of coarse-scale models and applications, such as hydrological forecasting. Since high-resolution snow cover models are computational expensive, simplified snow models are often used. Ground measured snow depth at single stations provide a chance for snow depth data assimilation to improve coarse-scale model forecasts. Snow depth is however commonly recorded at so-called flat fields, often in large measurement networks. While these ground measurement networks provide a wealth of information, various studies questioned the representativity of such flat field snow depth measurements for the surrounding topography. We developed two parameterizations to compute domain-averaged snow depth for coarse model grid cells over complex topography using easy to derive topographic parameters. To derive the two parameterizations we performed a scale dependent analysis for domain sizes ranging from 50m to 3km using highly-resolved snow depth maps at the peak of winter from two distinct climatic regions in Switzerland and in the Spanish Pyrenees. The first, simpler parameterization uses a commonly applied linear lapse rate. For the second parameterization, we first removed the obvious elevation gradient in mean snow depth, which revealed an additional correlation with the subgrid sky view factor. We evaluated domain-averaged snow depth derived with both parameterizations using flat field measurements nearby with the domain-averaged highly-resolved snow depth. This revealed an overall improved performance for the parameterization combining a power law elevation trend scaled with the subgrid parameterized sky view factor. We therefore suggest the parameterization could be used to assimilate flat field snow depth into coarse-scale snow model frameworks in order to improve coarse-scale snow depth estimates over complex topography.

The DMRT-ML Model: Numerical Simulations of the Microwave Emission of Snowpacks Based on the Dense Media Radiative Transfer Theory

NASA Technical Reports Server (NTRS)

Brucker, Ludovic; Picard, Ghislain; Roy, Alexandre; Dupont, Florent; Fily, Michel; Royer, Alain

2014-01-01

Microwave radiometer observations have been used to retrieve snow depth and snow water equivalent on both land and sea ice, snow accumulation on ice sheets, melt events, snow temperature, and snow grain size. Modeling the microwave emission from snow and ice physical properties is crucial to improve the quality of these retrievals. It also is crucial to improve our understanding of the radiative transfer processes within the snow cover, and the snow properties most relevant in microwave remote sensing. Our objective is to present a recent microwave emission model and its validation. The model is named DMRT-ML (DMRT Multi-Layer), and is available at http:lgge.osug.frpicarddmrtml.

A comparison study of two snow models using data from different Alpine sites

NASA Astrophysics Data System (ADS)

Piazzi, Gaia; Riboust, Philippe; Campo, Lorenzo; Cremonese, Edoardo; Gabellani, Simone; Le Moine, Nicolas; Morra di Cella, Umberto; Ribstein, Pierre; Thirel, Guillaume

2017-04-01

The hydrological balance of an Alpine catchment is strongly affected by snowpack dynamics. Melt-water supplies a significant component of the annual water budget, both in terms of soil moisture and runoff, which play a critical role in floods generation and impact water resource management in snow-dominated basins. Several snow models have been developed with variable degrees of complexity, mainly depending on their target application and the availability of computational resources and data. According to the level of detail, snow models range from statistical snowmelt-runoff and degree-day methods using composite snow-soil or explicit snow layer(s), to physically-based and energy balance snow models, consisting of detailed internal snow-process schemes. Intermediate-complexity approaches have been widely developed resulting in simplified versions of the physical parameterization schemes with a reduced snowpack layering. Nevertheless, an increasing model complexity does not necessarily entail improved model simulations. This study presents a comparison analysis between two snow models designed for hydrological purposes. The snow module developed at UPMC and IRSTEA is a mono-layer energy balance model analytically resolving heat and phase change equations into the snowpack. Vertical mass exchange into the snowpack is also analytically resolved. The model is intended to be used for hydrological studies but also to give a realistic estimation of the snowpack state at watershed scale (SWE and snow depth). The structure of the model allows it to be easily calibrated using snow observation. This model is further presented in EGU2017-7492. The snow module of SMASH (Snow Multidata Assimilation System for Hydrology) consists in a multi-layer snow dynamic scheme. It is physically based on mass and energy balances and it reproduces the main physical processes occurring within the snowpack: accumulation, density dynamics, melting, sublimation, radiative balance, heat and mass exchanges. The model is driven by observed forcing meteorological data (air temperature, wind velocity, relative air humidity, precipitation and incident solar radiation) to provide an estimation of the snowpack state. In this study, no DA is used. For more details on the DA scheme, please see EGU2017-7777. Observed data supplied by meteorological stations located in three experimental Alpine sites are used: Col de Porte (1325 m, France); Torgnon (2160 m, Italy); Weissfluhjoch (2540 m, Switzerland). Performances of the two models are compared through evaluations of snow mass, snow depth, albedo and surface temperature simulations in order to better understand and pinpoint limits and potentialities of the analyzed schemes and the impact of different parameterizations on models simulations.

Modeling multi-layer effects in passive microwave remote sensing of dry snow using Dense Media Radiative Transfer Theory (DMRT) based on quasicrystalline approximation

USGS Publications Warehouse

Liang, D.; Xu, X.; Tsang, L.; Andreadis, K.M.; Josberger, E.G.

2008-01-01

The Dense Media Radiative Transfer theory (DMRT) of Quasicrystalline Approximation of Mie scattering by sticky particles is used to study the multiple scattering effects in layered snow in microwave remote sensing. Results are illustrated for various snow profile characteristics. Polarization differences and frequency dependences of multilayer snow model are significantly different from that of the single-layer snow model. Comparisons are also made with CLPX data using snow parameters as given by the VIC model. ?? 2007 IEEE.

Land Surface Model Biases and their Impacts on the Assimilation of Snow-related Observations

NASA Astrophysics Data System (ADS)

Arsenault, K. R.; Kumar, S.; Hunter, S. M.; Aman, R.; Houser, P. R.; Toll, D.; Engman, T.; Nigro, J.

2007-12-01

Some recent snow modeling studies have employed a wide range of assimilation methods to incorporate snow cover or other snow-related observations into different hydrological or land surface models. These methods often include taking both model and observation biases into account throughout the model integration. This study focuses more on diagnosing the model biases and presenting their subsequent impacts on assimilating snow observations and modeled snowmelt processes. In this study, the land surface model, the Community Land Model (CLM), is used within the Land Information System (LIS) modeling framework to show how such biases impact the assimilation of MODIS snow cover observations. Alternative in-situ and satellite-based observations are used to help guide the CLM LSM in better predicting snowpack conditions and more realistic timing of snowmelt for a western US mountainous region. Also, MODIS snow cover observation biases will be discussed, and validation results will be provided. The issues faced with inserting or assimilating MODIS snow cover at moderate spatial resolutions (like 1km or less) will be addressed, and the impacts on CLM will be presented.

Mesocell study area snow distributions for the Cold Land Processes Experiment (CLPX)

Treesearch

Glen E. Liston; Christopher A. Hiemstra; Kelly Elder; Donald W. Cline

2008-01-01

The Cold Land Processes Experiment (CLPX) had a goal of describing snow-related features over a wide range of spatial and temporal scales. This required linking disparate snow tools and datasets into one coherent, integrated package. Simulating realistic high-resolution snow distributions and features requires a snow-evolution modeling system (SnowModel) that can...

Coupling the snow thermodynamic model SNOWPACK with the microwave emission model of layered snowpacks for subarctic and arctic snow water equivalent retrievals

NASA Astrophysics Data System (ADS)

Langlois, A.; Royer, A.; Derksen, C.; Montpetit, B.; Dupont, F.; GoïTa, K.

2012-12-01

Satellite-passive microwave remote sensing has been extensively used to estimate snow water equivalent (SWE) in northern regions. Although passive microwave sensors operate independent of solar illumination and the lower frequencies are independent of atmospheric conditions, the coarse spatial resolution introduces uncertainties to SWE retrievals due to the surface heterogeneity within individual pixels. In this article, we investigate the coupling of a thermodynamic multilayered snow model with a passive microwave emission model. Results show that the snow model itself provides poor SWE simulations when compared to field measurements from two major field campaigns. Coupling the snow and microwave emission models with successive iterations to correct the influence of snow grain size and density significantly improves SWE simulations. This method was further validated using an additional independent data set, which also showed significant improvement using the two-step iteration method compared to standalone simulations with the snow model.

Polar View Snow Service- Operational Snow Cover Mapping for Downstream Runoff Modeling and Hydropower Predictions

NASA Astrophysics Data System (ADS)

Bach, Heike; Appel, Florian; Rust, Felix; Mauser, Wolfram

2010-12-01

Information on snow cover and snow properties are important for hydrology and runoff modelling. Frequent updates of snow cover observation, especially for areas characterized by short-term snow dynamics, can help to improve water balance and discharge calculations. Within the GMES service element Polar View, VISTA offers a snow mapping service for Central Europe since several years [1, 2]. We outline the use of this near-real- time product for hydrological applications in Alpine environment. In particular we discuss the integration of the Polar View product into a physically based hydrological model (PROMET). This allows not only the provision of snow equivalent values, but also enhances river runoff modelling and its use in hydropower energy yield prediction. The GMES snow products of Polar View are thus used in a downstream service for water resources management, providing information services for renewable energy suppliers and energy traders.

Experimental and model based investigation of the links between snow bidirectional reflectance and snow microstructure

NASA Astrophysics Data System (ADS)

Dumont, M.; Flin, F.; Malinka, A.; Brissaud, O.; Hagenmuller, P.; Dufour, A.; Lapalus, P.; Lesaffre, B.; Calonne, N.; Rolland du Roscoat, S.; Ando, E.

2017-12-01

Snow optical properties are unique among Earth surface and crucial for a wide range of applications. The bi-directional reflectance, hereafter BRDF, of snow is sensible to snow microstructure. However the complex interplays between different parameters of snow microstructure namely size parameters and shape parameters on reflectance are challenging to disentangle both theoretically and experimentally. An accurate understanding and modelling of snow BRDF is required to correctly process satellite data. BRDF measurements might also provide means of characterizing snow morphology. This study presents one of the very few dataset that combined bi-directional reflectance measurements over 500-2500 nm and X-ray tomography of the snow microstructure for three different snow samples and two snow types. The dataset is used to evaluate the approach from Malinka, 2014 that relates snow optical properties to the chord length distribution in the snow microstructure. For low and medium absorption, the model accurately reproduces the measurements but tends to slightly overestimate the anisotropy of the reflectance. The model indicates that the deviation of the ice chord length distribution from an exponential distribution, that can be understood as a characterization of snow types, does not impact the reflectance for such absorptions. The simulations are also impacted by the uncertainties in the ice refractive index values. At high absorption and high viewing/incident zenith angle, the simulations and the measurements disagree indicating that some of the assumptions made in the model are not met anymore. The study also indicates that crystal habits might play a significant role for the reflectance under such geometries and wavelengths. However quantitative relationship between crystal habits and reflectance alongside with potential optical methodologies to classify snow morphology would require an extended dataset over more snow types. This extended dataset can likely be obtained thanks to the use of ray tracing models on tomography images of the snow microstructure.

Quantifying Fish Backscattering using SONAR Instrument and Kirchhoff Ray Mode (KRM) Model

NASA Astrophysics Data System (ADS)

Manik, Henry M.

2016-08-01

Sonar instrument was used to study backscattering from tuna fish. Extraction of target strength, incidence angle, and frequency dependence of the backscattered signal for individual scatterer was important for biological information. For this purpose, acoustic measurement of fish backscatter was conducted in the laboratory. Characteristics and general trends of the target strength of fish with special reference to tuna fish were investigated by using a Kirchhoff Ray Mode (KRM) model. Backscattering strength were calculated for the KRM having typical morphological and physical parameters of actual fish. Those backscattering amplitudes were shown as frequency, body length, backscattering patterns, the density and sound speed dependences, and orientation dependence. These results were compared with experimentally measured target strength data and good agreement was found. Measurement and model showed the target strength from the fish are depend on the presence of swimbladder. Target Strength increase with increasing the frequency and fish length.

A Comparison of the SNICAR Radiative Transfer Model to In Situ Snow Characterization Measurements at Sites in New England, USA

NASA Astrophysics Data System (ADS)

Adolph, A. C.; Albert, M. R.; Dibb, J. E.; Lazarcik, J.; Amante, J.

2016-12-01

As a highly reflective material, snow serves as an important control on surface energy balance. Given the current changes in climate and the sensitivity of snow cover to rising temperatures, it is critical that we understand the role of snow and its associated feedbacks in the climate system. Much of snow albedo research has focused on polar or high altitude snow packs, but rapid changes are also occurring in temperate regions; in the northeastern United States of America, changing climate has resulted in shallower snow packs and fewer days of snow cover. As these changes occur and we seek to understand the associated implications for snow albedo within climate dynamics, it is imperative that we are able to accurately represent snow in models. The SNow, ICe, and Aerosol Radiation model (SNICAR), developed by Flanner and Zender (2005) and used in the IPCC assessments, provides upward and downward radiative fluxes of one or many snow layers based on the following inputs: snow depth, density, grain size, and impurity content; solar zenith angle; lighting conditions; and albedo of the surface beneath the snowpack. To our knowledge, the SNICAR model has not been validated with data from a mid-latitude temperate region. Through a measurement campaign that occurred from winter 2013-2016, we have collected over 400 independent observations of a suite of snow characterization measurements and spectral snow albedo from three different sites in New Hampshire, USA. Comparison of our spectral albedo measurements to the SNICAR albedo derived from measured snow properties and illumination conditions will allow for validation of the model or recommendations for improvement based on the sensitivities found in the data.

Effects of Vegetation and of Heat and Vapor Fluxes from Soil on Snowpack Evolution and Radiobrightness

NASA Technical Reports Server (NTRS)

Chung, Y. C.; England, A. W.; DeRoo, R. D.; Weininger, Etai

2006-01-01

The radiobrightness of a snowpack is strongly linked to the snow moisture content profile, to the point that the only operational inversion algorithms require dry snow. Forward dynamic models do not include the effects of freezing and thawing of the soil beneath the snowpack and the effect of vegetation within the snow or above the snow. To get a more realistic description of the evolution of the snowpack, we reported an addition to the Snow-Soil-Vegetation-Atmosphere- Transfer (SSVAT) model, wherein we coupled soil processes of the Land Surface Process (LSP) model with the snow model SNTHERM. In the near future we will be adding a radiobrightness prediction based on the modeled moisture, temperature and snow grain size profiles. The initial investigations with this SSVAT for a late winter and early spring snow pack indicate that soil processes warm the snowpack and the soil. Vapor diffusion needs to be considered whenever the ground is thawed. In the early spring, heat flow from the ground into a snow and a strong temperature gradient across the snow lead to thermal convection. The buried vegetation can be ignored for a late winter snow pack. The warmer surface snow temperature will affect radiobrightness since it is most sensitive to snow surface characteristics. Comparison to data shows that SSVAT provides a more realistic representation of the temperature and moisture profiles in the snowpack and its underlying soil than SNTHERM. The radiobrightness module will be optimized for the prediction of brightness when the snow is moist. The liquid water content of snow causes considerable absorption compared to dry snow, and so longer wavelengths are likely to be most revealing as to the state of a moist snowpack. For volumetric moisture contents below about 7% (the pendular regime), the water forms rings around the contact points between snow grains. Electrostatic modeling of these pendular rings shows that the absorption of these rings is significantly higher than a sphere of the same volume. The first implementation of the radiobrightness module will therefore be a simple radiative transfer model without scattering.

Effects of snow grain shape on climate simulations: sensitivity tests with the Norwegian Earth System Model

NASA Astrophysics Data System (ADS)

Räisänen, Petri; Makkonen, Risto; Kirkevåg, Alf; Debernard, Jens B.

2017-12-01

Snow consists of non-spherical grains of various shapes and sizes. Still, in radiative transfer calculations, snow grains are often treated as spherical. This also applies to the computation of snow albedo in the Snow, Ice, and Aerosol Radiation (SNICAR) model and in the Los Alamos sea ice model, version 4 (CICE4), both of which are employed in the Community Earth System Model and in the Norwegian Earth System Model (NorESM). In this study, we evaluate the effect of snow grain shape on climate simulated by NorESM in a slab ocean configuration of the model. An experiment with spherical snow grains (SPH) is compared with another (NONSPH) in which the snow shortwave single-scattering properties are based on a combination of three non-spherical snow grain shapes optimized using measurements of angular scattering by blowing snow. The key difference between these treatments is that the asymmetry parameter is smaller in the non-spherical case (0.77-0.78 in the visible region) than in the spherical case ( ≈ 0.89). Therefore, for the same effective snow grain size (or equivalently, the same specific projected area), the snow broadband albedo is higher when assuming non-spherical rather than spherical snow grains, typically by 0.02-0.03. Considering the spherical case as the baseline, this results in an instantaneous negative change in net shortwave radiation with a global-mean top-of-the-model value of ca. -0.22 W m-2. Although this global-mean radiative effect is rather modest, the impacts on the climate simulated by NorESM are substantial. The global annual-mean 2 m air temperature in NONSPH is 1.17 K lower than in SPH, with substantially larger differences at high latitudes. The climatic response is amplified by strong snow and sea ice feedbacks. It is further demonstrated that the effect of snow grain shape could be largely offset by adjusting the snow grain size. When assuming non-spherical snow grains with the parameterized grain size increased by ca. 70 %, the climatic differences to the SPH experiment become very small. Finally, the impact of assumed snow grain shape on the radiative effects of absorbing aerosols in snow is discussed.

Integration of snow management practices into a detailed snow pack model

NASA Astrophysics Data System (ADS)

Spandre, Pierre; Morin, Samuel; Lafaysse, Matthieu; Lejeune, Yves; François, Hugues; George-Marcelpoil, Emmanuelle

2016-04-01

The management of snow on ski slopes is a key socio-economic and environmental issue in mountain regions. Indeed the winter sports industry has become a very competitive global market although this economy remains particularly sensitive to weather and snow conditions. The understanding and implementation of snow management in detailed snowpack models is a major step towards a more realistic assessment of the evolution of snow conditions in ski resorts concerning past, present and future climate conditions. Here we describe in a detailed manner the integration of snow management processes (grooming, snowmaking) into the snowpack model Crocus (Spandre et al., Cold Reg. Sci. Technol., in press). The effect of the tiller is explicitly taken into account and its effects on snow properties (density, snow microstructure) are simulated in addition to the compaction induced by the weight of the grooming machine. The production of snow in Crocus is carried out with respect to specific rules and current meteorological conditions. Model configurations and results are described in detail through sensitivity tests of the model of all parameters related to snow management processes. In-situ observations were carried out in four resorts in the French Alps during the 2014-2015 winter season considering for each resort natural, groomed only and groomed plus snowmaking conditions. The model provides realistic simulations of the snowpack properties with respect to these observations. The main uncertainty pertains to the efficiency of the snowmaking process. The observed ratio between the mass of machine-made snow on ski slopes and the water mass used for production was found to be lower than was expected from the literature, in every resort. The model now referred to as "Crocus-Resort" has been proven to provide realistic simulations of snow conditions on ski slopes and may be used for further investigations. Spandre, P., S. Morin, M. Lafaysse, Y. Lejeune, H. François and E. George-Marcelpoil, Integration of snow management processes into a detailed snowpack model, Cold Reg. Sci. Technol., in press.

Distributed snow modeling suitable for use with operational data for the American River watershed.

NASA Astrophysics Data System (ADS)

Shamir, E.; Georgakakos, K. P.

2004-12-01

The mountainous terrain of the American River watershed (~4300 km2) at the Western slope of the Northern Sierra Nevada is subject to significant variability in the atmospheric forcing that controls the snow accumulation and ablations processes (i.e., precipitation, surface temperature, and radiation). For a hydrologic model that attempts to predict both short- and long-term streamflow discharges, a plausible description of the seasonal and intermittent winter snow pack accumulation and ablation is crucial. At present the NWS-CNRFC operational snow model is implemented in a semi distributed manner (modeling unit of about 100-1000 km2) and therefore lump distinct spatial variability of snow processes. In this study we attempt to account for the precipitation, temperature, and radiation spatial variability by constructing a distributed snow accumulation and melting model suitable for use with commonly available sparse data. An adaptation of the NWS-Snow17 energy and mass balance that is used operationally at the NWS River Forecast Centers is implemented at 1 km2 grid cells with distributed input and model parameters. The input to the model (i.e., precipitation and surface temperature) is interpolated from observed point data. The surface temperature was interpolated over the basin based on adiabatic lapse rates using topographic information whereas the precipitation was interpolated based on maps of climatic mean annual rainfall distribution acquired from PRISM. The model parameters that control the melting rate due to radiation were interpolated based on aspect. The study was conducted for the entire American basin for the snow seasons of 1999-2000. Validation of the Snow Water Equivalent (SWE) prediction is done by comparing to observation from 12 snow Sensors. The Snow Cover Area (SCA) prediction was evaluated by comparing to remotely sensed 500m daily snow cover derived from MODIS. The results that the distribution of snow over the area is well captured and the quantity compared to the snow gauges are well estimated in the high elevation.

Snow Physics and Meltwater Hydrology of the SSiB Model Employed for Climate Simulation Studies with GEOS 2 GCM

NASA Technical Reports Server (NTRS)

Mocko, David M.; Sud, Y. C.; Einaudi, Franco (Technical Monitor)

2000-01-01

Present-day climate models produce large climate drifts that interfere with the climate signals simulated in modelling studies. The simplifying assumptions of the physical parameterization of snow and ice processes lead to large biases in the annual cycles of surface temperature, evapotranspiration, and the water budget, which in turn causes erroneous land-atmosphere interactions. Since land processes are vital for climate prediction, and snow and snowmelt processes have been shown to affect Indian monsoons and North American rainfall and hydrology, special attention is now being given to cold land processes and their influence on the simulated annual cycle in GCMs. The snow model of the SSiB land-surface model being used at Goddard has evolved from a unified single snow-soil layer interacting with a deep soil layer through a force-restore procedure to a two-layer snow model atop a ground layer separated by a snow-ground interface. When the snow cover is deep, force-restore occurs within the snow layers. However, several other simplifying assumptions such as homogeneous snow cover, an empirical depth related surface albedo, snowmelt and melt-freeze in the diurnal cycles, and neglect of latent heat of soil freezing and thawing still remain as nagging problems. Several important influences of these assumptions will be discussed with the goal of improving them to better simulate the snowmelt and meltwater hydrology. Nevertheless, the current snow model (Mocko and Sud, 2000, submitted) better simulates cold land processes as compared to the original SSiB. This was confirmed against observations of soil moisture, runoff, and snow cover in global GSWP (Sud and Mocko, 1999) and point-scale Valdai simulations over seasonal snow regions. New results from the current snow model SSiB from the 10-year PILPS 2e intercomparison in northern Scandinavia will be presented.

Investigating the effect and uncertainties of light absorbing impurities in snow and ice on snow melt and discharge generation using a hydrologic catchment model and satellite data

NASA Astrophysics Data System (ADS)

Matt, Felix; Burkhart, John F.

2017-04-01

Light absorbing impurities in snow and ice (LAISI) originating from atmospheric deposition enhance snow melt by increasing the absorption of short wave radiation. The consequences are a shortening of the snow cover duration due to increased snow melt and, with respect to hydrologic processes, a temporal shift in the discharge generation. However, the magnitude of these effects as simulated in numerical models have large uncertainties, originating mainly from uncertainties in the wet and dry deposition of light absorbing aerosols, limitations in the model representation of the snowpack, and the lack of observable variables required to estimate model parameters and evaluate the simulated variables connected with the representation of LAISI. This leads to high uncertainties in the additional energy absorbed by the snow due to the presence of LAISI, a key variable in understanding snowpack energy-balance dynamics. In this study, we assess the effect of LAISI on snow melt and discharge generation and the involved uncertainties in a high mountain catchment located in the western Himalayas by using a distributed hydrological catchment model with focus on the representation of the seasonal snow pack. The snow albedo is hereby calculated from a radiative transfer model for snow, taking the increased absorption of short wave radiation by LAISI into account. Meteorological forcing data is generated from an assimilation of observations and high resolution WRF simulations, and LAISI mixing ratios from deposition rates of Black Carbon simulated with the FLEXPART model. To asses the quality of our simulations and the related uncertainties, we compare the simulated additional energy absorbed by the snow due to the presence of LAISI to the MODIS Dust Radiative Forcing in Snow (MODDRFS) algorithm satellite product.

Snow water equivalent mapping in Norway

NASA Astrophysics Data System (ADS)

Tveito, O. E.; Udnæs, H.-C.; Engeset, R.; Førland, E. J.; Isaksen, K.; Mengistu, Z.

2003-04-01

In high latitude area snow covers the ground large parts of the year. Information about the water volume as snow is of major importance in many respects. Flood forecasters at NVE need it in order to assess possible flood risks. Hydropower producers need it to plan the most efficient production of the water in their reservoirs, traders to estimate the potential energy available for the market. Meteorologists on their side use the information as boundary conditions in weather forecasting models. The Norwegian meteorological institute has provided snow accumulation maps for Norway for more than 50 years. These maps are now produced twice a month in the winter season. They show the accumulated precipitation in the winter season from the day the permanent snow cover is established. They do however not take melting into account, and do therefore not give a good description of the actual snow amounts during and after periods with snowmelt. Due to an increased need for a direct measure of water volumes as snow cover, met.no and NVE initialized a joint project in order to establish maps of the actual snow cover expressed in water equivalents. The project utilizes recent developments in the use of GIS in spatial modeling. Daily precipitation and temperature are distributed in space by using objective spatial interpolation methods. The interpolation considers topographical and other geographical parameters as well as weather type information. A degree-day model is used at each modeling point to calculate snow-accumulation and snowmelt. The maps represent a spatial scale of 1x1 km2. The modeled snow reservoir is validated by snow pillow values as well traditional snow depth observations. Preliminary results show that the new snow modeling approach reproduces the snow water equivalent well. The spatial approach also opens for a wide use in the terms of areal analysis.

A new spatial snow distribution in hydrological models parameterized from observed spatial variability of precipitation.

NASA Astrophysics Data System (ADS)

Skaugen, Thomas; Weltzien, Ingunn

2016-04-01

The traditional catchment hydrological model with its many free calibration parameters is not a well suited tool for prediction under conditions for which is has not been calibrated. Important tasks for hydrological modelling such as prediction in ungauged basins and assessing hydrological effects of climate change are hence not solved satisfactory. In order to reduce the number of calibration parameters in hydrological models we have introduced a new model which uses a dynamic gamma distribution as the spatial frequency distribution of snow water equivalent (SWE). The parameters are estimated from observed spatial variability of precipitation and the magnitude of accumulation and melting events and are hence not subject to calibration. The relationship between spatial mean and variance of precipitation is found to follow a pattern where decreasing temporal correlation with increasing accumulation or duration of the event leads to a levelling off or even a decrease of the spatial variance. The new model for snow distribution is implemented in the, already parameter parsimonious, DDD (Distance Distribution Dynamics) hydrological model and was tested for 71 Norwegian catchments. We compared the new snow distribution model with the current operational snow distribution model where a fixed, calibrated coefficient of variation parameterizes a log-normal model for snow distribution. Results show that the precision of runoff simulations is equal, but that the new snow distribution model better simulates snow covered area (SCA) when compared with MODIS satellite derived snow cover. In addition, SWE is simulated more realistically in that seasonal snow is melted out and the building up of "snow towers" is prevented and hence spurious trends in SWE.

Analysis of C and Ku band ocean backscatter measurements under low-wind conditions

NASA Astrophysics Data System (ADS)

Carswell, James R.; Donnelly, William J.; McIntosh, Robert E.; Donelan, Mark A.; Vandemark, Douglas C.

1999-09-01

Airborne ocean backscatter measurements at C and Ku band wavelengths obtained in low to moderate-wind conditions are presented. The differences between the low-wind backscatter data and the CMOD4 and SASS-II models are reported. The measurements show that the upwind/crosswind backscatter ratio is greater than predicted. These large upwind/crosswind backscatter ratios are attributed to a rapid decrease in the crosswind backscatter at low winds. Qualitative agreement with the composite surface model proposed by Donelan and Pierson suggests the rapid decrease in the crosswind backscatter may be caused by viscous dampening of the Bragg-resonant capillary-gravity waves. We show that for larger antenna footprints typical of satellite-based scatterometers, the variability in the observed wind field smooths out the backscatter response such that the rapid decrease in the crosswind direction is not observed.

Polarimetric C-/X-band Synthetic Aperture Radar Observations of Melting Sea Ice in the Canadian Arctic Archipelago

NASA Astrophysics Data System (ADS)

Casey, J. A.; Beckers, J. F.; Brossier, E.; Haas, C.

2013-12-01

Operational ice information services rely heavily on space-borne synthetic aperture radar (SAR) data for the production of ice charts to meet their mandate of providing timely and accurate sea ice information to support safe and efficient marine operations. During the summer melt period, the usefulness of SAR data for sea ice monitoring is limited by the presence of wet snow and melt ponds on the ice surface, which can mask the signature of the underlying ice. This is a critical concern for ice services whose clients (e.g. commercial shipping, cruise tourism, resource exploration and extraction) are most active at this time of year when sea ice is at its minimum extent, concentration and thickness. As a result, there is a need to further quantify the loss of ice information in SAR data during the melt season and to identify what information can still be retrieved about ice surface conditions and melt pond evolution at this time of year. To date the majority of studies have been limited to analysis of single-polarization C-band SAR data. This study will investigate the potential complimentary and unique sea ice information that polarimetric C- and X-band SAR data can provide to supplement the information available from traditional single co-polarized C-band SAR data. A time-series of polarimetric C- and X-band SAR data was acquired over Jones Sound in the Canadian Arctic Archipelago, in the vicinity of the Grise Fiord, Nunavut. Five RADARSAT-2 Wide Fine Quad-pol images and 11 TerraSAR-X StripMap dual-pol (HH/VV) images were acquired. The time-series begins at the onset of melt in early June and extends through advanced melt conditions in late July. Over this period several ponding and drainage events and two snowfall events occurred. Field observations of sea ice properties were collected using an Ice Mass Balance (IMB) buoy, hourly photos from a time-lapse camera deployed on a coastal cliff, and manual in situ measurements of snow thickness and melt pond depth. Where available, clear-sky data from optical sensors (MODIS, Landsat-8, and WorldView) are also used to provide supplementary information on melt pond coverage and evolution. Meteorological data are available from an Environment Canada weather station in Grise Fiord. In this presentation we will discuss the sea ice information provided by each polarization and frequency and evaluate the impact of melt pond evolution on SAR backscatter. Results to date indicate that C- and X-band provide predominantly redundant information, and cross-polarized backscatter (only acquired at C-band) is often very low and near the system noise floor. Early in the melt season a thick wet snow pack is present and both frequencies provide very little ice information. This is attributed to the strong attenuation of the microwave signal by the wet snow. At this time the underlying ice is effectively obscured. During heavily ponded periods backscatter is highly variable, attributed to changing winds and thus variable melt pond surface roughness. In the final week of observations the fast ice in the region is breaking up and open water is present in some images. In these images C-band appears to provide greater contrast between the melting ice and open water than X-band. Analysis of polarimetric parameters is ongoing.

Electromagnetic backscattering from one-dimensional drifting fractal sea surface II: Electromagnetic backscattering model

NASA Astrophysics Data System (ADS)

Tao, Xie; William, Perrie; Shang-Zhuo, Zhao; He, Fang; Wen-Jin, Yu; Yi-Jun, He

2016-07-01

Sea surface current has a significant influence on electromagnetic (EM) backscattering signals and may constitute a dominant synthetic aperture radar (SAR) imaging mechanism. An effective EM backscattering model for a one-dimensional drifting fractal sea surface is presented in this paper. This model is used to simulate EM backscattering signals from the drifting sea surface. Numerical results show that ocean currents have a significant influence on EM backscattering signals from the sea surface. The normalized radar cross section (NRCS) discrepancies between the model for a coupled wave-current fractal sea surface and the model for an uncoupled fractal sea surface increase with the increase of incidence angle, as well as with increasing ocean currents. Ocean currents that are parallel to the direction of the wave can weaken the EM backscattering signal intensity, while the EM backscattering signal is intensified by ocean currents propagating oppositely to the wave direction. The model presented in this paper can be used to study the SAR imaging mechanism for a drifting sea surface. Project supported by the National Natural Science Foundation of China (Grant No. 41276187), the Global Change Research Program of China (Grant No. 2015CB953901), the Priority Academic Program Development of Jiangsu Higher Education Institutions, China, the Program for the Innovation Research and Entrepreneurship Team in Jiangsu Province, China, the Canadian Program on Energy Research and Development, and the Canadian World Class Tanker Safety Service Program.

What Does a Multilayer Canopy Model Tell Us About Our Current Understanding of Snow-Canopy Unloading?

NASA Astrophysics Data System (ADS)

McGowan, L. E.; Paw U, K. T.; Dahlke, H. E.

2017-12-01

In the Western U.S., future water resources depend on the forested mountain snowpack. The variations in and estimates of forest mountain snow volume are vital to projecting annual water availability; yet, snow forest processes are not fully known. Most snow models calculate snow-canopy unloading based on time, temperature, Leaf Area Index (LAI), and/or wind speed. While models crudely consider the canopy shape via LAI, current models typically do not consider the vertical canopy structure or varied energetics within multiple canopy layers. Vertical canopy structure influences the spatiotemporal distribution of snow, and therefore ultimately determines the degree and extent by which snow alters both the surface energy balance and water availability. Within the canopy both the snowpack and energetic exposures to the snowpack (wind, shortwave and longwave radiation, turbulent heat fluxes etc.) vary widely in the vertical. The water and energy balance in each layer is dependent on all other layers. For example, increased snow canopy content in the top of the canopy will reduce available shortwave radiation at the bottom and snow unloading in a mid-layer can cascade and remove snow from all the lower layers. We examined vertical interactions and structures of the forest canopy on the impact of unloading utilizing the Advanced Canopy-Atmosphere-Soil-Algorithm (ACASA), a multilayer soil-vegetation-atmosphere numerical model based on higher-order closure of turbulence equations. Our results demonstrate how a multilayer model can be used to elucidate the physical processes of snow unloading, and could help researchers better parameterize unloading in snow-hydrology models.

Snow Radar Derived Surface Elevations and Snow Depths Multi-Year Time Series over Greenland Sea-Ice During IceBridge Campaigns

NASA Astrophysics Data System (ADS)

Perkovic-Martin, D.; Johnson, M. P.; Holt, B.; Panzer, B.; Leuschen, C.

2012-12-01

This paper presents estimates of snow depth over sea ice from the 2009 through 2011 NASA Operation IceBridge [1] spring campaigns over Greenland and the Arctic Ocean, derived from Kansas University's wideband Snow Radar [2] over annually repeated sea-ice transects. We compare the estimates of the top surface interface heights between NASA's Atmospheric Topographic Mapper (ATM) [3] and the Snow Radar. We follow this by comparison of multi-year snow depth records over repeated sea-ice transects to derive snow depth changes over the area. For the purpose of this paper our analysis will concentrate on flights over North/South basin transects off Greenland, which are the closest overlapping tracks over this time period. The Snow Radar backscatter returns allow for surface and interface layer types to be differentiated between snow, ice, land and water using a tracking and classification algorithm developed and discussed in the paper. The classification is possible due to different scattering properties of surfaces and volumes at the radar's operating frequencies (2-6.5 GHz), as well as the geometries in which they are viewed by the radar. These properties allow the returns to be classified by a set of features that can be used to identify the type of the surface or interfaces preset in each vertical profile. We applied a Support Vector Machine (SVM) learning algorithm [4] to the Snow Radar data to classify each detected interface into one of four types. The SVM algorithm was trained on radar echograms whose interfaces were visually classified and verified against coincident aircraft data obtained by CAMBOT [5] and DMS [6] imaging sensors as well as the scanning ATM lidar. Once the interface locations were detected for each vertical profile we derived a range to each interface that was used to estimate the heights above the WGS84 ellipsoid for direct comparisons with ATM. Snow Radar measurements were calibrated against ATM data over areas free of snow cover and over GPS land surveyed areas of Thule and Sondrestrom air bases. The radar measurements were compared against the ATM and the GPS measurements that were located in the estimated radar footprints, which resulted in an overall error of ~ 0.3 m between the radar and ATM. The agreement between ATM and GPS survey is within +/- 0.1 m. References: [1] http://www.nasa.gov/mission_pages/icebridge/ [2] Panzer, B. et. al, "An ultra-wideband, microwave radar for measuring snow thickness on sea ice and mapping near-surface internal layers in polar firn," Submitted to J. of Glaciology Instr. and Tech., July 23, 2012. [3] Krabill, William B. 2009 and 2011, updated current year. IceBridge ATM L1B Qfit Elevation and Return Strength. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media. [4] Chih-Chung Chang and Chih-Jen Lin. "Libsvm: a library for support vector machines", ACM Transactions on Intelligent Systems and Technology, 2:2:27:1-27:27, 2011. [5] Krabill, William B. 2009 and 2011, updated current year. IceBridge CAMBOT L1B Geolocated Images, [2009-04-25, 2011-04-15]. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media. [6] Dominguez, Roseanne. 2011, updated current year. IceBridge DMS L1B Geolocated and Orthorectified Images. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media

Modeling the influence of snow cover temperature and water content on wet-snow avalanche runout

NASA Astrophysics Data System (ADS)

Valero, Cesar Vera; Wever, Nander; Christen, Marc; Bartelt, Perry

2018-03-01

Snow avalanche motion is strongly dependent on the temperature and water content of the snow cover. In this paper we use a snow cover model, driven by measured meteorological data, to set the initial and boundary conditions for wet-snow avalanche calculations. The snow cover model provides estimates of snow height, density, temperature and liquid water content. This information is used to prescribe fracture heights and erosion heights for an avalanche dynamics model. We compare simulated runout distances with observed avalanche deposition fields using a contingency table analysis. Our analysis of the simulations reveals a large variability in predicted runout for tracks with flat terraces and gradual slope transitions to the runout zone. Reliable estimates of avalanche mass (height and density) in the release and erosion zones are identified to be more important than an exact specification of temperature and water content. For wet-snow avalanches, this implies that the layers where meltwater accumulates in the release zone must be identified accurately as this defines the height of the fracture slab and therefore the release mass. Advanced thermomechanical models appear to be better suited to simulate wet-snow avalanche inundation areas than existing guideline procedures if and only if accurate snow cover information is available.

Mapping the spatial distribution and time evolution of snow water equivalent with passive microwave measurements

USGS Publications Warehouse

Guo, J.; Tsang, L.; Josberger, E.G.; Wood, A.W.; Hwang, J.-N.; Lettenmaier, D.P.

2003-01-01

This paper presents an algorithm that estimates the spatial distribution and temporal evolution of snow water equivalent and snow depth based on passive remote sensing measurements. It combines the inversion of passive microwave remote sensing measurements via dense media radiative transfer modeling results with snow accumulation and melt model predictions to yield improved estimates of snow depth and snow water equivalent, at a pixel resolution of 5 arc-min. In the inversion, snow grain size evolution is constrained based on pattern matching by using the local snow temperature history. This algorithm is applied to produce spatial snow maps of Upper Rio Grande River basin in Colorado. The simulation results are compared with that of the snow accumulation and melt model and a linear regression method. The quantitative comparison with the ground truth measurements from four Snowpack Telemetry (SNOTEL) sites in the basin shows that this algorithm is able to improve the estimation of snow parameters.

Experimental measurement and modeling of snow accumulation and snowmelt in a mountain microcatchment

NASA Astrophysics Data System (ADS)

Danko, Michal; Krajčí, Pavel; Hlavčo, Jozef; Kostka, Zdeněk; Holko, Ladislav

2016-04-01

Fieldwork is a very useful source of data in all geosciences. This naturally applies also to the snow hydrology. Snow accumulation and snowmelt are spatially very heterogeneous especially in non-forested, mountain environments. Direct field measurements provide the most accurate information about it. Quantification and understanding of processes, that cause these spatial differences are crucial in prediction and modelling of runoff volumes in spring snowmelt period. This study presents possibilities of detailed measurement and modeling of snow cover characteristics in a mountain experimental microcatchment located in northern part of Slovakia in Western Tatra mountains. Catchment area is 0.059 km2 and mean altitude is 1500 m a.s.l. Measurement network consists of 27 snow poles, 3 small snow lysimeters, discharge measurement device and standard automatic weather station. Snow depth and snow water equivalent (SWE) were measured twice a month near the snow poles. These measurements were used to estimate spatial differences in accumulation of SWE. Snowmelt outflow was measured by small snow lysimeters. Measurements were performed in winter 2014/2015. Snow water equivalent variability was very high in such a small area. Differences between particular measuring points reached 600 mm in time of maximum SWE. The results indicated good performance of a snow lysimeter in case of snowmelt timing identification. Increase of snowmelt measured by the snow lysimeter had the same timing as increase in discharge at catchment's outlet and the same timing as the increase in air temperature above the freezing point. Measured data were afterwards used in distributed rainfall-runoff model MIKE-SHE. Several methods were used for spatial distribution of precipitation and snow water equivalent. The model was able to simulate snow water equivalent and snowmelt timing in daily step reasonably well. Simulated discharges were slightly overestimated in later spring.

An Integrated Uncertainty Analysis and Ensemble-based Data Assimilation Framework for Operational Snow Predictions

NASA Astrophysics Data System (ADS)

He, M.; Hogue, T. S.; Franz, K.; Margulis, S. A.; Vrugt, J. A.

2009-12-01

The National Weather Service (NWS), the agency responsible for short- and long-term streamflow predictions across the nation, primarily applies the SNOW17 model for operational forecasting of snow accumulation and melt. The SNOW17-forecasted snowmelt serves as an input to a rainfall-runoff model for streamflow forecasts in snow-dominated areas. The accuracy of streamflow predictions in these areas largely relies on the accuracy of snowmelt. However, no direct snowmelt measurements are available to validate the SNOW17 predictions. Instead, indirect measurements such as snow water equivalent (SWE) measurements or discharge are typically used to calibrate SNOW17 parameters. In addition, the forecast practice is inherently deterministic, lacking tools to systematically address forecasting uncertainties (e.g., uncertainties in parameters, forcing, SWE and discharge observations, etc.). The current research presents an Integrated Uncertainty analysis and Ensemble-based data Assimilation (IUEA) framework to improve predictions of snowmelt and discharge while simultaneously providing meaningful estimates of the associated uncertainty. The IUEA approach uses the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM) to simultaneously estimate uncertainties in model parameters, forcing, and observations. The robustness and usefulness of the IUEA-SNOW17 framework is evaluated for snow-dominated watersheds in the northern Sierra Mountains, using the coupled IUEA-SNOW17 and an operational soil moisture accounting model (SAC-SMA). Preliminary results are promising and indicate successful performance of the coupled IUEA-SNOW17 framework. Implementation of the SNOW17 with the IUEA is straightforward and requires no major modification to the SNOW17 model structure. The IUEA-SNOW17 framework is intended to be modular and transferable and should assist the NWS in advancing the current forecasting system and reinforcing current operational forecasting skill.

A novel linear physical model for remote sensing of snow wetness and snow density using the visible and infrared bands

NASA Astrophysics Data System (ADS)

Varade, D. M.; Dikshit, O.

2017-12-01

Modeling and forecasting of snowmelt runoff are significant for understanding the hydrological processes in the cryosphere which requires timely information regarding snow physical properties such as liquid water content and density of snow in the topmost layer of the snowpack. Both the seasonal runoffs and avalanche forecasting are vastly dependent on the inherent physical characteristics of the snowpack which are conventionally measured by field surveys in difficult terrains at larger impending costs and manpower. With advances in remote sensing technology and the increase in the availability of satellite data, the frequency and extent of these surveys could see a declining trend in future. In this study, we present a novel approach for estimating snow wetness and snow density using visible and infrared bands that are available with most multi-spectral sensors. We define a trapezoidal feature space based on the spectral reflectance in the near infrared band and the Normalized Differenced Snow Index (NDSI), referred to as NIR-NDSI space, where dry snow and wet snow are observed in the left diagonal upper and lower right corners, respectively. The corresponding pixels are extracted by approximating the dry and wet edges which are used to develop a linear physical model to estimate snow wetness. Snow density is then estimated using the modeled snow wetness. Although the proposed approach has used Sentinel-2 data, it can be extended to incorporate data from other multi-spectral sensors. The estimated values for snow wetness and snow density show a high correlation with respect to in-situ measurements. The proposed model opens a new avenue for remote sensing of snow physical properties using multi-spectral data, which were limited in the literature.

Effects of snow grain non-sphericity on climate simulations: Sensitivity tests with the NorESM model

NASA Astrophysics Data System (ADS)

Räisänen, Petri; Makkonen, Risto; Kirkevåg, Alf

2017-04-01

Snow grains are non-spherical and generally irregular in shape. Still, in radiative transfer calculations, they are often treated as spheres. This also applies to the computation of snow albedo in the Snow, Ice, and Aerosol Radiation (SNICAR) model and in the Los Alamos sea ice model, version 4 (CICE4), both of which are employed in the Community Earth System Model and in the Norwegian Earth System Model (NorESM). In this work, we evaluate the effect of snow grain shape on climate simulated by NorESM in a slab ocean configuration of the model. An experiment with spherical snow grains (SPH) is compared with another (NONSPH) in which the snow shortwave single-scattering properties are based on a combination of non-spherical snow grain shapes optimized using measurements of angular scattering by blowing snow. The key difference between these treatments is that the asymmetry parameter is smaller in the non-spherical case (≈ 0.78 in the visible region) than in the spherical case (≈ 0.89). Therefore, for a given snow grain size, the use of non-spherical snow grains yields a higher snow broadband albedo, typically by ≈0.03. Consequently, considering the spherical case as the baseline, the use of non-spherical snow grains results in a negative radiative forcing (RF), with a global-mean top-of-the-model value of ≈ -0.22 W m-2. Although this global-mean RF is modest, it has a rather substantial impact on the climate simulated by NoRESM. In particular, the global annual-mean 2-m air temperature in NONSPH is 1.17 K lower than in SPH, with substantially larger differences at high latitudes. The climatic response is amplified by strong snow and sea ice feedbacks. It is further found that the difference between NONSPH and SPH could be largely "tuned away" by adjusting the snow grain size in the NONSPH experiment by ≈ 70%. The impact of snow grain shape on the radiative effect (RE) of absorbing aerosols in snow (black carbon and mineral dust) is also discussed. For an optically thick snowpack with a given snow grain effective size, the absorbing aerosol RE is smaller for non-spherical than for spherical snow grains. The reason for this is that due to the lower asymmetry parameter of the non-spherical snow grains, solar radiation does not penetrate as deep in snow as in the case of spherical snow grains. However, in a climate model simulation, the RE is sensitive to patterns of aerosol deposition and simulated snow cover. In fact, the global land-area mean absorbing aerosol RE is larger in the NONSPH than SPH experiment (0.193 vs. 0.168 W m-2), owing to later snowmelt in spring.

A Backscattering Enhanced Microwave Canopy Scattering Model Based On MIMICS

NASA Astrophysics Data System (ADS)

Shen, X.; Hong, Y.; Qin, Q.; Chen, S.; Grout, T.

2010-12-01

For modeling microwave scattering of vegetated areas, several microwave canopy scattering models, based on the vectorized radiative transfer equation (VRT) that use different solving techniques, have been proposed in the past three decades. As an iterative solution of VRT at low orders, the Michigan Microwave Canopy Scattering Model (MIMICS) gives an analytical expression for calculating scattering as long as the volume scattering is not too strong. The most important usage of such models is to predict scattering in the backscattering direction. Unfortunately, the simplified assumption of MIMICS is that the scattering between the ground and trunk layers only includes the specular reflection. As a result, MIMICS includes a dominant coherent term which vanishes in the backscattering direction because this term contains a delta function factor of zero in this direction. This assumption needs reconsideration for accurately calculating the backscattering. In the framework of MIMICS, any incoherent terms that involve surface scattering factors must at least undergo surface scattering twice and volume scattering once. Therefore, these incoherent terms are usually very weak. On the other hand, due to the phenomenon of backscattering enhancement, the surface scattering in the backscattering direction is very strong compared to most other directions. Considering the facts discussed above, it is reasonable to add a surface backscattering term to the last equation of the boundary conditions of MIMICS. More terms appear in the final result including a backscattering coherent term which enhances the backscattering. The modified model is compared with the original MIMICS (version 1.0) using JPL/AIRSAR data from NASA Campaign Soil Moisture Experimental 2003 (SMEX03) and Washita92. Significant improvement is observed.

Combining model and satellite data to investigate the effect of light absorbing impurities on snow melt and discharge generation

NASA Astrophysics Data System (ADS)

Matt, F.; Burkhart, J. F.

2017-12-01

Light absorbing impurities in snow and ice (LAISI) originating from atmospheric deposition enhance snow melt by increasing the absorption of solar radiation. The consequences are a shortening of the snow cover duration due to increased snow melt and, with respect to hydrologic processes, a temporal shift in the discharge generation. However, the effects as simulated in numerical models have large uncertainties. These uncertainties originate mainly from uncertainties in the wet and dry deposition of light absorbing aerosols, limitations in the model representation of the snowpack, and the lack of observable variables required to estimate model parameters. This leads to high uncertainties in the additional energy absorbed by the snow due to the presence of LAISI (the so called radiative forcing of LAISI), a key variable in understanding snowpack energy-balance dynamics. In this study, we present an approach combining distributed model simulations on the catchment scale and remotely sensed radiative forcing from LAISI in order to evaluate and improve model predictions. In a case study, we assess the effect of LAISI on snow melt and discharge generation in a high mountain catchment located in the western Himalaya using the distributed hydrologic model, Shyft. The snow albedo is hereby calculated from a radiative transfer model for snow, taking the increased absorption of solar radiation by LAISI into account. LAISI mixing ratios in snow are determined from atmospheric aerosol deposition rates. To asses the quality of our simulations, we model the instantaneous clear sky radiative forcing at MODIS overpass times, and compare it to the MODIS Dust Radiative Forcing in Snow (MODDRFS) satellite product. By scaling the deposition input to the model, we can optimize the simulated radiative forcing towards the satellite observations.

Evaluating Multispectral Snowpack Reflectivity With Changing Snow Correlation Lengths

NASA Technical Reports Server (NTRS)

Kang, Do Hyuk; Barros, Ana P.; Kim, Edward J.

2016-01-01

This study investigates the sensitivity of multispectral reflectivity to changing snow correlation lengths. Matzler's ice-lamellae radiative transfer model was implemented and tested to evaluate the reflectivity of snow correlation lengths at multiple frequencies from the ultraviolet (UV) to the microwave bands. The model reveals that, in the UV to infrared (IR) frequency range, the reflectivity and correlation length are inversely related, whereas reflectivity increases with snow correlation length in the microwave frequency range. The model further shows that the reflectivity behavior can be mainly attributed to scattering rather than absorption for shallow snowpacks. The largest scattering coefficients and reflectivity occur at very small correlation lengths (approximately 10(exp -5 m) for frequencies higher than the IR band. In the microwave range, the largest scattering coefficients are found at millimeter wavelengths. For validation purposes, the ice-lamella model is coupled with a multilayer snow physics model to characterize the reflectivity response of realistic snow hydrological processes. The evolution of the coupled model simulated reflectivities in both the visible and the microwave bands is consistent with satellite-based reflectivity observations in the same frequencies. The model results are also compared with colocated in situ snow correlation length measurements (Cold Land Processes Field Experiment 2002-2003). The analysis and evaluation of model results indicate that the coupled multifrequency radiative transfer and snow hydrology modeling system can be used as a forward operator in a data-assimilation framework to predict the status of snow physical properties, including snow correlation length.

Developing a hydrological model in the absence of field data

NASA Astrophysics Data System (ADS)

Sproles, E. A.; Orrego Nelson, C.; Kerr, T.; Lopez Aspe, D.

2014-12-01

We present two runoff models that use remotely-sensed snow cover products from the Moderate Resolution Imaging Spectrometer (MODIS) as the first order hydrologic input. These simplistic models are the first step in developing an operational model for the Elqui River watershed located in northern Central Chile (30°S). In this semi-arid region, snow and glacier melt are the dominant hydrologic inputs where annual precipitation is limited to three or four winter events. Unfortunately winter access to the Andean Cordillera where snow accumulates is limited. While a monitoring network to measure snow where it accumulates in the upper elevations is under development, management decisions regarding water resources cannot wait. The two models we present differ in structure. The first applies a Monte Carlo approach to determine relationships between lagged changes in monthly snow cover frequency and monthly discharge. The second is a modified degree-day melt model, utilizing the MODIS snow cover product to determine where and when snow melt occurs. These models are not watershed specific and are applicable in other regions where snow dominates hydrologic inputs, but measurements are minimal.

Improving snow water equivalent simulations in an alpine basin using blended gage precipitation and snow pillow measurements

NASA Astrophysics Data System (ADS)

Sohrabi, M.; Safeeq, M.; Conklin, M. H.

2017-12-01

Snowpack is a critical freshwater reservoir that sustains ecosystem, natural habitat, hydropower, agriculture, and urban water supply in many areas around the world. Accurate estimation of basin scale snow water equivalent (SWE), through both measurement and modeling, has been significantly recognized to improve regional water resource management. Recent advances in remote data acquisition techniques have improved snow measurements but our ability to model snowpack evolution is largely hampered by poor knowledge of inherently variable high-elevation precipitation patterns. For a variety of reasons, majority of the precipitation gages are located in low and mid-elevation range and function as drivers for basin scale hydrologic modeling. Here, we blend observed gage precipitation from low and mid-elevation with point observations of SWE from high-elevation snow pillow into a physically based snow evolution model (SnowModel) to better represent the basin-scale precipitation field and improve snow simulations. To do this, we constructed two scenarios that differed in only precipitation. In WTH scenario, we forced the SnowModel using spatially distributed gage precipitation data. In WTH+SP scenario, the model was forced with spatially distributed precipitation data derived from gage precipitation along with observed precipitation from snow pillows. Since snow pillows do not directly measure precipitation, we uses positive change in SWE as a proxy for precipitation. The SnowModel was implemented at daily time step and 100 m resolution for the Kings River Basin, USA over 2000-2014. Our results show an improvement in snow simulation under WTH+SP as compared to WTH scenario, which can be attributed to better representation in high-elevation precipitation patterns under WTH+SP. The average Nash Sutcliffe efficiency over all snow pillow and course sites was substantially higher for WTH+SP (0.77) than for WTH scenario (0.47). The maximum difference in observed and simulated peak SWE was 810 mm for WTH and 380 mm for WTH+SP, which led to underestimation of snow season length and melt rate by up to 30 days and 12 mm/day, respectively, in WTH scenario. These results indicate that point scale snow observations at higher elevation can be used to improve precipitation input to hydrologic modeling in mountainous basins.

(abstract) Monitoring Seasonal Change in Taiga Forests Using ERS-1 SAR Data

NASA Technical Reports Server (NTRS)

Way, JoBea; Rignot, Eric; McDonald, Kyle; Viereck, Leslie; Williams, Cynthia; Adams, Phyllis; Payne, Cheryl; Wood, William

1993-01-01

Sensitivity of radar backscatter to the dielectric and geometric character of forested regions suggests significant changes in backscatter are expected with season due to freezing temperatures, snow, wind, leaf fall, and drought. The first European Remote Sensing Satellite, ERS-1, offers a unique opportunity to monitor a complete seasonal cycle for the Alaskan taiga forest ecosystem with synthetic aperture radar. During the 3-day repeat Commissioning Phase of ERS-1, from August 1991to December 1991, ERS-1 SAR data were collected in the region of Manley Hot Springs, Alaska, along the Tanana River, west of Fairbanks. In parallel with the SAR data collection, meteorological data from three weather stations positioned in three forest stands were collected continuously along with in situ measurements of the dielectric and moisture properties of the canopy and of ground cover which were collected during each overflight. The in situ data were collected in floodplain forest stands dominated by balsam poplar, white spruce, and black spruce. These results from the Commissioning Phase as well as preliminary results from the 35-day Repeat Phase will be presented.

Snow in Earth System Models: Recent Progress and Future Challenges

NASA Astrophysics Data System (ADS)

Clark, M. P.; Slater, A. G.

2016-12-01

Snow is the most variable of terrestrial boundary conditions. Some 50 million km^2 of the Northern Hemisphere typically sees periods of accumulation and ablation in the annual cycle. The wonderous properties of snow, such as high albedo, thermal insulation and its ability to act as a water store make it an integral part of the global climate system. Earliest inclusions of snow within climate models were simple adjustments to albedo and a moisture store. Modern Earth Syetem Models now represent snow through a myriad of model architectures and parameterizations that span a broad range of complexity. Understanding the impacts of modeling decisions upon simulation of snow and other Earth System components (either directly or via feedbacks) is an ongoing area of research. Snow models are progressing with multi-layer representations and capabilities such as complex albedo schemes that include contaminants. While considerable advances have been made, numerous challenges also remain. Simply getting a grasp on the mass of snow (seasonal or permanent) has proved more difficult than expected over the past 30 years. Snow interactions with vegetation has improved but the details of vegetation masking and emergence are still limited. Inclusion of blowing snow processes, in terms of transport and sublimation, is typically rare and sublimation remains a difficult quantity to measure. Contemplation of snow crystal form within models and integration with radiative transfer schemes for better understanding of full spectrum interations (from UV to long microwave) may simultaneously advance simulation and remote sensing. A series of international modeling experiments and directed field campaigns are planned in the near future with the aim of pushing our knowledge forward.

The Electrical Self-Potential Method as a Non-Intrusive Snow-Hydrological Sensor

NASA Astrophysics Data System (ADS)

Kulessa, B.; Thompson, S. S.; Luethi, M. P.; Essery, R.

2015-12-01

Building on growing momentum in the application of geophysical techniques to snow problems and, specifically, on new theory and an electrical geophysical snow hydrological model published recently; we demonstrate for the first time that the electrical self-potential geophysical technique can sense in-situ bulk meltwater fluxes. This has broad and immediate implications for snow measurement practice, modelling and operational snow forecasting. Our ability to measure, quantify and assimilate hydrological properties and processes of snow in operational models is disproportionally poor compared to the significance of seasonal snowmelt as a global water resource and major risk factor in flood and avalanche forecasting. Encouraged by recent theoretical, modelling and laboratory work, we show here that the diurnal evolution of aerially-distributed self-potential magnitudes closely track those of bulk meltwater fluxes in melting in-situ snowpacks at Rhone and Jungfraujoch glaciers, Switzerland. Numerical modelling infers temporally-evolving liquid water contents in the snowpacks on successive days in close agreement with snow-pit measurements. Muting previous concerns, the governing physical and chemical properties of snow and meltwater became temporally invariant for modelling purposes. Because measurement procedure is straightforward and readily automated for continuous monitoring over significant spatial scales, we conclude that the self-potential geophysical method is a highly-promising non-intrusive snow-hydrological sensor for measurement practice, modelling and operational snow forecasting.

Boundary Layer Temporal Evolution Observed by Doppler LiDAR Upwind of a Lake-Effect Snow Event

NASA Astrophysics Data System (ADS)

King, D.; Kristovich, D.

2017-12-01

Lake-effect snow (LES) annually affects the Great Lakes region. It can impact communities economically, recreationally and perhaps result in fatalities. Previous studies have shown that the upwind shore of a LES system tends to be a region for mesoscale downdrafts. This study intends to show how the depth of the boundary (BL) on the upwind shore and how it could influence a LES event downstream. From December 7-10, 2016, we deployed a Halo-Photonics Streamline pulsed Doppler LiDAR at Illinois Beach State Park in Zion, Illinois, to observe the evolving BL wind structure and depth upwind of the growing LES over eastern Lake Michigan. The LiDAR scans included vertical stare, velocity-azimuth display (VAD), and range height indicator (RHI) modes to display the BL depth as well as LES cloud band structure. The BL depth was observed by turbulent velocities and backscatter profiles from the LiDAR. The BL was found to be approximately one kilometer during the day, and reduced to near surface at night. The BL depth, overall, increased from the 8th to the 9th, while snowfall rate decreased on the downwind shore. This suggests that local BL dynamics have less influence on downwind convection and snow production than originally anticipated. The larger scale environment appears to play a larger role in the multi-day BL evolution.

Assessing seasonal backscatter variations with respect to uncertainties in soil moisture retrieval in Siberian tundra regions

NASA Astrophysics Data System (ADS)

Högström, Elin; Trofaier, Anna Maria; Gouttevin, Isabella; Bartsch, Annett

2015-04-01

Data from the Advanced Scatterometer (ASCAT) instrument provide the basis of a near real-time, coarse scale, global soil moisture product. Numerous studies have shown the applicability of this product, including recent operational use for numerical weather forecasts. Soil moisture is a key element in the global cycles of water, energy and carbon. Among many application areas, it is essential for the understanding of permafrost development in a future climate change scenario. Dramatic climate changes are expected in the Arctic, where ca 25% of the land is underlain by permafrost, and it is to a large extent remote and inaccessible. The availability and applicability of satellite derived land-surface data relevant for permafrost studies, such as surface soil moisture, is thus crucial to landscape-scale analyses of climate-induced change. However, there are challenges in the soil moisture retrieval that are specific to the Arctic. This study investigates backscatter variability unrelated to soil moisture variations in order to understand the possible impact on the soil moisture retrieval. The focus is on tundra lakes, which are a common feature in the Arctic and are expected to affect the retrieval. ENVISAT Advanced Synthetic Aperture Radar (ASAR) Wide Swath (120 m) data are used to resolve lakes and later understand and quantify their impacts on Metop ASCAT (25 km) soil moisture retrieval during the snow free period. Sites of interest are chosen according to high or low agreement between output from the land surface model ORCHIDEE and ASCAT derived SSM. The results show that in most cases low model agreement is related to high water fraction. The water fraction correlates with backscatter deviations (relative to a smooth water surface reference image) within the ASCAT footprint areas (R = 0.91-0.97). Backscatter deviations of up to 5 dB can occur in areas with less than 50% water fraction and an assumed soil moisture related range (sensitivity) of 7 dB in the ASCAT data. The study demonstrates that the usage of higher spatial resolution data than currently available for SSM is required in lowland permafrost environments. Furthermore, the results show that in the flat and open Arctic tundra areas, wind likely affects the soil moisture retrieval procedure rather than rain or remaining ice cover on the water surface. Therefore, the potential of a wind correction method is explored for sites where meteorological field data are available.

Do we need a dynamic snow depth threshold when comparing hydrological models with remote sensing products in mountain catchments?

NASA Astrophysics Data System (ADS)

Engel, Michael; Bertoldi, Giacomo; Notarnicola, Claudia; Comiti, Francesco

2017-04-01

To assess the performance of simulated snow cover of hydrological models, it is common practice to compare simulated data with observed ones derived from satellite images such as MODIS. However, technical and methodological limitations such as data availability of MODIS products, its spatial resolution or difficulties in finding appropriate parameterisations of the model need to be solved previously. Another important assumption usually made is the threshold of minimum simulated snow depth, generally set to 10 mm of snow depth, to respect the MODIS detection thresholds for snow cover. But is such a constant threshold appropriate for complex alpine terrain? How important is the impact of different snow depth thresholds on the spatial and temporal distribution of the pixel-based overall accuracy (OA)? To address this aspect, we compared the snow covered area (SCA) simulated by the GEOtop 2.0 snow model to the daily composite 250 m EURAC MODIS SCA in the upper Saldur basin (61 km2, Eastern Italian Alps) during the period October 2011 - October 2013. Initially, we calibrated the snow model against snow depths and snow water equivalents at point scale, taken from measurements at different meteorological stations. We applied different snow depth thresholds (0 mm, 10 mm, 50 mm, and 100 mm) to obtain the simulated snow cover and assessed the changes in OA both in time (during the entire evaluation period, accumulation and melting season) and space (entire catchment and specific areas of topographic characteristics such as elevation, slope, aspect, landcover, and roughness). Results show remarkable spatial and temporal differences in OA with respect to different snow depth thresholds. Inaccuracies of simulated and observed SCA during the accumulation season September to November 2012 were located in areas with north-west aspect, slopes of 30° or little elevation differences at sub-pixel scale (-0.25 to 0 m). We obtained best agreements with MODIS SCA for a snow depth threshold of 100 mm, leading to increased OA (> 0.8) in 13‰ of the catchment area. SCA agreement in January 2012 and 2013 was slightly limited by MODIS sensor detection due to shading effects and low illumination in areas exposed north-west to north. On the contrary, during the melting season in April 2013 and after the September 2013 snowfall event seemed to depend more on parameterisation than on snow depth thresholds. In contrast, inaccuracies during the melting season March to June 2013 could hardly be attributed to topographic characteristics and different snow depth thresholds but rather on model parameterisation. We identified specific conditions (p.e. specific snowfall events in autumn 2012 and spring 2013) when either MODIS data or the hydrological model was less accurate, thus justifying the need for improvements of precision in the snow cover detection algorithms or in the model's process description. In consequence, our study observations could support future snow cover evaluations in mountain areas, where spatially and temporally dynamic snow depth thresholds are transferred from the catchment scale to the regional scale. Keywords: snow cover, snow modelling, MODIS, snow depth sensitivity, alpine catchment

Sensitivity of modelled snow cover to turbulent flux parameterization and forcing data: a case study in a high altitude basin of the dry Andes, northern Chile

NASA Astrophysics Data System (ADS)

Kinnard, C.; Irarrazaval, I.; Campos, C.; Gascoin, S.; MacDonell, S.; Macdonell, S.; Herrero, J.

2016-12-01

Snow cover in the central-northern Andes of Chile is the main runoff source, providing water for the irrigation of cultures in the fertile valleys downstream. The prospect of adverse climate warming impacts on the hydrological cycle calls for a better understanding of the snow cover dynamics in response to climate, an aspect that has been little studied in the dry Andes. The heterogeneous and often thin snow cover, as well as the paucity of long-term hydrometeorological data makes snow modelling a challenging task in these regions. In this work we applied a physically-based, spatially-distributed snow model (Wimmed) to the La Laguna headwater catchment in the dry Andes (30°S, 70°W) during three hydrological years (2010-2013) when forcing data was available. Model testing at the point scale revealed a large sensitivity of simulated snow depths to the choice of snow roughness parameter (z0), which controls turbulent fluxes, while wind-induced snow erosion at the station in 2010 and 2011 complicated model validation. The inclusion of a mean wind speed map from a previous simulation with the WRF atmospheric model was found to improve the simulation results, while excluding the highest mountain ridge weather station had detrimental effects on the results. A snow roughness (z0) of 1 mm yielded the best comparison between the simulated and observed snow depth at the reference weather station, and between the simulated and MODIS-derived snow cover at the catchment scale. The simulation resulted in large sublimation losses (up to 4 mm day-1), corresponding to more than 80% of snow ablation in the catchment. While such high sublimation rates have been reported before in this region, remaining uncertainties in precipitation data and snow compaction processes call for more detailed studies and increased instrumentation in order to improve future modelling efforts.

Simulation of Seasonal Snow Microwave TB Using Coupled Multi-Layered Snow Evolution and Microwave Emission Models

NASA Technical Reports Server (NTRS)

Brucker, Ludovic; Royer, Alain; Picard, Ghislain; Langlois, Alex; Fily, Michel

2014-01-01

The accurate quantification of SWE has important societal benefits, including improving domestic and agricultural water planning, flood forecasting and electric power generation. However, passive-microwave SWE algorithms suffer from variations in TB due to snow metamorphism, difficult to distinguish from those due to SWE variations. Coupled snow evolution-emission models are able to predict snow metamorphism, allowing us to account for emissivity changes. They can also be used to identify weaknesses in the snow evolution model. Moreover, thoroughly evaluating coupled models is a contribution toward the assimilation of TB, which leads to a significant increase in the accuracy of SWE estimates.

A multilayer physically based snowpack model simulating direct and indirect radiative impacts of light-absorbing impurities in snow

NASA Astrophysics Data System (ADS)

Tuzet, Francois; Dumont, Marie; Lafaysse, Matthieu; Picard, Ghislain; Arnaud, Laurent; Voisin, Didier; Lejeune, Yves; Charrois, Luc; Nabat, Pierre; Morin, Samuel

2017-11-01

Light-absorbing impurities (LAIs) decrease snow albedo, increasing the amount of solar energy absorbed by the snowpack. Its most intuitive and direct impact is to accelerate snowmelt. Enhanced energy absorption in snow also modifies snow metamorphism, which can indirectly drive further variations of snow albedo in the near-infrared part of the solar spectrum because of the evolution of the near-surface snow microstructure. New capabilities have been implemented in the detailed snowpack model SURFEX/ISBA-Crocus (referred to as Crocus) to account for impurities' deposition and evolution within the snowpack and their direct and indirect impacts. Once deposited, the model computes impurities' mass evolution until snow melts out, accounting for scavenging by meltwater. Taking advantage of the recent inclusion of the spectral radiative transfer model TARTES (Two-stream Analytical Radiative TransfEr in Snow model) in Crocus, the model explicitly represents the radiative impacts of light-absorbing impurities in snow. The model was evaluated at the Col de Porte experimental site (French Alps) during the 2013-2014 snow season against in situ standard snow measurements and spectral albedo measurements. In situ meteorological measurements were used to drive the snowpack model, except for aerosol deposition fluxes. Black carbon (BC) and dust deposition fluxes used to drive the model were extracted from simulations of the atmospheric model ALADIN-Climate. The model simulates snowpack evolution reasonably, providing similar performances to our reference Crocus version in terms of snow depth, snow water equivalent (SWE), near-surface specific surface area (SSA) and shortwave albedo. Since the reference empirical albedo scheme was calibrated at the Col de Porte, improvements were not expected to be significant in this study. We show that the deposition fluxes from the ALADIN-Climate model provide a reasonable estimate of the amount of light-absorbing impurities deposited on the snowpack except for extreme deposition events which are greatly underestimated. For this particular season, the simulated melt-out date advances by 6 to 9 days due to the presence of light-absorbing impurities. The model makes it possible to apportion the relative importance of direct and indirect impacts of light-absorbing impurities on energy absorption in snow. For the snow season considered, the direct impact in the visible part of the solar spectrum accounts for 85 % of the total impact, while the indirect impact related to accelerated snow metamorphism decreasing near-surface specific surface area and thus decreasing near-infrared albedo accounts for 15 % of the total impact. Our model results demonstrate that these relative proportions vary with time during the season, with potentially significant impacts for snowmelt and avalanche prediction.

