Snow depth and snow cover retrieval from FengYun3B microwave radiation imagery based on a snow passive microwave unmixing method in Northeast China

NASA Astrophysics Data System (ADS)

Gu, Lingjia; Ren, Ruizhi; Zhao, Kai; Li, Xiaofeng

2014-01-01

The precision of snow parameter retrieval is unsatisfactory for current practical demands. The primary reason is because of the problem of mixed pixels that are caused by low spatial resolution of satellite passive microwave data. A snow passive microwave unmixing method is proposed in this paper, based on land cover type data and the antenna gain function of passive microwaves. The land cover type of Northeast China is partitioned into grass, farmland, bare soil, forest, and water body types. The component brightness temperatures (CBT), namely unmixed data, with 1 km data resolution are obtained using the proposed unmixing method. The snow depth determined by the CBT and three snow depth retrieval algorithms are validated through field measurements taken in forest and farmland areas of Northeast China in January 2012 and 2013. The results show that the overall of the retrieval precision of the snow depth is improved by 17% in farmland areas and 10% in forest areas when using the CBT in comparison with the mixed pixels. The snow cover results based on the CBT are compared with existing MODIS snow cover products. The results demonstrate that more snow cover information can be obtained with up to 86% accuracy.

A novel approach for automatic snow depth estimation using UAV-taken images without ground control points

NASA Astrophysics Data System (ADS)

Mizinski, Bartlomiej; Niedzielski, Tomasz

2017-04-01

Recent developments in snow depth reconstruction based on remote sensing techniques include the use of photographs of snow-covered terrain taken by unmanned aerial vehicles (UAVs). There are several approaches that utilize visible-light photos (RGB) or near infrared images (NIR). The majority of the methods in question are based on reconstructing the digital surface model (DSM) of the snow-covered area with the use of the Structure-from-Motion (SfM) algorithm and the stereo-vision software. Having reconstructed the above-mentioned DSM it is straightforward to calculate the snow depth map which may be produced as a difference between the DSM of snow-covered terrain and the snow-free DSM, known as the reference surface. In order to use the aforementioned procedure, the high spatial accuracy of the two DSMs must be ensured. Traditionally, this is done using the ground control points (GCPs), either artificial or natural terrain features that are visible on aerial images, the coordinates of which are measured in the field using the Global Navigation Satellite System (GNSS) receiver by qualified personnel. The field measurements may be time-taking (GCPs must be well distributed in the study area, therefore the field experts should travel over long distances) and dangerous (the field experts may be exposed to avalanche risk or cold). Thus, there is a need to elaborate methods that enable the above-mentioned automatic snow depth map production without the use of GCPs. One of such attempts is shown in this paper which aims to present the novel method which is based on real-time processing of snow-covered and snow-free dense point clouds produced by SfM. The two stage georeferencing is proposed. The initial (low accuracy) one assigns true geographic, and subsequently projected, coordinates to the two dense point clouds, while the said initially-registered dense point clouds are matched using the iterative closest point (ICP) algorithm in the final (high accuracy) stage. The stable reference is offered by specially-selected trees which are located in the vicinity of the terrain of interest. The method has already been implemented and along with the presentation of its concept, a few case studies from the Izerskie Mountains (southwestern Poland) are discussed. Although the method reveals several constraints, it may serve the purpose of generating the snow depth maps with reasonable accuracy, in particular in the absence of GCPs. The snow depth estimation algorithm has been elaborated in frame of the research grant no. LIDER/012/223/L-5/13/NCBR/2014 financed by the National Centre for Research and Development of Poland.

Winter snow conditions on Arctic sea ice north of Svalbard during the Norwegian young sea ICE (N-ICE2015) expedition

NASA Astrophysics Data System (ADS)

Merkouriadi, Ioanna; Gallet, Jean-Charles; Graham, Robert M.; Liston, Glen E.; Polashenski, Chris; Rösel, Anja; Gerland, Sebastian

2017-10-01

Snow is a crucial component of the Arctic sea ice system. Its thickness and thermal properties control heat conduction and radiative fluxes across the ocean, ice, and atmosphere interfaces. Hence, observations of the evolution of snow depth, density, thermal conductivity, and stratigraphy are crucial for the development of detailed snow numerical models predicting energy transfer through the snow pack. Snow depth is also a major uncertainty in predicting ice thickness using remote sensing algorithms. Here we examine the winter spatial and temporal evolution of snow physical properties on first-year (FYI) and second-year ice (SYI) in the Atlantic sector of the Arctic Ocean, during the Norwegian young sea ICE (N-ICE2015) expedition (January to March 2015). During N-ICE2015, the snow pack consisted of faceted grains (47%), depth hoar (28%), and wind slab (13%), indicating very different snow stratigraphy compared to what was observed in the Pacific sector of the Arctic Ocean during the SHEBA campaign (1997-1998). Average snow bulk density was 345 kg m-3 and it varied with ice type. Snow depth was 41 ± 19 cm in January and 56 ± 17 cm in February, which is significantly greater than earlier suggestions for this region. The snow water equivalent was 14.5 ± 5.3 cm over first-year ice and 19 ± 5.4 cm over second-year ice.

Radiance Assimilation Shows Promise for Snowpack Characterization: A 1-D Case Study

NASA Technical Reports Server (NTRS)

Durand, Michael; Kim, Edward; Margulis, Steve

2008-01-01

We demonstrate an ensemble-based radiometric data assimilation (DA) methodology for estimating snow depth and snow grain size using ground-based passive microwave (PM) observations at 18.7 and 36.5 GHz collected during the NASA CLPX-1, March 2003, Colorado, USA. A land surface model was used to develop a prior estimate of the snowpack states, and a radiative transfer model was used to relate the modeled states to the observations. Snow depth bias was -53.3 cm prior to the assimilation, and -7.3 cm after the assimilation. Snow depth estimated by a non-DA-based retrieval algorithm using the same PM data had a bias of -18.3 cm. The sensitivity of the assimilation scheme to the grain size uncertainty was evaluated; over the range of grain size uncertainty tested, the posterior snow depth estimate bias ranges from -2.99 cm to -9.85 cm, which is uniformly better than both the prior and retrieval estimates. This study demonstrates the potential applicability of radiometric DA at larger scales.

The Operation IceBridge Sea Ice Freeboard, Snow Septh and Thickness Product: An In-Depth Look at Past, Current and Future Versions

NASA Astrophysics Data System (ADS)

Harbeck, J.; Kurtz, N. T.; Studinger, M.; Onana, V.; Yi, D.

2015-12-01

The NASA Operation IceBridge Project Science Office has recently released an updated version of the sea ice freeboard, snow depth and thickness product (IDCSI4). This product is generated through the combination of multiple IceBridge instrument data, primarily the ATM laser altimeter, DMS georeferenced imagery and the CReSIS snow radar, and is available on a campaign-specific basis as all upstream data sets become available. Version 1 data (IDCSI2) was the initial data production; we have subsequently received community feedback that has now been incorporated, allowing us to provide an improved data product. All data now available to the public at the National Snow and Ice Data Center (NSIDC) have been homogeneously reprocessed using the new IDCSI4 algorithm. This algorithm contains significant upgrades that improve the quality and consistency of the dataset, including updated atmospheric and oceanic tidal models and replacement of the geoid with a more representative mean sea surface height product. Known errors with the IDCSI2 algorithm, identified by the Project Science Office as well as feedback from the scientific community, have been incorporated into the new algorithm as well. We will describe in detail the various steps of the IDCSI4 algorithm, show the improvements made over the IDCSI2 dataset and their beneficial impact and discuss future upgrades planned for the next version.

Simultaneous retrieval of sea ice thickness and snow depth using concurrent active altimetry and passive L-band remote sensing data

NASA Astrophysics Data System (ADS)

Zhou, L.; Xu, S.; Liu, J.

2017-12-01

The retrieval of sea ice thickness mainly relies on satellite altimetry, and the freeboard measurements are converted to sea ice thickness (hi) under certain assumptions over snow loading. The uncertain in snow depth (hs) is a major source of uncertainty in the retrieved sea ice thickness and total volume for both radar and laser altimetry. In this study, novel algorithms for the simultaneous retrieval of hi and hs are proposed for the data synergy of L-band (1.4 GHz) passive remote sensing and both types of active altimetry: (1) L-band (1.4GHz) brightness temperature (TB) from Soil Moisture Ocean Salinity (SMOS) satellite and sea ice freeboard (FBice) from radar altimetry, (2) L-band TB data and snow freeboard (FBsnow) from laser altimetry. Two physical models serve as the forward models for the retrieval: L-band radiation model, and the hydrostatic equilibrium model. Verification with SMOS and Operational IceBridge (OIB) data is carried out, showing overall good retrieval accuracy for both sea ice parameters. Specifically, we show that the covariability between hs and FBsnow is crucial for the synergy between TB and FBsnow. Comparison with existing algorithms shows lower uncertainty in both sea ice parameters, and that the uncertainty in the retrieved sea ice thickness as caused by that of snow depth is spatially uncorrelated, with the potential reduction of the volume uncertainty through spatial sampling. The proposed algorithms can be applied to the retrieval of sea ice parameters at basin-scale, using concurrent active and passive remote sensing data based on satellites.

Freeboard, Snow Depth and Sea-Ice Roughness in East Antarctica from In Situ and Multiple Satellite Data

NASA Technical Reports Server (NTRS)

Markus, Thorsten; Masson, Robert; Worby, Anthony; Lytle, Victoria; Kurtz, Nathan; Maksym, Ted

2011-01-01

In October 2003 a campaign on board the Australian icebreaker Aurora Australis had the objective to validate standard Aqua Advanced Microwave Scanning Radiometer (AMSR-E) sea-ice products. Additionally, the satellite laser altimeter on the Ice, Cloud and land Elevation Satellite (ICESat) was in operation. To capture the large-scale information on the sea-ice conditions necessary for satellite validation, the measurement strategy was to obtain large-scale sea-ice statistics using extensive sea-ice measurements in a Lagrangian approach. A drifting buoy array, spanning initially 50 km 100 km, was surveyed during the campaign. In situ measurements consisted of 12 transects, 50 500 m, with detailed snow and ice measurements as well as random snow depth sampling of floes within the buoy array using helicopters. In order to increase the amount of coincident in situ and satellite data an approach has been developed to extrapolate measurements in time and in space. Assuming no change in snow depth and freeboard occurred during the period of the campaign on the floes surveyed, we use buoy ice-drift information as well as daily estimates of thin-ice fraction and rough-ice vs smooth-ice fractions from AMSR-E and QuikSCAT, respectively, to estimate kilometer-scale snow depth and freeboard for other days. The results show that ICESat freeboard estimates have a mean difference of 1.8 cm when compared with the in situ data and a correlation coefficient of 0.6. Furthermore, incorporating ICESat roughness information into the AMSR-E snow depth algorithm significantly improves snow depth retrievals. Snow depth retrievals using a combination of AMSR-E and ICESat data agree with in situ data with a mean difference of 2.3 cm and a correlation coefficient of 0.84 with a negligible bias.

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

Sea Ice Thickness, Freeboard, and Snow Depth products from Operation IceBridge Airborne Data

NASA Technical Reports Server (NTRS)

Kurtz, N. T.; Farrell, S. L.; Studinger, M.; Galin, N.; Harbeck, J. P.; Lindsay, R.; Onana, V. D.; Panzer, B.; Sonntag, J. G.

2013-01-01

The study of sea ice using airborne remote sensing platforms provides unique capabilities to measure a wide variety of sea ice properties. These measurements are useful for a variety of topics including model evaluation and improvement, assessment of satellite retrievals, and incorporation into climate data records for analysis of interannual variability and long-term trends in sea ice properties. In this paper we describe methods for the retrieval of sea ice thickness, freeboard, and snow depth using data from a multisensor suite of instruments on NASA's Operation IceBridge airborne campaign. We assess the consistency of the results through comparison with independent data sets that demonstrate that the IceBridge products are capable of providing a reliable record of snow depth and sea ice thickness. We explore the impact of inter-campaign instrument changes and associated algorithm adaptations as well as the applicability of the adapted algorithms to the ongoing IceBridge mission. The uncertainties associated with the retrieval methods are determined and placed in the context of their impact on the retrieved sea ice thickness. Lastly, we present results for the 2009 and 2010 IceBridge campaigns, which are currently available in product form via the National Snow and Ice Data Center

Impact of Surface Roughness on AMSR-E Sea Ice Products

NASA Technical Reports Server (NTRS)

Stroeve, Julienne C.; Markus, Thorsten; Maslanik, James A.; Cavalieri, Donald J.; Gasiewski, Albin J.; Heinrichs, John F.; Holmgren, Jon; Perovich, Donald K.; Sturm, Matthew

2006-01-01

This paper examines the sensitivity of Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperatures (Tbs) to surface roughness by a using radiative transfer model to simulate AMSR-E Tbs as a function of incidence angle at which the surface is viewed. The simulated Tbs are then used to examine the influence that surface roughness has on two operational sea ice algorithms, namely: 1) the National Aeronautics and Space Administration Team (NT) algorithm and 2) the enhanced NT algorithm, as well as the impact of roughness on the AMSR-E snow depth algorithm. Surface snow and ice data collected during the AMSR-Ice03 field campaign held in March 2003 near Barrow, AK, were used to force the radiative transfer model, and resultant modeled Tbs are compared with airborne passive microwave observations from the Polarimetric Scanning Radiometer. Results indicate that passive microwave Tbs are very sensitive even to small variations in incidence angle, which can cause either an over or underestimation of the true amount of sea ice in the pixel area viewed. For example, this paper showed that if the sea ice areas modeled in this paper mere assumed to be completely smooth, sea ice concentrations were underestimated by nearly 14% using the NT sea ice algorithm and by 7% using the enhanced NT algorithm. A comparison of polarization ratios (PRs) at 10.7,18.7, and 37 GHz indicates that each channel responds to different degrees of surface roughness and suggests that the PR at 10.7 GHz can be useful for identifying locations of heavily ridged or rubbled ice. Using the PR at 10.7 GHz to derive an "effective" viewing angle, which is used as a proxy for surface roughness, resulted in more accurate retrievals of sea ice concentration for both algorithms. The AMSR-E snow depth algorithm was found to be extremely sensitive to instrument calibration and sensor viewing angle, and it is concluded that more work is needed to investigate the sensitivity of the gradient ratio at 37 and 18.7 GHz to these factors to improve snow depth retrievals from spaceborne passive microwave sensors.

Potential for Monitoring Snow Cover in Boreal Forests by Combining MODIS Snow Cover and AMSR-E SWE Maps

NASA Technical Reports Server (NTRS)

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

2009-01-01

Monitoring of snow cover extent and snow water equivalent (SWE) in boreal forests is important for determining the amount of potential runoff and beginning date of snowmelt. The great expanse of the boreal forest necessitates the use of satellite measurements to monitor snow cover. Snow cover in the boreal forest can be mapped with either the Moderate Resolution Imaging Spectroradiometer (MODIS) or the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) microwave instrument. The extent of snow cover is estimated from the MODIS data and SWE is estimated from the AMSR-E. Environmental limitations affect both sensors in different ways to limit their ability to detect snow in some situations. Forest density, snow wetness, and snow depth are factors that limit the effectiveness of both sensors for snow detection. Cloud cover is a significant hindrance to monitoring snow cover extent Using MODIS but is not a hindrance to the use of the AMSR-E. These limitations could be mitigated by combining MODIS and AMSR-E data to allow for improved interpretation of snow cover extent and SWE on a daily basis and provide temporal continuity of snow mapping across the boreal forest regions in Canada. The purpose of this study is to investigate if temporal monitoring of snow cover using a combination of MODIS and AMSR-E data could yield a better interpretation of changing snow cover conditions. The MODIS snow mapping algorithm is based on snow detection using the Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI) to enhance snow detection in dense vegetation. (Other spectral threshold tests are also used to map snow using MODIS.) Snow cover under a forest canopy may have an effect on the NDVI thus we use the NDVI in snow detection. A MODIS snow fraction product is also generated but not used in this study. In this study the NDSI and NDVI components of the snow mapping algorithm were calculated and analyzed to determine how they changed through the seasons. A blended snow product, the Air Force Weather Agency and NASA (ANSA) snow algorithm and product has recently been developed. The ANSA algorithm blends the MODIS snow cover and AMSR-E SWE products into a single snow product that has been shown to improve the performance of snow cover mapping. In this study components of the ANSA snow algorithm are used along with additional MODIS data to monitor daily changes in snow cover over the period of 1 February to 30 June 2008.

A Comparison of Sea Ice Type, Sea Ice Temperature, and Snow Thickness Distributions in the Arctic Seasonal Ice Zones with the DMSP SSM/I

NASA Technical Reports Server (NTRS)

St.Germain, Karen; Cavalieri, Donald J.; Markus, Thorsten

1997-01-01

Global climate studies have shown that sea ice is a critical component in the global climate system through its effect on the ocean and atmosphere, and on the earth's radiation balance. Polar energy studies have further shown that the distribution of thin ice and open water largely controls the distribution of surface heat exchange between the ocean and atmosphere within the winter Arctic ice pack. The thickness of the ice, the depth of snow on the ice, and the temperature profile of the snow/ice composite are all important parameters in calculating surface heat fluxes. In recent years, researchers have used various combinations of DMSP SSMI channels to independently estimate the thin ice type (which is related to ice thickness), the thin ice temperature, and the depth of snow on the ice. In each case validation efforts provided encouraging results, but taken individually each algorithm gives only one piece of the information necessary to compute the energy fluxes through the ice and snow. In this paper we present a comparison of the results from each of these algorithms to provide a more comprehensive picture of the seasonal ice zone using passive microwave observations.

Airborne radar surveys of snow depth over Antarctic sea ice during Operation IceBridge

NASA Astrophysics Data System (ADS)

Panzer, B.; Gomez-Garcia, D.; Leuschen, C.; Paden, J. D.; Gogineni, P. S.

2012-12-01

Over the last decade, multiple satellite-based laser and radar altimeters, optimized for polar observations, have been launched with one of the major objectives being the determination of global sea ice thickness and distribution [5, 6]. Estimation of sea-ice thickness from these altimeters relies on freeboard measurements and the presence of snow cover on sea ice affects this estimate. Current means of estimating the snow depth rely on daily precipitation products and/or data from passive microwave sensors [2, 7]. Even a small uncertainty in the snow depth leads to a large uncertainty in the sea-ice thickness estimate. To improve the accuracy of the sea-ice thickness estimates and provide validation for measurements from satellite-based sensors, the Center for Remote Sensing of Ice Sheets deploys the Snow Radar as a part of NASA Operation IceBridge. The Snow Radar is an ultra-wideband, frequency-modulated, continuous-wave radar capable of resolving snow depth on sea ice from 5 cm to more than 2 meters from long-range, airborne platforms [4]. This paper will discuss the algorithm used to directly extract snow depth estimates exclusively using the Snow Radar data set by tracking both the air-snow and snow-ice interfaces. Prior work in this regard used data from a laser altimeter for tracking the air-snow interface or worked under the assumption that the return from the snow-ice interface was greater than that from the air-snow interface due to a larger dielectric contrast, which is not true for thick or higher loss snow cover [1, 3]. This paper will also present snow depth estimates from Snow Radar data during the NASA Operation IceBridge 2010-2011 Antarctic campaigns. In 2010, three sea ice flights were flown, two in the Weddell Sea and one in the Amundsen and Bellingshausen Seas. All three flight lines were repeated in 2011, allowing an annual comparison of snow depth. In 2011, a repeat pass of an earlier flight in the Weddell Sea was flown, allowing for a comparison of snow depths with two weeks elapsed between passes. [1] Farrell, S.L., et al., "A First Assessment of IceBridge Snow and Ice Thickness Data Over Arctic Sea Ice," IEEE Tran. Geoscience and Remote Sensing, Vol. 50, No. 6, pp. 2098-2111, June 2012. [2] Kwok, R., and G. F. Cunningham, "ICESat over Arctic sea ice: Estimation of snow depth and ice thickness," J. Geophys. Res., 113, C08010, 2008. [3] Kwok, R., et al., "Airborne surveys of snow depth over Arctic sea ice," J. Geophys. Res., 116, C11018, 2011. [4] 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. Glaciology, July 23, 2012. [5] Wingham, D.J., et al., "CryoSat: A Mission to Determine the Fluctuations in Earth's Land and Marine Ice Fields," Advances in Space Research, Vol. 37, No. 4, pp. 841-871, 2006. [6] Zwally, H. J., et al., "ICESat's laser measurements of polar ice, atmosphere, ocean, and land," J. Geodynamics, Vol. 34, No. 3-4, pp. 405-445, Oct-Nov 2002. [7] Zwally, H. J., et al., "ICESat measurements of sea ice freeboard and estimates of sea ice thickness in the Weddell Sea," J. Geophys. Res., 113, C02S15, 2008.

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

Enhanced hemispheric-scale snow mapping through the blending of optical and microwave satellite data

NASA Astrophysics Data System (ADS)

Armstrong, R. L.; Brodzik, M. J.; Savoie, M.; Knowles, K.

2003-04-01

Snow cover is an important variable for climate and hydrologic models due to its effects on energy and moisture budgets. Seasonal snow can cover more than 50% of the Northern Hemisphere land surface during the winter resulting in snow cover being the land surface characteristic responsible for the largest annual and interannual differences in albedo. Passive microwave satellite remote sensing can augment measurements based on visible satellite data alone because of the ability to acquire data through most clouds or during darkness as well as to provide a measure of snow depth or water equivalent. Global snow cover fluctuation can now be monitored over a 24 year period using passive microwave data (Scanning Multichannel Microwave Radiometer (SMMR) 1978-1987 and Special Sensor Microwave/Imager (SSM/I), 1987-present). Evaluation of snow extent derived from passive microwave algorithms is presented through comparison with the NOAA Northern Hemisphere weekly snow extent data. For the period 1978 to 2002, both passive microwave and visible data sets show a similar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are consistently less than those provided by the visible satellite data and the visible data typically show higher monthly variability. Decadal trends and their significance are compared for the two data types. During shallow snow conditions of the early winter season microwave data consistently indicate less snow-covered area than the visible data. This underestimate of snow extent results from the fact that shallow snow cover (less than about 5.0 cm) does not provide a scattering signal of sufficient strength to be detected by the algorithms. As the snow cover continues to build during the months of January through March, as well as throughout the melt season, agreement between the two data types continually improves. This occurs because as the snow becomes deeper and the layered structure more complex, the negative spectral gradient driving the passive microwave algorithm is enhanced. Because the current generation of microwave snow algorithms is unable to consistently detect shallow and intermittent snow, we combine visible satellite data with the microwave data in a single blended product to overcome this problem. For the period 1978 to 2002 we combine data from the NOAA weekly snow charts with passive microwave data from the SMMR and SSM/I brightness temperature record. For the current and future time period we blend MODIS and AMSR-E data sets, both of which have greatly enhanced spatial resolution compared to the earlier data sources. Because it is not possible to determine snow depth or snow water equivalent from visible data, the regions where only the NOAA or MODIS data indicate snow are defined as "shallow snow". However, because our current blended product is being developed in the 25 km EASE-Grid and the MODIS data being used are in the Climate Modelers Grid (CMG) at approximately 5 km (0.05 deg.) the blended product also includes percent snow cover over the larger grid cell. A prototype version of the blended MODIS/AMSR-E product will be available in near real-time from NSIDC during the 2002-2003 winter season.

On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data

NASA Astrophysics Data System (ADS)

Zhou, Lu; Xu, Shiming; Liu, Jiping; Wang, Bin

2018-03-01

The accurate knowledge of sea ice parameters, including sea ice thickness and snow depth over the sea ice cover, is key to both climate studies and data assimilation in operational forecasts. Large-scale active and passive remote sensing is the basis for the estimation of these parameters. In traditional altimetry or the retrieval of snow depth with passive microwave remote sensing, although the sea ice thickness and the snow depth are closely related, the retrieval of one parameter is usually carried out under assumptions over the other. For example, climatological snow depth data or as derived from reanalyses contain large or unconstrained uncertainty, which result in large uncertainty in the derived sea ice thickness and volume. In this study, we explore the potential of combined retrieval of both sea ice thickness and snow depth using the concurrent active altimetry and passive microwave remote sensing of the sea ice cover. Specifically, laser altimetry and L-band passive remote sensing data are combined using two forward models: the L-band radiation model and the isostatic relationship based on buoyancy model. Since the laser altimetry usually features much higher spatial resolution than L-band data from the Soil Moisture Ocean Salinity (SMOS) satellite, there is potentially covariability between the observed snow freeboard by altimetry and the retrieval target of snow depth on the spatial scale of altimetry samples. Statistically significant correlation is discovered based on high-resolution observations from Operation IceBridge (OIB), and with a nonlinear fitting the covariability is incorporated in the retrieval algorithm. By using fitting parameters derived from large-scale surveys, the retrievability is greatly improved compared with the retrieval that assumes flat snow cover (i.e., no covariability). Verifications with OIB data show good match between the observed and the retrieved parameters, including both sea ice thickness and snow depth. With detailed analysis, we show that the error of the retrieval mainly arises from the difference between the modeled and the observed (SMOS) L-band brightness temperature (TB). The narrow swath and the limited coverage of the sea ice cover by altimetry is the potential source of error associated with the modeling of L-band TB and retrieval. The proposed retrieval methodology can be applied to the basin-scale retrieval of sea ice thickness and snow depth, using concurrent passive remote sensing and active laser altimetry based on satellites such as ICESat-2 and WCOM.

Design and development of a wireless sensor network to monitor snow depth in multiple catchments in the American River basin, California: hardware selection and sensor placement techniques

NASA Astrophysics Data System (ADS)

Kerkez, B.; Rice, R.; Glaser, S. D.; Bales, R. C.; Saksa, P. C.

2010-12-01

A 100-node wireless sensor network (WSN) was designed for the purpose of monitoring snow depth in two watersheds, spanning 3 km2 in the American River basin, in the central Sierra Nevada of California. The network will be deployed as a prototype project that will become a core element of a larger water information system for the Sierra Nevada. The site conditions range from mid-elevation forested areas to sub-alpine terrain with light forest cover. Extreme temperature and humidity fluctuations, along with heavy rain and snowfall events, create particularly challenging conditions for wireless communications. We show how statistics gathered from a previously deployed 60-node WSN, located in the Southern Sierra Critical Zone Observatory, were used to inform design. We adapted robust network hardware, manufactured by Dust Networks for highly demanding industrial monitoring, and added linear amplifiers to the radios to improve transmission distances. We also designed a custom data-logging board to interface the WSN hardware with snow-depth sensors. Due to the large distance between sensing locations, and complexity of terrain, we analyzed network statistics to select the location of repeater nodes, to create a redundant and reliable mesh. This optimized network topology will maximize transmission distances, while ensuring power-efficient network operations throughout harsh winter conditions. At least 30 of the 100 nodes will actively sense snow depth, while the remainder will act as sensor-ready repeaters in the mesh. Data from a previously conducted snow survey was used to create a Gaussian Process model of snow depth; variance estimates produced by this model were used to suggest near-optimal locations for snow-depth sensors to measure the variability across a 1 km2 grid. We compare the locations selected by the sensor placement algorithm to those made through expert opinion, and offer explanations for differences resulting from each approach.

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).

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.

Twenty-four year record of Northern Hemisphere snow cover derived from passive microwave remote sensing

NASA Astrophysics Data System (ADS)

Armstrong, Richard L.; Brodzik, Mary Jo

2003-04-01

Snow cover is an important variable for climate and hydrologic models due to its effects on energy and moisture budgets. Seasonal snow can cover more than 50% of the Northern Hemisphere land surface during the winter resulting in snow cover being the land surface characteristic responsible for the largest annual and interannual differences in albedo. Passive microwave satellite remote sensing can augment measurements based on visible satellite data alone because of the ability to acquire data through most clouds or during darkness as well as to provide a measure of snow depth or water equivalent. It is now possible to monitor the global fluctuation of snow cover over a 24 year period using passive microwave data (Scanning Multichannel Microwave Radiometer (SMMR) 1978-1987 and Special Sensor Microwave/Imager (SSM/I), 1987-present). Evaluation of snow extent derived from passive microwave algorithms is presented through comparison with the NOAA Northern Hemisphere snow extent data. For the period 1978 to 2002, both passive microwave and visible data sets show a smiliar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are consistently less than those provided by the visible statellite data and the visible data typically show higher monthly variability. During shallow snow conditions of the early winter season microwave data consistently indicate less snow-covered area than the visible data. This underestimate of snow extent results from the fact that shallow snow cover (less than about 5.0 cm) does not provide a scattering signal of sufficient strength to be detected by the algorithms. As the snow cover continues to build during the months of January through March, as well as on into the melt season, agreement between the two data types continually improves. This occurs because as the snow becomes deeper and the layered structure more complex, the negative spectral gradient driving the passive microwave algorithm is enhanced. Trends in annual averages are similar, decreasing at rates of approximately 2% per decade. The only region where the passive microwave data consistently indicate snow and the visible data do not is over the Tibetan Plateau and surrounding mountain areas. In the effort to determine the accuracy of the microwave algorithm over this region we are acquiring surface snow observations through a collaborative study with CAREERI/Lanzhou. In order to provide an optimal snow cover product in the future, we are developing a procedure that blends snow extent maps derived from MODIS data with snow water equivalent maps derived from both SSM/I and AMSR.

BOREAS HYD-4 Standard Snow Course Data

NASA Technical Reports Server (NTRS)

Metcalfe, John R.; Goodison, Barry E.; Walker, Anne; Hall, Forrest G. (Editor); Knapp, David E. (Editor); Smith, David E. (Technical Monitor)

2000-01-01

The Boreal Ecosystem-Atmosphere Study (BOREAS) Hydrology (HYD)-4 work was focused on collecting data during the winter focused field campaign (FFC-W) to improve the understanding of winter processes within the boreal forest. Knowledge of snow cover and its variability in the boreal forest is fundamental if BOREAS is to achieve its goals of understanding the processes and states involved in the exchange of energy and water. The development and validation of remote sensing algorithms will provide the means to extend the knowledge of these processes and states from the local to the regional scale. A specific thrust of the research is the development and validation of snow cover algorithms from airborne passive microwave measurements. Snow surveys were conducted at special snow courses throughout the 1993/94, 1994/95, 1995/96, and 1996/97 winter seasons. These snow courses were located in different boreal forest land cover types (i.e., old aspen, old black spruce, young jack pine, forest clearing, etc.) to document snow cover variations throughout the season as a function of different land cover. Measurements of snow depth, density, and water equivalent were acquired on or near the first and fifteenth of each month during the snow cover season. The data are provided in tabular ASCII files. The HYD-4 standard snow course data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

Machine Learning Algorithms for Automated Satellite Snow and Sea Ice Detection

NASA Astrophysics Data System (ADS)

Bonev, George

The continuous mapping of snow and ice cover, particularly in the arctic and poles, are critical to understanding the earth and atmospheric science. Much of the world's sea ice and snow covers the most inhospitable places, making measurements from satellite-based remote sensors essential. Despite the wealth of data from these instruments many challenges remain. For instance, remote sensing instruments reside on-board different satellites and observe the earth at different portions of the electromagnetic spectrum with different spatial footprints. Integrating and fusing this information to make estimates of the surface is a subject of active research. In response to these challenges, this dissertation will present two algorithms that utilize methods from statistics and machine learning, with the goal of improving on the quality and accuracy of current snow and sea ice detection products. The first algorithm aims at implementing snow detection using optical/infrared instrument data. The novelty in this approach is that the classifier is trained using ground station measurements of snow depth that are collocated with the reflectance observed at the satellite. Several classification methods are compared using this training data to identify the one yielding the highest accuracy and optimal space/time complexity. The algorithm is then evaluated against the current operational NASA snow product and it is found that it produces comparable and in some cases superior accuracy results. The second algorithm presents a fully automated approach to sea ice detection that integrates data obtained from passive microwave and optical/infrared satellite instruments. For a particular region of interest the algorithm generates sea ice maps of each individual satellite overpass and then aggregates them to a daily composite level, maximizing the amount of high resolution information available. The algorithm is evaluated at both, the individual satellite overpass level, and at the daily composite level. Results show that at the single overpass level for clear-sky regions, the developed multi-sensor algorithm performs with accuracy similar to that of the optical/infrared products, with the advantage of being able to also classify partially cloud-obscured regions with the help of passive microwave data. At the daily composite level, results show that the algorithm's performance with respect to total ice extent is in line with other daily products, with the novelty of being fully automated and having higher resolution.

Spatial Variability of Barrow-Area Shore-Fast Sea Ice and Its Relationships to Passive Microwave Emissivity

NASA Technical Reports Server (NTRS)

Maslanik, J. A.; Rivas, M. Belmonte; Holmgren, J.; Gasiewski, A. J.; Heinrichs, J. F.; Stroeve, J. C.; Klein, M.; Markus, T.; Perovich, D. K.; Sonntag, J. G.;

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.

Observed Differences between North American Snow Extent and Snow Depth Variability

NASA Astrophysics Data System (ADS)

Ge, Y.; Gong, G.

2006-12-01

Snow extent and snow depth are two related characteristics of a snowpack, but they need not be mutually consistent. Differences between these two variables at local scales are readily apparent. However at larger scales which interact with atmospheric circulation and climate, snow extent is typically the variable used, while snow depth is often assumed to be minor and/or mutually consistent compared to snow extent, though this is rarely verified. In this study, a new regional/continental-scale gridded dataset derived from field observations is utilized to quantitatively evaluate the relationship between snow extent and snow depth over North America. Various statistical methods are applied to assess the mutual consistency of monthly snow depth vs. snow extent, including correlations, composites and principal components. Results indicate that snow depth variations are significant in their own rights, and that depth and extent anomalies are largely unrelated, especially over broad high latitude regions north of the snowline. In the vicinity of the snowline, where precipitation and ablation can affect both snow extent and snow depth, the two variables vary concurrently, especially in autumn and spring. It is also found that deeper winter snow translates into larger snow-covered area in the subsequent spring/summer season, which suggests a possible influence of winter snow depth on summer climate. The observed lack of mutual consistency at continental/regional scales suggests that snowpack depth variations may be of sufficiently large magnitude, spatial scope and temporal duration to influence regional-hemispheric climate, in a manner unrelated to the more extensively studied snow extent variations.

Catchment-scale snow depth monitoring with balloon photogrammetry

NASA Astrophysics Data System (ADS)

Durand, M. T.; Li, D.; Wigmore, O.; Vanderjagt, B. J.; Molotch, N. P.; Bales, R. C.

2016-12-01

Field campaigns and permanent in-situ facilities provide extensive measurements of snowpack properties at catchment (or smaller) scales, and have consistently improved our understanding of snow processes and the estimation of snow water resources. However, snow depth, one of the most important snow states, has been measured almost entirely with discrete point-scale samplings in field measurements; spatiotemporally continuous snow depth measurements are nearly nonexistent, mainly due to the high cost of airborne flights and the ban of Unmanned Aerial Systems in many areas (e.g. in all the national parks). In this study, we estimate spatially continuous snow depth from photogrammetric reconstruction of aerial photos taken from a weather balloon. The study was conducted in a 0.2 km2 watershed in Wolverton, Sequoia National Park, California. We tied a point-and-shoot camera on a helium-inflated weather balloon to take aerial images; the camera was scripted to automatically capture images every 3 seconds and to record the camera position and orientation at the imaging times using a built-in GPS. With the 2D images of the snow-covered ground and the camera position and orientation data, the 3D coordinates of the snow surface were reconstructed at 10 cm resolution using photogrammetry software PhotoScan. Similar measurements were taken for the snow-free ground after snowmelt, and the snow depth was estimated from the difference between the snow-on and snow-off measurements. Comparing the photogrammetric-estimated snow depths with the 32 manually measured depths, taken at the same time as the snow-on balloon flight, we find the RMSE of the photogrammetric snow depth is 7 cm, which is 2% of the long-term peak snow depth in the study area. This study suggests that the balloon photogrammetry is a repeatable, economical, simple, and environmental-friendly method to continuously monitor snow at small-scales. Spatiotemporally continuous snow depth could be regularly measured in future field measurements to supplement traditional snow property observations. In addition, since the process of collecting and processing balloon photogrammetry data is straightforward, the photogrammetric snow depth could be shared with the public in real time using our cloud platform that is currently under development.

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.

Snow depth evolution on sea ice from Snow Buoy measurement

NASA Astrophysics Data System (ADS)

Nicolaus, M.; Arndt, S.; Hendricks, S.; Hoppmann, M.; Katlein, C.; König-Langlo, G.; Nicolaus, A.; Rossmann, H. L.; Schiller, M.; Schwegmann, S.; Langevin, D.