Evaluating the value of ENVISAT ASAR Data for the mapping and monitoring of peatland water table depths

NASA Astrophysics Data System (ADS)

Bechtold, Michel; Schlaffer, Stefan

2015-04-01

The Advanced Synthetic Aperture Radar (ASAR) onboard ENVISAT collected C-Band microwave backscatter data from 2005 to 2012. Backscatter in the C-Band depends to a large degree on the roughness and the moisture status of vegetation and soil surface with a penetration depth of ca. 3 cm. In wetlands with stable high water levels, the annual soil surface moisture dynamics are very distinct compared to the surrounding areas, which allows the monitoring of such environments with ASAR data (Reschke et al. 2012). Also in drained peatlands, moisture status of vegetation and soil surface strongly depends on water table depth due to high hydraulic conductivities of many peat soils in the low suction range (Dettmann et al. 2014). We hypothesize that this allows the characterization of water table depths with ASAR data. Here we analyze whether ASAR data can be used for the spatial and temporal estimation of water table depths in different peatlands (natural, near-natural, agriculturally-used and rewetted). Mapping and monitoring of water table depths is of crucial importance, e.g. for upscaling greenhouse gas emissions and evaluating the success of peatland rewetting projects. Here, ASAR data is analyzed with a new map of water table depths for the organic soils in Germany (Bechtold et al. 2014) as well as with a comprehensive data set of monitored peatland water levels from 1100 dip wells and 54 peatlands. ASAR time series from the years 2005-2012 with irregular temporal sampling intervals of 3-14 days were processed. Areas covered by snow were masked. Primary results about the accuracy of spatial estimates show significant correlations between long-term backscatter statistics and spatially-averaged water table depths extracted from the map at the resolution of the ASAR data. Backscatter also correlates with long-term averages of point-scale water table depth data of the monitoring wells. For the latter, correlation is highest between the dry reference backscatter values and summer mean water table depth. Using the boosted regression tree model of Bechtold et al., we evaluate whether the ASAR data can improve prediction accuracy and/or replace parts of ancillary data that is often not available in other countries. In the temporal domain primary results often show a better dependency between backscatter and water table depths compared to the spatial domain. For a variety of vegetation covers the temporal monitoring potential of ASAR data is evaluated at the level of annual water table depth statistics. Bechtold, M., Tiemeyer, B., Laggner, A., Leppelt, T., Frahm, E., and Belting, S., 2014. Large-scale regionalization of water table depth in peatlands optimized for greenhouse gas emission upscaling, Hydrol. Earth Syst. Sci., 18, 3319-3339. Dettmann, U., Bechtold, M., Frahm, E., Tiemeyer, B., 2014. On the applicability of unimodal and bimodal van Genuchten-Mualem based models to peat and other organic soils under evaporation conditions. Journal of Hydrology, 515, 103-115. Reschke, J., Bartsch, A., Schlaffer, S., Schepaschenko, D., 2012. Capability of C-Band SAR for Operational Wetland Monitoring at High Latitudes. Remote Sens. 4, 2923-2943.

The ASMEx snow slab experiment: snow microwave radiative transfer (SMRT) model evaluation

NASA Astrophysics Data System (ADS)

Sandells, Melody; Löwe, Henning; Picard, Ghislain; Dumont, Marie; Essery, Richard; Floury, Nicolas; Kontu, Anna; Lemmetyinen, Juha; Maslanka, William; Mätzler, Christian; Morin, Samuel; Wiesmann, Andreas

2017-04-01

A major uncertainty in snow microwave modelling to date has been the treatment of the snow microstructure. Although observations of microstructural parameters such as the optical grain diameter, specific surface area and correlation length have improved drastically over the last few years, scale factors have been used to derive the parameters needed in microwave emission models from these observations. Previous work has shown that a major difference between electromagnetic models of scattering coefficients is due to the specific snow microstructure models used. The snow microwave radiative transfer model (SMRT) is a new model developed to advance understanding of the role of microstructure and isolate different assumptions in existing microwave models that collectively hinder interpretation of model intercomparison studies. SMRT is implemented in Python and is modular, thus allows switching between different representations in its various components. Here, the role of microstructure is examined with the Improved Born Approximation electromagnetic model. The model is evaluated against scattering and absorption coefficients derived from radiometer measurements of snow slabs taken as part of the Arctic Snow Microstructure Experiment (ASMEx), which took place in Sodankylä, Finland over two seasons. Microtomography observations of slab samples were used to determine parameters for five microstructure models: spherical, exponential, sticky hard sphere, Teubner-Strey and Gaussian random field. SMRT brightness temperature simulations are also compared with radiometric observations of the snow slabs over a reflector plate and an absorber substrate. Agreement between simulations and observations is generally good except for slabs that are highly anisotropic.

a Physical Parameterization of Snow Albedo for Use in Climate Models.

NASA Astrophysics Data System (ADS)

Marshall, Susan Elaine

The albedo of a natural snowcover is highly variable ranging from 90 percent for clean, new snow to 30 percent for old, dirty snow. This range in albedo represents a difference in surface energy absorption of 10 to 70 percent of incident solar radiation. Most general circulation models (GCMs) fail to calculate the surface snow albedo accurately, yet the results of these models are sensitive to the assumed value of the snow albedo. This study replaces the current simple empirical parameterizations of snow albedo with a physically-based parameterization which is accurate (within +/- 3% of theoretical estimates) yet efficient to compute. The parameterization is designed as a FORTRAN subroutine (called SNOALB) which can be easily implemented into model code. The subroutine requires less then 0.02 seconds of computer time (CRAY X-MP) per call and adds only one new parameter to the model calculations, the snow grain size. The snow grain size can be calculated according to one of the two methods offered in this thesis. All other input variables to the subroutine are available from a climate model. The subroutine calculates a visible, near-infrared and solar (0.2-5 μm) snow albedo and offers a choice of two wavelengths (0.7 and 0.9 mu m) at which the solar spectrum is separated into the visible and near-infrared components. The parameterization is incorporated into the National Center for Atmospheric Research (NCAR) Community Climate Model, version 1 (CCM1), and the results of a five -year, seasonal cycle, fixed hydrology experiment are compared to the current model snow albedo parameterization. The results show the SNOALB albedos to be comparable to the old CCM1 snow albedos for current climate conditions, with generally higher visible and lower near-infrared snow albedos using the new subroutine. However, this parameterization offers a greater predictability for climate change experiments outside the range of current snow conditions because it is physically-based and not tuned to current empirical results.

NOAA's National Snow Analyses

NASA Astrophysics Data System (ADS)

Carroll, T. R.; Cline, D. W.; Olheiser, C. M.; Rost, A. A.; Nilsson, A. O.; Fall, G. M.; Li, L.; Bovitz, C. T.

2005-12-01

NOAA's National Operational Hydrologic Remote Sensing Center (NOHRSC) routinely ingests all of the electronically available, real-time, ground-based, snow data; airborne snow water equivalent data; satellite areal extent of snow cover information; and numerical weather prediction (NWP) model forcings for the coterminous U.S. The NWP model forcings are physically downscaled from their native 13 km2 spatial resolution to a 1 km2 resolution for the CONUS. The downscaled NWP forcings drive an energy-and-mass-balance snow accumulation and ablation model at a 1 km2 spatial resolution and at a 1 hour temporal resolution for the country. The ground-based, airborne, and satellite snow observations are assimilated into the snow model's simulated state variables using a Newtonian nudging technique. The principle advantages of the assimilation technique are: (1) approximate balance is maintained in the snow model, (2) physical processes are easily accommodated in the model, and (3) asynoptic data are incorporated at the appropriate times. The snow model is reinitialized with the assimilated snow observations to generate a variety of snow products that combine to form NOAA's NOHRSC National Snow Analyses (NSA). The NOHRSC NSA incorporate all of the available information necessary and available to produce a "best estimate" of real-time snow cover conditions at 1 km2 spatial resolution and 1 hour temporal resolution for the country. The NOHRSC NSA consist of a variety of daily, operational, products that characterize real-time snowpack conditions including: snow water equivalent, snow depth, surface and internal snowpack temperatures, surface and blowing snow sublimation, and snowmelt for the CONUS. The products are generated and distributed in a variety of formats including: interactive maps, time-series, alphanumeric products (e.g., mean areal snow water equivalent on a hydrologic basin-by-basin basis), text and map discussions, map animations, and quantitative gridded products. The NOHRSC NSA products are used operationally by NOAA's National Weather Service field offices when issuing hydrologic forecasts and warnings including river and flood forecasts, water supply forecasts, and spring flood outlooks for the nation. Additionally, the NOHRSC NSA products are used by a wide variety of federal, state, local, municipal, private-sector, and general-public end-users with a requirement for real-time snowpack information. The paper discusses, in detail, the techniques and procedures used to create the NOHRSC NSA products and gives a number of examples of the real-time snow products generated and distributed over the NOHRSC web site (www.nohrsc.noaa.gov). Also discussed are major limitations of the approach, the most notable being deficiencies in observation of snow water equivalent. Snow observation networks generally lack the consistency and coverage needed to significantly improve confidence in snow model states through updating. Many regions of the world simply lack snow water equivalent observations altogether, a significant constraint on global application of the NSA approach.

Combining point and distributed snowpack data with landscape-based discretization for hydrologic modeling of the snow-dominated Maipo River basin, in the semi-arid Andes of Central Chile.

NASA Astrophysics Data System (ADS)

McPhee, James; Videla, Yohann

2014-05-01

The 5000-km2 upper Maipo River Basin, in central Chile's Andes, has an adequate streamgage network but almost no meteorological or snow accumulation data. Therefore, hydrologic model parameterization is strongly subject to model errors stemming from input and model-state uncertainty. In this research, we apply the Cold Regions Hydrologic Model (CRHM) to the basin, force it with reanalysis data downscaled to an appropriate resolution, and inform a parsimonious basin discretization, based on the hydrologic response unit concept, with distributed data on snowpack properties obtained through snow surveys for two seasons. With minimal calibration the model is able to reproduce the seasonal accumulation and melt cycle as recorded in the one snow pillow available for the basin, and although a bias in maximum accumulation persists, snowpack persistence in time is appropriately simulated based on snow water equivalent and snow covered area observations. Blowing snow events were simulated by the model whenever daily wind speed surpassed 8 m/s, although the use of daily instead of hourly data to force the model suggests that this phenomenon could be underestimated. We investigate the representation of snow redistribution by the model, and compare it with small-scale observations of wintertime snow accumulation on glaciers, in a first step towards characterizing ice distribution within a HRU spatial discretization. Although built at a different spatial scale, we present a comparison of simulated results with distributed snow depth data obtained within a 40 km2 sub-basin of the main Maipo watershed in two snow surveys carried out at the end of winter seasons 2011 and 2012, and compare basin-wide SWE estimates with a regression tree extrapolation of the observed data.

A coupled melt-freeze temperature index approach in a one-layer model to predict bulk volumetric liquid water content dynamics in snow

NASA Astrophysics Data System (ADS)

Avanzi, Francesco; Yamaguchi, Satoru; Hirashima, Hiroyuki; De Michele, Carlo

2016-04-01

Liquid water in snow rules runoff dynamics and wet snow avalanches release. Moreover, it affects snow viscosity and snow albedo. As a result, measuring and modeling liquid water dynamics in snow have important implications for many scientific applications. However, measurements are usually challenging, while modeling is difficult due to an overlap of mechanical, thermal and hydraulic processes. Here, we evaluate the use of a simple one-layer one-dimensional model to predict hourly time-series of bulk volumetric liquid water content in seasonal snow. The model considers both a simple temperature-index approach (melt only) and a coupled melt-freeze temperature-index approach that is able to reconstruct melt-freeze dynamics. Performance of this approach is evaluated at three sites in Japan. These sites (Nagaoka, Shinjo and Sapporo) present multi-year time-series of snow and meteorological data, vertical profiles of snow physical properties and snow melt lysimeters data. These data-sets are an interesting opportunity to test this application in different climatic conditions, as sites span a wide latitudinal range and are subjected to different snow conditions during the season. When melt-freeze dynamics are included in the model, results show that median absolute differences between observations and predictions of bulk volumetric liquid water content are consistently lower than 1 vol%. Moreover, the model is able to predict an observed dry condition of the snowpack in 80% of observed cases at a non-calibration site, where parameters from calibration sites are transferred. Overall, the analysis show that a coupled melt-freeze temperature-index approach may be a valid solution to predict average wetness conditions of a snow cover at local scale.

SAR Tomography for Terrestrial Snow Stratigraphy

NASA Astrophysics Data System (ADS)

Lei, Y.; Xu, X.; Baldi, C.; Bleser, J. W. D.; Yueh, S. H.; Elder, K.

2017-12-01

Traditional microwave observation of snowpack includes brightness temperature and backscatter. The single baseline configuration and loss of phase information hinders the retrieval of snow stratigraphy information from microwave observations. In this paper, we are investigating the tomography of polarimetric SAR to measure snow stratigraphy. In the past two years, we have developed a homodyne frequency modulated continuous wave radar (FMCW), operation at three earth exploration satellite bands within the X-band and Ku-band spectrums (centered at 9.6 GHz, 13.5 GHz, and 17.2 GHz) at Jet Propulsion Laboratory. The transceiver is mounted to a dual-axis planar scanner (60cm in each direction), which translates the antenna beams across the target area creating a tomographic baseline in two directions. Dual-antenna architecture was implemented to improve the isolation between the transmitter and receiver. This technique offers a 50 dB improvement in signal-to-noise ratio versus conventional single-antenna FMCW radar systems. With current setting, we could have around 30cm vertical resolution. The system was deployed on a ground based tower at the Fraser Experimental Forest (FEF) Headquarters, near Fraser, CO, USA (39.847°N, 105.912°W) from February 1 to April 30, 2017 and run continuously with some gaps for required optional supports. FEF is a 93-km2 research watershed in the heart of the central Rocky Mountains approximately 80-km West of Denver. During the campaign, in situ measurements of snow depth and other snowpack properties were performed every week for comparison with the remotely sensed data. A network of soil moisture sensors, time-lapse cameras, acoustic depth sensors, laser depth sensor and meteorological instruments was installed next to the site to collect in situ measurements of snow, weather, and soil conditions. Preliminary tomographic processing of ground based SAR data of snowpack at X- and Ku- band has revealed the presence of multiple layers within the snowpack and clear melting/refrozen cycle, which is consistant with the in-situ measurement.

A method for removing arm backscatter from EPID images

DOE Office of Scientific and Technical Information (OSTI.GOV)

King, Brian W.; Greer, Peter B.; School of Mathematical and Physical Sciences, University of Newcastle, Newcastle, New South Wales 2308

2013-07-15

Purpose: To develop a method for removing the support arm backscatter from images acquired using current Varian electronic portal imaging devices (EPIDs).Methods: The effect of arm backscatter on EPID images was modeled using a kernel convolution method. The parameters of the model were optimized by comparing on-arm images to off-arm images. The model was used to develop a method to remove the effect of backscatter from measured EPID images. The performance of the backscatter removal method was tested by comparing backscatter corrected on-arm images to measured off-arm images for 17 rectangular fields of different sizes and locations on the imager.more » The method was also tested using on- and off-arm images from 42 intensity modulated radiotherapy (IMRT) fields.Results: Images generated by the backscatter removal method gave consistently better agreement with off-arm images than images without backscatter correction. For the 17 rectangular fields studied, the root mean square difference of in-plane profiles compared to off-arm profiles was reduced from 1.19% (standard deviation 0.59%) on average without backscatter removal to 0.38% (standard deviation 0.18%) when using the backscatter removal method. When comparing to the off-arm images from the 42 IMRT fields, the mean {gamma} and percentage of pixels with {gamma} < 1 were improved by the backscatter removal method in all but one of the images studied. The mean {gamma} value (1%, 1 mm) for the IMRT fields studied was reduced from 0.80 to 0.57 by using the backscatter removal method, while the mean {gamma} pass rate was increased from 72.2% to 84.6%.Conclusions: A backscatter removal method has been developed to estimate the image acquired by the EPID without any arm backscatter from an image acquired in the presence of arm backscatter. The method has been shown to produce consistently reliable results for a wide range of field sizes and jaw configurations.« less

A new fractional snow-covered area parameterization for the Community Land Model and its effect on the surface energy balance

NASA Astrophysics Data System (ADS)

Swenson, S. C.; Lawrence, D. M.

2011-11-01

One function of the Community Land Model (CLM4) is the determination of surface albedo in the Community Earth System Model (CESM1). Because the typical spatial scales of CESM1 simulations are large compared to the scales of variability of surface properties such as snow cover and vegetation, unresolved surface heterogeneity is parameterized. Fractional snow-covered area, or snow-covered fraction (SCF), within a CLM4 grid cell is parameterized as a function of grid cell mean snow depth and snow density. This parameterization is based on an analysis of monthly averaged SCF and snow depth that showed a seasonal shift in the snow depth-SCF relationship. In this paper, we show that this shift is an artifact of the monthly sampling and that the current parameterization does not reflect the relationship observed between snow depth and SCF at the daily time scale. We demonstrate that the snow depth analysis used in the original study exhibits a bias toward early melt when compared to satellite-observed SCF. This bias results in a tendency to overestimate SCF as a function of snow depth. Using a more consistent, higher spatial and temporal resolution snow depth analysis reveals a clear hysteresis between snow accumulation and melt seasons. Here, a new SCF parameterization based on snow water equivalent is developed to capture the observed seasonal snow depth-SCF evolution. The effects of the new SCF parameterization on the surface energy budget are described. In CLM4, surface energy fluxes are calculated assuming a uniform snow cover. To more realistically simulate environments having patchy snow cover, we modify the model by computing the surface fluxes separately for snow-free and snow-covered fractions of a grid cell. In this configuration, the form of the parameterized snow depth-SCF relationship is shown to greatly affect the surface energy budget. The direct exposure of the snow-free surfaces to the atmosphere leads to greater heat loss from the ground during autumn and greater heat gain during spring. The net effect is to reduce annual mean soil temperatures by up to 3°C in snow-affected regions.

A new fractional snow-covered area parameterization for the Community Land Model and its effect on the surface energy balance

NASA Astrophysics Data System (ADS)

Swenson, S. C.; Lawrence, D. M.

2012-11-01

One function of the Community Land Model (CLM4) is the determination of surface albedo in the Community Earth System Model (CESM1). Because the typical spatial scales of CESM1 simulations are large compared to the scales of variability of surface properties such as snow cover and vegetation, unresolved surface heterogeneity is parameterized. Fractional snow-covered area, or snow-covered fraction (SCF), within a CLM4 grid cell is parameterized as a function of grid cell mean snow depth and snow density. This parameterization is based on an analysis of monthly averaged SCF and snow depth that showed a seasonal shift in the snow depth-SCF relationship. In this paper, we show that this shift is an artifact of the monthly sampling and that the current parameterization does not reflect the relationship observed between snow depth and SCF at the daily time scale. We demonstrate that the snow depth analysis used in the original study exhibits a bias toward early melt when compared to satellite-observed SCF. This bias results in a tendency to overestimate SCF as a function of snow depth. Using a more consistent, higher spatial and temporal resolution snow depth analysis reveals a clear hysteresis between snow accumulation and melt seasons. Here, a new SCF parameterization based on snow water equivalent is developed to capture the observed seasonal snow depth-SCF evolution. The effects of the new SCF parameterization on the surface energy budget are described. In CLM4, surface energy fluxes are calculated assuming a uniform snow cover. To more realistically simulate environments having patchy snow cover, we modify the model by computing the surface fluxes separately for snow-free and snow-covered fractions of a grid cell. In this configuration, the form of the parameterized snow depth-SCF relationship is shown to greatly affect the surface energy budget. The direct exposure of the snow-free surfaces to the atmosphere leads to greater heat loss from the ground during autumn and greater heat gain during spring. The net effect is to reduce annual mean soil temperatures by up to 3°C in snow-affected regions.

Evaluation of an improved intermediate complexity snow scheme in the ORCHIDEE land surface model

NASA Astrophysics Data System (ADS)

Wang, Tao; Ottlé, Catherine; Boone, Aaron; Ciais, Philippe; Brun, Eric; Morin, Samuel; Krinner, Gerhard; Piao, Shilong; Peng, Shushi

2013-06-01

Snow plays an important role in land surface models (LSM) for climate and model applied over Fran studies, but its current treatment as a single layer of constant density and thermal conductivity in ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) induces significant deficiencies. The intermediate complexity snow scheme ISBA-ES (Interaction between Soil, Biosphere and Atmosphere-Explicit Snow) that includes key snow processes has been adapted and implemented into ORCHIDEE, referred to here as ORCHIDEE-ES. In this study, the adapted scheme is evaluated against the observations from the alpine site Col de Porte (CDP) with a continuous 18 year data set and from sites distributed in northern Eurasia. At CDP, the comparisons of snow depth, snow water equivalent, surface temperature, snow albedo, and snowmelt runoff reveal that the improved scheme in ORCHIDEE is capable of simulating the internal snow processes better than the original one. Preliminary sensitivity tests indicate that snow albedo parameterization is the main cause for the large difference in snow-related variables but not for soil temperature simulated by the two models. The ability of the ORCHIDEE-ES to better simulate snow thermal conductivity mainly results in differences in soil temperatures. These are confirmed by performing sensitivity analysis of ORCHIDEE-ES parameters using the Morris method. These features can enable us to more realistically investigate interactions between snow and soil thermal regimes (and related soil carbon decomposition). When the two models are compared over sites located in northern Eurasia from 1979 to 1993, snow-related variables and 20 cm soil temperature are better reproduced by ORCHIDEE-ES than ORCHIDEE, revealing a more accurate representation of spatio-temporal variability.

Changing Arctic snow cover: A review of recent developments and assessment of future needs for observations, modelling, and impacts.

PubMed

Bokhorst, Stef; Pedersen, Stine Højlund; Brucker, Ludovic; Anisimov, Oleg; Bjerke, Jarle W; Brown, Ross D; Ehrich, Dorothee; Essery, Richard L H; Heilig, Achim; Ingvander, Susanne; Johansson, Cecilia; Johansson, Margareta; Jónsdóttir, Ingibjörg Svala; Inga, Niila; Luojus, Kari; Macelloni, Giovanni; Mariash, Heather; McLennan, Donald; Rosqvist, Gunhild Ninis; Sato, Atsushi; Savela, Hannele; Schneebeli, Martin; Sokolov, Aleksandr; Sokratov, Sergey A; Terzago, Silvia; Vikhamar-Schuler, Dagrun; Williamson, Scott; Qiu, Yubao; Callaghan, Terry V

2016-09-01

Snow is a critically important and rapidly changing feature of the Arctic. However, snow-cover and snowpack conditions change through time pose challenges for measuring and prediction of snow. Plausible scenarios of how Arctic snow cover will respond to changing Arctic climate are important for impact assessments and adaptation strategies. Although much progress has been made in understanding and predicting snow-cover changes and their multiple consequences, many uncertainties remain. In this paper, we review advances in snow monitoring and modelling, and the impact of snow changes on ecosystems and society in Arctic regions. Interdisciplinary activities are required to resolve the current limitations on measuring and modelling snow characteristics through the cold season and at different spatial scales to assure human well-being, economic stability, and improve the ability to predict manage and adapt to natural hazards in the Arctic region.

Changing Arctic Snow Cover: A Review of Recent Developments and Assessment of Future Needs for Observations, Modelling, and Impacts

NASA Technical Reports Server (NTRS)

Bokhorst, Stef; Pedersen, Stine Hojlund; Brucker, Ludovic; Anisimov, Oleg; Bjerke, Jarle W.; Brown, Ross D.; Ehrich, Dorothee; Essery, Richard L. H.; Heilig, Achim; Ingvander, Susanne;

Snow-Cover Variability in North America in the 2000-2001 Winter as Determined from MODIS Snow Products

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Salomonson, Vincent V.; Riggs, George A.; Chien, Janet Y. L.; Houser, Paul R. (Technical Monitor)

2001-01-01

Moderate Resolution Imaging Spectroradiometer (MODIS) snow-cover maps have been available since September 13, 2000. These products, at 500 m spatial resolution, are available through the National Snow and Ice Data Center Distributed Active Archive Center in Boulder, Colorado. By the 2001-02 winter, 5 km climate-modeling grid (CMG) products will be available for presentation of global views of snow cover and for use in climate models. All MODIS snow-cover products are produced from automated algorithms that map snow in an objective manner. In this paper, we describe the MODIS snow products, and show snow maps from the fall of 2000 in North America.

Snow-Cover Variability in North America in the 2000-2001 Winter as Determined from MODIS Snow Products

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Salomonson, Vincent V.; Riggs, George A.; Chien, Y. L.; Houser, Paul R. (Technical Monitor)

2001-01-01

Moderate Resolution Imaging Spectroradiometer (MODIS) snow-cover maps have been available since September 13, 2000. These products, at 500-m spatial resolution, are available through the National Snow and Ice Data Center Distributed Active Archive Center in Boulder, Colorado. By the 2001-02 winter, 5-km climate-modeling grid (CMG) products will be available for presentation of global views of snow cover and for use in climate models. All MODIS snow-cover products are produced from automated algorithms that map snow in an objective manner. In this paper, we describe the MODIS snow products, and show snow maps from the fall of 2000 in North America.

Performance of complex snow cover descriptions in a distributed hydrological model system: A case study for the high Alpine terrain of the Berchtesgaden Alps.

PubMed

Warscher, M; Strasser, U; Kraller, G; Marke, T; Franz, H; Kunstmann, H

2013-05-01

[1] Runoff generation in Alpine regions is typically affected by snow processes. Snow accumulation, storage, redistribution, and ablation control the availability of water. In this study, several robust parameterizations describing snow processes in Alpine environments were implemented in a fully distributed, physically based hydrological model. Snow cover development is simulated using different methods from a simple temperature index approach, followed by an energy balance scheme, to additionally accounting for gravitational and wind-driven lateral snow redistribution. Test site for the study is the Berchtesgaden National Park (Bavarian Alps, Germany) which is characterized by extreme topography and climate conditions. The performance of the model system in reproducing snow cover dynamics and resulting discharge generation is analyzed and validated via measurements of snow water equivalent and snow depth, satellite-based remote sensing data, and runoff gauge data. Model efficiency (the Nash-Sutcliffe coefficient) for simulated runoff increases from 0.57 to 0.68 in a high Alpine headwater catchment and from 0.62 to 0.64 in total with increasing snow model complexity. In particular, the results show that the introduction of the energy balance scheme reproduces daily fluctuations in the snowmelt rates that trace down to the channel stream. These daily cycles measured in snowmelt and resulting runoff rates could not be reproduced by using the temperature index approach. In addition, accounting for lateral snow transport changes the seasonal distribution of modeled snowmelt amounts, which leads to a higher accuracy in modeling runoff characteristics.

The Spectral and Chemical Measurement of Pollutants on Snow Near South Pole, Antarctica

NASA Technical Reports Server (NTRS)

Casey, K. A.; Kaspari, S. D.; Skiles, S. M.; Kreutz, K.; Handley, M. J.

2017-01-01

Remote sensing of light-absorbing particles (LAPs), or dark colored impurities, such as black carbon (BC) and dust on snow, is a key remaining challenge in cryospheric surface characterization and application to snow, ice, and climate models. We present a quantitative data set of in situ snow reflectance, measured and modeled albedo, and BC and trace element concentrations from clean to heavily fossil fuel emission contaminated snow near South Pole, Antarctica. Over 380 snow reflectance spectra (350-2500 nm) and 28 surface snow samples were collected at seven distinct sites in the austral summer season of 2014-2015. Snow samples were analyzed for BC concentration via a single particle soot photometer and for trace element concentration via an inductively coupled plasma mass spectrometer. Snow impurity concentrations ranged from 0.14 to 7000 part per billion (ppb) BC, 9.5 to 1200 ppb sulfur, 0.19 to 660 ppb iron, 0.013 to 1.9 ppb chromium, 0.13 to 120 ppb copper, 0.63 to 6.3 ppb zinc, 0.45 to 82 parts per trillion (ppt) arsenic, 0.0028 to 6.1 ppb cadmium, 0.062 to 22 ppb barium, and 0.0044 to 6.2 ppb lead. Broadband visible to shortwave infrared albedo ranged from 0.85 in pristine snow to 0.62 in contaminated snow. LAP radiative forcing, the enhanced surface absorption due to BC and trace elements, spanned from less than 1 W m(exp. -2) for clean snow to approximately 70 W m(exp. -2) for snow with high BC and trace element content. Measured snow reflectance differed from modeled snow albedo due to specific impurity-dependent absorption features, which we recommend be further studied and improved in snow albedo models.

The spectral and chemical measurement of pollutants on snow near South Pole, Antarctica

NASA Astrophysics Data System (ADS)

Casey, K. A.; Kaspari, S. D.; Skiles, S. M.; Kreutz, K.; Handley, M. J.

2017-06-01

Remote sensing of light-absorbing particles (LAPs), or dark colored impurities, such as black carbon (BC) and dust on snow, is a key remaining challenge in cryospheric surface characterization and application to snow, ice, and climate models. We present a quantitative data set of in situ snow reflectance, measured and modeled albedo, and BC and trace element concentrations from clean to heavily fossil fuel emission contaminated snow near South Pole, Antarctica. Over 380 snow reflectance spectra (350-2500 nm) and 28 surface snow samples were collected at seven distinct sites in the austral summer season of 2014-2015. Snow samples were analyzed for BC concentration via a single particle soot photometer and for trace element concentration via an inductively coupled plasma mass spectrometer. Snow impurity concentrations ranged from 0.14 to 7000 part per billion (ppb) BC, 9.5 to 1200 ppb sulfur, 0.19 to 660 ppb iron, 0.013 to 1.9 ppb chromium, 0.13 to 120 ppb copper, 0.63 to 6.3 ppb zinc, 0.45 to 82 parts per trillion (ppt) arsenic, 0.0028 to 6.1 ppb cadmium, 0.062 to 22 ppb barium, and 0.0044 to 6.2 ppb lead. Broadband visible to shortwave infrared albedo ranged from 0.85 in pristine snow to 0.62 in contaminated snow. LAP radiative forcing, the enhanced surface absorption due to BC and trace elements, spanned from <1 W m-2 for clean snow to 70 W m-2 for snow with high BC and trace element content. Measured snow reflectance differed from modeled snow albedo due to specific impurity-dependent absorption features, which we recommend be further studied and improved in snow albedo models.

Resolving Size Distribution of Black Carbon Internally Mixed With Snow: Impact on Snow Optical Properties and Albedo

NASA Astrophysics Data System (ADS)

He, Cenlin; Liou, Kuo-Nan; Takano, Yoshi

2018-03-01

We develop a stochastic aerosol-snow albedo model that explicitly resolves size distribution of aerosols internally mixed with various snow grains. We use the model to quantify black carbon (BC) size effects on snow albedo and optical properties for BC-snow internal mixing. Results show that BC-induced snow single-scattering coalbedo enhancement and albedo reduction decrease by a factor of 2-3 with increasing BC effective radii from 0.05 to 0.25 μm, while polydisperse BC results in up to 40% smaller visible single-scattering coalbedo enhancement and albedo reduction compared to monodisperse BC with equivalent effective radii. We further develop parameterizations for BC size effects for application to climate models. Compared with a realistic polydisperse assumption and observed shifts to larger BC sizes in snow, respectively, assuming monodisperse BC and typical atmospheric BC effective radii could lead to overestimates of 24% and 40% in BC-snow albedo forcing averaged over different BC and snow conditions.

PBSM3D: A finite volume, scalar-transport blowing snow model for use with variable resolution meshes

NASA Astrophysics Data System (ADS)

Marsh, C.; Wayand, N. E.; Pomeroy, J. W.; Wheater, H. S.; Spiteri, R. J.

2017-12-01

Blowing snow redistribution results in heterogeneous snowcovers that are ubiquitous in cold, windswept environments. Capturing this spatial and temporal variability is important for melt and runoff simulations. Point scale blowing snow transport models are difficult to apply in fully distributed hydrological models due to landscape heterogeneity and complex wind fields. Many existing distributed snow transport models have empirical wind flow and/or simplified wind direction algorithms that perform poorly in calculating snow redistribution where there are divergent wind flows, sharp topography, and over large spatial extents. Herein, a steady-state scalar transport model is discretized using the finite volume method (FVM), using parameterizations from the Prairie Blowing Snow Model (PBSM). PBSM has been applied in hydrological response units and grids to prairie, arctic, glacier, and alpine terrain and shows a good capability to represent snow redistribution over complex terrain. The FVM discretization takes advantage of the variable resolution mesh in the Canadian Hydrological Model (CHM) to ensure efficient calculations over small and large spatial extents. Variable resolution unstructured meshes preserve surface heterogeneity but result in fewer computational elements versus high-resolution structured (raster) grids. Snowpack, soil moisture, and streamflow observations were used to evaluate CHM-modelled outputs in a sub-arctic and an alpine basin. Newly developed remotely sensed snowcover indices allowed for validation over large basins. CHM simulations of snow hydrology were improved by inclusion of the blowing snow model. The results demonstrate the key role of snow transport processes in creating pre-melt snowcover heterogeneity and therefore governing post-melt soil moisture and runoff generation dynamics.

Snow multivariable data assimilation for hydrological predictions in Alpine sites

NASA Astrophysics Data System (ADS)

Piazzi, Gaia; Thirel, Guillaume; Campo, Lorenzo; Gabellani, Simone; Stevenin, Hervè

2017-04-01

Snowpack dynamics (snow accumulation and ablation) strongly impacts on hydrological processes in Alpine areas. During the winter season the presence of snow cover (snow accumulation) reduces the drainage in the basin with a resulting lower watershed time of concentration in case of possible rainfall events. Moreover, the release of the significant water volume stored in winter (snowmelt) considerably contributes to the total discharge during the melting period. Therefore when modeling hydrological processes in snow-dominated catchments the quality of predictions deeply depends on how the model succeeds in catching snowpack dynamics. The integration of a hydrological model with a snow module allows improving predictions of river discharges. Besides the well-known modeling limitations (uncertainty in parameterizations; possible errors affecting both meteorological forcing data and initial conditions; approximations in boundary conditions), there are physical factors that make an exhaustive reconstruction of snow dynamics complicated: snow intermittence in space and time, stratification and slow phenomena like metamorphism processes, uncertainty in snowfall evaluation, wind transportation, etc. Data Assimilation (DA) techniques provide an objective methodology to combine several independent snow-related data sources (model simulations, ground-based measurements and remote sensed observations) in order to obtain the most likely estimate of snowpack state. This study presents SMASH (Snow Multidata Assimilation System for Hydrology), a multi-layer snow dynamic model strengthened by a multivariable DA framework for hydrological purposes. The model is physically based on mass and energy balances and can be used to reproduce the main physical processes occurring within the snowpack: accumulation, density dynamics, melting, sublimation, radiative balance, heat and mass exchanges. The model is driven by observed forcing meteorological data (air temperature, wind velocity, relative air humidity, precipitation and incident solar radiation) to provide a complete estimate of snowpack state. The implementation of a DA scheme enables to assimilate simultaneously ground-based observations of different snow-related variables (snow depth, snow density, surface temperature and albedo). SMASH performances are evaluated by using observed data supplied by meteorological stations located in three experimental Alpine sites: Col de Porte (1325 m, France); Torgnon (2160 m, Italy); Weissfluhjoch (2540 m, Switzerland). A comparison analysis between the resulting performaces of Particle Filter and Ensemble Kalman Filter schemes is shown.

Demonstrating the Uneven Importance of Fine-Scale Forest Structure on Snow Distributions using High Resolution Modeling

NASA Astrophysics Data System (ADS)

Broxton, P. D.; Harpold, A. A.; van Leeuwen, W.; Biederman, J. A.

2016-12-01

Quantifying the amount of snow in forested mountainous environments, as well as how it may change due to warming and forest disturbance, is critical given its importance for water supply and ecosystem health. Forest canopies affect snow accumulation and ablation in ways that are difficult to observe and model. Furthermore, fine-scale forest structure can accentuate or diminish the effects of forest-snow interactions. Despite decades of research demonstrating the importance of fine-scale forest structure (e.g. canopy edges and gaps) on snow, we still lack a comprehensive understanding of where and when forest structure has the largest impact on snowpack mass and energy budgets. Here, we use a hyper-resolution (1 meter spatial resolution) mass and energy balance snow model called the Snow Physics and Laser Mapping (SnowPALM) model along with LIDAR-derived forest structure to determine where spatial variability of fine-scale forest structure has the largest influence on large scale mass and energy budgets. SnowPALM was set up and calibrated at sites representing diverse climates in New Mexico, Arizona, and California. Then, we compared simulations at different model resolutions (i.e. 1, 10, and 100 m) to elucidate the effects of including versus not including information about fine scale canopy structure. These experiments were repeated for different prescribed topographies (i.e. flat, 30% slope north, and south-facing) at each site. Higher resolution simulations had more snow at lower canopy cover, with the opposite being true at high canopy cover. Furthermore, there is considerable scatter, indicating that different canopy arrangements can lead to different amounts of snow, even when the overall canopy coverage is the same. This modeling is contributing to the development of a high resolution machine learning algorithm called the Snow Water Artificial Network (SWANN) model to generate predictions of snow distributions over much larger domains, which has implications for improving land surface models that do not currently resolve or parameterize fine-scale canopy structure. In addition, these findings have implications for understanding the potential of different forest management strategies (i.e. thinning) based on local topography and climate to maximize the amount and retention of snow.

Monitoring global snow cover

NASA Technical Reports Server (NTRS)

Armstrong, Richard; Hardman, Molly

1991-01-01

A snow model that supports the daily, operational analysis of global snow depth and age has been developed. It provides improved spatial interpolation of surface reports by incorporating digital elevation data, and by the application of regionalized variables (kriging) through the use of a global snow depth climatology. Where surface observations are inadequate, the model applies satellite remote sensing. Techniques for extrapolation into data-void mountain areas and a procedure to compute snow melt are also contained in the model.

Antarctic surface temperature and sea ice biases in coupled climate models linked with cloud and land surface properties

NASA Astrophysics Data System (ADS)

Skiles, M.; Painter, T. H.; Marks, D. G.; Hedrick, A. R.

2014-12-01

Since 2013 the Airborne Snow Observatory (ASO) has been measuring spatial and temporal distribution of both snow water equivalent and snow albedo, the two most critical properties for understanding snowmelt runoff and timing, across key basins in the Western US. It is generally understood that net solar radiation (as controlled by variations in snow albedo and irradiance) provides the energy available for melt in almost all snow-covered environments. Until now, sparse measurements have restricted the ability to utilize measured net solar radiation in energy balance models, and current process simulations and model prediction of albedo evolution rely on oversimplifications of the processes. Data from ASO offers the unprecedented opportunity to utilize weekly measurements of spatially extensive spectral snow albedo to constrain and update snow albedo in a distributed snowmelt model for the first time. Here, we first investigate the sensitivity of the snow energy balance model SNOBAL to prescribed changes in snow albedo at two instrumented alpine catchments: at the point scale across 10 years at Senator Beck Basin Study Area in the San Juan Mountains, southwestern Colorado, and at the distributed scale across 25 years at Reynolds Creek Experimental Watershed, Idaho. We then compare distributed energy balance and snowmelt results across the ASO measurement record in the Tuolumne Basin in the Sierra Nevada Mountains, California, for model runs with and without integrated snow albedo from ASO.

Snow: A New Model Diagnostic and Seasonal Forecast Influences

NASA Astrophysics Data System (ADS)

Slater, A. G.; Lawrence, D. M.; Koven, C.

2015-12-01

Snow is the most variable of terrestrial surface condition on the planet with the seasonal extent of snow cover varying by about 48% of land area in the Northern Hemisphere. Physical properties of snow such as high albedo, high insulation along with its ability to store moisture make it an integral component of mid- and high-latitude climates and it is therefore important that models capture these properties and associated processes. In this work we explore two items associated with snow and their role in the climate system. Firstly, a diagnostic measure of snow insulation that is rooted in the physics of heat transfer is introduced. Insulation of the ground during cold Arctic winters heavily influences the rate and depth of ground freezing (or thawing), which can then influence hydrologic and biogeochemical fluxes. The ability of models to simulate snow insulation varies widely. Secondly, the role of snow upon seasonal forecasts is demonstrated within a currently operational modeling system. Due to model system biases, mass and longevity of snow can vary with forecasts. In turn, a longer lasting and greater moisture store can have impacts upon the surface temperature. These impacts can linger for over two months after all snow has melted. The cause of the biases is identified and a solution posed.

Applying A Multi-Objective Based Procedure to SWAT Modelling in Alpine Catchments

NASA Astrophysics Data System (ADS)

Tuo, Y.; Disse, M.; Chiogna, G.

2017-12-01

In alpine catchments, water management practices can lead to conflicts between upstream and downstream stakeholders, like in the Adige river basin (Italy). A correct prediction of available water resources plays an important part, for example, in defining how much water can be stored for hydropower production in upstream reservoirs without affecting agricultural activities downstream. Snow is a crucial hydrological component that highly affects seasonal behavior of streamflow. Therefore, a realistic representation of snow dynamics is fundamental for water management operations in alpine catchments. The Soil and Water Assessment Tool (SWAT) model has been applied in alpine catchments worldwide. However, during model calibration of catchment scale applications, snow parameters were generally estimated based on streamflow records rather than on snow measurements. This may lead to streamflow predictions with wrong snow melt contribution. This work highlights the importance of considering snow measurements in the calibration of the SWAT model for alpine hydrology and compares various calibration methodologies. In addition to discharge records, snow water equivalent time series of both subbasin scale and monitoring station were also utilized to evaluate the model performance by comparing with the SWAT subbasin and elevation band snow outputs. Comparing model results obtained calibrating the model using discharge data only and discharge data along with snow water equivalent data, we show that the latter approach allows us to improve the reliability of snow simulations while maintaining good estimations of streamflow. With a more reliable representation of snow dynamics, the hydrological model can provide more accurate references for proposing adequate water management solutions. This study offers to the wide SWAT user community an effective approach to improve streamflow predictions in alpine catchments and hence support decision makers in water allocation.

Spatial analysis and statistical modelling of snow cover dynamics in the Central Himalayas, Nepal

NASA Astrophysics Data System (ADS)

Weidinger, Johannes; Gerlitz, Lars; Böhner, Jürgen

2017-04-01

General circulation models are able to predict large scale climate variations in global dimensions, however small scale dynamic characteristics, such as snow cover and its temporal variations in high mountain regions, are not represented sufficiently. Detailed knowledge about shifts in seasonal ablation times and spatial distribution of snow cover are crucial for various research interests. Since high mountain areas, for instance the Central Himalayas in Nepal, are generally remote, it is difficult to obtain data in high spatio-temporal resolutions. Regional climate models and downscaling techniques are implemented to compensate coarse resolution. Furthermore earth observation systems, such as MODIS, also permit bridging this gap to a certain extent. They offer snow (cover) data in daily temporal and medium spatial resolution of around 500 m, which can be applied as evaluation and training data for dynamical hydrological and statistical analyses. Within this approach two snow distribution models (binary snow cover and fractional snow cover) as well as one snow recession model were implemented for a research domain in the Rolwaling Himal in Nepal, employing the random forest technique, which represents a state of the art machine learning algorithm. Both bottom-up strategies provide inductive reasoning to derive rules for snow related processes out of climate (temperature, precipitation and irradiance) and climate-related topographic data sets (elevation, aspect and convergence index) obtained by meteorological network stations, remote sensing products (snow cover - MOD10-A1 and land surface temperatures - MOD11-A1) along with GIS. Snow distribution is predicted reliably on a daily basis in the research area, whereas further effort is necessary for predicting daily snow cover recession processes adequately. Swift changes induced by clear sky conditions with high insolation rates are well represented, whereas steady snow loss still needs continuing effort. All approaches underline the technical difficulties of snow cover modelling during the monsoon season, in accordance with previous studies. The developed methods in combination with continuous in situ measurements provide a basis for further downscaling approaches.

Modelling technical snow production for skiing areas in the Austrian Alps with the physically based snow model AMUNDSEN

NASA Astrophysics Data System (ADS)

Hanzer, F.; Marke, T.; Steiger, R.; Strasser, U.

2012-04-01

Tourism and particularly winter tourism is a key factor for the Austrian economy. Judging from currently available climate simulations, the Austrian Alps show a particularly high vulnerability to climatic changes. To reduce the exposure of ski areas towards changes in natural snow conditions as well as to generally enhance snow conditions at skiing sites, technical snowmaking is widely utilized across Austrian ski areas. While such measures result in better snow conditions at the skiing sites and are important for the local skiing industry, its economic efficiency has also to be taken into account. The current work emerges from the project CC-Snow II, where improved future climate scenario simulations are used to determine future natural and artificial snow conditions and their effects on tourism and economy in the Austrian Alps. In a first step, a simple technical snowmaking approach is incorporated into the process based snow model AMUNDSEN, which operates at a spatial resolution of 10-50 m and a temporal resolution of 1-3 hours. Locations of skiing slopes within a ski area in Styria, Austria, were digitized and imported into the model environment. During a predefined time frame in the beginning of the ski season, the model produces a maximum possible amount of technical snow and distributes the associated snow on the slopes, whereas afterwards, until to the end of the ski season, the model tries to maintain a certain snow depth threshold value on the slopes. Due to only few required input parameters, this approach is easily transferable to other ski areas. In our poster contribution, we present first results of this snowmaking approach and give an overview of the data and methodology applied. In a further step in CC-Snow, this simple bulk approach will be extended to consider actual snow cannon locations and technical specifications, which will allow a more detailed description of technical snow production as well as cannon-based recordings of water and energy consumption.

Remote sensing, hydrological modeling and in situ observations in snow cover research: A review

NASA Astrophysics Data System (ADS)

Dong, Chunyu

2018-06-01

Snow is an important component of the hydrological cycle. As a major part of the cryosphere, snow cover also represents a valuable terrestrial water resource. In the context of climate change, the dynamics of snow cover play a crucial role in rebalancing the global energy and water budgets. Remote sensing, hydrological modeling and in situ observations are three techniques frequently utilized for snow cover investigations. However, the uncertainties caused by systematic errors, scale gaps, and complicated snow physics, among other factors, limit the usability of these three approaches in snow studies. In this paper, an overview of the advantages, limitations and recent progress of the three methods is presented, and more effective ways to estimate snow cover properties are evaluated. The possibility of improving remotely sensed snow information using ground-based observations is discussed. As a rapidly growing source of volunteered geographic information (VGI), web-based geotagged photos have great potential to provide ground truth data for remotely sensed products and hydrological models and thus contribute to procedures for cloud removal, correction, validation, forcing and assimilation. Finally, this review proposes a synergistic framework for the future of snow cover research. This framework highlights the cross-scale integration of in situ and remotely sensed snow measurements and the assimilation of improved remote sensing data into hydrological models.

An improved snow scheme for the ECMWF land surface model: Description and offline validation

Treesearch

Emanuel Dutra; Gianpaolo Balsamo; Pedro Viterbo; Pedro M. A. Miranda; Anton Beljaars; Christoph Schar; Kelly Elder

2010-01-01

A new snow scheme for the European Centre for Medium-Range Weather Forecasts (ECMWF) land surface model has been tested and validated. The scheme includes a new parameterization of snow density, incorporating a liquid water reservoir, and revised formulations for the subgrid snow cover fraction and snow albedo. Offline validation (covering a wide range of spatial and...

Evaluation of forest snow processes models (SnowMKIP2)

Treesearch

Nick Rutter; Richard Essery; John Pomeroy; Nuria Altimir; Kostas Andreadis; Ian Baker; Alan Barr; Paul Bartlett; Aaron Boone; Huiping Deng; Herve Douville; Emanuel Dutra; Kelly Elder; others

2009-01-01

Thirty-three snowpack models of varying complexity and purpose were evaluated across a wide range of hydrometeorological and forest canopy conditions at five Northern Hemisphere locations, for up to two winter snow seasons. Modeled estimates of snow water equivalent (SWE) or depth were compared to observations at forest and open sites at each location. Precipitation...

Relating multifrequency radar backscattering to forest biomass: Modeling and AIRSAR measurement

NASA Technical Reports Server (NTRS)

Sun, Guo-Qing; Ranson, K. Jon

1992-01-01

During the last several years, significant efforts in microwave remote sensing were devoted to relating forest parameters to radar backscattering coefficients. These and other studies showed that in most cases, the longer wavelength (i.e. P band) and cross-polarization (HV) backscattering had higher sensitivity and better correlation to forest biomass. This research examines this relationship in a northern forest area through both backscatter modeling and synthetic aperture radar (SAR) data analysis. The field measurements were used to estimate stand biomass from forest weight tables. The backscatter model described by Sun et al. was modified to simulate the backscattering coefficients with respect to stand biomass. The average number of trees per square meter or radar resolution cell, and the average tree height or diameter breast height (dbh) in the forest stand are the driving parameters of the model. The rest of the soil surface, orientation, and size distributions of leaves and branches, remain unchanged in the simulations.

Estimating snow depth of alpine snowpack via airborne multifrequency passive microwave radiance observations: Colorado, USA

NASA Astrophysics Data System (ADS)

Kim, R. S.; Durand, M. T.; Li, D.; Baldo, E.; Margulis, S. A.; Dumont, M.; Morin, S.

2017-12-01

This paper presents a newly-proposed snow depth retrieval approach for mountainous deep snow using airborne multifrequency passive microwave (PM) radiance observation. In contrast to previous snow depth estimations using satellite PM radiance assimilation, the newly-proposed method utilized single flight observation and deployed the snow hydrologic models. This method is promising since the satellite-based retrieval methods have difficulties to estimate snow depth due to their coarse resolution and computational effort. Indeed, this approach consists of particle filter using combinations of multiple PM frequencies and multi-layer snow physical model (i.e., Crocus) to resolve melt-refreeze crusts. The method was performed over NASA Cold Land Processes Experiment (CLPX) area in Colorado during 2002 and 2003. Results showed that there was a significant improvement over the prior snow depth estimates and the capability to reduce the prior snow depth biases. When applying our snow depth retrieval algorithm using a combination of four PM frequencies (10.7,18.7, 37.0 and 89.0 GHz), the RMSE values were reduced by 48 % at the snow depth transects sites where forest density was less than 5% despite deep snow conditions. This method displayed a sensitivity to different combinations of frequencies, model stratigraphy (i.e. different number of layering scheme for snow physical model) and estimation methods (particle filter and Kalman filter). The prior RMSE values at the forest-covered areas were reduced by 37 - 42 % even in the presence of forest cover.

A Physical Based Formula for Calculating the Critical Stress of Snow Movement

NASA Astrophysics Data System (ADS)

He, S.; Ohara, N.

2016-12-01

In snow redistribution modeling, one of the most important parameters is the critical stress of snow movement, which is difficult to estimate from field data because it is influenced by various factors. In this study, a new formula for calculating critical stress of snow movement was derived based on the ice particle sintering process modeling and the moment balance of a snow particle. Through this formula, the influences of snow particle size, air temperature, and deposited time on the critical stress were explicitly taken into consideration. It was found that some of the model parameters were sensitive to the critical stress estimation through the sensitivity analysis using Sobol's method. The two sensitive parameters of the sintering process modeling were determined by a calibration-validation procedure using the observed snow flux data via FlowCapt. Based on the snow flux and metrological data observed at the ISAW stations (http://www.iav.ch), it was shown that the results of this formula were able to describe very well the evolution of the minimum friction wind speed required for the snow motion. This new formula suggested that when the snow just reaches the surface, the smaller snowflake can move easier than the larger particles. However, smaller snow particles require more force to move as the sintering between the snowflakes progresses. This implied that compact snow with small snow particles may be harder to erode by wind although smaller particles may have a higher chance to be suspended once they take off.

Impacts of Satellite-Based Snow Albedo Assimilation on Offline and Coupled Land Surface Model Simulations.

PubMed

Wang, Tao; Peng, Shushi; Krinner, Gerhard; Ryder, James; Li, Yue; Dantec-Nédélec, Sarah; Ottlé, Catherine

2015-01-01

Seasonal snow cover in the Northern Hemisphere is the largest component of the terrestrial cryosphere and plays a major role in the climate system through strong positive feedbacks related to albedo. The snow-albedo feedback is invoked as an important cause for the polar amplification of ongoing and projected climate change, and its parameterization across models is an important source of uncertainty in climate simulations. Here, instead of developing a physical snow albedo scheme, we use a direct insertion approach to assimilate satellite-based surface albedo during the snow season (hereafter as snow albedo assimilation) into the land surface model ORCHIDEE (ORganizing Carbon and Hydrology In Dynamic EcosystEms) and assess the influences of such assimilation on offline and coupled simulations. Our results have shown that snow albedo assimilation in both ORCHIDEE and ORCHIDEE-LMDZ (a general circulation model of Laboratoire de Météorologie Dynamique) improve the simulation accuracy of mean seasonal (October throughout May) snow water equivalent over the region north of 40 degrees. The sensitivity of snow water equivalent to snow albedo assimilation is more pronounced in the coupled simulation than the offline simulation since the feedback of albedo on air temperature is allowed in ORCHIDEE-LMDZ. We have also shown that simulations of air temperature at 2 meters in ORCHIDEE-LMDZ due to snow albedo assimilation are significantly improved during the spring in particular over the eastern Siberia region. This is a result of the fact that high amounts of shortwave radiation during the spring can maximize its snow albedo feedback, which is also supported by the finding that the spatial sensitivity of temperature change to albedo change is much larger during the spring than during the autumn and winter. In addition, the radiative forcing at the top of the atmosphere induced by snow albedo assimilation during the spring is estimated to be -2.50 W m-2, the magnitude of which is almost comparable to that due to CO2 (2.83 W m-2) increases since 1750. Our results thus highlight the necessity of realistic representation of snow albedo in the model and demonstrate the use of satellite-based snow albedo to improve model behaviors, which opens new avenues for constraining snow albedo feedback in earth system models.

Impacts of Satellite-Based Snow Albedo Assimilation on Offline and Coupled Land Surface Model Simulations

PubMed Central

Wang, Tao; Peng, Shushi; Krinner, Gerhard; Ryder, James; Li, Yue; Dantec-Nédélec, Sarah; Ottlé, Catherine

2015-01-01

Seasonal snow cover in the Northern Hemisphere is the largest component of the terrestrial cryosphere and plays a major role in the climate system through strong positive feedbacks related to albedo. The snow-albedo feedback is invoked as an important cause for the polar amplification of ongoing and projected climate change, and its parameterization across models is an important source of uncertainty in climate simulations. Here, instead of developing a physical snow albedo scheme, we use a direct insertion approach to assimilate satellite-based surface albedo during the snow season (hereafter as snow albedo assimilation) into the land surface model ORCHIDEE (ORganizing Carbon and Hydrology In Dynamic EcosystEms) and assess the influences of such assimilation on offline and coupled simulations. Our results have shown that snow albedo assimilation in both ORCHIDEE and ORCHIDEE-LMDZ (a general circulation model of Laboratoire de Météorologie Dynamique) improve the simulation accuracy of mean seasonal (October throughout May) snow water equivalent over the region north of 40 degrees. The sensitivity of snow water equivalent to snow albedo assimilation is more pronounced in the coupled simulation than the offline simulation since the feedback of albedo on air temperature is allowed in ORCHIDEE-LMDZ. We have also shown that simulations of air temperature at 2 meters in ORCHIDEE-LMDZ due to snow albedo assimilation are significantly improved during the spring in particular over the eastern Siberia region. This is a result of the fact that high amounts of shortwave radiation during the spring can maximize its snow albedo feedback, which is also supported by the finding that the spatial sensitivity of temperature change to albedo change is much larger during the spring than during the autumn and winter. In addition, the radiative forcing at the top of the atmosphere induced by snow albedo assimilation during the spring is estimated to be -2.50 W m-2, the magnitude of which is almost comparable to that due to CO2 (2.83 W m-2) increases since 1750. Our results thus highlight the necessity of realistic representation of snow albedo in the model and demonstrate the use of satellite-based snow albedo to improve model behaviors, which opens new avenues for constraining snow albedo feedback in earth system models. PMID:26366564

Snow Climatology of Arctic Sea Ice: Comparison of Reanalysis and Climate Model Data with In Situ Measurements

NASA Astrophysics Data System (ADS)

Chevooruvalappil Chandran, B.; Pittana, M.; Haas, C.

2015-12-01

Snow on sea ice is a critical and complex factor influencing sea ice processes. Deep snow with a high albedo and low thermal conductivity inhibits ice growth in winter and minimizes ice loss in summer. Very shallow or absent snow promotes ice growth in winter and ice loss in summer. The timing of snow ablation critically impacts summer sea ice mass balance. Here we assess the accuracy of various snow on sea ice data products from reanalysis and modeling comparing them with in situ measurements. The latter are based on the Warren et al. (1999) monthly climatology derived from snow ruler measurements between 1954-1991, and on daily snow depth retrievals from few drifting ice mass balance buoys (IMB) with sufficiently long observations spanning the summer season. These were compared with snow depth data from the National Center for Environmental Prediction Department of Energy Reanalysis 2 (NCEP), the Community Climate System Model 4 (CCSM4), and the Canadian Earth System Model 2 (CanESM2). Results are quite variable in different years and regions. However, there is often good agreement between CanESM2 and IMB snow depth during the winter accumulation and spring melt periods. Regional analyses show that over the western Arctic covered primarily with multiyear ice NCEP snow depths are in good agreement with the Warren climatology while CCSM4 overestimates snow depth. However, in the Eastern Arctic which is dominated by first-year ice the opposite behavior is observed. Compared to the Warren climatology CanESM2 underestimates snow depth in all regions. Differences between different snow depth products are as large as 10 to 20 cm, with large consequences for the sea ice mass balance. However, it is also very difficult to evaluate the accuracy of reanalysis and model snow depths due to a lack of extensive, continuous in situ measurements.

The AMSR2 Satellite-based Microwave Snow Algorithm (SMSA) to estimate regional to global snow depth and snow water equivalent

NASA Astrophysics Data System (ADS)

Kelly, R. E. J.; Saberi, N.; Li, Q.

2017-12-01

With moderate to high spatial resolution (<1 km) regional to global snow water equivalent (SWE) observation approaches yet to be fully scoped and developed, the long-term satellite passive microwave record remains an important tool for cryosphere-climate diagnostics. A new satellite microwave remote sensing approach is described for estimating snow depth (SD) and snow water equivalent (SWE). The algorithm, called the Satellite-based Microwave Snow Algorithm (SMSA), uses Advanced Microwave Scanning Radiometer - 2 (AMSR2) observations aboard the Global Change Observation Mission - Water mission launched by the Japan Aerospace Exploration Agency in 2012. The approach is unique since it leverages observed brightness temperatures (Tb) with static ancillary data to parameterize a physically-based retrieval without requiring parameter constraints from in situ snow depth observations or historical snow depth climatology. After screening snow from non-snow surface targets (water bodies [including freeze/thaw state], rainfall, high altitude plateau regions [e.g. Tibetan plateau]), moderate and shallow snow depths are estimated by minimizing the difference between Dense Media Radiative Transfer model estimates (Tsang et al., 2000; Picard et al., 2011) and AMSR2 Tb observations to retrieve SWE and SD. Parameterization of the model combines a parsimonious snow grain size and density approach originally developed by Kelly et al. (2003). Evaluation of the SMSA performance is achieved using in situ snow depth data from a variety of standard and experiment data sources. Results presented from winter seasons 2012-13 to 2016-17 illustrate the improved performance of the new approach in comparison with the baseline AMSR2 algorithm estimates and approach the performance of the model assimilation-based approach of GlobSnow. Given the variation in estimation power of SWE by different land surface/climate models and selected satellite-derived passive microwave approaches, SMSA provides SWE estimates that are independent of real or near real-time in situ and model data.

Modelling the influence of elevation and snow regime on winter stream temperature in the rain-on-snow zone

NASA Astrophysics Data System (ADS)

Leach, J.; Moore, D.

2015-12-01

Winter stream temperature of coastal mountain catchments influences fish growth and development. Transient snow cover and advection associated with lateral throughflow inputs are dominant controls on stream thermal regimes in these regions. Existing stream temperature models lack the ability to properly simulate these processes. Therefore, we developed and evaluated a conceptual-parametric catchment-scale stream temperature model that includes the role of transient snow cover and lateral advection associated with throughflow. The model provided reasonable estimates of observed stream temperature at three test catchments. We used the model to simulate winter stream temperature for virtual catchments located at different elevations within the rain-on-snow zone. The modelling exercise examined stream temperature response associated with interactions between elevation, snow regime, and changes in air temperature. Modelling results highlight that the sensitivity of winter stream temperature response to changes in climate may be dependent on catchment elevation and landscape position.

Estimation of global snow cover using passive microwave data

NASA Astrophysics Data System (ADS)

Chang, Alfred T. C.; Kelly, Richard E.; Foster, James L.; Hall, Dorothy K.

2003-04-01

This paper describes an approach to estimate global snow cover using satellite passive microwave data. Snow cover is detected using the high frequency scattering signal from natural microwave radiation, which is observed by passive microwave instruments. Developed for the retrieval of global snow depth and snow water equivalent using Advanced Microwave Scanning Radiometer EOS (AMSR-E), the algorithm uses passive microwave radiation along with a microwave emission model and a snow grain growth model to estimate snow depth. The microwave emission model is based on the Dense Media Radiative Transfer (DMRT) model that uses the quasi-crystalline approach and sticky particle theory to predict the brightness temperature from a single layered snowpack. The grain growth model is a generic single layer model based on an empirical approach to predict snow grain size evolution with time. Gridding to the 25 km EASE-grid projection, a daily record of Special Sensor Microwave Imager (SSM/I) snow depth estimates was generated for December 2000 to March 2001. The estimates are tested using ground measurements from two continental-scale river catchments (Nelson River and the Ob River in Russia). This regional-scale testing of the algorithm shows that for passive microwave estimates, the average daily snow depth retrieval standard error between estimated and measured snow depths ranges from 0 cm to 40 cm of point observations. Bias characteristics are different for each basin. A fraction of the error is related to uncertainties about the grain growth initialization states and uncertainties about grain size changes through the winter season that directly affect the parameterization of the snow depth estimation in the DMRT model. Also, the algorithm does not include a correction for forest cover and this effect is clearly observed in the retrieval. Finally, error is also related to scale differences between in situ ground measurements and area-integrated satellite estimates. With AMSR-E data, improvements to snow depth and water equivalent estimates are expected since AMSR-E will have twice the spatial resolution of the SSM/I and will be able to characterize better the subnivean snow environment from an expanded range of microwave frequencies.

Estimation of snow emissivity via assimilation of multi-frequency passive microwave data into an ensemble-based data assimilation system

NASA Astrophysics Data System (ADS)

Farhadi, L.; Bateni, S. M.; Auligne, T.; Navari, M.

2017-12-01

Snow emissivity is a key parameter for the estimation of snow surface temperature, which is needed as an initial value in climate models and determination of the outgoing long-wave radiation. Moreover, snow emissivity is required for retrieval of atmospheric parameters (e.g., temperature and humidity profiles) from satellite measurements and satellite data assimilations in numerical weather prediction systems. Microwave emission models and remote sensing data cannot accurately estimate snow emissivity due to limitations attributed to each of them. Existing microwave emission models introduce significant uncertainties in their snow emissivity estimates. This is mainly due to shortcomings of the dense media theory for snow medium at high frequencies, and erroneous forcing variables. The well-known limitations of passive microwave data such as coarse spatial resolution, saturation in deep snowpack, and signal loss in wet snow are the major drawbacks of passive microwave retrieval algorithms for estimation of snow emissivity. A full exploitation of the information contained in the remote sensing data can be achieved by merging them with snow emission models within a data assimilation framework. Such an optimal merging can overcome the specific limitations of models and remote sensing data. An Ensemble Batch Smoother (EnBS) data assimilation framework was developed in this study to combine the synthetically generated passive microwave brightness temperatures at 1.4-, 18.7-, 36.5-, and 89-GHz frequencies with the MEMLS microwave emission model to reduce the uncertainty of the snow emissivity estimates. We have used the EnBS algorithm in the context of observing system simulation experiment (or synthetic experiment) at the local scale observation site (LSOS) of the NASA CLPX field campaign. Our findings showed that the developed methodology significantly improves the estimates of the snow emissivity. The simultaneous assimilation of passive microwave brightness temperatures at all frequencies (i.e., 1.4-, 18.7-, 36.5-, and 89-GHz) reduce the root-mean-square-error (RMSE) of snow emissivity at 1.4-, 18.7-, 36.5-, and 89-GHz (H-pol.) by 80%, 42%, 52%, 40%, respectively compared to the corresponding snow emissivity estimates from the open-loop model.

Subgrid parameterization of snow distribution at a Mediterranean site using terrestrial photography

NASA Astrophysics Data System (ADS)

Pimentel, Rafael; Herrero, Javier; José Polo, María

2017-02-01

Subgrid variability introduces non-negligible scale effects on the grid-based representation of snow. This heterogeneity is even more evident in semiarid regions, where the high variability of the climate produces various accumulation melting cycles throughout the year and a large spatial heterogeneity of the snow cover. This variability in a watershed can often be represented by snow accumulation-depletion curves (ADCs). In this study, terrestrial photography (TP) of a cell-sized area (30 × 30 m) was used to define local snow ADCs at a Mediterranean site. Snow-cover fraction (SCF) and snow-depth (h) values obtained with this technique constituted the two datasets used to define ADCs. A flexible sigmoid function was selected to parameterize snow behaviour on this subgrid scale. It was then fitted to meet five different snow patterns in the control area: one for the accumulation phase and four for the melting phase in a cycle within the snow season. Each pattern was successfully associated with the snow conditions and previous evolution. The resulting ADCs were associated to certain physical features of the snow, which were used to incorporate them in the point snow model formulated by Herrero et al. (2009) by means of a decision tree. The final performance of this model was tested against field observations recorded over four hydrological years (2009-2013). The calibration and validation of this ADC snow model was found to have a high level of accuracy, with global RMSE values of 105.8 mm for the average snow depth and 0.21 m2 m-2 for the snow-cover fraction in the control area. The use of ADCs on the cell scale proposed in this research provided a sound basis for the extension of point snow models to larger areas by means of a gridded distributed calculation.

High resolution humidity, temperature and aerosol profiling with MeteoSwiss Raman lidar

NASA Astrophysics Data System (ADS)

Dinoev, Todor; Arshinov, Yuri; Bobrovnikov, Sergei; Serikov, Ilya; Calpini, Bertrand; van den Bergh, Hubert; Parlange, Marc B.; Simeonov, Valentin

2010-05-01

Meteorological services rely, in part, on numerical weather prediction (NWP). Twice a day radiosonde observations of water vapor provide the required data for assimilation but this time resolution is insufficient to resolve certain meteorological phenomena. High time resolution temperature profiles from microwave radiometers are available as well but have rather low vertical resolution. The Raman LIDARs are able to provide temperature and humidity profiles with high time and range resolution, suitable for NWP model assimilation and validation. They are as well indispensible tools for continuous aerosol profiling for high resolution atmospheric boundary layer studies. To improve the database available for direct meteorological applications the Swiss meteo-service (MeteoSwiss), the Swiss Federal Institute of Technology in Lausanne (EPFL) and the Swiss National Science Foundation (SNSF) initiated a project to design and build an automated Raman lidar for day and night vertical profiling of tropospheric water vapor with the possibility to further upgrade it with an aerosol and temperature channels. The project was initiated in 2004 and RALMO (Raman Lidar for meteorological observations) was inaugurated in August 2008 at MeteoSwiss aerological station at Payerne. RALMO is currently operational and continuously profiles water vapor mixing ratio, aerosol backscatter ratio and aerosol extinction. The instrument is a fully automated, self-contained, eye-safe Raman lidar operated at 355 nm. Narrow field-of-view multi-telescope receiver and narrow band detection allow day and night-time vertical profiling of the atmospheric humidity. The rotational-vibrational Raman lidar responses from water vapor and nitrogen are spectrally separated by a high-throughput fiber coupled diffraction grating polychromator. The elastic backscatter and pure-rotational Raman lidar responses (PRR) from oxygen and nitrogen are spectrally isolated by a double grating polychromator and are used to derive vertical profiles of aerosol backscatter ratio and aerosol extinction at 355 nm. Set of Stokes and anti-Stokes PRR lines are separated by the polychromator to derive temperature profiles. The humidity profiles have vertical resolution from 15 m (within the boundary layer) to 100-450 m (within the free troposphere), time resolution of 30 min and 5 km vertical range at daytime and 10 km at night-time. The aerosol backscatter ratio and extinction profiles have similar resolution with vertical range of approximately 10 km. The temperature profiles are derived from PRR lidar signals, simultaneously recorded in analog and photon counting mode, allowing vertical range of approximately 10 km. Vaisala RS-92 and Snow-White chilled mirror hygrometer radiosondes were used for calibration of the water vapor and temperature channels. Continuous temperature profiles were obtained and were coupled with the available water vapor mixing ratio profiles to obtain relative humidity time series. Lidar derived aerosol backscatter ratio profiles will be used for estimation of the boundary layer height and validation of NWP model results. Optical thickness time series are currently compared to independent measurements from a collocated sun photometer to assess the performance of the aerosol channel.

How Much Water is in That Snowpack? Improving Basin-wide Snow Water Equivalent Estimates from the Airborne Snow Observatory

NASA Astrophysics Data System (ADS)

Bormann, K.; Painter, T. H.; Marks, D. G.; Kirchner, P. B.; Winstral, A. H.; Ramirez, P.; Goodale, C. E.; Richardson, M.; Berisford, D. F.

2014-12-01

In the western US, snowmelt from the mountains contribute the vast majority of fresh water supply, in an otherwise dry region. With much of California currently experiencing extreme drought, it is critical for water managers to have accurate basin-wide estimations of snow water content during the spring melt season. At the forefront of basin-scale snow monitoring is the Jet Propulsion Laboratory's Airborne Snow Observatory (ASO). With combined LiDAR /spectrometer instruments and weekly flights over key basins throughout California, the ASO suite is capable of retrieving high-resolution basin-wide snow depth and albedo observations. To make best use of these high-resolution snow depths, spatially distributed snow density data are required to leverage snow water equivalent (SWE) from the measured depths. Snow density is a spatially and temporally variable property and is difficult to estimate at basin scales. Currently, ASO uses a physically based snow model (iSnobal) to resolve distributed snow density dynamics across the basin. However, there are issues with the density algorithms in iSnobal, particularly with snow depths below 0.50 m. This shortcoming limited the use of snow density fields from iSnobal during the poor snowfall year of 2014 in the Sierra Nevada, where snow depths were generally low. A deeper understanding of iSnobal model performance and uncertainty for snow density estimation is required. In this study, the model is compared to an existing climate-based statistical method for basin-wide snow density estimation in the Tuolumne basin in the Sierra Nevada and sparse field density measurements. The objective of this study is to improve the water resource information provided to water managers during ASO operation in the future by reducing the uncertainty introduced during the snow depth to SWE conversion.

Linking laser scanning to snowpack modeling: Data processing and visualization

NASA Astrophysics Data System (ADS)

Teufelsbauer, H.

2009-07-01

SnowSim is a newly developed physical snowpack model that can use three-dimensional terrestrial laser scanning data to generate model domains. This greatly simplifies the input and numerical simulation of snow covers in complex terrains. The program can model two-dimensional cross sections of general slopes, with complicated snow distributions. The model predicts temperature distributions and snow settlements in this cross section. Thus, the model can be used for a wide range of problems in snow science and engineering, including numerical investigations of avalanche formation. The governing partial differential equations are solved by means of the finite element method, using triangular elements. All essential data for defining the boundary conditions and evaluating the simulation results are gathered by automatic weather and snow measurement sites. This work focuses on the treatment of these measurements and the simulation results, and presents a pre- and post-processing graphical user interface (GUI) programmed in Matlab.

Validation of A One-Dimensional Snow-Land Surface Model at the Sleepers River Watershed

NASA Astrophysics Data System (ADS)

Sun, Wen-Yih; Chern, Jiun-Dar

A one-dimensional land surface model, based on conservations of heat and water substance inside the soil and snow, is presented. To validate the model, a stand-alone experiment is carried out with five years of meteorological and hydrological observations collected from the NOAA-ARS Cooperative Snow Research Project (1966-1974) at the Sleepers River watershed in Danville, Vermont, U.S.A. The numerical results show that the model is capable of reproducing the observed soil temperature at different depths during the winter as well as a rapid increase of soil temperature after snow melts in the spring. The model also simulates the density, temperature, thickness, and equivalent water depth of snow reasonably well. The numerical results are sensitive to the fresh snow density and the soil properties used in the model, which affect the heat exchange between the snowpack and the soil.