2016-12-01

Snow cover is an Essential Climate Variable. On sea ice, snow dominates the energy and momentum exchanges across the atmosphere-ice-ocean interfaces, and actively contributes to sea ice mass balance. Yet, snow depth on sea ice is one of the least known and most difficult to observe parameters of the Arctic and Antarctic; mainly due to its exceptionally high spatial and temporal variability. In this study; we present a unique time series dataset of snow depth and air temperature evolution on Arctic and Antarctic sea ice recorded by autonomous instruments. Snow Buoys record snow depth with four independent ultrasonic sensors, increasing the reliability of the measurements and allowing for additional analyses. Auxiliary measurements include surface and air temperature, barometric pressure and GPS position. 39 deployments of such Snow Buoys were achieved over the last three years either on drifting pack ice, on landfast sea ice or on an ice shelf. Here we highlight results from two pairs of Snow Buoys installed on drifting pack ice in the Weddell Sea. The data reveals large regional differences in the annual cycle of snow depth. Almost no reduction in snow depth (snow melt) was observed in the inner and southern part of the Weddell Sea, allowing a net snow accumulation of 0.2 to 0.9 m per year. In contrast, summer snow melt close to the ice edge resulted in a decrease of about 0.5 m during the summer 2015/16. Another array of eight Snow Buoys was installed on central Arctic sea ice in September 2015. Their air temperature record revealed exceptionally high air temperatures in the subsequent winter, even exceeding the melting point but with almost no impact on snow depth at that time. Future applications of Snow Buoys on Arctic and Antarctic sea ice will allow additional inter-annual studies of snow depth and snow processes, e.g. to support the development of snow depth data products from airborne and satellite data or though assimilation in numerical models.

[A snow depth inversion method for the HJ-1B satellite data].

PubMed

Dong, Ting-Xu; Jiang, Hong-Bo; Chen, Chao; Qin, Qi-Ming

2011-10-01

The importance of the snow is self-evident, while the harms caused by the snow have also received more and more attention. At present, the retrieval of snow depth mainly focused on the use of microwave remote sensing data or a small amount of optical remote sensing data, such as the meteorological data or the MODIS data. The small satellites for environment and disaster monitoring of China are quite different form the meteorological data and MODIS data, both in the spectral resolution or spatial resolution. In this paper, aimed at the HJ-1B data, snow spectral of different underlying surfaces and depths were surveyed. The correlation between snow cover index and snow depth was also analyzed to establish the model for the snow depth retrieval using the HJ-1B data. The validation results showed that it can meet the requirements of real-time monitoring the snow depth on the condition of conventional snow depth.

Mapping snow depth distribution in forested terrain using unmanned aerial vehicles and structure-from-motion

NASA Astrophysics Data System (ADS)

Webster, C.; Bühler, Y.; Schirmer, M.; Stoffel, A.; Giulia, M.; Jonas, T.

2017-12-01

Snow depth distribution in forests exhibits strong spatial heterogeneity compared to adjacent open sites. Measurement of snow depths in forests is currently limited to a) manual point measurements, which are sparse and time-intensive, b) ground-penetrating radar surveys, which have limited spatial coverage, or c) airborne LiDAR acquisition, which are expensive and may deteriorate in denser forests. We present the application of unmanned aerial vehicles in combination with structure-from-motion (SfM) methods to photogrammetrically map snow depth distribution in forested terrain. Two separate flights were carried out 10 days apart across a heterogeneous forested area of 900 x 500 m. Corresponding snow depth maps were derived using both, LiDAR-based and SfM-based DTM data, obtained during snow-off conditions. Manual measurements collected following each flight were used to validate the snow depth maps. Snow depths were resolved at 5cm resolution and forest snow depth distribution structures such as tree wells and other areas of preferential melt were represented well. Differential snow depth maps showed maximum ablation in the exposed south sides of trees and smaller differences in the centre of gaps and on the north side of trees. This new application of SfM to map snow depth distribution in forests demonstrates a straightforward method for obtaining information that was previously only available through manual spatially limited ground-based measurements. These methods could therefore be extended to more frequent observation of snow depths in forests as well as estimating snow accumulation and depletion rates.

[Effect of different snow depth and area on the snow cover retrieval using remote sensing data].

PubMed

Jiang, Hong-bo; Qin, Qi-ming; Zhang, Ning; Dong, Heng; Chen, Chao

2011-12-01

For the needs of snow cover monitoring using multi-source remote sensing data, in the present article, based on the spectrum analysis of different depth and area of snow, the effect of snow depth on the results of snow cover retrieval using normalized difference snow index (NDSI) is discussed. Meanwhile, taking the HJ-1B and MODIS remote sensing data as an example, the snow area effect on the snow cover monitoring is also studied. The results show that: the difference of snow depth does not contribute to the retrieval results, while the snow area affects the results of retrieval to some extents because of the constraints of spatial resolution.

Snow depth on Arctic sea ice from historical in situ data

NASA Astrophysics Data System (ADS)

Shalina, Elena V.; Sandven, Stein

2018-06-01

The snow data from the Soviet airborne expeditions Sever in the Arctic collected over several decades in March, April and May have been analyzed in this study. The Sever data included more measurements and covered a much wider area, particularly in the Eurasian marginal seas (Kara Sea, Laptev Sea, East Siberian Sea and Chukchi Sea), compared to the Soviet North Pole drifting stations. The latter collected data mainly in the central part of the Arctic Basin. The following snow parameters have been analyzed: average snow depth on the level ice (undisturbed snow) height and area of sastrugi, depth of snow dunes attached to ice ridges and depth of snow on hummocks. In the 1970s-1980s, in the central Arctic, the average depth of undisturbed snow was 21.2 cm, the depth of sastrugi (that occupied about 30 % of the ice surface) was 36.2 cm and the average depth of snow near hummocks and ridges was about 65 cm. For the marginal seas, the average depth of undisturbed snow on the level ice varied from 9.8 cm in the Laptev Sea to 15.3 cm in the East Siberian Sea, which had a larger fraction of multiyear ice. In the marginal seas the spatial variability of snow depth was characterized by standard deviation varying between 66 and 100 %. The average height of sastrugi varied from 23 cm to about 32 cm with standard deviation between 50 and 56 %. The average area covered by sastrugi in the marginal seas was estimated to be 36.5 % of the total ice area where sastrugi were observed. The main result of the study is a new snow depth climatology for the late winter using data from both the Sever expeditions and the North Pole drifting stations. The snow load on the ice observed by Sever expeditions has been described as a combination of the depth of undisturbed snow on the level ice and snow depth of sastrugi weighted in proportion to the sastrugi area. The height of snow accumulated near the ice ridges was not included in the calculations because there are no estimates of the area covered by those features from the Sever expeditions. The effect of not including that data can lead to some underestimation of the average snow depth. The new climatology refines the description of snow depth in the central Arctic compared to the results by Warren et al. (1999) and provides additional detailed data in the marginal seas. The snow depth climatology is based on 94 % Sever data and 6 % North Pole data. The new climatology shows lower snow depth in the central Arctic comparing to Warren climatology and more detailed data in the Eurasian seas.

Interannual consistency in fractal snow depth patterns at two Colorado mountain sites

Treesearch

Jeffrey S. Deems; Steven R. Fassnacht; Kelly J. Elder

2008-01-01

Fractal dimensions derived from log-log variograms are useful for characterizing spatial structure and scaling behavior in snow depth distributions. This study examines the temporal consistency of snow depth scaling features at two sites using snow depth distributions derived from lidar datasets collected in 2003 and 2005. The temporal snow accumulation patterns in...

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.

Sea Ice Mass Balance Buoys (IMBs): First Results from a Data Processing Intercomparison Study

NASA Astrophysics Data System (ADS)

Hoppmann, Mario; Tiemann, Louisa; Itkin, Polona

2017-04-01

IMBs are autonomous instruments able to continuously monitor the growth and melt of sea ice and its snow cover at a single point on an ice floe. Complementing field expeditions, remote sensing observations and modelling studies, these in-situ data are crucial to assess the mass balance and seasonal evolution of sea ice and snow in the polar oceans. Established subtypes of IMBs combine coarse-resolution temperature profiles through air, snow, ice and ocean with ultrasonic pingers to detect snow accumulation and ice thermodynamic growth. Recent technological advancements enable the use of high-resolution temperature chains, which are also able to identify the surrounding medium through a „heating cycle". The temperature change during this heating cycle provides additional information on the internal properties and processes of the ice. However, a unified data processing technique to reliably and accurately determine sea ice thickness and snow depth from this kind of data is still missing, and an unambiguous interpretation remains a challenge. Following the need to improve techniques for remotely measuring sea ice mass balance, an international IMB working group has recently been established. The main goals are 1) to coordinate IMB deployments, 2) to enhance current IMB data processing and -interpretation techniques, and 3) to provide standardized IMB data products to a broader community. Here we present first results from two different data processing algorithms, applied to selected IMB datasets from the Arctic and Antarctic. Their performance with regard to sea ice thickness and snow depth retrieval is evaluated, and an uncertainty is determined. Although several challenges and caveats in IMB data processing and -interpretation are found, such datasets bear great potential and yield plenty of useful information about sea ice properties and processes. It is planned to include many more algorithms from contributors within the working group, and we explicitly invite other interested scientists to join this promising effort.

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.

Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods

DOE PAGES

Wainwright, Haruko M.; Liljedahl, Anna K.; Dafflon, Baptiste; ...

2017-04-03

This paper compares and integrates different strategies to characterize the variability of end-of-winter snow depth and its relationship to topography in ice-wedge polygon tundra of Arctic Alaska. Snow depth was measured using in situ snow depth probes and estimated using ground-penetrating radar (GPR) surveys and the photogrammetric detection and ranging (phodar) technique with an unmanned aerial system (UAS). We found that GPR data provided high-precision estimates of snow depth (RMSE=2.9cm), with a spatial sampling of 10cm along transects. Phodar-based approaches provided snow depth estimates in a less laborious manner compared to GPR and probing, while yielding a high precision (RMSE=6.0cm) andmore » a fine spatial sampling (4cm×4cm). We then investigated the spatial variability of snow depth and its correlation to micro- and macrotopography using the snow-free lidar digital elevation map (DEM) and the wavelet approach. We found that the end-of-winter snow depth was highly variable over short (several meter) distances, and the variability was correlated with microtopography. Microtopographic lows (i.e., troughs and centers of low-centered polygons) were filled in with snow, which resulted in a smooth and even snow surface following macrotopography. We developed and implemented a Bayesian approach to integrate the snow-free lidar DEM and multiscale measurements (probe and GPR) as well as the topographic correlation for estimating snow depth over the landscape. Our approach led to high-precision estimates of snow depth (RMSE=6.0cm), at 0.5m resolution and over the lidar domain (750m×700m).« less

Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods

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

Wainwright, Haruko M.; Liljedahl, Anna K.; Dafflon, Baptiste

This paper compares and integrates different strategies to characterize the variability of end-of-winter snow depth and its relationship to topography in ice-wedge polygon tundra of Arctic Alaska. Snow depth was measured using in situ snow depth probes and estimated using ground-penetrating radar (GPR) surveys and the photogrammetric detection and ranging (phodar) technique with an unmanned aerial system (UAS). We found that GPR data provided high-precision estimates of snow depth (RMSE=2.9cm), with a spatial sampling of 10cm along transects. Phodar-based approaches provided snow depth estimates in a less laborious manner compared to GPR and probing, while yielding a high precision (RMSE=6.0cm) andmore » a fine spatial sampling (4cm×4cm). We then investigated the spatial variability of snow depth and its correlation to micro- and macrotopography using the snow-free lidar digital elevation map (DEM) and the wavelet approach. We found that the end-of-winter snow depth was highly variable over short (several meter) distances, and the variability was correlated with microtopography. Microtopographic lows (i.e., troughs and centers of low-centered polygons) were filled in with snow, which resulted in a smooth and even snow surface following macrotopography. We developed and implemented a Bayesian approach to integrate the snow-free lidar DEM and multiscale measurements (probe and GPR) as well as the topographic correlation for estimating snow depth over the landscape. Our approach led to high-precision estimates of snow depth (RMSE=6.0cm), at 0.5m resolution and over the lidar domain (750m×700m).« less

Impact of intra- versus inter-annual snow depth variation on water relations and photosynthesis for two Great Basin Desert shrubs.

PubMed

Loik, Michael E; Griffith, Alden B; Alpert, Holly; Concilio, Amy L; Wade, Catherine E; Martinson, Sharon J

2015-06-01

Snowfall provides the majority of soil water in certain ecosystems of North America. We tested the hypothesis that snow depth variation affects soil water content, which in turn drives water potential (Ψ) and photosynthesis, over 10 years for two widespread shrubs of the western USA. Stem Ψ (Ψ stem) and photosynthetic gas exchange [stomatal conductance to water vapor (g s), and CO2 assimilation (A)] were measured in mid-June each year from 2004 to 2013 for Artemisia tridentata var. vaseyana (Asteraceae) and Purshia tridentata (Rosaceae). Snow fences were used to create increased or decreased snow depth plots. Snow depth on +snow plots was about twice that of ambient plots in most years, and 20 % lower on -snow plots, consistent with several down-scaled climate model projections. Maximal soil water content at 40- and 100-cm depths was correlated with February snow depth. For both species, multivariate ANOVA (MANOVA) showed that Ψ stem, g s, and A were significantly affected by intra-annual variation in snow depth. Within years, MANOVA showed that only A was significantly affected by spatial snow depth treatments for A. tridentata, and Ψ stem was significantly affected by snow depth for P. tridentata. Results show that stem water relations and photosynthetic gas exchange for these two cold desert shrub species in mid-June were more affected by inter-annual variation in snow depth by comparison to within-year spatial variation in snow depth. The results highlight the potential importance of changes in inter-annual variation in snowfall for future shrub photosynthesis in the western Great Basin Desert.

Characteristics and Limitations of Submerged GPS L1 Observations

NASA Astrophysics Data System (ADS)

Steiner, Ladina; Geiger, Alain

2017-04-01

Extensive amount of water stored in snow covers has a high impact on flood development during snow melting periods. Early assessment of these parameters in mountain environments enhance early-warning and thus prevention of major impacts. Sub-snow GNSS techniques are lately suggested to determine liquid water content, snow water equivalent or considered for avalanche rescue. This technique is affordable, flexible, and provides accurate and continuous observations independent on weather conditions. However, the characteristics of GNSS observations for applications within a snow-pack still need to be further investigated. The magnitude of the main interaction processes involved for the GPS wavelength propagating through different layers of snow, ice or water is theoretically examined. Liquid water exerts the largest influence on GPS signal propagation through a snow-pack. Therefore, we focus on determining the characteristics of GNSS observables under water. An experiment was set-up to investigate the characteristics and limitations of submerged GPS observations using a pool, a level control by communicating pipes, a geodetic and a low-cost GPS antenna, and a water level sensor. The GPS antennas were placed into the water. The water level was increased daily by a step of two millimeters up to thirty millimeters above the antenna. Based on this experiment, the signal penetration depth, satellite availability, the attenuation of signal strength and the quality of solutions are analyzed. Our experimental results show an agreement with the theoretically derived attenuation parameter and signal penetration depth. The assumption of water as the limiting parameter for GPS observations within a snow-pack can be confirmed. Higher wetness in a snow-pack leads to less transmission, higher refraction, higher attenuation and thus a decreased penetration depth as well as a reduced quality of the solutions. In consequence, GPS applications within a snow-pack are heavily impacted by wetness which is even more pronounced during melting period. In this poster, we present a short introduction to the principle, explain the developed algorithms and show results of experiments dedicated to the signal propagation in water.

Responses of Plant Community Composition to Long-term Changes in Snow Depth at the Great Basin Desert - Sierra Nevada ecotone.

NASA Astrophysics Data System (ADS)

Loik, M. E.

2015-12-01

Snowfall is the dominant hydrologic input for many high-elevation ecosystems of the western United States. Many climate models envision changes in California's Sierra Nevada snow pack characteristics, which would severely impact the storage and release of water for one of the world's largest economies. Given the importance of snowfall for future carbon cycling in high elevation ecosystems, how will these changes affect seedling recruitment, plant mortality, and community composition? To address this question, experiments utilize snow fences to manipulate snow depth and melt timing at a desert-montane ecotone in eastern California, USA. Long-term April 1 snow pack depth averages 1344 mm (1928-2015) but is highly variable from year to year. Snow fences increased equilibrium drift snow depth by 100%. Long-term changes in snow depth and melt timing are associated with s shift from shurbs to graminoids where snow depth was increased for >50 years. Changes in snow have impacted growth for only three plant species. Moreover, annual growth ring increments of the conifers Pinus jeffreyi and Pi. contorta were not equally sensitive to snow depth. There were over 8000 seedlings of the shrubs Artemisia tridentata and Purshia tridentata found in 6300 m2 in summer 2009, following about 1400 mm of winter snow and spring rain. The frequency of seedlings of A. tridentata and P. tridentata were much lower on increased-depth plots compared to ambient-depth, and reduced-depth plots. Survival of the first year was lowest for A. tridentata. Survival of seedlings from the 2008 cohort was much higher for P. tridentata than A. tridentata during the 2011-2015 drought. Results indicate complex interactions between snow depth and plant community characteristics, and that responses of plants at this ecotone may not respond similarly to increases vs. decreases in snow depth. These changes portend altered carbon uptake in this region under future snowfall scenarios.

Influence of tundra snow layer thickness on measured and modelled radar backscatter

NASA Astrophysics Data System (ADS)

Rutter, N.; Sandells, M. J.; Derksen, C.; King, J. M.; Toose, P.; Wake, L. M.; Watts, T.

2017-12-01

Microwave radar backscatter within a tundra snowpack is strongly influenced by spatial variability of the thickness of internal layering. Arctic tundra snowpacks often comprise layers consisting of two dominant snow microstructures; a basal depth hoar layer overlain by a layer of wind slab. Occasionally there is also a surface layer of decomposing fresh snow. The two main layers have strongly different microwave scattering properties. Depth hoar has a greater capacity for scattering electromagnetic energy than wind slab, however, wind slab usually has a larger snow water equivalent (SWE) than depth hoar per unit volume due to having a higher density. So, determining the relative proportions of depth hoar and wind slab from a snowpack of a known depth may help our future capacity to invert forward models of electromagnetic backscatter within a data assimilation scheme to improve modelled estimates of SWE. Extensive snow measurements were made within Trail Valley Creek, NWT, Canada in April 2013. Snow microstructure was measured at 18 pit and 9 trench locations throughout the catchment (trench extent ranged between 5 to 50 m). Ground microstructure measurements included traditional stratigraphy, near infrared stratigraphy, Specific Surface Area (SSA), and density. Coincident airborne Lidar measurements were made to estimate distributed snow depth across the catchment, in addition to airborne radar snow backscatter using a dual polarized (VV/VH) X- and Ku-band Synthetic Aperture Radar (SnowSAR). Ground measurements showed the mean proportion of depth hoar was just under 30% of total snow depth and was largely unresponsive to increasing snow depth. The mean proportion of wind slab is consistently greater than 50% and showed an increasing trend with increasing total snow depth. A decreasing trend in the mean proportion of surface snow (approximately 25% to 10%) with increasing total depth accounted for this increase in wind slab. This new knowledge of variability in stratigraphic thickness, relative to respective proportions of total snow depth, was used to investigate the representativeness of point measurements of density and microstructure for forward simulations of the SMRT microwave scattering model, using Lidar derived snow depths.

Measurement of snow depth distribution in the Kamikochi-Azusa river basin using an airborne laser scanning

NASA Astrophysics Data System (ADS)

Suzuki, K.; Sasaki, A.

2013-12-01

In the Japanese Alps region, large amounts of precipitation in the form of snow constitute a more important water resource than rain. During the winter, precipitation that is deposited as snowfall accumulates in the river basins, and it forms natural dams known as 'white dams.' A quantitative understanding of snow depth distribution in these mountainous areas is important not only for evaluating water resource volume, but also for understanding the effects of snow in terms of its impact on landforms and its effect on the distribution of vegetation. However, it is not easy to perform a quantitative evaluation of snow depth distribution in mountainous areas. Several methods have been proposed for clarifying snow depth distribution. The most widely used of these is a method of inserting a sounding rod into the snow to measure its depth at each geographic position. Another method is to dig a trench in the snow and then perform an observational measurement of the side of the trench. These methods enable accurate measurement of the snow depth; however, when the snow is several meters deep, the methods may be limited by the measuring capacity of the equipment, or by the time restrictions of the survey. For these reasons, wide area measurement of the spatial distribution of snow is very difficult, and it is not suitable for investigating snow depth distribution in river basins. There is a method of using ultrasonics or radar to measure the depth of snow and to make observations of snow depth at certain positions. This method offers high measurement precision and high time resolution at the observation points. However, for observations in areas of very deep snow, it becomes technically difficult to install the equipment, and it is difficult to make a large number of installations to cover a wide area. There are also methods of indirectly measuring snow depth. One of these is to use aerial photographs taken when there is no snow cover and when there is snow cover, draw contour lines, and then use the difference between them to clarify the snow depth. This method allows researchers to grasp the snow depth over a wide area, but it needs to be made more precise if it is to incorporate high-precision information on equivalent elevation points on the snow surface. In recent years, a measurement technology has been developed that uses laser scanners mounted on aircraft. This method enables researchers to obtain ground surface coordinate data with high precision over a wide area from the air. Using such a scanner to measure the ground surface during snow coverage and during no snow coverage, and then finding the differences between the surface elevations, has made it possible to ascertain snow depth with high precision. Airborne laser measurement enables high-precision measurements over a wide area and in a short amount of time, and measurements can be made regardless of geographical factors such as sloping ground. As such, it enables measurement of snow depth distribution over a wide area without having to worry about the undulations of the land. In this study, airborne laser scanning was carried out on the snow surface in the upstream region of the Kamikochi-Azusa River in Japan on March 29, 2012, in order to clarify the snow depth distribution.

NOHRSC Interactive Snow Information

Science.gov Websites

-present) RFC Basin Other (non-RFC) Basin State NSA region (Discussion) NSA subregion (Disc.) Basins by None Snow Water Equivalent Snow Depth Shallow SWE Shallow Snow Depth Snow Temperature Snow Density Snow Melt Snow Precipitation Non-Snow Precipitation Air Temperature Solar Radiation Relative Humidity

Improved Cloud and Snow Screening in MAIAC Aerosol Retrievals Using Spectral and Spatial Analysis

NASA Technical Reports Server (NTRS)

Lyapustin, A.; Wang, Y.; Laszlo, I.; Kokrkin, S.

2012-01-01

An improved cloud/snow screening technique in the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm is described. It is implemented as part of MAIAC aerosol retrievals based on analysis of spectral residuals and spatial variability. Comparisons with AERONET aerosol observations and a large-scale MODIS data analysis show strong suppression of aerosol optical thickness outliers due to unresolved clouds and snow. At the same time, the developed filter does not reduce the aerosol retrieval capability at high 1 km resolution in strongly inhomogeneous environments, such as near centers of the active fires. Despite significant improvement, the optical depth outliers in high spatial resolution data are and will remain the problem to be addressed by the application-dependent specialized filtering techniques.

Quantifying small-scale spatio-temporal variability of snow stratigraphy in forests based on high-resolution snow penetrometry

NASA Astrophysics Data System (ADS)

Teich, M.; Hagenmuller, P.; Bebi, P.; Jenkins, M. J.; Giunta, A. D.; Schneebeli, M.

2017-12-01

Snow stratigraphy, the characteristic layering within a seasonal snowpack, has important implications for snow remote sensing, hydrology and avalanches. Forests modify snowpack properties through interception, wind speed reduction, and changes to the energy balance. The lack of snowpack observations in forests limits our ability to understand the evolution of snow stratigraphy and its spatio-temporal variability as a function of forest structure and to observe snowpack response to changes in forest cover. We examined the snowpack under canopies of a spruce forest in the central Rocky Mountains, USA, using the SnowMicroPen (SMP), a high resolution digital penetrometer. Weekly-repeated penetration force measurements were recorded along 10 m transects every 0.3 m in winter 2015 and bi-weekly along 20 m transects every 0.5 m in 2016 in three study plots beneath canopies of undisturbed, bark beetle-disturbed and harvested forest stands, and an open meadow. To disentangle information about layer hardness and depth variabilities, and to quantitatively compare the different SMP profiles, we applied a matching algorithm to our dataset, which combines several profiles by automatically adjusting their layer thicknesses. We linked spatial and temporal variabilities of penetration force and depth, and thus snow stratigraphy to forest and meteorological conditions. Throughout the season, snow stratigraphy was more heterogeneous in undisturbed but also beneath bark beetle-disturbed forests. In contrast, and despite remaining small diameter trees and woody debris, snow stratigraphy was rather homogenous at the harvested plot. As expected, layering at the non-forested plot varied only slightly over the small spatial extent sampled. At the open and harvested plots, persistent crusts and ice lenses were clearly present in the snowpack, while such hard layers barely occurred beneath undisturbed and disturbed canopies. Due to settling, hardness significantly increased with depth at open and harvested plots, which was less distinctive at the other two plots. Our results contribute to the general understanding of forest-snowpack interactions and, if combined with density and specific surface area estimates, can be used to validate snowpack and microwave models for avalanche formation and SWE retrieval in forests.

Estimating Snow Water Equivalent over the American River in the Sierra Nevada Basin Using Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Welch, S. C.; Kerkez, B.; Glaser, S. D.; Bales, R. C.; Rice, R.

2011-12-01

We have designed a basin-scale (>2000 km2) instrument cluster, made up of 20 local-scale (1-km footprint) wireless sensor networks (WSNs), to measure patterns of snow depth and snow water equivalent (SWE) across the main snowmelt producing area within the American River basin. Each of the 20 WSNs has on the order of 25 wireless nodes, with over 10 nodes actively sensing snow depth, and thus snow accumulation and melt. When combined with existing snow density measurements and full-basin satellite snowcover data, these measurements are designed to provide dense ground-truth snow properties for research and real-time SWE for water management. The design of this large-scale network is based on rigorous testing of previous, smaller-scale studies, permitting for the development of methods to significantly, and efficiently scale up network operations. Recent advances in WSN technology have resulted in a modularized strategy that permits rapid future network deployment. To select network and sensor locations, various sensor placement approaches were compared, including random placement, placement of WSNs in locations that have captured the historical basin mean, as well as a placement algorithm leveraging the covariance structure of the SWE distribution. We show that that the optimal network locations do not exhibit a uniform grid, but rather follow strategic patterns based on physiographic terrain parameters. Uncertainty estimates are also provided to assess the confidence in the placement approach. To ensure near-optimal coverage of the full basin, we validated each placement approach with a multi-year record of SWE derived from reconstruction of historical satellite measurements.

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.

Improved Snow Mapping Accuracy with Revised MODIS Snow Algorithm

NASA Technical Reports Server (NTRS)

Riggs, George; Hall, Dorothy K.

2012-01-01

The MODIS snow cover products have been used in over 225 published studies. From those reports, and our ongoing analysis, we have learned about the accuracy and errors in the snow products. Revisions have been made in the algorithms to improve the accuracy of snow cover detection in Collection 6 (C6), the next processing/reprocessing of the MODIS data archive planned to start in September 2012. Our objective in the C6 revision of the MODIS snow-cover algorithms and products is to maximize the capability to detect snow cover while minimizing snow detection errors of commission and omission. While the basic snow detection algorithm will not change, new screens will be applied to alleviate snow detection commission and omission errors, and only the fractional snow cover (FSC) will be output (the binary snow cover area (SCA) map will no longer be included).

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.

Scales of snow depth variability in high elevation rangeland sagebrush

NASA Astrophysics Data System (ADS)

Tedesche, Molly E.; Fassnacht, Steven R.; Meiman, Paul J.

2017-09-01

In high elevation semi-arid rangelands, sagebrush and other shrubs can affect transport and deposition of wind-blown snow, enabling the formation of snowdrifts. Datasets from three field experiments were used to investigate the scales of spatial variability of snow depth around big mountain sagebrush ( Artemisia tridentata Nutt.) at a high elevation plateau rangeland in North Park, Colorado, during the winters of 2002, 2003, and 2008. Data were collected at multiple resolutions (0.05 to 25 m) and extents (2 to 1000 m). Finer scale data were collected specifically for this study to examine the correlation between snow depth, sagebrush microtopography, the ground surface, and the snow surface, as well as the temporal consistency of snow depth patterns. Variograms were used to identify the spatial structure and the Moran's I statistic was used to determine the spatial correlation. Results show some temporal consistency in snow depth at several scales. Plot scale snow depth variability is partly a function of the nature of individual shrubs, as there is some correlation between the spatial structure of snow depth and sagebrush, as well as between the ground and snow depth. The optimal sampling resolution appears to be 25-cm, but over a large area, this would require a multitude of samples, and thus a random stratified approach is recommended with a fine measurement resolution of 5-cm.

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.

Antarctic Sea Ice Thickness and Snow-to-Ice Conversion from Atmospheric Reanalysis and Passive Microwave Snow Depth

NASA Technical Reports Server (NTRS)

Markus, Thorsten; Maksym, Ted

2007-01-01

Passive microwave snow depth, ice concentration, and ice motion estimates are combined with snowfall from the European Centre for Medium Range Weather Forecasting (ECMWF) reanalysis (ERA-40) from 1979-200 1 to estimate the prevalence of snow-to-ice conversion (snow-ice formation) on level sea ice in the Antarctic for April-October. Snow ice is ubiquitous in all regions throughout the growth season. Calculated snow- ice thicknesses fall within the range of estimates from ice core analysis for most regions. However, uncertainties in both this analysis and in situ data limit the usefulness of snow depth and snow-ice production to evaluate the accuracy of ERA-40 snowfall. The East Antarctic is an exception, where calculated snow-ice production exceeds observed ice thickness over wide areas, suggesting that ERA-40 precipitation is too high there. Snow-ice thickness variability is strongly controlled not just by snow accumulation rates, but also by ice divergence. Surprisingly, snow-ice production is largely independent of snow depth, indicating that the latter may be a poor indicator of total snow accumulation. Using the presence of snow-ice formation as a proxy indicator for near-zero freeboard, we examine the possibility of estimating level ice thickness from satellite snow depths. A best estimate for the mean level ice thickness in September is 53 cm, comparing well with 51 cm from ship-based observations. The error is estimated to be 10-20 cm, which is similar to the observed interannual and regional variability. Nevertheless, this is comparable to expected errors for ice thickness determined by satellite altimeters. Improvement in satellite snow depth retrievals would benefit both of these methods.

Using high resolution Lidar data from SnowEx to characterize the sensitivity of snow depth retrievals to point-cloud density and vegetation

NASA Astrophysics Data System (ADS)

Patterson, V. M.; Bormann, K.; Deems, J. S.; Painter, T. H.

2017-12-01

The NASA SnowEx campaign conducted in 2016 and 2017 provides a rich source of high-resolution Lidar data from JPL's Airborne Snow Observatory (ASO - http://aso.jpl.nasa.gov) combined with extensive in-situ measurements in two key areas in Colorado: Grand Mesa and Senator Beck. While the uncertainty in the 50m snow depth retrievals from NASA's ASO been estimated at 1-2cm in non-vegetated exposed areas (Painter et al., 2016), the impact of forest cover and point-cloud density on ASO snow lidar depth retrievals is relatively unknown. Dense forest canopies are known to reduce lidar penetration and ground strikes thus affecting the elevation surface retrieved from in the forest. Using high-resolution lidar point cloud data from the ASO SnowEx campaigns (26pt/m2) we applied a series of data decimations (up to 90% point reduction) to the point cloud data to quantify the relationship between vegetation, ground point density, resulting snow-off and snow-on surface elevations and finally snow depth. We observed non-linear reductions in lidar ground point density in forested areas that were strongly correlated to structural forest cover metrics. Previously, the impacts of these data decimations on a small study area in Grand Mesa showed a sharp increase in under-canopy surface elevation errors of -0.18m when ground point densities were reduced to 1.5pt/m2. In this study, we expanded the evaluation to the more topographically challenging Senator Beck basin, have conducted analysis along a vegetation gradient and are considering snow the impacts of snow depth rather than snow-off surface elevation. Preliminary analysis suggest that snow depth retrievals inferred from airborne lidar elevation differentials may systematically underestimate snow depth in forests where canopy density exceeds 1.75 and where tree heights exceed 5m. These results provide a basis from which to identify areas that may suffer from vegetation-induced biases in surface elevation models and snow depths derived from airborne lidar data, and help quantify expected spatial distributions of errors in the snow depth that can be used to improve the accuracy of ASO basin-scale depth and water equivalent products.

Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle

NASA Astrophysics Data System (ADS)

Harder, Phillip; Schirmer, Michael; Pomeroy, John; Helgason, Warren

2016-11-01

Quantifying the spatial distribution of snow is crucial to predict and assess its water resource potential and understand land-atmosphere interactions. High-resolution remote sensing of snow depth has been limited to terrestrial and airborne laser scanning and more recently with application of structure from motion (SfM) techniques to airborne (manned and unmanned) imagery. In this study, photography from a small unmanned aerial vehicle (UAV) was used to generate digital surface models (DSMs) and orthomosaics for snow cover at a cultivated agricultural Canadian prairie and a sparsely vegetated Rocky Mountain alpine ridgetop site using SfM. The accuracy and repeatability of this method to quantify snow depth, changes in depth and its spatial variability was assessed for different terrain types over time. Root mean square errors in snow depth estimation from differencing snow-covered and non-snow-covered DSMs were 8.8 cm for a short prairie grain stubble surface, 13.7 cm for a tall prairie grain stubble surface and 8.5 cm for an alpine mountain surface. This technique provided useful information on maximum snow accumulation and snow-covered area depletion at all sites, while temporal changes in snow depth could also be quantified at the alpine site due to the deeper snowpack and consequent higher signal-to-noise ratio. The application of SfM to UAV photographs returns meaningful information in areas with mean snow depth > 30 cm, but the direct observation of snow depth depletion of shallow snowpacks with this method is not feasible. Accuracy varied with surface characteristics, sunlight and wind speed during the flight, with the most consistent performance found for wind speeds < 10 m s-1, clear skies, high sun angles and surfaces with negligible vegetation cover.

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.

Spatiotemporal variability of snow depth across the Eurasian continent from 1966 to 2012

NASA Astrophysics Data System (ADS)

Zhong, Xinyue; Zhang, Tingjun; Kang, Shichang; Wang, Kang; Zheng, Lei; Hu, Yuantao; Wang, Huijuan

2018-01-01

Snow depth is one of the key physical parameters for understanding land surface energy balance, soil thermal regime, water cycle, and assessing water resources from local community to regional industrial water supply. Previous studies by using in situ data are mostly site specific; data from satellite remote sensing may cover a large area or global scale, but uncertainties remain large. The primary objective of this study is to investigate spatial variability and temporal change in snow depth across the Eurasian continent. Data used include long-term (1966-2012) ground-based measurements from 1814 stations. Spatially, long-term (1971-2000) mean annual snow depths of >20 cm were recorded in northeastern European Russia, the Yenisei River basin, Kamchatka Peninsula, and Sakhalin. Annual mean and maximum snow depth increased by 0.2 and 0.6 cm decade-1 from 1966 through 2012. Seasonally, monthly mean snow depth decreased in autumn and increased in winter and spring over the study period. Regionally, snow depth significantly increased in areas north of 50° N. Compared with air temperature, snowfall had greater influence on snow depth during November through March across the former Soviet Union. This study provides a baseline for snow depth climatology and changes across the Eurasian continent, which would significantly help to better understanding climate system and climate changes on regional, hemispheric, or even global scales.

The Effect of Climate Change on Snow Pack at Sleepers River, Vermont, USA

NASA Astrophysics Data System (ADS)

Shanley, J. B.; Chalmers, A.; Denner, J.; Clark, S.

2017-12-01

Sleepers River Research Watershed, a U.S. Geological Survey Water, Energy, and Biogeochemical Budgets (WEBB) site in northeastern Vermont, has a 58-year record (since 1959) of snow depth and snow water equivalence (SWE), one of the longest continuous records in eastern North America. Snow measurements occur weekly during the winter at the watershed using an Adirondack type snow tube sampler. Sleepers River averages about 1100 mm of precipitation annually of which 20 to 30 percent falls as snow. Snow cover typically persists from December to April. Length of snow cover and snow depth vary with elevation, aspect, and cover type. Sites include open field, and hardwood and conifer stand clearings from 225 to 630 meters elevation. We evaluated changes in snow depth, snow cover duration, and SWE relative to elevation, soil frost depth, air temperature, total precipitation, and the El Niño - Southern Oscillation (ENSO) cycle. Overall, warmer winter temperatures have resulted in more midwinter thaws, more rain during the winter, and more variable soil frost depth. Trends in snowpack amount and duration were compared to winter-spring streamflow center-of-mass to evaluate if shifts in the snow pack regime were leading to earlier snowmelt.

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.

Estimating terrestrial snow depth with the Topex-Poseidon altimeter and radiometer

USGS Publications Warehouse

Papa, F.; Legresy, B.; Mognard, N.M.; Josberger, E.G.; Remy, F.

2002-01-01

Active and passive microwave measurements obtained by the dual-frequency Topex-Poseidon radar altimeter from the Northern Great Plains of the United States are used to develop a snow pack radar backscatter model. The model results are compared with daily time series of surface snow observations made by the U.S. National Weather Service. The model results show that Ku-band provides more accurate snow depth determinations than does C-band. Comparing the snow depth determinations derived from the Topex-Poseidon nadir-looking passive microwave radiometers with the oblique-looking Satellite Sensor Microwave Imager (SSM/I) passive microwave observations and surface observations shows that both instruments accurately portray the temporal characteristics of the snow depth time series. While both retrievals consistently underestimate the actual snow depths, the Topex-Poseidon results are more accurate.

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.

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 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.

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.

A radiation closure study of Arctic stratus cloud microphysical properties using the collocated satellite-surface data and Fu-Liou radiative transfer model

NASA Astrophysics Data System (ADS)

Dong, Xiquan; Xi, Baike; Qiu, Shaoyue; Minnis, Patrick; Sun-Mack, Sunny; Rose, Fred

2016-09-01

Retrievals of cloud microphysical properties based on passive satellite imagery are especially difficult over snow-covered surfaces because of the bright and cold surface. To help quantify their uncertainties, single-layered overcast liquid-phase Arctic stratus cloud microphysical properties retrieved by using the Clouds and the Earth's Radiant Energy System Edition 2 and Edition 4 (CERES Ed2 and Ed4) algorithms are compared with ground-based retrievals at the Atmospheric Radiation Measurement North Slope of Alaska (ARM NSA) site at Barrow, AK, during the period from March 2000 to December 2006. A total of 206 and 140 snow-free cases (Rsfc ≤ 0.3), and 108 and 106 snow cases (Rsfc > 0.3), respectively, were selected from Terra and Aqua satellite passes over the ARM NSA site. The CERES Ed4 and Ed2 optical depth (τ) and liquid water path (LWP) retrievals from both Terra and Aqua are almost identical and have excellent agreement with ARM retrievals under snow-free and snow conditions. In order to reach a radiation closure study for both the surface and top of atmosphere (TOA) radiation budgets, the ARM precision spectral pyranometer-measured surface albedos were adjusted (63.6% and 80% of the ARM surface albedos for snow-free and snow cases, respectively) to account for the water and land components of the domain of 30 km × 30 km. Most of the radiative transfer model calculated SW↓sfc and SW↑TOA fluxes by using ARM and CERES cloud retrievals and the domain mean albedos as input agree with the ARM and CERES flux observations within 10 W m-2 for both snow-free and snow conditions. Sensitivity studies show that the ARM LWP and re retrievals are less dependent on solar zenith angle (SZA), but all retrieved optical depths increase with SZA.

Resilience to Changing Snow Depth in a Shrubland Ecosystem.

NASA Astrophysics Data System (ADS)

Loik, M. E.

2008-12-01

Snowfall is the dominant hydrologic input for high elevations and latitudes of the arid- and semi-arid western United States. Sierra Nevada snowpack provides numerous important services for California, but is vulnerable to anthropogenic forcing of the coupled ocean-atmosphere system. GCM and RCM scenarios envision reduced snowpack and earlier melt under a warmer climate, but how will these changes affect soil and plant water relations and ecosystem processes? And, how resilient will this ecosystem be to short- and long-term forcing of snow depth and melt timing? To address these questions, our experiments utilize large- scale, long-term roadside snow fences to manipulate snow depth and melt timing in eastern California, USA. Interannual snow depth averages 1344 mm with a CV of 48% (April 1, 1928-2008). Snow fences altered snow melt timing by up to 18 days in high-snowfall years, and affected short-term soil moisture pulses less in low- than medium- or high-snowfall years. Sublimation in this arid location accounted for about 2 mol m- 2 of water loss from the snowpack in 2005. Plant water potential increased after the ENSO winter of 2005 and stayed relatively constant for the following three years, even after the low snowfall of winter 2007. Over the long-term, changes in snow depth and melt timing have impacted cover or biomass of Achnatherum thurberianum, Elymus elemoides, and Purshia tridentata. Growth of adult conifers (Pinus jeffreyi and Pi. contorta) was not equally sensitive to snow depth. Thus, complex interactions between snow depth, soil water inputs, physiological processes, and population patterns help drive the resilience of this ecosystem to changes in snow depth and melt timing.

High Resolution Insights into Snow Distribution Provided by Drone Photogrammetry

NASA Astrophysics Data System (ADS)

Redpath, T.; Sirguey, P. J.; Cullen, N. J.; Fitzsimons, S.

2017-12-01

Dynamic in time and space, New Zealand's seasonal snow is largely confined to remote alpine areas, complicating ongoing in situ measurement and characterisation. Improved understanding and modeling of the seasonal snowpack requires fine scale resolution of snow distribution and spatial variability. The potential of remotely piloted aircraft system (RPAS) photogrammetry to resolve spatial and temporal variability of snow depth and water equivalent in a New Zealand alpine catchment is assessed in the Pisa Range, Central Otago. This approach yielded orthophotomosaics and digital surface models (DSM) at 0.05 and 0.15 m spatial resolution, respectively. An autumn reference DSM allowed mapping of winter (02/08/2016) and spring (10/09/2016) snow depth at 0.15 m spatial resolution, via DSM differencing. The consistency and accuracy of the RPAS-derived surface was assessed by comparison of snow-free regions of the spring and autumn DSMs, while accuracy of RPAS retrieved snow depth was assessed with 86 in situ snow probe measurements. Results show a mean vertical residual of 0.024 m between DSMs acquired in autumn and spring. This residual approximated a Laplace distribution, reflecting the influence of large outliers on the small overall bias. Propagation of errors associated with successive DSMs saw snow depth mapped with an accuracy of ± 0.09 m (95% c.l.). Comparing RPAS and in situ snow depth measurements revealed the influence of geo-location uncertainty and interactions between vegetation and the snowpack on snow depth uncertainty and bias. Semi-variogram analysis revealed that the RPAS outperformed systematic in situ measurements in resolving fine scale spatial variability. Despite limitations accompanying RPAS photogrammetry, this study demonstrates a repeatable means of accurately mapping snow depth for an entire, yet relatively small, hydrological basin ( 0.5 km2), at high resolution. Resolving snowpack features associated with re-distribution and preferential accumulation and ablation, snow depth maps provide geostatistically robust insights into seasonal snow processes, with unprecedented detail. Such data may enhance understanding of physical processes controlling spatial and temporal distribution of seasonal snow, and their relative importance at varying spatial and temporal scales.

On the characterization of subpixel effects for passive microwave remote sensing of snow in montane environments

NASA Astrophysics Data System (ADS)

Vander Jagt, Benjamin John

Snow and its water equivalent plays a vital role in global water and energy balances, with particular relevance in mountainous areas with arid and semi-arid climate regimes. Spaceborne passive microwave (PM) remote sensing measurements are attractive for snowpack characterization due to their continuous global coverage and historical record; over 30 years of research has been invested in the development of methods to characterize large-scale snow water resources from PM-based measurements. Historically, use of PM data for snowpack characterization in montane enviroments has been obstructed by the complex subpixel variability of snow properties within the PM measurement footprint. The main subpixel effects can be grouped as: the effect of snow microstructure (e.g. snow grain size) and stratigraphy on snow microwave emission, vegetation attenuation of PM measurements, and the sensitivity PM brightness temperature (Tb) observation to the variability of different subpixel properties at spaceborne measurement scales. This dissertation is focused on a systematic examination of these issues, which thus far have prevented the widespread integration of snow water equivalent (SWE) retrieval methods. It is meant to further our comprehension of the underlying processes at work in these rugged, remote, a hydrologically important areas. The role that snow microstructure plays in the PM retrievals of SWE is examined first. Traditional estimates of grain size are subjective and prone to error. Objective techniques to characterize grain size are described and implemented, including near infrared (NIR), stereology, and autocorrelation based approaches. Results from an intensive Colorado field study in which independent estimates of grain size and their modeled brightness temperature (Tb) emission are evaluated against PM Tb observations are included. The coarse resolution of the passive microwave measurements provides additional challenges when trying to resolve snow states via remote sensing observations. The natural heterogeneity of snowpack (e.g. depth, stratigraphy, etc) and vegetative states within the PM footprint occurs at spatial scales smaller than PM observation scales. The sensitivity to changes in snow depth given sub-pixel variability in snow and vegetation is explored and quantified using the comprehensive dataset acquired during the Cold Land Processes experiment (CLPX). Lastly, vegetation has long been an obstacle in efforts to derive snow depth and mass estimates from passive microwave (PM) measurements of brightness temperature (Tb). We introduce a vegetation transmissivity model that is derived entirely from multi-scale and multi-temporal PM Tb observations and a globally available vegetation dataset, specifically the Leaf Area Index (LAI). This newly constructed model characterizes the attenuation of PM Tb observations at frequencies typically employed for snow retrieval algorithms, as a function of LAI. Additionally, the model is used to predict how much SWE is observable within the major river basins of Colorado and the central Rockies.

Using Commercial Digital Cameras and Structure-for-Motion Software to Map Snow Cover Depth from Small Aircraft

NASA Astrophysics Data System (ADS)

Sturm, M.; Nolan, M.; Larsen, C. F.

2014-12-01

A long-standing goal in snow hydrology has been to map snow cover in detail, either mapping snow depth or snow water equivalent (SWE) with sub-meter resolution. Airborne LiDAR and air photogrammetry have been used successfully for this purpose, but both require significant investments in equipment and substantial processing effort. Here we detail a relatively inexpensive and simple airborne photogrammetric technique that can be used to measure snow depth. The main airborne hardware consists of a consumer-grade digital camera attached to a survey-quality, dual-frequency GPS. Photogrammetric processing is done using commercially available Structure from Motion (SfM) software that does not require ground control points. Digital elevation models (DEMs) are made from snow-free acquisitions in the summer and snow-covered acquisitions in winter, and the maps are then differenced to arrive at snow thickness. We tested the accuracy and precision of snow depths measured using this system through 1) a comparison with airborne scanning LiDAR, 2) a comparison of results from two independent and slightly different photogrameteric systems, and 3) comparison to extensive on-the-ground measured snow depths. Vertical accuracy and precision are on the order of +/-30 cm and +/- 8 cm, respectively. The accuracy can be made to approach that of the precision if suitable snow-free ground control points exists and are used to co-register summer to winter DEM maps. Final snow depth accuracy from our series of tests was on the order of ±15 cm. This photogrammetric method substantially lowers the economic and expertise barriers to entry for mapping snow.

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.

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.

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.

A research on snow distribution in mountainous area using airborne laser scanning

NASA Astrophysics Data System (ADS)

Nishihara, T.; Tanise, A.

2015-12-01

In snowy cold regions, the snowmelt water stored in dams in early spring meets the water demand for the summer season. Thus, snowmelt water serves as an important water resource. However, snowmelt water also can cause snowmelt floods. Therefore, it's necessary to estimate snow water equivalent in a dam basin as accurately as possible. For this reason, the dam operation offices in Hokkaido, Japan conduct snow surveys every March to estimate snow water equivalent in the dam basin. In estimating, we generally apply a relationship between elevation and snow water equivalent. But above the forest line, snow surveys are generally conducted along ridges due to the risk of avalanches or other hazards. As a result, snow water equivalent above the forest line is significantly underestimated. In this study, we conducted airborne laser scanning to measure snow depth in the high elevation area including above the forest line twice in the same target area (in 2012 and 2015) and analyzed the relationships of snow depth above the forest line and some indicators of terrain. Our target area was the Chubetsu dam basin. It's located in central Hokkaido, a high elevation area in a mountainous region. Hokkaido is a northernmost island of Japan. Therefore it's a cold and snowy region. The target range for airborne laser scanning was 10km2. About 60% of the target range was above the forest line. First, we analyzed the relationship between elevation and snow depth. Below the forest line, the snow depth increased linearly with elevation increase. On the other hand, above the forest line, the snow depth varied greatly. Second, we analyzed the relationship between overground-openness and snow depth above the forest line. Overground-openness is an indicator quantifying how far a target point is above or below the surrounding surface. As a result, a simple relationship was clarified. Snow depth decreased linearly as overground-openness increases. This means that areas with heavy snow cover are distributed in valleys and that of light cover are on ridges. Lastly we compared the result of 2012 and that of 2015. The same characteristic of snow depth, above mentioned, was found. However, regression coefficients of linear equations were different according to the weather conditions of each year.

Snow depth retrieval from L-band satellite measurements on Arctic and Antarctic sea ice

NASA Astrophysics Data System (ADS)

Maaß, N.; Kaleschke, L.; Wever, N.; Lehning, M.; Nicolaus, M.; Rossmann, H. L.

2017-12-01

The passive microwave mission SMOS provides daily coverage of the polar regions and measures at a low frequency of 1.4 GHz (L-band). SMOS observations have been used to operationally retrieve sea ice thickness up to 1 m and to estimate snow depth in the Arctic for thicker ice. Here, we present how SMOS-retrieved snow depths compare with airborne measurements from NASA's Operation IceBridge mission (OIB) and with AMSR-2 satellite retrievals at higher frequencies, and we show first applications to Antarctic sea ice. In previous studies, SMOS and OIB snow depths showed good agreement on spatial scales from 50 to 1000 km for some days and disagreement for other days. Here, we present a more comprehensive comparison of OIB and SMOS snow depths in the Arctic for 2011 to 2015. We find that the SMOS retrieval works best for cold conditions and depends on auxiliary information on ice surface temperature, here provided by MODIS thermal imagery satellite data. However, comparing SMOS and OIB snow depths is difficult because of the different spatial resolutions (SMOS: 40 km, OIB: 40 m). Spatial variability within the SMOS footprint can lead to different snow conditions as seen from SMOS and OIB. Ideally the comparison is made for uniform conditions: Low lead and open water fraction, low spatial and temporal variability of ice surface temperature, no mixture of multi- and first-year ice. Under these conditions and cold temperatures (surface temperatures below -25°C), correlation coefficients between SMOS and OIB snow depths increase from 0.3 to 0.6. A finding from the comparison with AMSR-2 snow depths is that the SMOS-based maps depend less on the age of the sea ice than the maps derived from higher frequencies. Additionally, we show first results of SMOS snow depths for Antarctic sea ice. SMOS observations are compared to measurements of autonomous snow buoys drifting in the Weddell Sea since 2014. For a better comparability of these point measurements with SMOS data, we use model simulations along these trajectories made with a sea ice version of SNOWPACK, a 1D multi-layer thermodynamic snow model driven by reanalysis data. These simulations are especially helpful for indicating the occurrence of snow-ice-transformation, which cannot be identified in the buoy data and contributes to the measured snow height.

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

Effect of snow cover on soil frost penetration

NASA Astrophysics Data System (ADS)

Rožnovský, Jaroslav; Brzezina, Jáchym

2017-12-01

Snow cover occurrence affects wintering and lives of organisms because it has a significant effect on soil frost penetration. An analysis of the dependence of soil frost penetration and snow depth between November and March was performed using data from 12 automated climatological stations located in Southern Moravia, with a minimum period of measurement of 5 years since 2001, which belong to the Czech Hydrometeorological institute. The soil temperatures at 5 cm depth fluctuate much less in the presence of snow cover. In contrast, the effect of snow cover on the air temperature at 2 m height is only very small. During clear sky conditions and no snow cover, soil can warm up substantially and the soil temperature range can be even higher than the range of air temperature at 2 m height. The actual height of snow is also important - increased snow depth means lower soil temperature range. However, even just 1 cm snow depth substantially lowers the soil temperature range and it can therefore be clearly seen that snow acts as an insulator and has a major effect on soil frost penetration and soil temperature range.

Estimation of Sea Ice Thickness Distributions through the Combination of Snow Depth and Satellite Laser Altimetry Data

NASA Technical Reports Server (NTRS)

Kurtz, Nathan T.; Markus, Thorsten; Cavalieri, Donald J.; Sparling, Lynn C.; Krabill, William B.; Gasiewski, Albin J.; Sonntag, John G.

2009-01-01

Combinations of sea ice freeboard and snow depth measurements from satellite data have the potential to provide a means to derive global sea ice thickness values. However, large differences in spatial coverage and resolution between the measurements lead to uncertainties when combining the data. High resolution airborne laser altimeter retrievals of snow-ice freeboard and passive microwave retrievals of snow depth taken in March 2006 provide insight into the spatial variability of these quantities as well as optimal methods for combining high resolution satellite altimeter measurements with low resolution snow depth data. The aircraft measurements show a relationship between freeboard and snow depth for thin ice allowing the development of a method for estimating sea ice thickness from satellite laser altimetry data at their full spatial resolution. This method is used to estimate snow and ice thicknesses for the Arctic basin through the combination of freeboard data from ICESat, snow depth data over first-year ice from AMSR-E, and snow depth over multiyear ice from climatological data. Due to the non-linear dependence of heat flux on ice thickness, the impact on heat flux calculations when maintaining the full resolution of the ICESat data for ice thickness estimates is explored for typical winter conditions. Calculations of the basin-wide mean heat flux and ice growth rate using snow and ice thickness values at the 70 m spatial resolution of ICESat are found to be approximately one-third higher than those calculated from 25 km mean ice thickness values.

A Comparison of Snow Depth on Sea Ice Retrievals Using Airborne Altimeters and an AMSR-E Simulator

NASA Technical Reports Server (NTRS)

Cavalieri, D. J.; Marksu, T.; Ivanoff, A.; Miller, J. A.; Brucker, L.; Sturm, M.; Maslanik, J. A.; Heinrichs, J. F.; Gasiewski, A.; Leuschen, C.;

Trace gas and vegetation feedback responses of Alaskan tussock tundra to long-term snow depth manipulations

NASA Astrophysics Data System (ADS)

Ebbs, L. M.; Taneva, L.; Sullivan, P.; Welker, J. M.

2009-12-01

Changes in the precipitation and temperature regimes in Northern Alaska are manifesting themselves through shifts in sea ice, vegetation traits, animal migration timing and hydrologic dynamics. Changes in precipitation and soil temperature result in changes in plant mineral nutrition, soil nutrient availability, trace gas exchanges and differential nutrient acquisition strategies by arctic plants. In this study, we report on the extent to which long-term increases in snow depth, along with reductions in snow depth alter the magnitudes and pattern of CO2 exchange, soil properties and vegetation traits. A doubling of snow depth (from ~0.5 to ~1.0m) results in a delay of the growing season by ~ 2 weeks, however, by peak season, the rates of CO2 exchange are 50% higher in areas which had experienced deeper snow depth levels. To the contrary, long-term reductions in snow depth results in accelerated rates of plant phenology, however CO2 exchange rates at peak season are 30% less than those areas under ambient snow cover in the preceding winter. Reduced snow depth areas had the coldest winter soil temperatures while the deeper areas had the warmest winter soil temperatures, which may partially explain the summer CO2 fluxes indirectly via different rates of winter N mineralization and differences in leaf N properties. Our results indicate that shifting fall, winter and spring when snow is the primary form of precipitation, may have profound effects on tussock tundra systems.

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.

Snow Depth Depicted on Mt. Lyell by NASA Airborne Snow Observatory

NASA Image and Video Library

2013-05-02

A natural color image of Mt. Lyell, the highest point in the Tuolumne River Basin top image is compared with a three-dimensional color composite image of Mt. Lyell from NASA Airborne Snow Observatory depicting snow depth bottom image.

Snow accumulation on Arctic sea ice: is it a matter of how much or when?

NASA Astrophysics Data System (ADS)

Webster, M.; Petty, A.; Boisvert, L.; Markus, T.

2017-12-01

Snow on sea ice plays an important, yet sometimes opposing role in sea ice mass balance depending on the season. In autumn and winter, snow reduces the heat exchange from the ocean to the atmosphere, reducing sea ice growth. In spring and summer, snow shields sea ice from solar radiation, delaying sea ice surface melt. Changes in snow depth and distribution in any season therefore directly affect the mass balance of Arctic sea ice. In the western Arctic, a decreasing trend in spring snow depth distribution has been observed and attributed to the combined effect of peak snowfall rates in autumn and the coincident delay in sea ice freeze-up. Here, we build on this work and present an in-depth analysis on the relationship between snow accumulation and the timing of sea ice freeze-up across all Arctic regions. A newly developed two-layer snow model is forced with eight reanalysis precipitation products to: (1) identify the seasonal distribution of snowfall accumulation for different regions, (2) highlight which regions are most sensitive to the timing of sea ice freeze-up with regard to snow accumulation, and (3) show, if precipitation were to increase, which regions would be most susceptible to thicker snow covers. We also utilize a comprehensive sensitivity study to better understand the factors most important in controlling winter/spring snow depths, and to explore what could happen to snow depth on sea ice in a warming Arctic climate.

Measurements of seasonal frost depth by frost tube in Japan

NASA Astrophysics Data System (ADS)

Harada, K.; Yoshikawa, K.; Iwahana, G.; Stanilovskaya, J. V.; Sawada, Y.; Sone, T.

2017-12-01

Since 2011 winter season, frost depths have been measured as an outreach program in Hokkaido, northern part of Japan, where seasonal ground freezing occurs in winter. Frost depths were measured in elementary, junior high and high schools in order to emphasis their interest for earth sciences. At schools, using simple frost tube, measurements were conducted directly once a week by students or teacher during ground freezing under no snow-removal condition. A lecture was made in class and a frost tube was set at schoolyard, as the same tube and protocol as UAF's Permafrost Outreach Program, using clear tube with blue-colored water. In 2011 winter season, we started measurements at three schools, and the number of school extended to 32 in 2016 season, 26 elementary schools, 5 junior high schools and one high school. We visited schools in summer time or just before frost season to talk about the method of measurement, and measurements by students started just after ground freezing. After the end of frozen period, we visited schools again to explain results of each school or another schools in Japan, Alaska, Canada or Russia. The measured frost depths in Hokkaido ranged widely, from only a few centimeter to more than 50 cm. However, some schools had no frost depth due to heavy snow. We confirmed that the frost depth strongly depends on air temperature and snow depth. The lecture was made to student why the frost depth ranged widely, and the effect of snow was explained by using the example of igloo. In order to validate the effect of snow and to compare frost depths, we tried to measure frost depths under snow-removal and no snow-removal conditions at the same elementary school. At the end of December, depths had no significant difference between these conditions, and the difference went to 14 cm after one month, with about 30 cm of snow depth. After these measurements and lectures, students noticed snow has a role as insulator and affects the frost depth.

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.

Mapping Snow Depth with Automated Terrestrial Laser Scanning - Investigating Potential Applications

NASA Astrophysics Data System (ADS)

Adams, M. S.; Gigele, T.; Fromm, R.

2017-11-01

This contribution presents an automated terrestrial laser scanning (ATLS) setup, which was used during the winter 2016/17 to monitor the snow depth distribution on a NW-facing slope at a high-alpine study site. We collected data at high temporal [(sub-)daily] and spatial resolution (decimetre-range) over 0.8 km² with a Riegl LPM-321, set in a weather-proof glass fibre enclosure. Two potential ATLS-applications are investigated here: monitoring medium-sized snow avalanche events, and tracking snow depth change caused by snow drift. The results show the ATLS data's high explanatory power and versatility for different snow research questions.

Soil thermal dynamics, snow cover, and frozen depth under five temperature treatments in an ombrotrophic bog: Constrained forecast with data assimilation: Forecast With Data Assimilation

DOE PAGES

Huang, Yuanyuan; Jiang, Jiang; Ma, Shuang; ...

2017-08-18

We report that accurate simulation of soil thermal dynamics is essential for realistic prediction of soil biogeochemical responses to climate change. To facilitate ecological forecasting at the Spruce and Peatland Responses Under Climatic and Environmental change site, we incorporated a soil temperature module into a Terrestrial ECOsystem (TECO) model by accounting for surface energy budget, snow dynamics, and heat transfer among soil layers and during freeze-thaw events. We conditioned TECO with detailed soil temperature and snow depth observations through data assimilation before the model was used for forecasting. The constrained model reproduced variations in observed temperature from different soil layers,more » the magnitude of snow depth, the timing of snowfall and snowmelt, and the range of frozen depth. The conditioned TECO forecasted probabilistic distributions of soil temperature dynamics in six soil layers, snow, and frozen depths under temperature treatments of +0.0, +2.25, +4.5, +6.75, and +9.0°C. Air warming caused stronger elevation in soil temperature during summer than winter due to winter snow and ice. And soil temperature increased more in shallow soil layers in summer in response to air warming. Whole ecosystem warming (peat + air warmings) generally reduced snow and frozen depths. The accuracy of forecasted snow and frozen depths relied on the precision of weather forcing. Uncertainty is smaller for forecasting soil temperature but large for snow and frozen depths. Lastly, timely and effective soil thermal forecast, constrained through data assimilation that combines process-based understanding and detailed observations, provides boundary conditions for better predictions of future biogeochemical cycles.« less

Soil thermal dynamics, snow cover, and frozen depth under five temperature treatments in an ombrotrophic bog: Constrained forecast with data assimilation: Forecast With Data Assimilation

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

Huang, Yuanyuan; Jiang, Jiang; Ma, Shuang

We report that accurate simulation of soil thermal dynamics is essential for realistic prediction of soil biogeochemical responses to climate change. To facilitate ecological forecasting at the Spruce and Peatland Responses Under Climatic and Environmental change site, we incorporated a soil temperature module into a Terrestrial ECOsystem (TECO) model by accounting for surface energy budget, snow dynamics, and heat transfer among soil layers and during freeze-thaw events. We conditioned TECO with detailed soil temperature and snow depth observations through data assimilation before the model was used for forecasting. The constrained model reproduced variations in observed temperature from different soil layers,more » the magnitude of snow depth, the timing of snowfall and snowmelt, and the range of frozen depth. The conditioned TECO forecasted probabilistic distributions of soil temperature dynamics in six soil layers, snow, and frozen depths under temperature treatments of +0.0, +2.25, +4.5, +6.75, and +9.0°C. Air warming caused stronger elevation in soil temperature during summer than winter due to winter snow and ice. And soil temperature increased more in shallow soil layers in summer in response to air warming. Whole ecosystem warming (peat + air warmings) generally reduced snow and frozen depths. The accuracy of forecasted snow and frozen depths relied on the precision of weather forcing. Uncertainty is smaller for forecasting soil temperature but large for snow and frozen depths. Lastly, timely and effective soil thermal forecast, constrained through data assimilation that combines process-based understanding and detailed observations, provides boundary conditions for better predictions of future biogeochemical cycles.« less

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.

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.

A snow cover climatology for the Pyrenees from MODIS snow products

NASA Astrophysics Data System (ADS)

Gascoin, S.; Hagolle, O.; Huc, M.; Jarlan, L.; Dejoux, J.-F.; Szczypta, C.; Marti, R.; Sanchez, R.

2015-05-01

The seasonal snow in the Pyrenees is critical for hydropower production, crop irrigation and tourism in France, Spain and Andorra. Complementary to in situ observations, satellite remote sensing is useful to monitor the effect of climate on the snow dynamics. The MODIS daily snow products (Terra/MOD10A1 and Aqua/MYD10A1) are widely used to generate snow cover climatologies, yet it is preferable to assess their accuracies prior to their use. Here, we use both in situ snow observations and remote sensing data to evaluate the MODIS snow products in the Pyrenees. First, we compare the MODIS products to in situ snow depth (SD) and snow water equivalent (SWE) measurements. We estimate the values of the SWE and SD best detection thresholds to 40 mm water equivalent (w.e.) and 150 mm, respectively, for both MOD10A1 and MYD10A1. κ coefficients are within 0.74 and 0.92 depending on the product and the variable for these thresholds. However, we also find a seasonal trend in the optimal SWE and SD thresholds, reflecting the hysteresis in the relationship between the depth of the snowpack (or SWE) and its extent within a MODIS pixel. Then, a set of Landsat images is used to validate MOD10A1 and MYD10A1 for 157 dates between 2002 and 2010. The resulting accuracies are 97% (κ = 0.85) for MOD10A1 and 96% (κ = 0.81) for MYD10A1, which indicates a good agreement between both data sets. The effect of vegetation on the results is analyzed by filtering the forested areas using a land cover map. As expected, the accuracies decrease over the forests but the agreement remains acceptable (MOD10A1: 96%, κ = 0.77; MYD10A1: 95%, κ = 0.67). We conclude that MODIS snow products have a sufficient accuracy for hydroclimate studies at the scale of the Pyrenees range. Using a gap-filling algorithm we generate a consistent snow cover climatology, which allows us to compute the mean monthly snow cover duration per elevation band and aspect classes. There is snow on the ground at least 50% of the time above 1600 m between December and April. We finally analyze the snow patterns for the atypical winter 2011-2012. Snow cover duration anomalies reveal a deficient snowpack on the Spanish side of the Pyrenees, which seems to have caused a drop in the national hydropower production.

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.

Improving the MODIS Global Snow-Mapping Algorithm

NASA Technical Reports Server (NTRS)

Klein, Andrew G.; Hall, Dorothy K.; Riggs, George A.

1997-01-01

An algorithm (Snowmap) is under development to produce global snow maps at 500 meter resolution on a daily basis using data from the NASA MODIS instrument. MODIS, the Moderate Resolution Imaging Spectroradiometer, will be launched as part of the first Earth Observing System (EOS) platform in 1998. Snowmap is a fully automated, computationally frugal algorithm that will be ready to implement at launch. Forests represent a major limitation to the global mapping of snow cover as a forest canopy both obscures and shadows the snow underneath. Landsat Thematic Mapper (TM) and MODIS Airborne Simulator (MAS) data are used to investigate the changes in reflectance that occur as a forest stand becomes snow covered and to propose changes to the Snowmap algorithm that will improve snow classification accuracy forested areas.

Under-canopy snow accumulation and ablation measured with airborne scanning LiDAR altimetry and in-situ instrumental measurements, southern Sierra Nevada, California

NASA Astrophysics Data System (ADS)

Kirchner, P. B.; Bales, R. C.; Musselman, K. N.; Molotch, N. P.

2012-12-01

We investigated the influence of canopy on snow accumulation and melt in a mountain forest using paired snow on and snow off scanning LiDAR altimetry, synoptic measurement campaigns and in-situ time series data of snow depth, SWE, and radiation collected from the Kaweah River watershed, Sierra Nevada, California. Our analysis of forest cover classified by dominant species and 1 m2 grided mean under canopy snow accumulation calculated from airborne scanning LiDAR, demonstrate distinct relationships between forest class and under-canopy snow depth. The five forest types were selected from carefully prepared 1 m vegetation classifications and named for their dominant tree species, Giant Sequoia, Jeffrey Pine, White Fir, Red Fir, Sierra Lodgepole, Western White Pine, and Foxtail Pine. Sufficient LiDAR returns for calculating mean snow depth per m2 were available for 31 - 44% of the canopy covered area and demonstrate a reduction in snow depth of 12 - 24% from adjacent open areas. The coefficient of variation in snow depth under canopies ranged from 0.2 - 0.42 and generally decreased as elevation increased. Our analysis of snow density snows no statistical significance between snow under canopies and in the open at higher elevations with a weak significance for snow under canopies at lower elevations. Incident radiation measurements made at 15 minute intervals under forest canopies show an input of up to 150 w/m2 of thermal radiation from vegetation to the snow surface on forest plots. Snow accumulated on the mid to high elevation forested slopes of the Sierra Nevada represents the majority of winter snow storage. However snow estimates in forested environments demonstrate a high level of uncertainty due to the limited number of in-situ observations and the inability of most remote sensing platforms to retrieve reflectance under dense vegetation. Snow under forest canopies is strongly mediated by forest cover and decoupled from the processes that dictate accumulation and ablation of snow in open locations, where almost all precipitation and meteorlogic measurements concerning snow are made. Snow accumulation is intercepted by vegetation until it accumulates to a depth equal to or greater than the height of the vegetation, is reduced by the amount of sublimation or evaporation occurring while on the canopy and is redistributed beneath the canopy at a different density or as liquid water. Ablation processes are dictated by the energy environment surrounding vegetation where sensible heat is mediated by shading of short wave radiation.

Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records

NASA Technical Reports Server (NTRS)

Riggs, George A.; Hall, Dorothy K.; Roman, Miguel O.

2017-01-01

Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long-term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record, which extends from 2000 to the present. MODIS Collection 6 (C6) and VIIRS Collection 1 (C1) represent the state-of-the-art global snow cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map.The increased data content allows flexibility in using the datasets for specific regions and end-user applications.Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375m native resolution compared to MODIS 500 m), the snow detection algorithms and data products are designed to be as similar as possible so that the 16C year MODIS ESDR of global SCE can be extended into the future with the S-NPP VIIRS snow products and with products from future Joint Polar Satellite System (JPSS) platforms.These NASA datasets are archived and accessible through the NASA Distributed Active Archive Center at the National Snow and Ice Data Center in Boulder, Colorado.

The cumulative effect of consecutive winters' snow depth on moose and deer populations: a defence

USGS Publications Warehouse

McRoberts, R.E.; Mech, L.D.; Peterson, R.O.

1995-01-01

1. L. D. Mech et al. presented evidence that moose Alces alces and deer Odocoileus virginianus population parameters re influenced by a cumulative effect of three winters' snow depth. They postulated that snow depth affects adult ungulates cumulatively from winter to winter and results in measurable offspring effects after the third winter. 2. F. Messier challenged those findings and claimed that the population parameters studied were instead affected by ungulate density and wolf indexes. 3. This paper refutes Messier's claims by demonstrating that his results were an artifact of two methodological errors. The first was that, in his main analyses, Messier used only the first previous winter's snow depth rather than the sum of the previous three winters' snow depth, which was the primary point of Mech et al. Secondly, Messier smoothed the ungulate population data, which removed 22-51% of the variability from the raw data. 4. When we repeated Messier's analyses on the raw data and using the sum of the previous three winter's snow depth, his findings did not hold up.

Blowing snow detection from ground-based ceilometers: application to East Antarctica

NASA Astrophysics Data System (ADS)

Gossart, Alexandra; Souverijns, Niels; Gorodetskaya, Irina V.; Lhermitte, Stef; Lenaerts, Jan T. M.; Schween, Jan H.; Mangold, Alexander; Laffineur, Quentin; van Lipzig, Nicole P. M.

2017-12-01

Blowing snow impacts Antarctic ice sheet surface mass balance by snow redistribution and sublimation. However, numerical models poorly represent blowing snow processes, while direct observations are limited in space and time. Satellite retrieval of blowing snow is hindered by clouds and only the strongest events are considered. Here, we develop a blowing snow detection (BSD) algorithm for ground-based remote-sensing ceilometers in polar regions and apply it to ceilometers at Neumayer III and Princess Elisabeth (PE) stations, East Antarctica. The algorithm is able to detect (heavy) blowing snow layers reaching 30 m height. Results show that 78 % of the detected events are in agreement with visual observations at Neumayer III station. The BSD algorithm detects heavy blowing snow 36 % of the time at Neumayer (2011-2015) and 13 % at PE station (2010-2016). Blowing snow occurrence peaks during the austral winter and shows around 5 % interannual variability. The BSD algorithm is capable of detecting blowing snow both lifted from the ground and occurring during precipitation, which is an added value since results indicate that 92 % of the blowing snow is during synoptic events, often combined with precipitation. Analysis of atmospheric meteorological variables shows that blowing snow occurrence strongly depends on fresh snow availability in addition to wind speed. This finding challenges the commonly used parametrizations, where the threshold for snow particles to be lifted is a function of wind speed only. Blowing snow occurs predominantly during storms and overcast conditions, shortly after precipitation events, and can reach up to 1300 m a. g. l. in the case of heavy mixed events (precipitation and blowing snow together). These results suggest that synoptic conditions play an important role in generating blowing snow events and that fresh snow availability should be considered in determining the blowing snow onset.

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.

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.

From drones to ASO: Using 'Structure-From-Motion' photogrammetry to quantify variations in snow depth at multiple scales

NASA Astrophysics Data System (ADS)

Skiles, M.

2017-12-01

The ability to accurately measure and manage the natural snow water reservoir in mountainous regions has its challenges, namely mapping of snowpack depth and snow water equivalent (SWE). Presented here is a scalable method that differentially maps snow depth using Structure from Motion (SfM); a photogrammetric technique that uses 2d images to create a 3D model/Digital Surface Model (DSM). There are challenges with applying SfM to snow, namely, relatively uniform snow brightness can make it difficult to produce quality images needed for processing, and vegetation can limit the ability to `see' through the canopy to map both the ground and snow beneath. New techniques implemented in the method to adapt to these challenges will be demonstrated. Results include a time series at (1) the plot scale, imaged with an unmanned areal vehicle (DJI Phantom 2 adapted with Sony A5100) over the Utah Department of Transportation Atwater Study Plot in Little Cottonwood Canyon, UT, and at (2) the mountain watershed scale, imaged from the RGB camera aboard the Airborne Snow Observatory (ASO), over the headwaters of the Uncompahgre River in the San Juan Mountains, CO. At the plot scale we present comparisons to measured snow depth, and at the watershed scale we present comparisons to the ASO lidar DSM. This method is of interest due to its low cost relative to lidar, making it an accessible tool for snow research and the management of water resources. With advancing unmanned aerial vehicle technology there are implications for scalability to map snow depth, and SWE, across large basins.

Variability in snow-depth time series within the Adige catchment

NASA Astrophysics Data System (ADS)

Marcolini, Giorgia; Bellin, Alberto; Disse, Markus; Gabriele, Chiogna

2017-04-01

Snow cover extension and duration is particularly sensitive to climate change because strongly influenced by changes in temperature and precipitation. It affects the hydrological cycle of Alpine catchments as well as many other aspects of life in mountainous regions, such as ecosystem functioning and economy. Despite its relevance, variability in snow related parameters has not been investigated in the Southern side of the Alps as extensively as in the Northern side of the Alps. In this work, we investigate the temporal variability of mean seasonal snow depth (computed by averaging the daily snow depth in the period 1 November-30 April between two following years) and of snow cover duration (defined, similarly, as the number of days in the period 1 November-30 April with snow depth higher than 30 cm) for the homogeneous stations within the Adige catchment (North-East Italy) by using wavelets transform. We focus our analysis on the period 1980-2010, which with 37 time series is the richest of data and we group the stations in four elevation classes (below 1350 m a.s.l., between 1350 m a.s.l. and 1650 m a.s.l., between 1650 m a.s.l. and 2000 m a.s.l. and above 2000 m a.s.l.). Stations located above and below 1650 m a.s.l. show different behaviors, with the latter showing in the last decades a larger reduction of mean seasonal snow depth and snow cover duration, than the former. We also observe that starting from the late '80s snow cover duration and mean seasonal snow depth display values below the average in the study area, confirming the observations performed in other regions of the Alps. We also find an elevation-dependent correlation between the increase in winter teperature and snow cover extension and duration.