The Goddard Snow Radiance Assimilation Project: An Integrated Snow Radiance and Snow Physics Modeling Framework for Snow/cold Land Surface Modeling

NASA Technical Reports Server (NTRS)

Kim, E.; Tedesco, M.; Reichle, R.; Choudhury, B.; Peters-Lidard C.; Foster, J.; Hall, D.; Riggs, G.

2006-01-01

Microwave-based retrievals of snow parameters from satellite observations have a long heritage and have so far been generated primarily by regression-based empirical "inversion" methods based on snapshots in time. Direct assimilation of microwave radiance into physical land surface models can be used to avoid errors associated with such retrieval/inversion methods, instead utilizing more straightforward forward models and temporal information. This approach has been used for years for atmospheric parameters by the operational weather forecasting community with great success. Recent developments in forward radiative transfer modeling, physical land surface modeling, and land data assimilation are converging to allow the assembly of an integrated framework for snow/cold lands modeling and radiance assimilation. The objective of the Goddard snow radiance assimilation project is to develop such a framework and explore its capabilities. The key elements of this framework include: a forward radiative transfer model (FRTM) for snow, a snowpack physical model, a land surface water/energy cycle model, and a data assimilation scheme. In fact, multiple models are available for each element enabling optimization to match the needs of a particular study. Together these form a modular and flexible framework for self-consistent, physically-based remote sensing and water/energy cycle studies. In this paper we will describe the elements and the integration plan. All modules will operate within the framework of the Land Information System (LIS), a land surface modeling framework with data assimilation capabilities running on a parallel-node computing cluster. Capabilities for assimilation of snow retrieval products are already under development for LIS. We will describe plans to add radiance-based assimilation capabilities. Plans for validation activities using field measurements will also be discussed.

Comparison of modeled backscatter with SAR data at P-band

NASA Technical Reports Server (NTRS)

Wang, Yong; Davis, Frank W.; Melack, John M.

1992-01-01

In recent years several analytical models were developed to predict microwave scattering by trees and forest canopies. These models contribute to the understanding of radar backscatter over forested regions to the extent that they capture the basic interactions between microwave radiation and tree canopies, understories, and ground layers as functions of incidence angle, wavelength, and polarization. The Santa Barbara microwave model backscatter model for woodland (i.e. with discontinuous tree canopies) combines a single-tree backscatter model and a gap probability model. Comparison of model predictions with synthetic aperture radar (SAR) data and L-band (lambda = 0.235 m) is promising, but much work is still needed to test the validity of model predictions at other wavelengths. The validity of the model predictions at P-band (lambda = 0.68 m) for woodland stands at our Mt. Shasta test site was tested.

Evaluation of distributed hydrologic impacts of temperature-index and energy-based snow models

USDA-ARS?s Scientific Manuscript database

Proper characterizations of snow melt and accumulation processes in the snow-dominated mountain environment are needed to understand and predict spatiotemporal distribution of water cycle components. Two commonly used strategies in modeling of snow accumulation and melt are the full energy based and...

Comparison of snow melt properties across multiple spatial scales and landscape units in interior sub-Arctic boreal Alaskan watersheds

NASA Astrophysics Data System (ADS)

Bennett, K. E.; Cherry, J. E.; Hiemstra, C. A.; Bolton, W. R.

2013-12-01

Interior sub-Arctic Alaskan snow cover is rapidly changing and requires further study for correct parameterization in physically based models. This project undertook field studies during the 2013 snow melt season to capture snow depth, snow temperature profiles, and snow cover extent to compare with observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor at four different sites underlain by discontinuous permafrost. The 2013 melt season, which turned out to be the latest snow melt period on record, was monitored using manual field measurements (SWE, snow depth data collection), iButtons to record temperature of the snow pack, GoPro cameras to capture time lapse of the snow melt, and low level orthoimagery collected at ~1500 m using a Navion L17a plane mounted with a Nikon D3s camera. Sites were selected across a range of landscape conditions, including a north facing black spruce hill slope, a south facing birch forest, an open tundra site, and a high alpine meadow. Initial results from the adjacent north and south facing sites indicate a highly sensitive system where snow cover melts over just a few days, illustrating the importance of high resolution temporal data capture at these locations. Field observations, iButtons and GoPro cameras show that the MODIS data captures the melt conditions at the south and the north site with accuracy (2.5% and 6.5% snow cover fraction present on date of melt, respectively), but MODIS data for the north site is less variable around the melt period, owing to open conditions and sparse tree cover. However, due to the rapid melt rate trajectory, shifting the melt date estimate by a day results in a doubling of the snow cover fraction estimate observed by MODIS. This information can assist in approximating uncertainty associated with remote sensing data that is being used to populate hydrologic and snow models (the Sacramento Soil Moisture Accounting model, coupled with SNOW-17, and the Variable Infiltration Capacity hydrologic model) and provide greater understanding of error and resultant model sensitivities associated with regional observations of snow cover across the sub-Arctic boreal landscape.

Monitoring Areal Snow Cover Using NASA Satellite Imagery

NASA Technical Reports Server (NTRS)

Harshburger, Brian J.; Blandford, Troy; Moore, Brandon

2011-01-01

The objective of this project is to develop products and tools to assist in the hydrologic modeling process, including tools to help prepare inputs for hydrologic models and improved methods for the visualization of streamflow forecasts. In addition, this project will facilitate the use of NASA satellite imagery (primarily snow cover imagery) by other federal and state agencies with operational streamflow forecasting responsibilities. A GIS software toolkit for monitoring areal snow cover extent and producing streamflow forecasts is being developed. This toolkit will be packaged as multiple extensions for ArcGIS 9.x and an opensource GIS software package. The toolkit will provide users with a means for ingesting NASA EOS satellite imagery (snow cover analysis), preparing hydrologic model inputs, and visualizing streamflow forecasts. Primary products include a software tool for predicting the presence of snow under clouds in satellite images; a software tool for producing gridded temperature and precipitation forecasts; and a suite of tools for visualizing hydrologic model forecasting results. The toolkit will be an expert system designed for operational users that need to generate accurate streamflow forecasts in a timely manner. The Remote Sensing of Snow Cover Toolbar will ingest snow cover imagery from multiple sources, including the MODIS Operational Snowcover Data and convert them to gridded datasets that can be readily used. Statistical techniques will then be applied to the gridded snow cover data to predict the presence of snow under cloud cover. The toolbar has the ability to ingest both binary and fractional snow cover data. Binary mapping techniques use a set of thresholds to determine whether a pixel contains snow or no snow. Fractional mapping techniques provide information regarding the percentage of each pixel that is covered with snow. After the imagery has been ingested, physiographic data is attached to each cell in the snow cover image. This data can be obtained from a digital elevation model (DEM) for the area of interest.

Evaluation of snow and frozen soil parameterization in a cryosphere land surface modeling framework in the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Zhou, J.

2017-12-01

Snow and frozen soil are important components in the Tibetan Plateau, and influence the water cycle and energy balances through snowpack accumulation and melt and soil freeze-thaw. In this study, a new cryosphere land surface model (LSM) with coupled snow and frozen soil parameterization was developed based on a hydrologically improved LSM (HydroSiB2). First, an energy-balance-based three-layer snow model was incorporated into HydroSiB2 (hereafter HydroSiB2-S) to provide an improved description of the internal processes of the snow pack. Second, a universal and simplified soil model was coupled with HydroSiB2-S to depict soil water freezing and thawing (hereafter HydroSiB2-SF). In order to avoid the instability caused by the uncertainty in estimating water phase changes, enthalpy was adopted as a prognostic variable instead of snow/soil temperature in the energy balance equation of the snow/frozen soil module. The newly developed models were then carefully evaluated at two typical sites of the Tibetan Plateau (TP) (one snow covered and the other snow free, both with underlying frozen soil). At the snow-covered site in northeastern TP (DY), HydroSiB2-SF demonstrated significant improvements over HydroSiB2-F (same as HydroSiB2-SF but using the original single-layer snow module of HydroSiB2), showing the importance of snow internal processes in three-layer snow parameterization. At the snow-free site in southwestern TP (Ngari), HydroSiB2-SF reasonably simulated soil water phase changes while HydroSiB2-S did not, indicating the crucial role of frozen soil parameterization in depicting the soil thermal and water dynamics. Finally, HydroSiB2-SF proved to be capable of simulating upward moisture fluxes toward the freezing front from the underlying soil layers in winter.

Development of a land surface model with coupled snow and frozen soil physics

NASA Astrophysics Data System (ADS)

Wang, Lei; Zhou, Jing; Qi, Jia; Sun, Litao; Yang, Kun; Tian, Lide; Lin, Yanluan; Liu, Wenbin; Shrestha, Maheswor; Xue, Yongkang; Koike, Toshio; Ma, Yaoming; Li, Xiuping; Chen, Yingying; Chen, Deliang; Piao, Shilong; Lu, Hui

2017-06-01

Snow and frozen soil are important factors that influence terrestrial water and energy balances through snowpack accumulation and melt and soil freeze-thaw. In this study, a new land surface model (LSM) with coupled snow and frozen soil physics was developed based on a hydrologically improved LSM (HydroSiB2). First, an energy-balance-based three-layer snow model was incorporated into HydroSiB2 (hereafter HydroSiB2-S) to provide an improved description of the internal processes of the snow pack. Second, a universal and simplified soil model was coupled with HydroSiB2-S to depict soil water freezing and thawing (hereafter HydroSiB2-SF). In order to avoid the instability caused by the uncertainty in estimating water phase changes, enthalpy was adopted as a prognostic variable instead of snow/soil temperature in the energy balance equation of the snow/frozen soil module. The newly developed models were then carefully evaluated at two typical sites of the Tibetan Plateau (TP) (one snow covered and the other snow free, both with underlying frozen soil). At the snow-covered site in northeastern TP (DY), HydroSiB2-SF demonstrated significant improvements over HydroSiB2-F (same as HydroSiB2-SF but using the original single-layer snow module of HydroSiB2), showing the importance of snow internal processes in three-layer snow parameterization. At the snow-free site in southwestern TP (Ngari), HydroSiB2-SF reasonably simulated soil water phase changes while HydroSiB2-S did not, indicating the crucial role of frozen soil parameterization in depicting the soil thermal and water dynamics. Finally, HydroSiB2-SF proved to be capable of simulating upward moisture fluxes toward the freezing front from the underlying soil layers in winter.

Numerical simulation of distributed snow processes in complex terrain utilizing triangulated irregular networks (TINs)

NASA Astrophysics Data System (ADS)

Rinehart, A. J.; Vivoni, E. R.

2005-12-01

Snow processes play a significant role in the hydrologic cycle of mountainous and high-latitude catchments in the western United States. Snowmelt runoff contributes to a large percentage of stream runoff while snow covered regions remain highly localized to small portions of the catchment area. The appropriate representation of snow dynamics at a given range of spatial and temporal scales is critical for adequately predicting runoff responses in snowmelt-dominated watersheds. In particular, the accurate depiction of snow cover patterns is important as a range of topographic, land-use and geographic parameters create zones of preferential snow accumulation or ablation that significantly affect the timing of a region's snow melt and the persistence of a snow pack. In this study, we present the development and testing of a distributed snow model designed for simulations over complex terrain. The snow model is developed within the context of the TIN-based Real-time Integrated Basin Simulator (tRIBS), a fully-distributed watershed model capable of continuous simulations of coupled hydrological processes, including unsaturated-saturated zone dynamics, land-atmosphere interactions and runoff generation via multiple mechanisms. The use of triangulated irregular networks as a domain discretization allows tRIBS to accurately represent topography with a reduced number of computational nodes, as compared to traditional grid-based models. This representation is developed using a Delauney optimization criterion that causes areas of topographic homogeneity to be represented at larger spatial scales than the original grid, while more heterogeneous areas are represented at higher resolutions. We utilize the TIN-based terrain representation to simulate microscale (10-m to 100-m) snow pack dynamics over a catchment. The model includes processes such as the snow pack energy balance, wind and bulk redistribution, and snow interception by vegetation. For this study, we present tests from a distributed one-layer energy balance model as applied to a northern New Mexico hillslope in a ponderosa pine forest using both synthetic and real meteorological forcing. We also provide tests of the model's capability to represent spatial patterns within a small watershed in the Jemez Mountain region. Finally, we discuss the interaction of the tested snow process module with existing components in the watershed model and additional applications and capabilities under development.

Radiative transfer model of snow for bare ice regions

NASA Astrophysics Data System (ADS)

Tanikawa, T.; Aoki, T.; Niwano, M.; Hosaka, M.; Shimada, R.; Hori, M.; Yamaguchi, S.

2016-12-01

Modeling a radiative transfer (RT) for coupled atmosphere-snow-bare ice systems is of fundamental importance for remote sensing applications to monitor snow and bare ice regions in the Greenland ice sheet and for accurate climate change predictions by regional and global climate models. Recently, the RT model for atmosphere-snow system was implemented for our regional and global climate models. However, the bare ice region where recently it has been expanded on the Greenland ice sheet due to the global warming, has not been implemented for these models, implying that this region leads miscalculations in these climate models. Thus, the RT model of snow for bare ice regions is needed for accurate climate change predictions. We developed the RT model for coupled atmosphere-snow-bare ice systems, and conducted a sensitivity analysis of the RT model to know the effect of snow, bare ice and geometry parameters on the spectral radiant quantities. The RT model considers snow and bare-ice inherent optical properties (IOPs), including snow grain size, air bubble size and its concentration and bare ice thickness. The conventional light scattering theory, Mie theory, was used for IOP calculations. Monte Carlo method was used for the multiple scattering. The sensitivity analyses showed that spectral albedo for the bare ice increased with increasing the concentration of the air bubble in the bare ice for visible wavelengths because the air bubble is scatterer with no absorption. For near infrared wavelengths, spectral albedo has no dependence on the air bubble due to the strong light absorption by ice. When increasing solar zenith angle, the spectral albedo were increased for all wavelengths. This is the similar trend with spectral snow albedo. Cloud cover influenced the bare ice spectral albedo by covering direct radiation into diffuse radiation. The purely diffuse radiation has an effective solar zenith angle near 50°. Converting direct into diffuse radiation reduces the effective solar zenith angle, resulting in reducing the spectral albedo. This is also the similar trend with spectral snow albedo. Further work should focus on the validation of the RT model using in situ measurement data through field and laboratory experiments.

Evaluation of the satellite derived snow cover area - Runoff forecasting models for the inaccessible basins of western Himalayas

NASA Technical Reports Server (NTRS)

Dey, B.

1985-01-01

In this study, the existing seasonal snow cover area runoff forecasting models of the Indus, Kabul, Sutlej and Chenab basins were evaluated with the concurrent flow correlation model for the period 1975-79. In all the basins under study, correlation of concurrent flow model explained the variability in flow better than by the snow cover area runoff models. Actually, the concurrent flow correlation model explained more than 90 percent of the variability in the flow of these rivers. Compared to this model, the snow cover area runoff models explained less of the variability in flow. In the Himalayan river basins under study and at least for the period under observation, the concurrent flow correlation model provided a set of results with which to compare the estimates from the snow cover area runoff models.

Evaluating the performance of coupled snow-soil models in SURFEXv8 to simulate the permafrost thermal regime at a high Arctic site

NASA Astrophysics Data System (ADS)

Barrere, Mathieu; Domine, Florent; Decharme, Bertrand; Morin, Samuel; Vionnet, Vincent; Lafaysse, Matthieu

2017-09-01

Climate change projections still suffer from a limited representation of the permafrost-carbon feedback. Predicting the response of permafrost temperature to climate change requires accurate simulations of Arctic snow and soil properties. This study assesses the capacity of the coupled land surface and snow models ISBA-Crocus and ISBA-ES to simulate snow and soil properties at Bylot Island, a high Arctic site. Field measurements complemented with ERA-Interim reanalyses were used to drive the models and to evaluate simulation outputs. Snow height, density, temperature, thermal conductivity and thermal insulance are examined to determine the critical variables involved in the soil and snow thermal regime. Simulated soil properties are compared to measurements of thermal conductivity, temperature and water content. The simulated snow density profiles are unrealistic, which is most likely caused by the lack of representation in snow models of the upward water vapor fluxes generated by the strong temperature gradients within the snowpack. The resulting vertical profiles of thermal conductivity are inverted compared to observations, with high simulated values at the bottom of the snowpack. Still, ISBA-Crocus manages to successfully simulate the soil temperature in winter. Results are satisfactory in summer, but the temperature of the top soil could be better reproduced by adequately representing surface organic layers, i.e., mosses and litter, and in particular their water retention capacity. Transition periods (soil freezing and thawing) are the least well reproduced because the high basal snow thermal conductivity induces an excessively rapid heat transfer between the soil and the snow in simulations. Hence, global climate models should carefully consider Arctic snow thermal properties, and especially the thermal conductivity of the basal snow layer, to perform accurate predictions of the permafrost evolution under climate change.

Global land-atmosphere coupling associated with cold climate processes

NASA Astrophysics Data System (ADS)

Dutra, Emanuel

This dissertation constitutes an assessment of the role of cold processes, associated with snow cover, in controlling the land-atmosphere coupling. The work was based on model simulations, including offline simulations with the land surface model HTESSEL, and coupled atmosphere simulations with the EC-EARTH climate model. A revised snow scheme was developed and tested in HTESSEL and EC-EARTH. The snow scheme is currently operational at the European Centre for Medium-Range Weather Forecasts integrated forecast system, and in the default configuration of EC-EARTH. The improved representation of the snowpack dynamics in HTESSEL resulted in improvements in the near surface temperature simulations of EC-EARTH. The new snow scheme development was complemented with the option of multi-layer version that showed its potential in modeling thick snowpacks. A key process was the snow thermal insulation that led to significant improvements of the surface water and energy balance components. Similar findings were observed when coupling the snow scheme to lake ice, where lake ice duration was significantly improved. An assessment on the snow cover sensitivity to horizontal resolution, parameterizations and atmospheric forcing within HTESSEL highlighted the role of the atmospheric forcing accuracy and snowpack parameterizations in detriment of horizontal resolution over flat regions. A set of experiments with and without free snow evolution was carried out with EC-EARTH to assess the impact of the interannual variability of snow cover on near surface and soil temperatures. It was found that snow cover interannual variability explained up to 60% of the total interannual variability of near surface temperature over snow covered regions. Although these findings are model dependent, the results showed consistency with previously published work. Furthermore, the detailed validation of the snow dynamics simulations in HTESSEL and EC-EARTH guarantees consistency of the results.

Modeling Snow Regime in Cores of Small Planetary Bodies

NASA Astrophysics Data System (ADS)

Boukaré, C. E.; Ricard, Y. R.; Parmentier, E.; Parman, S. W.

2017-12-01

Observations of present day magnetic field on small planetary bodies such as Ganymede or Mercury challenge our understanding of planetary dynamo. Several mechanisms have been proposed to explain the origin of magnetic fields. Among the proposed scenarios, one family of models relies on snow regime. Snow regime is supported by experimental studies showing that melting curves can first intersect adiabats in regions where the solidifying phase is not gravitationaly stable. First solids should thus remelt during their ascent or descent. The effect of the snow zone on magnetic field generation remains an open question. Could magnetic field be generated in the snow zone? If not, what is the depth extent of the snow zone? How remelting in the snow zone drive compositional convection in the liquid layer? Several authors have tackled this question with 1D-spherical models. Zhang and Schubert, 2012 model sinking of the dense phase as internally heated convection. However, to our knowledge, there is no study on the convection structure associated with sedimentation and phase change at planetary scale. We extend the numerical model developped in [Boukare et al., 2017] to model snow dynamics in 2D Cartesian geometry. We build a general approach for modeling double diffusive convection coupled with solid-liquid phase change and phase separation. We identify several aspects that may govern the convection structure of the solidifying system: viscosity contrast between the snow zone and the liquid layer, crystal size, rate of melting/solidification and partitioning of light components during phase change.

Modelling the snowmelt and the snow water equivalent by creating a simplified energy balance conceptual snow model

NASA Astrophysics Data System (ADS)

Riboust, Philippe; Thirel, Guillaume; Le Moine, Nicolas; Ribstein, Pierre

2016-04-01

A better knowledge of the accumulated snow on the watersheds will help flood forecasting centres and hydro-power companies to predict the amount of water released during spring snowmelt. Since precipitations gauges are sparse at high elevations and integrative measurements of the snow accumulated on watershed surface are hard to obtain, using snow models is an adequate way to estimate snow water equivalent (SWE) on watersheds. In addition to short term prediction, simulating accurately SWE with snow models should have many advantages. Validating the snow module on both SWE and snowmelt should give a more reliable model for climate change studies or regionalization for ungauged watersheds. The aim of this study is to create a new snow module, which has a structure that allows the use of measured snow data for calibration or assimilation. Energy balance modelling seems to be the logical choice for designing a model in which internal variables, such as SWE, could be compared to observations. Physical models are complex, needing high computational resources and many different types of inputs that are not widely measured at meteorological stations. At the opposite, simple conceptual degree-day models offer to simulate snowmelt using only temperature and precipitation as inputs with fast computing. Its major drawback is to be empirical, i.e. not taking into account all of the processes of the energy balance, which makes this kind of model more difficult to use when willing to compare SWE to observed measurements. In order to reach our objectives, we created a snow model structured by a simplified energy balance where each of the processes is empirically parameterized in order to be calculated using only temperature, precipitation and cloud cover variables. This model's structure is similar to the one created by M.T. Walter (2005), where parameterizations from the literature were used to compute all of the processes of the energy balance. The conductive fluxes into the snowpack were modelled by using analytical solutions to the heat equation taking phase change into account. This approach has the advantage to use few forcing variables and to take into account all the processes of the energy balance. Indeed, the simulations should be quick enough to allow, for example, ensemble prediction or simulation of numerous basins, more easily than physical snow models. The snow module formulation has been completed and is in its validation phase using data from the experimental station of Col de Porte, Alpes, France. Data from the US SNOTEL product will be used in order to test the model structure on a larger scale and to test diverse calibration procedures, since the aim is to use it on a basin scale for discharge modelling purposes.

Snow Micro-Structure Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Micah Johnson, Andrew Slaughter

PIKA is a MOOSE-based application for modeling micro-structure evolution of seasonal snow. The model will be useful for environmental, atmospheric, and climate scientists. Possible applications include application to energy balance models, ice sheet modeling, and avalanche forecasting. The model implements physics from published, peer-reviewed articles. The main purpose is to foster university and laboratory collaboration to build a larger multi-scale snow model using MOOSE. The main feature of the code is that it is implemented using the MOOSE framework, thus making features such as multiphysics coupling, adaptive mesh refinement, and parallel scalability native to the application. PIKA implements three equations:more » the phase-field equation for tracking the evolution of the ice-air interface within seasonal snow at the grain-scale; the heat equation for computing the temperature of both the ice and air within the snow; and the mass transport equation for monitoring the diffusion of water vapor in the pore space of the snow.« less

An integrated uncertainty analysis and data assimilation approach for improved streamflow predictions

NASA Astrophysics Data System (ADS)

Hogue, T. S.; He, M.; Franz, K. J.; Margulis, S. A.; Vrugt, J. A.

2010-12-01

The current study presents an integrated uncertainty analysis and data assimilation approach to improve streamflow predictions while simultaneously providing meaningful estimates of the associated uncertainty. Study models include the National Weather Service (NWS) operational snow model (SNOW17) and rainfall-runoff model (SAC-SMA). The proposed approach uses the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM) to simultaneously estimate uncertainties in model parameters, forcing, and observations. An ensemble Kalman filter (EnKF) is configured with the DREAM-identified uncertainty structure and applied to assimilating snow water equivalent data into the SNOW17 model for improved snowmelt simulations. Snowmelt estimates then serves as an input to the SAC-SMA model to provide streamflow predictions at the basin outlet. The robustness and usefulness of the approach is evaluated for a snow-dominated watershed in the northern Sierra Mountains. This presentation describes the implementation of DREAM and EnKF into the coupled SNOW17 and SAC-SMA models and summarizes study results and findings.

Probabilities and statistics for backscatter estimates obtained by a scatterometer

NASA Technical Reports Server (NTRS)

Pierson, Willard J., Jr.

1989-01-01

Methods for the recovery of winds near the surface of the ocean from measurements of the normalized radar backscattering cross section must recognize and make use of the statistics (i.e., the sampling variability) of the backscatter measurements. Radar backscatter values from a scatterometer are random variables with expected values given by a model. A model relates backscatter to properties of the waves on the ocean, which are in turn generated by the winds in the atmospheric marine boundary layer. The effective wind speed and direction at a known height for a neutrally stratified atmosphere are the values to be recovered from the model. The probability density function for the backscatter values is a normal probability distribution with the notable feature that the variance is a known function of the expected value. The sources of signal variability, the effects of this variability on the wind speed estimation, and criteria for the acceptance or rejection of models are discussed. A modified maximum likelihood method for estimating wind vectors is described. Ways to make corrections for the kinds of errors found for the Seasat SASS model function are described, and applications to a new scatterometer are given.

Subpixel Snow-covered Area Including Differentiated Grain Size from AVIRIS Data Over the Sierra Nevada Mountain Range

NASA Astrophysics Data System (ADS)

Hill, R.; Calvin, W. M.; Harpold, A. A.

2016-12-01

Mountain snow storage is the dominant source of water for humans and ecosystems in western North America. Consequently, the spatial distribution of snow-covered area is fundamental to both hydrological, ecological, and climate models. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were collected along the entire Sierra Nevada mountain range extending from north of Lake Tahoe to south of Mt. Whitney during the 2015 and 2016 snow-covered season. The AVIRIS dataset used in this experiment consists of 224 contiguous spectral channels with wavelengths ranging 400-2500 nanometers at a 15-meter spatial pixel size. Data from the Sierras were acquired on four days: 2/24/15 during a very low snow year, 3/24/16 near maximum snow accumulation, and 5/12/16 and 5/18/16 during snow ablation and snow loss. Previous retrieval of subpixel snow-covered area in alpine regions used multiple snow endmembers due to the sensitivity of snow spectral reflectance to grain size. We will present a model that analyzes multiple endmembers of varying snow grain size, vegetation, rock, and soil in segmented regions along the Sierra Nevada to determine snow-cover spatial extent, snow sub-pixel fraction and approximate grain size or melt state. The root mean squared error will provide a spectrum-wide assessment of the mixture model's goodness-of-fit. Analysis will compare snow-covered area and snow-cover depletion in the 2016 year, and annual variation from the 2015 year. Field data were also acquired on three days concurrent with the 2016 flights in the Sagehen Experimental Forest and will support ground validation of the airborne data set.

Development of a global model for atmospheric backscatter at CO2 wavelengths

NASA Technical Reports Server (NTRS)

Kent, G. S.; Wang, P. H.; Farrukh, U.; Deepak, A.; Patterson, E. M.

1986-01-01

The variation of the aerosol backscattering at 10.6 micrometers within the free troposphere was investigated and a model to describe this variation was developed. The analysis combines theoretical modeling with the results contained within three independent data sets. The data sets used were obtained by the SAGE I/SAM II satellite experiments, the GAMETAG flight series, and by direct backscatter measurements. The theoretical work includes use of a bimodal, two component aerosol model, and the study of the microphysical and associated optical changes occurring within an aerosol plume. A consistent picture is obtained that describes the variation of the aerosol backscattering function in the free troposphere with altitude, latitude, and season.

Studies on remote sensing method of particle size and water density distribution in mists and clouds using laser radar techniques

NASA Technical Reports Server (NTRS)

Shimizu, H.; Kobayasi, T.; Inaba, H.

1979-01-01

A method of remote measurement of the particle size and density distribution of water droplets was developed. In this method, the size of droplets is measured from the Mie scattering parameter which is defined as the total-to-backscattering ratio of the laser beam. The water density distribution is obtained by a combination of the Mie scattering parameter and the extinction coefficient of the laser beam. This method was examined experimentally for the mist generated by an ultrasonic mist generator and applied to clouds containing rain and snow. Compared with the conventional sampling method, the present method has advantages of remote measurement capability and improvement in accuracy.

SAR backscatter from coniferous forest gaps

NASA Technical Reports Server (NTRS)

Day, John L.; Davis, Frank W.

1992-01-01

A study is in progress comparing Airborne Synthetic Aperture Radar (AIRSAR) backscatter from coniferous forest plots containing gaps to backscatter from adjacent gap-free plots. Issues discussed are how do gaps in the range of 400 to 1600 sq m (approximately 4-14 pixels at intermediate incidence angles) affect forest backscatter statistics and what incidence angles, wavelengths, and polarizations are most sensitive to forest gaps. In order to visualize the slant-range imaging of forest and gaps, a simple conceptual model is used. This strictly qualitative model has led us to hypothesize that forest radar returns at short wavelengths (eg., C-band) and large incidence angles (e.g., 50 deg) should be most affected by the presence of gaps, whereas returns at long wavelengths and small angles should be least affected. Preliminary analysis of 1989 AIRSAR data from forest near Mt. Shasta supports the hypothesis. Current forest backscatter models such as MIMICS and Santa Barbara Discontinuous Canopy Backscatter Model have in several cases correctly predicted backscatter from forest stands based on inputs of measured or estimated forest parameters. These models do not, however, predict within-stand SAR scene texture, or 'intrinsic scene variability' as Ulaby et al. has referred to it. For instance, the Santa Barbara model, which may be the most spatially coupled of the existing models, is not truly spatial. Tree locations within a simulated pixel are distributed according to a Poisson process, as they are in many natural forests, but tree size is unrelated to location, which is not the case in nature. Furthermore, since pixels of a simulated stand are generated independently in the Santa Barbara model, spatial processes larger than one pixel are not modeled. Using a different approach, Oliver modeled scene texture based on an hypothetical forest geometry. His simulated scenes do not agree well with SAR data, perhaps due to the simple geometric model used. Insofar as texture is the expression of biological forest processes, such as succession and disease, and physical ones, such as fire and wind-throw, it contains useful information about the forest, and has value in image interpretation and classification. Forest gaps are undoubtedly important contributors to scene variance. By studying the localized effects of gaps on forest backscatter, guided by our qualitative model, we hope to understand more clearly the manner in which spatial heterogeneities in forests produce variations in backscatter, which collectively give rise to scene texture.

The Airborne Snow Observatory: fusion of imaging spectrometer and scanning lidar for studies of mountain snow cover (Invited)

NASA Astrophysics Data System (ADS)

Painter, T. H.; Andreadis, K.; Berisford, D. F.; Goodale, C. E.; Hart, A. F.; Heneghan, C.; Deems, J. S.; Gehrke, F.; Marks, D. G.; Mattmann, C. A.; McGurk, B. J.; Ramirez, P.; Seidel, F. C.; Skiles, M.; Trangsrud, A.; Winstral, A. H.; Kirchner, P.; Zimdars, P. A.; Yaghoobi, R.; Boustani, M.; Khudikyan, S.; Richardson, M.; Atwater, R.; Horn, J.; Goods, D.; Verma, R.; Boardman, J. W.

2013-12-01

Snow cover and its melt dominate regional climate and water resources in many of the world's mountainous regions. However, we face significant water resource challenges due to the intersection of increasing demand from population growth and changes in runoff total and timing due to climate change. Moreover, increasing temperatures in desert systems will increase dust loading to mountain snow cover, thus reducing the snow cover albedo and accelerating snowmelt runoff. The two most critical properties for understanding snowmelt runoff and timing are the spatial and temporal distributions of snow water equivalent (SWE) and snow albedo. Despite their importance in controlling volume and timing of runoff, snowpack albedo and SWE are still poorly quantified in the US and not at all in most of the globe, leaving runoff models poorly constrained. Recognizing this need, JPL developed the Airborne Snow Observatory (ASO), an imaging spectrometer and imaging LiDAR system, to quantify snow water equivalent and snow albedo, provide unprecedented knowledge of snow properties, and provide complete, robust inputs to snowmelt runoff models, water management models, and systems of the future. Critical in the design of the ASO system is the availability of snow water equivalent and albedo products within 24 hours of acquisition for timely constraint of snowmelt runoff forecast models. In spring 2013, ASO was deployed for its first year of a multi-year Demonstration Mission of weekly acquisitions in the Tuolumne River Basin (Sierra Nevada) and monthly acquisitions in the Uncompahgre River Basin (Colorado). The ASO data were used to constrain spatially distributed models of varying complexities and integrated into the operations of the O'Shaughnessy Dam on the Hetch Hetchy reservoir on the Tuolumne River. Here we present the first results from the ASO Demonstration Mission 1 along with modeling results with and without the constraint by the ASO's high spatial resolution and spatially complete acquisitions. ASO ultimately provides a potential foundation for coming spaceborne missions.

Evaluation of an assimilation scheme to estimate snow water equivalent in the High Atlas of Morocco.

NASA Astrophysics Data System (ADS)

Baba, W. M.; Baldo, E.; Gascoin, S.; Margulis, S. A.; Cortés, G.; Hanich, L.

2017-12-01

The snow melt from the Atlas mountains represents a crucial water resource for crop irrigation in Morocco. Due to the paucity of in situ measurements, and the high spatial variability of the snow cover in this semi-arid region, assimilation of snow cover area (SCA) from high resolution optical remote sensing into a snowpack energy-balance model is considered as a promising method to estimate the snow water equivalent (SWE) and snow melt at catchment scales. Here we present a preliminary evaluation of an uncalibrated particle batch smoother data assimilation scheme (Margulis et al., 2015, J. Hydrometeor., 16, 1752-1772) in the High-Atlas Rheraya pilot catchment (225 km2) over a snow season. This approach does not require in situ data since it is based on MERRA-2 reanalyses data and satellite fractional snow cover area data. We compared the output of this prior/posterior ensemble data assimilation system to output from the distributed snowpack evolution model SnowModel (Liston and Elder, 2006, J. Hydrometeor. 7, 1259-1276). SnowModel was forced with in situ meteorological data from five automatic weather stations (AWS) and some key parameters (precipitation correction factor and rain-snow phase transition parameters) were calibrated using a time series of 8-m resolution SCA maps from Formosat-2. The SnowModel simulation was validated using a continuous snow height record at one high elevation AWS. The results indicate that the open loop simulation was reasonably accurate (compared to SnowModel results) in spite of the coarse resolution of the MERRA-2 forcing. The assimilation of Formosat-2 SCA further improved the simulation in terms of the peak SWE and SWE evolution over the melt season. During the accumulation season, the differences between the modeled and estimated (posterior) SWE were more substantial. The differences appear to be due to some observed precipitation events not being captured in MERRA-2. Further investigation will determine whether additional improvement in the posterior estimates result from a calibration of uncertainty input parameters based on the in situ meteorological data. The positive preliminary results pave the way for a SWE reanalysis at the scale of the Atlas mountains using data from wide swath sensors such as Landsat and Sentinel-2.

Large area robust identification of snow cover from multitemporal COSMO-SkyMed images

NASA Astrophysics Data System (ADS)

Pettinato, S.; Santi, E.; Paloscia, S.; Aiazzi, B.; Baronti, S.; Palchetti, E.; Garzelli, A.

2015-10-01

This paper investigates the ability of the Information Theoretic Snow Detection Algorithm (ITSDA) in detecting changes due to snow cover between summer and winter seasons on large area images acquired by COSMO-SkyMed constellation. ITSDA is a method for change detection in multitemporal SAR images, which has been recently applied by the authors to a subset of Cosmo-SkyMed data. The proposed technique is based on a nonparametric approach in the framework of Shannon's information theory, and in particular it features the conditional probability of the local means between the two images taken at different times. Such an unsupervised approach does not require any preliminary despeckling procedure to be performed before the calculation of the change map. In the case of a low quantity of anomalous changes in relatively small-size images, a mean shift procedure can be utilized for refining the map. However, in the present investigation, the changes to be identified are pervasive in large size images. Consequently, for computational issues, the mean shift refinement has been omitted in the present work. However, a simplified implementation of mean shift procedure to save time will be possibly considered in future submissions. In any case, the present version of ITSDA method preserve its characteristics of flexibility and sensibility to backscattering changes, thanks to the possibility of setting up the number of quantization levels in the estimation of the conditional probability between the amplitude values at the two acquisition dates.

View Angle Effects on MODIS Snow Mapping in Forests

NASA Technical Reports Server (NTRS)

Xin, Qinchuan; Woodcock, Curtis E.; Liu, Jicheng; Tan, Bin; Melloh, Rae A.; Davis, Robert E.

2012-01-01

Binary snow maps and fractional snow cover data are provided routinely from MODIS (Moderate Resolution Imaging Spectroradiometer). This paper investigates how the wide observation angles of MODIS influence the current snow mapping algorithm in forested areas. Theoretical modeling results indicate that large view zenith angles (VZA) can lead to underestimation of fractional snow cover (FSC) by reducing the amount of the ground surface that is viewable through forest canopies, and by increasing uncertainties during the gridding of MODIS data. At the end of the MODIS scan line, the total modeled error can be as much as 50% for FSC. Empirical analysis of MODIS/Terra snow products in four forest sites shows high fluctuation in FSC estimates on consecutive days. In addition, the normalized difference snow index (NDSI) values, which are the primary input to the MODIS snow mapping algorithms, decrease as VZA increases at the site level. At the pixel level, NDSI values have higher variances, and are correlated with the normalized difference vegetation index (NDVI) in snow covered forests. These findings are consistent with our modeled results, and imply that consideration of view angle effects could improve MODIS snow monitoring in forested areas.

Incorporation of the Mass Concentration and the New Snow Albedo Schemes into the Global Forecasting Model, GEOS-5 and the Impact of the New Schemes over Himalayan Glaciers

NASA Technical Reports Server (NTRS)

Yasunari, Teppei

2012-01-01

Recently the issue on glacier retreats comes up and many factors should be relevant to the issue. The absorbing aerosols such as dust and black carbon (BC) are considered to be one of the factors. After they deposited onto the snow surface, it will reduce snow albedo (called snow darkening effect) and probably contribute to further melting of glacier. The Goddard Earth Observing System version 5 (GEOS-5) has developed at NASA/GSFC. However, the original snowpack model used in the land surface model in the GEOS-5 did not consider the snow darkening effect. Here we developed the new snow albedo scheme which can consider the snow darkening effect. In addition, another scheme on calculating mass concentrations on the absorbing aerosols in snowpack was also developed, in which the direct aerosol depositions from the chemical transport model in the GEOS-5 were used. The scheme has been validated with the observed data obtained at backyard of the Institute of Low Temperature Science, Hokkaido University, by Dr. Teruo Aoki (Meteorological Research Institute) et aL including me. The observed data was obtained when I was Ph.D. candidate. The original GEOS-5during 2007-2009 over the Himalayas and Tibetan Plateau region showed more reductions of snow than that of the new GEOS-5 because the original one used lower albedo settings. On snow cover fraction, the new GEOS-5 simulated more realistic snow-covered area comparing to the MODIS snow cover fraction. The reductions on snow albedo, snow cover fraction, and snow water equivalent were seen with statistically significance if we consider the snow darkening effect comparing to the results without the snow darkening effect. In the real world, debris cover, inside refreezing process, surface flow of glacier, etc. affect glacier mass balance and the simulated results immediately do not affect whole glacier retreating. However, our results indicate that some surface melting over non debris covered parts of the glacier would be explained by the snow darkening effect. Further discussion and observations are necessary to assess the glacier issue.

Influence of Projected Changes in North American Snow Cover Extent on Mid-Latitude Cyclone Progression

NASA Astrophysics Data System (ADS)

Clare, R. M.; Desai, A. R.; Martin, J. E.; Notaro, M.; Vavrus, S. J.

2017-12-01

It has long been hypothesized that snow cover and snow extent have an influence on the development or steering of synoptic mid-latitude cyclones (MLCs). Rydzik and Desai (2014) showed a robust statistical relationship among snow cover extent, generation of low-level baroclinicity, and MLC tracks. Though snow cover extent is highly variable year to year, the changing global climate is expected to continue an already observed pattern of poleward retreat of mean snow cover in North America, particularly in late winter and spring. For this experiment, large ensemble simulations with the Weather Research and Forecasting model (WRF) were forced with output from the Community Earth System Model (CESM) to test the effect contributed solely by snow cover and the projected effects of a changing climate. Our experiment induces an adjustment to the extent of snow cover in North America according to CESM RCP 8.5 projections for each decade from 2020 to 2100 before and during several cases of MLCs moving east across the Great Plains near the snow line. To evaluate mechanisms of pre-existing and current snow influence on MLCs, model cases are started with snow line adjustment occurring from three days prior up to the storm's arrival over the Great Plains. We demonstrate that snow cover changes do alter MLC intensity and path via modification of low-level potential vorticity.

Comparison of the Snow Simulations in Community Land Model Using Two Snow Cover Fraction Parameterizations

NASA Astrophysics Data System (ADS)

Xie, Zhipeng; Hu, Zeyong

2016-04-01

Snow cover is an important component of local- and regional-scale energy and water budgets, especially in mountainous areas. This paper evaluates the snow simulations by using two snow cover fraction schemes in CLM4.5 (NY07 is the original snow-covered area parameterization used in CLM4, and SL12 is the default scheme in CLM4.5). Off-line simulations are carried out forced by the China Meteorological forcing dataset from January 1, 2001 to December 31, 2010 over the Tibetan Plateau. Simulated snow cover fraction (SCF), snow depth, and snow water equivalent (SWE) were compared against a set of observations including the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover product, the daily snow depth dataset of China, and China Meteorological Administration (CMA) in-situ snow depth and SWE observations. The comparison results indicate significant differences existing between those two SCF parameterizations simulations. Overall, the SL12 formulation shows a certain improvement compared to the NY07 scheme used in CLM4, with the percentage of correctly modeled snow/no snow being 75.8% and 81.8% when compared with the IMS snow product, respectively. Yet, this improvement varies both temporally and spatially. Both these two snow cover schemes overestimated the snow depth, in comparison with the daily snow depth dataset of China, the average biases of simulated snow depth are 7.38cm (8.77cm), 6.97cm (8.2cm) and 5.49cm (5.76cm) NY07 (and SL12) in the snow accumulation period (September through next February), snowmelt period (March through May) and snow-free period (June through August), respectively. When compared with the CMA in-situ snow depth observations, averaged biases are 3.18cm (4.38cm), 2.85cm (4.34cm) and 0.34cm (0.34cm) for NY07 (SL12), respectively. Though SL12 does worse snow depth simulation than NY07, the simulated SWE by SL12 is better than that by NY07, with average biases being 2.64mm, 6.22mm, 1.33mm for NY07, and 1.47mm, 2.63mm, 0.31mm for SL12, respectively. This study demonstrates that future improvements on snow simulation over the Tibetan Plateau are in urgent need for better representing the variability of snow in CLM. Furthermore, these findings lay a foundation for follow-up studies on the modification of snow cover parameterization in the land surface model. Keywords: snow cover, CLM, Tibetan Plateau, simulation.

A vertically integrated snow/ice model over land/sea for climate models. I - Development. II - Impact on orbital change experiments

NASA Technical Reports Server (NTRS)

Neeman, Binyamin U.; Ohring, George; Joseph, Joachim H.

1988-01-01

A vertically integrated formulation (VIF) model for sea ice/snow and land snow is discussed which can simulate the nonlinear effects of heat storage and transfer through the layers of snow and ice. The VIF demonstates the accuracy of the multilayer formulation, while benefitting from the computational flexibility of linear formulations. In the second part, the model is implemented in a seasonal dynamic zonally averaged climate model. It is found that, in response to a change between extreme high and low summer insolation orbits, the winter orbital change dominates over the opposite summer change for sea ice. For snow over land the shorter but more pronounced summer orbital change is shown to dominate.

The assessment of EUMETSAT HSAF Snow Products for mountainuos areas in the eastern part of Turkey

NASA Astrophysics Data System (ADS)

Akyurek, Z.; Surer, S.; Beser, O.; Bolat, K.; Erturk, A. G.

2012-04-01

Monitoring the snow parameters (e.g. snow cover area, snow water equivalent) is a challenging work. Because of its natural physical properties, snow highly affects the evolution of weather from daily basis to climate on a longer time scale. The derivation of snow products over mountainous regions has been considered very challenging. This can be done by periodic and precise mapping of the snow cover. However inaccessibility and scarcity of the ground observations limit the snow cover mapping in the mountainous areas. Today, it is carried out operationally by means of optical satellite imagery and microwave radiometry. In retrieving the snow cover area from satellite images bring the problem of topographical variations within the footprint of satellite sensors and spatial and temporal variation of snow characteristics in the mountainous areas. Most of the global and regional operational snow products use generic algorithms for flat and mountainous areas. However the non-uniformity of the snow characteristics can only be modeled with different algorithms for mountain and flat areas. In this study the early findings of Satellite Application Facilities on Hydrology (H-SAF) project, which is financially supported by EUMETSAT, will be presented. Turkey is a part of the H-SAF project, both in product generation (eg. snow recognition, fractional snow cover and snow water equivalent) for mountainous regions for whole Europe, cal/val of satellite-derived snow products with ground observations and cal/val studies with hydrological modeling in the mountainous terrain of Europe. All the snow products are operational on a daily basis. For the snow recognition product (H10) for mountainous areas, spectral thresholding methods were applied on sub pixel scale of MSG-SEVIRI images. The different spectral characteristics of cloud, snow and land determined the structure of the algorithm and these characteristics were obtained from subjective classification of known snow cover features in the MSG/SEVIRI images. The fractional snow cover area (H12) algorithm is based on a sub-pixel reflectance model applied on METOP-AVHRR data. Knowing the effects of topography on satellite-measured radiances for rough terrain, the sun zenith and azimuth angles, as well as direction of observation relative to these are taken into account in estimating the target reflectances from the satellite images. The values of SWE products (H13) were obtained using an assimilation process based on the Helsinki University of Technology model using Advanced Microwave Scanning Radiometer for EOS (AMSR-E) daily brightness-temperature values. The validation studies for three products have been performed for the water years 2010 and 2011. Average values of 70% of probability of detection for snow recognition product, 60% of overall accuracy for the fractional snow cover product and 45 mm RMSE for the snow water equivalent product have been obtained from the validation studies. Final versions of these three products will be presented and discussed. Key words: snow, satellite images, mountain, HSAF, snow cover, snow water equivalent

Modeling snow accumulation and ablation processes in forested environments

NASA Astrophysics Data System (ADS)

Andreadis, Konstantinos M.; Storck, Pascal; Lettenmaier, Dennis P.

2009-05-01

The effects of forest canopies on snow accumulation and ablation processes can be very important for the hydrology of midlatitude and high-latitude areas. A mass and energy balance model for snow accumulation and ablation processes in forested environments was developed utilizing extensive measurements of snow interception and release in a maritime mountainous site in Oregon. The model was evaluated using 2 years of weighing lysimeter data and was able to reproduce the snow water equivalent (SWE) evolution throughout winters both beneath the canopy and in the nearby clearing, with correlations to observations ranging from 0.81 to 0.99. Additionally, the model was evaluated using measurements from a Boreal Ecosystem-Atmosphere Study (BOREAS) field site in Canada to test the robustness of the canopy snow interception algorithm in a much different climate. Simulated SWE was relatively close to the observations for the forested sites, with discrepancies evident in some cases. Although the model formulation appeared robust for both types of climates, sensitivity to parameters such as snow roughness length and maximum interception capacity suggested the magnitude of improvements of SWE simulations that might be achieved by calibration.

Sensitivity of the snowmelt runoff model to underestimates of remotely sensed snow covered area

USDA-ARS?s Scientific Manuscript database

Three methods for estimating snow covered area (SCA) from Terra MODIS data were used to derive conventional depletion curves for input to the Snowmelt Runoff Model (SRM). We compared the MOD10 binary and fractional snow cover products and a method for estimating sub-pixel snow cover using spectral m...

Boundary Layer Aerosol Composition over Sierra Nevada Mountains using 9.11- and 10.59-micron CW Lidars and Modeled Backscatter from Size Distribution Data

NASA Technical Reports Server (NTRS)

Cutten, D. R.; Jarzembski, M. A.; Srivastava, V.; Pueschel, R. F.; Howard, S. D.; McCaul, E. W., Jr.

2003-01-01

An inversion technique has been developed to determine volume fractions of an atmospheric aerosol composed primarily of ammonium sulfate and ammonium nitrate and water combined with fixed concentration of elemental and organic carbon. It is based on measured aerosol backscatter obtained with 9.11 - and 10.59-micron wavelength continuous wave CO2 lidars and modeled backscatter from aerosol size distribution data. The technique is demonstrated during a flight of the NASA DC-8 aircraft over the Sierra Nevada Mountain Range, California on 19 September, 1995. Volume fraction of each component and effective complex refractive index of the composite particle were determined assuming an internally mixed composite aerosol model. The volume fractions were also used to re-compute aerosol backscatter, providing good agreement with the lidar-measured data. The robustness of the technique for determining volume fractions was extended with a comparison of calculated 2.1,-micron backscatter from size distribution data with the measured lidar data converted to 2.1,-micron backscatter using an earlier derived algorithm, verifying the algorithm as well as the backscatter calculations.

Analysis of MODIS snow cover time series over the alpine regions as input for hydrological modeling

NASA Astrophysics Data System (ADS)

Notarnicola, Claudia; Rastner, Philipp; Irsara, Luca; Moelg, Nico; Bertoldi, Giacomo; Dalla Chiesa, Stefano; Endrizzi, Stefano; Zebisch, Marc

2010-05-01

Snow extent and relative physical properties are key parameters in hydrology, weather forecast and hazard warning as well as in climatological models. Satellite sensors offer a unique advantage in monitoring snow cover due to their temporal and spatial synoptic view. The Moderate Resolution Imaging Spectrometer (MODIS) from NASA is especially useful for this purpose due to its high frequency. However, in order to evaluate the role of snow on the water cycle of a catchment such as runoff generation due to snowmelt, remote sensing data need to be assimilated in hydrological models. This study presents a comparison on a multi-temporal basis between snow cover data derived from (1) MODIS images, (2) LANDSAT images, and (3) predictions by the hydrological model GEOtop [1,3]. The test area is located in the catchment of the Matscher Valley (South Tyrol, Northern Italy). The snow cover maps derived from MODIS-images are obtained using a newly developed algorithm taking into account the specific requirements of mountain regions with a focus on the Alps [2]. This algorithm requires the standard MODIS-products MOD09 and MOD02 as input data and generates snow cover maps at a spatial resolution of 250 m. The final output is a combination of MODIS AQUA and MODIS TERRA snow cover maps, thus reducing the presence of cloudy pixels and no-data-values due to topography. By using these maps, daily time series starting from the winter season (November - May) 2002 till 2008/2009 have been created. Along with snow maps from MODIS images, also some snow cover maps derived from LANDSAT images have been used. Due to their high resolution (< 30 m) they have been considered as an evaluation tool. The snow cover maps are then compared with the hydrological GEOtop model outputs. The main objectives of this work are: 1. Evaluation of the MODIS snow cover algorithm using LANDSAT data 2. Investigation of snow cover, and snow cover duration for the area of interest for South Tyrol 3. Derivation and interpretation of the snow line for the seven winter seasons 4. An evaluation of the model outputs in order to determine the situations in which the remotely sensed data can be used to improve the model prediction of snow coverage and related variables References [1] Rigon R., Bertoldi G. and Over T.M. 2006. GEOtop: A Distributed Hydrological Model with Coupled Water and Energy Budgets, Journal of Hydrometeorology, 7: 371-388. [2] Rastner P., Irsara L., Schellenberger T., Della Chiesa S., Bertoldi G., Endrizzi S., Notarnicola C., Steurer C., Zebisch M. 2009. Monitoraggio del manto nevoso in aree alpine con dati MODIS multi-temporali e modelli idrologici, 13th ASITA National Conference, 1-4.12.2009, Bari, Italy. [3] Zanotti F., Endrizzi S., Bertoldi G. and Rigon R. 2004. The GEOtop snow module. Hydrological Processes, 18: 3667-3679. DOI:10.1002/hyp.5794.

The Effects of Snow Depth Forcing on Southern Ocean Sea Ice Simulations

NASA Technical Reports Server (NTRS)

Powel, Dylan C.; Markus, Thorsten; Stoessel, Achim

2003-01-01

The spatial and temporal distribution of snow on sea ice is an important factor for sea ice and climate models. First, it acts as an efficient insulator between the ocean and the atmosphere, and second, snow is a source of fresh water for altering the already weak Southern Ocean stratification. For the Antarctic, where the ice thickness is relatively thin, snow can impact the ice thickness in two ways: a) As mentioned above snow on sea ice reduces the ocean-atmosphere heat flux and thus reduces freezing at the base of the ice flows; b) a heavy snow load can suppress the ice below sea level which causes flooding and, with subsequent freezing, a thickening of the sea ice (snow-to-ice conversion). In this paper, we compare different snow fall paramterizations (incl. the incorporation of satellite-derived snow depth) and study the effect on the sea ice using a sea ice model.

Climatic Effects of Black Carbon Aerosols Over the Tibetan Plateau

NASA Astrophysics Data System (ADS)

He, Cenlin

Black carbon (BC), also known as soot, has been identified as the second most important anthropogenic emissions in terms of global climate forcing in the current atmosphere. Ample evidence has shown that BC deposition is an important driver of rapid snow melting and glacier retreat over the Tibetan Plateau, which holds the largest snow/ice mass outside polar regions. However, the climatic effects of BC over the Tibetan Plateau have not been thoroughly investigated in such a manner as to understand, quantify, and reduce large uncertainties in the estimate of radiative and hydrological effects. Thus, this Ph.D. study seeks to understand and improve key processes controlling BC life cycle in global and regional models and to quantify BC radiative effects over the Tibetan Plateau. First, the capability of a state-of-the-art global chemical transport model (CTM), GEOS-Chem, and the associated model uncertainties are systematically evaluated in simulating BC over the Tibetan Plateau, using in situ measurements of BC in surface air, BC in snow, and BC absorption optical depth. The effects of three key factors on the simulation are also delineated, including Asian anthropogenic emissions, BC aging process, and model resolution. Subsequently, a microphysics-based BC aging scheme that accounts for condensation, coagulation, and heterogeneous chemical oxidation processes is developed and examined in GEOS-Chem by comparing with aircraft measurements. Compared to the default aging scheme, the microphysical scheme reduces model-observation discrepancies by a factor of 3, particularly in the middle and upper troposphere. In addition, a theoretical BC aging-optics model is developed to account for three typical evolution stages, namely, freshly emitted aggregates, coated BC by soluble material, and BC particles undergoing further hygroscopic growth. The geometric-optics surface-wave (GOS) approach is employed to compute the BC single-scattering properties at each aging stage, which are subsequently compared with laboratory measurements. Results show large variations in BC optical properties caused by coating morphology and aging stages. Furthermore, a comprehensive intercomparison of the GOS approach, the superposition T-matrix method, and laboratory measurements is performed for optical properties of BC with complex structures during aging. Moreover, a new snow albedo model is developed for widely-observed close-packed snow grains internally mixed with BC. Results indicate that albedo simulations that account for snow close packing match closer to observations. Close packing enhances BC-induced snow albedo reduction and associated surface radiative forcing by up to 15% (20%) for fresh (old) snow, which suggests that BC-snow albedo forcing is underestimated in previous modeling studies without accounting for close packing. Finally, the snow albedo forcing and direct radiative forcing (DRF) of BC in the Tibetan Plateau are estimated using GEOS-Chem in conjunction with a stochastic snow model and a radiative transfer model. This, for the first time, accounts for realistic non-spherical snow grain shape and stochastic multiple inclusions of BC within snow in assessing BC-snow interactions. The annual mean BC snow albedo forcing is 2.9 W m-2 over snow-covered Plateau regions. BC-snow internal mixing increases the albedo forcing by 40-60% compared with external mixing, whereas Koch snowflakes reduce the forcing by 20-40% relative to spherical snow grains. BC DRF at the top of the atmosphere is 2.3 W m-2 with uncertainties of -70% - +85% in the Plateau. The BC forcings are further attributed to emissions from different regions.

Soil erosion by snow gliding - a first quantification attempt in a sub-alpine area, Switzerland

NASA Astrophysics Data System (ADS)

Meusburger, K.; Leitinger, G.; Mabit, L.; Mueller, M. H.; Walter, A.; Alewell, C.

2014-03-01

Snow processes might be one important driver of soil erosion in Alpine grasslands and thus the unknown variable when erosion modelling is attempted. The aim of this study is to assess the importance of snow gliding as soil erosion agent for four different land use/land cover types in a sub-alpine area in Switzerland. We used three different approaches to estimate soil erosion rates: sediment yield measurements in snow glide deposits, the fallout radionuclide 137Cs, and modelling with the Revised Universal Soil Loss Equation (RUSLE). The RUSLE model is suitable to estimate soil loss by water erosion, while the 137Cs method integrates soil loss due to all erosion agents involved. Thus, we hypothesise that the soil erosion rates determined with the 137Cs method are higher and that the observed discrepancy between the soil erosion rate of RUSLE and the 137Cs method is related to snow gliding and sediment concentrations in the snow glide deposits. Cumulative snow glide distance was measured for the sites in the winter 2009/10 and modelled for the surrounding area with the Spatial Snow Glide Model (SSGM). Measured snow glide distance ranged from 2 to 189 cm, with lower values at the north facing slopes. We observed a reduction of snow glide distance with increasing surface roughness of the vegetation, which is important information with respect to conservation planning and expected land use changes in the Alps. Our hypothesis was confirmed: the difference of RUSLE and 137Cs erosion rates was related to the measured snow glide distance (R2= 0.64; p < 0.005) and snow sediment yields (R2 = 0.39; p = 0.13). A high difference (lower proportion of water erosion compared to total net erosion) was observed for high snow glide rates and vice versa. The SSGM reproduced the relative difference of the measured snow glide values under different land uses and land cover types. The resulting map highlighted the relevance of snow gliding for large parts of the investigated area. Based on these results, we conclude that snow gliding is a key process impacting soil erosion pattern and magnitude in sub-alpine areas with similar topographic and climatic conditions.

Data sets for snow cover monitoring and modelling from the National Snow and Ice Data Center

NASA Astrophysics Data System (ADS)

Holm, M.; Daniels, K.; Scott, D.; McLean, B.; Weaver, R.

2003-04-01

A wide range of snow cover monitoring and modelling data sets are pending or are currently available from the National Snow and Ice Data Center (NSIDC). In-situ observations support validation experiments that enhance the accuracy of remote sensing data. In addition, remote sensing data are available in near-real time, providing coarse-resolution snow monitoring capability. Time series data beginning in 1966 are valuable for modelling efforts. NSIDC holdings include SMMR and SSM/I snow cover data, MODIS snow cover extent products, in-situ and satellite data collected for NASA's recent Cold Land Processes Experiment, and soon-to-be-released ASMR-E passive microwave products. The AMSR-E and MODIS sensors are part of NASA's Earth Observing System flying on the Terra and Aqua satellites Characteristics of these NSIDC-held data sets, appropriateness of products for specific applications, and data set access and availability will be presented.

Energy balance-based distributed modeling of snow and glacier melt runoff for the Hunza river basin in the Pakistan Karakoram Himalayan region

NASA Astrophysics Data System (ADS)

Shrestha, M.; Wang, L.; Koike, T.; Xue, Y.; Hirabayashi, Y.; Ahmad, S.

2012-12-01

A spatially distributed biosphere hydrological model with energy balance-based multilayer snow physics and multilayer glacier model, including debris free and debris covered surface (enhanced WEB-DHM-S) has been developed and applied to the Hunza river basin in the Pakistan Karakoram Himalayan region, where about 34% of the basin area is covered by glaciers. The spatial distribution of seasonal snow and glacier cover, snow and glacier melt runoff along with rainfall-contributed runoff, and glacier mass balances are simulated. The simulations are carried out at hourly time steps and at 1-km spatial resolution for the two hydrological years (2002-2003) with the use of APHRODITE precipitation dataset, observed temperature, and other atmospheric forcing variables from the Global Land Data Assimilation System (GLDAS). The pixel-to-pixel comparisons for the snow-free and snow-covered grids over the region reveal that the simulation agrees well with the Moderate Resolution Imaging Spectroradiometer (MODIS) eight-day maximum snow-cover extent data (MOD10A2) with an accuracy of 83% and a positive bias of 2.8 %. The quantitative evaluation also shows that the model is able to reproduce the river discharge satisfactorily with Nash efficiency of 0.92. It is found that the contribution of rainfall to total streamflow is small (about 10-12%) while the contribution of snow and glacier is considerably large (35-40% for snowmelt and 50-53% for glaciermelt, respectively). The model simulates the state of snow and glaciers at each model grid prognostically and thus can estimate the net annual mass balance. The net mass balance varies from -2 m to +2 m water equivalent. Additionally, the hypsography analysis for the equilibrium line altitude (ELA) suggests that the average ELA in this region is about 5700 m with substantial variation from glacier to glacier and region to region. This study is the first to adopt a distributed biosphere hydrological model with the energy balance- based multilayer snow and glacier module to estimate the spatial distribution of snow/glacier cover and snow and glacier melt runoff for a river basin in the Karakoram Himalayan region.

Assimilation of ground and satellite snow observations in a distributed hydrologic model to improve water supply forecasts in the Upper Colorado River Basin

NASA Astrophysics Data System (ADS)

Micheletty, P. D.; Day, G. N.; Quebbeman, J.; Carney, S.; Park, G. H.

2016-12-01

The Upper Colorado River Basin above Lake Powell is a major source of water supply for 25 million people and provides irrigation water for 3.5 million acres. Approximately 85% of the annual runoff is produced from snowmelt. Water supply forecasts of the April-July runoff produced by the National Weather Service (NWS) Colorado Basin River Forecast Center (CBRFC), are critical to basin water management. This project leverages advanced distributed models, datasets, and snow data assimilation techniques to improve operational water supply forecasts made by CBRFC in the Upper Colorado River Basin. The current work will specifically focus on improving water supply forecasts through the implementation of a snow data assimilation process coupled with the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM). Three types of observations will be used in the snow data assimilation system: satellite Snow Covered Area (MODSCAG), satellite Dust Radiative Forcing in Snow (MODDRFS), and SNOTEL Snow Water Equivalent (SWE). SNOTEL SWE provides the main source of high elevation snowpack information during the snow season, however, these point measurement sites are carefully selected to provide consistent indices of snowpack, and may not be representative of the surrounding watershed. We address this problem by transforming the SWE observations to standardized deviates and interpolating the standardized deviates using a spatial regression model. The interpolation process will also take advantage of the MODIS Snow Covered Area and Grainsize (MODSCAG) product to inform the model on the spatial distribution of snow. The interpolated standardized deviates are back-transformed and used in an Ensemble Kalman Filter (EnKF) to update the model simulated SWE. The MODIS Dust Radiative Forcing in Snow (MODDRFS) product will be used more directly through temporary adjustments to model snowmelt parameters, which should improve melt estimates in areas affected by dust on snow. In order to assess the value of different data sources, reforecasts will be produced for a historical period and performance measures will be computed to assess forecast skill. The existing CBRFC Ensemble Streamflow Prediction (ESP) reforecasts will provide a baseline for comparison to determine the added-value of the data assimilation process.

An analytical model for light backscattering by coccoliths and coccospheres of Emiliania huxleyi.

PubMed

Fournier, Georges; Neukermans, Griet

2017-06-26

We present an analytical model for light backscattering by coccoliths and coccolithophores of the marine calcifying phytoplankter Emiliania huxleyi. The model is based on the separation of the effects of diffraction, refraction, and reflection on scattering, a valid assumption for particle sizes typical of coccoliths and coccolithophores. Our model results match closely with results from an exact scattering code that uses complex particle geometry and our model also mimics well abrupt transitions in scattering magnitude. Finally, we apply our model to predict changes in the spectral backscattering coefficient during an Emiliania huxleyi bloom with results that closely match in situ measurements. Because our model captures the key features that control the light backscattering process, it can be generalized to coccoliths and coccolithophores of different morphologies which can be obtained from size-calibrated electron microphotographs. Matlab codes of this model are provided as supplementary material.

Use of In-Situ and Remotely Sensed Snow Observations for the National Water Model in Both an Analysis and Calibration Framework.

NASA Astrophysics Data System (ADS)

Karsten, L. R.; Gochis, D.; Dugger, A. L.; McCreight, J. L.; Barlage, M. J.; Fall, G. M.; Olheiser, C.

2017-12-01

Since version 1.0 of the National Water Model (NWM) has gone operational in Summer 2016, several upgrades to the model have occurred to improve hydrologic prediction for the continental United States. Version 1.1 of the NWM (Spring 2017) includes upgrades to parameter datasets impacting land surface hydrologic processes. These parameter datasets were upgraded using an automated calibration workflow that utilizes the Dynamic Data Search (DDS) algorithm to adjust parameter values using observed streamflow. As such, these upgrades to parameter values took advantage of various observations collected for snow analysis. In particular, in-situ SNOTEL observations in the Western US, volunteer in-situ observations across the entire US, gamma-derived snow water equivalent (SWE) observations courtesy of the NWS NOAA Corps program, gridded snow depth and SWE products from the Jet Propulsion Laboratory (JPL) Airborne Snow Observatory (ASO), gridded remotely sensed satellite-based snow products (MODIS,AMSR2,VIIRS,ATMS), and gridded SWE from the NWS Snow Data Assimilation System (SNODAS). This study explores the use of these observations to quantify NWM error and improvements from version 1.0 to version 1.1, along with subsequent work since then. In addition, this study explores the use of snow observations for use within the automated calibration workflow. Gridded parameter fields impacting the accumulation and ablation of snow states in the NWM were adjusted and calibrated using gridded remotely sensed snow states, SNODAS products, and in-situ snow observations. This calibration adjustment took place over various ecological regions in snow-dominated parts of the US for a retrospective period of time to capture a variety of climatological conditions. Specifically, the latest calibrated parameters impacting streamflow were held constant and only parameters impacting snow physics were tuned using snow observations and analysis. The adjusted parameter datasets were then used to force the model over an independent period for analysis against both snow and streamflow observations to see if improvements took place. The goal of this work is to further improve snow physics in the NWM, along with identifying areas where further work will take place in the future, such as data assimilation or further forcing improvements.

Comparing different snow products to assess spatio-temporal snow cover patterns in the Central Taurus Mountains, Turkey

NASA Astrophysics Data System (ADS)

Sturm, K.; Helmschrot, J.

2013-12-01

Snow and its spatial and temporal patterns are important for catchment hydrology in the semi-arid eastern Mediterranean. Since most of the annual rainfall is stored as snow during winter and released during drier conditions in spring and summer, downstream regions of the Taurus Mountains relying on snow water temporarily stored in reservoirs for agricultural use are heavily dependent on the timing of snowmelt discharge. Runoff is controlled by the amount of accumulated snow, its distribution, and the climatic conditions controlling spring snowmelt. Thus, knowledge about spatial and temporal snow cover dynamics is essential for sustainable water resources management. The lack of observations in high-altitude regions reinforces the application of different snow products for a better assessment of spatio-temporal snow cover patterns. To better assess the quality of such products, simulated daily snow cover and EO-based snow cover products were compared for the Egribuk subcatchment, in the Central Taurus Mountains, Turkey. Daily information on snow cover, depths, and snow water equivalent was derived from distributed hydrological modeling using the J2000 model. Furthermore, 8-day MODIS snow cover data from Terra (MOD10A2) and Aqua (MYD10A2) satellites at a spatial resolution of 500 m were synchronized to receive cloud-free images. From this effort, 253 images covering the period between 07/04/2002 and 12/27/2007 were used for further analyses. The products were analyzed individually to determine the number of snow-covered days in relation to freezing days, spring snowmelt onsets, and temporal patterns, reflecting the effect of altitude on the percentage snow-covered area (SCA) along a topographic gradient at various time-steps. Monthly and 8-day spatial patterns of a single snow season were also examined. When SCA peaks at all altitudes, in February and March, the results of both products show a good agreement regarding SCA extent. In contrast, the extent of SCA differs notably during snow accumulation and ablation periods, the highest deviations occurring in December, April, and May. The highest SCA inconsistencies are observed in the low and mid altitudes, whereas the higher elevations are snow-covered very early in the snow season as modeled by J2000. During these periods, J2000 simulates a significantly larger SCA than MODIS. The analysis of individual time steps suggests that the J2000 daily model does capture individual snow events, whereas the MODIS products fail to do so due to their temporal resolution. Furthermore, acquisition time and inner-daily melt and re-freezing effects may affect SCA estimates from MODIS data. In other cases, differences can clearly be associated to insufficient model input data, primarily due to limited spatial precipitation and temperature data. Our study indicates that individual products might provide inconsistent information on temporal and spatial snow cover. We recommend considering a combined analysis of different snow products in order to provide reliable information on snow cover dynamics, in particular in eastern Mediterranean high-altitude environments.

Development and application of a backscatter lidar forward operator for quantitative validation of aerosol dispersion models and future data assimilation

NASA Astrophysics Data System (ADS)

Geisinger, Armin; Behrendt, Andreas; Wulfmeyer, Volker; Strohbach, Jens; Förstner, Jochen; Potthast, Roland

2017-12-01

A new backscatter lidar forward operator was developed which is based on the distinct calculation of the aerosols' backscatter and extinction properties. The forward operator was adapted to the COSMO-ART ash dispersion simulation of the Eyjafjallajökull eruption in 2010. While the particle number concentration was provided as a model output variable, the scattering properties of each individual particle type were determined by dedicated scattering calculations. Sensitivity studies were performed to estimate the uncertainties related to the assumed particle properties. Scattering calculations for several types of non-spherical particles required the usage of T-matrix routines. Due to the distinct calculation of the backscatter and extinction properties of the models' volcanic ash size classes, the sensitivity studies could be made for each size class individually, which is not the case for forward models based on a fixed lidar ratio. Finally, the forward-modeled lidar profiles have been compared to automated ceilometer lidar (ACL) measurements both qualitatively and quantitatively while the attenuated backscatter coefficient was chosen as a suitable physical quantity. As the ACL measurements were not calibrated automatically, their calibration had to be performed using satellite lidar and ground-based Raman lidar measurements. A slight overestimation of the model-predicted volcanic ash number density was observed. Major requirements for future data assimilation of data from ACL have been identified, namely, the availability of calibrated lidar measurement data, a scattering database for atmospheric aerosols, a better representation and coverage of aerosols by the ash dispersion model, and more investigation in backscatter lidar forward operators which calculate the backscatter coefficient directly for each individual aerosol type. The introduced forward operator offers the flexibility to be adapted to a multitude of model systems and measurement setups.