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.

Assessing the utility of passive microwave data for Snow Water Equivalent (SWE) estimation in the Sutlej River Basin of the northwestern Himalaya

NASA Astrophysics Data System (ADS)

Brandt, T.; Bookhagen, B.; Dozier, J.

2014-12-01

Since 1978, space based passive microwave (PM) radiometers have been used to comprehensively measure Snow Water Equivalent (SWE) on a global basis. The ability of PM radiometers to directly measure SWE at high temporal frequencies offers some distinct advantages over optical remote sensors. Nevertheless, in mountainous terrain PM radiometers often struggle to accurately measure SWE because of wet snow, saturation in deep snow, forests, depth hoar and stratigraphy, variable relief, and subpixel heterogeneity inherent in large pixel sizes. The Himalaya, because of their high elevation and high relief—much above tree line—offer an opportunity to examine PM products in the mountains without the added complication of trees. The upper Sutlej River basin— the third largest Himalayan catchment—lies in the western Himalaya. The river is a tributary of the Indus River and seasonal snow constitutes a substantial part of the basin's hydrologic budget. The basin has a few surface stations and river gauges, which is unique for the region. As such, the Sutlej River basin is a good location to analyze the accuracy and effectiveness of the current National Snow and Ice Data Center's (NSIDC) standard AMSR-E/Aqua Daily SWE product in mountainous terrain. So far, we have observed that individual pixels can "flicker", i.e. fluctuate from day to day, over large parts of the basin. We consider whether this is an artifact of the algorithm or whether this is embedded in the raw brightness temperatures themselves. In addition, we examine how well the standard product registers winter storms, and how it varies over heavily glaciated pixels. Finally, we use a few common measures of algorithm performance (precision, recall and accuracy) to test how well the standard product detects the presence of snow, using optical imagery for validation. An improved understanding of the effectiveness of PM imagery in the mountains will help to clarify the technology's limits.

Daily Snow Depth Measurements from 195 Stations in the United States (1997) (NDP-059)

DOE Data Explorer

Easterling, D. R. [NOAA, National Climatic Data Center; Jamason, P. [NOAA, National Climatic Data Center; Bowman, D. P. [NOAA, National Climatic Data Center; Hughes, P. Y. [NOAA, National Climatic Data Center; Mason, E. H. [NOAA, National Climatic Data Center; Allison, L. J. [ORNL, Carbon Dioxide Information Analysis Center (CDIAC)

1997-02-01

This data package provides daily measurements of snow depth at 195 National Weather Service (NWS) first-order climatological stations in the United States. The data have been assembled and made available by the National Climatic Data Center (NCDC) in Asheville, North Carolina. The 195 stations encompass 388 unique sampling locations in 48 of the 50 states; no observations from Delaware or Hawaii are included in the database. Station selection criteria emphasized the quality and length of station records while seeking to provide a network with good geographic coverage. Snow depth at the 388 locations was measured once per day on ground open to the sky. The daily snow depth is the total depth of the snow on the ground at measurement time. The time period covered by the database is 1893-1992; however, not all station records encompass the complete period. While a station record ideally should contain daily data for at least the seven winter months (January through April and October through December), not all stations have complete records. Each logical record in the snow depth database contains one station's daily data values for a period of one month, including data source, measurement, and quality flags. The snow depth data have undergone extensive manual and automated quality assurance checks by NCDC and the Carbon Dioxide Information Analysis Center (CDIAC). These reviews involved examining the data for completeness, reasonableness, and accuracy, and included comparison of some data records with records in NCDC's Summary of the Day First Order online database. Since the snow depth measurements have been taken at NWS first-order stations that have long periods of record, they should prove useful in monitoring climate change.

The Snowtweets Project: communicating snow depth measurements from specialists and non-specialists via mobile communication technologies and social networks

NASA Astrophysics Data System (ADS)

King, J. M.; Cabrera, A. R.; Kelly, R. E.

2009-12-01

With the global decline of in situ snow measurements for hydrometeorological applications, there is an evolving need to find alternative ways to collect localized measurements of snow. The Snowtweets Project is an experiment aimed at providing a way for people interested in making snow measurements to quickly broadcast their measurements to the internet. The goal of the project is to encourage specialists and non-specialists alike to share simple snow depth measurements through widely available social networking sites. We are currently using the rapidly growing microblogging site Twitter (www.twitter.com) as a broadcasting vehicle to collect the snow depth measurements. Using 140 characters or less, users "tweet" their snow depth from their location through the Twitter website. This can be done from a computer or smartphone with internet access or through SMS messaging. The project has developed a Snowtweets web application that interrogates Twitter by parsing the 140 character string to obtain a geographic position and snow depth. GeoRSS and KML feeds are available to visualize the tweets in GoogleEarth or they can be viewed in our own visualiser, Snowbird. The emphasis is on achieving wide coverage to increase the number of microblogs. Furthermore, after some quality control filters, the project is able to combine the broadcast snow depths with traditional and objective satellite remote sensing-based observations or hydrologic model estimates. Our site, snowcore.uwaterloo.ca, was launched in July 2009 and is ready for the 2009-2010 northern hemisphere winter. We invite comments from experienced community participation projects to help improve our product.

On the influence of recrystallization on snow fabric and microstructure: study of a snow profile in Central East Antarctica

NASA Astrophysics Data System (ADS)

Calonne, Neige; Schneebeli, Martin; Montagnat, Maurine; Matzl, Margret

2016-04-01

Temperature gradient metamorphism affects the Antarctic snowpack up to 5 meters depth, which lead to a recrystallization of the ice grains by sublimation of ice and deposition of water vapor. By this way, it is well known that the snow microstructure evolves (geometrical changes). Also, a recent study shows an evolution of the snow fabric, based on a cold laboratory experiment. Both fabric and microstructure are required to better understand mechanical behavior and densification of snow, firn and ice, given polar climatology. The fabric of firn and ice has been extensively investigated, but the publications by Stephenson (1967, 1968) are to our knowledge the only ones describing the snow fabric in Antarctica. In this context, our work focuses on snow microstructure and fabric in the first meters depth of the Antarctic ice sheet, where temperature gradients driven recrystallization occurs. Accurate details of the snow microstructure are observed using micro-computed tomography. Snow fabrics were measured at various depths from thin sections of impregnated snow with an Automatic Ice Texture Analyzer (AITA). A definite relationship between microstructure and fabric is found and highlights the influence of metamorphism on both properties. Our results also show that the metamorphism enhances the differences between the snow layers properties. Our work stresses the significant and complex evolution of snow properties in the upper meters of the ice sheet and opens the question of how these layer properties will evolve at depth and may influence the densification.

COSMO-SkyMed Image Investigation of Snow Features in Alpine Environment

PubMed Central

Paloscia, Simonetta; Pettinato, Simone; Santi, Emanuele; Valt, Mauro

2017-01-01

In this work, X band images acquired by COSMO-SkyMed (CSK) on alpine environment have been analyzed for investigating snow characteristics and their effect on backscattering variations. Preliminary results confirmed the capability of simultaneous optical and Synthetic Aperture Radar (SAR) images (Landsat-8 and CSK) in separating snow/no-snow areas and in detecting wet snow. The sensitivity of backscattering to snow depth has not always been confirmed, depending on snow characteristics related to the season. A model based on Dense Media Radiative Transfer theory (DMRT-QMS) was applied for simulating the backscattering response on the X band from snow cover in different conditions of grain size, snow density and depth. By using DMRT-QMS and snow in-situ data collected on Cordevole basin in Italian Alps, the effect of grain size and snow density, beside snow depth and snow water equivalent, was pointed out, showing that the snow features affect the backscatter in different and sometimes opposite ways. Experimental values of backscattering were correctly simulated by using this model and selected intervals of ground parameters. The relationship between simulated and measured backscattering for the entire dataset shows slope >0.9, determination coefficient, R2 = 0.77, and root mean square error, RMSE = 1.1 dB, with p-value <0.05. PMID:28054962

Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records

NASA Astrophysics Data System (ADS)

Riggs, George A.; Hall, Dorothy K.; Román, Miguel O.

2017-10-01

Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long-term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record, which extends from 2000 to the present. MODIS Collection 6 (C6; https://nsidc.org/data/modis/data_summaries) and VIIRS Collection 1 (C1; https://doi.org/10.5067/VIIRS/VNP10.001) represent the state-of-the-art global snow-cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow-cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map. The increased data content allows flexibility in using the datasets for specific regions and end-user applications. Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375 m native resolution compared to MODIS 500 m), the snow detection algorithms and data products are designed to be as similar as possible so that the 16+ year MODIS ESDR of global SCE can be extended into the future with the S-NPP VIIRS snow products and with products from future Joint Polar Satellite System (JPSS) platforms. These NASA datasets are archived and accessible through the NASA Distributed Active Archive Center at the National Snow and Ice Data Center in Boulder, Colorado.

Outreach program by measurements of frost depth in Japan

NASA Astrophysics Data System (ADS)

Harada, K.; Yoshikawa, K.; Iwahana, G.; Stanilovskaya, J. V.; Sawada, Y.

2015-12-01

In order to emphasis their interest for earth sciences, an outreach program through measurements of frost depth is conducting in Japan since 2011. This program is made at elementary, junior high and high schools in Hokkaido, northern part of Japan where seasonal ground freezing occurs in winter. At schools, a lecture was made and a frost tube was set at schoolyard, as the same tube and protocol as UAF's Permafrost Outreach Program, using clear tube with blue-colored water. Frost depth was measured directly once a week at each school by students during ground freezing under no snow-removal condition. In 2011 season, we started this program at three schools, and the number of participated school is extended to 29 schools in 2014 winter season, 23 elementary schools, 5 junior high schools and one high school. We visited schools summer time and just before frost season to talk about the method of measurement. After the end of measured period, we also visited schools to explain measured results by each school and the other schools in Japan, Alaska, Canada and Russia. The measured values of frost depth in Hokkaido were ranged between 0cm and more than 50cm. We found that the frost depth depends on air temperature and snow depth. We discussed with student why the frost depth ranged widely and explained the effect of snow by using the example of igloo. In order to validate the effect of snow and to compare frost depths, we tried to measure frost depths under snow-removal and no snow-removal conditions at one elementary school. At the end of December, depths had no significant difference between these conditions, 11cm and 10cm, and the difference went to 14cm, 27cm and 13cm after one month, with about 30cm of snow depth. After these measurements and lectures, students noticed snow has a role as insulator and affects the frost depth. The network of this program will be expected to expand, finally more than a hundred schools.

Application of the Markov Chain Monte Carlo method for snow water equivalent retrieval based on passive microwave measurements

NASA Astrophysics Data System (ADS)

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

2015-12-01

Markov Chain Monte Carlo (MCMC) method is a retrieval algorithm based on Bayes' rule, which starts from an initial state of snow/soil parameters, and updates it to a series of new states by comparing the posterior probability of simulated snow microwave signals before and after each time of random walk. It is a realization of the Bayes' rule, which gives an approximation to the probability of the snow/soil parameters in condition of the measured microwave TB signals at different bands. Although this method could solve all snow parameters including depth, density, snow grain size and temperature at the same time, it still needs prior information of these parameters for posterior probability calculation. How the priors will influence the SWE retrieval is a big concern. Therefore, in this paper at first, a sensitivity test will be carried out to study how accurate the snow emission models and how explicit the snow priors need to be to maintain the SWE error within certain amount. The synthetic TB simulated from the measured snow properties plus a 2-K observation error will be used for this purpose. It aims to provide a guidance on the MCMC application under different circumstances. Later, the method will be used for the snowpits at different sites, including Sodankyla, Finland, Churchill, Canada and Colorado, USA, using the measured TB from ground-based radiometers at different bands. Based on the previous work, the error in these practical cases will be studied, and the error sources will be separated and quantified.

Monitoring Mountain Meteorology without Much Money (Invited)

NASA Astrophysics Data System (ADS)

Lundquist, J. D.

2009-12-01

Mountains are the water towers of the world, storing winter precipitation in the form of snow until summer, when it can be used for agriculture and cities. However, mountain weather is highly variable, and measurements are sparsely distributed. In order adequately sample snow and climate variables in complex terrain, we need as many measurements as possible. This means that instruments must be inexpensive and relatively simple to deploy. Here, we demonstrate how dime-sized temperature sensors developed for the refrigeration industry can be used to monitor air temperature (using evergreen trees as radiation shields) and snow cover duration (using the diurnal cycle in near-surface soil temperature). Together, these measurements can be used to recreate accumulated snow water equivalent over the prior year. We also demonstrate how buckets of water may be placed under networked acoustic snow depth sensors to provide an index of daily evaporation rates at SNOTEL stations. (a) Temperature sensor sealed for deployment in the soil. (b) Launching a temperature sensor into a tree. (c) Pulley system to keep sensor above the snow. (a) Photo of bucket underneath acoustic snow depth sensor. (b) Water depth in the bucket as calculated by the snow depth sensor and by a pressure sensor inside the bucket.

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.

Long-term snow and weather observations at Weissfluhjoch and its relation to other high-altitude observatories in the Alps

NASA Astrophysics Data System (ADS)

Marty, Christoph; Meister, Roland

2012-12-01

Snow and weather observations at Weissfluhjoch were initiated in 1936, when a research team set a snow stake and started digging snow pits on a plateau located at 2,540 m asl above Davos, Switzerland. This was the beginning of what is now the longest series of daily snow depth, new snow height and bi-monthly snow water equivalent measurements from a high-altitude research station. Our investigations reveal that the snow depth at Weissfluhjoch with regard to the evolution and inter-annual variability represents a good proxy for the entire Swiss Alps. In order to set the snow and weather observations from Weissfluhjoch in a broader context, this paper also shows some comparisons with measurements from five other high-altitude observatories in the European Alps. The results show a surprisingly uniform warming of 0.8°C during the last three decades at the six investigated mountain stations. The long-term snow measurements reveal no change in mid-winter, but decreasing trends (especially since the 1980s) for the solid precipitation ratio, snow fall, snow water equivalent and snow depth during the melt season due to a strong temperature increase of 2.5°C in the spring and summer months of the last three decades.

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.

Topographic, meteorologic, and canopy controls on the scaling characteristics of the spatial distribution of snow depth fields

Treesearch

Ernesto Trujillo; Jorge A. Ramirez; Kelly J. Elder

2007-01-01

In this study, LIDAR snow depths, bare ground elevations (topography), and elevations filtered to the top of vegetation (topography + vegetation) in five 1-km2 areas are used to determine whether the spatial distribution of snow depth exhibits scale invariance, and the control that vegetation, topography, and winds exert on such behavior. The one-dimensional and mean...

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.

GPS interferometric reflectometry for ground-based remote sensing of snow depth and density

NASA Astrophysics Data System (ADS)

Nievinski, F. G.; Larson, K. M.; Gutmann, E. D.; Zavorotny, V.; Williams, M. W.

2011-12-01

GPS interferometric reflectometry (GPS-IR) is a method that exploits multipath for ground-based remote sensing in the surroundings of a GPS antenna. It operates on L-band, leveraging hundreds of conventional GPS sites existing in the U.S., with a typical footprint of 30-meter radius. Multipath is the coherent interference of line-of-sight and reflected signals; as the two go in and out of phase, the power recorded by a GPS interferometer goes through peaks and troughs that can be related to land surface characteristics, such as soil moisture and snow depth. GPS-IR has been demonstrated to be capable of retrieving snow depth during extended periods at various locations, as validated by comparisons with a continuously-operating terrestrial scanning laser, an airborne LIDAR campaign, manual stake surveys, and ultrasonic depth sensors. Here we explore the possibility of retrieving snow density, too. This will determine the feasibility and limitations of GPS-IR for monitoring of snow water equivalent (SWE). Data were collected at Niwot Ridge LTER in Colorado, at a 3,500-m altitude alpine tundra site. Niwot receives around 1,000 mm of precipitation per year and has a mean annual air temperature of -3.8°C. Snow density and temperature is measured in 10-cm vertical increments at snowpits dug approximately every week. A continuously-operating GPS system established in 2009 allows for measurement of the snowpack several times a day at multiple azimuths as satellites rise and set. The typical peak snow depth at the GPS site is 1.5 m, with a peak depth during the study period of 1.7 m in 2009/2010 and 2.5 m in 2010/2011; density ranged 200-600 kg/m3. We employ a forward/inverse model originally developed for snow depth and recently extended to account for layering to study both synthetic and real observations. We present comparisons of density estimates obtained using GPS-IR observations to snowpit field data, focusing initially on dry snow. In addition, we explore the sensitivity of the model to roughness, density, snow depth, and random noise. Synthetic observations derived from the forward model based on realistic snow profiles are utilized in the inverse model to quantify both the formal uncertainty and the expected error in parameter retrievals.

Routine Mapping of the Snow Depth Distribution on Sea Ice

NASA Astrophysics Data System (ADS)

Farrell, S. L.; Newman, T.; Richter-Menge, J.; Dattler, M.; Paden, J. D.; Yan, S.; Li, J.; Leuschen, C.

2016-12-01

The annual growth and retreat of the polar sea ice cover is influenced by the seasonal accumulation, redistribution and melt of snow on sea ice. Due to its high albedo and low thermal conductivity, snow is also a controlling parameter in the mass and energy budgets of the polar climate system. Under a changing climate scenario it is critical to obtain reliable and routine measurements of snow depth, across basin scales, and long time periods, so as to understand regional, seasonal and inter-annual variability, and the subsequent impacts on the sea ice cover itself. Moreover the snow depth distribution remains a significant source of uncertainty in the derivation of sea ice thickness from remote sensing measurements, as well as in numerical model predictions of future climate state. Radar altimeter systems flown onboard NASA's Operation IceBridge (OIB) mission now provide annual measurements of snow across both the Arctic and Southern Ocean ice packs. We describe recent advances in the processing techniques used to interpret airborne radar waveforms and produce accurate and robust snow depth results. As a consequence of instrument effects and data quality issues associated with the initial release of the OIB airborne radar data, the entire data set was reprocessed to remove coherent noise and sidelobes in the radar echograms. These reprocessed data were released to the community in early 2016, and are available for improved derivation of snow depth. Here, using the reprocessed data, we present the results of seven years of radar measurements collected over Arctic sea ice at the end of winter, just prior to melt. Our analysis provides the snow depth distribution on both seasonal and multi-year sea ice. We present the inter-annual variability in snow depth for both the Central Arctic and the Beaufort/Chukchi Seas. We validate our results via comparison with temporally and spatially coincident in situ measurements gathered during many of the OIB surveys. The results will influence future sensor suite development for sea ice studies, and they provide a new metric for comparison with other sea ice observations. Integrating these novel snow depth observations with modeling studies will help inform model development, and advance our predictive capabilities to help better understand how sea ice is responding to a changing climate.

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.

Changes in snow cover over Northern Eurasia in the last few decades

NASA Astrophysics Data System (ADS)

Bulygina, O. N.; Razuvaev, V. N.; Korshunova, N. N.

2009-10-01

Daily snow depth (SD) and snow cover extent around 820 stations are used to analyse variations in snow cover characteristics in Northern Eurasia, a region that encompasses the Russian Federation. These analyses employ nearly five times more stations than in the previous studies and temporally span forty years. A representative judgement on the changes of snow depth over most of Russia is presented here for the first time. The number of days with greater than 50% of the near-station territory covered with snow, and the number of days with the snow depth greater than 1.0 cm, are used to characterize the duration of snow cover (SCD) season. Linear trends of the number of days and snow depth are calculated for each station from 1966 to 2007. This investigation reveals regional features in the change of snow cover characteristics. A decrease in the duration of snow cover is demonstrated in the northern regions of European Russia and in the mountainous regions of southern Siberia. An increase in SCD is found in Yakutia and in the Far East. In the western half of the Russian Federation, the winter-averaged SD is shown to increase, with the maximum trends being observed in Northern West Siberia. In contrast, in the mountainous regions of southern Siberia, the maximum SD decreases as the SCD decreases. While both snow cover characteristics (SCD and SD) play an important role in the hydrological cycle, ecosystems dynamics and societal wellbeing are quite different roles and the differences in their systematic changes (up to differences in the signs of changes) deserve further attention.

Understanding Snow Depth Variability with Respect to the Canopy in Multiple Climates Using Airborne LiDAR

NASA Astrophysics Data System (ADS)

Currier, W. R.; Giulia, M.; Pflug, J. M.; Jonas, T.; Jessica, L.

2017-12-01

Snow depth within a typical hydrologic model grid cell (150 m or 1 km) can vary from 0.5 meters to 6 meters, or more. This variability is driven by the meteorological conditions throughout the winter as well as the forest architecture. To better understand this variability, we used airborne LiDAR from Olympic National Park, WA, Yosemite National Park, CA, Jemez Caldera, NM, and Niwot Ridge, CO to determine unique spatial patterns of snow depth in forested regions. Specifically, we compared snow depth distributions along north facing forest edges and south facing forest edges to those in the open or directly under the canopy. When categorizing the north facing and south facing edges based on distance from the canopy, distances relative to tree height, and distances relative to the fraction of the sky that is visible (sky view factor) we found unique snow depth patterns for each of these regions. In all regions besides Olympic National Park, WA, north facing edges contained more snow than open areas, forested areas, or along the south facing edges. These snow distributions were relatively consistent regardless of the metric used to define the forest edge and the size of the domain (150 m through 1 km). The absence of the forest edge effect in Olympic National Park was attributed to the meteorological data and climate conditions, which showed significantly less incoming shortwave radiation and more incoming longwave radiation. Furthermore, this study evaluated the effect that wind speed and direction have on the spatial distribution of snow depth.

Mapping snow depth from stereo satellite imagery

NASA Astrophysics Data System (ADS)

Gascoin, S.; Marti, R.; Berthier, E.; Houet, T.; de Pinel, M.; Laffly, D.

2016-12-01

To date, there is no definitive approach to map snow depth in mountainous areas from spaceborne sensors. Here, we examine the potential of very-high-resolution (VHR) optical stereo satellites to this purpose. Two triplets of 0.70 m resolution images were acquired by the Pléiades satellite over an open alpine catchment (14.5 km²) under snow-free and snow-covered conditions. The open-source software Ame's Stereo Pipeline (ASP) was used to match the stereo pairs without ground control points to generate raw photogrammetric clouds and to convert them into high-resolution digital elevation models (DEMs) at 1, 2, and 4 m resolutions. The DEM differences (dDEMs) were computed after 3-D coregistration, including a correction of a -0.48 m vertical bias. The bias-corrected dDEM maps were compared to 451 snow-probe measurements. The results show a decimetric accuracy and precision in the Pléiades-derived snow depths. The median of the residuals is -0.16 m, with a standard deviation (SD) of 0.58 m at a pixel size of 2 m. We compared the 2 m Pléiades dDEM to a 2 m dDEM that was based on a winged unmanned aircraft vehicle (UAV) photogrammetric survey that was performed on the same winter date over a portion of the catchment (3.1 km²). The UAV-derived snow depth map exhibits the same patterns as the Pléiades-derived snow map, with a median of -0.11 m and a SD of 0.62 m when compared to the snow-probe measurements. The Pléiades images benefit from a very broad radiometric range (12 bits), allowing a high correlation success rate over the snow-covered areas. This study demonstrates the value of VHR stereo satellite imagery to map snow depth in remote mountainous areas even when no field data are available. Based on this method we have initiated a multi-year survey of the peak snow depth in the Bassiès catchment.

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.

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.

Global Snow from Space: Development of a Satellite-based, Terrestrial Snow Mission Planning Tool

NASA Astrophysics Data System (ADS)

Forman, B. A.; Kumar, S.; LeMoigne, J.; Nag, S.

2017-12-01

A global, satellite-based, terrestrial snow mission planning tool is proposed to help inform experimental mission design with relevance to snow depth and snow water equivalent (SWE). The idea leverages the capabilities of NASA's Land Information System (LIS) and the Tradespace Analysis Tool for Constellations (TAT-C) to harness the information content of Earth science mission data across a suite of hypothetical sensor designs, orbital configurations, data assimilation algorithms, and optimization and uncertainty techniques, including cost estimates and risk assessments of each hypothetical permutation. One objective of the proposed observing system simulation experiment (OSSE) is to assess the complementary - or perhaps contradictory - information content derived from the simultaneous collection of passive microwave (radiometer), active microwave (radar), and LIDAR observations from space-based platforms. The integrated system will enable a true end-to-end OSSE that can help quantify the value of observations based on their utility towards both scientific research and applications as well as to better guide future mission design. Science and mission planning questions addressed as part of this concept include: What observational records are needed (in space and time) to maximize terrestrial snow experimental utility? How might observations be coordinated (in space and time) to maximize this utility? What is the additional utility associated with an additional observation? How can future mission costs be minimized while ensuring Science requirements are fulfilled?

CREST-SAFE: Snow LST validation, wetness profiler creation, and depth/SWE product development

NASA Astrophysics Data System (ADS)

Perez Diaz, C. L.; Lakhankar, T.; Romanov, P.; Khanbilvardi, R.; Munoz Barreto, J.; Yu, Y.

2017-12-01

CREST-SAFE: Snow LST validation, wetness profiler creation, and depth/SWE product development The Field Snow Research Station (also referred to as Snow Analysis and Field Experiment, SAFE) is operated by the NOAA Center for Earth System Sciences and Remote Sensing Technologies (CREST) in the City University of New York (CUNY). The field station is located within the premises of the Caribou Municipal Airport (46°52'59'' N, 68°01'07'' W) and in close proximity to the National Weather Service (NWS) Regional Forecast Office. The station was established in 2010 to support studies in snow physics and snow remote sensing. The Visible Infrared Imager Radiometer Suite (VIIRS) Land Surface Temperature (LST) Environmental Data Record (EDR) and Moderate Resolution Imaging Spectroradiometer (MODIS) LST product (provided by the Terra and Aqua Earth Observing System satellites) were validated using in situ LST (T-skin) and near-surface air temperature (T-air) observations recorded at CREST-SAFE for the winters of 2013 and 2014. Results indicate that T-air correlates better than T-skin with VIIRS LST data and that the accuracy of nighttime LST retrievals is considerably better than that of daytime. Several trends in the MODIS LST data were observed, including the underestimation of daytime values and night-time values. Results indicate that, although all the data sets showed high correlation with ground measurements, day values yielded slightly higher accuracy ( 1°C). Additionally, we created a liquid water content (LWC)-profiling instrument using time-domain reflectometry (TDR) at CREST-SAFE and tested it during the snow melt period (February-April) immediately after installation in 2014. Results displayed high agreement when compared to LWC estimates obtained using empirical formulas developed in previous studies, and minor improvement over wet snow LWC estimates. Lastly, to improve on global snow cover mapping, a snow product capable of estimating snow depth and snow water equivalent (SWE) using microwave remote sensing and the CREST Snow Depth Regression Tree Model (SDRTM) was developed. Data from AMSR2 onboard the JAXA GCOM-W1 satellite is used to produce daily global snow depth and SWE maps in automated fashion at a 10-km resolution.

Improving NIR snow pit stratigraphy observations by introducing a controlled NIR light source

NASA Astrophysics Data System (ADS)

Dean, J.; Marshall, H.; Rutter, N.; Karlson, A.

2013-12-01

Near-infrared (NIR) photography in a prepared snow pit measures mm-/grain-scale variations in snow structure, as reflectivity is strongly dependent on microstructure and grain size at the NIR wavelengths. We explore using a controlled NIR light source to maximize signal to noise ratio and provide uniform incident, diffuse light on the snow pit wall. NIR light fired from the flash is diffused across and reflected by an umbrella onto the snow pit; the lens filter transmits NIR light onto the spectrum-modified sensor of the DSLR camera. Lenses are designed to refract visible light properly, not NIR light, so there must be a correction applied for the subsequent NIR bright spot. To avoid interpolation and debayering algorithms automatically performed by programs like Adobe's Photoshop on the images, the raw data are analyzed directly in MATLAB. NIR image data show a doubling of the amount of light collected in the same time for flash over ambient lighting. Transitions across layer boundaries in the flash-lit image are detailed by higher camera intensity values than ambient-lit images. Curves plotted using median intensity at each depth, normalized to the average profile intensity, show a separation between flash- and ambient-lit images in the upper 10-15 cm; the ambient-lit image curve asymptotically approaches the level of the flash-lit image curve below 15cm. We hypothesize that the difference is caused by additional ambient light penetrating the upper 10-15 cm of the snowpack from above and transmitting through the wall of the snow pit. This indicates that combining NIR ambient and flash photography could be a powerful technique for studying penetration depth of radiation as a function of microstructure and grain size. The NIR flash images do not increase the relative contrast at layer boundaries; however, the flash more than doubles the amount of recorded light and controls layer noise as well as layer boundary transition noise.

A Rapid Turn-around, Scalable Big Data Processing Capability for the JPL Airborne Snow Observatory (ASO) Mission

NASA Astrophysics Data System (ADS)

Mattmann, C. A.

2014-12-01

The JPL Airborne Snow Observatory (ASO) is an integrated LIDAR and Spectrometer measuring snow depth and rate of snow melt in the Sierra Nevadas, specifically, the Tuolumne River Basin, Sierra Nevada, California above the O'Shaughnessy Dam of the Hetch Hetchy reservoir, and the Uncompahgre Basin, Colorado, amongst other sites. The ASO data was delivered to water resource managers from the California Department of Water Resources in under 24 hours from the time that the Twin Otter aircraft landed in Mammoth Lakes, CA to the time disks were plugged in to the ASO Mobile Compute System (MCS) deployed at the Sierra Nevada Aquatic Research Laboratory (SNARL) near the airport. ASO performed weekly flights and each flight took between 500GB to 1 Terabyte of raw data, which was then processed from level 0 data products all the way to full level 4 maps of Snow Water Equivalent, albedo mosaics, and snow depth from LIDAR. These data were produced by Interactive Data analysis Language (IDL) algorithms which were then unobtrusively and automatically integrated into an Apache OODT and Apache Tika based Big Data processing system. Data movement was both electronic and physical including novel uses of LaCie 1 and 2 TeraByte (TB) data bricks and deployment in rugged terrain. The MCS was controlled remotely from the Jet Propulsion Laboratory, California Institute of Technology (JPL) in Pasadena, California on behalf of the National Aeronautics and Space Administration (NASA). Communication was aided through the use of novel Internet Relay Chat (IRC) command and control mechanisms and through the use of the Notifico open source communication tools. This talk will describe the high powered, and light-weight Big Data processing system that we developed for ASO and its implications more broadly for airborne missions at NASA and throughout the government. The lessons learned from ASO show the potential to have a large impact in the development of Big Data processing systems in the years to come.

Preliminary Evaluation of the AFWA-NASA (ANSA) Blended Snow-Cover Product over the Lower Great Lakes Region

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Foster, James L.; Riggs, George A.; Kelly, Richard E. J.; Chien, Janet Y. L.; Montesano, Paul M.

2009-01-01

The Air Force Weather Agency (AFWA) - NASA (ANSA) blended-snow product utilizes EOS standard snow products from the Moderate-Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) to map daily snow cover and snow-water equivalent (SWE) globally. We have compared ANSA-derived SWE. with SWE values calculated from snow depths reported at approx.1500 National Climatic Data Center (NCDC) coop stations in the Lower Great Lakes basin. Our preliminary results show that conversion of snow depth to SWE is very sensitive to the choice of snow density (we used either 0.2 or 03 as conversion factors). We found overall better agreement between the ANSA-derived SWE and the co-op station data when we use a snow density of 0.3 to convert the snow depths to SWE. In addition, we show that the ANSA underestimates SWE in densely-forested areas, using January and February 2008 ANSA and co-op data. Furthermore, apparent large SWE changes from one day to the next may be caused by thaw-re-freeze events, and do not always represent a real change in SWE. In the near future we will continue the analysis in the 2006-07 and 2007-08 snow seasons.

Towards Snowpack Characterization using C-band Synthetic Aperture Radar (SAR)

NASA Astrophysics Data System (ADS)

Park, J.; Forman, B. A.

2017-12-01

Sentinel 1A and 1B, operated by the European Space Agency (ESA), carries a C-band synthetic aperture radar (SAR) sensor that can be used to monitor terrestrial snow properties. This study explores the relationship between terrestrial snow-covered area, snow depth, and snow water equivalent with Sentinel 1 backscatter observations in order to better characterize snow mass. Ground-based observations collected by the National Oceanic and Atmospheric Administration - Cooperative Remote Sensing Science and Technology Center (NOAA-CREST) in Caribou, Maine in the United States are also used in the comparative analysis. Sentinel 1 Ground Range Detected (GRD) imagery with Interferometric Wide swath (IW) were preprocessed through a series of steps accounting for thermal noise, sensor orbit, radiometric calibration, speckle filtering, and terrain correction using ESA's Sentinel Application Platform (SNAP) software package, which is an open-source module written in Python. Comparisons of dual-polarized backscatter coefficients (i.e., σVV and σVH) with in-situ measurements of snow depth and SWE suggest that cross-polarized backscatter observations exhibit a modest correlation between both snow depth and SWE. In the case of the snow-covered area, a multi-temporal change detection method was used. Results using Sentinel 1 yield similar spatial patterns as when using hyperspectral observations collected by the MODerate Resolution Imaging Spectroradiometer (MODIS). These preliminary results suggest the potential application of Sentinel 1A/1B backscatter coefficients towards improved discrimination of snow cover, snow depth, and SWE. One goal of this research is to eventually merge C-band SAR backscatter observations with other snow information (e.g., passive microwave brightness temperatures) as part of a multi-sensor snow assimilation framework.

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...

Snow depth manipulation experiments in a dry and a moist tundra

NASA Astrophysics Data System (ADS)

Kwon, M. J.; Czimczik, C. I.; Jung, J. Y.; Kim, M.; Lee, Y. K.; Nam, S.; Wagner, I.

2017-12-01

As a result of global warming, precipitation in the Arctic is expected to increase by 25-50% by the end of this century, mostly in the form of snow. However, precipitation patterns vary considerable in space and time, and future precipitation patterns are highly uncertain at local and regional scales. The amount of snowfall (or snow depth) influences a number of ecosystem properties in Arctic ecosystems, such as soil temperature over winter and soil moisture in the following growing season. These modifications then affect rates of carbon-related soil processes and photosynthesis, thus CO2 exchange rates between terrestrial ecosystems and the atmosphere. In this study, we investigate the effects of snow depth on the magnitude, sources and temporal dynamics of CO2 fluxes. We installed snow fences in a dry dwarf-shrub (Cambridge Bay, Canada; 69° N, 105° W) and a moist low-shrub (Council, Alaska, USA; 64° N, 165° W) tundra in summer 2017, and established control, and increased and reduced snow depth plots at each snow fence. Summertime CO2 flux rates (net ecosystem exchange, ecosystem respiration, gross primary production) and the fractions of autotrophic and heterotrophic respiration to ecosystem respiration were measured using manual chambers and radiocarbon signatures. Wintertime CO2 flux rates will be measured using soda lime adsorption technique and forced diffusion chambers. Soil temperature and moisture at multiple depths, as well as changes in soil properties and microbial communities will be also observed, to research whether these changes affect CO2 flux rates or patterns. Our study will elucidate how future snow depth and its impact on soil physical and biogeochemical properties influence the magnitude and sources of tundra-atmosphere CO2 exchange in the rapidly warming Arctic.

Evaluating UAV and LiDAR Retrieval of Snow Depth in a Coniferous Forest in Arizona

NASA Astrophysics Data System (ADS)

Van Leeuwen, W. J. D.; Broxton, P.; Biederman, J. A.

2017-12-01

Remote sensing of snow depth and cover in forested environments is challenging. Trees interfere with the remote sensing of snowpack below the canopy and cause large variations in the spatial distribution of the snowpack itself (e.g. between below canopy environments to shaded gaps to open clearings). The distribution of trees and topographic variation make it challenging to monitor the snowpack with in-situ observations. Airborne LiDAR has improved our ability to monitor snowpack over large areas in montane and forested environments because of its high sampling rate and ability to penetrate the canopy. However, these LiDAR flights can be too expensive and time-consuming to process, making it hard to use them for real-time snow monitoring. In this research, we evaluate Structure from Motion (SfM) as an alternative to Airborne LiDAR to generate high-resolution snow depth data in forested environments. This past winter, we conducted a snow field campaign over Arizona's Mogollon Rim where we acquired aerial LiDAR, multi-angle aerial photography from a UAV, and extensive field observations of snow depth at two sites. LiDAR and SFM derived snow depth maps were generated by comparing "snow-on" and "snow-off" LiDAR and SfM data. The SfM- and LiDAR-generated snow depth maps were similar at a site with fewer trees, though there were more discrepancies at a site with more trees. Both compared reasonably well with the field observations at the sparser forested site, with poorer agreement at the denser forested site. Finally, although the SfM produced point clouds with much higher point densities than the aerial LiDAR, the SfM was not able to produce meaningful snow depth estimates directly underneath trees and had trouble in areas with deep shadows. Based on these findings, we are optimizing our UAV data acquisition strategies for this upcoming field season. We are using these data, along with high-resolution hydrological modeling, to gain a better understanding of how different forest structural, climatic and topographic conditions affect the snowpack and consequently the water resources available to the Salt River Project, a water utility providing power and water to millions of customers in the Phoenix area

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 Costs of Climate Change: Impact of Future Snow Cover Projections on Valuation of Albedo in Forest Management

NASA Astrophysics Data System (ADS)

Burakowski, E. A.; Lutz, D. A.

2014-12-01

Surface albedo provides an important climate regulating ecosystem service, particularly in the mid-latitudes where seasonal snow cover influences surface radiation budgets. In the case of substantial seasonal snow cover, the influence of albedo can equal or surpass the climatic benefits of carbon sequestration from forest growth. Climate mitigation platforms should therefore consider albedo in their framework in order to integrate these two climatic services in an economic context for the effective design and implementation of forest management projects. Over the next century, the influence of surface albedo is projected to diminish under higher emissions scenarios due to an overall decrease in snow depth and duration of snow cover in the mid-latitudes. In this study, we focus on the change in economic value of winter albedo in the northeastern United States projected through 2100 using the Special Report on Emissions Scenarios (SRES) a1 and b1 scenarios. Statistically downscaled temperature and precipitation are used as input to the Variable Infiltration Capacity (VIC) model to provide future daily snow depth fields through 2100. Using VIC projections of future snow depth, projected winter albedo fields over deforested lands were generated using an empirical logarithmic relationship between snow depth and albedo derived from a volunteer network of snow observers in New Hampshire over the period Nov 2011 through 2014. Our results show that greater reductions in snow depth and the number of winter days with snow cover in the a1 compared to the b1 scenario reduce wintertime albedo when forested lands are harvested. This result has implications on future trade-offs among albedo, carbon storage, and timber value that should be investigated in greater detail. The impacts of forest harvest on radiative forcing associated with energy redistribution (e.g., latent heat and surface roughness length) should also be considered in future work.

Mapping snow depth in open alpine terrain from stereo satellite imagery

NASA Astrophysics Data System (ADS)

Marti, R.; Gascoin, S.; Berthier, E.; de Pinel, M.; Houet, T.; Laffly, D.

2016-07-01

To date, there is no definitive approach to map snow depth in mountainous areas from spaceborne sensors. Here, we examine the potential of very-high-resolution (VHR) optical stereo satellites to this purpose. Two triplets of 0.70 m resolution images were acquired by the Pléiades satellite over an open alpine catchment (14.5 km2) under snow-free and snow-covered conditions. The open-source software Ame's Stereo Pipeline (ASP) was used to match the stereo pairs without ground control points to generate raw photogrammetric clouds and to convert them into high-resolution digital elevation models (DEMs) at 1, 2, and 4 m resolutions. The DEM differences (dDEMs) were computed after 3-D coregistration, including a correction of a -0.48 m vertical bias. The bias-corrected dDEM maps were compared to 451 snow-probe measurements. The results show a decimetric accuracy and precision in the Pléiades-derived snow depths. The median of the residuals is -0.16 m, with a standard deviation (SD) of 0.58 m at a pixel size of 2 m. We compared the 2 m Pléiades dDEM to a 2 m dDEM that was based on a winged unmanned aircraft vehicle (UAV) photogrammetric survey that was performed on the same winter date over a portion of the catchment (3.1 km2). The UAV-derived snow depth map exhibits the same patterns as the Pléiades-derived snow map, with a median of -0.11 m and a SD of 0.62 m when compared to the snow-probe measurements. The Pléiades images benefit from a very broad radiometric range (12 bits), allowing a high correlation success rate over the snow-covered areas. This study demonstrates the value of VHR stereo satellite imagery to map snow depth in remote mountainous areas even when no field data are available.

A simple algorithm for identifying periods of snow accumulation on a radiometer

NASA Astrophysics Data System (ADS)

Lapo, Karl E.; Hinkelman, Laura M.; Landry, Christopher C.; Massmann, Adam K.; Lundquist, Jessica D.

2015-09-01

Downwelling solar, Qsi, and longwave, Qli, irradiances at the earth's surface are the primary energy inputs for many hydrologic processes, and uncertainties in measurements of these two terms confound evaluations of estimated irradiances and negatively impact hydrologic modeling. Observations of Qsi and Qli in cold environments are subject to conditions that create additional uncertainties not encountered in other climates, specifically the accumulation of snow on uplooking radiometers. To address this issue, we present an automated method for estimating these periods of snow accumulation. Our method is based on forest interception of snow and uses common meteorological observations. In this algorithm, snow accumulation must exceed a threshold to obscure the sensor and is only removed through scouring by wind or melting. The algorithm is evaluated at two sites representing different mountain climates: (1) Snoqualmie Pass, Washington (maritime) and (2) the Senator Beck Basin Study Area, Colorado (continental). The algorithm agrees well with time-lapse camera observations at the Washington site and with multiple measurements at the Colorado site, with 70-80% of observed snow accumulation events correctly identified. We suggest using the method for quality controlling irradiance observations in snow-dominated climates where regular, daily maintenance is not possible.

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.

An Evaluation of Arctic Ocean Precipitation from Reanalyses for use in Snow Accumulation and Melt Models over Sea Ice

NASA Astrophysics Data System (ADS)

Barrett, A. P.; Stroeve, J.; Liston, G. E.; Tschudi, M. A.; Stewart, S.

2017-12-01

Retrievals of sea ice thickness from satellite- and air-borne sensors require knowledge of snow depth and density. Early retrievals used climatologies of snow depth and density - "The Warren Climatology" - based on observations from 31 Soviet drifting stations between 1957 and 1991. This climatology was the best available Arctic-wide data set at the time. However, it does not account for year-to-year variations in spatial and temporal patterns of snow depth, nor does it account for changes in snow depth over longer time periods. Recent efforts to retrieve ice thickness have used output from global and regional atmospheric reanalyses directly or as input to snow accumulation, density evolution, and melt models to estimate snow depth. While such efforts represent the state-of-the-art in terms of Arctic-wide snow depth fields, there can be large differences between precipitation (and other variables) from reanalyses. Knowledge about these differences and about biases in precipitation magnitude are important for getting the best-possible retrievals of ice thickness. Here, we evaluate fields of total precipitation and snow fall from the NASA MERRA and MERRA2, NOAA CFSR and CFSR version 2, ECMWF ERA-Interim, and Arctic System (ASR) reanalyses with a view to understanding differences in the magnitude, and temporal and spatial patterns of precipitation. Where possible we use observations to understand biases in the reanalysis output. Time series of annual total precipitation for the central Arctic correlate well with all reanalyses showing similar year-to-year variability. Time series for MERRA, MERRA2 and CFSR show no evidence of long-term trends. By contrast ERA-Interim appears to be wetter in the most recent decade. The ASR records only spans 2000 to 2012 but is similar to ERA-Interim. CFSR and MERRA2 are wetter than the other five reanalyses, especially over the eastern Arctic and North Atlantic.

Advances in Airborne Altimetric Techniques for the Measurement of Snow on Arctic Sea Ice

NASA Astrophysics Data System (ADS)

Newman, T.; Farrell, S. L.; Richter-Menge, J.; Elder, B. C.; Ruth, J.; Connor, L. N.

2014-12-01

Current sea ice observations and models indicate a transition towards a more seasonal Arctic ice pack with a smaller, and geographically more variable, multiyear ice component. To gain a comprehensive understanding of the processes governing this transition it is important to include the impact of the snow cover, determining the mechanisms by which snow is both responding to and forcing changes to the sea ice pack. Data from NASA's Operation IceBridge (OIB) snow radar system, which has been making yearly surveys of the western Arctic since 2009, offers a key resource for investigating the snow cover. In this work, we characterize the OIB snow radar instrument response to ascertain the location of 'side-lobes', aiding the interpretation of snow radar data. We apply novel wavelet-based techniques to identify the primary reflecting interfaces within the snow pack from which snow depth estimates are derived. We apply these techniques to the range of available snow radar data collected over the last 6 years during the NASA OIB mission. Our results are validated through comparison with a range of in-situ data. We discuss the impact of sea ice surface morphology on snow radar returns (with respect to ice type) and the topographic conditions over which accurate snow-radar-derived snow depths may be obtained. Finally we present improvements to in situ survey design that will allow for both an improved sampling of the snow radar footprint and more accurate assessment of the uncertainties in radar-derived snow depths in the future.

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.

Snow-depth and water-equivalent data for the Fairbanks area, Alaska, spring 1995

USGS Publications Warehouse

Plumb, E.W.; Lilly, M.R.

1996-01-01

Snow depths at 34 sites and snow-water equivalents at 13 sites in the Fairbanks area were monitored during the 1995 snowmelt period (March 30 to April 26) in the spring of 1995. The U.S. Geological Survey conducted this study in cooperation with the Fairbanks International Airport, the University of Alaska Fairbanks, the Alaska Department of Natural Resources-Division of Mining and Water Management, the U.S Army, Alaska, and the U.S. Army Corps of Engineers-Alaska District. These data were collected to provide information about potential recharge of the ground-and surface-water systems during the snowmelt period in the Fairbanks area. This information is needed by companion geohydrologic studies of areas with known or suspected contaminants in the subsurface. Data-collection sites selected had open, boggy, wooded, or brushy vegetation cover and had different slope aspects. The deepest snow at any site, 27.1 inches, was recorded on April 1, 1995; the shallowest snow measured that day was 19.1 inches. The snow-water equivalents at these two sites were 5.9 inches and 4.5 inches, respectively. Snow depths and water equivalents were comparatively greater at open and bog sites than at wooded or brushy sites. Snow depths and water equivalents at all sites decreased throughout the measuring period. The decrease was more rapid at open and boggy sites than at wooded and brushy sites. Snow had completely disappeared from all sites by April 26, 1995.

Snowpack monitoring in North America and Eurasia using passive microwave satellite data

NASA Technical Reports Server (NTRS)

Foster, J. L.; Rango, A.; Hall, D. K.; Chang, A. T. C.; Allison, L. J.; Diesen, B. C., III

1980-01-01

Areas of the Canadian high plains, the Montana and North Dakota high plains, and the steppes of central Russia have been studied in an effort to determine the utility of spaceborne microwave radiometers for monitoring snow depths in different geographic areas. Significant regression relationships between snow depth and microwave brightness temperatures were developed for each of these homogeneous areas. In each of the study areas investigated in this paper, Nimbus-6 (0.81 cm) ESMR data produced higher correlations than Nimbus-5 (1.55 cm) ESMR data in relating microwave brightness temperature to snow depth. It is difficult to extrapolate relationships between microwave brightness temperature and snow depth from one area to another because different geographic areas are likely to have different snowpack conditions.

Design of a High Resolution Open Access Global Snow Cover Web Map Service Using Ground and Satellite Observations

NASA Astrophysics Data System (ADS)

Kadlec, J.; Ames, D. P.

2014-12-01

The aim of the presented work is creating a freely accessible, dynamic and re-usable snow cover map of the world by combining snow extent and snow depth datasets from multiple sources. The examined data sources are: remote sensing datasets (MODIS, CryoLand), weather forecasting model outputs (OpenWeatherMap, forecast.io), ground observation networks (CUAHSI HIS, GSOD, GHCN, and selected national networks), and user-contributed snow reports on social networks (cross-country and backcountry skiing trip reports). For adding each type of dataset, an interface and an adapter is created. Each adapter supports queries by area, time range, or combination of area and time range. The combined dataset is published as an online snow cover mapping service. This web service lowers the learning curve that is required to view, access, and analyze snow depth maps and snow time-series. All data published by this service are licensed as open data; encouraging the re-use of the data in customized applications in climatology, hydrology, sports and other disciplines. The initial version of the interactive snow map is on the website snow.hydrodata.org. This website supports the view by time and view by site. In view by time, the spatial distribution of snow for a selected area and time period is shown. In view by site, the time-series charts of snow depth at a selected location is displayed. All snow extent and snow depth map layers and time series are accessible and discoverable through internationally approved protocols including WMS, WFS, WCS, WaterOneFlow and WaterML. Therefore they can also be easily added to GIS software or 3rd-party web map applications. The central hypothesis driving this research is that the integration of user contributed data and/or social-network derived snow data together with other open access data sources will result in more accurate and higher resolution - and hence more useful snow cover maps than satellite data or government agency produced data by itself.

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.

Combined Study of Snow Depth Determination and Winter Leaf Area Index Retrieval by Unmanned Aerial Vehicle Photogrammetry

NASA Astrophysics Data System (ADS)

Lendzioch, Theodora; Langhammer, Jakub; Jenicek, Michal

2017-04-01

A rapid and robust approach using Unmanned Aerial Vehicle (UAV) digital photogrammetry was performed for evaluating snow accumulation over different small localities (e.g. disturbed forest and open area) and for indirect field measurements of Leaf Area Index (LAI) of coniferous forest within the Šumava National Park, Czech Republic. The approach was used to reveal impacts related to changes in forest and snowpack and to determine winter effective LAI for monitoring the impact of forest canopy metrics on snow accumulation. Due to the advancement of the technique, snow depth and volumetric changes of snow depth over these selected study areas were estimated at high spatial resolution (1 cm) by subtracting a snow-free digital elevation model (DEM) from a snow-covered DEM. Both, downward-looking UAV images and upward-looking digital hemispherical photography (DHP), and additional widely used LAI-2200 canopy analyser measurements were applied to determine the winter LAI, controlling interception and transmitting radiation. For the performance of downward-looking UAV images the snow background instead of the sky fraction was used. The reliability of UAV-based LAI retrieval was tested by taking an independent data set during the snow cover mapping campaigns. The results showed the potential of digital photogrammetry for snow depth mapping and LAI determination by UAV techniques. The average difference obtained between ground-based and UAV-based measurements of snow depth was 7.1 cm with higher values obtained by UAV. The SD of 22 cm for the open area seemed competitive with the typical precision of point measurements. In contrast, the average difference in disturbed forest area was 25 cm with lower values obtained by UAV and a SD of 36 cm, which is in agreement with other studies. The UAV-based LAI measurements revealed the lowest effective LAI values and the plant canopy analyser LAI-2200 the highest effective LAI values. The biggest bias of effective LAI was observed between LAI-2200 and UAV-based analyses. Since the LAI parameter is important for snowpack modelling, this method presents the potential of simplifying LAI retrieval and mapping of snow dynamics while reducing running costs and time.

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.

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.

A snow cover climatology for the Pyrenees from MODIS snow products

NASA Astrophysics Data System (ADS)

Gascoin, S.; Hagolle, O.; Huc, M.; Jarlan, L.; Dejoux, J.-F.; Szczypta, C.; Marti, R.; Sánchez, R.

2014-11-01

The seasonal snow in the Pyrenees is critical for hydropower production, crop irrigation and tourism in France, Spain and Andorra. Complementary to in situ observations, satellite remote sensing is useful to monitor the effect of climate on the snow dynamics. The MODIS daily snow products (Terra/MOD10A1 and Aqua/MYD10A1) are widely used to generate snow cover climatologies, yet it is preferable to assess their accuracies prior to their use. Here, we use both in situ snow observations and remote sensing data to evaluate the MODIS snow products in the Pyrenees. First, we compare the MODIS products to in situ snow depth (SD) and snow water equivalent (SWE) measurements. We estimate the values of the SWE and SD best detection thresholds to 40 mm water equivalent (we) and 105 mm respectively, for both MOD10A1 and MYD10A1. Kappa coefficients are within 0.74 and 0.92 depending on the product and the variable. Then, a set of Landsat images is used to validate MOD10A1 and MYD10A1 for 157 dates between 2002 and 2010. The resulting accuracies are 97% (κ = 0.85) for MOD10A1 and 96% (κ = 0.81) for MYD10A1, which indicates a good agreement between both datasets. The effect of vegetation on the results is analyzed by filtering the forested areas using a land cover map. As expected, the accuracies decreases over the forests but the agreement remains acceptable (MOD10A1: 96%, κ = 0.77; MYD10A1: 95%, κ = 0.67). We conclude that MODIS snow products have a sufficient accuracy for hydroclimate studies at the scale of the Pyrenees range. Using a gapfilling algorithm we generate a consistent snow cover climatology, which allows us to compute the mean monthly snow cover duration per elevation band. We finally analyze the snow patterns for the atypical winter 2011-2012. Snow cover duration anomalies reveal a deficient snowpack on the Spanish side of the Pyrenees, which seems to have caused a drop in the national hydropower production.

BOREAS HYD-3 Snow Measurements

NASA Technical Reports Server (NTRS)

Hardy, Janet P.; Hall, Forrest G. (Editor); Knapp, David E. (Editor); Davis, Robert E.; Smith, David E. (Technical Monitor)

2000-01-01

The Boreal Ecosystem-Atmosphere Study (BOREAS) Hydrology (HYD)-3 team collected several data sets related to the hydrology of forested areas. This data set contains measurements of snow depth, snow density in three cm intervals, an integrated snow pack density and snow water equivalent (SWE), and snow pack physical properties from snow pit evaluation taken in 1994 and 1996. The data were collected from several sites in both the southern study area (SSA) and the northern study area (NSA). A variety of standard tools were used to measure the snow pack properties, including a meter stick (snow depth), a 100 cc snow density cutter, a dial stem thermometer, and the Canadian snow sampler as used by HYD-4 to obtain a snow pack-integrated measure of SWE. This study was undertaken to predict spatial distributions of snow properties important to the hydrology, remote sensing signatures, and the transmissivity of gases through the snow. The data are available in tabular ASCII files. The snow measurement data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

Observation of Snow cover glide on Sub-Alpine Coniferous Forests in Mount Zao, Northeastern Japan

NASA Astrophysics Data System (ADS)

Sasaki, A.; Suzuki, K.

2017-12-01

This is the study to clarify the snow cover glide behavior in the sub-alpine coniferous forests on Mount Zao, Northeastern Japan, in the winter of 2014-2015. We installed the glide-meter which is sled type, and measured the glide motion on the slope of Abies mariesii forest and its surrounding slope. In addition, we observed the air temperature, snow depth, density of snow, and snow temperature to discuss relationship between weather conditions and glide occurrence. The snow cover of the 2014-15 winter started on November 13th and disappeared on April 21st. The maximum snow depth was 242 cm thick, it was recorded at February 1st. The snow cover glide in the surrounding slope was occurred first at February 10th, although maximum snow depth recorded on February 1st. The glide motion in the surrounding slope is continuing and its velocity was 0.4 cm per day. The glide in the surrounding slope stopped at March 16th. The cumulative amount of the glide was 21.1 cm. The snow cover glide in the A. mariesii forest was even later occurred first at February 21st. The glide motion of it was intermittent and extremely small. On sub-alpine zone of Mount Zao, snow cover glide intensity is estimated to be 289 kg/m2 on March when snow water equivalent is maximum. At same period, maximum snow cover glide intensity is estimated to be about 1000 kg/m2 at very steep slopes where the slope angle is about 35 degree. Although potential of snow cover glide is enough high, the snow cover glide is suppressed by stem of A. mariesii trees, in the sub-alpine coniferous forest.

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.

Snow and Frost Depths on North and South Slopes

Treesearch

Richard S. Sartz

1973-01-01

Aspect affects soil frost depth by influencing the amount of solar radiation received at the ground or snow surface. Depending on the conditions, frost can be of equal depth on north and south slopes, deeper on north slopes, or deeper on south slopes. Data illustrate all three conditions

Uncertainty Quantification and Regional Sensitivity Analysis of Snow-related Parameters in the Canadian LAnd Surface Scheme (CLASS)

NASA Astrophysics Data System (ADS)

Badawy, B.; Fletcher, C. G.

2017-12-01

The parameterization of snow processes in land surface models is an important source of uncertainty in climate simulations. Quantifying the importance of snow-related parameters, and their uncertainties, may therefore lead to better understanding and quantification of uncertainty within integrated earth system models. However, quantifying the uncertainty arising from parameterized snow processes is challenging due to the high-dimensional parameter space, poor observational constraints, and parameter interaction. In this study, we investigate the sensitivity of the land simulation to uncertainty in snow microphysical parameters in the Canadian LAnd Surface Scheme (CLASS) using an uncertainty quantification (UQ) approach. A set of training cases (n=400) from CLASS is used to sample each parameter across its full range of empirical uncertainty, as determined from available observations and expert elicitation. A statistical learning model using support vector regression (SVR) is then constructed from the training data (CLASS output variables) to efficiently emulate the dynamical CLASS simulations over a much larger (n=220) set of cases. This approach is used to constrain the plausible range for each parameter using a skill score, and to identify the parameters with largest influence on the land simulation in CLASS at global and regional scales, using a random forest (RF) permutation importance algorithm. Preliminary sensitivity tests indicate that snow albedo refreshment threshold and the limiting snow depth, below which bare patches begin to appear, have the highest impact on snow output variables. The results also show a considerable reduction of the plausible ranges of the parameters values and hence reducing their uncertainty ranges, which can lead to a significant reduction of the model uncertainty. The implementation and results of this study will be presented and discussed in details.

State of Arctic Sea Ice North of Svalbard during N-ICE2015

NASA Astrophysics Data System (ADS)

Rösel, Anja; King, Jennifer; Gerland, Sebastian

2016-04-01

The N-ICE2015 cruise, led by the Norwegian Polar Institute, was a drift experiment with the research vessel R/V Lance from January to June 2015, where the ship started the drift North of Svalbard at 83°14.45' N, 21°31.41' E. The drift was repeated as soon as the vessel drifted free. Altogether, 4 ice stations where installed and the complex ocean-sea ice-atmosphere system was studied with an interdisciplinary Approach. During the N-ICE2015 cruise, extensive ice thickness and snow depth measurements were performed during both, winter and summer conditions. Total ice and snow thickness was measured with ground-based and airborne electromagnetic instruments; snow depth was measured with a GPS snow depth probe. Additionally, ice mass balance and snow buoys were deployed. Snow and ice thickness measurements were performed on repeated transects to quantify the ice growth or loss as well as the snow accumulation and melt rate. Additionally, we collected independent values on surveys to determine the general ice thickness distribution. Average snow depths of 32 cm on first year ice, and 52 cm on multi-year ice were measured in January, the mean snow depth on all ice types even increased until end of March to 49 cm. The average total ice and snow thickness in winter conditions was 1.92 m. During winter we found a small growth rate on multi-year ice of about 15 cm in 2 months, due to above-average snow depths and some extraordinary storm events that came along with mild temperatures. In contrast thereto, we also were able to study new ice formation and thin ice on newly formed leads. In summer conditions an enormous melt rate, mainly driven by a warm Atlantic water inflow in the marginal ice zone, was observed during two ice stations with melt rates of up to 20 cm per 24 hours. To reinforce the local measurements around the ship and to confirm their significance on a larger scale, we compare them to airborne thickness measurements and classified SAR-satellite scenes. The here presented data set is important for understanding the ocean-ice-atmosphere interactions, for calculating energy fluxes, and biogeochemical processes.

Continuous Snow Depth, Intensive Site 1, Barrow, Alaska

DOE Data Explorer

Bob Busey; Larry Hinzman; Vladimir Romanovsky; William Cable

2014-11-06

Continuous Snow depth data are being collected at several points within four intensive study areas in Barrow, Alaska. These data are being collected to better understand the energy dynamics above the active layer and permafrost. They complement in-situ snow and soil measurements at this location. The data could also be used as supporting measurements for other research and modeling activities.

Snow loads on roofs in areas of heavy snowfall

Treesearch

Robert D. Doty; Glenn H. Deitschman

1966-01-01

This study tested the feasibility of estimating snow loads on roofs from measurements of depth and water content of snow on nearby ground. The water content, and therefore the weight, of snow on the ground proved comparable to that of snow on roofs.

The Influence of Snowmobile Trails on Coyote Movements during Winter in High-Elevation Landscapes

PubMed Central

Gese, Eric M.; Dowd, Jennifer L. B.; Aubry, Lise M.

2013-01-01

Competition between sympatric carnivores has long been of interest to ecologists. Increased understanding of these interactions can be useful for conservation planning. Increased snowmobile traffic on public lands and in habitats used by Canada lynx (Lynx canadensis) remains controversial due to the concern of coyote (Canis latrans) use of snowmobile trails and potential competition with lynx. Determining the variables influencing coyote use of snowmobile trails has been a priority for managers attempting to conserve lynx and their critical habitat. During 2 winters in northwest Wyoming, we backtracked coyotes for 265 km to determine how varying snow characteristics influenced coyote movements; 278 km of random backtracking was conducted simultaneously for comparison. Despite deep snow (>1 m deep), radio-collared coyotes persisted at high elevations (>2,500 m) year-round. All coyotes used snowmobile trails for some portion of their travel. Coyotes used snowmobile trails for 35% of their travel distance (random: 13%) for a mean distance of 149 m (random: 59 m). Coyote use of snowmobile trails increased as snow depth and penetrability off trails increased. Essentially, snow characteristics were most influential on how much time coyotes spent on snowmobile trails. In the early months of winter, snow depth was low, yet the snow column remained dry and the coyotes traveled off trails. As winter progressed and snow depth increased and snow penetrability increased, coyotes spent more travel distance on snowmobile trails. As spring approached, the snow depth remained high but penetrability decreased, hence coyotes traveled less on snowmobile trails because the snow column off trail was more supportive. Additionally, coyotes traveled closer to snowmobile trails than randomly expected and selected shallower snow when traveling off trails. Coyotes also preferred using snowmobile trails to access ungulate kills. Snow compaction from winter recreation influenced coyote movements within an area containing lynx and designated lynx habitat. PMID:24367565

The influence of snowmobile trails on coyote movements during winter in high-elevation landscapes.

PubMed

Gese, Eric M; Dowd, Jennifer L B; Aubry, Lise M

2013-01-01

Competition between sympatric carnivores has long been of interest to ecologists. Increased understanding of these interactions can be useful for conservation planning. Increased snowmobile traffic on public lands and in habitats used by Canada lynx (Lynx canadensis) remains controversial due to the concern of coyote (Canis latrans) use of snowmobile trails and potential competition with lynx. Determining the variables influencing coyote use of snowmobile trails has been a priority for managers attempting to conserve lynx and their critical habitat. During 2 winters in northwest Wyoming, we backtracked coyotes for 265 km to determine how varying snow characteristics influenced coyote movements; 278 km of random backtracking was conducted simultaneously for comparison. Despite deep snow (>1 m deep), radio-collared coyotes persisted at high elevations (>2,500 m) year-round. All coyotes used snowmobile trails for some portion of their travel. Coyotes used snowmobile trails for 35% of their travel distance (random: 13%) for a mean distance of 149 m (random: 59 m). Coyote use of snowmobile trails increased as snow depth and penetrability off trails increased. Essentially, snow characteristics were most influential on how much time coyotes spent on snowmobile trails. In the early months of winter, snow depth was low, yet the snow column remained dry and the coyotes traveled off trails. As winter progressed and snow depth increased and snow penetrability increased, coyotes spent more travel distance on snowmobile trails. As spring approached, the snow depth remained high but penetrability decreased, hence coyotes traveled less on snowmobile trails because the snow column off trail was more supportive. Additionally, coyotes traveled closer to snowmobile trails than randomly expected and selected shallower snow when traveling off trails. Coyotes also preferred using snowmobile trails to access ungulate kills. Snow compaction from winter recreation influenced coyote movements within an area containing lynx and designated lynx habitat.

Quantifying the variability of snowpack properties and processes in a small-forested catchment representative of the boreal zone

NASA Astrophysics Data System (ADS)

Parajuli, A.; Nadeau, D.; Anctil, F.; Parent, A. C.; Bouchard, B.; Jutras, S.

2017-12-01

In snow-fed catchments, it is crucial to monitor and to model snow water equivalent (SWE), particularly to simulate the melt water runoff. However, the distribution of SWE can be highly heterogeneous, particularly within forested environments, mainly because of the large variability in snow depths. Although the boreal forest is the dominant land cover in Canada and in a few other northern countries, very few studies have quantified the spatiotemporal variability of snow depths and snowpack dynamics within this biome. The objective of this paper is to fill this research gap, through a detailed monitoring of snowpack dynamics at nine locations within a 3.57 km2 experimental forested catchment in southern Quebec, Canada (47°N, 71°W). The catchment receives 6 m of snow annually on average and is predominantly covered with balsam fir stand with some traces of spruce and white birch. In this study, we used a network of nine so-called `snow profiling stations', providing automated snow depth and snowpack temperature profile measurements, as well as three contrasting sites (juvenile, sapling and open areas) where sublimation rates were directly measured with flux towers. In addition, a total of 1401 manual snow samples supported by 20 snow pits measurements were collected throughout the winter of 2017. This paper presents some preliminary analyses of this unique dataset. Simple empirical relations relying SWE with easy-to-determine proxies, such as snow depths and snow temperature, are tested. Then, binary regression trees and multiple regression analysis are used to model SWE using topographic characteristics (slope, aspect, elevation), forest features (tree height, tree diameter, forest density and gap fraction) and meteorological forcing (solar radiation, wind speed, snow-pack temperature profile, air temperature, humidity). An analysis of sublimation rates comparing open area, saplings and juvenile forest is also presented in this paper.

From Drought to Flood: An Analysis of the Water Balance of the Tuolumne River Basin During Extreme Conditions (2015 - 2017)

NASA Astrophysics Data System (ADS)

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

2017-12-01

Closing the water balance of a snow-dominated mountain basin has long been a focal point of the hydrologic sciences. This study attempts to more precisely quantify the solid precipitation inputs to a basin using the iSnobal energy balance snowmelt model and assimilated snow depth information from the Airborne Snow Observatory (ASO). Throughout the ablation seasons of three highly dissimilar consecutive water years (2015 - 2017), the ASO captured high resolution snow depth snapshots over the Tuolumne River Basin in California's Central Sierra Nevada. These measurements were used to periodically update the snow depth state variable of iSnobal, thereby nudging the estimates of water storage (snow water equivalent, or SWE) and melt (surface water input, or SWI) toward a more accurate solution. Once precipitation inputs and streamflow outputs are better constrained, the additional loss terms of the water mass balance equation (i.e. groundwater recharge and evapotranspiration) can be estimated with less uncertainty.

Analysis of ground-measured and passive-microwave-derived snow depth variations in midwinter across the Northern Great Plains

USGS Publications Warehouse

Chang, A.T.C.; Kelly, R.E.J.; Josberger, E.G.; Armstrong, R.L.; Foster, J.L.; Mognard, N.M.

2005-01-01

Accurate estimation of snow mass is important for the characterization of the hydrological cycle at different space and time scales. For effective water resources management, accurate estimation of snow storage is needed. Conventionally, snow depth is measured at a point, and in order to monitor snow depth in a temporally and spatially comprehensive manner, optimum interpolation of the points is undertaken. Yet the spatial representation of point measurements at a basin or on a larger distance scale is uncertain. Spaceborne scanning sensors, which cover a wide swath and can provide rapid repeat global coverage, are ideally suited to augment the global snow information. Satellite-borne passive microwave sensors have been used to derive snow depth (SD) with some success. The uncertainties in point SD and areal SD of natural snowpacks need to be understood if comparisons are to be made between a point SD measurement and satellite SD. In this paper three issues are addressed relating satellite derivation of SD and ground measurements of SD in the northern Great Plains of the United States from 1988 to 1997. First, it is shown that in comparing samples of ground-measured point SD data with satellite-derived 25 ?? 25 km2 pixels of SD from the Defense Meteorological Satellite Program Special Sensor Microwave Imager, there are significant differences in yearly SD values even though the accumulated datasets showed similarities. Second, from variogram analysis, the spatial variability of SD from each dataset was comparable. Third, for a sampling grid cell domain of 1?? ?? 1?? in the study terrain, 10 distributed snow depth measurements per cell are required to produce a sampling error of 5 cm or better. This study has important implications for validating SD derivations from satellite microwave observations. ?? 2005 American Meteorological Society.

Multitemporal Accuracy and Precision Assessment of Unmanned Aerial System Photogrammetry for Slope-Scale Snow Depth Maps in Alpine Terrain

NASA Astrophysics Data System (ADS)

Adams, Marc S.; Bühler, Yves; Fromm, Reinhard

2017-12-01

Reliable and timely information on the spatio-temporal distribution of snow in alpine terrain plays an important role for a wide range of applications. Unmanned aerial system (UAS) photogrammetry is increasingly applied to cost-efficiently map the snow depth at very high resolution with flexible applicability. However, crucial questions regarding quality and repeatability of this technique are still under discussion. Here we present a multitemporal accuracy and precision assessment of UAS photogrammetry for snow depth mapping on the slope-scale. We mapped a 0.12 km2 large snow-covered study site, located in a high-alpine valley in Western Austria. 12 UAS flights were performed to acquire imagery at 0.05 m ground sampling distance in visible (VIS) and near-infrared (NIR) wavelengths with a modified commercial, off-the-shelf sensor mounted on a custom-built fixed-wing UAS. The imagery was processed with structure-from-motion photogrammetry software to generate orthophotos, digital surface models (DSMs) and snow depth maps (SDMs). Accuracy of DSMs and SDMs were assessed with terrestrial laser scanning and manual snow depth probing, respectively. The results show that under good illumination conditions (study site in full sunlight), the DSMs and SDMs were acquired with an accuracy of ≤ 0.25 and ≤ 0.29 m (both at 1σ), respectively. In case of poorly illuminated snow surfaces (study site shadowed), the NIR imagery provided higher accuracy (0.19 m; 0.23 m) than VIS imagery (0.49 m; 0.37 m). The precision of the UASSDMs was 0.04 m for a small, stable area and below 0.33 m for the whole study site (both at 1σ).

Assimilating MODIS-based albedo and snow cover fraction into the Common Land Model to improve snow depth simulation with direct insertion and deterministic ensemble Kalman filter methods

NASA Astrophysics Data System (ADS)

Xu, Jianhui; Shu, Hong

2014-09-01

This study assesses the analysis performance of assimilating the Moderate Resolution Imaging Spectroradiometer (MODIS)-based albedo and snow cover fraction (SCF) separately or jointly into the physically based Common Land Model (CoLM). A direct insertion method (DI) is proposed to assimilate the black and white-sky albedos into the CoLM. The MODIS-based albedo is calculated with the MODIS bidirectional reflectance distribution function (BRDF) model parameters product (MCD43B1) and the solar zenith angle as estimated in the CoLM for each time step. Meanwhile, the MODIS SCF (MOD10A1) is assimilated into the CoLM using the deterministic ensemble Kalman filter (DEnKF) method. A new DEnKF-albedo assimilation scheme for integrating the DI and DEnKF assimilation schemes is proposed. Our assimilation results are validated against in situ snow depth observations from November 2008 to March 2009 at five sites in the Altay region of China. The experimental results show that all three data assimilation schemes can improve snow depth simulations. But overall, the DEnKF-albedo assimilation shows the best analysis performance as it significantly reduces the bias and root-mean-square error (RMSE) during the snow accumulation and ablation periods at all sites except for the Fuyun site. The SCF assimilation via DEnKF produces better results than the albedo assimilation via DI, implying that the albedo assimilation that indirectly updates the snow depth state variable is less efficient than the direct SCF assimilation. For the Fuyun site, the DEnKF-albedo scheme tends to overestimate the snow depth accumulation with the maximum bias and RMSE values because of the large positive innovation (observation minus forecast).

Regional sensitivities of seasonal snowpack to elevation, aspect, and vegetation cover in western North America

NASA Astrophysics Data System (ADS)

Tennant, Christopher J.; Harpold, Adrian A.; Lohse, Kathleen Ann; Godsey, Sarah E.; Crosby, Benjamin T.; Larsen, Laurel G.; Brooks, Paul D.; Van Kirk, Robert W.; Glenn, Nancy F.

2017-08-01

In mountains with seasonal snow cover, the effects of climate change on snowpack will be constrained by landscape-vegetation interactions with the atmosphere. Airborne lidar surveys used to estimate snow depth, topography, and vegetation were coupled with reanalysis climate products to quantify these interactions and to highlight potential snowpack sensitivities to climate and vegetation change across the western U.S. at Rocky Mountain (RM), Northern Basin and Range (NBR), and Sierra Nevada (SNV) sites. In forest and shrub areas, elevation captured the greatest amount of variability in snow depth (16-79%) but aspect explained more variability (11-40%) in alpine areas. Aspect was most important at RM sites where incoming shortwave to incoming net radiation (SW:NetR↓) was highest (˜0.5), capturing 17-37% of snow depth variability in forests and 32-37% in shrub areas. Forest vegetation height exhibited negative relationships with snow depth and explained 3-6% of its variability at sites with greater longwave inputs (NBR and SNV). Variability in the importance of physiography suggests differential sensitivities of snowpack to climate and vegetation change. The high SW:NetR↓ and importance of aspect suggests RM sites may be more responsive to decreases in SW:NetR↓ driven by warming or increases in humidity or cloud cover. Reduced canopy-cover could increase snow depths at SNV sites, and NBR and SNV sites are currently more sensitive to shifts from snow to rain. The consistent importance of aspect and elevation indicates that changes in SW:NetR↓ and the elevation of the rain/snow transition zone could have widespread and varied effects on western U.S. snowpacks.

Estimating Catchment-Scale Snowpack Variability in Complex Forested Terrain, Valles Caldera National Preserve, NM

NASA Astrophysics Data System (ADS)

Harpold, A. A.; Brooks, P. D.; Biederman, J. A.; Swetnam, T.