Integration, Validation, and Application of a PV Snow Coverage Model in SAM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Freeman, Janine M.; Ryberg, David Severin

2017-08-01

Due to the increasing deployment of PV systems in snowy climates, there is significant interest in a method capable of estimating PV losses resulting from snow coverage that has been verified for a variety of system designs and locations. Many independent snow coverage models have been developed over the last 15 years; however, there has been very little effort verifying these models beyond the system designs and locations on which they were based. Moreover, major PV modeling software products have not yet incorporated any of these models into their workflows. In response to this deficiency, we have integrated the methodologymore » of the snow model developed in the paper by Marion et al. (2013) into the National Renewable Energy Laboratory's (NREL) System Advisor Model (SAM). In this work, we describe how the snow model is implemented in SAM and we discuss our demonstration of the model's effectiveness at reducing error in annual estimations for three PV arrays. Next, we use this new functionality in conjunction with a long term historical data set to estimate average snow losses across the United States for two typical PV system designs. The open availability of the snow loss estimation capability in SAM to the PV modeling community, coupled with our results of the nationwide study, will better equip the industry to accurately estimate PV energy production in areas affected by snowfall.« less

Merging a Terrain-Based Parameter and Snow Particle Counter Data for the Assessment of Snow Redistribution in the Col du Lac Blanc Area

NASA Astrophysics Data System (ADS)

Schön, Peter; Prokop, Alexander; Naaim-Bouvet, Florence; Vionnet, Vincent; Guyomarc'h, Gilbert; Heiser, Micha; Nishimura, Kouichi

2015-04-01

Wind and the associated snow drift are dominating factors determining the snow distribution and accumulation in alpine areas, resulting in a high spatial variability of snow depth that is difficult to evaluate and quantify. The terrain-based parameter Sx characterizes the degree of shelter or exposure of a grid point provided by the upwind terrain, without the computational complexity of numerical wind field models. The parameter has shown to qualitatively predict snow redistribution with good reproduction of spatial patterns. It does not, however, provide a quantitative estimate of changes in snow depths. The objective of our research was to introduce a new parameter to quantify changes in snow depths in our research area, the Col du Lac Blanc in the French Alps. The area is at an elevation of 2700 m and particularly suited for our study due to its consistently bi-modal wind directions. Our work focused on two pronounced, approximately 10 m high terrain breaks, and we worked with 1 m resolution digital snow surface models (DSM). The DSM and measured changes in snow depths were obtained with high-accuracy terrestrial laser scan (TLS) measurements. First we calculated the terrain-based parameter Sx on a digital snow surface model and correlated Sx with measured changes in snow-depths (Δ SH). Results showed that Δ SH can be approximated by Δ SHestimated = α * Sx, where α is a newly introduced parameter. The parameter α has shown to be linked to the amount of snow deposited influenced by blowing snow flux. At the Col du Lac Blanc test side, blowing snow flux is recorded with snow particle counters (SPC). Snow flux is the number of drifting snow particles per time and area. Hence, the SPC provide data about the duration and intensity of drifting snow events, two important factors not accounted for by the terrain parameter Sx. We analyse how the SPC snow flux data can be used to estimate the magnitude of the new variable parameter α . To simulate the development of the snow surface in dependency of Sx, SPC flux and time, we apply a simple cellular automata system. The system consists of raster cells that develop through discrete time steps according to a set of rules. The rules are based on the states of neighboring cells. Our model assumes snow transport in dependency of Sx gradients between neighboring cells. The cells evolve based on difference quotients between neighbouring cells. Our analyses and results are steps towards using the terrain-based parameter Sx, coupled with SPC data, to quantitatively estimate changes in snow depths, using high raster resolutions of 1 m.

Next Generation Snow Cover Mapping: Can Future Hyperspectral Satellite Spectrometer Systems Improve Subpixel Snow-covered Area and Grain Size in the Sierra Nevada?

NASA Astrophysics Data System (ADS)

Hill, R.; Calvin, W. M.; Harpold, A.

2017-12-01

Mountain snow storage is the dominant source of water for humans and ecosystems in western North America. Consequently, the spatial distribution of snow-covered area is fundamental to both hydrological, ecological, and climate models. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were collected along the entire Sierra Nevada mountain range extending from north of Lake Tahoe to south of Mt. Whitney during the 2015 and 2016 snow-covered season. The AVIRIS dataset used in this experiment consists of 224 contiguous spectral channels with wavelengths ranging 400-2500 nanometers at a 15-meter spatial pixel size. Data from the Sierras were acquired on four days: 2/24/15 during a very low snow year, 3/24/16 near maximum snow accumulation, and 5/12/16 and 5/18/16 during snow ablation and snow loss. Building on previous retrieval of subpixel snow-covered area algorithms that take into account varying grain size we present a model that analyzes multiple endmembers of varying snow grain size, vegetation, rock, and soil in segmented regions along the Sierra Nevada to determine snow-cover spatial extent, snow sub-pixel fraction, and approximate grain size. In addition, varying simulated models of the data will compare and contrast the retrieval of current snow products such as MODIS Snow-Covered Area and Grain Size (MODSCAG) and the Airborne Space Observatory (ASO). Specifically, does lower spatial resolution (MODIS), broader resolution bandwidth (MODIS), and limited spectral resolution (ASO) affect snow-cover area and grain size approximations? The implications of our findings will help refine snow mapping products for planned hyperspectral satellite spectrometer systems such as EnMAP (slated to launch in 2019), HISUI (planned for inclusion on the International Space Station in 2018), and HyspIRI (currently under consideration).

Validating SWE reconstruction using Airborne Snow Observatory measurements in the Sierra Nevada

NASA Astrophysics Data System (ADS)

Bair, N.; Rittger, K.; Davis, R. E.; Dozier, J.

2015-12-01

The Airborne Snow Observatory (ASO) program offers high resolution estimates of snow water equivalent (SWE) in several small basins across California during the melt season. Primarily, water managers use this information to model snowmelt runoff into reservoirs. Another, and potentially more impactful, use of ASO SWE measurements is in validating and improving satellite-based SWE estimates which can be used in austere regions with no ground-based snow or water measurements, such as Afghanistan's Hindu Kush. Using the entire ASO dataset to date (2013-2015) which is mostly from the Upper Tuolumne basin, but also includes measurements from 2015 in the Kings, Rush Creek, Merced, and Mammoth Lakes basins, we compare ASO measurements to those from a SWE reconstruction method. Briefly, SWE reconstruction involves downscaling energy balance forcings to compute potential melt energy, then using satellite-derived estimates of fractional snow covered area (fSCA) to estimate snow melt from potential melt. The snowpack can then be built in reverse, given a remotely-sensed date of snow disappearance (fSCA=0). Our model has improvements over previous iterations in that it: uses the full energy balance (compared to a modified degree-day) approach, models bulk and surface snow temperatures, accounts for ephemeral snow, and uses a remotely-sensed snow albedo adjusted for impurities. To check that ASO provides accurate snow measurements, we compare fSCA derived from ASO snow depth at 3 m resolution with fSCA from a spectral unmixing algorithm for LandSAT at 30 m, and from binary SCA estimates from Geoeye at 0.5 m from supervised classification. To conclude, we document how our reconstruction model has evolved over the years and provide specific examples where improvements have been made using ASO and other verification sources.

Development and Evaluation of a Cloud-Gap-Filled MODIS Daily Snow-Cover Product

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Riggs, George A.; Foster, James L.; Kumar, Sujay V.

2010-01-01

The utility of the Moderate Resolution Imaging Spectroradiometer (MODIS) snow-cover products is limited by cloud cover which causes gaps in the daily snow-cover map products. We describe a cloud-gap-filled (CGF) daily snowcover map using a simple algorithm to track cloud persistence, to account for the uncertainty created by the age of the snow observation. Developed from the 0.050 resolution climate-modeling grid daily snow-cover product, MOD10C1, each grid cell of the CGF map provides a cloud-persistence count (CPC) that tells whether the current or a prior day was used to make the snow decision. Percentage of grid cells "observable" is shown to increase dramatically when prior days are considered. The effectiveness of the CGF product is evaluated by conducting a suite of data assimilation experiments using the community Noah land surface model in the NASA Land Information System (LIS) framework. The Noah model forecasts of snow conditions, such as snow-water equivalent (SWE), are updated based on the observations of snow cover which are obtained either from the MOD1 OC1 standard product or the new CGF product. The assimilation integrations using the CGF maps provide domain averaged bias improvement of -11 %, whereas such improvement using the standard MOD1 OC1 maps is -3%. These improvements suggest that the Noah model underestimates SWE and snow depth fields, and that the assimilation integrations contribute to correcting this systematic error. We conclude that the gap-filling strategy is an effective approach for increasing cloud-free observations of snow cover.

Effects of soil water and heat relationship under various snow cover during freezing-thawing periods in Songnen Plain, China.

PubMed

Fu, Qiang; Hou, Renjie; Li, Tianxiao; Jiang, Ruiqi; Yan, Peiru; Ma, Ziao; Zhou, Zhaoqiang

2018-01-22

In this study, the spatial variations of soil water and heat under bare land (BL), natural snow (NS), compacted snow (CS) and thick snow (TS) treatments were analyzed. The relationship curve between soil temperature and water content conforms to the exponential filtering model, by means of the functional form of the model, it was defined as soil water and heat relation function model. On this basis, soil water and heat function models of 10, 20, 40, 60, 100, and 140 cm were established. Finally, a spatial variation law of the relationship effect was described based on analysising of the differences between the predicted and measured results. During freezing period, the effects of external factors on soil were hindered by snow cover. As the snow increased, the accuracy of the function model gradually improved. During melting period, infiltration by snowmelt affected the relationship between the soil temperature and moisture. With the increasing of snow, the accuracy of the function models gradually decreased. The relationship effects of soil water and heat increased with increasing depth within the frozen zone. In contrast, below the frozen layer, the relationship of soil water and heat was weaker, and the function models were less accurate.

Technical snow production in skiing areas: conditions, practice, monitoring and modelling. A case study in Mayrhofen/Austria

NASA Astrophysics Data System (ADS)

Strasser, Ulrich; Hanzer, Florian; Marke, Thomas; Rothleitner, Michael

2017-04-01

The production of technical snow today is a self-evident feature of modern alpine skiing resort management. Millions of Euros are invested every year for the technical infrastructure and its operation to produce a homogeneous and continuing snow cover on the skiing slopes for the winter season in almost every larger destination in the Alps. In Austria, skiing tourism is a significant factor of the national economic structure. We present the framing conditions of technical snow production in the mid-size skiing resort of Mayrhofen (Zillertal Alps/Austria, 136 km slopes, elevation range 630 - 2.500 m a.s.l.). Production conditions are defined by the availability of water, the planned date for the season opening, and the climatic conditions in the weeks before. By means of an adapted snow production strategy an attempt is made to ecologically and economically optimize the use of water and energy resources. Monitoring of the snow cover is supported by a network of low-cost sensors and mobile snow depth recordings. Finally, technical snow production is simulated with the spatially distributed, physically based hydroclimatological model AMUNDSEN. The model explicitly considers individual snow guns and distributes the produced snow along the slopes. The amount of simulated snow produced by each device is a function of its type, of actual wet-bulb temperature at the location, of ski area infrastructure (in terms of water supply and pumping capacity), and of snow demand.

Analysis of the Dielectric constant of saline-alkali soils and the effect on radar backscattering coefficient: a case study of soda alkaline saline soils in Western Jilin Province using RADARSAT-2 data.

PubMed

Li, Yang-yang; Zhao, Kai; Ren, Jian-hua; Ding, Yan-ling; Wu, Li-li

2014-01-01

Soil salinity is a global problem, especially in developing countries, which affects the environment and productivity of agriculture areas. Salt has a significant effect on the complex dielectric constant of wet soil. However, there is no suitable model to describe the variation in the backscattering coefficient due to changes in soil salinity content. The purpose of this paper is to use backscattering models to understand behaviors of the backscattering coefficient in saline soils based on the analysis of its dielectric constant. The effects of moisture and salinity on the dielectric constant by combined Dobson mixing model and seawater dielectric constant model are analyzed, and the backscattering coefficient is then simulated using the AIEM. Simultaneously, laboratory measurements were performed on ground samples. The frequency effect of the laboratory results was not the same as the simulated results. The frequency dependence of the ionic conductivity of an electrolyte solution is influenced by the ion's components. Finally, the simulated backscattering coefficients measured from the dielectric constant with the AIEM were analyzed using the extracted backscattering coefficient from the RADARSAT-2 image. The results show that RADARSAT-2 is potentially able to measure soil salinity; however, the mixed pixel problem needs to be more thoroughly considered.

Validation and application of MODIS-derived clean snow albedo and dust radiative forcing

NASA Astrophysics Data System (ADS)

Rittger, K. E.; Bryant, A. C.; Seidel, F. C.; Bair, E. H.; Skiles, M.; Goodale, C. E.; Ramirez, P.; Mattmann, C. A.; Dozier, J.; Painter, T.

2012-12-01

Snow albedo is an important control on snowmelt. Though albedo evolution of aging snow can be roughly modeled from grain growth, dust and other light absorbing impurities are extrinsic and therefore must be measured. Estimates of clean snow albedo and surface radiative forcing from impurities, which can be inferred from MODIS 500 m surface reflectance products, can provide this driving data for snowmelt models. Here we use MODSCAG (MODIS snow covered area and grain size) to estimate the clean snow albedo and MODDRFS (MODIS dust radiative forcing of snow) to estimate the additional absorbed solar radiation from dust and black carbon. With its finer spatial (20 m) and spectral (10 nm) resolutions, AVIRIS provides a way to estimate the accuracy of MODIS products and understand variability of snow albedo at a finer scale that we explore though a range of topography. The AVIRIS database includes images from late in the accumulation season through the melt season when we are most interested in changes in snow albedo. In addition to the spatial validation, we employ the best estimate of albedo from MODIS in an energy balance reconstruction model to estimate the maximum snow water equivalent. MODDRFS calculates radiative forcing only in pixels that are completely snow-covered, so we spatially interpolate the product to estimate the forcing in all pixels where MODSCAG has given us estimates of clean snow albedo. Comparisons with snow pillows and courses show better agreement when the radiative forcing from absorbing impurities is included in the energy balance reconstruction.

Dust on Snow Processes and Impacts in the Upper Colorado River Basin

NASA Astrophysics Data System (ADS)

Skiles, M.; Painter, T. H.; Okin, G. S.

2015-12-01

In the Upper Colorado River Basin episodic deposition of mineral dust onto mountain snow cover frequently occurs in the spring when wind speeds and dust emission peaks on the nearby Colorado Plateau, and deposition rates have increased since the intensive settlement in the western USA in the mid 1880s. Dust deposition darkens the snow surface, and accelerates snowmelt through reduction of albedo and further indirect reduction of albedo by accelerating the growth of snow grain size. Observation and modeling of dust-on-snow processes began in 2005 at Senator Beck Basin Study Area (SBBSA) in the San Juan Mountains, CO, work which has shown that dust advances melt, shifts runoff timing and intensity, and reduces total water yield. The consistency of deposition and magnitude of impacts highlighted the need for more detailed understanding of the radiative impacts of dust-on-snow in this region. Here I will present results from a novel, high resolution, daily snow property dataset, collected at SBBSA over the 2013 ablation season, to facilitate physically based radiative transfer and snowmelt modeling. Measurements included snow albedo and vertical profiles of snow density, optical snow grain size, and dust/black carbon concentrations. This dataset was used to assess the relationship between episodic dust events, snow grain growth, and albedo over time, and observe the relation between deposited dust and melt water. Additionally, modeling results include the determination of the regionally specific dust-on-snow complex refractive index and radiative forcing partitioning between dust and black carbon, and dust and snow grain growth.

Impact of Snow Grain Shape and Internal Mixing with Black Carbon Aerosol on Snow Optical Properties for use in Climate Models

NASA Astrophysics Data System (ADS)

He, C.; Liou, K. N.; Takano, Y.; Yang, P.; Li, Q.; Chen, F.

2017-12-01

A set of parameterizations is developed for spectral single-scattering properties of clean and black carbon (BC)-contaminated snow based on geometric-optic surface-wave (GOS) computations, which explicitly resolves BC-snow internal mixing and various snow grain shapes. GOS calculations show that, compared with nonspherical grains, volume-equivalent snow spheres show up to 20% larger asymmetry factors and hence stronger forward scattering, particularly at wavelengths <1 mm. In contrast, snow grain sizes have a rather small impact on the asymmetry factor at wavelengths <1 mm, whereas size effects are important at longer wavelengths. The snow asymmetry factor is parameterized as a function of effective size, aspect ratio, and shape factor, and shows excellent agreement with GOS calculations. According to GOS calculations, the single-scattering coalbedo of pure snow is predominantly affected by grain sizes, rather than grain shapes, with higher values for larger grains. The snow single-scattering coalbedo is parameterized in terms of the effective size that combines shape and size effects, with an accuracy of >99%. Based on GOS calculations, BC-snow internal mixing enhances the snow single-scattering coalbedo at wavelengths <1 mm, but it does not alter the snow asymmetry factor. The BC-induced enhancement ratio of snow single-scattering coalbedo, independent of snow grain size and shape, is parameterized as a function of BC concentration with an accuracy of >99%. Overall, in addition to snow grain size, both BC-snow internal mixing and snow grain shape play critical roles in quantifying BC effects on snow optical properties. The present parameterizations can be conveniently applied to snow, land surface, and climate models including snowpack radiative transfer processes.

Combining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed

USGS Publications Warehouse

Balk, Benjamin; Elder, Kelly

2000-01-01

We model the spatial distribution of snow across a mountain basin using an approach that combines binary decision tree and geostatistical techniques. In April 1997 and 1998, intensive snow surveys were conducted in the 6.9‐km2 Loch Vale watershed (LVWS), Rocky Mountain National Park, Colorado. Binary decision trees were used to model the large‐scale variations in snow depth, while the small‐scale variations were modeled through kriging interpolation methods. Binary decision trees related depth to the physically based independent variables of net solar radiation, elevation, slope, and vegetation cover type. These decision tree models explained 54–65% of the observed variance in the depth measurements. The tree‐based modeled depths were then subtracted from the measured depths, and the resulting residuals were spatially distributed across LVWS through kriging techniques. The kriged estimates of the residuals were added to the tree‐based modeled depths to produce a combined depth model. The combined depth estimates explained 60–85% of the variance in the measured depths. Snow densities were mapped across LVWS using regression analysis. Snow‐covered area was determined from high‐resolution aerial photographs. Combining the modeled depths and densities with a snow cover map produced estimates of the spatial distribution of snow water equivalence (SWE). This modeling approach offers improvement over previous methods of estimating SWE distribution in mountain basins.

Snow model design for operational purposes

NASA Astrophysics Data System (ADS)

Kolberg, Sjur

2017-04-01

A parsimonious distributed energy balance snow model intended for operational use is evaluated using discharge, snow covered area and grain size; the latter two as observed from the MODIS sensor. The snow model is an improvement of the existing GamSnow model, which is a part of the Enki modelling framework. Core requirements for the new version have been: 1. Reduction of calibration freedom, motivated by previous experience of non-identifiable parameters in the existing version 2. Improvement of process representation based on recent advances in physically based snow modelling 3. Limiting the sensitivity to forcing data which are poorly known over the spatial domain of interest (often in mountainous areas) 4. Preference for observable states, and the ability to improve from updates. The albedo calculation is completely revised, now based on grain size through an emulation of the SNICAR model (Flanner and Zender, 2006; Gardener and Sharp, 2010). The number of calibration parameters in the albedo model is reduced from 6 to 2. The wind function governing turbulent energy fluxes has been reduced from 2 to 1 parameter. Following Raleigh et al (2011), snow surface radiant temperature is split from the top layer thermodynamic temperature, using bias-corrected wet-bulb temperature to model the former. Analyses are ongoing, and the poster will bring evaluation results from 16 years of MODIS observations and more than 25 catchments in southern Norway.

White water: Fifty years of snow research in WRR and the outlook for the future

NASA Astrophysics Data System (ADS)

Sturm, Matthew

2015-07-01

Over the past 50 years, 239 papers related to snow have been published in Water Resources Research (WRR). Seminal papers on virtually every facet of snow physics and snow water resources have appeared in the journal. These include papers on drifting snow, the snow surface energy balance, the effect of grain size on albedo, chemical elution, water movement through snow, and canopy interception. In particular, papers in WRR have explored the distribution of snow across different landscapes, providing data, process knowledge, and the basis for virtually all of the distributed snow models in use today. In this paper, I review these key contributions and provide some personal thoughts on what is likely to be the focus and nature of papers published in the next few decades, a period that is likely to see an increasing ability to map snow cover in detail, which should serve as a basis for the further development and improvement of snow models. It will also be an uncertain future, with profound changes in snow climatology predicted. I expect WRR will continue to play a key role in documenting and understanding these important cryospheric changes.

Stochastic parameterization for light absorption by internally mixed BC/dust in snow grains for application to climate models

NASA Astrophysics Data System (ADS)

Liou, K. N.; Takano, Y.; He, C.; Yang, P.; Leung, L. R.; Gu, Y.; Lee, W. L.

2014-06-01

A stochastic approach has been developed to model the positions of BC (black carbon)/dust internally mixed with two snow grain types: hexagonal plate/column (convex) and Koch snowflake (concave). Subsequently, light absorption and scattering analysis can be followed by means of an improved geometric-optics approach coupled with Monte Carlo photon tracing to determine BC/dust single-scattering properties. For a given shape (plate, Koch snowflake, spheroid, or sphere), the action of internal mixing absorbs substantially more light than external mixing. The snow grain shape effect on absorption is relatively small, but its effect on asymmetry factor is substantial. Due to a greater probability of intercepting photons, multiple inclusions of BC/dust exhibit a larger absorption than an equal-volume single inclusion. The spectral absorption (0.2-5 µm) for snow grains internally mixed with BC/dust is confined to wavelengths shorter than about 1.4 µm, beyond which ice absorption predominates. Based on the single-scattering properties determined from stochastic and light absorption parameterizations and using the adding/doubling method for spectral radiative transfer, we find that internal mixing reduces snow albedo substantially more than external mixing and that the snow grain shape plays a critical role in snow albedo calculations through its forward scattering strength. Also, multiple inclusion of BC/dust significantly reduces snow albedo as compared to an equal-volume single sphere. For application to land/snow models, we propose a two-layer spectral snow parameterization involving contaminated fresh snow on top of old snow for investigating and understanding the climatic impact of multiple BC/dust internal mixing associated with snow grain metamorphism, particularly over mountain/snow topography.

Stochastic Parameterization for Light Absorption by Internally Mixed BC/dust in Snow Grains for Application to Climate Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liou, K. N.; Takano, Y.; He, Cenlin

2014-06-27

A stochastic approach to model the positions of BC/dust internally mixed with two snow-grain types has been developed, including hexagonal plate/column (convex) and Koch snowflake (concave). Subsequently, light absorption and scattering analysis can be followed by means of an improved geometric-optics approach coupled with Monte Carlo photon tracing to determine their single-scattering properties. For a given shape (plate, Koch snowflake, spheroid, or sphere), internal mixing absorbs more light than external mixing. The snow-grain shape effect on absorption is relatively small, but its effect on the asymmetry factor is substantial. Due to a greater probability of intercepting photons, multiple inclusions ofmore » BC/dust exhibit a larger absorption than an equal-volume single inclusion. The spectral absorption (0.2 – 5 um) for snow grains internally mixed with BC/dust is confined to wavelengths shorter than about 1.4 um, beyond which ice absorption predominates. Based on the single-scattering properties determined from stochastic and light absorption parameterizations and using the adding/doubling method for spectral radiative transfer, we find that internal mixing reduces snow albedo more than external mixing and that the snow-grain shape plays a critical role in snow albedo calculations through the asymmetry factor. Also, snow albedo reduces more in the case of multiple inclusion of BC/dust compared to that of an equal-volume single sphere. For application to land/snow models, we propose a two-layer spectral snow parameterization containing contaminated fresh snow on top of old snow for investigating and understanding the climatic impact of multiple BC/dust internal mixing associated with snow grain metamorphism, particularly over mountains/snow topography.« less

Georectification and snow classification of webcam images: potential for complementing satellite-derrived snow maps over Switzerland

NASA Astrophysics Data System (ADS)

Dizerens, Céline; Hüsler, Fabia; Wunderle, Stefan

2016-04-01

The spatial and temporal variability of snow cover has a significant impact on climate and environment and is of great socio-economic importance for the European Alps. Satellite remote sensing data is widely used to study snow cover variability and can provide spatially comprehensive information on snow cover extent. However, cloud cover strongly impedes the surface view and hence limits the number of useful snow observations. Outdoor webcam images not only offer unique potential for complementing satellite-derived snow retrieval under cloudy conditions but could also serve as a reference for improved validation of satellite-based approaches. Thousands of webcams are currently connected to the Internet and deliver freely available images with high temporal and spatial resolutions. To exploit the untapped potential of these webcams, a semi-automatic procedure was developed to generate snow cover maps based on webcam images. We used daily webcam images of the Swiss alpine region to apply, improve, and extend existing approaches dealing with the positioning of photographs within a terrain model, appropriate georectification, and the automatic snow classification of such photographs. In this presentation, we provide an overview of the implemented procedure and demonstrate how our registration approach automatically resolves the orientation of a webcam by using a high-resolution digital elevation model and the webcam's position. This allows snow-classified pixels of webcam images to be related to their real-world coordinates. We present several examples of resulting snow cover maps, which have the same resolution as the digital elevation model and indicate whether each grid cell is snow-covered, snow-free, or not visible from webcams' positions. The procedure is expected to work under almost any weather condition and demonstrates the feasibility of using webcams for the retrieval of high-resolution snow cover information.

Snow Sublimation in Mountain Environments and Its Sensitivity to Forest Disturbance and Climate Warming

NASA Astrophysics Data System (ADS)

Sexstone, Graham A.; Clow, David W.; Fassnacht, Steven R.; Liston, Glen E.; Hiemstra, Christopher A.; Knowles, John F.; Penn, Colin A.

2018-02-01

Snow sublimation is an important component of the snow mass balance, but the spatial and temporal variability of this process is not well understood in mountain environments. This study combines a process-based snow model (SnowModel) with eddy covariance (EC) measurements to investigate (1) the spatio-temporal variability of simulated snow sublimation with respect to station observations, (2) the contribution of snow sublimation to the ablation of the snowpack, and (3) the sensitivity and response of snow sublimation to bark beetle-induced forest mortality and climate warming across the north-central Colorado Rocky Mountains. EC-based observations of snow sublimation compared well with simulated snow sublimation at stations dominated by surface and canopy sublimation, but blowing snow sublimation in alpine areas was not well captured by the EC instrumentation. Water balance calculations provided an important validation of simulated sublimation at the watershed scale. Simulated snow sublimation across the study area was equivalent to 28% of winter precipitation on average, and the highest relative snow sublimation fluxes occurred during the lowest snow years. Snow sublimation from forested areas accounted for the majority of sublimation fluxes, highlighting the importance of canopy and sub-canopy surface sublimation in this region. Simulations incorporating the effects of tree mortality due to bark-beetle disturbance resulted in a 4% reduction in snow sublimation from forested areas. Snow sublimation rates corresponding to climate warming simulations remained unchanged or slightly increased, but total sublimation losses decreased by up to 6% because of a reduction in snow covered area and duration.

Snow sublimation in mountain environments and its sensitivity to forest disturbance and climate warming

USGS Publications Warehouse

Sexstone, Graham A.; Clow, David W.; Fassnacht, Steven R.; Liston, Glen E.; Hiemstra, Christopher A.; Knowles, John F.; Penn, Colin A.

2018-01-01

Snow sublimation is an important component of the snow mass balance, but the spatial and temporal variability of this process is not well understood in mountain environments. This study combines a process‐based snow model (SnowModel) with eddy covariance (EC) measurements to investigate (1) the spatio‐temporal variability of simulated snow sublimation with respect to station observations, (2) the contribution of snow sublimation to the ablation of the snowpack, and (3) the sensitivity and response of snow sublimation to bark beetle‐induced forest mortality and climate warming across the north‐central Colorado Rocky Mountains. EC‐based observations of snow sublimation compared well with simulated snow sublimation at stations dominated by surface and canopy sublimation, but blowing snow sublimation in alpine areas was not well captured by the EC instrumentation. Water balance calculations provided an important validation of simulated sublimation at the watershed scale. Simulated snow sublimation across the study area was equivalent to 28% of winter precipitation on average, and the highest relative snow sublimation fluxes occurred during the lowest snow years. Snow sublimation from forested areas accounted for the majority of sublimation fluxes, highlighting the importance of canopy and sub‐canopy surface sublimation in this region. Simulations incorporating the effects of tree mortality due to bark‐beetle disturbance resulted in a 4% reduction in snow sublimation from forested areas. Snow sublimation rates corresponding to climate warming simulations remained unchanged or slightly increased, but total sublimation losses decreased by up to 6% because of a reduction in snow covered area and duration.

The electrical self-potential method is a non-intrusive snow-hydrological sensor

NASA Astrophysics Data System (ADS)

Thompson, S. S.; Kulessa, B.; Essery, R. L. H.; Lüthi, M. P.

2015-08-01

Our ability to measure, quantify and assimilate hydrological properties and processes of snow in operational models is disproportionally poor compared to the significance of seasonal snowmelt as a global water resource and major risk factor in flood and avalanche forecasting. Encouraged by recent theoretical, modelling and laboratory work, we show here that the diurnal evolution of aerially-distributed self-potential magnitudes closely track those of bulk meltwater fluxes in melting in-situ snowpacks at Rhone and Jungfraujoch glaciers, Switzerland. Numerical modelling infers temporally-evolving liquid water contents in the snowpacks on successive days in close agreement with snow-pit measurements. Muting previous concerns, the governing physical and chemical properties of snow and meltwater became temporally invariant for modelling purposes. Because measurement procedure is straightforward and readily automated for continuous monitoring over significant spatial scales, we conclude that the self-potential geophysical method is a highly-promising non-intrusive snow-hydrological sensor for measurement practice, modelling and operational snow forecasting.

Using snowflake surface-area-to-volume ratio to model and interpret snowfall triple-frequency radar signatures

NASA Astrophysics Data System (ADS)

Gergely, Mathias; Cooper, Steven J.; Garrett, Timothy J.

2017-10-01

The snowflake microstructure determines the microwave scattering properties of individual snowflakes and has a strong impact on snowfall radar signatures. In this study, individual snowflakes are represented by collections of randomly distributed ice spheres where the size and number of the constituent ice spheres are specified by the snowflake mass and surface-area-to-volume ratio (SAV) and the bounding volume of each ice sphere collection is given by the snowflake maximum dimension. Radar backscatter cross sections for the ice sphere collections are calculated at X-, Ku-, Ka-, and W-band frequencies and then used to model triple-frequency radar signatures for exponential snowflake size distributions (SSDs). Additionally, snowflake complexity values obtained from high-resolution multi-view snowflake images are used as an indicator of snowflake SAV to derive snowfall triple-frequency radar signatures. The modeled snowfall triple-frequency radar signatures cover a wide range of triple-frequency signatures that were previously determined from radar reflectivity measurements and illustrate characteristic differences related to snow type, quantified through snowflake SAV, and snowflake size. The results show high sensitivity to snowflake SAV and SSD maximum size but are generally less affected by uncertainties in the parameterization of snowflake mass, indicating the importance of snowflake SAV for the interpretation of snowfall triple-frequency radar signatures.

Modeling the effect of snow and ice on the global environmental fate and long-range transport potential of semivolatile organic compounds.

PubMed

Stocker, Judith; Scheringer, Martin; Wegmann, Fabio; Hungerbuhler, Konrad

2007-09-01

Snow and ice have been implemented in a global multimedia box model to investigate the influence of these media on the environmental fate and long-range transport (LRT) of semivolatile organic compounds (SOCs). Investigated compounds include HCB, PCB28, PCB180, PBDE47, PBDE209, alpha-HCH, and dacthal. In low latitudes, snow acts as a transfer medium taking up chemicals from air and releasing them to water or soil during snowmelt. In high latitudes, snow and ice shield water, soil, and vegetation from chemical deposition. In the model version including snow and ice (scenario 2), the mass of chemicals in soil in high latitudes is between 27% (HCB) and 97% (alpha-HCH) of the mass calculated with the model version without snow and ice (scenario 1). Amounts in Arctic seawater in scenario 2 are 8% (alpha-HCH) to 21% (dacthal) of the amounts obtained in scenario 1. For all investigated chemicals except alpha-HCH, presence of snow and ice in the model increases the concentration in air by a factor of 2 (HCB)to 10 (PBDE209). Because of reduced net deposition to snow-covered surfaces in high latitudes, LRT to the Arctic is reduced for most chemicals whereas transport to the south is more pronounced than in scenario 1 ("southward shift"). The presence of snow and ice thus considerably changes the environmental fate of SOCs.

A Backscatter-Lidar Forward-Operator

NASA Astrophysics Data System (ADS)

Geisinger, Armin; Behrendt, Andreas; Wulfmeyer, Volker; Vogel, Bernhard; Mattis, Ina; Flentje, Harald; Förstner, Jochen; Potthast, Roland

2015-04-01

We have developed a forward-operator which is capable of calculating virtual lidar profiles from atmospheric state simulations. The operator allows us to compare lidar measurements and model simulations based on the same measurement parameter: the lidar backscatter profile. This method simplifies qualitative comparisons and also makes quantitative comparisons possible, including statistical error quantification. Implemented into an aerosol-capable model system, the operator will act as a component to assimilate backscatter-lidar measurements. As many weather services maintain already networks of backscatter-lidars, such data are acquired already in an operational manner. To estimate and quantify errors due to missing or uncertain aerosol information, we started sensitivity studies about several scattering parameters such as the aerosol size and both the real and imaginary part of the complex index of refraction. Furthermore, quantitative and statistical comparisons between measurements and virtual measurements are shown in this study, i.e. applying the backscatter-lidar forward-operator on model output.

Simulation of L-band and HH microwave backscattering from coniferous forest stands - A comparison with SIR-B data

NASA Technical Reports Server (NTRS)

Sun, Guo-Qing; Simonett, David S.

1988-01-01

SIR-B images of the Mt. Shasta region of northern California are used to evaluate a composite L-band HH backscattering model of coniferous forest stands. It is found that both SIR-B and simulated backscattering coefficients for eight stands studied have similar trends and relations to average tree height and average number of trees per pixel. Also, the dispersion and distribution of simulated backscattering coefficients from each stand broadly match SIR-B data from the same stand. Although the limited quality and quantity of experimental data makes it difficult to draw any strong conclusions, the comparisons indicate that a stand-based L-band HH composite model seems promising for explaining backscattering features.

Optical Benson: Following the Impact of Melt Season Progression Using Landsat and Sentinel 2 - Snow Zone Formation Imaged

NASA Astrophysics Data System (ADS)

Fahnestock, M. A.; Shuman, C. A.; Alley, K. E.

2017-12-01

Snow pit observations on a glaciologically-focussed surface traverse in Greenland allowed Benson [1962, SIPRE (now CRREL) Research Report 70] to define a series of snow zones based on the extent of post-depositional diagenesis of the snowpack. At high elevations, Benson found fine-grained "dry snow" where melt (at that time) was absent year-round, followed down-elevation by a "percolation zone" where surface melt penetrated the snowpack, then a "wet snow zone" where firn became saturated during the peak of the melt season, and finally "superimposed ice" and "bare ice" zones where refrozen surface melt and glacier ice were exposed in the melt season. These snow zones can be discriminated in winter synthetic aperture radar (SAR) imagery of the ice sheet (e.g. Fahnestock et al. 2001), but summer melt reduces radar backscatter and makes it difficult to follow the progression of diagenesis beyond the initial indications of surface melting. While some of the impacts of surface melt (especially bands of blue water-saturated firn) are observed from time to time in optical satellite imagery, it has only become possible to map effects of melt over the course of a summer season with the advent of large-data analysis tools such as Google Earth Engine and the inclusion of Landsat and Sentinel-2 data streams in these tools. A map of the maximum extent of this blue saturated zone through the 2016 melt season is shown in the figure. This image is a true color (RGB) composite, but each pixel in the image shows the color of the surface when the "blueness" of the pixel was at a maximum. This means each pixel can be from a different satellite image acquisition than adjacent pixels - but it also means that the maximum extent of the saturated firn (Benson's wet snow zone) is visible. Also visible are percolation, superimposed and bare ice zones. This analysis, using Landsat 8 Operational Land Imager data, was performed using Google Earth Engine to access and analyze the entire melt season's data. Similar spatial analyses for other years in the record, combined with pixel-by-pixel analysis of each time series through the year, can be used to track the progression and overall effect of the melt season in each year. This view of the progression of a melt season provides a new set of tools to help understand changing surface conditions for ice sheets and glaciers globally.

Impacts of 1, 1.5, and 2 Degree Warming on Arctic Terrestrial Snow and Sea Ice

NASA Astrophysics Data System (ADS)

Derksen, C.; Mudryk, L.; Howell, S.; Flato, G. M.; Fyfe, J. C.; Gillett, N. P.; Sigmond, M.; Kushner, P. J.; Dawson, J.; Zwiers, F. W.; Lemmen, D.; Duguay, C. R.; Zhang, X.; Fletcher, C. G.; Dery, S. J.

2017-12-01

The 2015 Paris Agreement of the United Nations Framework Convention on Climate Change (UNFCCC) established the global temperature goal of "holding the increase in the global average temperature to below 2°C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5°C above pre-industrial levels." In this study, we utilize multiple gridded snow and sea ice products (satellite retrievals; assimilation systems; physical models driven by reanalyses) and ensembles of climate model simulations to determine the impacts of observed warming, and project the relative impacts of the UNFCC future warming targets on Arctic seasonal terrestrial snow and sea ice cover. Observed changes during the satellite era represent the response to approximately 1°C of global warming. Consistent with other studies, analysis of the observational record (1970's to present) identifies changes including a shorter snow cover duration (due to later snow onset and earlier snow melt), significant reductions in spring snow cover and summer sea ice extent, and the loss of a large proportion of multi-year sea ice. The spatial patterns of observed snow and sea ice loss are coherent across adjacent terrestrial/marine regions. There are strong pattern correlations between snow and temperature trends, with weaker association between sea ice and temperature due to the additional influence of dynamical effects such wind-driven redistribution of sea ice. Climate model simulations from the Coupled Model Inter-comparison Project Phase 5(CMIP-5) multi-model ensemble, large initial condition ensembles of the Community Earth System Model (CESM) and Canadian Earth System Model (CanESM2) , and warming stabilization simulations from CESM were used to identify changes in snow and ice under further increases to 1.5°C and 2°C warming. The model projections indicate these levels of warming will be reached over the coming 2-4 decades. Warming to 1.5°C results in an increase in the number of melting days over snow and sea ice (and resultant increases in snow-free and ice-free duration), which are similar in magnitude to the change from pre-industrial conditions to present day. Continued warming to 2°C further intensifies the cryospheric response consistent with amplified Arctic warming relative to the global average trend.

A multi-objective approach to improve SWAT model calibration in alpine catchments

NASA Astrophysics Data System (ADS)

Tuo, Ye; Marcolini, Giorgia; Disse, Markus; Chiogna, Gabriele

2018-04-01

Multi-objective hydrological model calibration can represent a valuable solution to reduce model equifinality and parameter uncertainty. The Soil and Water Assessment Tool (SWAT) model is widely applied to investigate water quality and water management issues in alpine catchments. However, the model calibration is generally based on discharge records only, and most of the previous studies have defined a unique set of snow parameters for an entire basin. Only a few studies have considered snow observations to validate model results or have taken into account the possible variability of snow parameters for different subbasins. This work presents and compares three possible calibration approaches. The first two procedures are single-objective calibration procedures, for which all parameters of the SWAT model were calibrated according to river discharge alone. Procedures I and II differ from each other by the assumption used to define snow parameters: The first approach assigned a unique set of snow parameters to the entire basin, whereas the second approach assigned different subbasin-specific sets of snow parameters to each subbasin. The third procedure is a multi-objective calibration, in which we considered snow water equivalent (SWE) information at two different spatial scales (i.e. subbasin and elevation band), in addition to discharge measurements. We tested these approaches in the Upper Adige river basin where a dense network of snow depth measurement stations is available. Only the set of parameters obtained with this multi-objective procedure provided an acceptable prediction of both river discharge and SWE. These findings offer the large community of SWAT users a strategy to improve SWAT modeling in alpine catchments.

Evaluation of SNODAS snow depth and snow water equivalent estimates for the Colorado Rocky Mountains, USA

USGS Publications Warehouse

Clow, David W.; Nanus, Leora; Verdin, Kristine L.; Schmidt, Jeffrey

2012-01-01

The National Weather Service's Snow Data Assimilation (SNODAS) program provides daily, gridded estimates of snow depth, snow water equivalent (SWE), and related snow parameters at a 1-km2 resolution for the conterminous USA. In this study, SNODAS snow depth and SWE estimates were compared with independent, ground-based snow survey data in the Colorado Rocky Mountains to assess SNODAS accuracy at the 1-km2 scale. Accuracy also was evaluated at the basin scale by comparing SNODAS model output to snowmelt runoff in 31 headwater basins with US Geological Survey stream gauges. Results from the snow surveys indicated that SNODAS performed well in forested areas, explaining 72% of the variance in snow depths and 77% of the variance in SWE. However, SNODAS showed poor agreement with measurements in alpine areas, explaining 16% of the variance in snow depth and 30% of the variance in SWE. At the basin scale, snowmelt runoff was moderately correlated (R2 = 0.52) with SNODAS model estimates. A simple method for adjusting SNODAS SWE estimates in alpine areas was developed that uses relations between prevailing wind direction, terrain, and vegetation to account for wind redistribution of snow in alpine terrain. The adjustments substantially improved agreement between measurements and SNODAS estimates, with the R2 of measured SWE values against SNODAS SWE estimates increasing from 0.42 to 0.63 and the root mean square error decreasing from 12 to 6 cm. Results from this study indicate that SNODAS can provide reliable data for input to moderate-scale to large-scale hydrologic models, which are essential for creating accurate runoff forecasts. Refinement of SNODAS SWE estimates for alpine areas to account for wind redistribution of snow could further improve model performance. Published 2011. This article is a US Government work and is in the public domain in the USA.

Implementation of a physically based water percolation routine in the Crocus/SURFEX (V7.3) snowpack model

NASA Astrophysics Data System (ADS)

D'Amboise, Christopher J. L.; Müller, Karsten; Oxarango, Laurent; Morin, Samuel; Schuler, Thomas V.

2017-09-01

We present a new water percolation routine added to the one-dimensional snowpack model Crocus as an alternative to the empirical bucket routine. This routine solves the Richards equation, which describes flow of water through unsaturated porous snow governed by capillary suction, gravity and hydraulic conductivity of the snow layers. We tested the Richards routine on two data sets, one recorded from an automatic weather station over the winter of 2013-2014 at Filefjell, Norway, and the other an idealized synthetic data set. Model results using the Richards routine generally lead to higher water contents in the snow layers. Snow layers often reached a point at which the ice crystals' surface area is completely covered by a thin film of water (the transition between pendular and funicular regimes), at which feedback from the snow metamorphism and compaction routines are expected to be nonlinear. With the synthetic simulation 18 % of snow layers obtained a saturation of > 10 % and 0.57 % of layers reached saturation of > 15 %. The Richards routine had a maximum liquid water content of 173.6 kg m-3 whereas the bucket routine had a maximum of 42.1 kg m-3. We found that wet-snow processes, such as wet-snow metamorphism and wet-snow compaction rates, are not accurately represented at higher water contents. These routines feed back on the Richards routines, which rely heavily on grain size and snow density. The parameter sets for the water retention curve and hydraulic conductivity of snow layers, which are used in the Richards routine, do not represent all the snow types that can be found in a natural snowpack. We show that the new routine has been implemented in the Crocus model, but due to feedback amplification and parameter uncertainties, meaningful applicability is limited. Updating or adapting other routines in Crocus, specifically the snow compaction routine and the grain metamorphism routine, is needed before Crocus can accurately simulate the snowpack using the Richards routine.

Inferential consequences of modeling rather than measuring snow accumulation in studies of animal ecology

USGS Publications Warehouse

Cross, Paul C.; Klaver, Robert W.; Brennan, Angela; Creel, Scott; Beckmann, Jon P.; Higgs, Megan D.; Scurlock, Brandon M.

2013-01-01

Abstract. It is increasingly common for studies of animal ecology to use model-based predictions of environmental variables as explanatory or predictor variables, even though model prediction uncertainty is typically unknown. To demonstrate the potential for misleading inferences when model predictions with error are used in place of direct measurements, we compared snow water equivalent (SWE) and snow depth as predicted by the Snow Data Assimilation System (SNODAS) to field measurements of SWE and snow depth. We examined locations on elk (Cervus canadensis) winter ranges in western Wyoming, because modeled data such as SNODAS output are often used for inferences on elk ecology. Overall, SNODAS predictions tended to overestimate field measurements, prediction uncertainty was high, and the difference between SNODAS predictions and field measurements was greater in snow shadows for both snow variables compared to non-snow shadow areas. We used a simple simulation of snow effects on the probability of an elk being killed by a predator to show that, if SNODAS prediction uncertainty was ignored, we might have mistakenly concluded that SWE was not an important factor in where elk were killed in predatory attacks during the winter. In this simulation, we were interested in the effects of snow at finer scales (2) than the resolution of SNODAS. If bias were to decrease when SNODAS predictions are averaged over coarser scales, SNODAS would be applicable to population-level ecology studies. In our study, however, averaging predictions over moderate to broad spatial scales (9–2200 km2) did not reduce the differences between SNODAS predictions and field measurements. This study highlights the need to carefully evaluate two issues when using model output as an explanatory variable in subsequent analysis: (1) the model’s resolution relative to the scale of the ecological question of interest and (2) the implications of prediction uncertainty on inferences when using model predictions as explanatory or predictor variables.

Soil erosion by snow gliding - a first quantification attempt in a subalpine area in Switzerland

NASA Astrophysics Data System (ADS)

Meusburger, K.; Leitinger, G.; Mabit, L.; Mueller, M. H.; Walter, A.; Alewell, C.

2014-09-01

Snow processes might be one important driver of soil erosion in Alpine grasslands and thus the unknown variable when erosion modelling is attempted. The aim of this study is to assess the importance of snow gliding as a soil erosion agent for four different land use/land cover types in a subalpine area in Switzerland. We used three different approaches to estimate soil erosion rates: sediment yield measurements in snow glide depositions, the fallout radionuclide 137Cs and modelling with the Revised Universal Soil Loss Equation (RUSLE). RUSLE permits the evaluation of soil loss by water erosion, the 137Cs method integrates soil loss due to all erosion agents involved, and the measurement of snow glide deposition sediment yield can be directly related to snow-glide-induced erosion. Further, cumulative snow glide distance was measured for the sites in the winter of 2009/2010 and modelled for the surrounding area and long-term average winter precipitation (1959-2010) with the spatial snow glide model (SSGM). Measured snow glide distance confirmed the presence of snow gliding and ranged from 2 to 189 cm, with lower values on the north-facing slopes. We observed a reduction of snow glide distance with increasing surface roughness of the vegetation, which is an important information with respect to conservation planning and expected and ongoing land use changes in the Alps. Snow glide erosion estimated from the snow glide depositions was highly variable with values ranging from 0.03 to 22.9 t ha-1 yr-1 in the winter of 2012/2013. For sites affected by snow glide deposition, a mean erosion rate of 8.4 t ha-1 yr-1 was found. The difference in long-term erosion rates determined with RUSLE and 137Cs confirms the constant influence of snow-glide-induced erosion, since a large difference (lower proportion of water erosion compared to total net erosion) was observed for sites with high snow glide rates and vice versa. Moreover, the difference between RUSLE and 137Cs erosion rates was related to the measured snow glide distance (R2 = 0.64; p < 0.005) and to the snow deposition sediment yields (R2 = 0.39; p = 0.13). The SSGM reproduced the relative difference of the measured snow glide values under different land uses and land cover types. The resulting map highlighted the relevance of snow gliding for large parts of the investigated area. Based on these results, we conclude that snow gliding appears to be a crucial and non-negligible process impacting soil erosion patterns and magnitude in subalpine areas with similar topographic and climatic conditions.

An AeroCom Assessment of Black Carbon in Arctic Snow and Sea Ice

NASA Technical Reports Server (NTRS)

Jiao, C.; Flanner, M. G.; Balkanski, Y.; Bauer, S. E.; Bellouin, N.; Bernsten, T. K.; Bian, H.; Carslaw, K. S.; Chin, M.; DeLuca, N.;

Spatial properties of snow cover in the Upper Merced River Basin: implications for a distributed snow measurement network

NASA Astrophysics Data System (ADS)

Bouffon, T.; Rice, R.; Bales, R.

2006-12-01

The spatial distributions of snow water equivalent (SWE) and snow depth within a 1, 4, and 16 km2 grid element around two automated snow pillows in a forested and open- forested region of the Upper Merced River Basin (2,800 km2) of Yosemite National Park were characterized using field observations and analyzed using binary regression trees. Snow surveys occurred at the forested site during the accumulation and ablation seasons, while at the open-forest site a survey was performed only during the accumulation season. An average of 130 snow depth and 7 snow density measurements were made on each survey, within the 4 km2 grid. Snow depth was distributed using binary regression trees and geostatistical methods using the physiographic parameters (e.g. elevation, slope, vegetation, aspect). Results in the forest region indicate that the snow pillow overestimated average SWE within the 1, 4, and 16 km2 areas by 34 percent during ablation, but during accumulation the snow pillow provides a good estimate of the modeled mean SWE grid value, however it is suspected that the snow pillow was underestimating SWE. However, at the open forest site, during accumulation, the snow pillow was 28 percent greater than the mean modeled grid element. In addition, the binary regression trees indicate that the independent variables of vegetation, slope, and aspect are the most influential parameters of snow depth distribution. The binary regression tree and multivariate linear regression models explain about 60 percent of the initial variance for snow depth and 80 percent for density, respectively. This short-term study provides motivation and direction for the installation of a distributed snow measurement network to fill the information gap in basin-wide SWE and snow depth measurements. Guided by these results, a distributed snow measurement network was installed in the Fall 2006 at Gin Flat in the Upper Merced River Basin with the specific objective of measuring accumulation and ablation across topographic variables with the aim of providing guidance for future larger scale observation network designs.

Changes in the relation between snow station observations and basin scale snow water resources

NASA Astrophysics Data System (ADS)

Sexstone, G. A.; Penn, C. A.; Clow, D. W.; Moeser, D.; Liston, G. E.

2017-12-01

Snow monitoring stations that measure snow water equivalent or snow depth provide fundamental observations used for predicting water availability and flood risk in mountainous regions. In the western United States, snow station observations provided by the Natural Resources Conservation Service Snow Telemetry (SNOTEL) network are relied upon for forecasting spring and summer streamflow volume. Streamflow forecast accuracy has declined for many regions over the last several decades. Changes in snow accumulation and melt related to climate, land use, and forest cover are not accounted for in current forecasts, and are likely sources of error. Therefore, understanding and updating relations between snow station observations and basin scale snow water resources is crucial to improve accuracy of streamflow prediction. In this study, we investigated the representativeness of snow station observations when compared to simulated basin-wide snow water resources within the Rio Grande headwaters of Colorado. We used the combination of a process-based snow model (SnowModel), field-based measurements, and remote sensing observations to compare the spatiotemporal variability of simulated basin-wide snow accumulation and melt with that of SNOTEL station observations. Results indicated that observations are comparable to simulated basin-average winter precipitation but overestimate both the simulated basin-average snow water equivalent and snowmelt rate. Changes in the representation of snow station observations over time in the Rio Grande headwaters were also investigated and compared to observed streamflow and streamflow forecasting errors. Results from this study provide important insight in the context of non-stationarity for future water availability assessments and streamflow predictions.

Simulations of a Canadian snowpack brightness temperatures using SURFEX-Crocus for Snow Water Equivalent (SWE) retrievals

NASA Astrophysics Data System (ADS)

Larue, Fanny; Royer, Alain; De Sève, Danielle; Langlois, Alexandre; Roy, Alexandre; Saint-Jean-Rondeau, Olivier

2016-04-01

In Quebec, the water associated to snowmelt represents 30% of the annual electricity production so that the snow cover evaluation in real time is of primary interest. The key variable is snow water equivalent (SWE) which describes the evolution of a global seasonal snow cover. However, the sparse distribution of meteorological stations in northern Québec generates great uncertainty in the extrapolation of SWE. On the contrary, the spatial and temporal coverage of satellite data offer a source of information with a high potential when considered as an alternative to the poor spatial distribution of in-situ information. Thus, this project aims to improve the prediction of SWE by assimilation of satellite passive microwave brightness temperatures (Tb) observations, independently of any ground observations. The snowpack evolution is simulated by the French snow model SURFEX-Crocus, driven by the Canadian atmospheric model GEM with a spatial resolution of 10 km. The bias of the atmospheric model and the impact of initialization errors on the simulated SWE were quantified from our ground measurements. To assimilate satellite observations, the multi-layered snow model is first coupled with a radiative transfer model using the Dense Media Radiative transfer theory (the DMRT-ML model) to estimate the microwave snow emission of the simulated snowpack. In order to retrieve simulated Tb in frequencies of interest (i.e. sensitive to snow dielectric properties), the snow microstructure needs to be well parameterized. It was shown in previous studies that the specific surface area (SSA) of snow grains is a well-defined parameter to describe the size and the shape of snow grains and which allows reproducible field measurements. SURFEX-Crocus estimates a SSA for each simulated snow layer, however, the snow microstructure in DMRT-ML is defined per layer by monodisperse optical radius of grain (~ 1/SSA) and by the stickiness which is not known. It thus becomes necessary to introduce an empirical factor (noted φ) due to the simplification of the representation of snow as non-sticky spheres of ice in the model. In other words, the measured and simulated SSA has to be converted in an effective snow grain metric by optimizing this scaling factor to minimize the root-mean-square error between the measured and simulated brightness temperatures. The φ factor scaling the Crocus simulated SSA was estimated using ground-based radiometric measurements made during several field campaigns in the James Bay territory, Nunavik (in 2013 and 2015), and Churchill, Manitoba in 2010. This new parameterization, adapted to the Canadian arctic and subarctic snowpack, represents an essential step to optimize SWE maps in this remote region which have yet to be proven accurate.

Optimizing placements of ground-based snow sensors for areal snow cover estimation using a machine-learning algorithm and melt-season snow-LiDAR data

NASA Astrophysics Data System (ADS)

Oroza, C.; Zheng, Z.; Glaser, S. D.; Bales, R. C.; Conklin, M. H.

2016-12-01

We present a structured, analytical approach to optimize ground-sensor placements based on time-series remotely sensed (LiDAR) data and machine-learning algorithms. We focused on catchments within the Merced and Tuolumne river basins, covered by the JPL Airborne Snow Observatory LiDAR program. First, we used a Gaussian mixture model to identify representative sensor locations in the space of independent variables for each catchment. Multiple independent variables that govern the distribution of snow depth were used, including elevation, slope, and aspect. Second, we used a Gaussian process to estimate the areal distribution of snow depth from the initial set of measurements. This is a covariance-based model that also estimates the areal distribution of model uncertainty based on the independent variable weights and autocorrelation. The uncertainty raster was used to strategically add sensors to minimize model uncertainty. We assessed the temporal accuracy of the method using LiDAR-derived snow-depth rasters collected in water-year 2014. In each area, optimal sensor placements were determined using the first available snow raster for the year. The accuracy in the remaining LiDAR surveys was compared to 100 configurations of sensors selected at random. We found the accuracy of the model from the proposed placements to be higher and more consistent in each remaining survey than the average random configuration. We found that a relatively small number of sensors can be used to accurately reproduce the spatial patterns of snow depth across the basins, when placed using spatial snow data. Our approach also simplifies sensor placement. At present, field surveys are required to identify representative locations for such networks, a process that is labor intensive and provides limited guarantees on the networks' representation of catchment independent variables.

Development of global model for atmospheric backscatter at CO2 wavelengths

NASA Technical Reports Server (NTRS)

Kent, G. S.; Wang, P. H.; Farrukh, U.; Deepak, A.; Patterson, E. M.

1985-01-01

The improvement of an understanding of the variation of the aerosol backscattering at 10.6 micron within the free troposphere and the development model to describe this was undertaken. The analysis combines theoretical modeling with the results contained within three independent data sets. The data sets are obtained by the SAGE I/SAM II satellite experiments, the GAMETAG flight series and by direct backscatter measurements. The theoretical work includes use of a bimodal, two component aerosol model, and the study of the microphysical and associated optical changes occurring within an aerosol plume. A consistent picture is obtained, which describes the variation of the aerosol backscattering function in the free troposphere with altitude, latitude, and season. Most data are available and greatest consistency is found inside the Northern Hemisphere.

Objective Characterization of Snow Microstructure for Microwave Emission Modeling

NASA Technical Reports Server (NTRS)

Durand, Michael; Kim, Edward J.; Molotch, Noah P.; Margulis, Steven A.; Courville, Zoe; Malzler, Christian

2012-01-01

Passive microwave (PM) measurements are sensitive to the presence and quantity of snow, a fact that has long been used to monitor snowcover from space. In order to estimate total snow water equivalent (SWE) within PM footprints (on the order of approx 100 sq km), it is prerequisite to understand snow microwave emission at the point scale and how microwave radiation integrates spatially; the former is the topic of this paper. Snow microstructure is one of the fundamental controls on the propagation of microwave radiation through snow. Our goal in this study is to evaluate the prospects for driving the Microwave Emission Model of Layered Snowpacks with objective measurements of snow specific surface area to reproduce measured brightness temperatures when forced with objective measurements of snow specific surface area (S). This eliminates the need to treat the grain size as a free-fit parameter.

Integration, Validation, and Application of a PV Snow Coverage Model in SAM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ryberg, David; Freeman, Janine

2015-09-01

Due to the increasing deployment of PV systems in snowy climates, there is significant interest in a method capable of estimating PV losses resulting from snow coverage that has been verified for a wide variety of system designs and locations. A scattering of independent snow coverage models have been developed over the last 15 years; however, there has been very little effort spent on verifying these models beyond the system design and location on which they were based. Moreover, none of the major PV modeling software products have incorporated any of these models into their workflow. In response to thismore » deficiency, we have integrated the methodology of the snow model developed in the paper by Marion et al. [1] into the National Renewable Energy Laboratory's (NREL) System Advisor Model (SAM). In this work we describe how the snow model is implemented in SAM and discuss our demonstration of the model's effectiveness at reducing error in annual estimations for two PV arrays. Following this, we use this new functionality in conjunction with a long term historical dataset to estimate average snow losses across the United States for a typical PV system design. The open availability of the snow loss estimation capability in SAM to the PV modeling community, coupled with our results of the nation-wide study, will better equip the industry to accurately estimate PV energy production in areas affected by snowfall.« less

Process-level model evaluation: a snow and heat transfer metric

NASA Astrophysics Data System (ADS)

Slater, Andrew G.; Lawrence, David M.; Koven, Charles D.

2017-04-01

Land models require evaluation in order to understand results and guide future development. Examining functional relationships between model variables can provide insight into the ability of models to capture fundamental processes and aid in minimizing uncertainties or deficiencies in model forcing. This study quantifies the proficiency of land models to appropriately transfer heat from the soil through a snowpack to the atmosphere during the cooling season (Northern Hemisphere: October-March). Using the basic physics of heat diffusion, we investigate the relationship between seasonal amplitudes of soil versus air temperatures due to insulation from seasonal snow. Observations demonstrate the anticipated exponential relationship of attenuated soil temperature amplitude with increasing snow depth and indicate that the marginal influence of snow insulation diminishes beyond an effective snow depth of about 50 cm. A snow and heat transfer metric (SHTM) is developed to quantify model skill compared to observations. Land models within the CMIP5 experiment vary widely in SHTM scores, and deficiencies can often be traced to model structural weaknesses. The SHTM value for individual models is stable over 150 years of climate, 1850-2005, indicating that the metric is insensitive to climate forcing and can be used to evaluate each model's representation of the insulation process.

Process-level model evaluation: a snow and heat transfer metric

DOE PAGES

Slater, Andrew G.; Lawrence, David M.; Koven, Charles D.

2017-04-20

Land models require evaluation in order to understand results and guide future development. Examining functional relationships between model variables can provide insight into the ability of models to capture fundamental processes and aid in minimizing uncertainties or deficiencies in model forcing. This study quantifies the proficiency of land models to appropriately transfer heat from the soil through a snowpack to the atmosphere during the cooling season (Northern Hemisphere: October–March). Using the basic physics of heat diffusion, we investigate the relationship between seasonal amplitudes of soil versus air temperatures due to insulation from seasonal snow. Observations demonstrate the anticipated exponential relationshipmore » of attenuated soil temperature amplitude with increasing snow depth and indicate that the marginal influence of snow insulation diminishes beyond an effective snow depth of about 50 cm. A snow and heat transfer metric (SHTM) is developed to quantify model skill compared to observations. Land models within the CMIP5 experiment vary widely in SHTM scores, and deficiencies can often be traced to model structural weaknesses. The SHTM value for individual models is stable over 150 years of climate, 1850–2005, indicating that the metric is insensitive to climate forcing and can be used to evaluate each model's representation of the insulation process.« less

Process-level model evaluation: a snow and heat transfer metric

DOE Office of Scientific and Technical Information (OSTI.GOV)

Slater, Andrew G.; Lawrence, David M.; Koven, Charles D.

Land models require evaluation in order to understand results and guide future development. Examining functional relationships between model variables can provide insight into the ability of models to capture fundamental processes and aid in minimizing uncertainties or deficiencies in model forcing. This study quantifies the proficiency of land models to appropriately transfer heat from the soil through a snowpack to the atmosphere during the cooling season (Northern Hemisphere: October–March). Using the basic physics of heat diffusion, we investigate the relationship between seasonal amplitudes of soil versus air temperatures due to insulation from seasonal snow. Observations demonstrate the anticipated exponential relationshipmore » of attenuated soil temperature amplitude with increasing snow depth and indicate that the marginal influence of snow insulation diminishes beyond an effective snow depth of about 50 cm. A snow and heat transfer metric (SHTM) is developed to quantify model skill compared to observations. Land models within the CMIP5 experiment vary widely in SHTM scores, and deficiencies can often be traced to model structural weaknesses. The SHTM value for individual models is stable over 150 years of climate, 1850–2005, indicating that the metric is insensitive to climate forcing and can be used to evaluate each model's representation of the insulation process.« less

The Contribution to High Asia Runoff from Ice and Snow (CHARIS): Understanding the source and trends of cryospheric contributions to the water balance

NASA Astrophysics Data System (ADS)

Rittger, K.; Armstrong, R. L.; Bair, N.; Racoviteanu, A.; Brodzik, M. J.; Hill, A. F.; Wilson, A. M.; Khan, A. L.; Ramage, J. M.; Khalsa, S. J. S.; Barrett, A. P.; Raup, B. H.; Painter, T. H.

2017-12-01

The Contribution to High Asia Runoff from Ice and Snow, or CHARIS, project is systematically assessing the role that glaciers and seasonal snow play in the freshwater resources of Central and South Asia. The study area encompasses roughly 3 million square kilometers of the Himalaya, Karakoram, Hindu Kush, Pamir and Tien Shan mountain ranges that drain to five major rivers: the Ganges, Brahmaputra, Indus, Amu Darya and Syr Darya. We estimate daily snow and glacier ice contributions to the water balance. Our automated partitioning method generates daily maps of 1) snow over ice (SOI), 2) exposed glacier ice (EGI), 3) debris covered glacier ice (DGI) and 4) snow over land (SOL) using fractional snow cover, snow grain size, and annual minimum ice and snow from the 500 m MODIS-derived MODSCAG and MODICE products. Maps of snow and ice cover are validated using high-resolution (30 m) maps of snow, ice, and debris cover from Landsat. The probability of detection is 0.91 and precision is 0.85 for MODICE. We examine trends in annual and monthly snow and ice maps and use daily maps as inputs to a calibrated temperature-index model and an uncalibrated energy balance model, ParBal. Melt model results and measurements of isotopes and specific ions used as an independent validation of melt modeling indicate a sharp geographic contrast in the role of snow and ice melt to downstream water supplies between the arid Tien Shan and Pamir ranges of Central Asia, where melt water dominates dry season flows, and the monsoon influenced central and eastern Himalaya where rain controls runoff. We also compare melt onset and duration from the melt models to the Calibrated, Enhanced Resolution Passive Microwave Brightness Temperature Earth Science Data Record. Trend analysis of annual and monthly area of permanent snow and ice (the union of SOI and EGI) for 2000 to 2016 shows statistically significant negative trends in the Ganges and Brahmaputra basins. There are no statistically significant trends in permanent snow and ice in the other basins and no statistically significant trends in SOL, the renewable and seasonal component of snow and ice cover, in any of the five basins. This work gives a better understanding of the current hydrologic regime to guide realistic estimates of the future availability and vulnerability of water resources in these regions.

Differences between the MEMLS and the multiple-layer HUT model and their comparisons with in-situ snowpack observations

NASA Astrophysics Data System (ADS)

Pan, J.; Durand, M. T.; Sandells, M. J.; Lemmetyinen, J.; Kim, E. J.

2013-12-01

Application of passive microwave (PM) brightness temperature for snow water equivalent retrieval requires deep understanding of snow emission models, not only for their performance to reproduce in-situ PM observations, but also for their theoretical differences to approximate radiative transfer theory. In this paper, differences between the multiple-layer HUT (or TKK) model and the Microwave Emission Model of Layered Snowpacks (MEMLS) were listed, and the two models were compared with snow ground-based PM observations at Streamboat Springs, Colorado, USA; Churchill, Canada; and Sodankyla, Finland. The two models were chosen for their multiple-layer schemes are close to actual layer-by-layer snow measurements. Both the two models are semi-empirical models; whereas the HUT model uses the mean snow grain size, MEMLS uses the correlation length to relate the snow microstructure with the scattering coefficients. The two parameters are related according to previous studies. The Specific Surface Area (SSA) was measured at three test sites to derive the correlation length, while the mean snow grain sizes was available at Stream Springs and Sodankyla. It was shown that with different apparent forms of radiative transfer equations, the different parts of the two models have one-to-one correspondence however, and intermediate parameters are comparable. Regarding the multiple-layer structure of the models, it was found that the HUT model considers the internal reflectivity of each snow layer to be zero. The two-flux radiative transfer equations of the two models were compared, and the correspondence of the semi-empirical parameter q in the HUT model was found in the MEMLS. The effect of consideration of transverse radiation scattered into the direction under consideration via the six-flux approximation in MEMLS is compared. Based on model comparisons, we analyzed the differences of TB predictions at the three test sites.

Field measurements and modeling of wave propagation and subsequent weak layer failure in snow due to explosive loading

NASA Astrophysics Data System (ADS)

Simioni, Stephan; Sidler, Rolf; Dual, Jürg; Schweizer, Jürg

2015-04-01

Avalanche control by explosives is among the key temporary preventive measures. Yet, little is known about the mechanism involved in releasing avalanches by the effect of an explosion. Here, we test the hypothesis that the stress induced by acoustic waves exceeds the strength of weak snow layers. Consequently the snow fails and the onset of rapid crack propagation might finally lead to the release of a snow slab avalanche. We performed experiments with explosive charges over a snowpack. We installed microphones above the snowpack to measure near-surface air pressure and accelerometers within three snow pits. We also recorded pit walls of each pit with high speed cameras to detect weak layer failure. Empirical relationships and a priori information from ice and air were used to characterize a porous layered model from density measurements of snow profiles in the snow pits. This model was used to perform two-dimensional numerical simulations of wave propagation in Biot-type porous material. Locations of snow failure were identified in the simulation by comparing the axial and deviatoric stress field of the simulation to the corresponding snow strength. The identified snow failure locations corresponded well with the observed failure locations in the experiment. The acceleration measured in the snowpack best correlated with the modeled acceleration of the fluid relative to the ice frame. Even though the near field of the explosion is expected to be governed by non-linear effects as for example the observed supersonic wave propagation in the air above the snow surface, the results of the linear poroelastic simulation fit well with the measured air pressure and snowpack accelerations. The results of this comparison are an important step towards quantifying the effectiveness of avalanche control by explosives.

PULSE: A numerical model for the simulation of snowpack solute dynamics to capture runoff ionic pulses during snowmelt

NASA Astrophysics Data System (ADS)

Costa, D.; Pomeroy, J. W.; Wheater, H. S.

2017-12-01

Early ionic pulses in spring snowmelt can cause the temporary acidification of streams and account for a significant portion of the total annual nutrient export, particularly in seasonally snow-covered areas where the frozen ground may limit runoff-soil contact and cause the rapid delivery of these ions to streams. Ionic pulses are a consequence of snow ion exclusion, a process induced by snow metamorphism where ions are segregated from the snow grains losing mass to the surface of the grains gaining mass. While numerous studies have been successful in providing quantitative evidence of this process, few mechanistic mathematical models have been proposed for diagnostic and prediction. A few early modelling attempts have been successful in capturing this process assuming transport through porous media with variable porosity, however their implementation is difficult because they require complex models of snow physics to resolve the evolution of in-snow properties and processes during snowmelt, such as heat conduction, metamorphism, melt and water flow. Furthermore, initial snowpack to snow-surface ion concentration ratios are difficult to measure but are required to initiate these models and ion exclusion processes are not represented in a physically-based transparent fashion. In this research, a standalone numerical model has been developed to capture ionic pulses in snowmelt by emulating solute leaching from snow grains during melt and its subsequent transport by the percolating meltwater. Estimating snow porosity and water content dynamics is shown to be a viable alternative to deployment of complex snow physics models for this purpose. The model was applied to four study sites located in the Arctic and in Sierra Nevada to test for different climatic and hydrological conditions. The model compares very well with observations and could capture both the timing and magnitude of early melt ionic pulses accurately. This study demonstrates how physically based approaches can provide successful simulations of the spatial and temporal fluxes of snowmelt ions, which can be used to improve the prediction of nutrient export in cold regions for the spring freshet.

PULSE: A numerical model for the simulation of snowpack solute dynamics to capture runoff ionic pulses during snowmelt

NASA Astrophysics Data System (ADS)

Clark, M. P.; Nijssen, B.; Lundquist, J. D.; Luce, C. H.; Musselman, K. N.; Wayand, N. E.; Ou, M.; Lapo, K. E.

2016-12-01

Early ionic pulses in spring snowmelt can cause the temporary acidification of streams and account for a significant portion of the total annual nutrient export, particularly in seasonally snow-covered areas where the frozen ground may limit runoff-soil contact and cause the rapid delivery of these ions to streams. Ionic pulses are a consequence of snow ion exclusion, a process induced by snow metamorphism where ions are segregated from the snow grains losing mass to the surface of the grains gaining mass. While numerous studies have been successful in providing quantitative evidence of this process, few mechanistic mathematical models have been proposed for diagnostic and prediction. A few early modelling attempts have been successful in capturing this process assuming transport through porous media with variable porosity, however their implementation is difficult because they require complex models of snow physics to resolve the evolution of in-snow properties and processes during snowmelt, such as heat conduction, metamorphism, melt and water flow. Furthermore, initial snowpack to snow-surface ion concentration ratios are difficult to measure but are required to initiate these models and ion exclusion processes are not represented in a physically-based transparent fashion. In this research, a standalone numerical model has been developed to capture ionic pulses in snowmelt by emulating solute leaching from snow grains during melt and its subsequent transport by the percolating meltwater. Estimating snow porosity and water content dynamics is shown to be a viable alternative to deployment of complex snow physics models for this purpose. The model was applied to four study sites located in the Arctic and in Sierra Nevada to test for different climatic and hydrological conditions. The model compares very well with observations and could capture both the timing and magnitude of early melt ionic pulses accurately. This study demonstrates how physically based approaches can provide successful simulations of the spatial and temporal fluxes of snowmelt ions, which can be used to improve the prediction of nutrient export in cold regions for the spring freshet.

Role of Forcing Uncertainty and Background Model Error Characterization in Snow Data Assimilation

NASA Technical Reports Server (NTRS)

Kumar, Sujay V.; Dong, Jiarul; Peters-Lidard, Christa D.; Mocko, David; Gomez, Breogan

2017-01-01

Accurate specification of the model error covariances in data assimilation systems is a challenging issue. Ensemble land data assimilation methods rely on stochastic perturbations of input forcing and model prognostic fields for developing representations of input model error covariances. This article examines the limitations of using a single forcing dataset for specifying forcing uncertainty inputs for assimilating snow depth retrievals. Using an idealized data assimilation experiment, the article demonstrates that the use of hybrid forcing input strategies (either through the use of an ensemble of forcing products or through the added use of the forcing climatology) provide a better characterization of the background model error, which leads to improved data assimilation results, especially during the snow accumulation and melt-time periods. The use of hybrid forcing ensembles is then employed for assimilating snow depth retrievals from the AMSR2 (Advanced Microwave Scanning Radiometer 2) instrument over two domains in the continental USA with different snow evolution characteristics. Over a region near the Great Lakes, where the snow evolution tends to be ephemeral, the use of hybrid forcing ensembles provides significant improvements relative to the use of a single forcing dataset. Over the Colorado headwaters characterized by large snow accumulation, the impact of using the forcing ensemble is less prominent and is largely limited to the snow transition time periods. The results of the article demonstrate that improving the background model error through the use of a forcing ensemble enables the assimilation system to better incorporate the observational information.

Discrimination Between Clouds and Snow in Landsat 8 Imagery: an Assessment of Current Methods and a New Approach

NASA Astrophysics Data System (ADS)

Stillinger, T.; Dozier, J.; Phares, N.; Rittger, K.

2015-12-01

Discrimination between snow and clouds poses a serious but tractable challenge to the consistent delivery of high-quality information on mountain snow from remote sensing. Clouds obstruct the surface from the sensor's view, and the similar optical properties of clouds and snow make accurate discrimination difficult. We assess the performance of the current Landsat 8 operational snow and cloud mask products (LDCM CCA and CFmask), along with a new method, using over one million manually identified snow and clouds pixels in Landsat 8 scenes. The new method uses physically based scattering models to generate spectra in each Landsat 8 band, at that scene's solar illumination, for snow and cloud particle sizes that cover the plausible range for each. The modeled spectra are compared to pixels' spectra via several independent ways to identify snow and clouds. The results are synthesized to create a final snow/cloud mask, and the method can be applied to any multispectral imager with bands covering the visible, near-infrared, and shortwave-infrared regions. Each algorithm we tested misidentifies snow and clouds in both directions to varying degrees. We assess performance with measures of Precision, Recall, and the F statistic, which are based on counts of true and false positives and negatives. Tests for significance in differences between spectra in the measured and modeled values among incorrectly identified pixels help ascertain reasons for misidentification. A cloud mask specifically designed to separate snow from clouds is a valuable tool for those interested in remotely sensing snow cover. Given freely available remote sensing datasets and computational tools to feasibly process entire mission histories for an area of interest, enabling researchers to reliably identify and separate snow and clouds increases the usability of the data for hydrological and climatological studies.

SMRT: A new, modular snow microwave radiative transfer model

NASA Astrophysics Data System (ADS)

Picard, Ghislain; Sandells, Melody; Löwe, Henning; Dumont, Marie; Essery, Richard; Floury, Nicolas; Kontu, Anna; Lemmetyinen, Juha; Maslanka, William; Mätzler, Christian; Morin, Samuel; Wiesmann, Andreas

2017-04-01

Forward models of radiative transfer processes are needed to interpret remote sensing data and derive measurements of snow properties such as snow mass. A key requirement and challenge for microwave emission and scattering models is an accurate description of the snow microstructure. The snow microwave radiative transfer model (SMRT) was designed to cater for potential future active and/or passive satellite missions and developed to improve understanding of how to parameterize snow microstructure. SMRT is implemented in Python and is modular to allow easy intercomparison of different theoretical approaches. Separate modules are included for the snow microstructure model, electromagnetic module, radiative transfer solver, substrate, interface reflectivities, atmosphere and permittivities. An object-oriented approach is used with carefully specified exchanges between modules to allow future extensibility i.e. without constraining the parameter list requirements. This presentation illustrates the capabilities of SMRT. At present, five different snow microstructure models have been implemented, and direct insertion of the autocorrelation function from microtomography data is also foreseen with SMRT. Three electromagnetic modules are currently available. While DMRT-QCA and Rayleigh models need specific microstructure models, the Improved Born Approximation may be used with any microstructure representation. A discrete ordinates approach with stream connection is used to solve the radiative transfer equations, although future inclusion of 6-flux and 2-flux solvers are envisioned. Wrappers have been included to allow existing microwave emission models (MEMLS, HUT, DMRT-QMS) to be run with the same inputs and minimal extra code (2 lines). Comparisons between theoretical approaches will be shown, and evaluation against field experiments in the frequency range 5-150 GHz. SMRT is simple and elegant to use whilst providing a framework for future development within the community.

Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

NASA Astrophysics Data System (ADS)

Raleigh, M. S.; Lundquist, J. D.; Clark, M. P.

2015-07-01

Physically based models provide insights into key hydrologic processes but are associated with uncertainties due to deficiencies in forcing data, model parameters, and model structure. Forcing uncertainty is enhanced in snow-affected catchments, where weather stations are scarce and prone to measurement errors, and meteorological variables exhibit high variability. Hence, there is limited understanding of how forcing error characteristics affect simulations of cold region hydrology and which error characteristics are most important. Here we employ global sensitivity analysis to explore how (1) different error types (i.e., bias, random errors), (2) different error probability distributions, and (3) different error magnitudes influence physically based simulations of four snow variables (snow water equivalent, ablation rates, snow disappearance, and sublimation). We use the Sobol' global sensitivity analysis, which is typically used for model parameters but adapted here for testing model sensitivity to coexisting errors in all forcings. We quantify the Utah Energy Balance model's sensitivity to forcing errors with 1 840 000 Monte Carlo simulations across four sites and five different scenarios. Model outputs were (1) consistently more sensitive to forcing biases than random errors, (2) generally less sensitive to forcing error distributions, and (3) critically sensitive to different forcings depending on the relative magnitude of errors. For typical error magnitudes found in areas with drifting snow, precipitation bias was the most important factor for snow water equivalent, ablation rates, and snow disappearance timing, but other forcings had a more dominant impact when precipitation uncertainty was due solely to gauge undercatch. Additionally, the relative importance of forcing errors depended on the model output of interest. Sensitivity analysis can reveal which forcing error characteristics matter most for hydrologic modeling.

Snow particles extracted from X-ray computed microtomography imagery and their single-scattering properties

NASA Astrophysics Data System (ADS)

Ishimoto, Hiroshi; Adachi, Satoru; Yamaguchi, Satoru; Tanikawa, Tomonori; Aoki, Teruo; Masuda, Kazuhiko

2018-04-01

Sizes and shapes of snow particles were determined from X-ray computed microtomography (micro-CT) images, and their single-scattering properties were calculated at visible and near-infrared wavelengths using a Geometrical Optics Method (GOM). We analyzed seven snow samples including fresh and aged artificial snow and natural snow obtained from field samples. Individual snow particles were numerically extracted, and the shape of each snow particle was defined by applying a rendering method. The size distribution and specific surface area distribution were estimated from the geometrical properties of the snow particles, and an effective particle radius was derived for each snow sample. The GOM calculations at wavelengths of 0.532 and 1.242 μm revealed that the realistic snow particles had similar scattering phase functions as those of previously modeled irregular shaped particles. Furthermore, distinct dendritic particles had a characteristic scattering phase function and asymmetry factor. The single-scattering properties of particles of effective radius reff were compared with the size-averaged single-scattering properties. We found that the particles of reff could be used as representative particles for calculating the average single-scattering properties of the snow. Furthermore, the single-scattering properties of the micro-CT particles were compared to those of particle shape models using our current snow retrieval algorithm. For the single-scattering phase function, the results of the micro-CT particles were consistent with those of a conceptual two-shape model. However, the particle size dependence differed for the single-scattering albedo and asymmetry factor.

Improved quantification of mountain snowpack properties using observations from Unmanned Air Vehicles (UAVs)

NASA Astrophysics Data System (ADS)

Shea, J. M.; Harder, P.; Pomeroy, J. W.; Kraaijenbrink, P. D. A.

2017-12-01

Mountain snowpacks represent a critical seasonal reservoir of water for downstream needs, and snowmelt is a significant component of mountain hydrological budgets. Ground-based point measurements are unable to describe the full spatial variability of snow accumulation and melt rates, and repeat Unmanned Air Vehicle (UAV) surveys provide an unparalleled opportunity to measure snow accumulation, redistribution and melt in alpine environments. This study presents results from a UAV-based observation campaign conducted at the Fortress Mountain Snow Laboratory in the Canadian Rockies in 2017. Seven survey flights were conducted between April (maximum snow accumulation) and mid-July (bare ground) to collect imagery with both an RGB camera and thermal infrared imager with the sensefly eBee RTK platform. UAV imagery are processed with structure from motion techniques, and orthoimages, digital elevation models, and surface temperature maps are validated against concurrent ground observations of snow depth, snow water equivalent, and snow surface temperature. We examine the seasonal evolution of snow depth and snow surface temperature, and explore the spatial covariances of these variables with respect to topographic factors and snow ablation rates. Our results have direct implications for scaling snow ablation calculations and model resolution and discretization.

Canadian snow and sea ice: assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system

NASA Astrophysics Data System (ADS)

Kushner, Paul J.; Mudryk, Lawrence R.; Merryfield, William; Ambadan, Jaison T.; Berg, Aaron; Bichet, Adéline; Brown, Ross; Derksen, Chris; Déry, Stephen J.; Dirkson, Arlan; Flato, Greg; Fletcher, Christopher G.; Fyfe, John C.; Gillett, Nathan; Haas, Christian; Howell, Stephen; Laliberté, Frédéric; McCusker, Kelly; Sigmond, Michael; Sospedra-Alfonso, Reinel; Tandon, Neil F.; Thackeray, Chad; Tremblay, Bruno; Zwiers, Francis W.

2018-04-01

The Canadian Sea Ice and Snow Evolution (CanSISE) Network is a climate research network focused on developing and applying state-of-the-art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. This study presents an assessment from the CanSISE Network of the ability of the second-generation Canadian Earth System Model (CanESM2) and the Canadian Seasonal to Interannual Prediction System (CanSIPS) to simulate and predict snow and sea ice from seasonal to multi-decadal timescales, with a focus on the Canadian sector. To account for observational uncertainty, model structural uncertainty, and internal climate variability, the analysis uses multi-source observations, multiple Earth system models (ESMs) in Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and large initial-condition ensembles of CanESM2 and other models. It is found that the ability of the CanESM2 simulation to capture snow-related climate parameters, such as cold-region surface temperature and precipitation, lies within the range of currently available international models. Accounting for the considerable disagreement among satellite-era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow mass over Canada, reflecting a broader northern hemispheric positive bias. Biases in seasonal snow cover extent are generally less pronounced. CanESM2 also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational uncertainty and internal variability. Sea ice is biased low in the Canadian Arctic, which makes it difficult to assess the realism of long-term sea ice trends there. The strengths and weaknesses of the modelling system need to be understood as a practical tradeoff: the Canadian models are relatively inexpensive computationally because of their moderate resolution, thus enabling their use in operational seasonal prediction and for generating large ensembles of multidecadal simulations. Improvements in climate-prediction systems like CanSIPS rely not just on simulation quality but also on using novel observational constraints and the ready transfer of research to an operational setting. Improvements in seasonal forecasting practice arising from recent research include accurate initialization of snow and frozen soil, accounting for observational uncertainty in forecast verification, and sea ice thickness initialization using statistical predictors available in real time.

Estimate of temperature change due to ice and snow accretion in the boreal forest regions

NASA Astrophysics Data System (ADS)

Sugiura, K.; Nagai, S.; Suzuki, R.; Eicken, H.; Maximov, T. C.

2016-12-01

Previous research has demonstrated that there is a wide difference between the surface albedo in winter/spring in snow-covered forest regions in various global climate models. If the forest is covered with snow, the surface albedo would increase. In this study, we carried out field observations to monitor the frequency of ice and snow accretion in the boreal forest regions. The time-lapse digital camera was set up on each side of the observation towers at the site located to the north of Fairbanks (USA) and at the site located to the north of Yakutsk (Russia). It was confirmed that both forests were not necessarily covered with snow without a break from the start of continuous snow cover until the end. In addition, the boreal forest at the Yakutsk site is covered with snow in comparison with the boreal forest at the Fairbanks site for a long term such as for about five month. Using a one-dimensional mathematics model about the energy flow including atmospheric multiple scattering, we estimated temperature change due to ice and snow accretion in the boreal forest regions. The result show that the mean surface temperature rises approximately 0.5 [oC] when the boreal forest is not covered with snow. In this presentation, we discuss the snow albedo parameterization in the boreal forest regions and the one-dimensional mathematics model to provide a basis for a better understanding of the role of snow in the climate system.

Snow-atmosphere coupling and its impact on temperature variability and extremes over North America

NASA Astrophysics Data System (ADS)

Diro, G. T.; Sushama, L.; Huziy, O.

2018-04-01

The impact of snow-atmosphere coupling on climate variability and extremes over North America is investigated using modeling experiments with the fifth generation Canadian Regional Climate Model (CRCM5). To this end, two CRCM5 simulations driven by ERA-Interim reanalysis for the 1981-2010 period are performed, where snow cover and depth are prescribed (uncoupled) in one simulation while they evolve interactively (coupled) during model integration in the second one. Results indicate systematic influence of snow cover and snow depth variability on the inter-annual variability of soil and air temperatures during winter and spring seasons. Inter-annual variability of air temperature is larger in the coupled simulation, with snow cover and depth variability accounting for 40-60% of winter temperature variability over the Mid-west, Northern Great Plains and over the Canadian Prairies. The contribution of snow variability reaches even more than 70% during spring and the regions of high snow-temperature coupling extend north of the boreal forests. The dominant process contributing to the snow-atmosphere coupling is the albedo effect in winter, while the hydrological effect controls the coupling in spring. Snow cover/depth variability at different locations is also found to affect extremes. For instance, variability of cold-spell characteristics is sensitive to snow cover/depth variation over the Mid-west and Northern Great Plains, whereas, warm-spell variability is sensitive to snow variation primarily in regions with climatologically extensive snow cover such as northeast Canada and the Rockies. Furthermore, snow-atmosphere interactions appear to have contributed to enhancing the number of cold spell days during the 2002 spring, which is the coldest recorded during the study period, by over 50%, over western North America. Additional results also provide useful information on the importance of the interactions of snow with large-scale mode of variability in modulating temperature extreme characteristics.

How much runoff originates as snow in the western United States and what its future changes tell us?

NASA Astrophysics Data System (ADS)

Li, D.; Wrzesien, M.; Durand, M. T.; Adam, J. C.; Lettenmaier, D. P.

2017-12-01

Snow is a vital hydrologic cycle component in the western United States. The seasonal phase of snowmelt bridges between winter-dominant precipitation and summer-dominant human and ecosystem water demand. Current estimates of the fraction of total annual runoff generated by snowmelt (f_Q,snow) are not based on defensible, systematic analyses. Here, based on hydrological model simulations, we describe a new algorithm that explicitly quantifies the contribution of snow to runoff in the Western U.S. Specifically, the algorithm tracks the fate of the snowmelt runoff in the modeled hydrological fluxes in the soil, surface water, and the atmosphere, and accounts for the exchanges among the three. The hydrological fluxes are simulated by the Variable Infiltration Capacity (VIC) model using an ensemble of ten general circulation model (GCM) outputs trained by ground observations. We conducted the tracking to the VIC modeling ensemble and reported the mean of the ten tracking results. We computed the historical f_Q,snow with the modeling estimates from 1960 to 2005, and predicted the future f_Q,snow using the modeling estimates from 2006 to 2100 in the RCP4.5 and RCP8.5 scenarios. Our tracking results show that from 1960 to 2005, slightly over one-half of the total runoff in the western United States originated as snowmelt, despite only 37% of the region's total precipitation falling as snow; snowfall is more efficient than rainfall in runoff generation. Snow's importance varies physiographically: snowmelt from the mountains is responsible for over 70% of the total runoff in the West. Snowmelt-derived runoff currently makes up about 2/3 of the inflow to the region's major reservoirs; for Lake Mead and Lake Powell, which are the two largest reservoirs of the nation, snow contributes over 70% of their storage. The contribution of snowmelt to the total runoff will decrease in a warmer climate, by about 1/3 over the West by 2100. Snow will melt earlier and the snowmelt-induced peak flow will shift earlier by 1.5 to up to 4 weeks. Thus, in the context of predicted reductions and earlier shifts of the snow-induced runoff, and the fact that the region's major reservoirs were designed for the historical snow climatology, we argue that substantial impacts on water supply may occur especially in the summer season when water demand peaks.

Intercomparison of Pulsed Lidar Data with Flight Level CW Lidar Data and Modeled Backscatter from Measured Aerosol Microphysics Near Japan and Hawaii

NASA Technical Reports Server (NTRS)

Cutten, D. R.; Spinhirne, J. D.; Menzies, R. T.; Bowdle, D. A.; Srivastava, V.; Pueschel, R. F.; Clarke, A. D.; Rothermel, J.

1998-01-01

Aerosol backscatter coefficient data were examined from two nights near Japan and Hawaii undertaken during NASA's Global Backscatter Experiment (GLOBE) in May-June 1990. During each of these two nights the aircraft traversed different altitudes within a region of the atmosphere defined by the same set of latitude and longitude coordinates. This provided an ideal opportunity to allow flight level focused continuous wave (CW) lidar backscatter measured at 9.11-micron wavelength and modeled aerosol backscatter from two aerosol optical counters to be compared with pulsed lidar aerosol backscatter data at 1.06- and 9.25-micron wavelengths. The best agreement between all sensors was found in the altitude region below 7 km, where backscatter values were moderately high at all three wavelengths. Above this altitude the pulsed lidar backscatter data at 1.06- and 9.25-micron wavelengths were higher than the flight level data obtained from the CW lidar or derived from the optical counters, suggesting sample volume effects were responsible for this. Aerosol microphysics analysis of data near Japan revealed a strong sea-salt aerosol plume extending upward from the marine boundary layer. On the basis of sample volume differences, it was found that large particles were of different composition compared with the small particles for low backscatter conditions.

Snow drift: acoustic sensors for avalanche warning and research

NASA Astrophysics Data System (ADS)

Lehning, M.; Naaim, F.; Naaim, M.; Brabec, B.; Doorschot, J.; Durand, Y.; Guyomarc'h, G.; Michaux, J.-L.; Zimmerli, M.

Based on wind tunnel measurements at the CSTB (Jules Verne) facility in Nantes and based on field observations at the SLF experimental site Versuchsfeld Weissfluhjoch, two acoustic wind drift sensors are evaluated against different mechanical snow traps and one optical snow particle counter. The focus of the work is the suitability of the acoustic sensors for applications such as avalanche warning and research. Although the acoustic sensors have not yet reached the accuracy required for typical research applications, they can, however, be useful for snow drift monitoring to help avalanche forecasters. The main problem of the acoustic sensors is a difficult calibration that has to take into account the variable snow properties. Further difficulties arise from snow fall and high wind speeds. However, the sensor is robust and can be operated remotely under harsh conditions. It is emphasized that due to the lack of an accurate reference method for snow drift measurements, all sensors play a role in improving and evaluating snow drift models. Finally, current operational snow drift models and snow drift sensors are compared with respect to their usefulness as an aid for avalanche warning. While drift sensors always make a point measurement, the models are able to give a more representative drift index that is valid for a larger area. Therefore, models have the potential to replace difficult observations such as snow drift in operational applications. Current models on snow drift are either only applicable in flat terrain, are still too complex for an operational application (Lehning et al., 2000b), or offer only limited information on snow drift, such as the SNOWPACK drift index (Lehning et al., 2000a). On the other hand, snow drift is also difficult to measure. While mechanical traps (Mellor 1960; Budd et al., 1966) are probably still the best reference, they require more or less continuous manual operation and are thus not suitable for remote locations or long-term monitoring. Optical sensors (Schmidt, 1977; Brown and Pomeroy, 1989; Sato and Kimura, 1993) have been very successful for research applications, but suffer from the fact that they give a single flux value at one specific height. In addition, they have not been used, to our knowledge, for long-term monitoring applications or at remote sites. New developments of acoustic sensors have taken place recently (Chritin et al., 1999; Font et al., 1998). Jaedicke (2001) gives examples of possible applications of acoustic snow drift sensors. He emphasizes the advantages of acoustic sensors for snow drift monitoring at remote locations, but could not present any evaluation of the accuracy of the measurements. We present a complete evaluation of the new acoustic sensors for snow drift and discuss their applications for research or avalanche warning. We compare the suitability of sensors for operational applications.

Reproducing snow making strategies with deterministic modeling and image-based validation

NASA Astrophysics Data System (ADS)

Allamano, P.; Claps, P.; Poggi, D.

2012-04-01

Almost all winter resorts rely on artificial snow production as a surrogate for natural snow when the natural snow cover is missing or inadequate. The sustainability of snowmaking practices represents a debated issue, with two contrasting views: on the one hand the need for enhancing the value of mountain regions in terms of touristic appeal; on the other hand, the question whether the production of artificial snow is sustainable from an environmental point of view. We present here the outcomes of a pilot study aimed at assessing the impact of snowmaking practices on water resources management in the Gressoney valley. The study area is located in the Aosta Valley (North-Western Italy). The total area covered by ski runs is of about 95 ha, with an elevation range of 2000 m and an average snow production over the last 5 seasons of 200.000 m3 of water per year. Daily records of water volume used for artificial snow making were made available by the ski runs administrators for the last 5 seasons along with webcam images taken for the last 2 years. Daily meteorological records (of temperature and precipitation) were retrieved in 5 meteo stations within the district area since 1928 (83 years). The snowpack evolution in the skiable domain is modeled by means of a distributed water balance model which adopts a radiation-temperature index representation to describe snowmelt, and accounts for the topographic complexity of the area by modeling radiation over a very fine terrain grid (10 by 10 m cells). The model requires distributed daily temperature and precipitation as inputs. The snowmelt module is calibrated locally at the five stations. The snow-making module, aimed at synthesizing the production strategies at the district scale, is calibrated by keeping the required average snow cover depths on the ski runs as a free parameter. After calibrating the model parameters, also with the aid of visual comparison of modeled and real snow patterns registered by the webcams, we were able to reconstruct the seasonal evolution of natural and artificial snow cover over the whole district since 1928. A 83 years-long synthetic record of seasonal volumes potentially allocated for artificial snow production was obtained and a preliminary evaluation of the probability to have insufficient resource to face both domestic and snow production needs was performed. The system was found to have a 10% probability of deficiency, with deficit volumes ranging from 10.000 to 100.000 m3.

Crossing physical simulations of snow conditions and a geographic model of ski area to assess ski resorts vulnerability

NASA Astrophysics Data System (ADS)

François, Hugues; Spandre, Pierre; Morin, Samuel; George-Marcelpoil, Emmanuelle; Lafaysse, Matthieu; Lejeune, Yves

2016-04-01

In order to face climate change, meteorological variability and the recurrent lack of natural snow on the ground, ski resorts adaptation often rely on technical responses. Indeed, since the occurrence of episodes with insufficient snowfalls in the early 1990's, snowmaking has become an ordinary practice of snow management, comparable to grooming, and contributes to optimise the operation of ski resorts. It also participates to the growth of investments and is associated with significant operating costs, and thus represents a new source of vulnerability. The assessment of the actual effects of snowmaking and of snow management practices in general is a real concern for the future of the ski industry. The principal model use to simulate snow conditions in resorts, Ski Sim, has also been moving this way. Its developers introduced an artificial input of snow on ski area to complete natural snowfalls and considered different organisations of ski lifts (lower and upper zones). However the use of a degree-day model prevents them to consider the specific properties of artificial snow and the impact of grooming on the snowpack. A first proof of concept in the French Alps has shown the feasibility and the interest to cross the geographic model of ski areas and the output of the physically-based reanalysis of snow conditions SAFRAN - Crocus (François et al., CRST 2014). Since these initial developments, several ways have been explored to refine our model. A new model of ski areas has been developed. Our representation is now based on gravity derived from a DEM and ski lift localisation. A survey about snow management practices also allowed us to define criteria in order to model snowmaking areas given ski areas properties and tourism infrastructures localisation. We also suggest to revisit the assessment of ski resort viability based on the "one hundred days rule" based on natural snow depth only. Indeed, the impact of snow management must be considered so as to propose reliability indices that are both physically and socio-economically meaningful. Our contribution proposes to present these works in the context of a test resort in the French Alps and its snow conditions during the period 2000-2012. In order to show the impact of different management practices three configurations are considered: natural snowfalls, groomed natural snowfalls and managed snow (natural and artificial snowpack combined with grooming) and discuss the implications of the results in terms of the assessment of the climate vulnerability of ski resorts.

Calculations of radar backscattering coefficient of vegetation-covered soils

NASA Technical Reports Server (NTRS)

Mo, T.; Schmugge, T. J.; Jackson, T. J. (Principal Investigator)

1983-01-01

A model for simulating the measured backscattering coefficient of vegetation-covered soil surfaces includes both coherent and incoherent components of the backscattered radar pulses from a rough sil surface. The effect of vegetation canopy scattering is also incorporated into the model by making the radar pulse subject to two-way attenuation and volume scattering when it passes through the vegetation layer. Model results agree well with the measured angular distributions of the radar backscattering coefficient for HH polarization at the 1.6 GHz and 4.75 GHz frequencies over grass-covered fields. It was found that the coherent scattering component is very important at angles near nadir, while the vegetation volume scattering is dominant at incident angles 30 degrees.

Black carbon radiative forcing over the Tibetan Plateau

NASA Astrophysics Data System (ADS)

He, Cenlin; Li, Qinbin; Liou, Kuo-Nan; Takano, Yoshi; Gu, Yu; Qi, Ling; Mao, Yuhao; Leung, L. Ruby

2014-11-01

We estimate the snow albedo forcing and direct radiative forcing (DRF) of black carbon (BC) in the Tibetan Plateau using a global chemical transport model in conjunction with a stochastic snow model and a radiative transfer model. The annual mean BC snow albedo forcing is 2.9 W m-2 averaged over snow-covered plateau regions, which is a factor of 3 larger than the value over global land snowpack. BC-snow internal mixing increases the albedo forcing by 40-60% compared with external mixing, and coated BC increases the forcing by 30-50% compared with uncoated BC aggregates, whereas Koch snowflakes reduce the forcing by 20-40% relative to spherical snow grains. The annual BC DRF at the top of the atmosphere is 2.3 W m-2 with uncertainties of -70-85% in the plateau after scaling the modeled BC absorption optical depth to Aerosol Robotic Network observations. The BC forcings are attributed to emissions from different regions.

Estimation of Subpixel Snow-Covered Area by Nonparametric Regression Splines

NASA Astrophysics Data System (ADS)

Kuter, S.; Akyürek, Z.; Weber, G.-W.

2016-10-01

Measurement of the areal extent of snow cover with high accuracy plays an important role in hydrological and climate modeling. Remotely-sensed data acquired by earth-observing satellites offer great advantages for timely monitoring of snow cover. However, the main obstacle is the tradeoff between temporal and spatial resolution of satellite imageries. Soft or subpixel classification of low or moderate resolution satellite images is a preferred technique to overcome this problem. The most frequently employed snow cover fraction methods applied on Moderate Resolution Imaging Spectroradiometer (MODIS) data have evolved from spectral unmixing and empirical Normalized Difference Snow Index (NDSI) methods to latest machine learning-based artificial neural networks (ANNs). This study demonstrates the implementation of subpixel snow-covered area estimation based on the state-of-the-art nonparametric spline regression method, namely, Multivariate Adaptive Regression Splines (MARS). MARS models were trained by using MODIS top of atmospheric reflectance values of bands 1-7 as predictor variables. Reference percentage snow cover maps were generated from higher spatial resolution Landsat ETM+ binary snow cover maps. A multilayer feed-forward ANN with one hidden layer trained with backpropagation was also employed to estimate the percentage snow-covered area on the same data set. The results indicated that the developed MARS model performed better than th

Modelling the physical multiphase interactions of HNO3 between snow and air on the Antarctic Plateau (Dome C) and coast (Halley)

NASA Astrophysics Data System (ADS)

Chan, Hoi Ga; Frey, Markus M.; King, Martin D.

2018-02-01

Emissions of nitrogen oxide (NOx = NO + NO2) from the photolysis of nitrate (NO3-) in snow affect the oxidising capacity of the lower troposphere especially in remote regions of high latitudes with little pollution. Current air-snow exchange models are limited by poor understanding of processes and often require unphysical tuning parameters. Here, two multiphase models were developed from physically based parameterisations to describe the interaction of nitrate between the surface layer of the snowpack and the overlying atmosphere. The first model is similar to previous approaches and assumes that below a threshold temperature, To, the air-snow grain interface is pure ice and above To a disordered interface (DI) emerges covering the entire grain surface. The second model assumes that air-ice interactions dominate over all temperatures below melting of ice and that any liquid present above the eutectic temperature is concentrated in micropockets. The models are used to predict the nitrate in surface snow constrained by year-round observations of mixing ratios of nitric acid in air at a cold site on the Antarctic Plateau (Dome C; 75°06' S, 123°33' E; 3233 m a.s.l.) and at a relatively warm site on the Antarctic coast (Halley; 75°35' S, 26°39' E; 35 m a.s.l). The first model agrees reasonably well with observations at Dome C (Cv(RMSE) = 1.34) but performs poorly at Halley (Cv(RMSE) = 89.28) while the second model reproduces with good agreement observations at both sites (Cv(RMSE) = 0.84 at both sites). It is therefore suggested that in winter air-snow interactions of nitrate are determined by non-equilibrium surface adsorption and co-condensation on ice coupled with solid-state diffusion inside the grain, similar to Bock et al. (2016). In summer, however, the air-snow exchange of nitrate is mainly driven by solvation into liquid micropockets following Henry's law with contributions to total surface snow NO3- concentrations of 75 and 80 % at Dome C and Halley, respectively. It is also found that the liquid volume of the snow grain and air-micropocket partitioning of HNO3 are sensitive to both the total solute concentration of mineral ions within the snow and pH of the snow. The second model provides an alternative method to predict nitrate concentration in the surface snow layer which is applicable over the entire range of environmental conditions typical for Antarctica and forms a basis for a future full 1-D snowpack model as well as parameterisations in regional or global atmospheric chemistry models.

Impact of the snow cover scheme on snow distribution and energy budget modeling over the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Xie, Zhipeng; Hu, Zeyong; Xie, Zhenghui; Jia, Binghao; Sun, Genhou; Du, Yizhen; Song, Haiqing

2018-02-01

This paper presents the impact of two snow cover schemes (NY07 and SL12) in the Community Land Model version 4.5 (CLM4.5) on the snow distribution and surface energy budget over the Tibetan Plateau. The simulated snow cover fraction (SCF), snow depth, and snow cover days were evaluated against in situ snow depth observations and a satellite-based snow cover product and snow depth dataset. The results show that the SL12 scheme, which considers snow accumulation and snowmelt processes separately, has a higher overall accuracy (81.8%) than the NY07 (75.8%). The newer scheme performs better in the prediction of overall accuracy compared with the NY07; however, SL12 yields a 15.1% underestimation rate while NY07 overestimated the SCF with a 15.2% overestimation rate. Both two schemes capture the distribution of the maximum snow depth well but show large positive biases in the average value through all periods (3.37, 3.15, and 1.48 cm for NY07; 3.91, 3.52, and 1.17 cm for SL12) and overestimate snow cover days compared with the satellite-based product and in situ observations. Higher altitudes show larger root-mean-square errors (RMSEs) in the simulations of snow depth and snow cover days during the snow-free period. Moreover, the surface energy flux estimations from the SL12 scheme are generally superior to the simulation from NY07 when evaluated against ground-based observations, in particular for net radiation and sensible heat flux. This study has great implications for further improvement of the subgrid-scale snow variations over the Tibetan Plateau.

Comparison of Commonly-Used Microwave Radiative Transfer Models for Snow Remote Sensing

NASA Technical Reports Server (NTRS)

Royer, Alain; Roy, Alexandre; Montpetit, Benoit; Saint-Jean-Rondeau, Olivier; Picard, Ghislain; Brucker, Ludovic; Langlois, Alexandre

2017-01-01

This paper reviews four commonly-used microwave radiative transfer models that take different electromagnetic approaches to simulate snow brightness temperature (T(sub B)): the Dense Media Radiative Transfer - Multi-Layer model (DMRT-ML), the Dense Media Radiative Transfer - Quasi-Crystalline Approximation Mie scattering of Sticky spheres (DMRT-QMS), the Helsinki University of Technology n-Layers model (HUT-nlayers) and the Microwave Emission Model of Layered Snowpacks (MEMLS). Using the same extensively measured physical snowpack properties, we compared the simulated T(sub B) at 11, 19 and 37 GHz from these four models. The analysis focuses on the impact of using different types of measured snow microstructure metrics in the simulations. In addition to density, snow microstructure is defined for each snow layer by grain optical diameter (Do) and stickiness for DMRT-ML and DMRT-QMS, mean grain geometrical maximum extent (D(sub max)) for HUT n-layers and the exponential correlation length for MEMLS. These metrics were derived from either in-situ measurements of snow specific surface area (SSA) or macrophotos of grain sizes (D(sub max)), assuming non-sticky spheres for the DMRT models. Simulated T(sub B) sensitivity analysis using the same inputs shows relatively consistent T(sub B) behavior as a function of Do and density variations for the vertical polarization (maximum deviation of 18 K and 27 K, respectively), while some divergences appear in simulated variations for the polarization ratio (PR). Comparisons with ground based radiometric measurements show that the simulations based on snow SSA measurements have to be scaled with a model-specific factor of Do in order to minimize the root mean square error (RMSE) between measured and simulated T(sub B). Results using in-situ grain size measurements (SSA or D(sub max), depending on the model) give a mean T(sub B) RMSE (19 and 37 GHz) of the order of 16-26 K, which is similar for all models when the snow microstructure metrics are scaled. However, the MEMLS model converges to better results when driven by the correlation length estimated from in-situ SSA measurements rather than D(sub max) measurements. On a practical level, this paper shows that the SSA parameter, a snow property that is easy to retrieve in-situ, appears to be the most relevant parameter for characterizing snow microstructure, despite the need for a scaling factor.

Estimation of daily Snow Cover Area combining MODIS and LANDSAT information by using cellular automata

NASA Astrophysics Data System (ADS)

Pardo-Iguzquiza, Eulogio; Juan Collados Lara, Antonio; Pulido-Velazquez, David

2016-04-01

The snow availability in Alpine catchments is essential for the economy of these areas. It plays an important role in tourist development but also in the management of the Water Resources Snow is an important water resource in many river basins with mountains in the catchment area. The determination of the snow water equivalent requires the estimation of the evolution of the snow pack (cover area, thickness and snow density) along the time. Although there are complex physical models of the dynamics of the snow pack, sometimes the data available are scarce and a stochastic model like the cellular automata (CA) can be of great practical interest. CA can be used to model the dynamics of growth and wane of the snow pack. The CA is calibrated with historical data. This requires the determination of transition rules that are capable of modeling the evolution of the spatial pattern of snow cover area. Furthermore, CA requires the definition of states and neighborhoods. We have included topographical variables and climatological variables in order to define the state of each pixel. The evolution of snow cover in a pixel depends on its state, the state of the neighboring pixels and the transition rules. The calibration of the CA is done using daily MODIS data, available for the period 24/02/2002 to present with a spatial resolution of 500 m, and the LANDSAT information available with a sixteen-day periodicity from 1984 to the present and with spatial resolution of 30 m. The methodology has been applied to estimation of the snow cover area of Sierra Nevada mountain range in the Southern of Spain to obtain snow cover area daily information with 500 m spatial resolution for the period 1980-2014. Acknowledgments: This research has been partially supported by the GESINHIMPADAPT project (CGL2013-48424-C2-2-R) with Spanish MINECO funds. We would also like to thank NASA DAAC and LANDSAT project for the data provided for this study.

Dynamics of Phase Transitions in a Snow Mass Containing Water-Soluble Salt Particles

NASA Astrophysics Data System (ADS)

Zelenko, V. L.; Heifets, L. I.; Orlov, Yu. N.; Voskresenskiy, N. M.

2018-07-01

A macrokinetic approach is used to describe the dynamics of phase transitions in a snow mass containing water-soluble salt particles. Equations are derived that describe the rate of salt granule dissolution and the change in the phase composition and temperature of a snow mass under the conditions of heat transfer with an isothermal surface. An experimental setup that models the change in the state of a snow mass placed on an isothermal surface is created to verify theoretical conclusions. Experimental observations of the change in temperature of the snow mass are compared to theoretical calculations. The mathematical model that is developed can be used to predict the state of a snow mass on roads treated with a deicing agent, or to analyze the state of snow masses containing water-soluble salt inclusions and resting on mountain slopes.

Application of SNODAS and hydrologic models to enhance entropy-based snow monitoring network design

NASA Astrophysics Data System (ADS)

Keum, Jongho; Coulibaly, Paulin; Razavi, Tara; Tapsoba, Dominique; Gobena, Adam; Weber, Frank; Pietroniro, Alain

2018-06-01

Snow has a unique characteristic in the water cycle, that is, snow falls during the entire winter season, but the discharge from snowmelt is typically delayed until the melting period and occurs in a relatively short period. Therefore, reliable observations from an optimal snow monitoring network are necessary for an efficient management of snowmelt water for flood prevention and hydropower generation. The Dual Entropy and Multiobjective Optimization is applied to design snow monitoring networks in La Grande River Basin in Québec and Columbia River Basin in British Columbia. While the networks are optimized to have the maximum amount of information with minimum redundancy based on entropy concepts, this study extends the traditional entropy applications to the hydrometric network design by introducing several improvements. First, several data quantization cases and their effects on the snow network design problems were explored. Second, the applicability the Snow Data Assimilation System (SNODAS) products as synthetic datasets of potential stations was demonstrated in the design of the snow monitoring network of the Columbia River Basin. Third, beyond finding the Pareto-optimal networks from the entropy with multi-objective optimization, the networks obtained for La Grande River Basin were further evaluated by applying three hydrologic models. The calibrated hydrologic models simulated discharges using the updated snow water equivalent data from the Pareto-optimal networks. Then, the model performances for high flows were compared to determine the best optimal network for enhanced spring runoff forecasting.

Remote sensing of snow and ice

NASA Technical Reports Server (NTRS)

Rango, A.

1979-01-01

This paper reviews remote sensing of snow and ice, techniques for improved monitoring, and incorporation of the new data into forecasting and management systems. The snowcover interpretation of visible and infrared data from satellites, automated digital methods, radiative transfer modeling to calculate the solar reflectance of snow, and models using snowcover input data and elevation zones for calculating snowmelt are discussed. The use of visible and near infrared techniques for inferring snow properties, microwave monitoring of snowpack characteristics, use of Landsat images for collecting glacier data, monitoring of river ice with visible imagery from NOAA satellites, use of sequential imagery for tracking ice flow movement, and microwave studies of sea ice are described. Applications of snow and ice research to commercial use are examined, and it is concluded that a major problem to be solved is characterization of snow and ice in nature, since assigning of the correct properties to a real system to be modeled has been difficult.

Climate Sensitivity to Realistic Solar Heating of Snow and Ice

NASA Astrophysics Data System (ADS)

Flanner, M.; Zender, C. S.

2004-12-01

Snow and ice-covered surfaces are highly reflective and play an integral role in the planetary radiation budget. However, GCMs typically prescribe snow reflection and absorption based on minimal knowledge of snow physical characteristics. We performed climate sensitivity simulations with the NCAR CCSM including a new physically-based multi-layer snow radiative transfer model. The model predicts the effects of vertically resolved heating, absorbing aerosol, and snowpack transparency on snowpack evolution and climate. These processes significantly reduce the model's near-infrared albedo bias over deep snowpacks. While the current CCSM implementation prescribes all solar radiative absorption to occur in the top 2 cm of snow, we estimate that about 65% occurs beneath this level. Accounting for the vertical distribution of snowpack heating and more realistic reflectance significantly alters snowpack depth, surface albedo, and surface air temperature over Northern Hemisphere regions. Implications for the strength of the ice-albedo feedback will be discussed.

Macroscopic modeling for heat and water vapor transfer in dry snow by homogenization.

PubMed

Calonne, Neige; Geindreau, Christian; Flin, Frédéric

2014-11-26

Dry snow metamorphism, involved in several topics related to cryospheric sciences, is mainly linked to heat and water vapor transfers through snow including sublimation and deposition at the ice-pore interface. In this paper, the macroscopic equivalent modeling of heat and water vapor transfers through a snow layer was derived from the physics at the pore scale using the homogenization of multiple scale expansions. The microscopic phenomena under consideration are heat conduction, vapor diffusion, sublimation, and deposition. The obtained macroscopic equivalent model is described by two coupled transient diffusion equations including a source term arising from phase change at the pore scale. By dimensional analysis, it was shown that the influence of such source terms on the overall transfers can generally not be neglected, except typically under small temperature gradients. The precision and the robustness of the proposed macroscopic modeling were illustrated through 2D numerical simulations. Finally, the effective vapor diffusion tensor arising in the macroscopic modeling was computed on 3D images of snow. The self-consistent formula offers a good estimate of the effective diffusion coefficient with respect to the snow density, within an average relative error of 10%. Our results confirm recent work that the effective vapor diffusion is not enhanced in snow.

Use of distributed snow cover information to update snow storages of a lumped rainfall-runoff model operationally

NASA Astrophysics Data System (ADS)

Lisniak, D.; Meissner, D.; Klein, B.; Pinzinger, R.

2013-12-01

The German Federal Institute of Hydrology (BfG) offers navigational water-level forecasting services on the Federal Waterways, like the rivers Rhine and Danube. In cooperation with the Federal States this mandate also includes the forecasting of flood events. For the River Rhine, the most frequented inland waterway in Central Europe, the BfG employs a hydrological model (HBV) coupled to a hydraulic model (SOBEK) by the FEWS-framework to perform daily forecasts of water-levels operationally. Sensitivity studies have shown that the state of soil water storage in the hydrological model is a major factor of uncertainty when performing short- to medium-range forecasts some days ahead. Taking into account the various additional sources of uncertainty associated with hydrological modeling, including measurement uncertainties, it is essential to estimate an optimal initial state of the soil water storage before propagating it in time, forced by meteorological forecasts, and transforming it into discharge. We show, that using the Ensemble Kalman Filter these initial states can be updated straightforward under certain hydrologic conditions. However, this approach is not sufficient if the runoff is mainly generated by snow melt. Since the snow cover evolution is modeled rather poorly by the HBV-model in our operational setting, flood events caused by snow melt are consistently underestimated by the HBV-model, which has long term effects in basins characterized by a nival runoff regime. Thus, it appears beneficial to update the snow storage of the HBV-model with information derived from regionalized snow cover observations. We present a method to incorporate spatially distributed snow cover observations into the lumped HBV-model. We show the plausibility of this approach and asses the benefits of a coupled snow cover and soil water storage updating, which combine a direct insertion with an Ensemble Kalman Filter. The Ensemble Kalman Filter used here takes into account the internal routing mechanism of the HBV-model, which causes a delayed response of the simulated discharge at the catchment outlet to changes in internal states.

SUBGRID PARAMETERIZATION OF SNOW DISTRIBUTION FOR AN ENERGY AND MASS BALANCE SNOW COVER MODEL. (R824784)

EPA Science Inventory

Representation of sub-element scale variability in snow accumulation and ablation is increasingly recognized as important in distributed hydrologic modelling. Representing sub-grid scale variability may be accomplished through numerical integration of a nested grid or through a l...

Measuring and modelling the impact of the bark beetle forest disturbance on snow accumulation and ablation at a plot scale

NASA Astrophysics Data System (ADS)

Jenicek, Michal; Matejka, Ondrej; Hotovy, Ondrej

2017-04-01

The knowledge of water volume stored in the snowpack and its spatial distribution is important to predict the snowmelt runoff. The objective of this study was to quantify the role of different forest structures on the snowpack distribution at a plot scale during snow accumulation and snow ablation periods. Special interest was put in the role of the forest affected by the bark beetle (Ips typographus). We performed repeated detailed manual field survey at selected mountain plots with different canopy structure located at the same elevation and without influence of topography and wind on the snow distribution. The forest canopy structure was described using parameters calculated from hemispherical photographs, such as canopy closure, leaf area index (LAI) and potential irradiance. Additionally, we used shortwave radiation measured using CNR4 Net radiometers placed in plots with different canopy structure. Two snow accumulation and ablation models were set-up to simulate the snow water equivalent (SWE) in plots with different vegetation cover. First model was physically-based using the energy balance approach, second model was conceptual and it was based on the degree-day approach. Both models accounted for snow interception in different forest types using LAI as a parameter. The measured SWE in the plot with healthy forest was on average by 41% lower than in open area during snow accumulation period. The disturbed forest caused the SWE reduction by 22% compared to open area indicating increasing snow storage after forest defoliation. The snow ablation in healthy forest was by 32% slower compared to open area. On the contrary, the snow ablation in disturbed forest (due to the bark beetle) was on average only by 7% slower than in open area. The relative decrease in incoming solar radiation in the forest compared to open area was much bigger compared to the relative decrease in snowmelt rates. This indicated that the decrease in snowmelt rates cannot be explained only by the decrease in incoming solar radiation. Both models simulated sufficiently compared to observations with slightly accurate simulations in open area compared to healthy forest. This was expected, since both models were forced to fit with observations. However, the energy balance approach simulated snowmelt in the forest environment accurately since it accounts also for longwave radiation which might largely influence snowmelt in the forested plots. Both models showed faster snowmelt after forest defoliation which also resulted in earlier snow melt-out in the disturbed forest compared to the healthy coniferous forest.

The measurement of ultrasound scattering from individual micron-sized objects and its application in single cell scattering.

PubMed

Falou, Omar; Rui, Min; El Kaffas, Ahmed; Kumaradas, J Carl; Kolios, Michael C

2010-08-01

The measurement of the ultrasound backscatter from individual micron-sized objects such as cells is required for various applications such as tissue characterization. However, performing such a measurement remains a challenge. For example, the presence of air bubbles in a suspension of cells during the measurements may lead to the incorrect interpretation of the acoustic signals. This work introduces a technique for measuring the ultrasound backscatter from individual micron-sized objects by combining a microinjection system with a co-registered optical microscope and an ultrasound imaging device. This allowed the measurement of the ultrasound backscatter response from a single object under optical microscope guidance. The optical and ultrasonic data were used to determine the size of the object and to deduce its backscatter responses, respectively. In order to calibrate the system, the backscatter frequency responses from polystyrene microspheres were measured and compared to theoretical predictions. A very good agreement was found between the measured backscatter responses of individual microspheres and theoretical predictions of an elastic sphere. The backscatter responses from single OCI-AML-5 cells were also investigated. It was found that the backscatter responses from AML cells are best modeled using the fluid sphere model. The advantages, limitations, and future applications of the developed technique are discussed.

Linking livestock snow disaster mortality and environmental stressors in the Qinghai-Tibetan Plateau: Quantification based on generalized additive models.

PubMed

Li, Yijia; Ye, Tao; Liu, Weihang; Gao, Yu

2018-06-01

Livestock snow disaster occurs widely in Central-to-Eastern Asian temperate and alpine grasslands. The effects of snow disaster on livestock involve a complex interaction between precipitation, vegetation, livestock, and herder communities. Quantifying the relationship among livestock mortality, snow hazard intensity, and seasonal environmental stressors is of great importance for snow disaster early warning, risk assessments, and adaptation strategies. Using a wide-spatial extent, long-time series, and event-based livestock snow disaster dataset, this study quantified those relationships and established a quantitative model of livestock mortality for prediction purpose for the Qinghai-Tibet Plateau region. Estimations using generalized additive models (GAMs) were shown to accurately predict livestock mortality and mortality rate due to snow disaster, with adjusted-R 2 up to 0.794 and 0.666, respectively. These results showed that a longer snow disaster duration, lower temperatures during the disaster, and a drier summer with less vegetation all contribute significantly and non-linearly to higher mortality (rate), after controlling for elevation and socioeconomic conditions. These results can be readily applied to risk assessment and risk-based adaptation actions. Copyright © 2017 Elsevier B.V. All rights reserved.

Modeling outcomes of approaches to sustained human and snow leopard coexistence.

PubMed

Wilman, Elizabeth A; Wilman, Elspeth N

2016-02-01

The snow leopard (Uncia uncia) is in danger of extinction. Killing to protect livestock is among the primary causes of its decline. Efforts to mitigate this threat have focused on balancing the need to conserve the snow leopard with the needs of local people in snow leopard habitat, many of whom rely on raising livestock for their livelihoods. Conservation of the snow leopard has the characteristics of a public good, and outside funding is required to support conservation efforts. There are 5 commonly discussed approaches to resolving this issue: (1) direct payments for conservation, (2) investments in protection from predation, (3) damage compensation payments, (4) investments in better livestock husbandry, and (5) leases of pastureland for wild prey. After a review of these 5 conservation strategies, an economic-ecologic model, which includes the interactions between the snow leopard, its wild prey, and livestock, is used to evaluate the 2 most promising conservation strategies. The model reveals that investments in protection from predation and leases of pastureland for wild prey are effective but only in delaying the eventual extinction of the snow leopard. To preserve the snow leopard, these approaches must be applied more aggressively and new ones explored. © 2015 Society for Conservation Biology.

The effect of leaf size on the microwave backscattering by corn

NASA Technical Reports Server (NTRS)

Paris, J. F.

1986-01-01

Attema and Ulaby (1978) proposed the cloud model to predict the microwave backscattering properties of vegetation. This paper describes a modification in which the biophysical properties and microwave properties of vegetation are related at the level of the individual scatterer (e.g., the leaf or the stalk) rather than at the level of the aggregated canopy (e.g., the green leaf area index). Assuming that the extinction cross section of an average leaf was proportional to its water content, that a power law relationship existed between the backscattering cross section of an average green corn leaf and its area, and that the backscattering coefficient of the surface was a linear function of its volumetric soil moisture content, it is found that the explicit inclusion of the effects of corn leaf size in the model led to an excellent fit between the observed and predicted backscattering coefficients. Also, an excellent power law relationship existed between the backscattering cross section of a corn leaf and its area.

Impacts of absorbing aerosol deposition on snowpack and hydrologic cycle in the Rocky Mountain region using variable-resolution CESM (VR-CESM)

NASA Astrophysics Data System (ADS)

Wu, C.; Liu, X.; Lin, Z.; Rahimi-Esfarjani, S. R.; Lu, Z.

2017-12-01

Deposition of light-absorbing aerosols (LAAs) including black carbon (BC) and dust onto snow surface has been suggested to reduce the snow albedo, and modulate the snowpack and consequent hydrologic cycle. In this study we use the variable-resolution Community Earth System Model (VR-CESM) to quantify the impacts of LAAs deposition onto snow in the Rocky Mountain region (RMR) during the period of 1981-2005. We first evaluate the model simulation of LAA concentrations both in the atmosphere and in snow, and then investigate the snowpack and runoff changes induced by LAAs-in-snow. The model simulates similar magnitudes of surface atmospheric dust concentrations as observations, but underestimates surface atmospheric BC concentrations by about a factor of two. Despite of this, the magnitude of BC-in-snow concentrations is overall comparable to observations. Regional mean surface radiative effect (SRE) due to LAAs-in-snow reaches up to 0.6-1.7 W m-2 in spring, and dust contributes to about 21-43% of total SRE. Maximum surface air temperature increase due to the LLA's SRE is around 0.9-1.1oC. Snow water equivalent and snow cover fraction reduce by around 2-50 mm and 0.05-0.2, respectively in the two regions around the mountains (Eastern Snake River Plain and Southwestern Wyoming) due to positive snow-albedo feedbacks. During the snow melting period, LAAs accelerate the hydrologic cycle with runoff increased by 7%-42% in April-May and reduced by 2-23% in June-July in the mountainous regions. Under the influence of LAAs-in-snow, Southern Rockies experience the most significant reduction of runoff by about 15% in the later stage of snow melt (i.e., June-July). Our results highlight the potentially important role of LAAs-in-snow in the historical and future changes of snowpack in the RMR.

Evaluation of backscatter dose from internal lead shielding in clinical electron beams using EGSnrc Monte Carlo simulations.

PubMed

De Vries, Rowen J; Marsh, Steven

2015-11-08

Internal lead shielding is utilized during superficial electron beam treatments of the head and neck, such as lip carcinoma. Methods for predicting backscattered dose include the use of empirical equations or performing physical measurements. The accuracy of these empirical equations required verification for the local electron beams. In this study, a Monte Carlo model of a Siemens Artiste linac was developed for 6, 9, 12, and 15 MeV electron beams using the EGSnrc MC package. The model was verified against physical measurements to an accuracy of better than 2% and 2mm. Multiple MC simulations of lead interfaces at different depths, corresponding to mean electron energies in the range of 0.2-14 MeV at the interfaces, were performed to calculate electron backscatter values. The simulated electron backscatter was compared with current empirical equations to ascertain their accuracy. The major finding was that the current set of backscatter equations does not accurately predict electron backscatter, particularly in the lower energies region. A new equation was derived which enables estimation of electron backscatter factor at any depth upstream from the interface for the local treatment machines. The derived equation agreed to within 1.5% of the MC simulated electron backscatter at the lead interface and upstream positions. Verification of the equation was performed by comparing to measurements of the electron backscatter factor using Gafchromic EBT2 film. These results show a mean value of 0.997 ± 0.022 to 1σ of the predicted values of electron backscatter. The new empirical equation presented can accurately estimate electron backscatter factor from lead shielding in the range of 0.2 to 14 MeV for the local linacs.

Evaluation of backscatter dose from internal lead shielding in clinical electron beams using EGSnrc Monte Carlo simulations

PubMed Central

Marsh, Steven

2015-01-01

Internal lead shielding is utilized during superficial electron beam treatments of the head and neck, such as lip carcinoma. Methods for predicting backscattered dose include the use of empirical equations or performing physical measurements. The accuracy of these empirical equations required verification for the local electron beams. In this study, a Monte Carlo model of a Siemens Artiste linac was developed for 6, 9, 12, and 15 MeV electron beams using the EGSnrc MC package. The model was verified against physical measurements to an accuracy of better than 2% and 2 mm. Multiple MC simulations of lead interfaces at different depths, corresponding to mean electron energies in the range of 0.2–14 MeV at the interfaces, were performed to calculate electron backscatter values. The simulated electron backscatter was compared with current empirical equations to ascertain their accuracy. The major finding was that the current set of backscatter equations does not accurately predict electron backscatter, particularly in the lower energies region. A new equation was derived which enables estimation of electron backscatter factor at any depth upstream from the interface for the local treatment machines. The derived equation agreed to within 1.5% of the MC simulated electron backscatter at the lead interface and upstream positions. Verification of the equation was performed by comparing to measurements of the electron backscatter factor using Gafchromic EBT2 film. These results show a mean value of 0.997±0.022 to 1σ of the predicted values of electron backscatter. The new empirical equation presented can accurately estimate electron backscatter factor from lead shielding in the range of 0.2 to 14 MeV for the local linacs. PACS numbers: 87.53.Bn, 87.55.K‐, 87.56.bd PMID:26699566

Separating snow, clean and debris covered ice in the Upper Indus Basin, Hindukush-Karakoram-Himalayas, using Landsat images between 1998 and 2002

NASA Astrophysics Data System (ADS)

Khan, Asif; Naz, Bibi S.; Bowling, Laura C.

2015-02-01

The Hindukush Karakoram Himalayan mountains contain some of the largest glaciers of the world, and supply melt water from perennial snow and glaciers to the Upper Indus Basin (UIB) upstream of Tarbela dam, which constitutes greater than 80% of the annual flows, and caters to the needs of millions of people in the Indus Basin. It is therefore important to study the response of perennial snow and glaciers in the UIB under changing climatic conditions, using improved hydrological modeling, glacier mass balance, and observations of glacier responses. However, the available glacier inventories and datasets only provide total perennial-snow and glacier cover areas, despite the fact that snow, clean ice and debris covered ice have different melt rates and densities. This distinction is vital for improved hydrological modeling and mass balance studies. This study, therefore, presents a separated perennial snow and glacier inventory (perennial snow-cover on steep slopes, perennial snow-covered ice, clean and debris covered ice) based on a semi-automated method that combines Landsat images and surface slope information in a supervised maximum likelihood classification to map distinct glacier zones, followed by manual post processing. The accuracy of the presented inventory falls well within the accuracy limits of available snow and glacier inventory products. For the entire UIB, estimates of perennial and/or seasonal snow on steep slopes, snow-covered ice, clean and debris covered ice zones are 7238 ± 724, 5226 ± 522, 4695 ± 469 and 2126 ± 212 km2 respectively. Thus total snow and glacier cover is 19,285 ± 1928 km2, out of which 12,075 ± 1207 km2 is glacier cover (excluding steep slope snow-cover). Equilibrium Line Altitude (ELA) estimates based on the Snow Line Elevation (SLE) in various watersheds range between 4800 and 5500 m, while the Accumulation Area Ratio (AAR) ranges between 7% and 80%. 0 °C isotherms during peak ablation months (July and August) range between ∼ 5500 and 6200 m in various watersheds. These outputs can be used as input to hydrological models, to estimate spatially-variable degree day factors for hydrological modeling, to separate glacier and snow-melt contributions in river flows, and to study glacier mass balance, and glacier responses to changing climate.

The Studies on Aerosol Transport, Its Deposition, and Its Impact on Climate - the Study on the Surface Material Circulation can Connect from the Past to the Future

NASA Technical Reports Server (NTRS)

Yasunari, Teppei

2012-01-01

Recently the issue on glacier retreats comes up and many factors should be relevant to the issue. The absorbing aerosols such as dust and black carbon (BC) are considered to be one of the factors. After they deposited onto the snow surface, it will reduce snow albedo (called snow darkening effect) and probably contribute to further melting of glacier. The Goddard Earth Observing System version 5 (GEOS-5) has developed at NASAlGSFC. However, the original snowpack model used in the land surface model in the GEOS-5 did not consider the snow darkening effect. Here we developed the new snow albedo scheme which can consider the snow darkening effect. In addition, another scheme on calculating mass concentrations on the absorbing aerosols in snowpack was also developed, in which the direct aerosol depositions from the chemical transport model in the GEOS-5 were used. The scheme has been validated with the observed data obtained at backyard of the Institute of Low Temperature Science, Hokkaido University, by Dr. Teruo Aoki (Meteorological Research Institute) et al. including me. The observed data was obtained when I was Ph.D. candidate. The original GEOS-5 during 2007-2009 over the Himalayas and Tibetan Plateau region showed more reductions of snow than that of the new GEOS-5 because the original one used lower albedo settings. On snow cover fraction, the new GEOS-5 simulated more realistic snow-covered area comparing to the MODIS snow cover fraction. The reductions on snow albedo, snow cover fraction, and snow water equivalent were seen with statistically significance if we consider the snow darkening effect comparing to the results without the snow darkening effect. In the real world, debris-cover, inside refreezing process, surface flow of lacier, etc. affect glacier mass balance and the simu.latedresults immediately do not affect whole glacier retreating. However, our results indicate that some surface melting over non debris-covered parts of the glacier would be explained by the snow darkening effect. Further discussion and observations are necessary to assess the glacier issue.

The Various Influences due to Aerosol Depositions

NASA Technical Reports Server (NTRS)

Yasunari, Teppei

2011-01-01

Recently the issue on glacier retreats comes up and many factors should be relevant to the issue. The absorbing aerosols such as dust and black carbon (BC) are considered to be one of the factors. After they deposited onto the snow surface, it will reduce snow albedo (called snow darkening effect) and probably contribute to further melting of glacier. The Goddard Earth Observing System version 5 (GEOS-5) has developed at NASA/GSFC. However, the original snowpack model used in the land surface model in the GEOS-5 did not consider the snow darkening effect. Here we developed the new snow albedo scheme which can consider the snow darkening effect. In addition, another scheme on calculating mass concentrations on the absorbing aerosols in snowpack was also developed, in which the direct aerosol depositions from the chemical transport model in the GEOS-5 were used. The scheme has been validated with the observed data obtained at backyard of the Institute of Low Temperature Science, Hokkaido University, by Dr. Teruo Aoki (Meteorological Research Institute) et al. including me. The observed data was obtained when I was Ph.D.caftdidate. The original GEOS-5 during 2007-2009 over the Himalayas and Tibetan Plateau region showed more reductions of snow than that of the new GEOS-5 because the original one used lower albedo settings. On snow cover fraction, the new GEOS-5 simulated more realistic snow-covered area comparing to the MODIS snow cover fraction. The reductions on snow albedo, snow cover fraction, and snow water equivalent were seen with statistically significance if we consider the snow darkening effect comparing to the results without the snow darkening effect. In the real world, debris cover, inside refreezing process, surface flow of glacier, etc. affect glacier mass balance and the simulated results immediately do not affect whole glacier retreating. However, our results indicate that some surface melting over non debris covered parts of the glacier would be explained by the snow darkening effect. Further discussion and observations are necessary to assess the glacier issue.

Acoustic Wave Propagation in Snow Based on a Biot-Type Porous Model

NASA Astrophysics Data System (ADS)

Sidler, R.

2014-12-01

Despite the fact that acoustic methods are inexpensive, robust and simple, the application of seismic waves to snow has been sparse. This might be due to the strong attenuation inherent to snow that prevents large scale seismic applications or due to the somewhat counterintuitive acoustic behavior of snow as a porous material. Such materials support a second kind of compressional wave that can be measured in fresh snow and which has a decreasing wave velocity with increasing density of snow. To investigate wave propagation in snow we construct a Biot-type porous model of snow as a function of porosity based on the assumptions that the solid frame is build of ice, the pore space is filled with a mix of air, or air and water, and empirical relationships for the tortuosity, the permeability, the bulk, and the shear modulus.We use this reduced model to investigate compressional and shear wave velocities of snow as a function of porosity and to asses the consequences of liquid water in the snowpack on acoustic wave propagation by solving Biot's differential equations with plain wave solutions. We find that the fast compressional wave velocity increases significantly with increasing density, but also that the fast compressional wave velocity might be even lower than the slow compressional wave velocity for very light snow. By using compressional and shear strength criteria and solving Biot's differential equations with a pseudo-spectral approach we evaluate snow failure due to acoustic waves in a heterogeneous snowpack, which we think is an important mechanism in triggering avalanches by explosives as well as by skiers. Finally, we developed a low cost seismic acquisition device to assess the theoretically obtained wave velocities in the field and to explore the possibility of an inexpensive tool to remotely gather snow water equivalent.

Electromagnetic reflection from multi-layered snow models

NASA Technical Reports Server (NTRS)

Linlor, W. I.; Jiracek, G. R.

1975-01-01

The remote sensing of snow-pack characteristics with surface installations or an airborne system could have important applications in water-resource management and flood prediction. To derive some insight into such applications, the electromagnetic response of multilayered snow models is analyzed in this paper. Normally incident plane waves at frequencies ranging from 1 MHz to 10 GHz are assumed, and amplitude reflection coefficients are calculated for models having various snow-layer combinations, including ice layers. Layers are defined by thickness, permittivity, and conductivity; the electrical parameters are constant or prescribed functions of frequency. To illustrate the effect of various layering combinations, results are given in the form of curves of amplitude reflection coefficients versus frequency for a variety of models. Under simplifying assumptions, the snow thickness and effective dielectric constant can be estimated from the variations of reflection coefficient as a function of frequency.

Modeling snow-crystal growth: a three-dimensional mesoscopic approach.

PubMed

Gravner, Janko; Griffeath, David

2009-01-01

We introduce a three-dimensional, computationally feasible, mesoscopic model for snow-crystal growth, based on diffusion of vapor, anisotropic attachment, and a boundary layer. Several case studies are presented that faithfully replicate most observed snow-crystal morphology, an unusual achievement for a mathematical model. In particular, many of the most striking physical specimens feature both facets and branches, and our model provides an explanation for this phenomenon. We also duplicate many other observed traits, including ridges, ribs, sandwich plates, and hollow columns, as well as various dynamic instabilities. The concordance of observed phenomena suggests that the ingredients in our model are the most important ones in the development of physical snow crystals.

A mixed multiscale model better accounting for the cross term of the subgrid-scale stress and for backscatter

NASA Astrophysics Data System (ADS)

Thiry, Olivier; Winckelmans, Grégoire

2016-02-01

In the large-eddy simulation (LES) of turbulent flows, models are used to account for the subgrid-scale (SGS) stress. We here consider LES with "truncation filtering only" (i.e., that due to the LES grid), thus without regular explicit filtering added. The SGS stress tensor is then composed of two terms: the cross term that accounts for interactions between resolved scales and unresolved scales, and the Reynolds term that accounts for interactions between unresolved scales. Both terms provide forward- (dissipation) and backward (production, also called backscatter) energy transfer. Purely dissipative, eddy-viscosity type, SGS models are widely used: Smagorinsky-type models, or more advanced multiscale-type models. Dynamic versions have also been developed, where the model coefficient is determined using a dynamic procedure. Being dissipative by nature, those models do not provide backscatter. Even when using the dynamic version with local averaging, one typically uses clipping to forbid negative values of the model coefficient and hence ensure the stability of the simulation; hence removing the backscatter produced by the dynamic procedure. More advanced SGS model are thus desirable, and that better conform to the physics of the true SGS stress, while remaining stable. We here investigate, in decaying homogeneous isotropic turbulence, and using a de-aliased pseudo-spectral method, the behavior of the cross term and of the Reynolds term: in terms of dissipation spectra, and in terms of probability density function (pdf) of dissipation in physical space: positive and negative (backscatter). We then develop a new mixed model that better accounts for the physics of the SGS stress and for the backscatter. It has a cross term part which is built using a scale-similarity argument, further combined with a correction for Galilean invariance using a pseudo-Leonard term: this is the term that also does backscatter. It also has an eddy-viscosity multiscale model part that accounts for all the remaining phenomena (also for the incompleteness of the cross term model), that is dynamic and that adjusts the overall dissipation. The model is tested, both a priori and a posteriori, and is compared to the direct numerical simulation and to the exact SGS terms, also in time. The model is seen to provide accurate energy spectra, also in comparison to the dynamic Smagorinsky model. It also provides significant backscatter (although four times less than the real SGS stress), while remaining stable.

Towards Year-round Estimation of Terrestrial Water Storage over Snow-Covered Terrain via Multi-sensor Assimilation of GRACE/GRACE-FO and AMSR-E/AMSR-2.

NASA Astrophysics Data System (ADS)

Wang, J.; Xue, Y.; Forman, B. A.; Girotto, M.; Reichle, R. H.

2017-12-01

The Gravity and Recovery Climate Experiment (GRACE) has revolutionized large-scale remote sensing of the Earth's terrestrial hydrologic cycle and has provided an unprecedented observational constraint for global land surface models. However, the coarse-scale (in space and time), vertically-integrated measure of terrestrial water storage (TWS) limits GRACE's applicability to smaller scale hydrologic applications. In order to enhance model-based estimates of TWS while effectively adding resolution (in space and time) to the coarse-scale TWS retrievals, a multi-variate, multi-sensor data assimilation framework is presented here that simultaneously assimilates gravimetric retrievals of TWS in conjunction with passive microwave (PMW) brightness temperature (Tb) observations over snow-covered terrain. The framework uses the NASA Catchment Land Surface Model (Catchment) and an ensemble Kalman filter (EnKF). A synthetic assimilation experiment is presented for the Volga river basin in Russia. The skill of the output from the assimilation of synthetic observations is compared with that of model estimates generated without the benefit of assimilating the synthetic observations. It is shown that the EnKF framework improves modeled estimates of TWS, snow depth, and snow mass (a.k.a. snow water equivalent). The data assimilation routine produces a conditioned (updated) estimate that is more accurate and contains less uncertainty during both the snow accumulation phase of the snow season as well as during the snow ablation season.

Cartographic modelling of aerotechnogenic pollution in snow cover in the landscapes of the Kola Peninsula.

PubMed

Ratkin, N E; Asming, V E; Koshkin, V V

2001-01-01

The goal of this work was to develop computational techniques for sulphates, nickel and copper accumulation in the snow in the local pollution zone. The main task was to reveal the peculiarities of formation and pollution of snow cover on the region with complex cross-relief. A digital cartographic model of aerotechnogenic pollution of snow cover in the landscapes of the local zone has been developed, based on five-year experimental data. Data regarding annual emissions from the industrial complex, information about distribution of wind and the sum of precipitation from meteostation "Nikel" for the winter period, allowed the model to ensure: * material presentation in the form of maps of water capacity and accumulation of sulphates, nickel and copper in the snow over any winter period in retrospective; * calculation of water capacity and accumulation of pollutants for watersheds and other natural-territorial complexes; * solution of the opposite problem about the determination of the emissions of sulphates, nickel and copper from the enterprise by measuring snow pollution in datum points. The model can be used in other northern regions of the Russian Federation with similar physical-geographical and climatic conditions. The relationships between the sum of precipitation and water capacity in the landscapes of the same type and also the relationships between pollution content in snow and relief, pollution content in snow and distance from the source of emissions, were used as the basis for the model.

Assessing the ability of operational snow models to predict snowmelt runoff extremes (Invited)

NASA Astrophysics Data System (ADS)

Wood, A. W.; Restrepo, P. J.; Clark, M. P.

2013-12-01

In the western US, the snow accumulation and melt cycle of winter and spring plays a critical role in the region's water management strategies. Consequently, the ability to predict snowmelt runoff at time scales from days to seasons is a key input for decisions in reservoir management, whether for avoiding flood hazards or supporting environmental flows through the scheduling of releases in spring, or for allocating releases for multi-state water distribution in dry seasons of year (using reservoir systems to provide an invaluable buffer for many sectors against drought). Runoff forecasts thus have important benefits at both wet and dry extremes of the climatological spectrum. The importance of the prediction of the snow cycle motivates an assessment of the strengths and weaknesses of the US's central operational snow model, SNOW17, in contrast to process-modeling alternatives, as they relate to simulating observed snowmelt variability and extremes. To this end, we use a flexible modeling approach that enables an investigation of different choices in model structure, including model physics, parameterization and degree of spatiotemporal discretization. We draw from examples of recent extreme events in western US watersheds and an overall assessment of retrospective model performance to identify fruitful avenues for advancing the modeling basis for the operational prediction of snow-related runoff extremes.

The Airborne Snow Observatory: fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo

USDA-ARS?s Scientific Manuscript database

Snow cover and its melt dominate regional climate and water resources in many of the world’s mountainous regions. Snowmelt timing and magnitude in mountains tend to be controlled by absorption of solar radiation and snow water equivalent, respectively, and yet both of these are very poorly known ev...