2011-12-01

Difficulty estimating snowpack variability across complex forested terrain currently hinders the prediction of water resources in the semi-arid Southwestern U.S. Catchment-scale estimates of snowpack variability are necessary for addressing ecological, hydrological, and water resources issues, but are often interpolated from a small number of point-scale observations. In this study, we used LiDAR-derived distributed datasets to investigate how elevation, aspect, topography, and vegetation interact to control catchment-scale snowpack variability. The study area is the Redondo massif in the Valles Caldera National Preserve, NM, a resurgent dome that varies from 2500 to 3430 m and drains from all aspects. Mean LiDAR-derived snow depths from four catchments (2.2 to 3.4 km^2) draining different aspects of the Redondo massif varied by 30%, despite similar mean elevations and mixed conifer forest cover. To better quantify this variability in snow depths we performed a multiple linear regression (MLR) at a 7.3 by 7.3 km study area (5 x 106 snow depth measurements) comprising the four catchments. The MLR showed that elevation explained 45% of the variability in snow depths across the study area, aspect explained 18% (dominated by N-S aspect), and vegetation 2% (canopy density and height). This linear relationship was not transferable to the catchment-scale however, where additional MLR analyses showed the influence of aspect and elevation differed between the catchments. The strong influence of North-South aspect in most catchments indicated that the solar radiation is an important control on snow depth variability. To explore the role of solar radiation, a model was used to generate winter solar forcing index (SFI) values based on the local and remote topography. The SFI was able to explain a large amount of snow depth variability in areas with similar elevation and aspect. Finally, the SFI was modified to include the effects of shading from vegetation (in and out of canopy), which further explained snow depth variability. The importance of SFI for explaining catchment-scale snow depth variability demonstrates that aspect is not a sufficient metric for direct radiation in complex terrain where slope and remote topographic shading are significant. Surprisingly, the net effects of interception and shading by vegetation on snow depths were minimal compared to elevation and aspect in these catchments. These results suggest that snowpack losses from interception may be balanced by increased shading to reduce the overall impacts from vegetation compared to topographic factors in this high radiation environment. Our analysis indicated that elevation and solar radiation are likely to control snow variability in larger catchments, with interception and shading from vegetation becoming more important at smaller scales.

Towards the Development of a Global, Satellite-based, Terrestrial Snow Mission Planning Tool

NASA Technical Reports Server (NTRS)

Forman, Bart; Kumar, Sujay; Le Moigne, Jacqueline; Nag, Sreeja

2017-01-01

A global, satellite-based, terrestrial snow mission planning tool is proposed to help inform experimental mission design with relevance to snow depth and snow water equivalent (SWE). The idea leverages the capabilities of NASAs Land Information System (LIS) and the Tradespace Analysis Tool for Constellations (TAT C) to harness the information content of Earth science mission data across a suite of hypothetical sensor designs, orbital configurations, data assimilation algorithms, and optimization and uncertainty techniques, including cost estimates and risk assessments of each hypothetical orbital configuration.One objective the proposed observing system simulation experiment (OSSE) is to assess the complementary or perhaps contradictory information content derived from the simultaneous collection of passive microwave (radiometer), active microwave (radar), and LIDAR observations from space-based platforms. The integrated system will enable a true end-to-end OSSE that can help quantify the value of observations based on their utility towards both scientific research and applications as well as to better guide future mission design. Science and mission planning questions addressed as part of this concept include:1. What observational records are needed (in space and time) to maximize terrestrial snow experimental utility?2. How might observations be coordinated (in space and time) to maximize utility? 3. What is the additional utility associated with an additional observation?4. How can future mission costs being minimized while ensuring Science requirements are fulfilled?

Towards the Development of a Global, Satellite-Based, Terrestrial Snow Mission Planning Tool

NASA Technical Reports Server (NTRS)

Forman, Bart; Kumar, Sujay; Le Moigne, Jacqueline; Nag, Sreeja

2017-01-01

A global, satellite-based, terrestrial snow mission planning tool is proposed to help inform experimental mission design with relevance to snow depth and snow water equivalent (SWE). The idea leverages the capabilities of NASA's Land Information System (LIS) and the Tradespace Analysis Tool for Constellations (TAT-C) to harness the information content of Earth science mission data across a suite of hypothetical sensor designs, orbital configurations, data assimilation algorithms, and optimization and uncertainty techniques, including cost estimates and risk assessments of each hypothetical permutation. One objective of the proposed observing system simulation experiment (OSSE) is to assess the complementary or perhaps contradictory information content derived from the simultaneous collection of passive microwave (radiometer), active microwave (radar), and LIDAR observations from space-based platforms. The integrated system will enable a true end-to-end OSSE that can help quantify the value of observations based on their utility towards both scientific research and applications as well as to better guide future mission design. Science and mission planning questions addressed as part of this concept include: What observational records are needed (in space and time) to maximize terrestrial snow experimental utility? How might observations be coordinated (in space and time) to maximize this utility? What is the additional utility associated with an additional observation? How can future mission costs be minimized while ensuring Science requirements are fulfilled?

Wind tunnel experiments: influence of erosion and deposition on wind-packing of new snow

NASA Astrophysics Data System (ADS)

Sommer, Christian G.; Lehning, Michael; Fierz, Charles

2018-01-01

Wind sometimes creates a hard, wind-packed layer at the surface of a snowpack. The formation of such wind crusts was observed during wind tunnel experiments with combined SnowMicroPen and Microsoft Kinect sensors. The former provides the hardness of new and wind-packed snow and the latter spatial snow depth data in the test section. Previous experiments showed that saltation is necessary but not sufficient for wind-packing. The combination of hardness and snow depth data now allows to study the case with saltation in more detail. The Kinect data requires complex processing but with the appropriate corrections, snow depth changes can be measured with an accuracy of about 1 mm. The Kinect is therefore well suited to quantify erosion and deposition. We found that no hardening occurred during erosion and that a wind crust may or may not form when snow is deposited. Deposition is more efficient at hardening snow in wind-exposed than in wind-sheltered areas. The snow hardness increased more on the windward side of artificial obstacles placed in the wind tunnel. Similarly, the snow was harder in positions with a low Sx parameter. Sx describes how wind-sheltered (high Sx) or wind-exposed (low Sx) a position is and was calculated based on the Kinect data. The correlation between Sx and snow hardness was -0.63. We also found a negative correlation of -0.4 between the snow hardness and the deposition rate. Slowly deposited snow is harder than a rapidly growing accumulation. Sx and the deposition rate together explain about half of the observed variability of snow hardness.

Improvement and further development of SSM/I overland parameter algorithms using the WetNet workstation

NASA Technical Reports Server (NTRS)

Neale, Christopher M. U.; Mcdonnell, Jeffrey J.; Ramsey, Douglas; Hipps, Lawrence; Tarboton, David

1993-01-01

Since the launch of the DMSP Special Sensor Microwave/Imager (SSM/I), several algorithms have been developed to retrieve overland parameters. These include the present operational algorithms resulting from the Navy calibration/validation effort such as land surface type (Neale et al. 1990), land surface temperature (McFarland et al. 1990), surface moisture (McFarland and Neale, 1991) and snow parameters (McFarland and Neale, 1991). In addition, other work has been done including the classification of snow cover and precipitation using the SSM/I (Grody, 1991). Due to the empirical nature of most of the above mentioned algorithms, further research is warranted and improvements can probably be obtained through a combination of radiative transfer modelling to study the physical processes governing the microwave emissions at the SSM/I frequencies, and the incorporation of additional ground truth data and special cases into the regression data sets. We have proposed specifically to improve the retrieval of surface moisture and snow parameters using the WetNet SSM/I data sets along with ground truth information namely climatic variables from the NOAA cooperative network of weather stations as well as imagery from other satellite sensors such as the AVHRR and Thematic Mapper. In the case of surface moisture retrievals the characterization of vegetation density is of primary concern. The higher spatial resolution satellite imagery collected at concurrent periods will be used to characterize vegetation types and amounts which, along with radiative transfer modelling should lead to more physically based retrievals. Snow parameter retrieval algorithm improvement will initially concentrate on the classification of snowpacks (dry snow, wet snow, refrozen snow) and later on specific products such as snow water equivalent. Significant accomplishments in the past year are presented.

Validation of Airborne FMCW Radar Measurements of Snow Thickness Over Sea Ice in Antarctica

NASA Technical Reports Server (NTRS)

Galin, Natalia; Worby, Anthony; Markus, Thorsten; Leuschen, Carl; Gogineni, Prasad

2012-01-01

Antarctic sea ice and its snow cover are integral components of the global climate system, yet many aspects of their vertical dimensions are poorly understood, making their representation in global climate models poor. Remote sensing is the key to monitoring the dynamic nature of sea ice and its snow cover. Reliable and accurate snow thickness data are currently a highly sought after data product. Remotely sensed snow thickness measurements can provide an indication of precipitation levels, predicted to increase with effects of climate change in the polar regions. Airborne techniques provide a means for regional-scale estimation of snow depth and distribution. Accurate regional-scale snow thickness data will also facilitate an increase in the accuracy of sea ice thickness retrieval from satellite altimeter freeboard estimates. The airborne data sets are easier to validate with in situ measurements and are better suited to validating satellite algorithms when compared with in situ techniques. This is primarily due to two factors: better chance of getting coincident in situ and airborne data sets and the tractability of comparison between an in situ data set and the airborne data set averaged over the footprint of the antennas. A 28-GHz frequency modulated continuous wave (FMCW) radar loaned by the Center for Remote Sensing of Ice Sheets to the Australian Antarctic Division is used to measure snow thickness over sea ice in East Antarctica. Provided with the radar design parameters, the expected performance parameters of the radar are summarized. The necessary conditions for unambiguous identification of the airsnow and snowice layers for the radar are presented. Roughnesses of the snow and ice surfaces are found to be dominant determinants in the effectiveness of layer identification for this radar. Finally, this paper presents the first in situ validated snow thickness estimates over sea ice in Antarctica derived from an FMCW radar on a helicopterborne platform.

About uncertainties in sea ice thickness retrieval from satellite radar altimetry: results from the ESA-CCI Sea Ice ECV Project Round Robin Exercise

NASA Astrophysics Data System (ADS)

Kern, S.; Khvorostovsky, K.; Skourup, H.; Rinne, E.; Parsakhoo, Z. S.; Djepa, V.; Wadhams, P.; Sandven, S.

2014-03-01

One goal of the European Space Agency Climate Change Initiative sea ice Essential Climate Variable project is to provide a quality controlled 20 year long data set of Arctic Ocean winter-time sea ice thickness distribution. An important step to achieve this goal is to assess the accuracy of sea ice thickness retrieval based on satellite radar altimetry. For this purpose a data base is created comprising sea ice freeboard derived from satellite radar altimetry between 1993 and 2012 and collocated observations of snow and sea ice freeboard from Operation Ice Bridge (OIB) and CryoSat Validation Experiment (CryoVEx) air-borne campaigns, of sea ice draft from moored and submarine Upward Looking Sonar (ULS), and of snow depth from OIB campaigns, Advanced Microwave Scanning Radiometer aboard EOS (AMSR-E) and the Warren Climatology (Warren et al., 1999). An inter-comparison of the snow depth data sets stresses the limited usefulness of Warren climatology snow depth for freeboard-to-thickness conversion under current Arctic Ocean conditions reported in other studies. This is confirmed by a comparison of snow freeboard measured during OIB and CryoVEx and snow freeboard computed from radar altimetry. For first-year ice the agreement between OIB and AMSR-E snow depth within 0.02 m suggests AMSR-E snow depth as an appropriate alternative. Different freeboard-to-thickness and freeboard-to-draft conversion approaches are realized. The mean observed ULS sea ice draft agrees with the mean sea ice draft computed from radar altimetry within the uncertainty bounds of the data sets involved. However, none of the realized approaches is able to reproduce the seasonal cycle in sea ice draft observed by moored ULS satisfactorily. A sensitivity analysis of the freeboard-to-thickness conversion suggests: in order to obtain sea ice thickness as accurate as 0.5 m from radar altimetry, besides a freeboard estimate with centimetre accuracy, an ice-type dependent sea ice density is as mandatory as a snow depth with centimetre accuracy.

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

Snow Pattern Delineation, Scaling, Fidelity, and Landscape Factors

NASA Astrophysics Data System (ADS)

Hiemstra, C. A.; Wagner, A. M.; Deeb, E. J.; Morriss, B. F.; Sturm, M.

2014-12-01

In many snow-covered landscapes, snow tends to be shallow or deep in the same locations year after year. As snowmelt progresses in spring, areas of shallow snow become snow-free earlier than areas with deep snow. This pattern (Sturm and Wagner 2010) could likely be used to inform or improve modeled snow depth estimates where ground measurements are not collected; however, we must be certain of their utility before ingesting them into model calculations. Do patterns, as we detect them, have a relationship with earlier measured snow distributions? Second, are certain areas on the landscape likely to yield patterns that are influenced too highly by melting to be useful? Our Imnavait Creek Study Area (11 by 19 km) is on Alaska's North Slope, where we have examined a vast library of spring satellite imagery (ranging from mostly snow-covered to mostly snow-free). Landsat TM Imagery has been collected from the early 1980s-present, and the temporal and spatial resolution is roughly two weeks and 30 m, respectively. High resolution satellite imagery (WorldView 1, WorldView 2, IKONOS) has been obtained from 2010-2013 for the same area with almost daily- to monthly-temporal and at 2.5 m spatial resolutions, respectively. We found that there is a striking similarity among patterns from year to year across the span of decades and resolutions. However, the relationship of pattern with observed snow depths was strong in some areas and less clear in others. Overall, we suspect spatial scaling, spatial mismatch, sampling errors, and melt patterns explain most of the areas of pattern and depth disparity.

MODIS Collection 6 Data at the National Snow and Ice Data Center (NSIDC)

NASA Astrophysics Data System (ADS)

Fowler, D. K.; Steiker, A. E.; Johnston, T.; Haran, T. M.; Fowler, C.; Wyatt, P.

2015-12-01

For over 15 years, the NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC) has archived and distributed snow and sea ice products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the NASA Earth Observing System (EOS) Aqua and Terra satellites. Collection 6 represents the next revision to NSIDC's MODIS archive, mainly affecting the snow-cover products. Collection 6 specifically addresses the needs of the MODIS science community by targeting the scenarios that have historically confounded snow detection and introduced errors into the snow-cover and fractional snow-cover maps even though MODIS snow-cover maps are typically 90 percent accurate or better under good observing conditions, Collection 6 uses revised algorithms to discriminate between snow and clouds, resolve uncertainties along the edges of snow-covered regions, and detect summer snow cover in mountains. Furthermore, Collection 6 applies modified and additional snow detection screens and new Quality Assessment protocols that enhance the overall accuracy of the snow maps compared with Collection 5. Collection 6 also introduces several new MODIS snow products, including a daily Climate Modelling Grid (CMG) cloud gap-filled (CGF) snow-cover map which generates cloud-free maps by using the most recent clear observations.. The MODIS Collection 6 sea ice extent and ice surface temperature algorithms and products are much the same as Collection 5; however, Collection 6 updates to algorithm inputs—in particular, the L1B calibrated radiances, land and water mask, and cloud mask products—have improved the sea ice outputs. The MODIS sea ice products are currently available at NSIDC, and the snow cover products are soon to follow in 2016 NSIDC offers a variety of methods for obtaining these data. Users can download data directly from an online archive or use the NASA Reverb Search & Order Tool to perform spatial, temporal, and parameter subsetting, reformatting, and re-projection of the data.

The layered evolution of fabric and microstructure of snow at Point Barnola, Central East Antarctica

NASA Astrophysics Data System (ADS)

Calonne, Neige; Montagnat, Maurine; Matzl, Margret; Schneebeli, Martin

2017-02-01

Snow fabric, defined as the distribution of the c-axis orientations of the ice crystals in snow, is poorly known. So far, only one study exits that measured snow fabric based on a statistically representative technique. This recent study has revealed the impact of temperature gradient metamorphism on the evolution of fabric in natural snow, based on cold laboratory experiments. On polar ice sheets, snow properties are currently investigated regarding their strong variability in time and space, notably because of their potential influence on firn processes and consequently on ice core analysis. Here, we present measurements of fabric and microstructure of snow from Point Barnola, East Antarctica (close to Dome C). We analyzed a snow profile from 0 to 3 m depth, where temperature gradients occur. The main contributions of the paper are (1) a detailed characterization of snow in the upper meters of the ice sheet, especially by providing data on snow fabric, and (2) the study of a fundamental snow process, never observed up to now in a natural snowpack, namely the role of temperature gradient metamorphism on the evolution of the snow fabric. Snow samples were scanned by micro-tomography to measure continuous profiles of microstructural properties (density, specific surface area and pore thickness). Fabric analysis was performed using an automatic ice texture analyzer on 77 representative thin sections cut out from the samples. Different types of snow fabric could be identified and persist at depth. Snow fabric is significantly correlated with snow microstructure, pointing to the simultaneous influence of temperature gradient metamorphism on both properties. We propose a mechanism based on preferential grain growth to explain the fabric evolution under temperature gradients. Our work opens the question of how such a layered profile of fabric and microstructure evolves at depth and further influences the physical and mechanical properties of snow and firn. More generally, it opens the way to further studies on the influence of the snow fabric in snow processes related to anisotropic properties of ice such as grain growth, mechanical response, electromagnetic behavior.

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.

Meteorological and snow distribution data in the Izas Experimental Catchment (Spanish Pyrenees) from 2011 to 2017

NASA Astrophysics Data System (ADS)

Revuelto, Jesús; Azorin-Molina, Cesar; Alonso-González, Esteban; Sanmiguel-Vallelado, Alba; Navarro-Serrano, Francisco; Rico, Ibai; López-Moreno, Juan Ignacio

2017-12-01

This work describes the snow and meteorological data set available for the Izas Experimental Catchment in the Central Spanish Pyrenees, from the 2011 to 2017 snow seasons. The experimental site is located on the southern side of the Pyrenees between 2000 and 2300 m above sea level, covering an area of 55 ha. The site is a good example of a subalpine environment in which the evolution of snow accumulation and melt are of major importance in many mountain processes. The climatic data set consists of (i) continuous meteorological variables acquired from an automatic weather station (AWS), (ii) detailed information on snow depth distribution collected with a terrestrial laser scanner (TLS, lidar technology) for certain dates across the snow season (between three and six TLS surveys per snow season) and (iii) time-lapse images showing the evolution of the snow-covered area (SCA). The meteorological variables acquired at the AWS are precipitation, air temperature, incoming and reflected solar radiation, infrared surface temperature, relative humidity, wind speed and direction, atmospheric air pressure, surface temperature (snow or soil surface), and soil temperature; all were taken at 10 min intervals. Snow depth distribution was measured during 23 field campaigns using a TLS, and daily information on the SCA was also retrieved from time-lapse photography. The data set (https://doi.org/10.5281/zenodo.848277) is valuable since it provides high-spatial-resolution information on the snow depth and snow cover, which is particularly useful when combined with meteorological variables to simulate snow energy and mass balance. This information has already been analyzed in various scientific studies on snow pack dynamics and its interaction with the local climatology or topographical characteristics. However, the database generated has great potential for understanding other environmental processes from a hydrometeorological or ecological perspective in which snow dynamics play a determinant role.

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.

Some relationships among air, snow, and soil temperatures and soil frost

Treesearch

George Hart; Howard W. Lull

1963-01-01

Each winter gives examples of the insulating properties of snow cover. Seeds and soil fauna are protected from the cold by snow. Underground water pipes are less likely to freeze under snow cover. And, according to many observers, the occurrence, penetration, and thaw of soil frost are affected by snow cover. The depth of snow necessary to protect soil from freezing...

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...

Facilitating the exploitation of ERTS imagery using snow enhancement techniques

NASA Technical Reports Server (NTRS)

Wobber, F. J. (Principal Investigator); Martin, K. R.; Amato, R. V.

1973-01-01

The author has identified the following significant results. New fracture detail within New England test area has been interpreted from ERTS-1 images. Comparative analysis of snow-free imagery (1096-15065 and 1096-15072) has demonstrated that MSS bands 5 and 7 supply the greatest amount of geological fracture detail. Interpretation of the first snow-covered ERTS-1 images (1132-15074 and 1168-15065) in correlation with ground snow depth data indicates that a heavy blanket of snow (less than 9 inches) accentuates major structural features while a light dusting (greater than 1 inch) accentuates more subtle topographic expressions. Snow cover was found to accentuate drainage patterns which are indicative of lithological and/or structural variations. Snow cover provided added enhancement for viewing and detecting topographically expressed fractures and faults. A recent field investigation was conducted within the New England test area to field check lineaments observed from analysis of ERTS-1 imagery, collect snow depth readings, and obtain structural joint readings at key locations in the test area.

Snowcover influence on backscattering from terrain

NASA Technical Reports Server (NTRS)

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

1984-01-01

The effects of snowcover on the microwave backscattering from terrain in the 8-35 GHz region are examined through the analysis of experimental data and by application of a semiempirical model. The model accounts for surface backscattering contributions by the snow-air and snow-soil interfaces, and for volume backscattering contributions by the snow layer. Through comparisons of backscattering data for different terrain surfaces measured both with and without snowcover, the masking effects of snow are evaluated as a function of snow water equivalent and liquid water content. The results indicate that with dry snowcover it is not possible to discriminate between different types of ground surface (concrete, asphalt, grass, and bare ground) if the snow water equivalent is greater than about 20 cm (or a depth greater than 60 cm for a snow density of 0.3 g/cu cm). For the same density, however, if the snow is wet, a depth of 10 cm is sufficient to mask the underlying surface.

Using wireless sensor networks to improve understanding of rain-on-snow events across the Sierra Nevada

NASA Astrophysics Data System (ADS)

Maurer, T.; Avanzi, F.; Oroza, C.; Malek, S. A.; Glaser, S. D.; Bales, R. C.; Conklin, M. H.

2017-12-01

We use data gathered from Wireless Sensor Networks (WSNs) between 2008 and 2017 to investigate the temporal/spatial patterns of rain-on-snow events in three river basins of California's Sierra Nevada. Rain-on-snow transitions occur across a broad elevation range (several hundred meters), both between storms and within a given storm, creating an opportunity to use spatially and temporally dense data to forecast and study them. WSNs collect snow depth; meteorological data; and soil moisture and temperature data across relatively dense sensor clusters. Ten to twelve measurement nodes per cluster are placed across 1-km2 areas in locations representative of snow patterns at larger scales. Combining precipitation and snow data from snow-pillow and climate stations with an estimation of dew-point temperature from WSNs, we determine the frequency, timing, and geographic extent of rain-on-snow events. We compare these results to WSN data to evaluate the impact of rain-on-snow events on snowpack energy balance, density, and depth as well as on soil moisture. Rain-on-snow events are compared to dry warm-weather days to identify the relative importance of rain and radiation as the primary energy input to the snowpack for snowmelt generation. An intercomparison of rain-on-snow events for the WSNs in the Feather, American, and Kings River basins captures the behavior across a 2° latitudinal range of the Sierra Nevada. Rain-on-snow events are potentially a more important streamflow generation mechanism in the lower-elevation Feather River basin. Snowmelt response to rain-on-snow events changes throughout the wet season, with later events resulting in more melt due to snow isothermal conditions, coarser grain size, and more-homogeneous snow stratigraphy. Regardless of snowmelt response, rain-on-snow events tend to result in decreasing snow depth and a corresponding increase in snow density. Our results demonstrate that strategically placed WSNs can provide the necessary data at high temporal resolution to investigate how hydrologic responses evolve in both space and time, data not available from operational networks.

Inventory of File sref_em.t03z.pgrb212.p1.f06.grib2

Science.gov Websites

surface WEASD 6 hour fcst Water Equivalent of Accumulated Snow Depth [kg/m^2] 016 surface APCP 0-6 hour surface WEASD 0-6 hour acc Water Equivalent of Accumulated Snow Depth [kg/m^2] 019 surface CSNOW 6 hour -6 hour acc Large-Scale Precipitation (non-convective) [kg/m^2] 415 surface SNOM 0-6 hour acc Snow

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.

Variability in snowpack accumulation and ablation associated with mountain pine beetle infestation in western forests

NASA Astrophysics Data System (ADS)

Biederman, J. A.; Harpold, A. A.; Gochis, D. J.; Reed, D.; Brooks, P. D.

2010-12-01

Seasonal snowcover is a primary source of water to urban and agricultural regions in the western United States, where Mountain Pine Beetle (MPB) has caused rapid and extensive changes to vegetation in montane forests. Levels of MPB infestation in these seasonally snow-covered systems are unprecedented, and it is unknown how this will affect water yield, especially in changing climate conditions. To address this unknown we ask: How does snow accumulation and ablation vary across forest with differing levels of impact? Our study areas in the Rocky Mountains of CO and WY are similar in latitude, elevation and forest structure before infestation, but they vary in the intensity and timing of beetle infestation and tree mortality. We present a record for winter 2010 that includes continuous snow depth as well as stand-scale snow surveys at maximum accumulation. Additional measurements include snowfall, net radiation, temperature and wind speed as well as characterization of forest structure by leaf area index. In a stand uninfested by MPB, maximum snow depth was fairly uniform under canopy (mean = 86 cm, coefficient of variation = 0.021), while canopy gaps showed greater and more variable depth (mean = 117 cm, CV = 0.111). This is consistent with several studies demonstrating that snowfall into canopy gaps depends upon gap size, orientation, wind speed and storm size. In a stand impacted in 2007, snow depth under canopy was less uniform, and there were smaller differences in both mean depth and variability between canopy (mean = 93 cm, CV = 0.072) and gaps (mean = 97 cm, CV = 0.070), consistent with decreased canopy density. In a more recently infested (2009) stand with an intermediate level of MPB impact, mean snow depths were similar between canopy (96 cm, CV = 0.016) and gaps (95 cm, CV = 0.185) but gaps showed much greater variability, suggesting controls similar to those in effect in the uninfested stand. We further use these data to model snow accumulation and ablation as a function of vegetation, topography and fine-scale climate variability, with preliminary results presented at the meeting.

Snow Depth Calibrations for Electromagnetic Induction Investigations at a Former Munitions Waste Disposal Site in Alaska

NASA Astrophysics Data System (ADS)

Glaser, D. R., II; Wagner, A. M.; Gelvin, A.; Saari, S.; Staples, A.; Larsen, G.

2017-12-01

A US Army legacy munitions waste site was identified adjacent to a river near a small arms range in Alaska. As part of remediation efforts, geophysical studies were conducted to characterize the extent of buried metal debris at the site. Time-domain electromagnetic surveys were completed over the site to meet the regulatory guidance for site cleanup. Time-domain and frequency-domain electromagnetic induction, magnetic gradiometry, and ground penetrating radar subsurface geophysical studies were deployed over soil, water, and snow surface conditions throughout the impacted area. The time-domain electromagnetic induction results acquired during summer months, presented clear indications of trenches located directly perpendicular to and adjacent to the river. However, in the follow up investigation where the snow-pack was greater than one meter, the response amplitude of the metallic debris was dampened and possible targets were missed. This was confirmed by the subsequent magnetic gradiometry survey which identified a suspected extension of one of the trenches through the river on to the seasonal sand bar island. The region is subject to extremely cold temperatures as well as significant snow pack and permafrost soil conditions. The snow presented a negative impact to the accurate assessment of the site by changing the effective investigation depth. To address this we developed an approach using ground penetrating radar data calibrated with physical snow depth measurements to generate continuous estimates of snow depth and spatially correct the electromagnetic induction data to the corresponding regulatory amplitude limit as if the snow were not present. Limitations of the approach as related to the signal floor of the electromagnetic induction response were also assessed.

Inventory of File sref_nmb.t03z.pgrb212.p1.f06.grib2

Science.gov Websites

surface WEASD 6 hour fcst Water Equivalent of Accumulated Snow Depth [kg/m^2] 016 surface APCP 3-6 hour surface WEASD 3-6 hour acc Water Equivalent of Accumulated Snow Depth [kg/m^2] 019 surface CSNOW 6 hour (non-convective) [kg/m^2] 417 surface SNOM 3-6 hour acc Snow Melt [kg/m^2] 418 surface LHTFL 3-6 hour

Inventory of File sref_nmm.t03z.pgrb212.p1.f06.grib2

Science.gov Websites

surface WEASD 6 hour fcst Water Equivalent of Accumulated Snow Depth [kg/m^2] 016 surface APCP 3-6 hour surface WEASD 3-6 hour acc Water Equivalent of Accumulated Snow Depth [kg/m^2] 019 surface CSNOW 6 hour (non-convective) [kg/m^2] 417 surface SNOM 3-6 hour acc Snow Melt [kg/m^2] 418 surface LHTFL 0-6 hour

7 CFR 612.3 - Data collected and forecasts.

Code of Federal Regulations, 2010 CFR

2010-01-01

..., DEPARTMENT OF AGRICULTURE CONSERVATION OPERATIONS SNOW SURVEYS AND WATER SUPPLY FORECASTS § 612.3 Data..., and wind. (b) Water supply forecasts in the western states area are generally made monthly from.... Data sites generally include a snow course where both snow depth and water equivalent of snow are...

Snowpack monitoring in North America and Eurasia using passive microwave satellite data

NASA Technical Reports Server (NTRS)

Foster, J. L.; Rango, A.; Hall, D. K.

1980-01-01

Areas of the Canadian high plains, the Montana and North Dakota high plains, and the steppes of central Russia were studied in an effort to determine the utility of spaceborne electrical scanning microwave radiometers (ESMR) for monitoring snow depths in different geographic areas. Significant regression relationships between snow depth and microwave brightness temperatures were developed for each of these homogeneous areas. In the areas investigated, Nimbus 6 (.081 cm) ESMR data produced higher correlations than Nimbus 5 (1.55 cm) ESMR data in relating microwave brightness temperature and snow depth from one area to another because different geographic areas are likely to have different snowpack conditions.

Mapping snow depth in alpine terrain with remotely piloted aerial systems and structure-from-motion photogrammetry - first results from a pilot study

NASA Astrophysics Data System (ADS)

Adams, Marc; Fromm, Reinhard; Bühler, Yves; Bösch, Ruedi; Ginzler, Christian

2016-04-01

Detailed information on the spatio-temporal distribution of seasonal snow in the alpine terrain plays a major role for the hydrological cycle, natural hazard management, flora and fauna, as well as tourism. Current methods are mostly only valid on a regional scale or require a trade-off between the data's availability, cost and resolution. During a one-year pilot study, we investigated the potential of remotely piloted aerial systems (RPAS) and structure-from-motion photogrammetry for snow depth mapping. We employed multi-copter and fixed-wing RPAS, equipped with different low-cost, off-the shelf sensors, at four test sites in Austria and Switzerland. Over 30 flights were performed during the winter 2014/15, where different camera settings, filters and lenses, as well as data collection routines were tested. Orthophotos and digital surface models (DSM) where calculated from the imagery using structure-from-motion photogrammetry software. Snow height was derived by subtracting snow-free from snow-covered DSMs. The RPAS-results were validated against data collected using a variety of well-established remote sensing (i.e. terrestrial laser scanning, large frame aerial sensors) and in-situ measurement techniques. The results show, that RPAS i) are able to map snow depth within accuracies of 0.07-0.15 m root mean square error (RMSE), when compared to traditional in-situ data; ii) can be operated at lower cost, easier repeatability, less operational constraints and higher GSD than large frame aerial sensors on-board manned aircraft, while achieving significantly higher accuracies; iii) are able to acquire meaningful data even under harsh environmental conditions above 2000 m a.s.l. (turbulence, low temperature and high irradiance, low air density). While providing a first prove-of-concept, the study also showed future challenges and limitations of RPAS-based snow depth mapping, including a high dependency on correct co-registration of snow-free and snow-covered height measurements, as well as a significant impact of the underlying vegetation and illumination of the snow surface on the fidelity of the results.

The Impacts of Changes in Snowfall on Soil Greenhouse Gas Emissions Using an Automated Chamber System

NASA Astrophysics Data System (ADS)

Ruan, L.; Kahmark, K.; Robertson, G.

2012-12-01

Snow cover has decreased in many regions of the northern hemisphere and is projected to decrease further in most. The reduced snow cover may enhance soil freezing and increase the depth of frost. The frequency of freeze-thaw cycles is likely to increase due to the reduction of snowpack thickness. Freeze and thaw cycles can strongly affect soil C and N dynamics. The pulses of N2O and CO2 emissions from soil after thawing have been reported in various studies. However, most studies were based on the controlled laboratory conditions or low resolution static chamber methods in situ. Near-continuous automated chambers provide the temporal resolution needed for capturing short-lived pulses of greenhouse gases after intermittent melting events. We investigated the winter and spring response of soil greenhouse gas emissions (CO2, CH4 and N2O) to changes of snow depth using an automated chamber system. This study was established in 2010 at the Kellogg Biological Station (KBS) in southwest Michigan. The plot was no till rotational (corn-soybean-wheat) cropland, most recently in corn. The experiment was a completely randomized design (CRD) with three levels of snow depth: ambient, double, and no snow. Each level had four replicates. Twelve automated chambers were randomly assigned to treatments and greenhouse gas fluxes measured 4 times per day in each plot. There were more freeze-thaw cycles in the no snow treatment than in the ambient and double snow treatments. Soil temperature at 5 cm depth was more variable in the no snow treatment than in the ambient and double snow treatments. CH4 fluxes were uniformly low with no significant difference across three treatments. CO2 showed expected seasonal changes with the highest emission in spring and lowest emissions through the winter. N2O peaks were higher in spring due to freeze thaw effects and cumulative N2O fluxes were substantially higher in the no snow treatment than in the ambient and double snow treatments.

The Impact Of Snow Melt On Surface Runoff Of Sava River In Slovenia

NASA Astrophysics Data System (ADS)

Horvat, A.; Brilly, M.; Vidmar, A.; Kobold, M.

2009-04-01

Snow is a type of precipitation in the form of crystalline water ice, consisting of a multitude of snowflakes that fall from clouds. Snow remains on the ground until it melts or sublimates. Spring snow melt is a major source of water supply to areas in temperate zones near mountains that catch and hold winter snow, especially those with a prolonged dry summer. In such places, water equivalent is of great interest to water managers wishing to predict spring runoff and the water supply of cities downstream. In temperate zone like in Slovenia the snow melts in the spring and contributes certain amount of water to surface flow. This amount of water can be great and can cause serious floods in case of fast snow melt. For this reason we tried to determine the influence of snow melt on the largest river basin in Slovenia - Sava River basin, on surface runoff. We would like to find out if snow melt in Slovenian Alps can cause spring floods and how serious it can be. First of all we studied the caracteristics of Sava River basin - geology, hydrology, clima, relief and snow conditions in details for each subbasin. Furtermore we focused on snow and described the snow phenomenom in Slovenia, detailed on Sava River basin. We collected all available data on snow - snow water equivalent and snow depth. Snow water equivalent is a much more useful measurement to hydrologists than snow depth, as the density of cool freshly fallen snow widely varies. New snow commonly has a density of between 5% and 15% of water. But unfortunately there is not a lot of available data of SWE available for Slovenia. Later on we compared the data of snow depth and river runoff for some of the 40 winter seasons. Finally we analyzed the use of satellite images for Slovenia to determine the snow cover for hydrology reason. We concluded that snow melt in Slovenia does not have a greater influence on Sava River flow. The snow cover in Alps can melt fast due to higher temperatures but the water distributes and runs off slowly and does not cause floods. About use of satellite images we concluded that first of all, weather is unfavorable - lots of cloudiness in winter, and furthermore a grater part of land is covered by forest which prevents to see the snow cover on image clearly.

The Impacts of Pine Tree Die-Off on Snow Accumulation and Distribution at Plot to Catchment Scales

NASA Astrophysics Data System (ADS)

Biederman, J. A.; Harpold, A. A.; Gutmann, E. D.; Reed, D. E.; Gochis, D. J.; Brooks, P. D.

2011-12-01

Seasonal snow cover is a primary water source throughout much of Western North America, where insect-induced tree die-off is changing the montane landscape. Widespread mortality from insects or drought differs from well-studied cases of fire and logging in that tree mortality is not accompanied by other immediate biophysical changes. Much of the impacted landscape is a mosaic of stands of varying species, structure, management history and health overlain on complex terrain. To address the challenge of predicting the effects of forest die-off on snow water input, we quantified snow accumulation and ablation at scales ranging from individual trees, through forest stands, to nested small catchments. Our study sites in Northern Colorado and Southern Wyoming are dominated by lodgepole pine, but they include forest stands that are naturally developed, managed and clear-cut with varying mortality from Mountain Pine Beetle (MPB). Our record for winters 2010 and 2011 includes continuous meteorological data and snow depth in plots with varying MPB impact as well as stand- to catchment-scale snow surveys mid-winter and near maximal accumulation. At the plot scale, snow depth sensors in healthy stands recorded greater inputs during storms (21-42% of depth) and greater seasonal accumulation (15-40%) in canopy gaps than under trees, whereas no spatial effects of canopy geometry were observed in stands with heavy mortality. Similar patterns were observed in snow surveys near peak accumulation. At both impacted and thinned sites, spatial variability in snow depth was more closely associated with larger scale topography and changes in stand structure than with canopy cover. The role of aspect in ablation was observed to increase in impacted stands as both shading and wind attenuation decreased. Evidence of wind-controlled snow distribution was found 80-100 meters from exposed stand edges in impacted forest as compared to 10-15 meters in healthy forest. Integrating from the scale of stands to small catchments, maximal snow water equivalent (SWE) as a fraction of winter precipitation (P) ranged from 62 to 74%. Despite an expectation of decreased interception and increased snow accumulation with advanced mortality, surveys at stand and catchment scales found no significant increases in net snow water input between healthy and impacted forests. These observations suggest that the spatial scale of processes affecting net snow accumulation and ablation increase following die-off. Using data from our sites and other studies, this presentation will develop a predictive model of how interception, shading, and wind redistribution interact to control net snow water input following large-scale forest mortality.