The magnitude of the snow-sourced reactive nitrogen flux to the boundary layer in the Uintah Basin, Utah, USA

NASA Astrophysics Data System (ADS)

Zatko, Maria; Erbland, Joseph; Savarino, Joel; Geng, Lei; Easley, Lauren; Schauer, Andrew; Bates, Timothy; Quinn, Patricia K.; Light, Bonnie; Morison, David; Osthoff, Hans D.; Lyman, Seth; Neff, William; Yuan, Bin; Alexander, Becky

2016-11-01

Reactive nitrogen (Nr = NO, NO2, HONO) and volatile organic carbon emissions from oil and gas extraction activities play a major role in wintertime ground-level ozone exceedance events of up to 140 ppb in the Uintah Basin in eastern Utah. Such events occur only when the ground is snow covered, due to the impacts of snow on the stability and depth of the boundary layer and ultraviolet actinic flux at the surface. Recycling of reactive nitrogen from the photolysis of snow nitrate has been observed in polar and mid-latitude snow, but snow-sourced reactive nitrogen fluxes in mid-latitude regions have not yet been quantified in the field. Here we present vertical profiles of snow nitrate concentration and nitrogen isotopes (δ15N) collected during the Uintah Basin Winter Ozone Study 2014 (UBWOS 2014), along with observations of insoluble light-absorbing impurities, radiation equivalent mean ice grain radii, and snow density that determine snow optical properties. We use the snow optical properties and nitrate concentrations to calculate ultraviolet actinic flux in snow and the production of Nr from the photolysis of snow nitrate. The observed δ15N(NO3-) is used to constrain modeled fractional loss of snow nitrate in a snow chemistry column model, and thus the source of Nr to the overlying boundary layer. Snow-surface δ15N(NO3-) measurements range from -5 to 10 ‰ and suggest that the local nitrate burden in the Uintah Basin is dominated by primary emissions from anthropogenic sources, except during fresh snowfall events, where remote NOx sources from beyond the basin are dominant. Modeled daily averaged snow-sourced Nr fluxes range from 5.6 to 71 × 107 molec cm-2 s-1 over the course of the field campaign, with a maximum noontime value of 3.1 × 109 molec cm-2 s-1. The top-down emission estimate of primary, anthropogenic NOx in Uintah and Duchesne counties is at least 300 times higher than the estimated snow NOx emissions presented in this study. Our results suggest that snow-sourced reactive nitrogen fluxes are minor contributors to the Nr boundary layer budget in the highly polluted Uintah Basin boundary layer during winter 2014.

Modeling and measuring snow for assessing climate change impacts in Glacier National Park, Montana

USGS Publications Warehouse

Fagre, Daniel B.; Selkowitz, David J.; Reardon, Blase; Holzer, Karen; Mckeon, Lisa L.

2002-01-01

A 12-year program of global change research at Glacier National Park by the U.S. Geological Survey and numerous collaborators has made progress in quantifying the role of snow as a driver of mountain ecosystem processes. Spatially extensive snow surveys during the annual accumulation/ablation cycle covered two mountain watersheds and approximately 1,000 km2 . Over 7,000 snow depth and snow water equivalent (SWE) measurements have been made through spring 2002. These augment two SNOTEL sites, 9 NRCS snow courses, and approximately 150 snow pit analyses. Snow data were used to establish spatially-explicit interannual variability in snowpack SWE. East of the Continental Divide, snowpack SWE was lower but also less variable than west of the Divide. Analysis of snowpacks suggest downward trends in SWE, a reduction in snow cover duration, and earlier melt-out dates during the past 52 years. Concurrently, high elevation forests and treelines have responded with increased growth. However, the 80 year record of snow from 3 NRCS snow courses reflects a strong influence from the Pacific Decadal Oscillation, resulting in 20-30 year phases of greater or lesser mean SWE. Coupled with the fine-resolution spatial snow data from the two watersheds, the ecological consequences of changes in snowpack can be empirically assessed at a habitat patch scale. This will be required because snow distribution models have had varied success in simulating snowpack accumulation/ablation dynamics in these mountain watersheds, ranging from R2=0.38 for individual south-facing forested snow survey routes to R2=0.95 when aggregated to the watershed scale. Key ecological responses to snowpack changes occur below the watershed scale, such as snow-mediated expansion of forest into subalpine meadows, making continued spatially-explicit snow surveys a necessity. 

An AeroCom assessment of black carbon in Arctic snow and sea ice

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiao, C.; Flanner, M. G.; Balkanski, Y.

2014-01-01

Though many global aerosols models prognose surface deposition, only a few models have been used to directly simulate the radiative effect from black carbon (BC) deposition to snow and sea ice. In this paper, we apply aerosol deposition fields from 25 models contributing to two phases of the Aerosol Comparisons between Observations and Models (AeroCom) project to simulate and evaluate within-snow BC concentrations and radiative effect in the Arctic. We accomplish this by driving the offline land and sea ice components of the Community Earth System Model with different deposition fields and meteorological conditions from 2004 to 2009, during whichmore » an extensive field campaign of BC measurements in Arctic snow occurred. We find that models generally underestimate BC concentrations in snow in northern Russia and Norway, while overestimating BC amounts elsewhere in the Arctic. Although simulated BC distributions in snow are poorly correlated with measurements, mean values are reasonable. The multi-model mean (range) bias in BC concentrations, sampled over the same grid cells, snow depths, and months of measurements, are -4.4 (-13.2 to +10.7) ng g -1 for an earlier phase of AeroCom models (phase I), and +4.1 (-13.0 to +21.4) ng g -1 for a more recent phase of AeroCom models (phase II), compared to the observational mean of 19.2 ng g -1. Factors determining model BC concentrations in Arctic snow include Arctic BC emissions, transport of extra-Arctic aerosols, precipitation, deposition efficiency of aerosols within the Arctic, and meltwater removal of particles in snow. Sensitivity studies show that the model–measurement evaluation is only weakly affected by meltwater scavenging efficiency because most measurements were conducted in non-melting snow. The Arctic (60–90° N) atmospheric residence time for BC in phase II models ranges from 3.7 to 23.2 days, implying large inter-model variation in local BC deposition efficiency. Combined with the fact that most Arctic BC deposition originates from extra-Arctic emissions, these results suggest that aerosol removal processes are a leading source of variation in model performance. The multi-model mean (full range) of Arctic radiative effect from BC in snow is 0.15 (0.07–0.25) W m -2 and 0.18 (0.06–0.28) W m -2 in phase I and phase II models, respectively. After correcting for model biases relative to observed BC concentrations in different regions of the Arctic, we obtain a multi-model mean Arctic radiative effect of 0.17 W m -2 for the combined AeroCom ensembles. Finally, there is a high correlation between modeled BC concentrations sampled over the observational sites and the Arctic as a whole, indicating that the field campaign provided a reasonable sample of the Arctic.« less

Scaling and Numerical Model Evaluation of Snow-Cover Effects on the Generation and Modification of Daytime Mesoscale Circulations.

NASA Astrophysics Data System (ADS)

Segal, M.; Garratt, J. R.; Pielke, R. A.; Ye, Z.

1991-04-01

Consideration of the sensible heat flux characteristics over a snow surface suggests a significant diminution in the magnitude of the flux, compared to that over a snow-free surface under the same environmental conditions. Consequently, the existence of snow-covered mesoscale areas adjacent to snow-free areas produces horizontal thermal gradients in the lower atmosphere during the daytime, possibly resulting in a `snow breeze.' In addition, suppression of the daytime thermally induced upslope flow over snow-covered slopes is likely to occur. The present paper provides scaling and modeling evaluations of these situations, with quantification of the generated and modified circulations. These evaluations suggest that under ideal situations involved with uniform snow cover over large areas, particularly in late winter and early spring, a noticeable `snow breeze' is likely to develop. Additionally: suppression of the daytime thermally induced upslope flow is significant and may even result in a daytime drainage flow. The effects of bare ground patchiness in the snow cover on these circulations are also explored, both for flat terrain and slope-flow situations. A patchiness fraction greater than 0.5 is found to result in a noticeably reduced snow-breeze circulation, while a patchiness fraction of only 0.1 caused the simulated daytime drainage flow over slopes to he reversed.

Sensitivity of snow process simulations to precipitation-phase transition method in forested and open areas

NASA Astrophysics Data System (ADS)

Lundberg, A.; Gustafsson, D.

2009-04-01

Modeling of forest snow processes is complicated and especially problematic seems to be the separation of precipitation phase in climates where a large part of the precipitation falls at temperatures near zero degrees Celsius. When the precipitation is classified as snow, the tree crowns can carry an order of magnitude more canopy storage as compared to when the precipitation is classified as rain, and snow in the trees also alters the albedo of the forest while rain does not. Many different schemes for the precipitation phase separation are used by various snow models. Some models use just one air temperature threshold (TR/S) below which all precipitation is assumed to be snow and above which all precipitation is classified as rain. A more common approach for forest snow models is to use two temperature thresholds. The snow fraction (SF) is then set to one below the snow threshold (TS) and to zero above the rain threshold (TR) and SF is assumed to decrease linearly between these two thresholds. Also more sophisticated schemes exist, but three seems to be a lack of agreement on how the precipitation phase separations should be performed. The aim with this study is to use a hydrological model including canopy snow processes to illustrate the sensitivity for different formulations of the precipitation phase separation on a) the simulated maximum snow pack storage b) the interception evaporation loss and c) snow melt runoff. In other words, to investigate of the choice of precipitation phase separation has an impact on the simulated wintertime water balance. Simulations are made for sites in different climates and for both open fields and forest sites in different regions of Sweden from north to south. In general, precipitation phase separation methods that classified snowfall at higher temperatures resulted in a larger proportion of the precipitation lost by interception evaporation as a result of the increased interception capacity. However, the maximum snow accumulation was also increased in some cases due to the overall increased snowfall, depending on canopy density and precipitation and temperature regimes. Results show that the choice of precipitation phase separation method can have an significant impact on the simulated wintertime water balance, especially in forested regions.

Estimated snow parameters for vehicle mobility modeling in Korea, Germany and interior Alaska

DOT National Transportation Integrated Search

1995-09-01

Snow is a crucial factor affecting the U.S. Army's operations in cold regions. Values for snow depth and snow density are needed for vehicle mobility studies, but unfortunately the available historical records of these parameters tend to be relativel...

Validation of automated supervised segmentation of multibeam backscatter data from the Chatham Rise, New Zealand

NASA Astrophysics Data System (ADS)

Hillman, Jess I. T.; Lamarche, Geoffroy; Pallentin, Arne; Pecher, Ingo A.; Gorman, Andrew R.; Schneider von Deimling, Jens

2018-06-01

Using automated supervised segmentation of multibeam backscatter data to delineate seafloor substrates is a relatively novel technique. Low-frequency multibeam echosounders (MBES), such as the 12-kHz EM120, present particular difficulties since the signal can penetrate several metres into the seafloor, depending on substrate type. We present a case study illustrating how a non-targeted dataset may be used to derive information from multibeam backscatter data regarding distribution of substrate types. The results allow us to assess limitations associated with low frequency MBES where sub-bottom layering is present, and test the accuracy of automated supervised segmentation performed using SonarScope® software. This is done through comparison of predicted and observed substrate from backscatter facies-derived classes and substrate data, reinforced using quantitative statistical analysis based on a confusion matrix. We use sediment samples, video transects and sub-bottom profiles acquired on the Chatham Rise, east of New Zealand. Inferences on the substrate types are made using the Generic Seafloor Acoustic Backscatter (GSAB) model, and the extents of the backscatter classes are delineated by automated supervised segmentation. Correlating substrate data to backscatter classes revealed that backscatter amplitude may correspond to lithologies up to 4 m below the seafloor. Our results emphasise several issues related to substrate characterisation using backscatter classification, primarily because the GSAB model does not only relate to grain size and roughness properties of substrate, but also accounts for other parameters that influence backscatter. Better understanding these limitations allows us to derive first-order interpretations of sediment properties from automated supervised segmentation.

Using geostatistical methods to estimate snow water equivalence distribution in a mountain watershed

USGS Publications Warehouse

Balk, B.; Elder, K.; Baron, Jill S.

1998-01-01

Knowledge of the spatial distribution of snow water equivalence (SWE) is necessary to adequately forecast the volume and timing of snowmelt runoff.  In April 1997, peak accumulation snow depth and density measurements were independently taken in the Loch Vale watershed (6.6 km2), Rocky Mountain National Park, Colorado.  Geostatistics and classical statistics were used to estimate SWE distribution across the watershed.  Snow depths were spatially distributed across the watershed through kriging interpolation methods which provide unbiased estimates that have minimum variances.  Snow densities were spatially modeled through regression analysis.  Combining the modeled depth and density with snow-covered area (SCA produced an estimate of the spatial distribution of SWE.  The kriged estimates of snow depth explained 37-68% of the observed variance in the measured depths.  Steep slopes, variably strong winds, and complex energy balance in the watershed contribute to a large degree of heterogeneity in snow depth.

[Multi-Scale Convergence of Cold-Land Process Representation in Land-Surface Models, Microwave Remote Sensing, and Field Observations

NASA Technical Reports Server (NTRS)

Shi, Jiancheng

2005-01-01

The cryosphere is a major component of the hydrosphere and interacts significantly with the global climate system, the geosphere, and the biosphere. Measurement of the amount of water stored in the snow pack and forecasting the rate of melt are thus essential for managing water supply and flood control systems. Snow hydrologists are confronted with the dual problems of estimating both the quantity of water held by seasonal snow packs and time of snow melt. Monitoring these snow parameters is essential for one of the objectives of the Earth Science Enterprise-understanding of the global hydrologic cycle. Measuring spatially distributed snow properties, such as snow water equivalence (SWE) and wetness, from space is a key component for improvement of our understanding of coupled atmosphere-surface processes. Through the GWEC project, we have significantly advanced our understandings and improved modeling capabilities of the microwave signatures in response to snow and underground properties.

Optimal Time Allocation in Backscatter Assisted Wireless Powered Communication Networks.

PubMed

Lyu, Bin; Yang, Zhen; Gui, Guan; Sari, Hikmet

2017-06-01

This paper proposes a wireless powered communication network (WPCN) assisted by backscatter communication (BackCom). This model consists of a power station, an information receiver and multiple users that can work in either BackCom mode or harvest-then-transmit (HTT) mode. The time block is mainly divided into two parts corresponding to the data backscattering and transmission periods, respectively. The users first backscatter data to the information receiver in time division multiple access (TDMA) during the data backscattering period. When one user works in the BackCom mode, the other users harvest energy from the power station. During the data transmission period, two schemes, i.e., non-orthogonal multiple access (NOMA) and TDMA, are considered. To maximize the system throughput, the optimal time allocation policies are obtained. Simulation results demonstrate the superiority of the proposed model.

Optimal Time Allocation in Backscatter Assisted Wireless Powered Communication Networks

PubMed Central

Lyu, Bin; Yang, Zhen; Gui, Guan; Sari, Hikmet

2017-01-01

This paper proposes a wireless powered communication network (WPCN) assisted by backscatter communication (BackCom). This model consists of a power station, an information receiver and multiple users that can work in either BackCom mode or harvest-then-transmit (HTT) mode. The time block is mainly divided into two parts corresponding to the data backscattering and transmission periods, respectively. The users first backscatter data to the information receiver in time division multiple access (TDMA) during the data backscattering period. When one user works in the BackCom mode, the other users harvest energy from the power station. During the data transmission period, two schemes, i.e., non-orthogonal multiple access (NOMA) and TDMA, are considered. To maximize the system throughput, the optimal time allocation policies are obtained. Simulation results demonstrate the superiority of the proposed model. PMID:28587171

Conceptualisation of Snowpack Isotope Dynamics in Spatially Distributed Tracer-Aided Runoff Models in Snow Influenced Northern Cathments

NASA Astrophysics Data System (ADS)

Ala-aho, P. O. A.; Tetzlaff, D.; Laudon, H.; McNamara, J. P.; Soulsby, C.

2016-12-01

We use the Spatially distributed Tracer-Aided Rainfall-Runoff (STARR) modelling framework to explore non-stationary flow and isotope response in three northern headwater catchments. The model simulates dynamic, spatially variable tracer concentration in different water stores and fluxes within a catchment, which can constrain internal catchment mixing processes, flow paths and associated water ages. To date, a major limitation in using such models in snow-dominated catchments has been the difficulties in paramaterising the isotopic transformations in snowpack accumulation and melt. We use high quality long term datasets for hydrometrics and stable water isotopes collected in three northern study catchments for model calibration and testing. The three catchments exhibit different hydroclimatic conditions, soil and vegetation types, and topographic relief, which brings about variable degree of snow dominance across the catchments. To account for the snow influence we develop novel formulations to estimate the isotope evolution in the snowpack and melt. Algorithms for the isotopic evolution parameterize an isotopic offset between snow evaporation and melt fluxes and the remaining snow storage. The model for each catchment is calibrated to match both streamflow and tracer concentration at the stream outlet to ensure internal consistency of the system behaviour. The model is able to reproduce the streamflow along with the spatio-temporal differences in tracer concentrations across the three studies catchments reasonably well. Incorporating the spatially distributed snowmelt processes and associated isotope transformations proved essential in capturing the stream tracer reponse for strongly snow-influenced cathments. This provides a transferrable tool which can be used to understand spatio-temporal variability of mixing and water ages for different storages and flow paths in other snow influenced, environments.

Impacts of Recent Climatic Wetting on Distributed Snow and Streamflow Responses in a Terminal Lake Basin.

NASA Astrophysics Data System (ADS)

Van Hoy, D.; Mahmood, T. H.; Jeannotte, T.; Todhunter, P. E.

2017-12-01

The recent shift in hydroclimatic conditions in the Northern Great Plains (NGP) has led to an increase in precipitation, rainfall rate, and wetland connectivity over the last few decades. These changes yield an integrated response resulting in high mean annual streamflow and subsequent flooding in many NGP basins such as the terminal Devils Lake Basin (DLB). In this study, we investigate the impacts of recent climatic wetting on distributed hydrologic responses such as snow processes and streamflow using a field-tested and physically-based cold region hydrologic model (CRHM). CHRM is designed for cold prairie regions and has modules to simulate major processes such as blowing snow transport, sublimation, interception, frozen soil infiltration, snowmelt and subsequent streamflow generation. Our modeling focuses on a tributary basin of the DLB known as the Mauvais Coulee Basin (MCB). Since there were no snow observations in the MCB, we conducted a detailed snow survey at distributed locations estimating snow depth, density, and snow water equivalent (SWE) using a prairie snow tube four times during winter of 2016-17. The MCB model was evaluated against distributed snow observations and streamflow measured at the basin outlet (USGS) for the year 2016-2017. Preliminary results indicate that the simulated SWEs at distributed locations and streamflow (NSE ≈ 0.70) are in good agreement with observations. The simulated SWE maps exhibit large spatiotemporal variation during 2016-17 winter due to spatial variability in precipitation, snow redistribution from stubble field to wooded areas, and snow accumulations in small depressions across the subbasins. The main source of snow appears to be the hills and ridges of the eastern and western edges of the basin, while the main sink is the large flat central valleys. The model will be used to examine the effect of recent changes to precipitation and temperature on snow processes and subsequent streamflow for 2004-2017 season. We will also investigate the hydrologic sensitivity to precipitation and temperature changes by altering input temperature and precipitation. Finally, our findings will point toward future process-based studies and simulated hydrologic responses that can be used to prepare flood hazard maps for cities around Devils Lake.

Characterization and predictability of basin scale SWE distributions using ASO snow depth and SWE retrievals

NASA Astrophysics Data System (ADS)

Bormann, K.; Hedrick, A. R.; Marks, D. G.; Painter, T. H.

2017-12-01

The spatial and temporal distribution of snow water resources (SWE) in the mountains has been examined extensively through the use of models, in-situ networks and remote sensing techniques. However, until the Airborne Snow Observatory (http://aso.jpl.nasa.gov), our understanding of SWE dynamics has been limited due to a lack of well-constrained spatial distributions of SWE in complex terrain, particularly at high elevations and at regional scales (100km+). ASO produces comprehensive snow depth measurements and well-constrained SWE products providing the opportunity to re-examine our current understanding of SWE distributions with a robust and rich data source. We collected spatially-distributed snow depth and SWE data from over 150 individual ASO acquisitions spanning seven basins in California during the five-year operational period of 2013 - 2017. For each of these acquisitions, we characterized the spatial distribution of snow depth and SWE and examined how these distributions changed with time during snowmelt. We compared these distribution patterns between each of the seven basins and finally, examined the predictability of the SWE distributions using statistical extrapolations through both space and time. We compare and contrast these observationally-based characteristics with those from a physically-based snow model to highlight the strengths and weaknesses of the implementation of our understanding of SWE processes in the model environment. In practice, these results may be used to support or challenge our current understanding of mountain SWE dynamics and provide techniques for enhanced evaluation of high-resolution snow models that go beyond in-situ point comparisons. In application, this work may provide guidance on the potential of ASO to guide backfilling of sparse spaceborne measurements of snow depth and snow water equivalent.

Assessment and application of a snowblow modelling approach for identifying enhanced snow accumulation in areas of former glaciation

NASA Astrophysics Data System (ADS)

Mills, Stephanie; Smith, Michael; Le Brocq, Anne; Ardakova, Ekaterina; Hillier, John; Boston, Clare

2016-04-01

The redistribution of snow by wind can play an important role in providing additional mass to the surface of glaciers and can, therefore, have an impact on the glacier's surface mass balance. In areas of marginal glaciation, this local topo-climatic effect may be prove crucial for the initiation and survival of glaciers, whilst it can also increase heterogeneity in the distribution of snow on ice caps and ice sheets. We present a newly developed snowblow model which calculates spatial variations in relative snow accumulation that result from variations in topography. We apply this model to areas of former marginal glaciation in the Brecon Beacons, Wales and an area of former plateau icefield glaciation in the Monadhliath, Scotland. We can then determine whether redistribution by snow can help explain variations in the estimated equilibrium line altitudes (ELAs) of these former glaciers. Specifically, we compare the areas where snow is modelled as accumulating, to the reconstructed glacier surface, which is based on mapped moraines believed to be of Younger Dryas age. The model is applied to 30 m resolution DEMs and potential snow accumulation is simulated from different wind directions in order to determine the most likely contributing sector. Total snow accumulation in sub-set areas is then calculated and compared to the reconstructed glacier area. The results suggest that areas with larger amounts of snow accumulation often correspond with those where the ELA is lower than surrounding glaciers and vice versa, in both the marginal and icefield setting, suggesting that the role of snowblow in supplying additional mass to the surface of glaciers is significant.

Spatio-temporal variability of snow water equivalent in the extra-tropical Andes Cordillera from distributed energy balance modeling and remotely sensed snow cover

NASA Astrophysics Data System (ADS)

Cornwell, E.; Molotch, N. P.; McPhee, J.

2016-01-01

Seasonal snow cover is the primary water source for human use and ecosystems along the extratropical Andes Cordillera. Despite its importance, relatively little research has been devoted to understanding the properties, distribution and variability of this natural resource. This research provides high-resolution (500 m), daily distributed estimates of end-of-winter and spring snow water equivalent over a 152 000 km2 domain that includes the mountainous reaches of central Chile and Argentina. Remotely sensed fractional snow-covered area and other relevant forcings are combined with extrapolated data from meteorological stations and a simplified physically based energy balance model in order to obtain melt-season melt fluxes that are then aggregated to estimate the end-of-winter (or peak) snow water equivalent (SWE). Peak SWE estimates show an overall coefficient of determination R2 of 0.68 and RMSE of 274 mm compared to observations at 12 automatic snow water equivalent sensors distributed across the model domain, with R2 values between 0.32 and 0.88. Regional estimates of peak SWE accumulation show differential patterns strongly modulated by elevation, latitude and position relative to the continental divide. The spatial distribution of peak SWE shows that the 4000-5000 m a.s.l. elevation band is significant for snow accumulation, despite having a smaller surface area than the 3000-4000 m a.s.l. band. On average, maximum snow accumulation is observed in early September in the western Andes, and in early October on the eastern side of the continental divide. The results presented here have the potential of informing applications such as seasonal forecast model assessment and improvement, regional climate model validation, as well as evaluation of observational networks and water resource infrastructure development.

Independent evaluation of the SNODAS snow depth product using regional scale LiDAR-derived measurements

NASA Astrophysics Data System (ADS)

Hedrick, A.; Marshall, H.-P.; Winstral, A.; Elder, K.; Yueh, S.; Cline, D.

2014-06-01

Repeated Light Detection and Ranging (LiDAR) surveys are quickly becoming the de facto method for measuring spatial variability of montane snowpacks at high resolution. This study examines the potential of a 750 km2 LiDAR-derived dataset of snow depths, collected during the 2007 northern Colorado Cold Lands Processes Experiment (CLPX-2), as a validation source for an operational hydrologic snow model. The SNOw Data Assimilation System (SNODAS) model framework, operated by the US National Weather Service, combines a physically-based energy-and-mass-balance snow model with satellite, airborne and automated ground-based observations to provide daily estimates of snowpack properties at nominally 1 km resolution over the coterminous United States. Independent validation data is scarce due to the assimilating nature of SNODAS, compelling the need for an independent validation dataset with substantial geographic coverage. Within twelve distinctive 500 m × 500 m study areas located throughout the survey swath, ground crews performed approximately 600 manual snow depth measurements during each of the CLPX-2 LiDAR acquisitions. This supplied a dataset for constraining the uncertainty of upscaled LiDAR estimates of snow depth at the 1 km SNODAS resolution, resulting in a root-mean-square difference of 13 cm. Upscaled LiDAR snow depths were then compared to the SNODAS-estimates over the entire study area for the dates of the LiDAR flights. The remotely-sensed snow depths provided a more spatially continuous comparison dataset and agreed more closely to the model estimates than that of the in situ measurements alone. Finally, the results revealed three distinct areas where the differences between LiDAR observations and SNODAS estimates were most drastic, suggesting natural processes specific to these regions as causal influences on model uncertainty.

Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements

NASA Astrophysics Data System (ADS)

Hedrick, A.; Marshall, H.-P.; Winstral, A.; Elder, K.; Yueh, S.; Cline, D.

2015-01-01

Repeated light detection and ranging (lidar) surveys are quickly becoming the de facto method for measuring spatial variability of montane snowpacks at high resolution. This study examines the potential of a 750 km2 lidar-derived data set of snow depths, collected during the 2007 northern Colorado Cold Lands Processes Experiment (CLPX-2), as a validation source for an operational hydrologic snow model. The SNOw Data Assimilation System (SNODAS) model framework, operated by the US National Weather Service, combines a physically based energy-and-mass-balance snow model with satellite, airborne and automated ground-based observations to provide daily estimates of snowpack properties at nominally 1 km resolution over the conterminous United States. Independent validation data are scarce due to the assimilating nature of SNODAS, compelling the need for an independent validation data set with substantial geographic coverage. Within 12 distinctive 500 × 500 m study areas located throughout the survey swath, ground crews performed approximately 600 manual snow depth measurements during each of the CLPX-2 lidar acquisitions. This supplied a data set for constraining the uncertainty of upscaled lidar estimates of snow depth at the 1 km SNODAS resolution, resulting in a root-mean-square difference of 13 cm. Upscaled lidar snow depths were then compared to the SNODAS estimates over the entire study area for the dates of the lidar flights. The remotely sensed snow depths provided a more spatially continuous comparison data set and agreed more closely to the model estimates than that of the in situ measurements alone. Finally, the results revealed three distinct areas where the differences between lidar observations and SNODAS estimates were most drastic, providing insight into the causal influences of natural processes on model uncertainty.

Improving the Terrain-Based Parameter for the Assessment of Snow Redistribution in the Col du Lac Blanc Area and Comparisons with TLS Snow Depth Data

NASA Astrophysics Data System (ADS)

Schön, Peter; Prokop, Alexander; Naaim-Bouvet, Florence; Nishimura, Kouichi; Vionnet, Vincent; Guyomarc'h, Gilbert

2014-05-01

Wind and the associated snow drift are dominating factors determining the snow distribution and accumulation in alpine areas, resulting in a high spatial variability of snow depth that is difficult to evaluate and quantify. The terrain-based parameter Sx characterizes the degree of shelter or exposure of a grid point provided by the upwind terrain, without the computational complexity of numerical wind field models. The parameter has shown to qualitatively predict snow redistribution with good reproduction of spatial patterns, but has failed to quantitatively describe the snow redistribution, and correlations with measured snow heights were poor. The objective of our research was to a) identify the sources of poor correlations between predicted and measured snow re-distribution and b) improve the parameters ability to qualitatively and quantitatively describe snow redistribution in our research area, the Col du Lac Blanc in the French Alps. The area is at an elevation of 2700 m and particularly suited for our study due to its constant wind direction and the availability of data from a meteorological station. Our work focused on areas with terrain edges of approximately 10 m height, and we worked with 1-2 m resolution digital terrain and snow surface data. We first compared the results of the terrain-based parameter calculations to measured snow-depths, obtained by high-accuracy terrestrial laser scan measurements. The results were similar to previous studies: The parameter was able to reproduce observed patterns in snow distribution, but regression analyses showed poor correlations between terrain-based parameter and measured snow-depths. We demonstrate how the correlations between measured and calculated snow heights improve if the parameter is calculated based on a snow surface model instead of a digital terrain model. We show how changing the parameter's search distance and how raster re-sampling and raster smoothing improve the results. To improve the parameter's quantitative abilities, we modified the parameter, based on the comparisons with TLS data and the terrain and wind conditions specific to the research site. The modification is in a linear form f(x) = a * Sx, where a is a newly introduced parameter; f(x) yields the estimates for the snow height. We found that the parameter depends on the time period between the compared snow surfaces and the intensity of drifting snow events, which are linked to wind velocities. At the Col du Lac Blanc test side, blowing snow flux is recorded with snow particle counters (SPC). Snow flux is the number of drifting snow particles per time and area. Hence, the SPC provide data about the duration and intensity of drifting snow events, two important factors not accounted for by the terrain parameter Sx. We analyse how the SPC snow flux data can be used to estimate the magnitude of the new variable parameter a. We could improve the parameters' correlations with measured snow heights and its ability to quantitatively describe snow distribution in the Col du Lac Blanc area. We believe that our work is also a prerequisite to further improve the parameter's ability to describe snow redistribution.

MODIS Snow Cover Mapping Decision Tree Technique: Snow and Cloud Discrimination

NASA Technical Reports Server (NTRS)

Riggs, George A.; Hall, Dorothy K.

2010-01-01

Accurate mapping of snow cover continues to challenge cryospheric scientists and modelers. The Moderate-Resolution Imaging Spectroradiometer (MODIS) snow data products have been used since 2000 by many investigators to map and monitor snow cover extent for various applications. Users have reported on the utility of the products and also on problems encountered. Three problems or hindrances in the use of the MODIS snow data products that have been reported in the literature are: cloud obscuration, snow/cloud confusion, and snow omission errors in thin or sparse snow cover conditions. Implementation of the MODIS snow algorithm in a decision tree technique using surface reflectance input to mitigate those problems is being investigated. The objective of this work is to use a decision tree structure for the snow algorithm. This should alleviate snow/cloud confusion and omission errors and provide a snow map with classes that convey information on how snow was detected, e.g. snow under clear sky, snow tinder cloud, to enable users' flexibility in interpreting and deriving a snow map. Results of a snow cover decision tree algorithm are compared to the standard MODIS snow map and found to exhibit improved ability to alleviate snow/cloud confusion in some situations allowing up to about 5% increase in mapped snow cover extent, thus accuracy, in some scenes.

Snow Depth Mapping at a Basin-Wide Scale in the Western Arctic Using UAS Technology

NASA Astrophysics Data System (ADS)

de Jong, T.; Marsh, P.; Mann, P.; Walker, B.

2015-12-01

Assessing snow depths across the Arctic has proven to be extremely difficult due to the variability of snow depths at scales from metres to 100's of metres. New Unmanned Aerial Systems (UAS) technology provides the possibility to obtain centimeter level resolution imagery (~3cm), and to create Digital Surface Models (DSM) based on the Structure from Motion method. However, there is an ongoing need to quantify the accuracy of this method over different terrain and vegetation types across the Arctic. In this study, we used a small UAS equipped with a high resolution RGB camera to create DSMs over a 1 km2 watershed in the western Canadian Arctic during snow (end of winter) and snow-free periods. To improve the image georeferencing, 15 Ground Control Points were marked across the watershed and incorporated into the DSM processing. The summer DSM was subtracted from the snowcovered DSM to deliver snow depth measurements across the entire watershed. These snow depth measurements were validated by over 2000 snow depth measurements. This technique has the potential to improve larger scale snow depth mapping across watersheds by providing snow depth measurements at a ~3 cm . The ability of mapping both shallow snow (less than 75cm) covering much of the basin and snow patches (up to 5 m in depth) that cover less than 10% of the basin, but contain a significant portion of total basin snowcover, is important for both water resource applications, as well as for testing snow models.

Remote Sensing of Snow Cover. Section; Snow Extent

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Frei, Allan; Drey, Stephen J.

2012-01-01

Snow was easily identified in the first image obtained from the Television Infrared Operational Satellite-1 (TIROS-1) weather satellite in 1960 because the high albedo of snow presents a good contrast with most other natural surfaces. Subsequently, the National Oceanic and Atmospheric Administration (NOAA) began to map snow using satellite-borne instruments in 1966. Snow plays an important role in the Earth s energy balance, causing more solar radiation to be reflected back into space as compared to most snow-free surfaces. Seasonal snow cover also provides a critical water resource through meltwater emanating from rivers that originate from high-mountain areas such as the Tibetan Plateau. Meltwater from mountain snow packs flows to some of the world s most densely-populated areas such as Southeast Asia, benefiting over 1 billion people (Immerzeel et al., 2010). In this section, we provide a brief overview of the remote sensing of snow cover using visible and near-infrared (VNIR) and passive-microwave (PM) data. Snow can be mapped using the microwave part of the electromagnetic spectrum, even in darkness and through cloud cover, but at a coarser spatial resolution than when using VNIR data. Fusing VNIR and PM algorithms to produce a blended product offers synergistic benefits. Snow-water equivalent (SWE), snow extent, and melt onset are important parameters for climate models and for the initialization of atmospheric forecasts at daily and seasonal time scales. Snowmelt data are also needed as input to hydrological models to improve flood control and irrigation management.

Refreezing on the Greenland ice sheet: a model comparison

NASA Astrophysics Data System (ADS)

Steger, Christian; Reijmer, Carleen; van den Broeke, Michiel; Ligtenberg, Stefan; Kuipers Munneke, Peter; Noël, Brice

2016-04-01

Mass loss of the Greenland ice sheet (GrIS) is an important contributor to global sea level rise. Besides calving, surface melt is the dominant source of mass loss. However, only part of the surface melt leaves the ice sheet as runoff whereas the other part percolates into the snow cover and refreezes. Due to this process, part of the meltwater is (intermediately) stored. Refreezing thus impacts the surface mass balance of the ice sheet but it also affects the vertical structure of the snow cover due to transport of mass and energy. Due to the sparse availability of in situ data and the demand of future projections, it is inevitable to use numerical models to simulate refreezing and related processes. Currently, the magnitude of refrozen mass is neither well constrained nor well validated. In this study, we model the snow and firn layer, and compare refreezing on the GrIS as modelled with two different numerical models. Both models are forced with meteorological data from the regional climate model RACMO 2 that has been shown to simulate realistic conditions for Greenland. One model is the UU/IMAU firn densification model (FDM) that can be used both in an on- and offline mode with RACMO 2. The other model is SNOWPACK; a model originally designed to simulate seasonal snow cover in alpine conditions. In contrast to FDM, SNOWPACK accounts for snow metamorphism and microstructure and contains a more physically based snow densification scheme. A first comparison of the models indicates that both seem to be able to capture the general spatial and temporal pattern of refreezing. Spatially, refreezing occurs mostly in the ablation zone and decreases in the accumulation zone towards the interior of the ice sheet. Below the equilibrium line altitude (ELA) where refreezing occurs in seasonal snow cover on bare ice, the storage effect is only intermediate. Temporal patterns on a seasonal range indicate two peaks in refreezing; one at the beginning of the melt season where water infiltrates the cold snow pack and one in early winter where the penetration of the cold surface temperature refreezes the retained liquid water. However, the model comparison reveals differences especially close to the equilibrium line where refreezing and runoff seem to be highly sensitive to the exact model formulation and fresh snow density initialization. Furthermore, SNOWPACK's densification scheme generally underestimates densification rates in case of high overburden pressure.

Observation and analysis of water inherent optical properties

NASA Astrophysics Data System (ADS)

Sun, Deyong; Li, Yunmei; Le, Chengfeng; Huang, Changchun

2008-03-01

Inherent optical property is an important part of water optical properties, and is the foundation of water color analytical model establishment. Through quantity filter technology (QFT) and backscattering meter BB9 (WETlabs Inc), absorption coefficients of CDOM, total suspended minerals and backscattering coefficients of total suspended minerals had been observed in Meiliang Bay of Taihu lake at summer and winter respectively. After analyzing the spectral characteristics of absorption and backscattering coefficients, the differences between two seasons had been illustrated adequately, and the reasons for the phenomena, which are related to the changes of water quality coefficient, had also been explained. So water environment states can be reflected by inherent optical properties. In addition, the relationship models between backscattering coefficients and suspended particle concentrations had been established, which can support coefficients for analytical models.

Towards a well-founded and reproducible snow load map for Austria

NASA Astrophysics Data System (ADS)

Winkler, Michael; Schellander, Harald

2017-04-01

"EN 1991-1-3 Eurocode 1: Part 1-3: Snow Loads" provides standard for the determination of the snow load to be used for the structural design of buildings etc. Since 2006 national specifications for Austria define a snow load map with four "load zones", allowing the calculation of the characteristic ground snow load sk for locations below 1500 m asl. A quadratic regression between altitude and sk is used, as suggested by EN 1991-1-3. The actual snow load map is based on best meteorological practice, but still it is somewhat subjective and non-reproducible. Underlying snow data series often end in the 1980s; in the best case data until about 2005 is used. Moreover, extreme value statistics only rely on the Gumbel distribution and the way in which snow depths are converted to snow loads is generally unknown. This might be enough reasons to rethink the snow load standard for Austria, all the more since today's situation is different to what it was some 15 years ago: Firstly, Austria is rich of multi-decadal, high quality snow depth measurements. These data are not well represented in the actual standard. Secondly, semi-empirical snow models allow sufficiently precise calculations of snow water equivalents and snow loads from snow depth measurements without the need of other parameters like temperature etc. which often are not available at the snow measurement sites. With the help of these tools, modelling of daily snow load series from daily snow depth measurements is possible. Finally, extreme value statistics nowadays offers convincing methods to calculate snow depths and loads with a return period of 50 years, which is the base of sk, and allows reproducible spatial extrapolation. The project introduced here will investigate these issues in order to update the Austrian snow load standard by providing a well-founded and reproducible snow load map for Austria. Not least, we seek for contact with standards bodies of neighboring countries to find intersections as well as to avoid inconsistencies and duplications of effort.

Physics-based distributed snow models in the operational arena: Current and future challenges

NASA Astrophysics Data System (ADS)

Winstral, A. H.; Jonas, T.; Schirmer, M.; Helbig, N.

2017-12-01

The demand for modeling tools robust to climate change and weather extremes along with coincident increases in computational capabilities have led to an increase in the use of physics-based snow models in operational applications. Current operational applications include the WSL-SLF's across Switzerland, ASO's in California, and USDA-ARS's in Idaho. While the physics-based approaches offer many advantages there remain limitations and modeling challenges. The most evident limitation remains computation times that often limit forecasters to a single, deterministic model run. Other limitations however remain less conspicuous amidst the assumptions that these models require little to no calibration based on their foundation on physical principles. Yet all energy balance snow models seemingly contain parameterizations or simplifications of processes where validation data are scarce or present understanding is limited. At the research-basin scale where many of these models were developed these modeling elements may prove adequate. However when applied over large areas, spatially invariable parameterizations of snow albedo, roughness lengths and atmospheric exchange coefficients - all vital to determining the snowcover energy balance - become problematic. Moreover as we apply models over larger grid cells, the representation of sub-grid variability such as the snow-covered fraction adds to the challenges. Here, we will demonstrate some of the major sensitivities of distributed energy balance snow models to particular model constructs, the need for advanced and spatially flexible methods and parameterizations, and prompt the community for open dialogue and future collaborations to further modeling capabilities.

Retrieval of Snow Properties for Ku- and Ka-band Dual-Frequency Radar

NASA Technical Reports Server (NTRS)

Liao, Liang; Meneghini, Robert; Tokay, Ali; Bliven, Larry F.

2016-01-01

The focus of this study is on the estimation of snow microphysical properties and the associated bulk parameters such as snow water content and water equivalent snowfall rate for Ku- and Ka-band dual-frequency radar. This is done by exploring a suitable scattering model and the proper particle size distribution (PSD) assumption that accurately represent, in the electromagnetic domain, the micro/macro-physical properties of snow. The scattering databases computed from simulated aggregates for small-to-moderate particle sizes are combined with a simple scattering model for large particle sizes to characterize snow scattering properties over the full range of particle sizes. With use of the single-scattering results, the snow retrieval lookup tables can be formed in a way that directly links the Ku- and Ka-band radar reflectivities to snow water content and equivalent snowfall rate without use of the derived PSD parameters. A sensitivity study of the retrieval results to the PSD and scattering models is performed to better understand the dual-wavelength retrieval uncertainties. To aid in the development of the Ku- and Ka-band dual-wavelength radar technique and to further evaluate its performance, self-consistency tests are conducted using measurements of the snow PSD and fall velocity acquired from the Snow Video Imager Particle Image Probe (SVIPIP) duringthe winter of 2014 at the NASA Wallops Flight Facility site in Wallops Island, Virginia.

Retrieval of Snow Properties for Ku- and Ka-Band Dual-Frequency Radar

NASA Technical Reports Server (NTRS)

Liao, Liang; Meneghini, Robert; Tokay, Ali; Bliven, Larry F.

2016-01-01

The focus of this study is on the estimation of snow microphysical properties and the associated bulk parameters such as snow water content and water equivalent snowfall rate for Ku- and Ka-band dual-frequency radar. This is done by exploring a suitable scattering model and the proper particle size distribution (PSD) assumption that accurately represent, in the electromagnetic domain, the micro-macrophysical properties of snow. The scattering databases computed from simulated aggregates for small-to-moderate particle sizes are combined with a simple scattering model for large particle sizes to characterize snow-scattering properties over the full range of particle sizes. With use of the single-scattering results, the snow retrieval lookup tables can be formed in a way that directly links the Ku- and Ka-band radar reflectivities to snow water content and equivalent snowfall rate without use of the derived PSD parameters. A sensitivity study of the retrieval results to the PSD and scattering models is performed to better understand the dual-wavelength retrieval uncertainties. To aid in the development of the Ku- and Ka-band dual-wavelength radar technique and to further evaluate its performance, self-consistency tests are conducted using measurements of the snow PSD and fall velocity acquired from the Snow Video Imager Particle Image Probe (SVIPIP) during the winter of 2014 at the NASA Wallops Flight Facility site in Wallops Island, Virginia.

Multiwavelength Comparison of Modeled and Measured Remote Tropospheric Aerosol Backscatter Over Pacific Ocean

NASA Technical Reports Server (NTRS)

Cutten, D. R.; Pueschel, R. F.; Srivastava, V.; Clarke, A. D.; Rothermel, J.; Spinhirne, J. D.; Menzies, R. T.

1996-01-01

Aerosol concentrations and size distributions in the middle and upper troposphere over the remote Pacific Ocean were measured with a forward scattering spectrometer probe (FSSP) on the NASA DC-8 aircraft during NASA's Global Backscatter Experiment (GLOBE) in May-June 1990. The FSSP size channels were recalibrated based on refractive index estimates from flight-level aerosol volatility measurements with a collocated laser optical particle counter (LOPC). The recalibrated FSSP size distributions were averaged over 100-s intervals, fitted with lo-normal distributions and used to calculate aerosol backscatter coefficients at selected wavelengths. The FSSP-derived backscatter estimates were averaged over 300-s intervals to reduce large random fluctuations. The smoothed FSSP aerosol backscatter coefficients were then compared with LOPC-derived backscatter values and with backscatter measured at or near flight level from four lidar systems operating at 0.53, 1.06, 9.11, 9.25, and 10.59 micrometers. Agreement between FSSP-derived and lidar-measured backscatter was generally best at flight level in homogeneous aerosol fields and at high backscatter values. FSSP data often underestimated low backscatter values especially at the longer wavelengths due to poor counting statistics for larger particles (greater than 0.8 micrometers diameter) that usually dominate aerosol backscatter at these wavelengths. FSSP data also underestimated backscatter at shorter wavelengths when particles smaller than the FSSP lower cutoff diameter (0.35 micrometers) made significant contributions to the total backscatter.

California's Snow Gun and its implications for mass balance predictions under greenhouse warming

NASA Astrophysics Data System (ADS)

Howat, I.; Snyder, M.; Tulaczyk, S.; Sloan, L.

2003-12-01

Precipitation has received limited treatment in glacier and snowpack mass balance models, largely due to the poor resolution and confidence of precipitation predictions relative to temperature predictions derived from atmospheric models. Most snow and glacier mass balance models rely on statistical or lapse rate-based downscaling of general or regional circulation models (GCM's and RCM's), essentially decoupling sub-grid scale, orographically-driven evolution of atmospheric heat and moisture. Such models invariably predict large losses in the snow and ice volume under greenhouse warming. However, positive trends in the mass balance of glaciers in some warming maritime climates, as well as at high elevations of the Greenland Ice Sheet, suggest that increased precipitation may play an important role in snow- and glacier-climate interactions. Here, we present a half century of April snowpack data from the Sierra Nevada and Cascade mountains of California, USA. This high-density network of snow-course data indicates that a gain in winter snow accumulation at higher elevations has compensated loss in snow volume at lower elevations by over 50% and has led to glacier expansion on Mt. Shasta. These trends are concurrent with a region-wide increase in winter temperatures up to 2° C. They result from the orographic lifting and saturation of warmer, more humid air leading to increased precipitation at higher elevations. Previous studies have invoked such a "Snow Gun" effect to explain contemporaneous records of Tertiary ocean warming and rapid glacial expansion. A climatological context of the California's "snow gun" effect is elucidated by correlation between the elevation distribution of April SWE observations and the phase of the Pacific Decadal Oscillation and the El Nino Southern Oscillation, both controlling the heat and moisture delivered to the U.S. Pacific coast. The existence of a significant "Snow Gun" effect presents two challenges to snow and glacier mass balance modeling. Firstly, the link between amplification of orographic precipitation and the temporal evolution of ocean-climate oscillations indicates that prediction of future mass balance trends requires consideration of the timing and amplitude of such oscillations. Only recently have ocean-atmosphere models begun to realistically produce such temporal variability. Secondly, the steepening snow mass-balance elevation-gradient associated with the "Snow Gun" implies greater spatial variability in balance with warming. In a warming climate, orographic processes at a scale finer that the highest resolution RCM (>20km grid) become increasingly important and predictions based on lower elevations become increasingly inaccurate for higher elevations. Therefore, thermodynamic interaction between atmospheric heat, moisture and topography must be included in downscaling techniques. In order to demonstrate the importance of the thermodynamic downscaling in mass balance predictions, we nest a high-resolution (100m grid), coupled Orographic Precipitation and Surface Energy balance Model (OPSEM) into the RegC2.5 RCM (40 km grid) and compare results. We apply this nesting technique to Mt. Shasta, California, an area of high topography (~4000m) relative to its RegCM2.5 grid elevation (1289m). These models compute average April snow volume under present and doubled-present Atmospheric CO2 concentrations. While the RegCM2.5 regional model predicts an 83% decrease in April SWE, OPSEM predicts a 16% increase. These results indicate that thermodynamic interactions between the atmosphere and topography at sub- RCM grid resolution must be considered in mass balance models.

Application of Snowfall and Wind Statistics to Snow Transport Modeling for Snowdrift Control in Minnesota.

NASA Astrophysics Data System (ADS)

Shulski, Martha D.; Seeley, Mark W.

2004-11-01

Models were utilized to determine the snow accumulation season (SAS) and to quantify windblown snow for the purpose of snowdrift control for locations in Minnesota. The models require mean monthly temperature, snowfall, density of snow, and wind frequency distribution statistics. Temperature and precipitation data were obtained from local cooperative observing sites, and wind data came from Automated Surface Observing System (ASOS)/Automated Weather Observing System (AWOS) sites in the region. The temperature-based algorithm used to define the SAS reveals a geographic variability in the starting and ending dates of the season, which is determined by latitude and elevation. Mean seasonal snowfall shows a geographic distribution that is affected by topography and proximity to Lake Superior. Mean snowfall density also exhibits variability, with lower-density snow events displaced to higher-latitude positions. Seasonal wind frequencies show a strong bimodal distribution with peaks from the northwest and southeast vector direction, with an exception for locations in close proximity to the Lake Superior shoreline. In addition, for western and south-central Minnesota there is a considerably higher frequency of wind speeds above the mean snow transport threshold of 7 m s-1. As such, this area is more conducive to higher potential snow transport totals. Snow relocation coefficients in this area are in the range of 0.4 0.9, and, according to the empirical models used in this analysis, this range implies that actual snow transport is 40% 90% of the total potential in south-central and western areas of the state.

SWANN: The Snow Water Artificial Neural Network Modelling System

NASA Astrophysics Data System (ADS)

Broxton, P. D.; van Leeuwen, W.; Biederman, J. A.

2017-12-01

Snowmelt from mountain forests is important for water supply and ecosystem health. Along Arizona's Mogollon Rim, snowmelt contributes to rivers and streams that provide a significant water supply for hydro-electric power generation, agriculture, and human consumption in central Arizona. In this project, we are building a snow monitoring system for the Salt River Project (SRP), which supplies water and power to millions of customers in the Phoenix metropolitan area. We are using process-based hydrological models and artificial neural networks (ANNs) to generate information about both snow water equivalent (SWE) and snow cover. The snow-cover data is generated with ANNs that are applied to Landsat and MODIS satellite reflectance data. The SWE data is generated using a combination of gridded SWE estimates generated by process-based snow models and ANNs that account for variations in topography, forest cover, and solar radiation. The models are trained and evaluated with snow data from SNOTEL stations as well as from aerial LiDAR and field data that we collected this past winter in northern Arizona, as well as with similar data from other sites in the Southwest US. These snow data are produced in near-real time, and we have built a prototype decision support tool to deliver them to SRP. This tool is designed to provide daily-to annual operational monitoring of spatial and temporal changes in SWE and snow cover conditions over the entire Salt River Watershed (covering 17,000 km2), and features advanced web mapping capabilities and watershed analytics displayed as graphical data.

Role of Tibetan Buddhist monasteries in snow leopard conservation.

PubMed

Li, Juan; Wang, Dajun; Yin, Hang; Zhaxi, Duojie; Jiagong, Zhala; Schaller, George B; Mishra, Charudutt; McCarthy, Thomas M; Wang, Hao; Wu, Lan; Xiao, Lingyun; Basang, Lamao; Zhang, Yuguang; Zhou, Yunyun; Lu, Zhi

2014-02-01

The snow leopard (Panthera uncia) inhabits the rugged mountains in 12 countries of Central Asia, including the Tibetan Plateau. Due to poaching, decreased abundance of prey, and habitat degradation, it was listed as endangered by the International Union for Conservation of Nature in 1972. Current conservation strategies, including nature reserves and incentive programs, have limited capacities to protect snow leopards. We investigated the role of Tibetan Buddhist monasteries in snow leopard conservation in the Sanjiangyuan region in China's Qinghai Province on the Tibetan Plateau. From 2009 to 2011, we systematically surveyed snow leopards in the Sanjiangyuan region. We used the MaxEnt model to determine the relation of their presence to environmental variables (e.g., elevation, ruggedness) and to predict snow leopard distribution. Model results showed 89,602 km(2) of snow leopard habitat in the Sanjiangyuan region, of which 7674 km(2) lay within Sanjiangyuan Nature Reserve's core zones. We analyzed the spatial relation between snow leopard habitat and Buddhist monasteries and found that 46% of monasteries were located in snow leopard habitat and 90% were within 5 km of snow leopard habitat. The 336 monasteries in the Sanjiangyuan region could protect more snow leopard habitat (8342 km(2) ) through social norms and active patrols than the nature reserve's core zones. We conducted 144 household interviews to identify local herders' attitudes and behavior toward snow leopards and other wildlife. Most local herders claimed that they did not kill wildlife, and 42% said they did not kill wildlife because it was a sin in Buddhism. Our results indicate monasteries play an important role in snow leopard conservation. Monastery-based snow leopard conservation could be extended to other Tibetan Buddhist regions that in total would encompass about 80% of the global range of snow leopards. © 2013 Society for Conservation Biology.

Estimating Snow Water Storage in North America Using CLM4, DART, and Snow Radiance Data Assimilation

NASA Technical Reports Server (NTRS)

Kwon, Yonghwan; Yang, Zong-Liang; Zhao, Long; Hoar, Timothy J.; Toure, Ally M.; Rodell, Matthew

2016-01-01

This paper addresses continental-scale snow estimates in North America using a recently developed snow radiance assimilation (RA) system. A series of RA experiments with the ensemble adjustment Kalman filter are conducted by assimilating the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) brightness temperature T(sub B) at 18.7- and 36.5-GHz vertical polarization channels. The overall RA performance in estimating snow depth for North America is improved by simultaneously updating the Community Land Model, version 4 (CLM4), snow/soil states and radiative transfer model (RTM) parameters involved in predicting T(sub B) based on their correlations with the prior T(sub B) (i.e., rule-based RA), although degradations are also observed. The RA system exhibits a more mixed performance for snow cover fraction estimates. Compared to the open-loop run (0.171m RMSE), the overall snow depth estimates are improved by 1.6% (0.168m RMSE) in the rule-based RA whereas the default RA (without a rule) results in a degradation of 3.6% (0.177mRMSE). Significant improvement of the snow depth estimates in the rule-based RA as observed for tundra snow class (11.5%, p < 0.05) and bare soil land-cover type (13.5%, p < 0.05). However, the overall improvement is not significant (p = 0.135) because snow estimates are degraded or marginally improved for other snow classes and land covers, especially the taiga snow class and forest land cover (7.1% and 7.3% degradations, respectively). The current RA system needs to be further refined to enhance snow estimates for various snow types and forested regions.

On Clear-Cut Mapping with Time-Series of Sentinel-1 Data in Boreal Forest

NASA Astrophysics Data System (ADS)

Rauste, Yrjo; Antropov, Oleg; Mutanen, Teemu; Hame, Tuomas

2016-08-01

Clear-cutting is the most drastic and wide-spread change that affects the hydrological and carbon-balance proper- ties of forested land in the Boreal forest zone1.A time-series of 36 Sentinel-1 images was used to study the potential for mapping clear-cut areas. The time series covered one and half year (2014-10-09 ... 2016-03-20) in a 200-km-by-200-km study site in Finland. The Sentinel- 1 images were acquired in Interferometric Wide-swath (IW), dual-polarized mode (VV+VH). All scenes were acquired in the same orbit configuration. Amplitude im- ages (GRDH product) were used. The Sentinel-1 scenes were ortho-rectified with in-house software using a digi- tal elevation model (DEM) produced by the Land Survey of Finland. The Sentinel-1 amplitude data were radio- metrically corrected for topographic effects.The temporal behaviour of C-band backscatter was stud- ied for areas representing 1) areas clear-cut during the ac- quisition of the Sentinel-1 time-series, 2) areas remaining forest during the acquisition of the Sentinel-1 time-series, and 3) areas that had been clear-cut before the acquisition of the Sentinel-1 time-series.The following observations were made:1. The separation between clear-cut areas and forest was generally low;2. Under certain acquisition conditions, clear-cut areas were well separable from forest;3. The good scenes were acquired: 1) in winter during thick snow cover, and 2) in late summer towards the end of a warm and dry period;4. The separation between clear-cut and forest was higher in VH polarized data than in VV-polarized data.5. The separation between clear-cut and forest was higher in the winter/snow scenes than in the dry summer scenes.

A Citizen Science Campaign to Validate Snow Remote-Sensing Products

NASA Astrophysics Data System (ADS)

Wikstrom Jones, K.; Wolken, G. J.; Arendt, A. A.; Hill, D. F.; Crumley, R. L.; Setiawan, L.; Markle, B.

2017-12-01

The ability to quantify seasonal water retention and storage in mountain snow packs has implications for an array of important topics, including ecosystem function, water resources, hazard mitigation, validation of remote sensing products, climate modeling, and the economy. Runoff simulation models, which typically rely on gridded climate data and snow remote sensing products, would be greatly improved if uncertainties in estimates of snow depth distribution in high-elevation complex terrain could be reduced. This requires an increase in the spatial and temporal coverage of observational snow data in high-elevation data-poor regions. To this end, we launched Community Snow Observations (CSO). Participating citizen scientists use Mountain Hub, a multi-platform mobile and web-based crowdsourcing application that allows users to record, submit, and instantly share geo-located snow depth, snow water equivalence (SWE) measurements, measurement location photos, and snow grain information with project scientists and other citizen scientists. The snow observations are used to validate remote sensing products and modeled snow depth distribution. The project's prototype phase focused on Thompson Pass in south-central Alaska, an important infrastructure corridor that includes avalanche terrain and the Lowe River drainage and is essential to the City of Valdez and the fisheries of Prince William Sound. This year's efforts included website development, expansion of the Mountain Hub tool, and recruitment of citizen scientists through a combination of social media outreach, community presentations, and targeted recruitment of local avalanche professionals. We also conducted two intensive field data collection campaigns that coincided with an aerial photogrammetric survey. With more than 400 snow depth observations, we have generated a new snow remote-sensing product that better matches actual SWE quantities for Thompson Pass. In the next phase of the citizen science portion of this project we will focus on expanding our group of participants to a larger geographic area in Alaska, further develop our partnership with Mountain Hub, and build relationships in new communities as we conduct a photogrammetric survey in a different region next year.

Geomatics contributions to key indicators for estimation and monitoring of snow cover input to hydrogeological resources

NASA Astrophysics Data System (ADS)

Somma, J.; Drapeau, L.; Abou Chakra, C.; El-Ali, T.

2014-12-01

Climate change is a subject of concern for the inhabitants of the semi-arid zones because water needs are greatly increasing with population growth. For the Middle East region, the karstic geology of Lebanon with its high and steep mountains makes it a real water tower and promotes an essential snow cover. Studies carried out on snow water equivalent reserve [1] remain still insufficient for the development of continuous monitoring. Modeling the lebanese high plateau made of sinkholes and undulations eases the computations of land capacity for snow retention. It is therefore an interesting testing ground for snow volumes calculations [2]. To improve previous attempts, a research project focuses on snow melting processes. It uses the cessation date of snow melt water infiltration which is crucial in the precocity or the delay of low water level [3]; and geomatics to determinate the major factor for the evaluation of storaged water (spatial or vertical extension of snow cover). The project studies the sensitivity of temporal snow melting variabilities to quantities of snow precipitations and climatic conditions. Field measurements were collected at very high topographic precision [4] in a specific sinkhole and were used to create volumes models for measuring indicators such as: snow water equivalent; melting speed in relation to climatic data; forecast of completed meting date; correlations with springs discharges. Other methodological procedures take into account snow depressions (sinkholes and ripples) capacity retention; daily webcam images to monitor the accumulation and melt rate and remotely sensed Pleiades stereoscopic images to create snow cover elevation model at the time of acquisition. [1]Corbane et al., 2004 ; 2005 ; Corbane, 2002 ; Bernier et al., 2001, 2003 ; Shaban et al., 2004; Aouad et al., 2004, Aouad-Rizk et al., 2005 ; Gédéon el al., 2004 [2] Somma et al ; 2014 [3] Drapeau et al ; 2013 [4] Drapeau et al, 2013; Somma et Drapeau, 2011 ; Somma et Luxey, 2006

General Report of the Researches of Snowpack Properties, Snowmelt Runoff and Evapotranspiration in Japan

NASA Technical Reports Server (NTRS)

Takeda, K.

1985-01-01

A method was developed for estimating the distribution of snow and the snow water equivalent in Japan by combining LANDSAT data with the degree day method. A snow runoff model was improved and applied to the Okutadami River basin. The Martinec Rango model from the U.S. was applied to Japanese river basins to verify its applicability. This model was then compared with the Japanese model. Analysis of microwave measurements obtained by a radiometer on a tower over dry snow in Hokkaido indicate a certain correlation between brightness temperature and snowpack properties. A correlation between brightness temperature and depth of dry snow in an inland plain area was revealed in NIMBUS SMMR data obtained from the U.S. Calculation of evaporation using airborne remote sensing data and a Priestley-Taylor type of equation shows that the differentiation of evaporation with vegetation type is not remarkable because of little evapotransportation in winter.

A Mass Diffusion Model for Dry Snow Utilizing a Fabric Tensor to Characterize Anisotropy

NASA Astrophysics Data System (ADS)

Shertzer, Richard H.; Adams, Edward E.

2018-03-01

A homogenization algorithm for randomly distributed microstructures is applied to develop a mass diffusion model for dry snow. Homogenization is a multiscale approach linking constituent behavior at the microscopic level—among ice and air—to the macroscopic material—snow. Principles of continuum mechanics at the microscopic scale describe water vapor diffusion across an ice grain's surface to the air-filled pore space. Volume averaging and a localization assumption scale up and down, respectively, between microscopic and macroscopic scales. The model yields a mass diffusivity expression at the macroscopic scale that is, in general, a second-order tensor parameterized by both bulk and microstructural variables. The model predicts a mass diffusivity of water vapor through snow that is less than that through air. Mass diffusivity is expected to decrease linearly with ice volume fraction. Potential anisotropy in snow's mass diffusivity is captured due to the tensor representation. The tensor is built from directional data assigned to specific, idealized microstructural features. Such anisotropy has been observed in the field and laboratories in snow morphologies of interest such as weak layers of depth hoar and near-surface facets.

Fractional Snow Cover Mapping by Artificial Neural Networks and Support Vector Machines

NASA Astrophysics Data System (ADS)

Çiftçi, B. B.; Kuter, S.; Akyürek, Z.; Weber, G.-W.

2017-11-01

Snow is an important land cover whose distribution over space and time plays a significant role in various environmental processes. Hence, snow cover mapping with high accuracy is necessary to have a real understanding for present and future climate, water cycle, and ecological changes. This study aims to investigate and compare the design and use of artificial neural networks (ANNs) and support vector machines (SVMs) algorithms for fractional snow cover (FSC) mapping from satellite data. ANN and SVM models with different model building settings are trained by using Moderate Resolution Imaging Spectroradiometer surface reflectance values of bands 1-7, normalized difference snow index and normalized difference vegetation index as predictor variables. Reference FSC maps are generated from higher spatial resolution Landsat ETM+ binary snow cover maps. Results on the independent test data set indicate that the developed ANN model with hyperbolic tangent transfer function in the output layer and the SVM model with radial basis function kernel produce high FSC mapping accuracies with the corresponding values of R = 0.93 and R = 0.92, respectively.

Numerical Modeling of Coupled Water Flow and Heat Transport in Soil and Snow

NASA Astrophysics Data System (ADS)

Kelleners, T.

2015-12-01

A numerical model is developed to calculate coupled water flow and heat transport in seasonally frozen soil and snow. Both liquid water flow and water vapor flow are included. The effect of dissolved ions on soil water freezing point depression is included by combining an expression for osmotic head with the Clapeyron equation and the van Genuchten soil water retention function. The coupled water flow and heat transport equations are solved using the Thomas algorithm and Picard iteration. Ice pressure is always assumed zero and frost heave is neglected. The new model is tested using data from a high-elevation rangeland soil that is subject to significant soil freezing and a mountainous forest soil that is snow-covered for about 8 months of the year. Soil hydraulic parameters are mostly based on measurements and only vegetation parameters are fine-tuned to match measured and calculated soil water content, soil & snow temperature, and snow height. Modeling statistics for both systems show good performance for temperature, intermediate performance for snow height, and relatively low performance for soil water content, in accordance with earlier results with an older version of the model.

Snow water equivalent in the Alps as seen by gridded data sets, CMIP5 and CORDEX climate models

NASA Astrophysics Data System (ADS)

Terzago, Silvia; von Hardenberg, Jost; Palazzi, Elisa; Provenzale, Antonello

2017-07-01

The estimate of the current and future conditions of snow resources in mountain areas would require reliable, kilometre-resolution, regional-observation-based gridded data sets and climate models capable of properly representing snow processes and snow-climate interactions. At the moment, the development of such tools is hampered by the sparseness of station-based reference observations. In past decades passive microwave remote sensing and reanalysis products have mainly been used to infer information on the snow water equivalent distribution. However, the investigation has usually been limited to flat terrains as the reliability of these products in mountain areas is poorly characterized.This work considers the available snow water equivalent data sets from remote sensing and from reanalyses for the greater Alpine region (GAR), and explores their ability to provide a coherent view of the snow water equivalent distribution and climatology in this area. Further we analyse the simulations from the latest-generation regional and global climate models (RCMs, GCMs), participating in the Coordinated Regional Climate Downscaling Experiment over the European domain (EURO-CORDEX) and in the Fifth Coupled Model Intercomparison Project (CMIP5) respectively. We evaluate their reliability in reproducing the main drivers of snow processes - near-surface air temperature and precipitation - against the observational data set EOBS, and compare the snow water equivalent climatology with the remote sensing and reanalysis data sets previously considered. We critically discuss the model limitations in the historical period and we explore their potential in providing reliable future projections.The results of the analysis show that the time-averaged spatial distribution of snow water equivalent and the amplitude of its annual cycle are reproduced quite differently by the different remote sensing and reanalysis data sets, which in fact exhibit a large spread around the ensemble mean. We find that GCMs at spatial resolutions equal to or finer than 1.25° longitude are in closer agreement with the ensemble mean of satellite and reanalysis products in terms of root mean square error and standard deviation than lower-resolution GCMs. The set of regional climate models from the EURO-CORDEX ensemble provides estimates of snow water equivalent at 0.11° resolution that are locally much larger than those indicated by the gridded data sets, and only in a few cases are these differences smoothed out when snow water equivalent is spatially averaged over the entire Alpine domain. ERA-Interim-driven RCM simulations show an annual snow cycle that is comparable in amplitude to those provided by the reference data sets, while GCM-driven RCMs present a large positive bias. RCMs and higher-resolution GCM simulations are used to provide an estimate of the snow reduction expected by the mid-21st century (RCP 8.5 scenario) compared to the historical climatology, with the main purpose of highlighting the limits of our current knowledge and the need for developing more reliable snow simulations.

Evaluation and Application of Gridded Snow Water Equivalent Products for Improving Snowmelt Flood Predictions in the Red River Basin of the North

NASA Astrophysics Data System (ADS)

Schroeder, R.; Jacobs, J. M.; Vuyovich, C.; Cho, E.; Tuttle, S. E.

2017-12-01

Each spring the Red River basin (RRB) of the North, located between the states of Minnesota and North Dakota and southern Manitoba, is vulnerable to dangerous spring snowmelt floods. Flat terrain, low permeability soils and a lack of satisfactory ground observations of snow pack conditions make accurate predictions of the onset and magnitude of major spring flood events in the RRB very challenging. This study investigated the potential benefit of using gridded snow water equivalent (SWE) products from passive microwave satellite missions and model output simulations to improve snowmelt flood predictions in the RRB using NOAA's operational Community Hydrologic Prediction System (CHPS). Level-3 satellite SWE products from AMSR-E, AMSR2 and SSM/I, as well as SWE computed from Level-2 brightness temperatures (Tb) measurements, including model output simulations of SWE from SNODAS and GlobSnow-2 were chosen to support the snowmelt modeling exercises. SWE observations were aggregated spatially (i.e. to the NOAA North Central River Forecast Center forecast basins) and temporally (i.e. by obtaining daily screened and weekly unscreened maximum SWE composites) to assess the value of daily satellite SWE observations relative to weekly maximums. Data screening methods removed the impacts of snow melt and cloud contamination on SWE and consisted of diurnal SWE differences and a temperature-insensitive polarization difference ratio, respectively. We examined the ability of the satellite and model output simulations to capture peak SWE and investigated temporal accuracies of screened and unscreened satellite and model output SWE. The resulting SWE observations were employed to update the SNOW-17 snow accumulation and ablation model of CHPS to assess the benefit of using temporally and spatially consistent SWE observations for snow melt predictions in two test basins in the RRB.

Multi-scale assimilation of remotely sensed snow observations for hydrologic estimation

NASA Astrophysics Data System (ADS)

Andreadis, K.; Lettenmaier, D.

2008-12-01

Data assimilation provides a framework for optimally merging model predictions and remote sensing observations of snow properties (snow cover extent, water equivalent, grain size, melt state), ideally overcoming limitations of both. A synthetic twin experiment is used to evaluate a data assimilation system that would ingest remotely sensed observations from passive microwave and visible wavelength sensors (brightness temperature and snow cover extent derived products, respectively) with the objective of estimating snow water equivalent. Two data assimilation techniques are used, the Ensemble Kalman filter and the Ensemble Multiscale Kalman filter (EnMKF). One of the challenges inherent in such a data assimilation system is the discrepancy in spatial scales between the different types of snow-related observations. The EnMKF represents the sample model error covariance with a tree that relates the system state variables at different locations and scales through a set of parent-child relationships. This provides an attractive framework to efficiently assimilate observations at different spatial scales. This study provides a first assessment of the feasibility of a system that would assimilate observations from multiple sensors (MODIS snow cover and AMSR-E brightness temperatures) and at different spatial scales for snow water equivalent estimation. The relative value of the different types of observations is examined. Additionally, the error characteristics of both model and observations are discussed.

Sensitivity of Alpine Snow and Streamflow Regimes to Climate Changes

NASA Astrophysics Data System (ADS)

Rasouli, K.; Pomeroy, J. W.; Marks, D. G.; Bernhardt, M.

2014-12-01

Understanding the sensitivity of hydrological processes to climate change in alpine areas with snow dominated regimes is of paramount importance as alpine basins show both high runoff efficiency associated with the melt of the seasonal snowpack and great sensitivity of snow processes to temperature change. In this study, meteorological data measured in a selection of alpine headwaters basins including Reynolds Mountain East, Idaho, USA, Wolf Creek, Yukon in Canada, and Zugspitze Mountain, Germany with climates ranging from arctic to continental temperate were used to study the snow and streamflow sensitivity to climate change. All research sites have detailed multi-decadal meteorological and snow measurements. The Cold Regions Hydrological Modelling platform (CRHM) was used to create a model representing a typical alpine headwater basin discretized into hydrological response units with physically based representations of snow redistribution by wind, complex terrain snowmelt energetics and runoff processes in alpine tundra. The sensitivity of snow hydrology to climate change was investigated by changing air temperature and precipitation using weather generating methods based on the change factors obtained from different climate model projections for future and current periods. The basin mean and spatial variability of peak snow water equivalent, sublimation loss, duration of snow season, snowmelt rates, streamflow peak, and basin discharge were assessed under varying climate scenarios and the most sensitive hydrological mechanisms to the changes in the different alpine climates were detected. The results show that snow hydrology in colder alpine climates is more resilient to warming than that in warmer climates, but that compensatory factors to warming such as reduced blowing snow sublimation loss and reduced melt rate should also be assessed when considering climate change impacts on alpine hydrology.

Black carbon in the atmosphere and deposition on snow, last 130 years

NASA Astrophysics Data System (ADS)

Skeie, R. B.; Berntsen, T.; Myhre, G.; Pedersen, C.; Gerland, S.; Ström, J.; Forsström, S.