Assimilation and High Resolution Forecasts of Surface and Near Surface Conditions for the 2010 Vancouver Winter Olympic and Paralympic Games

NASA Astrophysics Data System (ADS)

Bernier, Natacha B.; Bélair, Stéphane; Bilodeau, Bernard; Tong, Linying

2014-01-01

A dynamical model was experimentally implemented to provide high resolution forecasts at points of interests in the 2010 Vancouver Olympics and Paralympics Region. In a first experiment, GEM-Surf, the near surface and land surface modeling system, is driven by operational atmospheric forecasts and used to refine the surface forecasts according to local surface conditions such as elevation and vegetation type. In this simple form, temperature and snow depth forecasts are improved mainly as a result of the better representation of real elevation. In a second experiment, screen level observations and operational atmospheric forecasts are blended to drive a continuous cycle of near surface and land surface hindcasts. Hindcasts of the previous day conditions are then regarded as today's optimized initial conditions. Hence, in this experiment, given observations are available, observation driven hindcasts continuously ensure that daily forecasts are issued from improved initial conditions. GEM-Surf forecasts obtained from improved short-range hindcasts produced using these better conditions result in improved snow depth forecasts. In a third experiment, assimilation of snow depth data is applied to further optimize GEM-Surf's initial conditions, in addition to the use of blended observations and forecasts for forcing. Results show that snow depth and summer temperature forecasts are further improved by the addition of snow depth data assimilation.

Proposed hybrid-classifier ensemble algorithm to map snow cover area

NASA Astrophysics Data System (ADS)

Nijhawan, Rahul; Raman, Balasubramanian; Das, Josodhir

2018-01-01

Metaclassification ensemble approach is known to improve the prediction performance of snow-covered area. The methodology adopted in this case is based on neural network along with four state-of-art machine learning algorithms: support vector machine, artificial neural networks, spectral angle mapper, K-mean clustering, and a snow index: normalized difference snow index. An AdaBoost ensemble algorithm related to decision tree for snow-cover mapping is also proposed. According to available literature, these methods have been rarely used for snow-cover mapping. Employing the above techniques, a study was conducted for Raktavarn and Chaturangi Bamak glaciers, Uttarakhand, Himalaya using multispectral Landsat 7 ETM+ (enhanced thematic mapper) image. The study also compares the results with those obtained from statistical combination methods (majority rule and belief functions) and accuracies of individual classifiers. Accuracy assessment is performed by computing the quantity and allocation disagreement, analyzing statistic measures (accuracy, precision, specificity, AUC, and sensitivity) and receiver operating characteristic curves. A total of 225 combinations of parameters for individual classifiers were trained and tested on the dataset and results were compared with the proposed approach. It was observed that the proposed methodology produced the highest classification accuracy (95.21%), close to (94.01%) that was produced by the proposed AdaBoost ensemble algorithm. From the sets of observations, it was concluded that the ensemble of classifiers produced better results compared to individual classifiers.

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.

Further observations of snow and frost in the Adirondacks

Treesearch

Howard W. Lull; Francis M. Rushmore

1961-01-01

Snow-depth and water-content measurements were made in March and April 1960 in the vicinity of Paul Smiths, New York, to check on procedures developed the previous year for predicting snow accumulation and melt.

Snow Dunes: A Controlling Factor of Melt Pond Distribution on Arctic Sea Ice

NASA Technical Reports Server (NTRS)

Petrich, Chris; Eicken, Hajo; Polashenski, Christopher M.; Sturm, Matthew; Harbeck, Jeremy P.; Perovich, Donald K.; Finnegan, David C.

2012-01-01

The location of snow dunes over the course of the ice-growth season 2007/08 was mapped on level landfast first-year sea ice near Barrow, Alaska. Landfast ice formed in mid-December and exhibited essentially homogeneous snow depths of 4-6 cm in mid-January; by early February distinct snow dunes were observed. Despite additional snowfall and wind redistribution throughout the season, the location of the dunes was fixed by March, and these locations were highly correlated with the distribution of meltwater ponds at the beginning of June. Our observations, including ground-based light detection and ranging system (lidar) measurements, show that melt ponds initially form in the interstices between snow dunes, and that the outline of the melt ponds is controlled by snow depth contours. The resulting preferential surface ablation of ponded ice creates the surface topography that later determines the melt pond evolution.

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.

Relationship between snow depth and gray wolf predation on white-tailed deer

USGS Publications Warehouse

Nelson, M.E.; Mech, L.D.

1986-01-01

Survival of 203 yearling and adult white-tailed deer (Odocoileus virginianus) was monitored for 23,441 deer days from January through April 1975-85 in northeastern Minnesota. Gray wolf (Canis lupus) predation was the primary mortality cause, and from year to year during this period, the mean predation rate ranged from 0.00 to 0.29. The sum of weekly snow depths/month explained 51% of the variation in annual wolf predation rate, with the highest predation during the deepest snow.

Improving streamflow prediction using remotely-sensed soil moisture and snow depth

USDA-ARS?s Scientific Manuscript database

The monitoring of both cold and warm season hydrologic processes in headwater watersheds is critical for accurate water resource monitoring in many alpine regions. This work presents a new method that explores the simultaneous use of remotely sensed surface soil moisture (SM) and snow depth (SD) ret...

The Development of Snow Properties and Its Effect on Trafficability.

DTIC Science & Technology

1980-04-01

preferred to the horizontally applied NRC snow hardness tester. Hence the latter does not enter into graphical representation of the snow cover...depth was broken or cracked during vehicle passage. With the air temperature at 0°C, snow density was meassured in the trace of the right track: TABLE I

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.

Remote sensing: Snow monitoring tool for today and tomorrow

NASA Technical Reports Server (NTRS)

Rango, A.

1977-01-01

Various types of remote sensing are now available or will be in the future for snowpack monitoring. Aircraft reconnaissance is now used in a conventional manner by various water resources agencies to obtain information on snowlines, depth, and melting of the snowpack for forecasting purposes. The use of earth resources satellites for mapping snowcovered area, snowlines, and changes in snowcover during the spring has increased during the last five years. Gamma ray aircraft flights, although confined to an extremely low altitude, provide a means for obtaining valuable information on snow water equivalent. The most recently developed remote sensing technology for snow, namely, microwave monitoring, has provided initial results that may eventually allow us to infer snow water equivalent or depth, snow wetness, and the hydrologic condition of the underlying soil.

McChord AFB, Tacoma, Washington. Revised Uniform Summary of Surface Weather Observations (RUSSWO)

DTIC Science & Technology

1974-03-19

depth at 0W LST Jan L6-MaY 57 Snow depth at 1230 (JOT Jun 57-present Snow depth at 1.200 G CO’ U. S. Nlavy :mad Wather From neginning of record thru Jun...8217;. Wather Bureau and Navy stations did not report ceilings within the range 10,000 feet and higher prior to jant.ary 19h9. Summaries prepared from

Spatial Variability of Soil-Water Storage in the Southern Sierra Critical Zone Observatory: Measurement and Prediction

NASA Astrophysics Data System (ADS)

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

2017-12-01

Predicting the spatial distribution of soil moisture in mountain environments is confounded by multiple factors, including complex topography, spatial variably of soil texture, sub-surface flow paths, and snow-soil interactions. While remote-sensing tools such as passive-microwave monitoring can measure spatial variability of soil moisture, they only capture near-surface soil layers. Large-scale sensor networks are increasingly providing soil-moisture measurements at high temporal resolution across a broader range of depths than are accessible from remote sensing. It may be possible to combine these in-situ measurements with high-resolution LIDAR topography and canopy cover to estimate the spatial distribution of soil moisture at high spatial resolution at multiple depths. We study the feasibility of this approach using six years (2009-2014) of daily volumetric water content measurements at 10-, 30-, and 60-cm depths from the Southern Sierra Critical Zone Observatory. A non-parametric, multivariate regression algorithm, Random Forest, was used to predict the spatial distribution of depth-integrated soil-water storage, based on the in-situ measurements and a combination of node attributes (topographic wetness, northness, elevation, soil texture, and location with respect to canopy cover). We observe predictable patterns of predictor accuracy and independent variable ranking during the six-year study period. Predictor accuracy is highest during the snow-cover and early recession periods but declines during the dry period. Soil texture has consistently high feature importance. Other landscape attributes exhibit seasonal trends: northness peaks during the wet-up period, and elevation and topographic-wetness index peak during the recession and dry period, respectively.

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.

Measuring spatiotemporal variation in snow optical grain size under a subalpine forest canopy using contact spectroscopy

NASA Astrophysics Data System (ADS)

Molotch, Noah P.; Barnard, David M.; Burns, Sean P.; Painter, Thomas H.

2016-09-01

The distribution of forest cover exerts strong controls on the spatiotemporal distribution of snow accumulation and snowmelt. The physical processes that govern these controls are poorly understood given a lack of detailed measurements of snow states. In this study, we address one of many measurement gaps by using contact spectroscopy to measure snow optical grain size at high spatial resolution in trenches dug between tree boles in a subalpine forest. Trenches were collocated with continuous measurements of snow depth and vertical profiles of snow temperature and supplemented with manual measurements of snow temperature, geometric grain size, grain type, and density from trench walls. There was a distinct difference in snow optical grain size between winter and spring periods. In winter and early spring, when facetted snow crystal types were dominant, snow optical grain size was 6% larger in canopy gaps versus under canopy positions; a difference that was smaller than the measurement uncertainty. By midspring, the magnitude of snow optical grain size differences increased dramatically and patterns of snow optical grain size became highly directional with 34% larger snow grains in areas south versus north of trees. In winter, snow temperature gradients were up to 5-15°C m-1 greater under the canopy due to shallower snow accumulation. However, in canopy gaps, snow depths were greater in fall and early winter and therefore more significant kinetic growth metamorphism occurred relative to under canopy positions, resulting in larger snow grains in canopy gaps. Our findings illustrate the novelty of our method of measuring snow optical grain size, allowing for future studies to advance the understanding of how forest and meteorological conditions interact to impact snowpack evolution.

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.

Snow depth spatial structure from hillslope to basin scale

NASA Astrophysics Data System (ADS)

Deems, J. S.

2017-12-01

Knowledge of spatial patterns of snow accumulation is required for understanding the hydrology, climatology, and ecology of mountain regions. Spatial structure in snow accumulation patterns changes with the scale of observation, a feature that has been characterized using fractal dimensions calculated from lidar-derived snow depth maps: fractal scaling structure at short length scales, with a `scale break' transition to more stochastic patterns at longer separation distances. Previous work has shown that this fractal structure of snow depth distributions differs between sites with different vegetation and terrain characteristics. Forested areas showed a transition to a nearly random spatial distribution at a much shorter lag distance than do unforested sites, enabling a statistical characterization. Alpine areas, however, showed strong spatial structure for a much wider scale range, and were the source of the dominant spatial pattern observable over a wider area. These spatial structure characteristics suggest that the choice of measurement or model resolution (satellite sensor, DEM, field survey point spacing, etc.) will strongly affect the estimates of snow volume or mass, as well as the magnitude of spatial variability. These prior efforts used data sets that were high resolution ( 1 m laser point spacing) but of limited extent ( 1 km2), constraining detection of scale features such as fractal dimension or scale breaks to areas of relatively similar characteristics and to lag distances of under 500 m. New datasets available from the NASA JPL Airborne Snow Observatory (ASO) provide similar resolution but over large areas, enabling assessment of snow spatial structure across an entire watershed, or in similar vegetation or physiography but in different parts of the basin. Additionally, the multi-year ASO time series allows an investigation into the temporal stability of these scale characteristics, within a single snow season and between seasons of strongly varying accumulation totals and patterns. This presentation will explore initial results from this study, using data from the Tuolumne River Basin in California, USA. Fractal scaling characteristics derived from ASO lidar snow depth measurements are examined at the basin scale, as well as in varying topographic and forest cover environments.

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).

Analysis of Leaf Area Index and Fraction of PAR Absorbed by Vegetation Products from the Terra MODIS Sensor: 2000-2005

NASA Technical Reports Server (NTRS)

Yang, Wenze; Huang, Dong; Tan, Bin; Stroeve, Julienne C.; Shabanov, Nikolay V.; Knyazikhin, Yuri; Nemani, Ramakrishna R.; Myneni, Ranga B.

2006-01-01

The analysis of two years of Collection 3 and five years of Collection 4 Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) data sets is presented in this article with the goal of understanding product quality with respect to version (Collection 3 versus 4), algorithm (main versus backup), snow (snow-free versus snow on the ground), and cloud (cloud-free versus cloudy) conditions. Retrievals from the main radiative transfer algorithm increased from 55% in Collection 3 to 67% in Collection 4 due to algorithm refinements and improved inputs. Anomalously high LAI/FPAR values observed in Collection 3 product in some vegetation types were corrected in Collection 4. The problem of reflectance saturation and too few main algorithm retrievals in broadleaf forests persisted in Collection 4. The spurious seasonality in needleleaf LAI/FPAR fields was traced to fewer reliable input data and retrievals during the boreal winter period. About 97% of the snow covered pixels were processed by the backup Normalized Difference Vegetation Index-based algorithm. Similarly, a majority of retrievals under cloudy conditions were obtained from the backup algorithm. For these reasons, the users are advised to consult the quality flags accompanying the LAI and FPAR product.

Estimating the snow water equivalent on a glacierized high elevation site (Forni Glacier, Italy)

NASA Astrophysics Data System (ADS)

Senese, Antonella; Maugeri, Maurizio; Meraldi, Eraldo; Verza, Gian Pietro; Azzoni, Roberto Sergio; Compostella, Chiara; Diolaiuti, Guglielmina

2018-04-01

We present and compare 11 years of snow data (snow depth and snow water equivalent, SWE) measured by an automatic weather station (AWS) and corroborated by data from field campaigns on the Forni Glacier in Italy. The aim of the analysis is to estimate the SWE of new snowfall and the annual SWE peak based on the average density of the new snow at the site (corresponding to the snowfall during the standard observation period of 24 h) and automated snow depth measurements. The results indicate that the daily SR50 sonic ranger measurements and the available snow pit data can be used to estimate the mean new snow density value at the site, with an error of ±6 kg m-3. Once the new snow density is known, the sonic ranger makes it possible to derive SWE values with an RMSE of 45 mm water equivalent (if compared with snow pillow measurements), which turns out to be about 8 % of the total SWE yearly average. Therefore, the methodology we present is interesting for remote locations such as glaciers or high alpine regions, as it makes it possible to estimate the total SWE using a relatively inexpensive, low-power, low-maintenance, and reliable instrument such as the sonic ranger.

Tree-Ring Widths and Snow Cover Depth in High Tauern

NASA Astrophysics Data System (ADS)

Falarz, Malgorzata

2017-12-01

The aim of the study is to examine the correlation of Norway spruce tree-ring widths and the snow cover depth in the High Tauern mountains. The average standardized tree-ring widths indices for Nowary spruce posted by Bednarz and Niedzwiedz (2006) were taken into account. Increment cores were collected from 39 Norway spruces growing in the High Tauern near the upper limit of the forest at altitude of 1700-1800 m, 3 km from the meteorological station at Sonnblick. Moreover, the maximum of snow cover depth in Sonnblick (3105 m a.s.l.) for each winter season in the period from 1938/39 to 1994/95 (57 winter seasons) was taken into account. The main results of the research are as follows: (1) tree-ring widths in a given year does not reveal statistically significant dependency on the maximum snow cover depth observed in the winter season, which ended this year; (2) however, the tested relationship is statistically significant in the case of correlating of the tree-ring widths in a given year with a maximum snow cover depth in a season of previous year. The correlation coefficient for the entire period of the study is not very high (r=0.27) but shows a statistical significance at the 0.05 level; (3) the described relationship is not stable over time. 30-year moving correlations showed no significant dependencies till 1942 and after 1982 (probably due to the so-called divergence phenomenon). However, during the period of 1943-1981 the values of correlation coefficient for moving 30-year periods are statistically significant and range from 0.37 to 0.45; (4) the correlation coefficient between real and calibrated (on the base of the regression equation) values of maximum snow cover depth is statistically significant for calibration period and not significant for verification one; (5) due to a quite short period of statistically significant correlations and not very strict dependencies, the reconstruction of snow cover on Sonnblick for the period before regular measurements seems to be not reasonable.

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...

Development of an Algorithm for Satellite Remote Sensing of Sea and Lake Ice

NASA Astrophysics Data System (ADS)

Dorofy, Peter T.

Satellite remote sensing of snow and ice has a long history. The traditional method for many snow and ice detection algorithms has been the use of the Normalized Difference Snow Index (NDSI). This manuscript is composed of two parts. Chapter 1, Development of a Mid-Infrared Sea and Lake Ice Index (MISI) using the GOES Imager, discusses the desirability, development, and implementation of alternative index for an ice detection algorithm, application of the algorithm to the detection of lake ice, and qualitative validation against other ice mapping products; such as, the Ice Mapping System (IMS). Chapter 2, Application of Dynamic Threshold in a Lake Ice Detection Algorithm, continues with a discussion of the development of a method that considers the variable viewing and illumination geometry of observations throughout the day. The method is an alternative to Bidirectional Reflectance Distribution Function (BRDF) models. Evaluation of the performance of the algorithm is introduced by aggregating classified pixels within geometrical boundaries designated by IMS and obtaining sensitivity and specificity statistical measures.

Effects of multilayer snow scheme on the simulation of snow: Offline Noah and coupled with NCEP CFSv2

NASA Astrophysics Data System (ADS)

Saha, Subodh Kumar; Sujith, K.; Pokhrel, Samir; Chaudhari, Hemantkumar S.; Hazra, Anupam

2017-03-01

The Noah version 2.7.1 is a moderately complex land surface model (LSM), with a single layer snowpack, combined with vegetation and underlying soil layer. Many previous studies have pointed out biases in the simulation of snow, which may hinder the skill of a forecasting system coupled with the Noah. In order to improve the simulation of snow by the Noah, a multilayer snow scheme (up to a maximum of six layers) is introduced. As Noah is the land surface component of the Climate Forecast System version 2 (CFSv2) of the National Centers for Environmental Prediction (NCEP), the modified Noah is also coupled with the CFSv2. The offline LSM shows large improvements in the simulation of snow depth, snow water equivalent (SWE), and snow cover area during snow season (October to June). CFSv2 with the modified Noah reveals a dramatic improvements in the simulation of snow depth and 2 m air temperature and moderate improvements in SWE. As suggested in the previous diagnostic and sensitivity study, improvements in the simulation of snow by CFSv2 have lead to the reduction in dry bias over the Indian subcontinent (by a maximum of 2 mm d-1). The multilayer snow scheme shows promising results in the simulation of snow as well as Indian summer monsoon rainfall and hence this development may be the part of the future version of the CFS.

MODIS Snow Cover Recovery Using Variational Interpolation

NASA Astrophysics Data System (ADS)

Tran, H.; Nguyen, P.; Hsu, K. L.; Sorooshian, S.

2017-12-01

Cloud obscuration is one of the major problems that limit the usages of satellite images in general and in NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) global Snow-Covered Area (SCA) products in particular. Among the approaches to resolve the problem, the Variational Interpolation (VI) algorithm method, proposed by Xia et al., 2012, obtains cloud-free dynamic SCA images from MODIS. This method is automatic and robust. However, computational deficiency is a main drawback that degrades applying the method for larger scales (i.e., spatial and temporal scales). To overcome this difficulty, this study introduces an improved version of the original VI. The modified VI algorithm integrates the MINimum RESidual (MINRES) iteration (Paige and Saunders., 1975) to prevent the system from breaking up when applied to much broader scales. An experiment was done to demonstrate the crash-proof ability of the new algorithm in comparison with the original VI method, an ability that is obtained when maintaining the distribution of the weights set after solving the linear system. After that, the new VI algorithm was applied to the whole Contiguous United States (CONUS) over four winter months of 2016 and 2017, and validated using the snow station network (SNOTEL). The resulting cloud free images have high accuracy in capturing the dynamical changes of snow in contrast with the MODIS snow cover maps. Lastly, the algorithm was applied to create a Cloud free images dataset from March 10, 2000 to February 28, 2017, which is able to provide an overview of snow trends over CONUS for nearly two decades. ACKNOWLEDGMENTSWe would like to acknowledge NASA, NOAA Office of Hydrologic Development (OHD) National Weather Service (NWS), Cooperative Institute for Climate and Satellites (CICS), Army Research Office (ARO), ICIWaRM, and UNESCO for supporting this research.

Retrieval of aerosol optical depth over bare soil surfaces using time series of MODIS imagery

NASA Astrophysics Data System (ADS)

Yuan, Zhengwu; Yuan, Ranyin; Zhong, Bo

2014-11-01

Aerosol Optical Depth (AOD) is one of the key parameters which can not only reflect the characterization of atmospheric turbidity, but also identify the climate effects of aerosol. The current MODIS aerosol estimation algorithm over land is based on the "dark-target" approach which works only over densely vegetated surfaces. For non-densely vegetated surfaces (such as snow/ice, desert, and bare soil surfaces), this method will be failed. In this study, we develop an algorithm to derive AOD over the bare soil surfaces. Firstly, this method uses the time series of MODIS imagery to detect the " clearest" observations during the non-growing season in multiple years for each pixel. Secondly, the "clearest" observations after suitable atmospheric correction are used to fit the bare soil's bidirectional reflectance distribution function (BRDF) using Kernel model. As long as the bare soil's BRDF is established, the surface reflectance of "hazy" observations can be simulated. Eventually, the AOD over the bare soil surfaces are derived. Preliminary validation results by comparing with the ground measurements from AERONET at Xianghe sites show a good agreement.

Performance of the Falling Snow Retrieval Algorithms for the Global Precipitation Measurement (GPM) Mission

NASA Technical Reports Server (NTRS)

Skofronick-Jackson, Gail; Munchak, Stephen J.; Ringerud, Sarah

2016-01-01

Retrievals of falling snow from space represent an important data set for understanding the Earth's atmospheric, hydrological, and energy cycles, especially during climate change. Estimates of falling snow must be captured to obtain the true global precipitation water cycle, snowfall accumulations are required for hydrological studies, and without knowledge of the frozen particles in clouds one cannot adequately understand the energy and radiation budgets. While satellite-based remote sensing provides global coverage of falling snow events, the science is relatively new and retrievals are still undergoing development with challenges remaining). This work reports on the development and testing of retrieval algorithms for the Global Precipitation Measurement (GPM) mission Core Satellite, launched February 2014.

Mapping Snow Grain Size over Greenland from MODIS

NASA Technical Reports Server (NTRS)

Lyapustin, Alexei; Tedesco, Marco; Wang, Yujie; Kokhanovsky, Alexander

2008-01-01

This paper presents a new automatic algorithm to derive optical snow grain size (SGS) at 1 km resolution using Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. Differently from previous approaches, snow grains are not assumed to be spherical but a fractal approach is used to account for their irregular shape. The retrieval is conceptually based on an analytical asymptotic radiative transfer model which predicts spectral bidirectional snow reflectance as a function of the grain size and ice absorption. The analytical form of solution leads to an explicit and fast retrieval algorithm. The time series analysis of derived SGS shows a good sensitivity to snow metamorphism, including melting and snow precipitation events. Preprocessing is performed by a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, which includes gridding MODIS data to 1 km resolution, water vapor retrieval, cloud masking and an atmospheric correction. MAIAC cloud mask (CM) is a new algorithm based on a time series of gridded MODIS measurements and an image-based rather than pixel-based processing. Extensive processing of MODIS TERRA data over Greenland shows a robust performance of CM algorithm in discrimination of clouds over bright snow and ice. As part of the validation analysis, SGS derived from MODIS over selected sites in 2004 was compared to the microwave brightness temperature measurements of SSM\\I radiometer, which is sensitive to the amount of liquid water in the snowpack. The comparison showed a good qualitative agreement, with both datasets detecting two main periods of snowmelt. Additionally, MODIS SGS was compared with predictions of the snow model CROCUS driven by measurements of the automatic whether stations of the Greenland Climate Network. We found that CROCUS grain size is on average a factor of two larger than MODIS-derived SGS. Overall, the agreement between CROCUS and MODIS results was satisfactory, in particular before and during the first melting period in mid-June. Following detailed time series analysis of SGS for four permanent sites, the paper presents SGS maps over the Greenland ice sheet for the March-September period of 2004.

Snow distribution throughout small subalpine catchment post-insect infestation of spruce and pine beetle.

NASA Astrophysics Data System (ADS)

Beverly, D.; Ewers, B. E.; Hyde, K.; Ohara, N.; Speckman, H. N.

2015-12-01

High elevation watersheds of the Rocky Mountains region contribute over 70% of the streamflow needed for infrastructure, agriculture, and ecological processes. Snow-water yields are heterogeneous in space and time and are driven by a multitude of snow distribution processes, including snowpack evolution driven by physical and biological factors. Quantifying heterogeneity of snowpack is further complicated by vegetation perturbations; much of the Rocky Mountains have experienced significant tree mortality due to bark beetle outbreaks. Reduction of living crown area decreases canopy interception while increasing radiation to snow surfaces, which alters snowpack distribution throughout the catchment. We hypothesize that, in a complex watershed, topographic variation (i.e., slope and aspect) will have a greater effect on snowpack evolution and distribution than densities of canopy mortality due to beetle infestation. The 120 ha No Name watershed, located in southern Wyoming at 3000 m elevation was divided into twenty-one 175 m2 parcels, in which plots were randomly assigned within each parcel. Peak snow was measured in April; in the 50 m2 plots, depths were measured every 2 m along north-south and east-west transects. Twenty-one snow pits were excavated to quantify snow densities in 10 cm increments throughout the pit profile. Forest inventories occurred the following summer. Peak snowpack levels occurred in April with mean depth of 92.3 ­­± 2.4 cm and peak SWE of 34.0 ± 0.84 cm. Binary decision trees accounted for 63% of the variability after including topographic indices, beetle condition of the trees, LAI, and basal area. Snow depth showed a slight positive relationship with increased in beetle mortality on slopes less than 11 degrees. Overall, topographic indices are greater drivers for snow distributions compared to effects of tree mortality.

Combining snow depth and innovative skier flow measurements in order to improve snow grooming techniques

NASA Astrophysics Data System (ADS)

Carmagnola, Carlo Maria; Albrecht, Stéphane; Hargoaa, Olivier

2017-04-01

In the last decades, ski resort managers have massively improved their snow management practices, in order to adapt their strategies to the inter-annual variability in snow conditions and to the effects of climate change. New real-time informations, such as snow depth measurements carried out on the ski slopes by grooming machines during their daily operations, have become available, allowing high saving, efficiency and optimization gains (reducing for instance the groomer fuel consumption and operation time and the need for machine-made snow production). In order to take a step forward in improving the grooming techniques, it would be necessary to keep into account also the snow erosion by skiers, which depends mostly on the snow surface properties and on the skier attendance. Today, however, most ski resort managers have only a vague idea of the evolution of the skier flows on each slope during the winter season. In this context, we have developed a new sensor (named Skiflux) able to measure the skier attendance using an infrared beam crossing the slopes. Ten Skiflux sensors have been deployed during the 2016/17 winter season at Val Thorens ski area (French Alps), covering a whole sector of the resort. A dedicated software showing the number of skier passages in real time as been developed as well. Combining this new Skiflux dataset with the snow depth measurements from grooming machines (Snowsat System) and the snow and meteorological conditions measured in-situ (Liberty System from Technoalpin), we were able to create a "real-time skiability index" accounting for the quality of the surface snow and its evolution during the day. Moreover, this new framework allowed us to improve the preparation of ski slopes, suggesting new strategies for adapting the grooming working schedule to the snow quality and the skier attendance. In the near future, this work will benefit from the advances made within the H2020 PROSNOW project ("Provision of a prediction system allowing for management and optimization of snow in Alpine ski resorts"), which has been funded for the period 2017-2020.

Trends in annual minimum exposed snow and ice cover in High Mountain Asia from MODIS

NASA Astrophysics Data System (ADS)

Rittger, Karl; Brodzik, Mary J.; Painter, Thomas H.; Racoviteanu, Adina; Armstrong, Richard; Dozier, Jeff

2016-04-01

Though a relatively short record on climatological scales, data from the Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000-2014 can be used to evaluate changes in the cryosphere and provide a robust baseline for future observations from space. We use the MODIS Snow Covered Area and Grain size (MODSCAG) algorithm, based on spectral mixture analysis, to estimate daily fractional snow and ice cover and the MODICE Persistent Ice (MODICE) algorithm to estimate the annual minimum snow and ice fraction (fSCA) for each year from 2000 to 2014 in High Mountain Asia. We have found that MODSCAG performs better than other algorithms, such as the Normalized Difference Index (NDSI), at detecting snow. We use MODICE because it minimizes false positives (compared to maximum extents), for example, when bright soils or clouds are incorrectly classified as snow, a common problem with optical satellite snow mapping. We analyze changes in area using the annual MODICE maps of minimum snow and ice cover for over 15,000 individual glaciers as defined by the Randolph Glacier Inventory (RGI) Version 5, focusing on the Amu Darya, Syr Darya, Upper Indus, Ganges, and Brahmaputra River basins. For each glacier with an area of at least 1 km2 as defined by RGI, we sum the total minimum snow and ice covered area for each year from 2000 to 2014 and estimate the trends in area loss or gain. We find the largest loss in annual minimum snow and ice extent for 2000-2014 in the Brahmaputra and Ganges with 57% and 40%, respectively, of analyzed glaciers with significant losses (p-value<0.05). In the Upper Indus River basin, we see both gains and losses in minimum snow and ice extent, but more glaciers with losses than gains. Our analysis shows that a smaller proportion of glaciers in the Amu Darya and Syr Darya are experiencing significant changes in minimum snow and ice extent (3.5% and 12.2%), possibly because more of the glaciers in this region are smaller than 1 km2 than in the Indus, Ganges, and Brahmaputra making analysis from MODIS (pixel area ~0.25 km2) difficult. Overall, we see 23% of the glaciers in the 5 river basins with significant trends (in either direction). We relate these changes in area to topography and climate to understand the driving processes related to these changes. In addition to annual minimum snow and ice cover, the MODICE algorithm also provides the date of minimum fSCA for each pixel. To determine whether the surface was snow or ice we use the date of minimum fSCA from MODICE to index daily maps of snow on ice (SOI), or exposed glacier ice (EGI) and systematically derive an equilibrium line altitude (ELA) for each year from 2000-2014. We test this new algorithm in the Upper Indus basin and produce annual estimates of ELA. For the Upper Indus basin we are deriving annual ELAs that range from 5350 m to 5450 m which is slightly higher than published values of 5200 m for this region.

Intercomparison of Satellite-Derived Snow-Cover Maps

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Tait, Andrew B.; Foster, James L.; Chang, Alfred T. C.; Allen, Milan

1999-01-01

In anticipation of the launch of the Earth Observing System (EOS) Terra, and the PM-1 spacecraft in 1999 and 2000, respectively, efforts are ongoing to determine errors of satellite-derived snow-cover maps. EOS Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer-E (AMSR-E) snow-cover products will be produced. For this study we compare snow maps covering the same study area acquired from different sensors using different snow- mapping algorithms. Four locations are studied: 1) southern Saskatchewan; 2) a part of New England (New Hampshire, Vermont and Massachusetts) and eastern New York; 3) central Idaho and western Montana; and 4) parts of North and South Dakota. Snow maps were produced using a prototype MODIS snow-mapping algorithm used on Landsat Thematic Mapper (TM) scenes of each study area at 30-m and when the TM data were degraded to 1 -km resolution. National Operational Hydrologic Remote Sensing Center (NOHRSC) 1 -km resolution snow maps were also used, as were snow maps derived from 1/2 deg. x 1/2 deg. resolution Special Sensor Microwave Imager (SSM/1) data. A land-cover map derived from the International Geosphere-Biosphere Program (IGBP) land-cover map of North America was also registered to the scenes. The TM, NOHRSC and SSM/I snow maps, and land-cover maps were compared digitally. In most cases, TM-derived maps show less snow cover than the NOHRSC and SSM/I maps because areas of incomplete snow cover in forests (e.g., tree canopies, branches and trunks) are seen in the TM data, but not in the coarser-resolution maps. The snow maps generally agree with respect to the spatial variability of the snow cover. The 30-m resolution TM data provide the most accurate snow maps, and are thus used as the baseline for comparison with the other maps. Comparisons show that the percent change in amount of snow cover relative to the 3 0-m resolution TM maps is lowest using the TM I -km resolution maps, ranging from 0 to 40%. The highest percent change (less than 100%) is found in the New England study area, probably due to the presence of patchy snow cover. A scene with patchy snow cover is more difficult to map accurately than is a scene with a well-defined snowline such as is found on the North and South Dakota scene where the percent change ranged from 0 to 40%. There are also some important differences in the amount of snow mapped using the two different SSM/I algorithms because they utilize different channels.

NASA sea ice and snow validation plan for the Defense Meteorological Satellite Program special sensor microwave/imager

NASA Technical Reports Server (NTRS)

Cavalieri, Donald J. (Editor); Swift, Calvin T. (Editor)

1987-01-01

This document addresses the task of developing and executing a plan for validating the algorithm used for initial processing of sea ice data from the Special Sensor Microwave/Imager (SSMI). The document outlines a plan for monitoring the performance of the SSMI, for validating the derived sea ice parameters, and for providing quality data products before distribution to the research community. Because of recent advances in the application of passive microwave remote sensing to snow cover on land, the validation of snow algorithms is also addressed.

Global Precipitation Measurement (GPM) Core Observatory Falling Snow Estimates

NASA Astrophysics Data System (ADS)

Skofronick Jackson, G.; Kulie, M.; Milani, L.; Munchak, S. J.; Wood, N.; Levizzani, V.

2017-12-01

Retrievals of falling snow from space represent an important data set for understanding and linking the Earth's atmospheric, hydrological, and energy cycles. Estimates of falling snow must be captured to obtain the true global precipitation water cycle, snowfall accumulations are required for hydrological studies, and without knowledge of the frozen particles in clouds one cannot adequately understand the energy and radiation budgets. This work focuses on comparing the first stable falling snow retrieval products (released May 2017) for the Global Precipitation Measurement (GPM) Core Observatory (GPM-CO), which was launched February 2014, and carries both an active dual frequency (Ku- and Ka-band) precipitation radar (DPR) and a passive microwave radiometer (GPM Microwave Imager-GMI). Five separate GPM-CO falling snow retrieval algorithm products are analyzed including those from DPR Matched (Ka+Ku) Scan, DPR Normal Scan (Ku), DPR High Sensitivity Scan (Ka), combined DPR+GMI, and GMI. While satellite-based remote sensing provides global coverage of falling snow events, the science is relatively new, the different on-orbit instruments don't capture all snow rates equally, and retrieval algorithms differ. Thus a detailed comparison among the GPM-CO products elucidates advantages and disadvantages of the retrievals. GPM and CloudSat global snowfall evaluation exercises are natural investigative pathways to explore, but caution must be undertaken when analyzing these datasets for comparative purposes. This work includes outlining the challenges associated with comparing GPM-CO to CloudSat satellite snow estimates due to the different sampling, algorithms, and instrument capabilities. We will highlight some factors and assumptions that can be altered or statistically normalized and applied in an effort to make comparisons between GPM and CloudSat global satellite falling snow products as equitable as possible.

Overcoming the stauchwall: Viscoelastic stress redistribution and the start of full-depth gliding snow avalanches

NASA Astrophysics Data System (ADS)

Bartelt, P.; Feistl, T.; Bühler, Y.; Buser, O.

2012-08-01

When a full-depth tensile crack opens in the mountain snowcover, internal forces are transferred from the fracture crown to the stauchwall. The stauchwall is located at the lower limit of a gliding zone and must carry the weight of the snowcover. The stauchwall can fail, leading to full-depth snow avalanches, or, it can withstand the stress redistribution. The snowcover often finds a new static equilibrium, despite the initial crack. We present a model describing how the snowcover reacts to the sudden transfer of the forces from the crown to the stauchwall. Our goal is to find the conditions for failure and the start of full-depth avalanches. The model balances the inertial forces of the gliding snowcover with the viscoelastic response of the stauchwall. We compute stresses, strain-rates and deformations during the stress redistribution and show that a new equilibrium state is not found directly, but depends on the viscoelastic properties of the snow, which are density and temperature dependent. During the stress redistribution the stauchwall encounters stresses and strain-rates that can be much higher than at the final equilibrium state. Because of the excess strain-rates, the stauchwall can fail in brittle compression before reaching the new equilibrium. Snow viscosity and the length of the gliding snow region are the two critical parameters governing the transition from stable snowpack gliding to avalanche flow. The model reveals why the formation of gliding snow avalanches is height invariant and how technical measures to prevent snowpack glide can be optimized to improve avalanche mitigation.