2009-04-01

The transport of Black Carbon (BC) in the atmosphere and the deposition of BC on snow surfaces for the last 130 years, with special emphasis on the last 8 years, are modeled with the Oslo CTM2 model. In addition regional contribution to BC deposition on snow in the polar region is evaluated for some years. The model results are compared with observations including our own recent measurement of BC in snow. Radiative forcing due to the direct effect as well as the snow-albedo effect is also calculated. Oslo CTM2 is an offline chemical transport model with T42 horizontal resolution using meteorological data from the IFS model at ECMWF. The scheme for BC includes hydrophilic and hydrophobic particles, as well as emissions from fossil fuel, biofuel and open biomass burning. Data on snow fall, melt and evaporation from ECMWF are used to generate and remove snow layers in each grid box. In these snow layers the amounts of deposited BC are stored, and concentration of BC in each snow layer is calculated. For the period 1870-2000 time slice simulations are done every 10th year. The period is simulated with constant meteorological data for the year 2000-2001 and vertical resolution of 40 levels. The emission data used is from Bond [1] for fossil fuel and biofuel, and data from Ito and Penner [2] for open biomass burning. The period 2000 until present are modeled with real time meteorological data and vertical resolution of 60 levels. Fossil fuel emission data used are the year 2000 data from Bond [1] except for the Asian region where REAS emissions [3] are used. For biomass burning BC emission the GFED data set are used [4]. The results are compared with available BC measurements from ice cores, air and snow. The observed time history of the BC concentration in snow over Greenland, US, and Himalaya is compared to the model results. During the years 2006-2008 several measurements of BC concentrations in snow in the Arctic region have been done, showing significant spatial variability. Within the large spread in the observations of BC concentration in snow, the model gives results that are consistent with the observations. In addition to evaluating total effect of BC in snow and its radiative effects, regional contribution to BC deposition on snow in the Arctic region are calculated. Today China is the region with largest BC fossil fuel emissions. Our results using the Olso CTM2 model show however that it is the 4th region in contribution to BC deposition on snow north of 65 degrees. The largest contributor is Russia, followed by Western Europe and North America. In the historical period, the share of emissions between these regions differs from the present situation. The BC emissions from fossil fuel in North America and Western Europe were respectively 3 and 2 times larger in 1920-30 than the present emissions from these regions. Therefore those regions had a higher contribution to BC in snow in the Arctic region 80 years ago than they have today. References: 1. Bond, T.C., et al., Historical emissions of black and organic carbon aerosol from energy-related combustion, 1850-2000. Global Biogeochemical Cycles, 2007. 21(2): p. 16. 2. Ito, A. and J.E. Penner, Historical emissions of carbonaceous aerosols from biomass and fossil fuel burning for the period 1870-2000. Global Biogeochemical Cycles, 2005. 19(2): p. 14. 3. Ohara, T., et al., An Asian emission inventory of anthropogenic emission sources for the period 1980-2020. Atmospheric Chemistry and Physics, 2007. 7(16): p. 4419-4444. 4. van der Werf, G.R., et al., Interannual variability in global biomass burning emissions from 1997 to 2004. Atmos. Chem. Phys., 2006. 6(11): p. 3423-3441.

Evaluation of gridded snow water equivalent and satellite snow cover products for mountain basins in a hydrologic model

USGS Publications Warehouse

Dressler, K.A.; Leavesley, G.H.; Bales, R.C.; Fassnacht, S.R.

2006-01-01

The USGS precipitation-runoff modelling system (PRMS) hydrologic model was used to evaluate experimental, gridded, 1 km2 snow-covered area (SCA) and snow water equivalent (SWE) products for two headwater basins within the Rio Grande (i.e. upper Rio Grande River basin) and Salt River (i.e. Black River basin) drainages in the southwestern USA. The SCA product was the fraction of each 1 km2 pixel covered by snow and was derived from NOAA advanced very high-resolution radiometer imagery. The SWE product was developed by multiplying the SCA product by SWE estimates interpolated from National Resources Conservation Service snow telemetry point measurements for a 6 year period (1995-2000). Measured SCA and SWE estimates were consistently lower than values estimated from temperature and precipitation within PRMS. The greatest differences occurred in the relatively complex terrain of the Rio Grande basin, as opposed to the relatively homogeneous terrain of the Black River basin, where differences were small. Differences between modelled and measured snow were different for the accumulation period versus the ablation period and had an elevational trend. Assimilating the measured snowfields into a version of PRMS calibrated to achieve water balance without assimilation led to reduced performance in estimating streamflow for the Rio Grande and increased performance in estimating streamflow for the Black River basin. Correcting the measured SCA and SWE for canopy effects improved simulations by adding snow mostly in the mid-to-high elevations, where satellite estimates of SCA are lower than model estimates. Copyright ?? 2006 John Wiley & Sons, Ltd.

Improvement of Mars Surface Snow Albedo Modeling in LMD Mars GCM With SNICAR

NASA Astrophysics Data System (ADS)

Singh, D.; Flanner, M. G.; Millour, E.

2018-03-01

The current version of Laboratoire de Météorologie Dynamique (LMD) Mars GCM (original-MGCM) uses annually repeating (prescribed) CO2 snow albedo values based on the Thermal Emission Spectrometer observations. We integrate the Snow, Ice, and Aerosol Radiation (SNICAR) model with MGCM (SNICAR-MGCM) to prognostically determine H2O and CO2 snow albedos interactively in the model. Using the new diagnostic capabilities of this model, we find that cryospheric surfaces (with dust) increase the global surface albedo of Mars by 0.022. Over snow-covered regions, SNICAR-MGCM simulates mean albedo that is higher by about 0.034 than prescribed values in the original-MGCM. Globally, shortwave flux into the surface decreases by 1.26 W/m2, and net CO2 snow deposition increases by about 4% with SNICAR-MGCM over one Martian annual cycle as compared to the original-MGCM simulations. SNICAR integration reduces the mean global surface temperature and the surface pressure of Mars by about 0.87% and 2.5%, respectively. Changes in albedo also show a similar distribution to dust deposition over the globe. The SNICAR-MGCM model generates albedos with higher sensitivity to surface dust content as compared to original-MGCM. For snow-covered regions, we improve the correlation between albedo and optical depth of dust from -0.91 to -0.97 with SNICAR-MGCM as compared to the original-MGCM. Dust substantially darkens Mars's cryosphere, thereby reducing its impact on the global shortwave energy budget by more than half, relative to the impact of pure snow.

Ocean backscatter across the Gulf Stream sea surface temperature front

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nghiem, S.V.; Li, F.K.

1997-06-01

Ocean backscatter was measured by the Jet Propulsion Laboratory, with the airborne NUSCAT K{sub u}-band scatterometer, across the Gulf Stream sea surface temperature front during the Surface Wave Dynamics Experiment off the coast of Virginia and Maryland in the winter of 1991. Backscatter across the front between the National Oceanic and Atmospheric Administration experimental coastal buoy A (44024) on the cold side and Discus C buoy (44023) on the warm side shows a difference of more than 5 dB for vertical polarization in many cases. This large frontal backscatter change is observed in all upwind, downwind, and crosswind directions. Themore » sea surface temperature difference measured by the buoys was about 9{degrees}C. The corresponding difference in wind speed cannot account for the large backscatter change in view of geophysical model functions depending only on neutral wind velocity such as SASS. The measured backscatter also has larger upwind-downwind and upwind-crosswind ratios compared to the model results. Furthermore, NUSCAT data reveal that upwind backscatter on the cold side was smaller than or close to crosswind backscatter on the warm side for incidence angles between 30{degrees} to 50{degrees}. This suggests that the temperature front can be detected by the scatterometer at these incidence angles for different wind directions in the cold and warm sides.« less

Numerical run-out modelling used for reassessment of existing permanent avalanche paths in the Krkonose Mts., Czechia

NASA Astrophysics Data System (ADS)

Blahut, Jan; Klimes, Jan; Balek, Jan; Taborik, Petr; Juras, Roman; Pavlasek, Jiri

2015-04-01

Run-out modelling of snow avalanches is being widely applied in high mountain areas worldwide. This study presents application of snow avalanche run-out calculation applied to mid-mountain ranges - the Krkonose, Jeseniky and Kralicky Sneznik Mountains. All mentioned mountain ranges lie in the northern part of Czechia, close to the border with Poland. Its highest peak reaches only 1602 m a.s.l. However, climatic conditions and regular snowpack presence are the reason why these mountain ranges experience considerable snow avalanche activity every year, sometimes resulting in injuries or even fatalities. Within the aim of an applied project dealing with snow avalanche hazard prediction a re-assessment of permanent snow avalanche paths has been performed based on extensive statistics covering period from 1961/62 till present. On each avalanche path different avalanches with different return periods were modelled using the RAMMS code. As a result, an up-to-date snow avalanche hazard map was prepared.

Estimating snow depth in real time using unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Niedzielski, Tomasz; Mizinski, Bartlomiej; Witek, Matylda; Spallek, Waldemar; Szymanowski, Mariusz

2016-04-01

In frame of the project no. LIDER/012/223/L-5/13/NCBR/2014, financed by the National Centre for Research and Development of Poland, we elaborated a fully automated approach for estimating snow depth in real time in the field. The procedure uses oblique aerial photographs taken by the unmanned aerial vehicle (UAV). The geotagged images of snow-covered terrain are processed by the Structure-from-Motion (SfM) method which is used to produce a non-georeferenced dense point cloud. The workflow includes the enhanced RunSFM procedure (keypoint detection using the scale-invariant feature transform known as SIFT, image matching, bundling using the Bundler, executing the multi-view stereo PMVS and CMVS2 software) which is preceded by multicore image resizing. The dense point cloud is subsequently automatically georeferenced using the GRASS software, and the ground control points are borrowed from positions of image centres acquired from the UAV-mounted GPS receiver. Finally, the digital surface model (DSM) is produced which - to improve the accuracy of georeferencing - is shifted using a vector obtained through precise geodetic GPS observation of a single ground control point (GCP) placed on the Laboratory for Unmanned Observations of Earth (mobile lab established at the University of Wroclaw, Poland). The DSM includes snow cover and its difference with the corresponding snow-free DSM or digital terrain model (DTM), following the concept of the digital elevation model of differences (DOD), produces a map of snow depth. Since the final result depends on the snow-free model, two experiments are carried out. Firstly, we show the performance of the entire procedure when the snow-free model reveals a very high resolution (3 cm/px) and is produced using the UAV-taken photographs and the precise GCPs measured by the geodetic GPS receiver. Secondly, we perform a similar exercise but the 1-metre resolution light detection and ranging (LIDAR) DSM or DTM serves as the snow-free model. Thus, the main objective of the paper is to present the performance of the new procedure for estimating snow depth and to compare the two experiments.

Predicting the distributions of predator (snow leopard) and prey (blue sheep) under climate change in the Himalaya.

PubMed

Aryal, Achyut; Shrestha, Uttam Babu; Ji, Weihong; Ale, Som B; Shrestha, Sujata; Ingty, Tenzing; Maraseni, Tek; Cockfield, Geoff; Raubenheimer, David

2016-06-01

Future climate change is likely to affect distributions of species, disrupt biotic interactions, and cause spatial incongruity of predator-prey habitats. Understanding the impacts of future climate change on species distribution will help in the formulation of conservation policies to reduce the risks of future biodiversity losses. Using a species distribution modeling approach by MaxEnt, we modeled current and future distributions of snow leopard (Panthera uncia) and its common prey, blue sheep (Pseudois nayaur), and observed the changes in niche overlap in the Nepal Himalaya. Annual mean temperature is the major climatic factor responsible for the snow leopard and blue sheep distributions in the energy-deficient environments of high altitudes. Currently, about 15.32% and 15.93% area of the Nepal Himalaya are suitable for snow leopard and blue sheep habitats, respectively. The bioclimatic models show that the current suitable habitats of both snow leopard and blue sheep will be reduced under future climate change. The predicted suitable habitat of the snow leopard is decreased when blue sheep habitats is incorporated in the model. Our climate-only model shows that only 11.64% (17,190 km(2)) area of Nepal is suitable for the snow leopard under current climate and the suitable habitat reduces to 5,435 km(2) (reduced by 24.02%) after incorporating the predicted distribution of blue sheep. The predicted distribution of snow leopard reduces by 14.57% in 2030 and by 21.57% in 2050 when the predicted distribution of blue sheep is included as compared to 1.98% reduction in 2030 and 3.80% reduction in 2050 based on the climate-only model. It is predicted that future climate may alter the predator-prey spatial interaction inducing a lower degree of overlap and a higher degree of mismatch between snow leopard and blue sheep niches. This suggests increased energetic costs of finding preferred prey for snow leopards - a species already facing energetic constraints due to the limited dietary resources in its alpine habitat. Our findings provide valuable information for extension of protected areas in future.

Uncertainty in the Future of Seasonal Snowpack over North America.

NASA Astrophysics Data System (ADS)

McCrary, R. R.; Mearns, L.

2017-12-01

The uncertainty in future changes in seasonal snowpack (snow water equivalent, SWE) and snow cover extent (SCE) for North America are explored using the North American Regional Climate Change Assessment Program (NARCCAP) suite of regional climate models (RCMs) and their driving CMIP3 global circulation models (GCMs). The higher resolution of the NARCCAP RCMs is found to add significant value to the details of future projections of SWE in topographically complex regions such as the Pacific Northwest and the Rocky Mountains. The NARCCAP models also add detailed information regarding changes in the southernmost extent of snow cover. 11 of the 12 NARCCAP ensemble members contributed SWE output which we use to explore the uncertainty in future snowpack at higher resolution. In this study, we quantify the uncertainty in future projections by looking at the spread of the interquartile range of the different models. By mid-Century the RCMs consistently predict that winter SWE amounts will decrease over most of North America. The only exception to this is in Northern Canada, where increased moisture supply leads to increases in SWE in all but one of the RCMs. While the models generally agree on the sign of the change in SWE, there is considerable spread in the magnitude (absolute and percent) of the change. The RCMs also agree that the number of days with measureable snow on the ground is projected to decrease, with snow accumulation occurring later in the Fall/Winter and melting starting earlier in the Spring/Summer. As with SWE amount, spread across the models is large for changes in the timing of the snow season and can vary by over a month between models. While most of the NARCCAP models project a total loss of measurable snow along the southernmost edge of their historical range, there is considerable uncertainty about where this will occur within the ensemble due to the bias in snow cover extent in the historical simulations. We explore methods to increase our confidence about the regions that will lose any seasonal snow.

High resolution climate scenarios for snowmelt modelling in small alpine catchments

NASA Astrophysics Data System (ADS)

Schirmer, M.; Peleg, N.; Burlando, P.; Jonas, T.

2017-12-01

Snow in the Alps is affected by climate change with regard to duration, timing and amount. This has implications with respect to important societal issues as drinking water supply or hydropower generation. In Switzerland, the latter received a lot of attention following the political decision to phase out of nuclear electricity production. An increasing number of authorization requests for small hydropower plants located in small alpine catchments was observed in the recent years. This situation generates ecological conflicts, while the expected climate change poses a threat to water availability thus putting at risk investments in such hydropower plants. Reliable high-resolution climate scenarios are thus required, which account for small-scale processes to achieve realistic predictions of snowmelt runoff and its variability in small alpine catchments. We therefore used a novel model chain by coupling a stochastic 2-dimensional weather generator (AWE-GEN-2d) with a state-of-the-art energy balance snow cover model (FSM). AWE-GEN-2d was applied to generate ensembles of climate variables at very fine temporal and spatial resolution, thus providing all climatic input variables required for the energy balance modelling. The land-surface model FSM was used to describe spatially variable snow cover accumulation and melt processes. The FSM was refined to allow applications at very high spatial resolution by specifically accounting for small-scale processes, such as a subgrid-parametrization of snow covered area or an improved representation of forest-snow processes. For the present study, the model chain was tested for current climate conditions using extensive observational dataset of different spatial and temporal coverage. Small-scale spatial processes such as elevation gradients or aspect differences in the snow distribution were evaluated using airborne LiDAR data. 40-year of monitoring data for snow water equivalent, snowmelt and snow-covered area for entire Switzerland was used to verify snow distribution patterns at coarser spatial and temporal scale. The ability of the model chain to reproduce current climate conditions in small alpine catchments makes this model combination an outstanding candidate to produce high resolution climate scenarios of snowmelt in small alpine catchments.

Simulation of Surface Energy Fluxes and Snow Interception Using a Higher Order Closure Multi-Layer Soil-Vegetation-Atmospheric Model: The Effect of Canopy Shape and Structure

NASA Astrophysics Data System (ADS)

McGowan, L. E.; Dahlke, H. E.; Paw U, K. T.

2015-12-01

Snow cover is a critical driver of the Earth's surface energy budget, climate change, and water resources. Variations in snow cover not only affect the energy budget of the land surface but also represent a major water supply source. In California, US estimates of snow depth, extent, and melt in the Sierra Nevada are critical to estimating the amount of water available for both California agriculture and urban users. However, accurate estimates of snow cover and snow melt processes in forested area still remain a challenge. Canopy structure influences the vertical and spatiotemporal distribution of snow, and therefore ultimately determines the degree and extent by which snow alters both the surface energy balance and water availability in forested regions. In this study we use the Advanced Canopy-Atmosphere-Soil algorithm (ACASA), a multi-layer soil-vegetation-atmosphere numerical model, to simulate the effect of different snow-covered canopy structures on the energy budget, and temperature and other scalar profiles within different forest types in the Sierra Nevada, California. ACASA incorporates a higher order turbulence closure scheme which allows the detailed simulation of turbulent fluxes of heat and water vapor as well as the CO2 exchange of several layers within the canopy. As such ACASA can capture the counter gradient fluxes within canopies that may occur frequently, but are typically unaccounted for, in most snow hydrology models. Six different canopy types were modeled ranging from coniferous forests (e.g. most biomass near the ground) to top-heavy (e.g. most biomass near the top of the crown) deciduous forests to multi-layered forest canopies (e.g. mixture of young and mature trees). Preliminary results indicate that the canopy shape and structure associated with different canopy types fundamentally influence the vertical scalar profiles (including those of temperature, moisture, and wind speed) in the canopy and thus alter the interception and snow melt dynamics in forested land surfaces. The turbulent transport dynamics, including counter-gradient fluxes, and radiation features including land surface albedo, are discussed in the context of the snow energy balance.

Evaluation of alternative formulae for calculation of surface temperature in snowmelt models using frequency analysis of temperature observations

Treesearch

C. H. Luce; D. G. Tarboton

2010-01-01

The snow surface temperature is an important quantity in the snow energy balance, since it modulates the exchange of energy between the surface and the atmosphere as well as the conduction of energy into the snowpack. It is therefore important to correctly model snow surface temperatures in energy balance snowmelt models. This paper focuses on the relationship between...

Mapping snow depth in complex alpine terrain with close range aerial imagery - estimating the spatial uncertainties of repeat autonomous aerial surveys over an active rock glacier

NASA Astrophysics Data System (ADS)

Goetz, Jason; Marcer, Marco; Bodin, Xavier; Brenning, Alexander

2017-04-01

Snow depth mapping in open areas using close range aerial imagery is just one of the many cases where developments in structure-from-motion and multi-view-stereo (SfM-MVS) 3D reconstruction techniques have been applied for geosciences - and with good reason. Our ability to increase the spatial resolution and frequency of observations may allow us to improve our understanding of how snow depth distribution varies through space and time. However, to ensure accurate snow depth observations from close range sensing we must adequately characterize the uncertainty related to our measurement techniques. In this study, we explore the spatial uncertainties of snow elevation models for estimation of snow depth in a complex alpine terrain from close range aerial imagery. We accomplish this by conducting repeat autonomous aerial surveys over a snow-covered active-rock glacier located in the French Alps. The imagery obtained from each flight of an unmanned aerial vehicle (UAV) is used to create an individual digital elevation model (DEM) of the snow surface. As result, we obtain multiple DEMs of the snow surface for the same site. These DEMs are obtained from processing the imagery with the photogrammetry software Agisoft Photoscan. The elevation models are also georeferenced within Photoscan using the geotagged imagery from an onboard GNSS in combination with ground targets placed around the rock glacier, which have been surveyed with highly accurate RTK-GNSS equipment. The random error associated with multi-temporal DEMs of the snow surface is estimated from the repeat aerial survey data. The multiple flights are designed to follow the same flight path and altitude above the ground to simulate the optimal conditions of repeat survey of the site, and thus try to estimate the maximum precision associated with our snow-elevation measurement technique. The bias of the DEMs is assessed with RTK-GNSS survey observations of the snow surface elevation of the area on and surrounding the rock glacier. Additionally, one of the challenges with processing snow cover imagery with SfM-MVS is dealing with the general homogeneity of the surface, which makes is difficult for automated-feature detection algorithms to identify key features for point matching. This challenge depends on the snow cover surface conditions, such as scale, lighting conditions (high vs. low contrast), and availability of snow-free features within a scene, among others. We attempt to explore this aspect by spatial modelling the factors influencing the precision and bias of the DEMs from image, flight, and terrain attributes.

An improved Monte-Carlo model of the Varian EPID separating support arm and rear-housing backscatter

NASA Astrophysics Data System (ADS)

Monville, M. E.; Kuncic, Z.; Greer, P. B.

2014-03-01

Previous investigators of EPID dosimetric properties have ascribed the backscatter, that contaminates dosimetric EPID images, to its supporting arm. Accordingly, Monte-Carlo (MC) EPID models have approximated the backscatter signal from the layers under the detector and the robotic support arm using either uniform or non-uniform solid water slabs, or through convolutions with back-scatter kernels. The aim of this work is to improve the existent MC models by measuring and modelling the separate backscatter contributions of the robotic arm and the rear plastic housing of the EPID. The EPID plastic housing is non-uniform with a 11.9 cm wide indented section that runs across the cross-plane direction in the superior half of the EPID which is 1.75 cm closer to the EPID sensitive layer than the rest of the housing. The thickness of the plastic housing is 0.5 cm. Experiments were performed with and without the housing present by removing all components of the EPID from the housing. The robotic support arm was not present for these measurements. A MC model of the linear accelerator and the EPID was modified to include the rear-housing indentation and results compared to the measurement. The rear housing was found to contribute a maximum of 3% additional signal. The rear housing contribution to the image is non-uniform in the in-plane direction with 2% asymmetry across the central 20 cm of an image irradiating the entire detector. The MC model was able to reproduce this non-uniform contribution. The EPID rear housing contributes a non-uniform backscatter component to the EPID image, which has not been previously characterized. This has been incorporated into an improved MC model of the EPID.

Investigating the spectral characteristics of backscattering from heterogeneous spheroidal nuclei using broadband finite-difference time-domain simulations

NASA Astrophysics Data System (ADS)

Chao, Guo-Shan; Sung, Kung-Bin

2010-02-01

Backscattered light spectra have been used to extract size distribution of cell nuclei in epithelial tissues for noninvasive detection of precancerous lesions. In existing experimental studies, size estimation is achieved by assuming nuclei as homogeneous spheres or spheroids and fitting the measured data with models based on Mie theory. However, the validity of simplifying nuclei as homogeneous spheres has not been thoroughly examined. In this study, we investigate the spectral characteristics of backscattering from models of spheroidal nuclei under plane wave illumination using three-dimensional finite-difference time-domain (FDTD) simulation. A modulated Gaussian pulse is used to obtain wavelength dependent scattering intensity with a single FDTD run. The simulated model of nuclei consists of a nucleolus and randomly distributed chromatin condensation in homogeneous cytoplasm and nucleoplasm. The results show that backscattering spectra from spheroidal nuclei have similar oscillating patterns to those from homogeneous spheres with the diameter equal to the projective length of the spheroidal nucleus along the propagation direction. The strength of backscattering is enhanced in heterogeneous spheroids as compared to homogeneous spheroids. The degree of which backscattering spectra of heterogeneous nuclei deviate from Mie theory is highly dependent on the distribution of chromatin/nucleolus but not sensitive to nucleolar size, refractive index fluctuation or chromatin density.

Theoretical and experimental models of the diffuse radar backscatter from Mars

NASA Technical Reports Server (NTRS)

England, A. W.

1995-01-01

The general objective for this work was to develop a theoretically and experimentally consistent explanation for the diffuse component of radar backscatter from Mars. The strength, variability, and wavelength independence of Mars' diffuse backscatter are unique among our Moon and the terrestrial planets. This diffuse backscatter is generally attributed to wavelength-scale surface roughness and to rock clasts within the Martian regolith. Through the combination of theory and experiment, the authors attempted to bound the range of surface characteristics that could produce the observed diffuse backscatter. Through these bounds they gained a limited capability for data inversion. Within this umbrella, specific objectives were: (1) To better define the statistical roughness parameters of Mars' surface so that they are consistent with observed radar backscatter data, and with the physical and chemical characteristics of Mars' surface as inferred from Mariner 9, the Viking probes, and Earth-based spectroscopy; (2) To better understand the partitioning between surface and volume scattering in the Mars regolith; (3) To develop computational models of Mars' radio emission that incorporate frequency dependent, surface and volume scattering.

Snow cover distribution over elevation zones in a mountainous catchment

NASA Astrophysics Data System (ADS)

Panagoulia, D.; Panagopoulos, Y.

2009-04-01

A good understanding of the elevetional distribution of snow cover is necessary to predict the timing and volume of runoff. In a complex mountainous terrain the snow cover distribution within a watershed is highly variable in time and space and is dependent on elevation, slope, aspect, vegetation type, surface roughness, radiation load, and energy exchange at the snow-air interface. Decreases in snowpack due to climate change could disrupt the downstream urban and agricultural water supplies, while increases could lead to seasonal flooding. Solar and longwave radiation are dominant energy inputs driving the ablation process. Turbulent energy exchange at the snow cover surface is important during the snow season. The evaporation of blowing and drifting snow is strongly dependent upon wind speed. Much of the spatial heterogeneity of snow cover is the result of snow redistribution by wind. Elevation is important in determining temperature and precipitation gradients along hillslopes, while the temperature gradients determine where precipitation falls as rain and snow and contribute to variable melt rates within the hillslope. Under these premises, the snow accumulation and ablation (SAA) model of the US National Weather Service (US NWS) was applied to implement the snow cover extent over elevation zones of a mountainous catchment (the Mesochora catchment in Western-Central Greece), taking also into account the indirectly included processes of sublimation, interception, and snow redistribution. The catchment hydrology is controlled by snowfall and snowmelt and the simulated discharge was computed from the soil moisture accounting (SMA) model of the US NWS and compared to the measured discharge. The elevationally distributed snow cover extent presented different patterns with different time of maximization, extinction and return during the year, producing different timing of discharge that is a crucial factor for the control and management of water resources systems.

Origin of elemental carbon in snow from western Siberia and northwestern European Russia during winter-spring 2014, 2015 and 2016

NASA Astrophysics Data System (ADS)

Evangeliou, Nikolaos; Shevchenko, Vladimir P.; Espen Yttri, Karl; Eckhardt, Sabine; Sollum, Espen; Pokrovsky, Oleg S.; Kobelev, Vasily O.; Korobov, Vladimir B.; Lobanov, Andrey A.; Starodymova, Dina P.; Vorobiev, Sergey N.; Thompson, Rona L.; Stohl, Andreas

2018-01-01

Short-lived climate forcers have been proven important both for the climate and human health. In particular, black carbon (BC) is an important climate forcer both as an aerosol and when deposited on snow and ice surface because of its strong light absorption. This paper presents measurements of elemental carbon (EC; a measurement-based definition of BC) in snow collected from western Siberia and northwestern European Russia during 2014, 2015 and 2016. The Russian Arctic is of great interest to the scientific community due to the large uncertainty of emission sources there. We have determined the major contributing sources of BC in snow in western Siberia and northwestern European Russia using a Lagrangian atmospheric transport model. For the first time, we use a recently developed feature that calculates deposition in backward (so-called retroplume) simulations allowing estimation of the specific locations of sources that contribute to the deposited mass. EC concentrations in snow from western Siberia and northwestern European Russia were highly variable depending on the sampling location. Modelled BC and measured EC were moderately correlated (R = 0.53-0.83) and a systematic region-specific model underestimation was found. The model underestimated observations by 42 % (RMSE = 49 ng g-1) in 2014, 48 % (RMSE = 37 ng g-1) in 2015 and 27 % (RMSE = 43 ng g-1) in 2016. For EC sampled in northwestern European Russia the underestimation by the model was smaller (fractional bias, FB > -100 %). In this region, the major sources were transportation activities and domestic combustion in Finland. When sampling shifted to western Siberia, the model underestimation was more significant (FB < -100 %). There, the sources included emissions from gas flaring as a major contributor to snow BC. The accuracy of the model calculations was also evaluated using two independent datasets of BC measurements in snow covering the entire Arctic. The model underestimated BC concentrations in snow especially for samples collected in springtime.

Enhancement of the MODIS Daily Snow Albedo Product

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Schaaf, Crystal B.; Wang, Zhuosen; Riggs, George A.

2009-01-01

The MODIS daily snow albedo product is a data layer in the MOD10A1 snow-cover product that includes snow-covered area and fractional snow cover as well as quality information and other metadata. It was developed to augment the MODIS BRDF/Albedo algorithm (MCD43) that provides 16-day maps of albedo globally at 500-m resolution. But many modelers require daily snow albedo, especially during the snowmelt season when the snow albedo is changing rapidly. Many models have an unrealistic snow albedo feedback in both estimated albedo and change in albedo over the seasonal cycle context, Rapid changes in snow cover extent or brightness challenge the MCD43 algorithm; over a 16-day period, MCD43 determines whether the majority of clear observations was snow-covered or snow-free then only calculates albedo for the majority condition. Thus changes in snow albedo and snow cover are not portrayed accurately during times of rapid change, therefore the current MCD43 product is not ideal for snow work. The MODIS daily snow albedo from the MOD10 product provides more frequent, though less robust maps for pixels defined as "snow" by the MODIS snow-cover algorithm. Though useful, the daily snow albedo product can be improved using a daily version of the MCD43 product as described in this paper. There are important limitations to the MOD10A1 daily snow albedo product, some of which can be mitigated. Utilizing the appropriate per-pixel Bidirectional Reflectance Distribution Functions (BRDFs) can be problematic, and correction for anisotropic scattering must be included. The BRDF describes how the reflectance varies with view and illumination geometry. Also, narrow-to-broadband conversion specific for snow on different surfaces must be calculated and this can be difficult. In consideration of these limitations of MOD10A1, we are planning to improve the daily snow albedo algorithm by coupling the periodic per-pixel snow albedo from MCD43, with daily surface ref|outanoom, In this paper, we compare a daily version of MCD43B3 with the daily albedo from MOD10A1. and MCD43B3 with a 16-day average of MOD10A1, over Greenland. We also discuss some near-future planned enhancements to MOD10A1.

On the absorption of solar radiation in a layer of oil beneath a layer of snow

NASA Technical Reports Server (NTRS)

Larsen, J. C.; Barkstrom, B. R.

1976-01-01

Solar energy deposition in oil layers covered by snow is calculated for three model snow types using radiative transfer theory. It is suggested that excess absorbed energy is unlikely to escape, so that some melting is likely to occur for snow depths less than about 4 cm.

A modified force-restore approach to modeling snow-surface heat fluxes

Treesearch

Charles H. Luce; David G. Tarboton

2001-01-01

Accurate modeling of the energy balance of a snowpack requires good estimates of the snow surface temperature. The snow surface temperature allows a balance between atmospheric heat fluxes and the conductive flux into the snowpack. While the dependency of atmospheric fluxes on surface temperature is reasonably well understood and parameterized, conduction of heat from...

A breakdown surface model for thermal backscattering from the exhaust plume of a space-based HF laser

NASA Astrophysics Data System (ADS)

Falcovitz, J.

1986-06-01

The purpose of this report is to present a breakdown surface model for evaluating thermal backscattering flow from the supersonic exhaust plume of a gaseous mixture of H, HF, H2, DF and He. Fluxes of these species are considered separately. The model is carefully analyzed and is shown to overestimate the flux. Actual flux levels of the heavy corrosive molecules (HD, DF) have been found to be exceedingly low. It is concluded that the contribution of thermal backscattering to contaminating flux of HF and DF can be neglected.

Differences Between the HUT Snow Emission Model and MEMLS and Their Effects on Brightness Temperature Simulation

NASA Technical Reports Server (NTRS)

Pan, Jinmei; Durand, Michael; Sandells, Melody; Lemmetyinen, Juha; Kim, Edward J.; Pulliainen, Jouni; Kontu, Anna; Derksen, Chris

2015-01-01

Microwave emission models are a critical component of snow water equivalent retrieval algorithms applied to passive microwave measurements. Several such emission models exist, but their differences need to be systematically compared. This paper compares the basic theories of two models: the multiple-layer HUT (Helsinki University of Technology) model and MEMLS (Microwave Emission Model of Layered Snowpacks). By comparing the mathematical formulation side-by-side, three major differences were identified: (1) by assuming the scattered intensity is mostly (96) in the forward direction, the HUT model simplifies the radiative transfer (RT) equation into 1-flux; whereas MEMLS uses a 2-flux theory; (2) the HUT scattering coefficient is much larger than MEMLS; (3 ) MEMLS considers the trapped radiation inside snow due to internal reflection by a 6-flux model, which is not included in HUT. Simulation experiments indicate that, the large scattering coefficient of the HUT model compensates for its large forward scattering ratio to some extent, but the effects of 1-flux simplification and the trapped radiation still result in different T(sub B) simulations between the HUT model and MEMLS. The models were compared with observations of natural snow cover at Sodankyl, Finland; Churchill, Canada; and Colorado, USA. No optimization of the snow grain size was performed. It shows that HUT model tends to under estimate T(sub B) for deep snow. MEMLS with the physically-based improved Born approximation performed best among the models, with a bias of -1.4 K, and an RMSE of 11.0 K.

Forward-looking Assimilation of MODIS-derived Snow Covered Area into a Land Surface Model

NASA Technical Reports Server (NTRS)

Zaitchik, Benjamin F.; Rodell, Matthew

2008-01-01

Snow cover over land has a significant impact on the surface radiation budget, turbulent energy fluxes to the atmosphere, and local hydrological fluxes. For this reason, inaccuracies in the representation of snow covered area (SCA) within a land surface model (LSM) can lead to substantial errors in both offline and coupled simulations. Data assimilation algorithms have the potential to address this problem. However, the assimilation of SCA observations is complicated by an information deficit in the observation SCA indicates only the presence or absence of snow, and not snow volume and by the fact that assimilated SCA observations can introduce inconsistencies with atmospheric forcing data, leading to non-physical artifacts in the local water balance. In this paper we present a novel assimilation algorithm that introduces MODIS SCA observations to the Noah LSM in global, uncoupled simulations. The algorithm utilizes observations from up to 72 hours ahead of the model simulation in order to correct against emerging errors in the simulation of snow cover while preserving the local hydrologic balance. This is accomplished by using future snow observations to adjust air temperature and, when necessary, precipitation within the LSM. In global, offline integrations, this new assimilation algorithm provided improved simulation of SCA and snow water equivalent relative to open loop integrations and integrations that used an earlier SCA assimilation algorithm. These improvements, in turn, influenced the simulation of surface water and energy fluxes both during the snow season and, in some regions, on into the following spring.

Snowscape Ecology: Linking Snow Properties to Wildlife Movements and Demography

NASA Astrophysics Data System (ADS)

Prugh, L.; Verbyla, D.; van de Kerk, M.; Mahoney, P.; Sivy, K. J.; Liston, G. E.; Nolin, A. W.

2017-12-01

Snow enshrouds up to one third of the global land mass annually and exerts a major influence on animals that reside in these "snowscapes," (landscapes covered in snow). Dynamic snowscapes may have especially strong effects in arctic and boreal regions where dry snow persists for much of the year. Changes in temperature and hydrology are transforming northern regions, with profound implications for wildlife that are not well understood. We report initial findings from a NASA ABoVE project examining effects of snow properties on Dall sheep (Ovis dalli dalli). We used the MODSCAG snow fraction product to map spring snowline elevations and snow-off dates from 2000-2015 throughout the global range of Dall sheep in Alaska and northwestern Canada. We found a negative effect of spring snow cover on Dall sheep recruitment that increased with latitude. Using meteorological data and a daily freeze/thaw status product derived from passive microwave remote sensing from 1983-2012, we found that sheep survival rates increased in years with higher temperatures, less winter precipitation, fewer spring freeze-thaw events, and more winter freeze-thaw events. To examine the effects of snow depth and density on sheep movements, we used location data from GPS-collared sheep and a snowpack evolution model (SnowModel). We found that sheep selected for shallow, fluffy snow at high elevations, but they selected for denser snow as depth increased. Our field measurements identified a critical snow density threshold of 329 (± 18 SE) kg/m3 to support the weight of Dall sheep. Thus, sheep may require areas of shallow, fluffy snow for foraging, while relying on hard-packed snow for winter travel. These findings highlight the importance of multiple snowscape properties on wildlife movements and demography. The integrated study of snow properties and ecological processes, which we call snowscape ecology, will greatly improve global change forecasting.

Modeling of multi-phase interactions of reactive nitrogen between snow and air in Antarctica

NASA Astrophysics Data System (ADS)

McCrystall, M.; Chan, H. G. V.; Frey, M. M.; King, M. D.

2016-12-01

In polar and snow-covered regions, the snowpack is an important link between atmospheric, terrestrial and oceanic systems. Trace gases, including nitrogen oxides, produced via photochemical reactions in snow are partially released to the lower atmosphere with considerable impact on its composition. However, the post-depositional processes that change the chemical composition and physical properties of the snowpack are still poorly understood. Most current snow chemistry models oversimplify as they assume air-liquid interactions and aqueous phase chemistry taking place at the interface between the snow grain and air. Here, we develop a novel temperature dependent multi-phase (gas-liquid-ice) physical exchange model for reactive nitrogen. The model is validated with existing year-round observations of nitrate in the top 0.5-2 cm of snow and the overlying atmosphere at two very different Antarctic locations: Dome C on the East Antarctic Plateau with very low annual mean temperature (-54ºC) and accumulation rate (<30 kg m-2 yr-1); and Halley, a coastal site with at times at or above freezing temperatures during summer, high accumulation rate and high background level of sea salt aerosol. We find that below the eutectic temperature of the H2O/dominant ion mixture the surface snow nitrate is controlled by kinetic adsorption onto the surface of snow grains followed by grain diffusion. Above the eutectic temperature, in addition to the former two processes, thermodynamic equilibrium of HNO3 between interstitial air and liquid water pockets, possibly present at triple junctions or grooves at grain boundaries, greatly enhances the nitrate uptake by snow in agreement with the concentration peak observed in summer.

Improving alpine-region spectral unmixing with optimal-fit snow endmembers

NASA Technical Reports Server (NTRS)

Painter, Thomas H.; Roberts, Dar A.; Green, Robert O.; Dozier, Jeff

1995-01-01

Surface albedo and snow-covered-area (SCA) are crucial inputs to the hydrologic and climatologic modeling of alpine and seasonally snow-covered areas. Because the spectral albedo and thermal regime of pure snow depend on grain size, areal distribution of snow grain size is required. Remote sensing has been shown to be an effective (and necessary) means of deriving maps of grain size distribution and snow-covered-area. Developed here is a technique whereby maps of grain size distribution improve estimates of SCA from spectral mixture analysis with AVIRIS data.

Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

NASA Astrophysics Data System (ADS)

Alonso-González, Esteban; López-Moreno, J. Ignacio; Gascoin, Simon; García-Valdecasas Ojeda, Matilde; Sanmiguel-Vallelado, Alba; Navarro-Serrano, Francisco; Revuelto, Jesús; Ceballos, Antonio; Jesús Esteban-Parra, María; Essery, Richard

2018-02-01

We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfall occurs in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 km × 10 km resolution using the Weather Research and Forecasting (WRF) model. The WRF outputs were used directly, or as input to other submodels, to obtain data needed to drive the Factorial Snow Model (FSM). We used lapse rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands for each 10 km × 10 km grid cell in the Iberian Peninsula. The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism, and risk management. The data presented here are freely available for download from Zenodo (https://doi.org/10.5281/zenodo.854618). This paper fully describes the work flow, data validation, uncertainty assessment, and possible applications and limitations of the database.

Validation of snow characteristics and snow albedo feedback in the Canadian Regional Climate Model simulations over North America

NASA Astrophysics Data System (ADS)

Fang, B.; Sushama, L.; Diro, G. T.

2015-12-01

Snow characteristics and snow albedo feedback (SAF) over North America, as simulated by the fifth-generation Canadian Regional Climate Model (CRCM5), when driven by ERA-40/ERA-Interim, CanESM2 and MPI-ESM-LR at the lateral boundaries, are analyzed in this study. Validation of snow characteristics is performed by comparing simulations against available observations from MODIS, ISCCP and CMC. Results show that the model is able to represent the main spatial distribution of snow characteristics with some overestimation in snow mass and snow depth over the Canadian high Arctic. Some overestimation in surface albedo is also noted for the boreal region which is believed to be related to the snow unloading parameterization, as well as the overestimation of snow albedo. SAF is assessed both in seasonal and climate change contexts when possible. The strength of SAF is quantified as the amount of additional net shortwave radiation at the top of the atmosphere as surface albedo decreases in association with a 1°C increase in surface temperature. Following Qu and Hall (2007), this is expressed as the product of the variation in planetary albedo with surface albedo and the change in surface albedo for 1°C change in surface air temperature during the season, which in turn is determined by the strength of the snow cover and snowpack metamorphosis feedback loops. Analysis of the latter term in the seasonal cycle suggests that for CRCM5 simulations, the snow cover feedback loop is more dominant compared to the snowpack metamorphosis feedback loop, whereas for MODIS, the two feedback loops have more or less similar strength. Moreover, the SAF strength in the climate change context appears to be weaker than in the seasonal cycle and is sensitive to the driving GCM and the RCP scenario.

Intercomparison of different uncertainty sources in hydrological climate change projections for an alpine catchment (upper Clutha River, New Zealand)

NASA Astrophysics Data System (ADS)

Jobst, Andreas M.; Kingston, Daniel G.; Cullen, Nicolas J.; Schmid, Josef

2018-06-01

As climate change is projected to alter both temperature and precipitation, snow-controlled mid-latitude catchments are expected to experience substantial shifts in their seasonal regime, which will have direct implications for water management. In order to provide authoritative projections of climate change impacts, the uncertainty inherent to all components of the modelling chain needs to be accounted for. This study assesses the uncertainty in potential impacts of climate change on the hydro-climate of a headwater sub-catchment of New Zealand's largest catchment (the Clutha River) using a fully distributed hydrological model (WaSiM) and unique ensemble encompassing different uncertainty sources: general circulation model (GCM), emission scenario, bias correction and snow model. The inclusion of snow models is particularly important, given that (1) they are a rarely considered aspect of uncertainty in hydrological modelling studies, and (2) snow has a considerable influence on seasonal patterns of river flow in alpine catchments such as the Clutha. Projected changes in river flow for the 2050s and 2090s encompass substantial increases in streamflow from May to October, and a decline between December and March. The dominant drivers are changes in the seasonal distribution of precipitation (for the 2090s +29 to +84 % in winter) and substantial decreases in the seasonal snow storage due to temperature increase. A quantitative comparison of uncertainty identified GCM structure as the dominant contributor in the seasonal streamflow signal (44-57 %) followed by emission scenario (16-49 %), bias correction (4-22 %) and snow model (3-10 %). While these findings suggest that the role of the snow model is comparatively small, its contribution to the overall uncertainty was still found to be noticeable for winter and summer.

Snow model analysis.

DOT National Transportation Integrated Search

2014-01-01

This study developed a new snow model and a database which warehouses geometric, weather and traffic : data on New Jersey highways. The complexity of the model development lies in considering variable road : width, different spreading/plowing pattern...

Snow observations in Mount Lebanon (2011-2016)

NASA Astrophysics Data System (ADS)

Fayad, Abbas; Gascoin, Simon; Faour, Ghaleb; Fanise, Pascal; Drapeau, Laurent; Somma, Janine; Fadel, Ali; Bitar, Ahmad Al; Escadafal, Richard

2017-08-01

We present a unique meteorological and snow observational dataset in Mount Lebanon, a mountainous region with a Mediterranean climate, where snowmelt is an essential water resource. The study region covers the recharge area of three karstic river basins (total area of 1092 km2 and an elevation up to 3088 m). The dataset consists of (1) continuous meteorological and snow height observations, (2) snowpack field measurements, and (3) medium-resolution satellite snow cover data. The continuous meteorological measurements at three automatic weather stations (MZA, 2296 m; LAQ, 1840 m; and CED, 2834 m a.s.l.) include surface air temperature and humidity, precipitation, wind speed and direction, incoming and reflected shortwave irradiance, and snow height, at 30 min intervals for the snow seasons (November-June) between 2011 and 2016 for MZA and between 2014 and 2016 for CED and LAQ. Precipitation data were filtered and corrected for Geonor undercatch. Observations of snow height (HS), snow water equivalent, and snow density were collected at 30 snow courses located at elevations between 1300 and 2900 m a.s.l. during the two snow seasons of 2014-2016 with an average revisit time of 11 days. Daily gap-free snow cover extent (SCA) and snow cover duration (SCD) maps derived from MODIS snow products are provided for the same period (2011-2016). We used the dataset to characterize mean snow height, snow water equivalent (SWE), and density for the first time in Mount Lebanon. Snow seasonal variability was characterized with high HS and SWE variance and a relatively high snow density mean equal to 467 kg m-3. We find that the relationship between snow depth and snow density is specific to the Mediterranean climate. The current model explained 34 % of the variability in the entire dataset (all regions between 1300 and 2900 m a.s.l.) and 62 % for high mountain regions (elevation 2200-2900 m a.s.l.). The dataset is suitable for the investigation of snow dynamics and for the forcing and validation of energy balance models. Therefore, this dataset bears the potential to greatly improve the quantification of snowmelt and mountain hydrometeorological processes in this data-scarce region of the eastern Mediterranean. The DOI for the data is https://doi.org/10.5281/zenodo.583733.

Snow on Sea Ice Workshop - An Icy Meeting of the Minds: Modelers and Measurers

DTIC Science & Technology

2015-09-30

1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Snow on Sea Ice Workshop - An Icy Meeting of the Minds...workshop was to promote more seamless and better integration between measurements and modeling of snow on sea ice , thereby improving our predictive...capabilities for sea ice . OBJECTIVES The key objective was to improve the ability of modelers and measurers work together closely. To that end, we

Interpretation of snow-climate feedback as produced by 17 general circulation models

NASA Technical Reports Server (NTRS)

Cess, R. D.; Zhang, M.-H.; Potter, G. L.; Blanchet, J.-P.; Chalita, S.; Colman, R.; Dazlich, D. A.; Del Genio, A. D.; Lacis, A. A.; Dymnikov, V.

1991-01-01

Snow feedback is expected to amplify global warming caused by increasing concentrations of atmospheric greenhouse gases. The conventional explanation is that a warmer earth will have less snow cover, resulting in a darker planet that absorbs more solar radiation. An intercomparison of 17 general circulation models, for which perturbations of sea surface temperature were used as a surrogate climate change, suggests that this explanation is overly simplistic. The results instead indicate that additional amplification or moderation may be caused both by cloud interactions and longwave radiation. One measure of this net effect of snow feedback was found to differ markedly among the 17 climate models, ranging from weak negative feedback in some models to strong positive feedback in others.

Impacts of absorbing aerosol deposition on snowpack and hydrologic cycle in the Rocky Mountain region based on variable-resolution CESM (VR-CESM) simulations

NASA Astrophysics Data System (ADS)

Wu, Chenglai; Liu, Xiaohong; Lin, Zhaohui; Rahimi-Esfarjani, Stefan R.; Lu, Zheng

2018-01-01

The deposition of light-absorbing aerosols (LAAs), such as black carbon (BC) and dust, onto snow cover has been suggested to reduce the snow albedo and modulate the snowpack and consequent hydrologic cycle. In this study we use the variable-resolution Community Earth System Model (VR-CESM) with a regionally refined high-resolution (0.125°) grid to quantify the impacts of LAAs in snow in the Rocky Mountain region during the period 1981-2005. We first evaluate the model simulation of LAA concentrations both near the surface and in snow and then investigate the snowpack and runoff changes induced by LAAs in snow. The model simulates similar magnitudes of near-surface atmospheric dust concentrations as observations in the Rocky Mountain region. Although the model underestimates near-surface atmospheric BC concentrations, the model overestimates BC-in-snow concentrations by 35 % on average. The regional mean surface radiative effect (SRE) due to LAAs in snow reaches up to 0.6-1.7 W m-2 in spring, and dust contributes to about 21-42 % of total SRE. Due to positive snow albedo feedbacks induced by the LAA SRE, snow water equivalent is reduced by 2-50 mm and snow cover fraction by 5-20 % in the two regions around the mountains (eastern Snake River Plain and southwestern Wyoming), corresponding to an increase in surface air temperature by 0.9-1.1 °C. During the snow melting period, LAAs accelerate the hydrologic cycle with monthly runoff increases of 0.15-1.00 mm day-1 in April-May and reductions of 0.04-0.18 mm day-1 in June-July in the mountainous regions. Of all the mountainous regions, the Southern Rockies experience the largest reduction of total runoff by 15 % during the later stage of snowmelt (i.e., June and July). Compared to previous studies based on field observations, our estimation of dust-induced SRE is generally 1 order of magnitude smaller in the Southern Rockies, which is ascribed to the omission of larger dust particles (with the diameter > 10 µm) in the model. This calls for the inclusion of larger dust particles in the model to reduce the discrepancies. Overall these results highlight the potentially important role of LAA interactions with snowpack and the subsequent impacts on the hydrologic cycles across the Rocky Mountains.

Predicting the distribution and ecological niche of unexploited snow crab (Chionoecetes opilio) populations in Alaskan waters: a first open-access ensemble model.

PubMed

Hardy, Sarah M; Lindgren, Michael; Konakanchi, Hanumantharao; Huettmann, Falk

2011-10-01

Populations of the snow crab (Chionoecetes opilio) are widely distributed on high-latitude continental shelves of the North Pacific and North Atlantic, and represent a valuable resource in both the United States and Canada. In US waters, snow crabs are found throughout the Arctic and sub-Arctic seas surrounding Alaska, north of the Aleutian Islands, yet commercial harvest currently focuses on the more southerly population in the Bering Sea. Population dynamics are well-monitored in exploited areas, but few data exist for populations further north where climate trends in the Arctic appear to be affecting species' distributions and community structure on multiple trophic levels. Moreover, increased shipping traffic, as well as fisheries and petroleum resource development, may add additional pressures in northern portions of the range as seasonal ice cover continues to decline. In the face of these pressures, we examined the ecological niche and population distribution of snow crabs in Alaskan waters using a GIS-based spatial modeling approach. We present the first quantitative open-access model predictions of snow-crab distribution, abundance, and biomass in the Chukchi and Beaufort Seas. Multi-variate analysis of environmental drivers of species' distribution and community structure commonly rely on multiple linear regression methods. The spatial modeling approach employed here improves upon linear regression methods in allowing for exploration of nonlinear relationships and interactions between variables. Three machine-learning algorithms were used to evaluate relationships between snow-crab distribution and environmental parameters, including TreeNet, Random Forests, and MARS. An ensemble model was then generated by combining output from these three models to generate consensus predictions for presence-absence, abundance, and biomass of snow crabs. Each algorithm identified a suite of variables most important in predicting snow-crab distribution, including nutrient and chlorophyll-a concentrations in overlying waters, temperature, salinity, and annual sea-ice cover; this information may be used to develop and test hypotheses regarding the ecology of this species. This is the first such quantitative model for snow crabs, and all GIS-data layers compiled for this project are freely available from the authors, upon request, for public use and improvement.

Seasonal and inter-annual snowmelt patterns in the southern Sierra Nevada, California

NASA Astrophysics Data System (ADS)

Musselman, K. N.; Molotch, N. P.; Margulis, S. A.

2012-12-01

In the Sierra Nevada, seasonal snow represents a critical component of California's water resource infrastructure in that it affords reliable water during otherwise arid summers. Complex spatial, seasonal and inter-annual snowmelt patterns determine when and where that meltwater is available. Our knowledge of snowmelt dynamics is typically limited to what we can infer from sparse, point-scale snow measurement stations. Limitations such as these motivate the use of numerical snowmelt models. We evaluate the ability of the Alpine3D model system to represent three years of snow dynamics over an 1800 km2 area of Sequoia National Park. The domain spans a 3600 m elevation gradient and ecosystems ranging from semi-arid grasslands to massive sequoia stands to alpine tundra. The model results were evaluated against data from a multi-scale measurement campaign that included airborne LiDAR, clusters of snow depth sensors, repeated manual snow surveys, and automated SWE stations. Compared to these measurements, Alpine3D consistently performed well in middle elevation conifer forests; compared to LiDAR data, the mean snow depth error in forested regions was < 2%. The model also simulated the snow disappearance date within two days of that measured by regional automated sensors. At upper elevations, however, the model tended to overestimate SWE by 50% to as much as 100% in some areas and the errors were linearly correlated (R2 > 0.80, p<0.01) with the distance of the SWE measurements from the nearest precipitation gauge used to derive the model forcing. The results suggest that Alpine3D is highly accurate during the melt season and that precipitation uncertainty may be a critical limitation on snow model accuracy. Finally, an analysis of seasonal and inter-annual snowmelt patterns highlighted distinct melt differences between lower, middle, and upper elevations. Snowmelt was generally most frequent (70% - 95% of the snow-covered season) at the lower elevations where snow cover was episodic and seasonal mean melt rates computed on days when melt was simulated were generally low (< 3 mm day-1). At upper elevations, melt occurred during less than 65% of the snow-covered period, occurred later in the season and mean melt rates were the highest of the region (> 6 mm day-1). Middle elevations remained continuously snow covered throughout the winter and early spring, were prone to frequent but intermittent melt, and provided the most sustained period of seasonal mean snowmelt (~ 5 mm day-1). The melt dynamics (e.g. timing and melt rate) unique to these middle elevations may be critical to the local forest ecosystem. Furthermore, the three years evaluated in this study indicate a marked sensitivity of this elevation range to seasonal meteorology, suggesting that it could be highly sensitive to future changes in climate.

Snow multivariable data assimilation for hydrological predictions in mountain areas

NASA Astrophysics Data System (ADS)

Piazzi, Gaia; Campo, Lorenzo; Gabellani, Simone; Rudari, Roberto; Castelli, Fabio; Cremonese, Edoardo; Morra di Cella, Umberto; Stevenin, Hervé; Ratto, Sara Maria

2016-04-01

The seasonal presence of snow on alpine catchments strongly impacts both surface energy balance and water resource. Thus, the knowledge of the snowpack dynamics is of critical importance for several applications, such as water resource management, floods prediction and hydroelectric power production. Several independent data sources provide information about snowpack state: ground-based measurements, satellite data and physical models. Although all these data types are reliable, each of them is affected by specific flaws and errors (respectively dependency on local conditions, sensor biases and limitations, initialization and poor quality forcing data). Moreover, there are physical factors that make an exhaustive reconstruction of snow dynamics complicated: snow intermittence in space and time, stratification and slow phenomena like metamorphism processes, uncertainty in snowfall evaluation, wind transportation, etc. Data Assimilation (DA) techniques provide an objective methodology to combine observational and modeled information to obtain the most likely estimate of snowpack state. Indeed, by combining all the available sources of information, the implementation of DA schemes can quantify and reduce the uncertainties of the estimations. This study presents SMASH (Snow Multidata Assimilation System for Hydrology), a multi-layer snow dynamic model, strengthened by a robust multivariable data assimilation algorithm. The model is physically based on mass and energy balances and can be used to reproduce the main physical processes occurring within the snowpack: accumulation, density dynamics, melting, sublimation, radiative balance, heat and mass exchanges. The model is driven by observed forcing meteorological data (air temperature, wind velocity, relative air humidity, precipitation and incident solar radiation) to provide a complete estimate of snowpack state. The implementation of an Ensemble Kalman Filter (EnKF) scheme enables to assimilate simultaneously ground-based and remotely sensed data of different snow-related variables (snow albedo and surface temperature, Snow Water Equivalent from passive microwave sensors and Snow Cover Area). SMASH performance was evaluated in the period June 2012 - December 2013 at the meteorological station of Torgnon (Tellinod, 2 160 msl), located in Aosta Valley, a mountain region in northwestern Italy. The EnKF algorithm was firstly tested by assimilating several ground-based measurements: snow depth, land surface temperature, snow density and albedo. The assimilation of snow observed data revealed an overall considerable enhancement of model predictions with respect to the open loop experiments. A first attempt to integrate also remote sensed information was performed by assimilating the Land Surface Temperature (LST) from METEOSAT Second Generation (MSG), leading to good results. The analysis allowed identifying the snow depth and the snowpack surface temperature as the most impacting variables in the assimilation process. In order to pinpoint an optimal number of ensemble instances, SMASH performances were also quantitatively evaluated by varying the instances amount. Furthermore, the impact of the data assimilation frequency was analyzed by varying the assimilation time step (3h, 6h, 12h, 24h).

A Model for Backscattering from Quasi Periodic Corn Canopies at L-Band

NASA Technical Reports Server (NTRS)

Lang, R.; Utku, C.; Zhao, Q.; O'Neill, P.

2010-01-01

In this study, a model for backscattering at L-band from a corn canopy is proposed. The canopy consists of a quasi-periodic distribution of stalks and a random distribution of leaves. The Distorted Born Approximation (DBA) is employed to calculate the single scattered return from the corn field. The new feature of the method is that the coherence of the stalks in the row direction is incorporated in the model in a systematic fashion. Since the wavelength is on the order of the distance between corn stalks in a row, grating lobe behavior is observed at certain azimuth angles of incidence. The results are compared with experimental values measured in Huntsville, Alabama in 1998. The mean field and the effective dielectric constant of the canopy are obtained by using the Foldy approximation. The stalks are placed in the effective medium in a two dimensional lattice to simulate the row structure of a corn field. In order to mimic a real corn field, a quasi-periodic stalk distribution is assumed where the stalks are given small random perturbations about their lattice locations. Corn leaves are also embedded in the effective medium and the backscattered field from the stalks and the leaves is computed. The backscattering coefficient is calculated and averaged over successive stalk position perturbations. It is assumed that soil erosion has smoothed the soil sufficiently so that it can be assumed flat. Corn field backscatter data was collected from cornfields during the Huntsville 98 experimental campaign held at Alabama A&M University Research Station, Huntsville, Alabama in 1998 using the NASA/GW truck mounted radar. Extensive ground truth data was collected. This included soil moisture measurements and corn plant architectural data to be used in the model. In particular, the distances between the stalks in a single row have been measured. The L-band radar backscatter data was collected for both H and V polarizations and for look angles of 15o and 45o over a two week period under varying soil moisture conditions. These measured backscattering values will be compared with the model backscattering values and a discussion of the results will be presented.

MODIS Snow and Sea Ice Products

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Riggs, George A.; Salomonson, Vincent V.

2004-01-01

In this chapter, we describe the suite of Earth Observing System (EOS) Moderate-Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua snow and sea ice products. Global, daily products, developed at Goddard Space Flight Center, are archived and distributed through the National Snow and Ice Data Center at various resolutions and on different grids useful for different communities Snow products include binary snow cover, snow albedo, and in the near future, fraction of snow in a 5OO-m pixel. Sea ice products include ice extent determined with two different algorithms, and sea ice surface temperature. The algorithms used to develop these products are described. Both the snow and sea ice products, available since February 24,2000, are useful for modelers. Validation of the products is also discussed.

The Microwave Radiative Properties of Falling Snow Derived from Nonspherical Ice Particle Models. Part II: Initial Testing Using Radar, Radiometer and In Situ Observations

NASA Technical Reports Server (NTRS)

Olson, William S.; Tian, Lin; Grecu, Mircea; Kuo, Kwo-Sen; Johnson, Benjamin; Heymsfield, Andrew J.; Bansemer, Aaron; Heymsfield, Gerald M.; Wang, James R.; Meneghini, Robert

2016-01-01

In this study, two different particle models describing the structure and electromagnetic properties of snow are developed and evaluated for potential use in satellite combined radar-radiometer precipitation estimation algorithms. In the first model, snow particles are assumed to be homogeneous ice-air spheres with single-scattering properties derived from Mie theory. In the second model, snow particles are created by simulating the self-collection of pristine ice crystals into aggregate particles of different sizes, using different numbers and habits of the collected component crystals. Single-scattering properties of the resulting nonspherical snow particles are determined using the discrete dipole approximation. The size-distribution-integrated scattering properties of the spherical and nonspherical snow particles are incorporated into a dual-wavelength radar profiling algorithm that is applied to 14- and 34-GHz observations of stratiform precipitation from the ER-2 aircraft-borne High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) radar. The retrieved ice precipitation profiles are then input to a forward radiative transfer calculation in an attempt to simulate coincident radiance observations from the Conical Scanning Millimeter-Wave Imaging Radiometer (CoSMIR). Much greater consistency between the simulated and observed CoSMIR radiances is obtained using estimated profiles that are based upon the nonspherical crystal/aggregate snow particle model. Despite this greater consistency, there remain some discrepancies between the higher moments of the HIWRAP-retrieved precipitation size distributions and in situ distributions derived from microphysics probe observations obtained from Citation aircraft underflights of the ER-2. These discrepancies can only be eliminated if a subset of lower-density crystal/aggregate snow particles is assumed in the radar algorithm and in the interpretation of the in situ data.

Strategies for using remotely sensed data in hydrologic models

NASA Technical Reports Server (NTRS)

Peck, E. L.; Keefer, T. N.; Johnson, E. R. (Principal Investigator)

1981-01-01

Present and planned remote sensing capabilities were evaluated. The usefulness of six remote sensing capabilities (soil moisture, land cover, impervious area, areal extent of snow cover, areal extent of frozen ground, and water equivalent of the snow cover) with seven hydrologic models (API, CREAMS, NWSRFS, STORM, STANFORD, SSARR, and NWSRFS Snowmelt) were reviewed. The results indicate remote sensing information has only limited value for use with the hydrologic models in their present form. With minor modifications to the models the usefulness would be enhanced. Specific recommendations are made for incorporating snow covered area measurements in the NWSRFS Snowmelt model. Recommendations are also made for incorporating soil moisture measurements in NWSRFS. Suggestions are made for incorporating snow covered area, soil moisture, and others in STORM and SSARR. General characteristics of a hydrologic model needed to make maximum use of remotely sensed data are discussed. Suggested goals for improvements in remote sensing for use in models are also established.

A new parameterization of the post-fire snow albedo effect

NASA Astrophysics Data System (ADS)

Gleason, K. E.; Nolin, A. W.

2013-12-01

Mountain snowpack serves as an important natural reservoir of water: recharging aquifers, sustaining streams, and providing important ecosystem services. Reduced snowpacks and earlier snowmelt have been shown to affect fire size, frequency, and severity in the western United States. In turn, wildfire disturbance affects patterns of snow accumulation and ablation by reducing canopy interception, increasing turbulent fluxes, and modifying the surface radiation balance. Recent work shows that after a high severity forest fire, approximately 60% more solar radiation reaches the snow surface due to the reduction in canopy density. Also, significant amounts of pyrogenic carbon particles and larger burned woody debris (BWD) are shed from standing charred trees, which concentrate on the snowpack, darken its surface, and reduce snow albedo by 50% during ablation. Although the post-fire forest environment drives a substantial increase in net shortwave radiation at the snowpack surface, driving earlier and more rapid melt, hydrologic models do not explicitly incorporate forest fire disturbance effects to snowpack dynamics. The objective of this study was to parameterize the post-fire snow albedo effect due to BWD deposition on snow to better represent forest fire disturbance in modeling of snow-dominated hydrologic regimes. Based on empirical results from winter experiments, in-situ snow monitoring, and remote sensing data from a recent forest fire in the Oregon High Cascades, we characterized the post-fire snow albedo effect, and developed a simple parameterization of snowpack albedo decay in the post-fire forest environment. We modified the recession coefficient in the algorithm: α = α0 + K exp (-nr) where α = snowpack albedo, α0 = minimum snowpack albedo (≈0.4), K = constant (≈ 0.44), -n = number of days since last major snowfall, r = recession coefficient [Rohrer and Braun, 1994]. Our parameterization quantified BWD deposition and snow albedo decay rates and related these forest disturbance effects to radiative heating and snow melt rates. We validated our parameterization of the post-fire snow albedo effect at the plot scale using a physically-based, spatially-distributed snow accumulation and melt model, and in-situ eddy covariance and snow monitoring data. This research quantified wildfire impacts to snow dynamics in the Oregon High Cascades, and provided a new parameterization of post-fire drivers to changes in high elevation winter water storage.

75 FR 52255 - Airworthiness Directives; Air Tractor, Inc. Models AT-802 and AT-802A Airplanes

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-25

... replacement at whichever Follow Snow Engineering Co. Service caps, the web plates, the center of the following... Engineering Co. Service Letter 80GG, revised December 21, 2005; Snow Engineering Co. Service Letter 284, dated October 4, 2009; Snow Engineering Co. Service Letter 281, dated August 1, 2009; Snow Engineering Co...

Linkages between Snow Cover Seasonality, Terrain, and Land Surface Phenology in the Highland Pastures of Kyrgyzstan

NASA Astrophysics Data System (ADS)

Henebry, Geoffrey; Tomaszewska, Monika; Kelgenbaeva, Kamilya

2017-04-01

In the highlands of Kyrgyzstan, vertical transhumance is the foundation of montane agropastoralism. Terrain attributes, such as elevation, slope, and aspect, affect snow cover seasonality, which is a key influence on the timing of plant growth and forage availability. Our study areas include the highland pastures in Central Tien Shan mountains, specifically in the rayons of Naryn and At-Bashy in Naryn oblast, and Alay and Chong-Alay rayons in Osh oblast. To explore the linkages between snow cover seasonality and land surface phenology as modulated by terrain and variations in thermal time, we use 16 years (2001-2016) of Landsat surface reflectance data at 30 m resolution with MODIS land surface temperature and snow cover products at 1 km and 500 m resolution, respectively, and two digital elevation models, SRTM and ASTER GDEM. We model snow cover seasonality using frost degree-days and land surface phenology using growing degree-days as quadratic functions of thermal time: a convex quadratic (CxQ) model for land surface phenology and a concave quadratic (CvQ) model for snow cover seasonality. From the fitted parameter coefficients, we calculated phenometrics, including "peak height" and "thermal time to peak" for the CxQ models and "trough depth" and "thermal time to trough" for the CvQ models. We explore how these phenometrics change as a function of elevation and slope-aspect interactions and due to interannual variability. Further, we examine how snow cover duration and timing affects the subsequent peak height and thermal time to peak in wetter, drier, and normal years.

Evaluation TRMM Rainfall Data In Hydrological Modeling For An Ungaged In Lhasa River Basin

NASA Astrophysics Data System (ADS)

Ji, H. J.; Liu, J.

2017-12-01

Evaluation TRMM Rainfall Data In Hydrological Modeling For An Ungaged In Lhasa River BasinHaijuan Ji1* Jintao Liu1,2 Shanshan Xu1___________________ 1College of Hydrology and Water Resources, Hohai University, Nanjing 210098, People's Republic of China 2State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, People's Republic of China ___________________ * Corresponding author. Tel.: +86-025-83786973; Fax: +86-025-83786606. E-mail address: Hhu201510@163.com (H.J. Ji). Abstract: The Tibetan Plateau plays an important role in regulating the regional hydrological processes due to its high elevations and being the headwaters of many major Asian river basins. If familiar with the distribution of hydrological characteristics, will help us improve the level of development and utilization the water resources. However, there exist glaciers and snow with few sites. It is significance for us to understand the glacier and snow hydrological process in order to recognize the evolution of water resources in the Tibetan. This manuscript takes Lhasa River as the study area, taking use of ground, remote sensing and assimilation data, taking advantage of high precision TRMM precipitation data and MODIS snow cover data, first, according to the data from ground station evaluation of TRMM data in the application of the accuracy of the Lhasa River, and based on MODIS data fusion of multi source microwave snow making cloudless snow products, which are used for discriminant and analysis glacier and snow regulation mechanism on day scale, add snow and glacier unit into xinanjing model, this model can simulate the study region's runoff evolution, parameter sensitivity even spatial variation of hydrological characteristics the next ten years on region grid scale. The results of hydrological model in Lhasa River can simulate the glacier and snow runoff variation in high cold region better, to enhance the predictive ability of the spring snow disaster.

Using Gridded Snow Covered Area and Snow-Water Equivalence Spatial Data Sets to Improve Snow-Pack Depletion Simulation in a Continental Scale Hydrologic Model

NASA Astrophysics Data System (ADS)

Risley, J. C.; Tracey, J. A.; Markstrom, S. L.; Hay, L.

2014-12-01

Snow cover areal depletion curves were used in a continuous daily hydrologic model to simulate seasonal spring snowmelt during the period between maximum snowpack accumulation and total melt. The curves are defined as the ratio of snow-water equivalence (SWE) divided by the seasonal maximum snow-water equivalence (Ai) (Y axis) versus the percent snow cover area (SCA) (X axis). The slope of the curve can vary depending on local watershed conditions. Windy sparsely vegetated high elevation watersheds, for example, can have a steeper slope than lower elevation forested watersheds. To improve the accuracy of simulated runoff at ungaged watersheds, individual snow cover areal depletion curves were created for over 100,000 hydrologic response units (HRU) in the continental scale U.S. Geological Survey (USGS) National Hydrologic Model (NHM). NHM includes the same components of the USGS Precipitation-Runoff-Modeling System (PRMS), except it uses consistent land surface characterization and model parameterization across the U.S. continent. Weighted-mean daily time series of 1-kilometer gridded SWE, from Snow Data Assimilation System (SNODAS), and 500-meter gridded SCA, from Moderate Resolution Imaging Spectroradiometer (MODIS), for 2003-2014 were computed for each HRU using the USGS Geo Data Portal. Using a screening process, pairs of SWE/Ai and SCA from the snowmelt period of each year were selected. SCA values derived from imagery that did not have any cloud cover and were >0 and <100 percent were selected. Unrealistically low and high SCA values that were paired with high and low SWE/Ai ratios, respectively, were removed. Second order polynomial equations were then fit to the remaining pairs of SWE/Ai and SCA to create a unique curve for each HRU. Simulations comparing these new curves with an existing single default curve in NHM will be made to determine if there are significant improvements in runoff.

ALBEDO MODELS FOR SNOW AND ICE ON A FRESHWATER LAKE. (R824801)

EPA Science Inventory

Abstract

Snow and ice albedo measurements were taken over a freshwater lake in Minnesota for three months during the winter of 1996_¯1997 for use in a winter lake water quality model. The mean albedo of new snow was measured as 0.83±0.028, while the...

Arctic plant ecophysiology and water source utilization in response to altered snow: isotopic (δ18O and δ2H) evidence for meltwater subsidies to deciduous shrubs.

PubMed

Jespersen, R Gus; Leffler, A Joshua; Oberbauer, Steven F; Welker, Jeffrey M

2018-06-28

Warming-linked woody shrub expansion in the Arctic has critical consequences for ecosystem processes and climate feedbacks. The snow-shrub interaction model has been widely implicated in observed Arctic shrub increases, yet equivocal experimental results regarding nutrient-related components of this model have highlighted the need for a consideration of the increased meltwater predicted in expanding shrub stands. We used a 22-year snow manipulation experiment to simultaneously address the unexplored role of snow meltwater in arctic plant ecophysiology and nutrient-related components of the snow-shrub hypothesis. We coupled measurements of leaf-level gas exchange and leaf tissue chemistry (%N and δ 13 C) with an analysis of stable isotopes (δ 18 O and δ 2 H) in soil water, precipitation, and stem water. In deeper snow areas photosynthesis, conductance, and leaf N increased and δ 13 C values decreased in the deciduous shrubs, Betula nana and Salix pulchra, and the graminoid, Eriophorum vaginatum, with the strongest treatment effects observed in deciduous shrubs, consistent with predictions of the snow-shrub hypothesis. We also found that deciduous shrubs, especially S. pulchra, obtained much of their water from snow melt early in the growing season (40-50%), more than either E. vaginatum or the evergreen shrub, Rhododendron tomentosum (Ledum palustre). This result provides the basis for adding a meltwater-focused feedback loop to the snow-shrub interaction model of shrub expansion in the Arctic. Our results highlight the critical role of winter snow in the ecophysiology of Arctic plants, particularly deciduous shrubs, and underline the importance of understanding how global warming will affect the Arctic winter snowpack.

The influence of canopy shading of snow on effective albedo in forested environments

NASA Astrophysics Data System (ADS)

Webster, C.; Jonas, T.

2017-12-01

The overlap of highly reflective snow and absorbent forested areas creates strong heterogeneity in the effective surface albedo compared to forest-free areas. Current errors in calculations of effective forest snow albedo arise due to uncertainties in how models should treat masking of snow by vegetation but improvement of local and large scale models is currently limited by a lack of measurements that demonstrate both spatial and temporal variability over forests. We present above-canopy measurements of winter-time effective forest snow albedo using up- and down-looking radiometers mounted on an octocopter UAV for a total of fifteen flights on eight different days. Ground-view fractions across the flight path were between 0.12 and 0.81. Correlations between effective albedo and both ground-view fraction and canopy height were statistically significant during 14 out of 15 flights, but varied between flights due to solar angle and snow cover. Measured effective albedo across the flight path differed by up to 0.33 during snow-on canopy conditions. A comparison between maximum interception and no interception showed effective albedo varied by up 0.17, which was the same variation between effective albedo during high (46°) and low (23°) solar elevation angles. Temporal and spatial variations in effective albedo caused by canopy shading of the snow surface are therefore as important as temporal variations caused by interception of snow by the canopy. Calculation of effective albedo over forested areas therefore requires careful consideration of canopy height, canopy coverage, solar angle and interception load. The results of this study should be used to inform snow albedo and canopy structure parametrisations in local and larger scale land surface models.

Skin dose mapping for non-uniform x-ray fields using a backscatter point spread function

NASA Astrophysics Data System (ADS)

Vijayan, Sarath; Xiong, Zhenyu; Shankar, Alok; Rudin, Stephen; Bednarek, Daniel R.

2017-03-01

Beam shaping devices like ROI attenuators and compensation filters modulate the intensity distribution of the xray beam incident on the patient. This results in a spatial variation of skin dose due to the variation of primary radiation and also a variation in backscattered radiation from the patient. To determine the backscatter component, backscatter point spread functions (PSF) are generated using EGS Monte-Carlo software. For this study, PSF's were determined by simulating a 1 mm beam incident on the lateral surface of an anthropomorphic head phantom and a 20 cm thick PMMA block phantom. The backscatter PSF's for the head phantom and PMMA phantom are curve fit with a Lorentzian function after being normalized to the primary dose intensity (PSFn). PSFn is convolved with the primary dose distribution to generate the scatter dose distribution, which is added to the primary to obtain the total dose distribution. The backscatter convolution technique is incorporated in the dose tracking system (DTS), which tracks skin dose during fluoroscopic procedures and provides a color map of the dose distribution on a 3D patient graphic model. A convolution technique is developed for the backscatter dose determination for the nonuniformly spaced graphic-model surface vertices. A Gafchromic film validation was performed for shaped x-ray beams generated with an ROI attenuator and with two compensation filters inserted into the field. The total dose distribution calculated by the backscatter convolution technique closely agreed with that measured with the film.

Snow stratigraphic heterogeneity within ground-based passive microwave radiometer footprints: implications for emission modelling

NASA Astrophysics Data System (ADS)

Sandells, M.; Rutter, N.; Derksen, C.; Langlois, A.; Lemmetyinen, J.; Montpetit, B.; Pulliainen, J. T.; Royer, A.; Toose, P.

2012-12-01

Remote sensing of snow mass remains a challenging area of research. Scattering of electromagnetic radiation is sensitive to snow mass, but is also affected by contrasts in the dielectric properties of the snow. Although the argument that errors from simple algorithms average out at large scales has been used to justify current retrieval methods, it is not obvious why this should be the case. This hypothesis needs to be tested more rigorously. A ground-based field experiment was carried out to assess the impact of sub-footprint snow heterogeneity on microwave brightness temperature, in Churchill, Canada in winter in early 2010. Passive microwave measurements of snow were made using sled-mounted radiometers at 75cm intervals over a 5m transect. Measurements were made at horizontal and vertical polarizations at frequencies of 19 and 37 GHz. Snow beneath the radiometer footprints was subsequently excavated, creating a snow trench wall along the centrepoints of adjacent footprints. The trench wall was carefully smoothed and photographed with a near-infrared camera in order to determine the positions of stratigraphic snow layer boundaries. Three one-dimensional vertical profiles of snowpack properties (density and snow specific surface area) were taken at 75cm, 185cm and 355cm from the left hand side of the trench. These profile measurements were used to derive snow density and grain size for each of the layers identified from the NIR image. Microwave brightness temperatures for the 2-dimensional map of snow properties was simulated with the Helsinki University of Technology (HUT) model at 1cm intervals horizontally across the trench. Where each of five ice lenses was identified in the snow stratigraphy, a decrease in brightness temperature was simulated. However, the median brightness temperature simulated across the trench was substantially higher than the observations, of the order of tens of Kelvin, dependent on frequency and polarization. In order to understand and quantify possible sources of error in the simulations, a number of experiments were carried out to investigate the sensitivity of the brightness temperature to: 1) uncertainties in field observations, 2) representation of ice lenses, 3) model layering structure, and 4) near-infrared derived grain size representing snow grain size at microwave wavelengths. Field measurement error made little difference to the simulated brightness temperature, nor did the representation of ice lenses as crusts of high density snow. As the number of layers in the snow was reduced to 3, 2, or 1, the simulated brightness temperature increased slightly. However, scaling of snow grain size had a dramatic effect on the simulated brightness temperatures, reducing the median bias of the simulations to within measurement error for the statistically different brightness temperature distributions. This indicated that further investigation is required to define what is meant by the microwave grain size, and how this relates to the grain size that is used in the microwave emission model.

Simulation of the Microwave Emission of Multi-layered Snowpacks Using the Dense Media Radiative Transfer Theory: the DMRT-ML Model

NASA Technical Reports Server (NTRS)

Picard, G.; Brucker, Ludovic; Roy, A.; Dupont, F.; Fily, M.; Royer, A.; Harlow, C.

2013-01-01

DMRT-ML is a physically based numerical model designed to compute the thermal microwave emission of a given snowpack. Its main application is the simulation of brightness temperatures at frequencies in the range 1-200 GHz similar to those acquired routinely by spacebased microwave radiometers. The model is based on the Dense Media Radiative Transfer (DMRT) theory for the computation of the snow scattering and extinction coefficients and on the Discrete Ordinate Method (DISORT) to numerically solve the radiative transfer equation. The snowpack is modeled as a stack of multiple horizontal snow layers and an optional underlying interface representing the soil or the bottom ice. The model handles both dry and wet snow conditions. Such a general design allows the model to account for a wide range of snow conditions. Hitherto, the model has been used to simulate the thermal emission of the deep firn on ice sheets, shallow snowpacks overlying soil in Arctic and Alpine regions, and overlying ice on the large icesheet margins and glaciers. DMRT-ML has thus been validated in three very different conditions: Antarctica, Barnes Ice Cap (Canada) and Canadian tundra. It has been recently used in conjunction with inverse methods to retrieve snow grain size from remote sensing data. The model is written in Fortran90 and available to the snow remote sensing community as an open-source software. A convenient user interface is provided in Python.

Estimation of snow albedo reduction by light absorbing impurities using Monte Carlo radiative transfer model

NASA Astrophysics Data System (ADS)

Sengupta, D.; Gao, L.; Wilcox, E. M.; Beres, N. D.; Moosmüller, H.; Khlystov, A.

2017-12-01

Radiative forcing and climate change greatly depends on earth's surface albedo and its temporal and spatial variation. The surface albedo varies greatly depending on the surface characteristics ranging from 5-10% for calm ocean waters to 80% for some snow-covered areas. Clean and fresh snow surfaces have the highest albedo and are most sensitive to contamination with light absorbing impurities that can greatly reduce surface albedo and change overall radiative forcing estimates. Accurate estimation of snow albedo as well as understanding of feedbacks on climate from changes in snow-covered areas is important for radiative forcing, snow energy balance, predicting seasonal snowmelt, and run off rates. Such information is essential to inform timely decision making of stakeholders and policy makers. Light absorbing particles deposited onto the snow surface can greatly alter snow albedo and have been identified as a major contributor to regional climate forcing if seasonal snow cover is involved. However, uncertainty associated with quantification of albedo reduction by these light absorbing particles is high. Here, we use Mie theory (under the assumption of spherical snow grains) to reconstruct the single scattering parameters of snow (i.e., single scattering albedo ῶ and asymmetry parameter g) from observation-based size distribution information and retrieved refractive index values. The single scattering parameters of impurities are extracted with the same approach from datasets obtained during laboratory combustion of biomass samples. Instead of using plane-parallel approximation methods to account for multiple scattering, we have used the simple "Monte Carlo ray/photon tracing approach" to calculate the snow albedo. This simple approach considers multiple scattering to be the "collection" of single scattering events. Using this approach, we vary the effective snow grain size and impurity concentrations to explore the evolution of snow albedo over a wide wavelength range (300 nm - 2000 nm). Results will be compared with the SNICAR model to better understand the differences in snow albedo computation between plane-parallel methods and the statistical Monte Carlo methods.

Influence of snow cover changes on surface radiation and heat balance based on the WRF model

NASA Astrophysics Data System (ADS)

Yu, Lingxue; Liu, Tingxiang; Bu, Kun; Yang, Jiuchun; Chang, Liping; Zhang, Shuwen

2017-10-01

The snow cover extent in mid-high latitude areas of the Northern Hemisphere has significantly declined corresponding to the global warming, especially since the 1970s. Snow-climate feedbacks play a critical role in regulating the global radiation balance and influencing surface heat flux exchange. However, the degree to which snow cover changes affect the radiation budget and energy balance on a regional scale and the difference between snow-climate and land use/cover change (LUCC)-climate feedbacks have been rarely studied. In this paper, we selected Heilongjiang Basin, where the snow cover has changed obviously, as our study area and used the WRF model to simulate the influences of snow cover changes on the surface radiation budget and heat balance. In the scenario simulation, the localized surface parameter data improved the accuracy by 10 % compared with the control group. The spatial and temporal analysis of the surface variables showed that the net surface radiation, sensible heat flux, Bowen ratio, temperature and percentage of snow cover were negatively correlated and that the ground heat flux and latent heat flux were positively correlated with the percentage of snow cover. The spatial analysis also showed that a significant relationship existed between the surface variables and land cover types, which was not obviously as that for snow cover changes. Finally, six typical study areas were selected to quantitatively analyse the influence of land cover types beneath the snow cover on heat absorption and transfer, which showed that when the land was snow covered, the conversion of forest to farmland can dramatically influence the net radiation and other surface variables, whereas the snow-free land showed significantly reduced influence. Furthermore, compared with typical land cover changes, e.g., the conversion of forest into farmland, the influence of snow cover changes on net radiation and sensible heat flux were 60 % higher than that of land cover changes, indicating the importance of snow cover changes in the surface-atmospheric feedback system.

Everywhere and nowhere: snow and its linkages

NASA Astrophysics Data System (ADS)

Hiemstra, C. A.

2017-12-01

Interest has grown in quantifying higher latitude precipitation change and snow-related ecosystem and economic impacts. There is a high demand for creating and using snow-related datasets, yet available datasets contain limitations, aren't scale appropriate, or lack thorough validation. Much of the uncertainty in snow estimates relates to ongoing snow measurement problems that are chronic and pervasive in windy, Arctic environments. This, coupled with diminishing support for long-term snow field observations, creates formidable hydrologic gaps in snow dominated landscapes. Snow touches most aspects of high latitude landscapes and spans albedo, ecosystems, soils, permafrost, and sea ice. In turn, snow can be impacted by disturbances, landscape change, ecosystem, structure, and later arrival of sea or lake ice. Snow, and its changes touch infrastructure, housing, and transportation. Advances in snow measurements, modeling, and data assimilation are under way, but more attention and a concerted effort is needed in a time of dwindling resources to make required advances during a time of rapid change.

Analyzing dynamics of snow distribution and melt runoff in a meso-scaled watershed using the AgroEcoSystem-Watershed (AgES-W) model

NASA Astrophysics Data System (ADS)

Kunz, A.; Helmschrot, J.; Green, T. R.

2013-12-01

The seasonal snow cover in the western mountain regions of the United States functions as the primary supply and storage of water. Water management in these areas is often based on empirical relationships between point measurements of snow water equivalent (SWE) at selected sites and associated stream discharge. With a climate shifting towards more rain and less snow, due to the global warming, the patterns of snow deposition, and consequently the timing of melt, soil water content and the flow in streams and rivers will most likely alter. As a consequence, the established relationships between measured SWE and runoff will become unstable and unreliable, and consequently impacting the water resource management in this area. To better assess and understand the spatial and temporal dimension of altered snow cover on runoff generation in the intermountain region of the western United States, we set up the distributed hydrological AgroEcoSystem-Watershed (AgES-W) model for the Reynolds Creek Experimental Watershed (239 km2) in the Owyhee Mountains of Idaho. The study area with elevations ranging from 1101 to 2241 m is dominated by granitic and volcanic rocks and lake sediments. Deep moist soils allowing for mountain big sagebrush aspen and subalpine fir are found at higher elevations, whereas shallow, arid soils supporting sagebrush-grassland communities are common at lower elevations. Precipitation in the region varies from 230 mm at the lower elevations in the north up to 1100 mm in the higher regions at the southern margin south. The mean annual streamflow at the outlet is 0.56 m3/s. Since the Reynolds Creek Experimental Watershed (RCEW) was selected as a test basin in 1959, a comprehensive hydro-climatological network provides long-term records of daily snow, precipitation, temperature and streamflow measurements. Thus, we used a 30-year data record to calibrate and validate the AgES-W model to three nested sub-basins within the test site. First results show declining discharge volumes for RCEW, while volumes remain fairly constant for the 0.4 km2 Reynolds Mountain East (RME) headwater basin. Comparing simulated snow cover with snow-depth records measured across RME, the model was initially tested regarding its reliability to estimate spatio-temporal snow cover. AgES-W was able to simulate snow-depth dynamics quite well (>0.7 Nash-Sutcliffe Efficiency) for single measurement points, which were cross-validated using additional measurement points as well as stream discharge. The obtained parameter set was then used to model snow distribution for the entire RME basin for a period of 12 years. Applying the calibrated model to all catchments, we analyzed temporal shifts of seasonal runoff within and between the three nested subwatersheds to identify possible changes in the spatio-temporal pattern of snow accumulation and snowmelt. The model results were further used to analyze and map simulated snow water equivalents along a topographic gradient to identify spatial shifts of the snowline during the last 30 years. First results for RME indicate a decline of snow-covered area based on the course of monthly averages, with the largest declines in January and February.

Indices for estimating fractional snow cover in the western Tibetan Plateau

NASA Astrophysics Data System (ADS)

Shreve, Cheney M.; Okin, Gregory S.; Painter, Thomas H.

Snow cover in the Tibetan Plateau is highly variable in space and time and plays a key role in ecological processes of this cold-desert ecosystem. Resolution of passive microwave data is too low for regional-scale estimates of snow cover on the Tibetan Plateau, requiring an alternate data source. Optically derived snow indices allow for more accurate quantification of snow cover using higher-resolution datasets subject to the constraint of cloud cover. This paper introduces a new optical snow index and assesses four optically derived MODIS snow indices using Landsat-based validation scenes: MODIS Snow-Covered Area and Grain Size (MODSCAG), Relative Multiple Endmember Spectral Mixture Analysis (RMESMA), Relative Spectral Mixture Analysis (RSMA) and the normalized-difference snow index (NDSI). Pearson correlation coefficients were positively correlated with the validation datasets for all four optical snow indices, suggesting each provides a good measure of total snow extent. At the 95% confidence level, linear least-squares regression showed that MODSCAG and RMESMA had accuracy comparable to validation scenes. Fusion of optical snow indices with passive microwave products, which provide snow depth and snow water equivalent, has the potential to contribute to hydrologic and energy-balance modeling in the Tibetan Plateau.

Distributed Assimilation of Satellite-based Snow Extent for Improving Simulated Streamflow in Mountainous, Dense Forests: An Example Over the DMIP2 Western Basins

NASA Technical Reports Server (NTRS)

Yatheendradas, Soni; Peters-Lidard, Christa D.; Koren, Victor; Cosgrove, Brian A.; DeGoncalves, Luis G. D.; Smith, Michael; Geiger, James; Cui, Zhengtao; Borak, Jordan; Kumar, Sujay V.;

Assessment of snow modeling decisions in the extra-tropical Andes Cordillera

NASA Astrophysics Data System (ADS)

Mendoza, P. A.; Musselman, K. N.; Raleigh, M. S.; Clark, M. P.; McPhee, J. P.

2017-12-01

Improving model realism is an ongoing challenge for the cryosphere research community, not only to advance process understanding, but also to quantify and reduce uncertainty under global warming conditions. This work attempts to characterize the interplay and impact of user decisions about snow model structure and parameter specification on model uncertainty. Snow simulations were conducted in the extra-tropical Andes - a mountainous region that acts as a natural reservoir for Central Chile and Western Argentina. To address this topic, we apply the Structure for Unifying Multiple Modeling Alternatives (SUMMA) to simulate seasonal snowpack dynamics at three sites with different hydroclimatic regimes (semi-arid, Mediterranean, and temperate humid). Results are verified against extensive ground-based observations. Site elevations decrease from north to south, whereas precipitation amounts increase with latitude. Results highlight the impact of different windflow and snow transport decisions on model skill during the accumulation period, and different parameterizations (e.g., albedo decay) on spring simulations. We anticipate that the outcomes from this study will have important implications on current and future research, in particular on the configuration of snow models used to quantify the availability of water resources in this region.

CUES - A Study Site for Measuring Snowpack Energy Balance in the Sierra Nevada

NASA Astrophysics Data System (ADS)

Bair, Edward; Dozier, Jeff; Davis, Robert; Colee, Michael; Claffey, Keran

2015-09-01

Accurate measurement and modeling of the snowpack energy balance are critical to understanding the terrestrial water cycle. Most of the water resources in the western US come from snowmelt, yet statistical runoff models that rely on the historical record are becoming less reliable because of a changing climate. For physically based snow melt models that do not depend on past conditions, ground based measurements of the energy balance components are imperative for verification. For this purpose, the US Army Corps of Engineers Cold Regions Research and Engineering Laboratory (CRREL) and the University of California, Santa Barbara (UCSB) established the “CUES” snow study site (CRREL/UCSB Energy Site, http://www.snow.ucsb.edu/) at 2940 m elevation on Mammoth Mountain, California. We describe CUES, provide an overview of research, share our experience with scientific measurements, and encourage future collaborative research. Snow measurements began near the current CUES site for ski area operations in 1969. In the 1970s, researchers began taking scientific measurements. Today, CUES benefits from year round gondola access and a fiber optic internet connection. Data loggers and computers automatically record and store over 100 measurements from more than 50 instruments each minute. CUES is one of only five high altitude mountain sites in the Western US where a full suite of energy balance components are measured. In addition to measuring snow on the ground at multiple locations, extensive radiometric and meteorological measurements are recorded. Some of the more novel measurements include scans by an automated terrestrial LiDAR, passive and active microwave imaging of snow stratigraphy, microscopic imaging of snow grains, snowflake imaging with a multi-angle camera, fluxes from upward and downward looking radiometers, snow water equivalent from different types of snow pillows, snowmelt from lysimeters, and concentration of impurities in the snowpack. We give an example of terrain-corrected snow albedo measurements compared to several models and of sublimation measured from lysimeter and snow pillow melt. We conclude with some thoughts on the future of CUES.

In situ phytoplankton absorption, fluorescence emission, and particulate backscattering spectra determined from reflectance

NASA Technical Reports Server (NTRS)

Roesler, Collin S.; Pery, Mary Jane

1995-01-01

An inverse model was developed to extract the absortion and scattering (elastic and inelastic) properties of oceanic constituents from surface spectral reflectance measurements. In particular, phytoplankton spectral absorption coefficients, solar-stimulated chlorophyll a fluorescence spectra, and particle backscattering spectra were modeled. The model was tested on 35 reflectance spectra obtained from irradiance measurements in optically diverse ocean waters (0.07 to 25.35 mg/cu m range in surface chlorophyll a concentrations). The universality of the model was demonstrated by the accurate estimation of the spectral phytoplankton absorption coefficents over a range of 3 orders of magnitude (rho = 0.94 at 500 nm). Under most oceanic conditions (chlorophyll a less than 3 mg/cu m) the percent difference between measured and modeled phytoplankton absorption coefficents was less than 35%. Spectral variations in measured phytoplankton absorption spectra were well predicted by the inverse model. Modeled volume fluorescence was weakly correlated with measured chl a; fluorescence quantum yield varied from 0.008 to 0.09 as a function of environment and incident irradiance. Modeled particle backscattering coefficients were linearly related to total particle cross section over a twentyfold range in backscattering coefficents (rho = 0.996, n = 12).

Modeling the Interaction of Radiation Between Vegetation and the Seasonal Snowcover

NASA Astrophysics Data System (ADS)

Tribbeck, M. J.; Gurney, R. J.; Morris, E. M.; Pearson, D.

2001-12-01

Prediction of meltwater runoff is crucial to communities where the seasonal snowpack is the major water supply. Water is itself a vital resource and it carries nutrients both in solution and in suspension. Simulation of snowpack depletion at a point in open areas has previously been shown to produce accurate results using physically based models such as SNTHERM. However, the radiation balance is more complex under a forest canopy as radiation is scattered and absorbed by canopy elements. This can alter the timing and magnitude of snowpack runoff substantially. The interaction of radiation between a forest canopy and its underlying snowcover is modeled by the coupling of a physically based snow model and an optical and thermal radiation canopy model. The snow model, which is based on SNTHERM (Jordan, 1991), is a discrete, multi-layer, one-dimensional mass and energy budget model for snow and is formulated with an adaptive grid system that compresses with the compacting snowpack and allows retention of snowpack stratigraphy. The vegetation canopy model approximates the canopy as a series of discrete, randomly orientated elements that scatter and absorb optical and thermal radiation. Multiple scattering of radiation between canopy and snow surface is modeled to conserve energy. The coupled model SNOWCAN differs from other vegetation-snow models such as GORT or SNOBAL as it models the albedo feedback mechanism. This is important as the albedo both affects and is affected by (through grain growth) the radiation balance. SNOWCAN is driven by standard atmospheric variables (including incident solar and thermal radiation) measured outside of the canopy and simulates snowpack properties such as temperature and density profiles as well as the sub-canopy radiation balance. The coupled snow and vegetation energy budget model was used to simulate snow depth at an old jack pine site during the 1994 BOREAS campaign. Measured and simulated snow depth showed good agreement throughout the accumulation and ablation periods, yielding an r2 correlation coefficient of 0.94. The snowpack development was also simulated at a point site within a fir stand in Reynolds Creek Experimental Watershed, Idaho, USA for the water year 2000-2001. A sensitivity analysis was carried out and comparisons were made with field observations of snowpack properties and sub-canopy radiation data for model validation.

Spatiotemporal variability of snow depletion curves derived from SNODAS for the conterminous United States, 2004-2013

USGS Publications Warehouse

Driscoll, Jessica; Hay, Lauren E.; Bock, Andrew R.

2017-01-01

Assessment of water resources at a national scale is critical for understanding their vulnerability to future change in policy and climate. Representation of the spatiotemporal variability in snowmelt processes in continental-scale hydrologic models is critical for assessment of water resource response to continued climate change. Continental-extent hydrologic models such as the U.S. Geological Survey National Hydrologic Model (NHM) represent snowmelt processes through the application of snow depletion curves (SDCs). SDCs relate normalized snow water equivalent (SWE) to normalized snow covered area (SCA) over a snowmelt season for a given modeling unit. SDCs were derived using output from the operational Snow Data Assimilation System (SNODAS) snow model as daily 1-km gridded SWE over the conterminous United States. Daily SNODAS output were aggregated to a predefined watershed-scale geospatial fabric and used to also calculate SCA from October 1, 2004 to September 30, 2013. The spatiotemporal variability in SNODAS output at the watershed scale was evaluated through the spatial distribution of the median and standard deviation for the time period. Representative SDCs for each watershed-scale modeling unit over the conterminous United States (n = 54,104) were selected using a consistent methodology and used to create categories of snowmelt based on SDC shape. The relation of SDC categories to the topographic and climatic variables allow for national-scale categorization of snowmelt processes.

Correcting Inadequate Model Snow Process Descriptions Dramatically Improves Mountain Hydrology Simulations

NASA Astrophysics Data System (ADS)

Pomeroy, J. W.; Fang, X.

2014-12-01

The vast effort in hydrology devoted to parameter calibration as a means to improve model performance assumes that the models concerned are not fundamentally wrong. By focussing on finding optimal parameter sets and ascribing poor model performance to parameter or data uncertainty, these efforts may fail to consider the need to improve models with more intelligent descriptions of hydrological processes. To test this hypothesis, a flexible physically based hydrological model including a full suite of snow hydrology processes as well as warm season, hillslope and groundwater hydrology was applied to Marmot Creek Research Basin, Canadian Rocky Mountains where excellent driving meteorology and basin biophysical descriptions exist. Model parameters were set from values found in the basin or from similar environments; no parameters were calibrated. The model was tested against snow surveys and streamflow observations. The model used algorithms that describe snow redistribution, sublimation and forest canopy effects on snowmelt and evaporative processes that are rarely implemented in hydrological models. To investigate the contribution of these processes to model predictive capability, the model was "falsified" by deleting parameterisations for forest canopy snow mass and energy, blowing snow, intercepted rain evaporation, and sublimation. Model falsification by ignoring forest canopy processes contributed to a large increase in SWE errors for forested portions of the research basin with RMSE increasing from 19 to 55 mm and mean bias (MB) increasing from 0.004 to 0.62. In the alpine tundra portion, removing blowing processes resulted in an increase in model SWE MB from 0.04 to 2.55 on north-facing slopes and -0.006 to -0.48 on south-facing slopes. Eliminating these algorithms degraded streamflow prediction with the Nash Sutcliffe efficiency dropping from 0.58 to 0.22 and MB increasing from 0.01 to 0.09. These results show dramatic model improvements by including snow redistribution and melt processes associated with wind transport and forest canopies. As most hydrological models do not currently include these processes, it is suggested that modellers first improve the realism of model structures before trying to optimise what are inherently inadequate simulations of hydrology.

Estimation of Coastal Freshwater Discharge into Prince William Sound using a High-Resolution Hydrological Model

NASA Astrophysics Data System (ADS)

Beamer, J. P.; Hill, D. F.; Liston, G. E.; Arendt, A. A.; Hood, E. W.

2013-12-01

In Prince William Sound (PWS), Alaska, there is a pressing need for accurate estimates of the spatial and temporal variations in coastal freshwater discharge (FWD). FWD into PWS originates from streamflow due to rainfall, annual snowmelt, and changes in stored glacier mass and is important because it helps establish spatial and temporal patterns in ocean salinity and temperature, and is a time-varying boundary condition for oceanographic circulation models. Previous efforts to model FWD into PWS have been heavily empirical, with many physical processes absorbed into calibration coefficients that, in many cases, were calibrated to streams and rivers not hydrologically similar to those discharging into PWS. In this work we adapted and validated a suite of high-resolution (in space and time), physically-based, distributed weather, snowmelt, and runoff-routing models designed specifically for snow melt- and glacier melt-dominated watersheds like PWS in order to: 1) provide high-resolution, real-time simulations of snowpack and FWD, and 2) provide a record of historical variations of FWD. SnowModel, driven with gridded topography, land cover, and 32 years (1979-2011) of 3-hourly North American Regional Reanalysis (NARR) atmospheric forcing data, was used to simulate snowpack accumulation and melt across a PWS model domain. SnowModel outputs of daily snow water equivalent (SWE) depth and grid-cell runoff volumes were then coupled with HydroFlow, a runoff-routing model which routed snowmelt, glacier-melt, and rainfall to each watershed outlet (PWS coastline) in the simulation domain. The end product was a continuous 32-year simulation of daily FWD into PWS. In order to validate the models, SWE and snow depths from SnowModel were compared with observed SWE and snow depths from SnoTel and snow survey data, and discharge from HydroFlow was compared with observed streamflow measurements. As a second phase of this research effort, the coupled models will be set-up to run in real-time, where daily measurements from weather stations in the PWS will be used to drive simulations of snow cover and streamflow. In addition, we will deploy a strategic array of instrumentation aimed at validating the simulated weather estimates and the calculations of freshwater discharge. Upon successful implementation and validation of the modeling system, it will join established and ongoing computational and observational efforts that have a common goal of establishing a comprehensive understanding of the physical behavior of PWS.

Contribution of high resolution remote sensing data to the modeling of the snow cover the in Atlas Mountains

NASA Astrophysics Data System (ADS)

Baba, Wassim; Gascoin, Simon; Hanich, Lahoucine; Kinnard, Christophe

2017-04-01

Snow melt from the Atlas Mountains watersheds represent an important water resource for the semi-arid, cultivated, lowlands. Due to the high incoming solar radiation and low precipitation, the spatial-temporal variability of the snowpack is expected to be strongly influenced by the topography. We explore this hypothesis using a distributed energy balance snow model (SnowModel) in the experimental watershed of the Rheraya River in Morocco (225 km2). The digital elevation model (DEM) in SnowModel is used for the computation of the gridded meteorological forcing from the automatic weather stations data. We acquired three Pléiades stereo pairs in to produce an accurate, high resolution DEM of the Rheraya watershed at 4 m posting. Then, the DEM was resampled to different spatial resolutions (8 m, 30 m, 90 m, 250 m and 500 m) to simulate the snowpack evolution over 2008-2009 snow season. As validation data we used a time series of 15 maps of the snow cover area (SCA) from Formosat-2 imagery over the same snow season in the upper Rheraya watershed. These maps have a resolution of 8 m, which enables to capture small-scale variability in the snow cover. We found that the simulations at 90 m, 30 m and 8 m yield similar results at the catchment scale, with significant differences in areas of very steep topography only. From February to April, an overall good agreement was observed between the simulated SCA and the Formosat-2 SCA at 8 m and 90 m. Before the melting season, true positive (TP) column of confusion matrix is close to 1, but it drops to 0.6 during the melting season. Heidke Skill Score is higher than 0.7 for the most of the validation dates and averages 0.8. On the contrary, 500 m simulation underestimates the SCA throughout the snow season and the TP score is always inferior to the one obtained at 8 m and 90 m. We further analyzed the effect of topography by comparing the distribution of meteorological and snowpack variables along north-south and east-west transects. This analysis indicates that the impact of the topography on the simulated SWE and snow melt is mainly driven by changes in the solar radiations and the precipitations.

Modeling of microwave scattering from vegetated covered terrain

NASA Technical Reports Server (NTRS)

Lang, R. H.

1982-01-01

General formulation of resonant backscattering from vegetation, mean field and Green's function in three media, and electromagnetic backscattering coefficients from a layer of vegetation are discussed.

Relating P-band AIRSAR backscatter to forest stand parameters

NASA Technical Reports Server (NTRS)

Wang, Yong; Melack, John M.; Davis, Frank W.; Kasischke, Eric S.; Christensen, Norman L., Jr.

1993-01-01

As part of research on forest ecosystems, the Jet Propulsion Laboratory (JPL) and collaborating research teams have conducted multi-season airborne synthetic aperture radar (AIRSAR) experiments in three forest ecosystems including temperate pine forest (Duke, Forest, North Carolina), boreal forest (Bonanza Creek Experimental Forest, Alaska), and northern mixed hardwood-conifer forest (Michigan Biological Station, Michigan). The major research goals were to improve understanding of the relationships between radar backscatter and phenological variables (e.g. stand density, tree size, etc.), to improve radar backscatter models of tree canopy properties, and to develop a radar-based scheme for monitoring forest phenological changes. In September 1989, AIRSAR backscatter data were acquired over the Duke Forest. As the aboveground biomass of the loblolly pine forest stands at Duke Forest increased, the SAR backscatter at C-, L-, and P-bands increased and saturated at different biomass levels for the C-band, L-band, and P-band data. We only use the P-band backscatter data and ground measurements here to study the relationships between the backscatter and stand density, the backscatter and mean trunk dbh (diameter at breast height) of trees in the stands, and the backscatter and stand basal area.

Simulated cold bias being improved by using MODIS time-varying albedo in the Tibetan Plateau in WRF model

NASA Astrophysics Data System (ADS)

Meng, X.; Lyu, S.; Zhang, T.; Zhao, L.; Li, Z.; Han, B.; Li, S.; Ma, D.; Chen, H.; Ao, Y.; Luo, S.; Shen, Y.; Guo, J.; Wen, L.

2018-04-01

Systematic cold biases exist in the simulation for 2 m air temperature in the Tibetan Plateau (TP) when using regional climate models and global atmospheric general circulation models. We updated the albedo in the Weather Research and Forecasting (WRF) Model lower boundary condition using the Global LAnd Surface Satellite Moderate-Resolution Imaging Spectroradiometer albedo products and demonstrated evident improvement for cold temperature biases in the TP. It is the large overestimation of albedo in winter and spring in the WRF model that resulted in the large cold temperature biases. The overestimated albedo was caused by the simulated precipitation biases and over-parameterization of snow albedo. Furthermore, light-absorbing aerosols can result in a large reduction of albedo in snow and ice cover. The results suggest the necessity of developing snow albedo parameterization using observations in the TP, where snow cover and melting are very different from other low-elevation regions, and the influence of aerosols should be considered as well. In addition to defining snow albedo, our results show an urgent call for improving precipitation simulation in the TP.

Snow distribution and heat flow in the taiga

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sturm, M.

1992-05-01

The trees of the taiga intercept falling snow and cause it to become distributed in an uneven fashion. Around aspen and birch, cone-shaped accumulations form. Beneath large spruce trees, the snow cover is depleted, forming a bowl-shaped depression called a tree well. Small spruce trees become covered with snow, creating cavities that funnel cold air to the snow/ground interface. The depletion of snow under large spruce trees results in greater heat loss from the ground. A finite difference model suggests that heat flow from tree wells can be more than twice that of undisturbed snow. In forested watersheds, this increasemore » can be a significant percentage of the total winter energy exchange.« less

Towards Improved Snow Water Equivalent Estimation via GRACE Assimilation

NASA Technical Reports Server (NTRS)

Forman, Bart; Reichle, Rofl; Rodell, Matt

2011-01-01

Passive microwave (e.g. AMSR-E) and visible spectrum (e.g. MODIS) measurements of snow states have been used in conjunction with land surface models to better characterize snow pack states, most notably snow water equivalent (SWE). However, both types of measurements have limitations. AMSR-E, for example, suffers a loss of information in deep/wet snow packs. Similarly, MODIS suffers a loss of temporal correlation information beyond the initial accumulation and final ablation phases of the snow season. Gravimetric measurements, on the other hand, do not suffer from these limitations. In this study, gravimetric measurements from the Gravity Recovery and Climate Experiment (GRACE) mission are used in a land surface model data assimilation (DA) framework to better characterize SWE in the Mackenzie River basin located in northern Canada. Comparisons are made against independent, ground-based SWE observations, state-of-the-art modeled SWE estimates, and independent, ground-based river discharge observations. Preliminary results suggest improved SWE estimates, including improved timing of the subsequent ablation and runoff of the snow pack. Additionally, use of the DA procedure can add vertical and horizontal resolution to the coarse-scale GRACE measurements as well as effectively downscale the measurements in time. Such findings offer the potential for better understanding of the hydrologic cycle in snow-dominated basins located in remote regions of the globe where ground-based observation collection if difficult, if not impossible. This information could ultimately lead to improved freshwater resource management in communities dependent on snow melt as well as a reduction in the uncertainty of river discharge into the Arctic Ocean.

Inorganic carbon addition stimulates snow algae primary productivity

NASA Astrophysics Data System (ADS)

Hamilton, T. L.; Havig, J. R.

2017-12-01

Earth has experienced glacial/interglacial oscillations throughout its history. Today over 15 million square kilometers (5.8 million square miles) of Earth's land surface is covered in ice including glaciers, ice caps, and the ice sheets of Greenland and Antarctica, most of which are retreating as a consequence of increased atmospheric CO2. Glaciers are teeming with life and supraglacial snow and ice surfaces are often red due to blooms of photoautotrophic algae. Recent evidence suggests the red pigmentation, secondary carotenoids produced in part to thrive under high irradiation, lowers albedo and accelerates melt. However, there are relatively few studies that report the productivity of snow algae communities and the parameters that constrain their growth on snow and ice surfaces. Here, we demonstrate that snow algae primary productivity can be stimulated by the addition of inorganic carbon. We found an increase in light-dependent carbon assimilation in snow algae microcosms amended with increasing amounts of inorganic carbon. Our snow algae communities were dominated by typical cosmopolitan snow algae species recovered from Alpine and Arctic environments. The climate feedbacks necessary to enter and exit glacial/interglacial oscillations are poorly understood. Evidence and models agree that global Snowball events are accompanied by changes in atmospheric CO2 with increasing CO2 necessary for entering periods of interglacial time. Our results demonstrate a positive feedback between increased CO2 and snow algal productivity and presumably growth. With the recent call for bio-albedo effects to be considered in climate models, our results underscore the need for robust climate models to include feedbacks between supraglacial primary productivity, albedo, and atmospheric CO2.

Using Terrain Analysis and Remote Sensing to Improve Snow Mass Balance and Runoff Prediction

NASA Astrophysics Data System (ADS)

Venteris, E. R.; Coleman, A. M.; Wigmosta, M. S.

2010-12-01

Approximately 70-80% of the water in the international Columbia River basin is sourced from snowmelt. The demand for this water has competing needs, as it is used for agricultural irrigation, municipal, hydro and nuclear power generation, and environmental in-stream flow requirements. Accurate forecasting of water supply is essential for planning current needs and prediction of future demands due to growth and climate change. A significant limitation on current forecasting is spatial and temporal uncertainty in snowpack characteristics, particularly snow water equivalent. Currently, point measurements of snow mass balance are provided by the NRCS SNOTEL network. Each site consists of a snow mass sensor and meteorology station that monitors snow water equivalent, snow depth, precipitation, and temperature. There are currently 152 sites in the mountains of Oregon and Washington. An important step in improving forecasts is determining how representative each SNOTEL site is of the total mass balance of the watershed through a full accounting of the spatiotemporal variability in snowpack processes. This variation is driven by the interaction between meteorological processes, land cover, and landform. Statistical and geostatistical spatial models relate the state of the snowpack (characterized through SNOTEL, snow course measurements, and multispectral remote sensing) to terrain attributes derived from digital elevation models (elevation, aspect, slope, compound topographic index, topographic shading, etc.) and land cover. Time steps representing the progression of the snow season for several meteorologically distinct water years are investigated to identify and quantify dominant physical processes. The spatially distributed snow balance data can be used directly as model inputs to improve short- and long-range hydrologic forecasts.

On the radiative effects of light-absorbing impurities on snowpack evolution

NASA Astrophysics Data System (ADS)

Dumont, M.; Tuzet, F.; Lafaysse, M.; Arnaud, L.; Picard, G.; Lejeune, Y.; Lamare, M.; Morin, S.; Voisin, D.; Di Mauro, B.

2017-12-01

The presence of light absorbing impurities in snow strongly decreases snow reflectance leading to an increase in the amount of solar energy absorbed by the snowpack. This effect is also known as impurities direct radiative effect. The change in the amount of energy absorbed by the snowpack modifies the temperature profile inside the snowpack and in turn snow metamorphism (impurities indirect radiative effects). In this work, we used the detailed snowpack model SURFEX/ISBA-Crocus with an explicit representation of snow light-absorbing impurities content (Tuzet et al., 2017) fed by medium-resolution ALADIN-Climate atmospheric model to represent dust and black carbon atmospheric deposition fluxes. The model is used at two sites: Col de Porte (medium elevation site in the French Alps) and Torgnon (high elevation site in the Italian Alps). The simulations are compared to in-situ observations and used to quantify the effects of light-absorbing impurities on snow melt rate and timing. The respective parts of the direct and indirect radiative effects of light-absorbing impurities in snow are also computed for the two sites, emphasizing the need to account for the interactions between snow metamorphism and LAI radiative properties, to accurately predict the effects of light-absorbing impurities in snow. Moreover, we describe how automated hyperspectral reflectance can be used to estimate effective impurities surface content in snow. Finally we demonstrate how these reflectances measurements either from in situ or satellite data can be used via an assimilation scheme to constrain snowpack ensemble simulations and better predict the snowpack state and evolution.

Development and testing of a snow interceptometer to quantify canopy water storage and interception processes in the rain/snow transition zone of the North Cascades, Washington, USA

NASA Astrophysics Data System (ADS)

Martin, Kael A.; Van Stan, John T.; Dickerson-Lange, Susan E.; Lutz, James A.; Berman, Jeffrey W.; Gersonde, Rolf; Lundquist, Jessica D.

2013-06-01

Tree canopy snow interception is a significant hydrological process, capable of removing up to 60% of snow from the ground snowpack. Our understanding of canopy interception has been limited by our ability to measure whole canopy water storage in an undisturbed forest setting. This study presents a relatively inexpensive technique for directly measuring snow canopy water storage using an interceptometer, adapted from Friesen et al. (2008). The interceptometer is composed of four linear motion position sensors distributed evenly around the tree trunk. We incorporate a trunk laser-mapping installation method for precise sensor placement to reduce signal error due to sensor misalignment. Through calibration techniques, the amount of canopy snow required to produce the measured displacements can be calculated. We demonstrate instrument performance on a western hemlock (Tsuga heterophylla) for a snow interception event in November 2011. We find a snow capture efficiency of 83 ± 15% of accumulated ground snowfall with a maximum storage capacity of 50 ± 8 mm snow water equivalent (SWE). The observed interception event is compared to simulated interception, represented by the variable infiltration capacity (VIC) hydrologic model. The model generally underreported interception magnitude by 33% using a leaf area index (LAI) of 5 and 16% using an LAI of 10. The interceptometer captured intrastorm accumulation and melt rates up to 3 and 0.75 mm SWE h-1, respectively, which the model failed to represent. While further implementation and validation is necessary, our preliminary results indicate that forest interception magnitude may be underestimated in maritime areas.

Deriving Snow-Cover Depletion Curves for Different Spatial Scales from Remote Sensing and Snow Telemetry Data

NASA Technical Reports Server (NTRS)

Fassnacht, Steven R.; Sexstone, Graham A.; Kashipazha, Amir H.; Lopez-Moreno, Juan Ignacio; Jasinski, Michael F.; Kampf, Stephanie K.; Von Thaden, Benjamin C.

2015-01-01

During the melting of a snowpack, snow water equivalent (SWE) can be correlated to snow-covered area (SCA) once snow-free areas appear, which is when SCA begins to decrease below 100%. This amount of SWE is called the threshold SWE. Daily SWE data from snow telemetry stations were related to SCA derived from moderate-resolution imaging spectro radiometer images to produce snow-cover depletion curves. The snow depletion curves were created for an 80,000 sq km domain across southern Wyoming and northern Colorado encompassing 54 snow telemetry stations. Eight yearly snow depletion curves were compared, and it is shown that the slope of each is a function of the amount of snow received. Snow-cover depletion curves were also derived for all the individual stations, for which the threshold SWE could be estimated from peak SWE and the topography around each station. A stations peak SWE was much more important than the main topographic variables that included location, elevation, slope, and modelled clear sky solar radiation. The threshold SWE mostly illustrated inter-annual consistency.

Modelling of backscatter from vegetation layers

NASA Technical Reports Server (NTRS)

Van Zyl, J. J.; Engheta, N.; Papas, C. H.; Elachi, C.; Zebker, H.

1985-01-01

A simple way to build up a library of models which may be used to distinguish between the different types of vegetation and ground surfaces by means of their backscatter properties is presented. The curve of constant power received by the antenna (Gamma sphere) is calculated for the given Stokes Scattering Operator, and model parameters are adopted of the most similar library model Gamma sphere. Results calculated for a single scattering model resembling coniferous trees are compared with the Gamma spheres of a model resembling tropical region trees. The polarization which would minimize the effect of either the ground surface or the vegetation layer can be calculated and used to analyze the backscatter from the ground surface/vegetation layer combination, and enhance the power received from the desired part of the combination.

Early results from NASA's SnowEx campaign

NASA Astrophysics Data System (ADS)

Kim, Edward; Gatebe, Charles; Hall, Dorothy; Misakonis, Amy; Elder, Kelly; Marshall, Hans Peter; Hiemstra, Chris; Brucker, Ludovic; Crawford, Chris; Kang, Do Hyuk; De Marco, Eugenia; Beckley, Matt; Entin, Jared

2017-04-01

SnowEx is a multi-year airborne snow campaign with the primary goal of addressing the question: How much water is stored in Earth's terrestrial snow-covered regions? Year 1 (2016-17) focuses on the distribution of snow-water equivalent (SWE) and the snow energy balance in a forested environment. The year 1 primary site is Grand Mesa and the secondary site is the Senator Beck Basin, both in western, Colorado, USA. Ten core sensors on four core aircraft will make observations using a broad suite of airborne sensors including active and passive microwave, and active and passive optical/infrared sensing techniques to determine the sensitivity and accuracy of these potential satellite remote sensing techniques, along with models, to measure snow under a range of forest conditions. SnowEx also includes an extensive range of ground truth measurements—in-situ samples, snow pits, ground based remote sensing measurements, and sophisticated new techniques. A detailed description of the data collected will be given and some early results will be presented. Seasonal snow cover is the largest single component of the cryosphere in areal extent (covering an average of 46M km2 of Earth's surface (31 % of land areas) each year). This seasonal snow has major societal impacts in the areas of water resources, natural hazards (floods and droughts), water security, and weather and climate. The only practical way to estimate the quantity of snow on a consistent global basis is through satellites. Yet, current space-based techniques underestimate storage of snow water equivalent (SWE) by as much as 50%, and model-based estimates can differ greatly vs. estimates based on remotely-sensed observations. At peak coverage, as much as half of snow-covered terrestrial areas involve forested areas, so quantifying the challenge represented by forests is important to plan any future snow mission. Single-sensor approaches may work for certain snow types and certain conditions, but not for others. Snow simply varies too much. Thus, the snow community consensus is that a multi-sensor approach is needed to adequately address global snow, combined with modeling and data assimilation. What remains at issue, then, is how best to combine and use the various sensors in an optimal way. That requires field measurements. NASA's SnowEx airborne campaign is designed to do exactly that. A list of core sensors is as follows. All are from NASA unless otherwise noted. • Radar (volume scattering): European Space Agency's SnowSAR, operated by MetaSensing • Lidar & hyperspectral imager: Airborne Snow Observatory (ASO) • Passive microwave: Airborne Earth Science Microwave Imaging Radiometer (AESMIR) • Bi-directional Reflectance Function (BRDF): the Cloud Absorption Radiometer (CAR) • Thermal Infrared imager • Thermal infrared non-imager from U. Washington • Video camera The ASO suite flew on a King Air, and the other sensors flew on a Navy P-3. In addition, two NASA radars flew on G-III aircraft to test more experimental retrieval techniques: • InSAR altimetry: Glacier and Ice Surface Topography Interferometer (GLISTIN-A) • Radar phase delay: Uninhabited Aerial Vehicle Synthetic Aperture Radar, (UAVSAR)

Computation of Nonlinear Backscattering Using a High-Order Numerical Method

NASA Technical Reports Server (NTRS)

Fibich, G.; Ilan, B.; Tsynkov, S.

2001-01-01

The nonlinear Schrodinger equation (NLS) is the standard model for propagation of intense laser beams in Kerr media. The NLS is derived from the nonlinear Helmholtz equation (NLH) by employing the paraxial approximation and neglecting the backscattered waves. In this study we use a fourth-order finite-difference method supplemented by special two-way artificial boundary conditions (ABCs) to solve the NLH as a boundary value problem. Our numerical methodology allows for a direct comparison of the NLH and NLS models and for an accurate quantitative assessment of the backscattered signal.

Evaluating indices of lipid and protein content in lesser snow and Ross's geese during spring migration

USGS Publications Warehouse

Webb, Elisabeth B.; Fowler, Drew N.; Woodall, Brendan A.; Vrtiska, Mark P.

2018-01-01

Assessing nutrient stores in avian species is important for understanding the extent to which body condition influences success or failure in life‐history events. We evaluated predictive models using morphometric characteristics to estimate total body lipids (TBL) and total body protein (TBP), based on traditional proximate analyses, in spring migrating lesser snow geese (Anser caerulescens caerulescens) and Ross's geese (A. rossii). We also compared performance of our lipid model with a previously derived predictive equation for TBL developed for nesting lesser snow geese. We used external and internal measurements on 612 lesser snow and 125 Ross's geese collected during spring migration in 2015 and 2016 within the Central and Mississippi flyways to derive and evaluate predictive models. Using a validation data set, our best performing lipid model for snow geese better predicted TBL (root mean square error [RMSE] of 23.56) compared with a model derived from nesting individuals (RMSE = 48.60), suggesting the importance of season‐specific models for accurate lipid estimation. Models that included body mass and abdominal fat deposit best predicted TBL determined by proximate analysis in both species (lesser snow goose, R2 = 0.87, RMSE = 23.56: Ross's geese, R2 = 0.89, RMSE = 13.75). Models incorporating a combination of external structural measurements in addition to internal muscle and body mass best predicted protein values (R2 = 0.85, RMSE = 19.39 and R2 = 0.85, RMSE = 7.65, lesser snow and Ross's geese, respectively), but protein models including only body mass and body size were also competitive and provided extended utility to our equations for field applications. Therefore, our models indicated the importance of specimen dissection and measurement of the abdominal fat pad to provide the most accurate lipid estimates and provide alternative dissection‐free methods for estimating protein.

Reconfiguration of a flexible flat plate under snow loading

NASA Astrophysics Data System (ADS)

Gosselin, Frédérick; de Langre, Emmanuel

2015-11-01

Snow and wind constitute two of the main sources of mechanical loading on terrestrial plants. Plants bend and twist with large amplitude to bear these loads. For the past ten years, various authors have sought to decompose the problem of plant reconfiguration under fluid flow into its fundamental mechanical ingredients by studying the reconfiguration of simple flexible structures such as beams, plates, rods and strips. Here, we adopt a similar approach to these studies and consider the snow interception of a flexible flat plate. We performed two sets of experiments on thin flexible rectangular plates supported at their center: in the first one, a plate was subjected to real snowing events; in the second one, a plate was loaded with glass beads acting as a granular media similar to snow. Moreover, a theoretical model coupling the Elastica formulation to a loading with a set angle of repose is developed. The model is found to be in good agreement with the experiments on glass beads. Asymptotic scaling laws can be found similarly to the Vogel exponents of reconfiguring structures. For the real snow loading, it is found that the cohesive force in snow which is highly dependent on the snow temperature complicate things greatly.

Projected climate change impacts on winter recreation in the ...

EPA Pesticide Factsheets

A physically-based water and energy balance model is used to simulate natural snow accumulation at 247 winter recreation locations across the continental United States. We combine this model with projections of snowmaking conditions to determine downhill skiing, cross-country skiing, and snowmobiling season lengths under baseline and future climates, using data from five climate models and two emissions scenarios. The present-day simulations from the snow model without snowmaking are validated with observations of snow-water-equivalent from snow monitoring sites. Projected season lengths are combined with baseline estimates of winter recreation activity to monetize impacts to the selected winter recreation activity categories for the years 2050 and 2090. Estimate the physical and economic impact of climate change on winter recreation in the contiguous U.S.

Snow cover monitoring model and change over both time and space in pastoral area of northern China

NASA Astrophysics Data System (ADS)

Cui, Yan; Li, Suju; Wang, Ping; Zhang, Wei; Nie, Juan; Wen, Qi

2014-11-01

Snow disaster is a natural phenomenon owning to widespread snowfall for a long time and usually affect people's life, property and economic. During the whole disaster management circle, snow disaster in pastoral area of northern china which including Xinjiang, Inner Mongolia, Qinghai, Tibet has been paid more attention. Thus do a good job in snow cover monitoring then found snow disaster in time can help the people in disaster area to take effective rescue measures, which always been the central and local government great important work. Remote sensing has been used widely in snow cover monitoring for its wide range, high efficiency, less conditions, more methods and large information. NOAA/AVHRR data has been used for wide range, plenty bands information and timely acquired and act as an import data of Snow Cover Monitoring Model (SCMM). SCMM including functions list below: First after NOAA/AVHRR data has been acquired, geometric calibration, radiometric calibration and other pre-processing work has been operated. Second after band operation, four threshold conditions are used to extract snow spectrum information among water, cloud and other features in NOAA/AVHRR image. Third snow cover information has been analyzed one by one and the maximum snow cover from about twenty images in a week has been selected. Then selected image has been mosaic which covered the pastoral area of China. At last both time and space analysis has been carried out through this operational model ,such as analysis on the difference between this week and the same period of last year , this week and last week in three level regional. SCMM have been run successfully for three years, and the results have been take into account as one of the three factors which led to risk warning of snow disaster and analysis results from it always play an important role in disaster reduction and relief.

Effect of species structure and dielectric constant on C-band forest backscatter

NASA Technical Reports Server (NTRS)

Lang, R. H.; Landry, R.; Kilic, O.; Chauhan, N.; Khadr, N.; Leckie, D.

1993-01-01

A joint experiment between Canadian and USA research teams was conducted early in Oct. 1992 to determine the effect of species structure and dielectric variations on forest backscatter. Two stands, one red pine and one jack pine, in the Petawawa National Forestry Institute (PNFI) were utilized for the experiment. Extensive tree architecture measurements had been taken by the Canada Centre for Remote Sensing (CCRS) several months earlier by employing a Total Station surveying instrument which provides detailed information on branch structure. A second part of the experiment consisted of cutting down several trees and using dielectric probes to measure branch and needle permittivity values at both sites. The dielectric and the tree geometry data were used in the George Washington University (GWU) Vegetation Model to determine the C band backscattering coefficients of the individual stands for VV polarization. The model results show that backscatter at C band comes mainly from the needles and small branches and the upper portion of the trunks acts only as an attenuator. A discussion of variation of backscatter with specie structure and how dielectric variations in needles for both species may affect the total backscatter returns is provided.

Using mid-altitude regions as observatories of change in snow areas: the Natural Park of Cazorla, Segura y las Villas (South Spain) as study case for early snow regression.

NASA Astrophysics Data System (ADS)

Montilla, Soledad; Pimentel, Rafael; José Pérez-Palazón, María; Aguillar, Cristina; José Polo, María

2017-04-01

Snow plays a key role at the hydrological cycle in semiarid mountainous areas, modifying the energy and water balances that govern the regime of stored water in the snowpack, a key resource for the spring and summer river flow. The Natural and National Park of Sierra Nevada (SNNP), a coastal mountain range up to 3450 m a.s.l. in southern Spain, is a representative example of snow areas in Mediterranean-climate regions; its high altitudinal gradient results in a wide variety of eco-climatic environments, and it is part of the global monitoring network to study climate change. Both monitoring and modelling efforts have been performed to assess this variability and its significant scales; whereas increasing temperature trends have been found, no significant trends are observed so far regarding the precipitation regime both on a seasonal and annual basis, with a highly variable impact on the snow regime in this area, especially in the mid-altitude range. In this context, the study of the snow cover in the neighbouring Natural Park of Cazorla, Segura and Las Villas (CSLVNP), with similar climatic conditions but lower altitudes (up to 2107 m a.s.l.) is proposed as a parallel monitoring site for early warning of impacts of climate change on the snow regime. The CSLVNP is the most extensive protected area in Spain and it is located to the north of SNPN, with less influence of the Mediterranean Sea. This study carried out a first quantification of the snow importance in this area, which exhibits a large transitional zone with a dominant alpine environment, and its relationship with the observed local precipitation-temperature trends. For this, the snow cover fraction on a 30x30 m gridded resolution has been studied during a 5-yr period combining on-site meteorological observations and remote-sensing data analysis, and snow modelling by the distributed and physically based approach for Mediterranean regions proposed by Herrero et al. (2009; 2010). The analysis of the available series of satellite images Landsat 8 OLI/TIRS, Landsat 7 ETM+ and Landsat 4-5 TM were used to obtain snow cover fraction maps with 30x30 m resolution. The study period 2010-2015 was simulated with the distributed snow model and the results were compared against these snow map series. Additionally, the annual and seasonal trends of precipitation, mean daily temperature and global radiation were obtained from the available local data sets. Globally, the simulated results overestimate the snow presence in the study area, very likely due to the estimation of snowfall. However, on a local scale, the model performance improves in the region between 1750 and 2250 m altitude. On the other hand, those zones at lower altitudes, which are a transition of the clearly alpine environment above, present a high variability of results related to the spatial patterns of precipitation, temperature and radiation. Regarding the precipitation-temperature regime, an increasing 0.05 °/yr over the last 30 years (1970-2010) was found, but no significant conclusion can be achieved on precipitation trends. This is also observed in the SNNP, which confirms the potential representativeness of PNCSV as an early warning site. Further work is being carried out to improve the snow modelling at this site and generate longer snow cover fraction maps series and other characteristic variables of the snow in this area.

Modeling the Physical Multi-Phase Interactions of HNO3 Between Snow and Air on the Antarctic Plateau (Dome C) and coast (Halley)

NASA Astrophysics Data System (ADS)

Chan, Hoi Ga; Frey, Markus M.; King, Martin D.

2017-04-01

Nitrogen oxides (NOx = NO + NO2) emissions from nitrate (NO3-) photolysis in snow affect the oxidising capacity of the lower troposphere especially in remote regions of the high latitudes with low pollution levels. The porous structure of snowpack allows the exchange of gases with the atmosphere driven by physicochemical processes, and hence, snow can act as both source and sink of atmospheric chemical trace gases. Current models are limited by poor process understanding and often require tuning parameters. Here, two multi-phase physical models were developed from first principles constrained by observed atmospheric nitrate, HNO3, to describe the air-snow interaction of nitrate. Similar to most of the previous approaches, the first model assumes that below a threshold temperature, To, the air-snow grain interface is pure ice and above To, a disordered interface (DI) emerges assumed to be covering the entire grain surface. The second model assumes that Air-Ice interactions dominate over the entire temperature range below melting and that only above the eutectic temperature, liquid is present in the form of micropockets in grooves. The models are validated with available year-round observations of nitrate in snow and air at a cold site on the Antarctica Plateau (Dome C, 75°06'S, 123°33'E, 3233 m a.s.l.) and at a relatively warm site on the Antarctica coast (Halley, 75°35'S, 26°39'E, 35 m a.s.l). The first model agrees reasonably well with observations at Dome C (Cv(RMSE) = 1.34), but performs poorly at Halley (Cv(RMSE) = 89.28) while the second model reproduces with good agreement observations at both sites without any tuning (Cv(RMSE) = 0.84 at both sites). It is therefore suggested that air-snow interactions of nitrate in the winter are determined by non-equilibrium surface adsorption and co-condensation on ice coupled with solid-state diffusion inside the grain. In summer, however, the air-snow exchange of nitrate is mainly driven by solvation into liquid micropockets following Henry's law with contributions to total NO3- concentrations of 75% and 80% at Dome C and Halley respectively. It is also found that liquid volume of the snow grain and air-micropocket partitioning of HNO3 are sensitive to total solute concentration and pH. In conclusion, the second model can be used to predict nitrate concentration in surface snow over the entire range of environ- mental conditions typical for Antarctica and forms a basis for parameterisations in regional or global atmospheric chemistry models.

Through the Looking Glass: Droughtorama to Snowpocalypse in the Sierra Nevada as studied with the NASA Airborne Snow Observatory

NASA Astrophysics Data System (ADS)

Painter, T. H.; Bormann, K.; Deems, J. S.; Hedrick, A. R.; Marks, D. G.; Skiles, M.; Stock, G. M.

2017-12-01

Across the last five years, the Sierra Nevada has seen increasing drought and then an abrupt return to a top five snowpack. Fortunately, the NASA Airborne Snow Observatory has been flying the Central Sierra Nevada since the spring of 2013, quantifying critical mountain basins' snow water equivalent and snow albedo. The huge variation of snowpack years captured by the NASA ASO is of enormous benefit to water cycle science, ecosystem science, and water management utilization of ASO data and its modeling. It allows a much broader understanding of mountain basin snow season cases for understanding snowmelt runoff, snow/rain mixes, snowfall distribution, evapotranspiration, soil moisture, and glacier mass balance. For water management, trust in empirical and physically-based modeling from the ASO data for application anywhere in the range of snow years is greatly improved by having consistency in that modeling with the span of years ASO has characterized. The NASA ASO was designed to characterize mountain snowpack and fill this void in water cycle science. Our original conversations with partner California Department of Water Resources in 2011 focused on the utility of ASO for flood risk mitigation, given the large snowfall of that year. However, from 2012 through 2016, California snowpacks expressed horrible drought, reaching the nadir in 2015 with the lowest snowpack on record. The 2016 snowpack was nearly normal according to snow pillows and snow courses (ASO's record is too short to define a `normal' year). However, 2017 had enormous snowfall in January and February, keeping snow pillows on track with the largest year on record, 1982-83. However, March backed off and the record year was lost. Still, accumulation was enormous. In parts of the San Joaquin basin, snow depths were > 30 m. The sum of near April 1 ASO total basin SWE for 2013 through 2016 in the Tuolumne Basin was only 92% of the near April 1, 2017 acquisition. In addition to the large accumulation of snow in 2017, the snowpack was also covered with far greater impurities (dust, black carbon) across the snowmelt period than in the previous years, as expressed in the snow albedo and radiative forcing by dust and BC in snow from the ASO imaging spectrometer. In this presentation, we explore the importance of this opportunity for water cycle science and water management.

Two-threshold model for scaling laws of noninteracting snow avalanches

USGS Publications Warehouse

Faillettaz, J.; Louchet, F.; Grasso, J.-R.

2004-01-01

A two-threshold model was proposed for scaling laws of noninteracting snow avalanches. It was found that the sizes of the largest avalanches just preceding the lattice system were power-law distributed. The proposed model reproduced the range of power-law exponents observe for land, rock or snow avalanches, by tuning the maximum value of the ratio of the two failure thresholds. A two-threshold 2D cellular automation was introduced to study the scaling for gravity-driven systems.

Retrospective Snow Analysis Across the Continental United States for the National Water Model

NASA Astrophysics Data System (ADS)

Karsten, L. R.; Gochis, D.; Dugger, A. L.; McCreight, J. L.; Barlage, M. J.; Fall, G. M.; Olheiser, C.

2016-12-01

For large portions of the United States, snow plays a vital role in hydrologic prediction. This is particularly true in the mountain west where snowmelt contributes up to 80% of total streamflow runoff. The Office of Water Prediction (OWP) will begin running the National Water Model (NWM) during the second half of 2016, which is a continental-scale implementation of the WRF-Hydro community hydrologic modeling framework. Assessing and benchmarking the performance of the snow component of the NWM is important for future research-to-operations activities and for forecasters to better understand NWM output. For this study, WRF-Hydro was ran using the same domain and physics options as the NWM (1 km LSM, 250m overland routing, and NHDPlus Version 2.1 channel network). The land surface component chosen is Noah-MP land surface model. Forcing from the National Land Data Assimilation System (NLDAS-2) was downscaled from the native 0.125 degree resolution to the 1 km modeling domain to drive the model. The model was ran over a 5-year retrospective period to gauge multi-year performance of the snow states. Output was analyzed against both in-situ observations, such as SNOTEL, and the Snow Data Assimilation System (SNODAS). In addition, gridded snow states and SNODAS grids were aggregated to Omernik-derived ecological regions. This was done in order to break up snow analysis by regions that share similar ecological and physiographic characteristics. Results show WRF-Hydro is able to capture peak timing across most of the mountain west fairly well. In terms of magnitudes, the model struggles across portions of the west with a low bias. This is especially true in the Cascades, which could be traced back to precipitation partitioning issues in the model. Across the central Rockies, the model exhibits a lower dry bias showing improved performance there. Previous literature suggests a dry bias in the precipitation out west may be contributing to model performance. East of the Rockies, the model captures events well, including both timing and magnitude when compared to SNODAS. There are issues with particular events in these regions, but this may be due to the nature of the events being mixed-phase. Overall performance with snow simulation for the NWM shows promise for use in operations.

How autumn Eurasian snow anomalies affect east asian winter monsoon: a numerical study

NASA Astrophysics Data System (ADS)

Luo, Xiao; Wang, Bin

2018-03-01

Previous studies have found that snow Eurasian anomalies in autumn can affect East Asian winter monsoon (EAWM), but the mechanisms remain controversial and not well understood. The possible mechanisms by which Eurasian autumn snow anomalies affect EAWM are investigated by numerical experiments with a coupled general circulation model and its atmospheric general circulation model component. The leading empirical orthogonal function mode of the October-November mean Eurasian snow cover is characterized by a uniform anomaly over a broad region of central Eurasia (40°N-65°N, 60°E-140°E). However, the results from a 150-ensemble mean simulation with snow depth anomaly specified in October and November reveal that the Mongolian Plateau and Vicinity (MPV, 40°-55°N, 80°-120°E) is the key region for autumn snow anomalies to affect EAWM. The excessive snow forcing can significantly enhance EAWM and the snowfall over the northwestern China and along the EAWM front zone stretching from the southeast China to Japan. The physical process involves a snow-monsoon feedback mechanism. The excessive autumn snow anomalies over the MPV region can persist into the following winter, and significantly enhance winter snow anomalies, which increase surface albedo, reduce incoming solar radiation and cool the boundary layer air, leading to an enhanced Mongolian High and a deepened East Asian trough. The latter, in turn, strengthen surface northwesterly winds, cooling East Asia and increasing snow accumulation over the MPV region and the southeastern China. The increased snow covers feedback to EAWM system through changing albedo, extending its influence southeastward. It is also found that the atmosphere-ocean coupling process can amplify the delayed influence of Eurasian snow mass anomaly on EAWM. The autumn surface albedo anomalies, however, do not have a lasting "memory" effect. Only if the albedo anomalies are artificially extended into December and January, will the EAWM be affected in a similar way as the impacts of autumn snow mass anomalies.

Estimation of the spatiotemporal dynamics of snow covered area by using cellular automata models

NASA Astrophysics Data System (ADS)

Pardo-Igúzquiza, Eulogio; Collados-Lara, Antonio-Juan; Pulido-Velazquez, David

2017-07-01

Given the need to consider the cryosphere in water resources management for mountainous regions, the purpose of this paper is to model the daily spatially distributed dynamics of snow covered area (SCA) by using calibrated cellular automata models. For the operational use of the calibrated model, the only data requirements are the altitude of each cell of the spatial discretization of the area of interest and precipitation and temperature indexes for the area of interest. For the calibration step, experimental snow covered area data are needed. Potential uses of the model are to estimate the snow covered area when satellite data are absent, or when they provide a temporal resolution different from the operational resolution, or when the satellite images are useless because they are covered by clouds or because there has been a sensor failure. Another interesting application is the simulation of SCA dynamics for the snow covered area under future climatic scenarios. The model is applied to the Sierra Nevada mountain range, in southern Spain, which is home to significant biodiversity, contains important water resources in its snowpack, and contains the most meridional ski resort in Europe.

Diagnosing land management and climate change impacts on snowmelt in semi-arid agricultural cold regions with an improved snowmelt model

NASA Astrophysics Data System (ADS)

Harder, P.; Pomeroy, J. W.; Helgason, W.

2017-12-01

Spring snowmelt is the most important hydrological event in semi-arid agricultural cold regions, recharging soil moisture and generating the majority of annual runoff. Adoption of no-till agricultural practices means vast areas of the Canadian Prairies, and other analogous regions, are characterized by standing crop stubble. The emergence of stubble during snowmelt will have important implications for the snowpack energy balance. In addition, spatiotemporally dynamic snowcover heterogeneity leads to enhancement of turbulent flux contributions to melt by advection of energy from warm moist bare ground to snow. Stubble emergence and advection are generally unaccounted for in snow models. To address these challenges a stubble-snow-atmosphere surface energy balance model is developed that relates stubble parameters to the snow surface energy balance. Existing fractal understandings of snowcover geometry are applied to a conceptualized boundary layer integration model to estimate a sensible and latent heat advection efficiency. The small-scale nature of stubble-snow-atmosphere interactions makes direct validation of the energy balance terms challenging. However, the energy balance estimates are assessed by comparing to measured snow and stubble surface temperatures, snow surface incoming shortwave radiation and areal average turbulent fluxes. Advection estimates are validated from a two-dimensional air temperature, water vapor and windspeed profiles. Snowcover geometry relationships are validated/updated with unmanned air vehicle observations. Observations for model assessment occurred in 2015 and 2016 on wheat and canola stubble fields in north-central Saskatchewan, Canada. The model is not calibrated to melt rates, yet compares well with available observations, providing confidence in the model structure and parameterization. Sensitivity analysis using the model revealed compensatory relationships in energy balance terms resulting in limited reduction of energy available for snowmelt as stubble height increases. The proposed model is used to diagnose the influence of stubble management and climate change on melt processes to reveal the potential implications on runoff generation, infiltration and land-atmosphere interactions.

Potential and limitations of webcam images for snow cover monitoring in the Swiss Alps

NASA Astrophysics Data System (ADS)

Dizerens, Céline; Hüsler, Fabia; Wunderle, Stefan

2017-04-01

In Switzerland, several thousands of outdoor webcams are currently connected to the Internet. They deliver freely available images that can be used to analyze snow cover variability on a high spatio-temporal resolution. To make use of this big data source, we have implemented a webcam-based snow cover mapping procedure, which allows to almost automatically derive snow cover maps from such webcam images. As there is mostly no information about the webcams and its parameters available, our registration approach automatically resolves these parameters (camera orientation, principal point, field of view) by using an estimate of the webcams position, the mountain silhouette, and a high-resolution digital elevation model (DEM). Combined with an automatic snow classification and an image alignment using SIFT features, our procedure can be applied to arbitrary images to generate snow cover maps with a minimum of effort. Resulting snow cover maps have the same resolution as the digital elevation model and indicate whether each grid cell is snow-covered, snow-free, or hidden from webcams' positions. Up to now, we processed images of about 290 webcams from our archive, and evaluated images of 20 webcams using manually selected ground control points (GCPs) to evaluate the mapping accuracy of our procedure. We present methodological limitations and ongoing improvements, show some applications of our snow cover maps, and demonstrate that webcams not only offer a great opportunity to complement satellite-derived snow retrieval under cloudy conditions, but also serve as a reference for improved validation of satellite-based approaches.