Colonization in the Photic Zone and Subsequent Changes during Sinking Determine Bacterial Community Composition in Marine Snow

PubMed Central

Thiele, Stefan; Fuchs, Bernhard M.; Amann, Rudolf

2014-01-01

Due to sampling difficulties, little is known about microbial communities associated with sinking marine snow in the twilight zone. A drifting sediment trap was equipped with a viscous cryogel and deployed to collect intact marine snow from depths of 100 and 400 m off Cape Blanc (Mauritania). Marine snow aggregates were fixed and washed in situ to prevent changes in microbial community composition and to enable subsequent analysis using catalyzed reporter deposition fluorescence in situ hybridization (CARD-FISH). The attached microbial communities collected at 100 m were similar to the free-living community at the depth of the fluorescence maximum (20 m) but different from those at other depths (150, 400, 550, and 700 m). Therefore, the attached microbial community seemed to be “inherited” from that at the fluorescence maximum. The attached microbial community structure at 400 m differed from that of the attached community at 100 m and from that of any free-living community at the tested depths, except that collected near the sediment at 700 m. The differences between the particle-associated communities at 400 m and 100 m appeared to be due to internal changes in the attached microbial community rather than de novo colonization, detachment, or grazing during the sinking of marine snow. The new sampling method presented here will facilitate future investigations into the mechanisms that shape the bacterial community within sinking marine snow, leading to better understanding of the mechanisms which regulate biogeochemical cycling of settling organic matter. PMID:25527538

Alaska North Slope Tundra Travel Model and Validation Study

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

Harry R. Bader; Jacynthe Guimond

2006-03-01

The Alaska Department of Natural Resources (DNR), Division of Mining, Land, and Water manages cross-country travel, typically associated with hydrocarbon exploration and development, on Alaska's arctic North Slope. This project is intended to provide natural resource managers with objective, quantitative data to assist decision making regarding opening of the tundra to cross-country travel. DNR designed standardized, controlled field trials, with baseline data, to investigate the relationships present between winter exploration vehicle treatments and the independent variables of ground hardness, snow depth, and snow slab thickness, as they relate to the dependent variables of active layer depth, soil moisture, and photosyntheticallymore » active radiation (a proxy for plant disturbance). Changes in the dependent variables were used as indicators of tundra disturbance. Two main tundra community types were studied: Coastal Plain (wet graminoid/moist sedge shrub) and Foothills (tussock). DNR constructed four models to address physical soil properties: two models for each main community type, one predicting change in depth of active layer and a second predicting change in soil moisture. DNR also investigated the limited potential management utility in using soil temperature, the amount of photosynthetically active radiation (PAR) absorbed by plants, and changes in microphotography as tools for the identification of disturbance in the field. DNR operated under the assumption that changes in the abiotic factors of active layer depth and soil moisture drive alteration in tundra vegetation structure and composition. Statistically significant differences in depth of active layer, soil moisture at a 15 cm depth, soil temperature at a 15 cm depth, and the absorption of photosynthetically active radiation were found among treatment cells and among treatment types. The models were unable to thoroughly investigate the interacting role between snow depth and disturbance due to a lack of variability in snow depth cover throughout the period of field experimentation. The amount of change in disturbance indicators was greater in the tundra communities of the Foothills than in those of the Coastal Plain. However the overall level of change in both community types was less than expected. In Coastal Plain communities, ground hardness and snow slab thickness were found to play an important role in change in active layer depth and soil moisture as a result of treatment. In the Foothills communities, snow cover had the most influence on active layer depth and soil moisture as a result of treatment. Once certain minimum thresholds for ground hardness, snow slab thickness, and snow depth were attained, it appeared that little or no additive effect was realized regarding increased resistance to disturbance in the tundra communities studied. DNR used the results of this modeling project to set a standard for maximum permissible disturbance of cross-country tundra travel, with the threshold set below the widely accepted standard of Low Disturbance levels (as determined by the U.S. Fish and Wildlife Service). DNR followed the modeling project with a validation study, which seemed to support the field trial conclusions and indicated that the standard set for maximum permissible disturbance exhibits a conservative bias in favor of environmental protection. Finally DNR established a quick and efficient tool for visual estimations of disturbance to determine when investment in field measurements is warranted. This Visual Assessment System (VAS) seemed to support the plot disturbance measurements taking during the modeling and validation phases of this project.« less

Concentrations of Reactive Trace Gases In The Interstitial Air of Surface Snow

NASA Astrophysics Data System (ADS)

Jacobi, H.-W.; Honrath, R. E.; Peterson, M. C.; Lu, Y.; Dibb, J. E.; Arsenault, M. A.; Swanson, A. L.; Blake, N. J.; Bales, R. C.; Schrems, O.

Several measurements at Arctic and Antarctic sites have demonstrated that unexpected photochemical reactions occur in irradiated surface snow influencing the composi- tion of the boundary layer over snow-covered areas. The results of these reactions are probably most obvious in the interstitial air of the surface snow since it constitutes the interface between the surface snow and the boundary layer. Therefore, measurements of concentrations of nitrogen oxide and dioxide, nitrous acid, formaldehyde, hydro- gen peroxide, formic acid, acetic acid, and other organic compounds were performed in the interstitial air of the surface snow of the Greenland ice sheet. Concentrations were measured at variable depths between - 10 cm and - 50 cm during the summer field season in 2000 at the Summit Environmental Observatory. At shallow depths, the system NO-NO2-O3 exhibits large deviations from the calculated photostationary state. Using steady-state analyses applied to OH-HO2-CH3O2 cycling indicated the presence of high concentrations of OH and peroxy radicals in the firn air. Maximum concentrations calculated for a depth of - 10 cm are in the order of 6 105 molecules cm-3 and 1.4 * 107 molecules cm-3 for OH and HO2, respectively, although radia- tion levels at - 10 cm are reduced by approximately 50 % compared to levels above the snow surface. By far the most important OH source is the photolysis of HONO while the photolysis of ozone contributes less than 2 % to the overall production of OH in the firn air.

A Supplementary Clear-Sky Snow and Ice Recognition Technique for CERES Level 2 Products

NASA Technical Reports Server (NTRS)

Radkevich, Alexander; Khlopenkov, Konstantin; Rutan, David; Kato, Seiji

2013-01-01

Identification of clear-sky snow and ice is an important step in the production of cryosphere radiation budget products, which are used in the derivation of long-term data series for climate research. In this paper, a new method of clear-sky snow/ice identification for Moderate Resolution Imaging Spectroradiometer (MODIS) is presented. The algorithm's goal is to enhance the identification of snow and ice within the Clouds and the Earth's Radiant Energy System (CERES) data after application of the standard CERES scene identification scheme. The input of the algorithm uses spectral radiances from five MODIS bands and surface skin temperature available in the CERES Single Scanner Footprint (SSF) product. The algorithm produces a cryosphere rating from an aggregated test: a higher rating corresponds to a more certain identification of the clear-sky snow/ice-covered scene. Empirical analysis of regions of interest representing distinctive targets such as snow, ice, ice and water clouds, open waters, and snow-free land selected from a number of MODIS images shows that the cryosphere rating of snow/ice targets falls into 95% confidence intervals lying above the same confidence intervals of all other targets. This enables recognition of clear-sky cryosphere by using a single threshold applied to the rating, which makes this technique different from traditional branching techniques based on multiple thresholds. Limited tests show that the established threshold clearly separates the cryosphere rating values computed for the cryosphere from those computed for noncryosphere scenes, whereas individual tests applied consequently cannot reliably identify the cryosphere for complex scenes.

Exploitation of ERTS-1 imagery utilizing snow enhancement techniques

NASA Technical Reports Server (NTRS)

Wobber, F. J.; Martin, K. R.

1973-01-01

Photogeological analysis of ERTS-simulation and ERTS-1 imagery of snowcovered terrain within the ERAP Feather River site and within the New England (ERTS) test area provided new fracture detail which does not appear on available geological maps. Comparative analysis of snowfree ERTS-1 images has demonstrated that MSS Bands 5 and 7 supply the greatest amount of geological fracture detail. Interpretation of the first snow-covered ERTS-1 images in correlation with ground snow depth data indicates that a heavy blanket of snow (more than 9 inches) accentuates major structural features while a light "dusting", (less than 1 inch) accentuates more subtle topographic expressions. An effective mail-based method for acquiring timely ground-truth (snowdepth) information was established and provides a ready correlation of fracture detail with snow depth so as to establish the working limits of the technique. The method is both efficient and inexpensive compared with the cost of similarly scaled direct field observations.

[Spectrum similarities-based analysis of spatial difference of snow cover for multi-scale satellite data-a case study of MODIS and HJ-1B data].

PubMed

Liu, Yan; Li, Yang; Yang, Yun; Jian, Ji

2014-05-01

Vegetation and bare soil were collected in the areas of Miyaluo district in northwest of Sichuan province, the Qilian Mountains in Qinghai province and northern areas of Xinjiang during the years of 2007 and 2013. Then these data were converted to spectral reflectance by applying sensor response function of MODIS and HJ-1B respectively within the range of visible light, near-infrared and shortwave infrared. Comprehensive analysis was made on spectral characteristics and reflectivity similarities and differences of different sensors between old and new snowmelt, under the condition of different snow depth and different snow cover. The conclusions can be drawn That is, there exists high consistency of spectral response between new snow and dirty snow for each sensor in the visible wavelength range, also it is true for bare soil and low vegetation. However, low consistency happens to other types of snow; especially snowmelt and frozen snow. The range of NDSI is relatively stable under the condition of different snow depth for full snow cover and the trend of NDSI shows great consistency for different sensors; NDSI threshold method for monitoring snow by using MODIS and HJ-1B data showed very obvious difference in spatial scales, which is a reasonable explanation of the existence of mixed pixels.

American River Hydrologic Observatory

NASA Astrophysics Data System (ADS)

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

2016-12-01

We have set up fourteen large wireless sensor networks to measure hydrologic parameters over physiographical representative regions of the snow-dominated portion of the river basin. This is perhaps the largest wireless sensor network in the world. Each network covers about a 1 km2 area and consists of about 45 elements. We measure snow depth, temperature humidity soil moisture and temperature, and solar radiation in real time at ten locations per site, as opposed to the traditional once-a-month snow course. As part of the multi-PI SSCZO, we have installed a 62-node wireless sensor network to measure snow depth, temperature humidity soil moisture and temperature, and solar radiation in real time. This network has been operating for approximately six years. We are now installing four large wireless sensor networks to measure snow depth, temperature humidity soil moisture and temperature, and solar radiation in East Branch of the North Fork of the Feather River, CA. The presentation will discuss the planning and operation of the networks as well as some unique results. It will also present information about the networking hardware designed for these installations, which has resulted in a start-up, Metronome Systems.

Will changes in root-zone temperature in boreal spring affect recovery of photosynthesis in Picea mariana and Populus tremuloides in a future climate?

PubMed

Fréchette, Emmanuelle; Ensminger, Ingo; Bergeron, Yves; Gessler, Arthur; Berninger, Frank

2011-11-01

Future climate will alter the soil cover of mosses and snow depths in the boreal forests of eastern Canada. In field manipulation experiments, we assessed the effects of varying moss and snow depths on the physiology of black spruce (Picea -mariana (Mill.) B.S.P.) and trembling aspen (Populus tremuloides Michx.) in the boreal black spruce forest of western Québec. For 1 year, naturally regenerated 10-year-old spruce and aspen were grown with one of the following treatments: additional N fertilization, addition of sphagnum moss cover, removal of mosses, delayed soil thawing through snow and hay addition, or accelerated soil thawing through springtime snow removal. Treatments that involved the addition of insulating moss or snow in the spring caused lower soil temperature, while removing moss and snow in the spring caused elevated soil temperature and thus had a warming effect. Soil warming treatments were associated with greater temperature variability. Additional soil cover, whether moss or snow, increased the rate of photosynthetic recovery in the spring. Moss and snow removal, on the other hand, had the opposite effect and lowered photosynthetic activity, especially in spruce. Maximal electron transport rate (ETR(max)) was, for spruce, 39.5% lower after moss removal than with moss addition, and 16.3% lower with accelerated thawing than with delayed thawing. Impaired photosynthetic recovery in the absence of insulating moss or snow covers was associated with lower foliar N concentrations. Both species were affected in that way, but trembling aspen generally reacted less strongly to all treatments. Our results indicate that a clear negative response of black spruce to changes in root-zone temperature should be anticipated in a future climate. Reduced moss cover and snow depth could adversely affect the photosynthetic capacities of black spruce, while having only minor effects on trembling aspen.

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.

Calculation of new snow densities from sub-daily automated snow measurements

NASA Astrophysics Data System (ADS)

Helfricht, Kay; Hartl, Lea; Koch, Roland; Marty, Christoph; Lehning, Michael; Olefs, Marc

2017-04-01

In mountain regions there is an increasing demand for high-quality analysis, nowcasting and short-range forecasts of the spatial distribution of snowfall. Operational services, such as for avalanche warning, road maintenance and hydrology, as well as hydropower companies and ski resorts need reliable information on the depth of new snow (HN) and the corresponding water equivalent (HNW). However, the ratio of HNW to HN can vary from 1:3 to 1:30 because of the high variability of new snow density with respect to meteorological conditions. In the past, attempts were made to calculate new snow densities from meteorological parameters mainly using daily values of temperature and wind. Further complex statistical relationships have been used to calculate new snow densities on hourly to sub-hourly time intervals to drive multi-layer snow cover models. However, only a few long-term in-situ measurements of new snow density exist for sub-daily time intervals. Settling processes within the new snow due to loading and metamorphism need to be considered when computing new snow density. As the effect of these processes is more pronounced for long time intervals, a high temporal resolution of measurements is desirable. Within the pluSnow project data of several automatic weather stations with simultaneous measurements of precipitation (pluviometers), snow water equivalent (SWE) using snow pillows and snow depth (HS) measurements using ultrasonic rangers were analysed. New snow densities were calculated for a set of data filtered on the basis of meteorological thresholds. The calculated new snow densities were compared to results from existing new snow density parameterizations. To account for effects of settling of the snow cover, a case study based on a multi-year data set using the snow cover model SNOWPACK at Weissfluhjoch was performed. Measured median values of hourly new snow densities at the different stations range from 54 to 83 kgm-3. This is considerably lower than a 1:10 approximation (i.e. 100 kgm-3), which is mainly based on daily values in the Alps. Variations in new snow density could not be explained in a satisfactory manner using meteorological data measured at the same location. Likewise, some of the tested parametrizations of new snow density, which primarily use air temperature as a proxy, result in median new snow densities close to the ones from automated measurements, but show only a low correlation between calculated and measured new snow densities. The case study on the influence of snow settling on HN resulted on average in an underestimation of HN by 17%, which corresponds to 2-3% of the cumulated HN from the previous 24 hours. Therefore, the mean hourly new snow densities may be overestimated by 14%. The analysis in this study is especially limited with respect to the meteorological influence on the HS measurement using ultra-sonic rangers. Nevertheless, the reasonable mean values encourage calculating new snow densities from standard hydro-meteorological measurements using more precise observation devices such as optical snow depth sensors and more sensitive scales for SWE measurements also on sub-daily time-scales.

IceChrono v1: a probabilistic model to compute a common and optimal chronology for several ice cores

NASA Astrophysics Data System (ADS)

Parrenin, Frédéric

2015-04-01

Polar ice cores provide exceptional archives of past environmental conditions. The dating of ice cores is essential to interpret the paleo records that they contain, but it is a complicated problem since it involves different dating methods. Here I present IceChrono v1, a new probabilistic model to combine different kinds of chronological information to obtain a common and optimized chronology for several ice cores, as well as its uncertainty. It is based on the inversion of three quantities: the surface accumulation rate, the Lock-In Depth (LID) of air bubbles and the vertical thinning function. The chronological information used are: models of the sedimentation process (accumulation of snow, densification of snow into ice and air trapping, ice flow), ice and gas dated horizons, ice and gas dated depth intervals, Δdepth observations (depth shift between synchronous events recorded in the ice and in the air), stratigraphic links in between ice cores (ice-ice, air-air or mix ice-air and air-ice links). The optimization problem is formulated as a least squares problems, that is, all densities of probabilities are assumed gaussian. It is numerically solved using the Levenberg-Marquardt algorithm and a numerical evaluation of the model's Jacobian. IceChrono is similar in scope to the Datice model, but has differences from the mathematical, numerical and programming point of views. I apply IceChrono on an AICC2012-like experiment and I find similar results than Datice within a few centuries, which is a confirmation of both IceChrono and Datice codes. IceChrono v1 is freely available under the GPL v3 open source license.

Coupled long-term summer warming and deeper snow alters species composition and stimulates gross primary productivity in tussock tundra.

PubMed

Leffler, A Joshua; Klein, Eric S; Oberbauer, Steven F; Welker, Jeffrey M

2016-05-01

Climate change is expected to increase summer temperature and winter precipitation throughout the Arctic. The long-term implications of these changes for plant species composition, plant function, and ecosystem processes are difficult to predict. We report on the influence of enhanced snow depth and warmer summer temperature following 20 years of an ITEX experimental manipulation at Toolik Lake, Alaska. Winter snow depth was increased using snow fences and warming was accomplished during summer using passive open-top chambers. One of the most important consequences of these experimental treatments was an increase in active layer depth and rate of thaw, which has led to deeper drainage and lower soil moisture content. Vegetation concomitantly shifted from a relatively wet system with high cover of the sedge Eriophorum vaginatum to a drier system, dominated by deciduous shrubs including Betula nana and Salix pulchra. At the individual plant level, we observed higher leaf nitrogen concentration associated with warmer temperatures and increased snow in S. pulchra and B. nana, but high leaf nitrogen concentration did not lead to higher rates of net photosynthesis. At the ecosystem level, we observed higher GPP and NEE in response to summer warming. Our results suggest that deeper snow has a cascading set of biophysical consequences that include a deeper active layer that leads to altered species composition, greater leaf nitrogen concentration, and higher ecosystem-level carbon uptake.

A Case Study of Using a Multilayered Thermodynamical Snow Model for Radiance Assimilation

NASA Technical Reports Server (NTRS)

Toure, Ally M.; Goita, Kalifa; Royer, Alain; Kim, Edward J.; Durand, Michael; Margulis, Steven A.; Lu, Huizhong

2011-01-01

A microwave radiance assimilation (RA) scheme for the retrieval of snow physical state variables requires a snowpack physical model (SM) coupled to a radiative transfer model. In order to assimilate microwave brightness temperatures (Tbs) at horizontal polarization (h-pol), an SM capable of resolving melt-refreeze crusts is required. To date, it has not been shown whether an RA scheme is tractable with the large number of state variables present in such an SM or whether melt-refreeze crust densities can be estimated. In this paper, an RA scheme is presented using the CROCUS SM which is capable of resolving melt-refreeze crusts. We assimilated both vertical (v) and horizontal (h) Tbs at 18.7 and 36.5 GHz. We found that assimilating Tb at both h-pol and vertical polarization (v-pol) into CROCUS dramatically improved snow depth estimates, with a bias of 1.4 cm compared to-7.3 cm reported by previous studies. Assimilation of both h-pol and v-pol led to more accurate results than assimilation of v-pol alone. The snow water equivalent (SWE) bias of the RA scheme was 0.4 cm, while the bias of the SWE estimated by an empirical retrieval algorithm was -2.9 cm. Characterization of melt-refreeze crusts via an RA scheme is demonstrated here for the first time; the RA scheme correctly identified the location of melt-refreeze crusts observed in situ.

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).

Snow mechanics and avalanche formation: field experiments on the dynamic response of the snow cover

NASA Astrophysics Data System (ADS)

Schweizer, Jürg; Schneebeli, Martin; Fierz, Charles; Föhn, Paul M. B.

1995-11-01

Knowledge about snow mechanics and snow avalanche formation forms the basis of any hazard mitigation measures. The crucial point is the snow stability. The most relevant mechanical properties - the compressive, tensile and shear strength of the individual snow layers within the snow cover - vary substantially in space and time. Among other things the strength of the snow layers depends strongly on the state of stress and the strain rate. The evaluation of the stability of the snow cover is hence a difficult task involving many extrapolations. To gain insight in the release mechanism of slab avalanches triggered by skiers, the skier's impact is measured with a load cell at different depths within the snow cover and for different snow conditions. The study focused on the effects of the dynamic loading and of the damping by snow compaction. In accordance with earlier finite-element (FE) calculations the results show the importance of the depth of the weak layer or interface and the snow conditions, especially the sublayering. In order to directly measure the impact force and to study the snow properties in more detail, a new instrument, called rammrutsch was developed. It combines the properties of the rutschblock with the defined impact properties of the rammsonde. The mechanical properties are determined using (i) the impact energy of the rammrutsch and (ii) the deformations of the snow cover measured with accelerometers and digital image processing of video sequences. The new method is well suited to detect and to measure the mechanical processes and properties of the fracturing layers. The duration of one test is around 10 minutes and the method seems appropriate for determining the spatial variability of the snow cover. A series of experiments in a forest opening showed a clear difference in the snow stability between sites below trees and ones in the free field of the opening.

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.

Spatiotemporal dynamics of snow cover based on multi-source remote sensing data in China

NASA Astrophysics Data System (ADS)

Huang, Xiaodong; Deng, Jie; Ma, Xiaofang; Wang, Yunlong; Feng, Qisheng; Hao, Xiaohua; Liang, Tiangang

2016-10-01

By combining optical remote sensing snow cover products with passive microwave remote sensing snow depth (SD) data, we produced a MODIS (Moderate Resolution Imaging Spectroradiometer) cloudless binary snow cover product and a 500 m snow depth product. The temporal and spatial variations of snow cover from December 2000 to November 2014 in China were analyzed. The results indicate that, over the past 14 years, (1) the mean snow-covered area (SCA) in China was 11.3 % annually and 27 % in the winter season, with the mean SCA decreasing in summer and winter seasons, increasing in spring and fall seasons, and not much change annually; (2) the snow-covered days (SCDs) showed an increase in winter, spring, and fall, and annually, whereas they showed a decrease in summer; (3) the average SD decreased in winter, summer, and fall, while it increased in spring and annually; (4) the spatial distributions of SD and SCD were highly correlated seasonally and annually; and (5) the regional differences in the variation of snow cover in China were significant. Overall, the SCD and SD increased significantly in south and northeast China, and decreased significantly in the north of Xinjiang province. The SCD and SD increased on the southwest edge and in the southeast part of the Tibetan Plateau, whereas it decreased in the north and northwest regions.

An Ultra-Wideband, Microwave Radar for Measuring Snow Thickness on Sea Ice and Mapping Near-Surface Internal Layers in Polar Firn

NASA Technical Reports Server (NTRS)

Panzer, Ben; Gomez-Garcia, Daniel; Leuschen, Carl; Paden, John; Rodriguez-Morales, Fernando; Patel, Azsa; Markus, Thorsten; Holt, Benjamin; Gogineni, Prasad

2013-01-01

Sea ice is generally covered with snow, which can vary in thickness from a few centimeters to >1 m. Snow cover acts as a thermal insulator modulating the heat exchange between the ocean and the atmosphere, and it impacts sea-ice growth rates and overall thickness, a key indicator of climate change in polar regions. Snow depth is required to estimate sea-ice thickness using freeboard measurements made with satellite altimeters. The snow cover also acts as a mechanical load that depresses ice freeboard (snow and ice above sea level). Freeboard depression can result in flooding of the snow/ice interface and the formation of a thick slush layer, particularly in the Antarctic sea-ice cover. The Center for Remote Sensing of Ice Sheets (CReSIS) has developed an ultra-wideband, microwave radar capable of operation on long-endurance aircraft to characterize the thickness of snow over sea ice. The low-power, 100mW signal is swept from 2 to 8GHz allowing the air/snow and snow/ ice interfaces to be mapped with 5 c range resolution in snow; this is an improvement over the original system that worked from 2 to 6.5 GHz. From 2009 to 2012, CReSIS successfully operated the radar on the NASA P-3B and DC-8 aircraft to collect data on snow-covered sea ice in the Arctic and Antarctic for NASA Operation IceBridge. The radar was found capable of snow depth retrievals ranging from 10cm to >1 m. We also demonstrated that this radar can be used to map near-surface internal layers in polar firn with fine range resolution. Here we describe the instrument design, characteristics and performance of the radar.

Acoustic Imaging of Snowpack Physical Properties

NASA Astrophysics Data System (ADS)

Kinar, N. J.; Pomeroy, J. W.

2011-12-01

Measurements of snowpack depth, density, structure and temperature have often been conducted by the use of snowpits and invasive measurement devices. Previous research has shown that acoustic waves passing through snow are capable of measuring these properties. An experimental observation device (SAS2, System for the Acoustic Sounding of Snow) was used to autonomously send audible sound waves into the top of the snowpack and to receive and process the waves reflected from the interior and bottom of the snowpack. A loudspeaker and microphone array separated by an offset distance was suspended in the air above the surface of the snowpack. Sound waves produced from a loudspeaker as frequency-swept sequences and maximum length sequences were used as source signals. Up to 24 microphones measured the audible signal from the snowpack. The signal-to-noise ratio was compared between sequences in the presence of environmental noise contributed by wind and reflections from vegetation. Beamforming algorithms were used to reject spurious reflections and to compensate for movement of the sensor assembly during the time of data collection. A custom-designed circuit with digital signal processing hardware implemented an inversion algorithm to relate the reflected sound wave data to snowpack physical properties and to create a two-dimensional image of snowpack stratigraphy. The low power consumption circuit was powered by batteries and through WiFi and Bluetooth interfaces enabled the display of processed data on a mobile device. Acoustic observations were logged to an SD card after each measurement. The SAS2 system was deployed at remote field locations in the Rocky Mountains of Alberta, Canada. Acoustic snow properties data was compared with data collected from gravimetric sampling, thermocouple arrays, radiometers and snowpit observations of density, stratigraphy and crystal structure. Aspects for further research and limitations of the acoustic sensing system are 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.

Altitudinal gradients, midwinter melt, and wind effects on snow accumulation in semiarid midlatitude Andes under La Niña conditions

NASA Astrophysics Data System (ADS)

Ayala, A.; McPhee, J.; Vargas, X.

2014-04-01

The Andes Cordillera remains a sparsely monitored and studied snow hydrology environment in comparison to similar mountain ranges in the Northern Hemisphere. In order to uncover some of the key processes driving snow water equivalent (SWE) spatial variability, we present and analyze a distributed SWE data set, sampled at the end of accumulation season 2011. Three representative catchments across the region were monitored, obtaining measurements in an elevation range spanning 2000 to 3900 m asl and from 32.4° to 34.0°S in latitude. Climatic conditions during this season corresponded to a moderate La Niña phenomenon, which is generally correlated with lower-than normal accumulation. Collected measurements can be described at the regional and watershed extents by altitudinal gradients that imply an increase by a factor of two in snow depth between 2200 and 3000 m asl, though with significant variability at the upper sites. In these upper sites, we found north-facing, wind-sheltered slopes showing 25% less average SWE values than south-facing, wind-exposed ones. This suggests that under these conditions, solar radiation dominated wind transport effects in controlling end-of-winter variability. Nevertheless, we found clusters of snow depth measurements above 3000 m asl that can be explained by wind exposure differences. This is the first documented snow depth data set of this spatial extent for this region, and it is framed within an ongoing research effort aimed at improving understanding and modeling of snow hydrology in the extratropical Andes Cordillera.

Effects of climate and snow depth on Bromus tectorum population dynamics at high elevation.

PubMed

Griffith, Alden B; Loik, Michael E

2010-11-01

Invasive plants are thought to be especially capable of range shifts or expansion in response to climate change due to high dispersal and colonization abilities. Although highly invasive throughout the Intermountain West, the presence and impact of the grass Bromus tectorum has been limited at higher elevations in the eastern Sierra Nevada, potentially due to extreme wintertime conditions. However, climate models project an upward elevational shift of climate regimes in the Sierra Nevada that could favor B. tectorum expansion. This research specifically examined the effects of experimental snow depth manipulations and interannual climate variability over 5 years on B. tectorum populations at high elevation (2,175 m). Experimentally-increased snow depth had an effect on phenology and biomass, but no effect on individual fecundity. Instead an experimentally-increased snowpack inhibited population growth in 1 year by reducing seedling emergence and early survival. A similar negative effect of increased snow was observed 2 years later. However, a strong negative effect on B. tectorum was also associated with a naturally low-snow winter, when seedling emergence was reduced by 86%. Across 5 years, winters with greater snow cover and a slower accumulation of degree-days coincided with higher B. tectorum seedling density and population growth. Thus, we observed negative effects associated with both experimentally-increased and naturally-decreased snowpacks. It is likely that the effect of snow at high elevation is nonlinear and differs from lower elevations where wintertime germination can be favorable. Additionally, we observed a doubling of population size in 1 year, which is alarming at this elevation.

Local Scale Radiobrightness Modeling During the Intensive Observing Period-4 of the Cold Land Processes Experiment-1

NASA Astrophysics Data System (ADS)

Kim, E.; Tedesco, M.; de Roo, R.; England, A. W.; Gu, H.; Pham, H.; Boprie, D.; Graf, T.; Koike, T.; Armstrong, R.; Brodzik, M.; Hardy, J.; Cline, D.

2004-12-01

The NASA Cold Land Processes Field Experiment (CLPX-1) was designed to provide microwave remote sensing observations and ground truth for studies of snow and frozen ground remote sensing, particularly issues related to scaling. CLPX-1 was conducted in 2002 and 2003 in Colorado, USA. One of the goals of the experiment was to test the capabilities of microwave emission models at different scales. Initial forward model validation work has concentrated on the Local-Scale Observation Site (LSOS), a 0.8~ha study site consisting of open meadows separated by trees where the most detailed measurements were made of snow depth and temperature, density, and grain size profiles. Results obtained in the case of the 3rd Intensive Observing Period (IOP3) period (February, 2003, dry snow) suggest that a model based on Dense Medium Radiative Transfer (DMRT) theory is able to model the recorded brightness temperatures using snow parameters derived from field measurements. This paper focuses on the ability of forward DMRT modelling, combined with snowpack measurements, to reproduce the radiobrightness signatures observed by the University of Michigan's Truck-Mounted Radiometer System (TMRS) at 19 and 37~GHz during the 4th IOP (IOP4) in March, 2003. Unlike in IOP3, conditions during IOP4 include both wet and dry periods, providing a valuable test of DMRT model performance. In addition, a comparison will be made for the one day of coincident observations by the University of Tokyo's Ground-Based Microwave Radiometer-7 (GBMR-7) and the TMRS. The plot-scale study in this paper establishes a baseline of DMRT performance for later studies at successively larger scales. And these scaling studies will help guide the choice of future snow retrieval algorithms and the design of future Cold Lands observing systems.

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.

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.

Operational Applications of Satellite Snowcover Observations

NASA Technical Reports Server (NTRS)

Rango, A. (Editor)

1975-01-01

LANDSAT and NOAA satellites data were used to study snow depth. These snow measurements were used to help forecast runoff and flooding. Many areas of California, Arizona, Colorado, and Wyoming were emphasized.

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.

Snow instability evaluation: calculating the skier-induced stress in a multi-layered snowpack

NASA Astrophysics Data System (ADS)

Monti, Fabiano; Gaume, Johan; van Herwijnen, Alec; Schweizer, Jürg

2016-03-01

The process of dry-snow slab avalanche formation can be divided into two phases: failure initiation and crack propagation. Several approaches tried to quantify slab avalanche release probability in terms of failure initiation based on shear stress and strength. Though it is known that both the properties of the weak layer and the slab play a major role in avalanche release, most previous approaches only considered slab properties in terms of slab depth, average density and skier penetration. For example, for the skier stability index, the additional stress (e.g. due to a skier) at the depth of the weak layer is calculated by assuming that the snow cover can be considered a semi-infinite, elastic, half-space. We suggest a new approach based on a simplification of the multi-layered elasticity theory in order to easily compute the additional stress due to a skier at the depth of the weak layer, taking into account the layering of the snow slab and the substratum. We first tested the proposed approach on simplified snow profiles, then on manually observed snow profiles including a stability test and, finally, on simulated snow profiles. Our simple approach reproduced the additional stress obtained by finite element simulations for the simplified profiles well - except that the sequence of layering in the slab cannot be replicated. Once implemented into the classical skier stability index and applied to manually observed snow profiles classified into different stability classes, the classification accuracy improved with the new approach. Finally, we implemented the refined skier stability index into the 1-D snow cover model SNOWPACK. The two study cases presented in this paper showed promising results even though further verification is still needed. In the future, we intend to implement the proposed approach for describing skier-induced stress within a multi-layered snowpack into more complex models which take into account not only failure initiation but also crack propagation.

Snow instability evaluation: calculating the skier-induced stress in a multi-layered snowpack

NASA Astrophysics Data System (ADS)

Monti, F.; Gaume, J.; van Herwijnen, A.; Schweizer, J.

2015-08-01

The process of dry-snow slab avalanche formation can be divided into two phases: failure initiation and crack propagation. Several approaches tried to quantify slab avalanche release probability in terms of failure initiation based on shear stress and strength. Though it is known that both the properties of the weak layer and the slab play a major role in avalanche release, most previous approaches only considered slab properties in terms of slab depth, average density and skier penetration. For example, for the skier stability index, the additional stress (e.g. due to a skier) at the depth of the weak layer is calculated by assuming that the snow cover can be considered a semi-infinite, elastic half-space. We suggest a new approach based on a simplification of the multi-layered elasticity theory in order to easily compute the additional stress due to a skier at the depth of the weak layer taking into account the layering of the snow slab and the substratum. We first tested the proposed approach on simplified snow profiles, then on manually observed snow profiles including a stability test and, finally, on simulated snow profiles. Our simple approach well reproduced the additional stress obtained by finite element simulations for the simplified profiles - except that the sequence of layering in the slab cannot be replicated. Once implemented into the classical skier stability index and applied to manually observed snow profiles classified into different stability classes, the classification accuracy improved with the new approach. Finally, we implemented the refined skier stability index into the 1-D snow cover model SNOWPACK. For the two study cases presented in this paper, this approach showed promising results even though further verification is still needed. In the future, we intend to implement the proposed approach for describing skier-induced stress within a multi-layered snowpack into more complex models which take into account not only failure initiation but also crack propagation.

Differential ecophysiological response of deciduous shrubs and a graminoid to long-term experimental snow reductions and additions in moist acidic tundra, northern Alaska

Treesearch

Robert R. Pattison; Jeffrey M. Welker

2014-01-01

Changes in winter precipitation that include both decreases and increases in winter snow are underway across the Arctic. In this study, we used a 14-year experiment that has increased and decreased winter snow in the moist acidic tussock tundra of northern Alaska to understand impacts of variation in winter snow depth on summer leaf-level ecophysiology of two deciduous...

Radiative transfer in falling snow: A two-stream approximation

NASA Astrophysics Data System (ADS)

Koh, Gary

1989-04-01

Light transmission measurements through falling snow have produced results unexplainable by single scattering arguments. A two-stream approximation to radiative transfer is used to derive an analytical expression that describes the effects of multiple scattering as a function of the snow optical depth and the snow asymmetry parameter. The approximate solution is simple and it may be as accurate as the exact solution for describing the transmission measurements within the limits of experimental uncertainties.

Observational Evidence of Changes in Soil Temperatures across Eurasian Continent

NASA Astrophysics Data System (ADS)

Zhang, T.

2015-12-01

Soil temperature is one of the key climate change indicators and plays an important role in plant growth, agriculture, carbon cycle and ecosystems as a whole. In this study, variability and changes in ground surface and soil temperatures up to 3.20 m were investigated based on data and information obtained from hydrometeorological stations across Eurasian continent since the early 1950s. Ground surface and soil temperatures were measured daily by using the same standard method and by the trained professionals across Eurasian continent, which makes the dataset unique and comparable over a large study region. Using the daily soil temperature profiles, soil seasonal freeze depth was also obtained through linear interpolation. Preliminary results show that soil temperatures at various depths have increased dramatically, almost twice as much as air temperature increase over the same period. Regionally, soil temperature increase was more dramatically in high northern latitudes than mid/lower latitude regions. Air temperature changes alone may not be able to fully explain the magnitude of changes in soil temperatures. Further study indicates that snow cover establishment started later in autumn and snow cover disappearance occurred earlier in spring, while winter snow depth became thicker with a decreasing trend of snow density. Changes in snow cover conditions may play an important role in changes of soil temperatures over the Eurasian continent.

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.

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.

Snowpack ground truth: Radar test site, Steamboat Springs, Colorado, 8-16 April 1976

NASA Technical Reports Server (NTRS)

Howell, S.; Jones, E. B.; Leaf, C. F.

1976-01-01

Ground-truth data taken at Steamboat Springs, Colorado is presented. Data taken during the period April 8, 1976 - April 16, 1976 included the following: (1) snow depths and densities at selected locations (using a Mount Rose snow tube); (2) snow pits for temperature, density, and liquid water determinations using the freezing calorimetry technique and vertical layer classification; (3) snow walls were also constructed of various cross sections and documented with respect to sizes and snow characteristics; (4) soil moisture at selected locations; and (5) appropriate air temperature and weather data.

Towards better understanding of high-mountain cryosphere changes using GPM data: A Joint Snowfall and Snow-cover Passive Microwave Retrieval Algorithm

NASA Astrophysics Data System (ADS)

Ebtehaj, A.; Foufoula-Georgiou, E.

2016-12-01

Scientific evidence suggests that the duration and frequency of snowfall and the extent of snow cover are rapidly declining under global warming. Both precipitation and snow cover scatter the upwelling surface microwave emission and decrease the observed high-frequency brightness temperatures. The mixture of these two scattering signals is amongst the largest sources of ambiguities and errors in passive microwave retrievals of both precipitation and snow-cover. The dual frequency radar and the high-frequency radiometer on board the GPM satellite provide a unique opportunity to improve passive retrievals of precipitation and snow-cover physical properties and fill the gaps in our understating of their variability in view of climate change. Recently, a new Bayesian rainfall retrieval algorithm (called ShARP) was developed using modern approximation methods and shown to yield improvements against other algorithms in retrieval of rainfall over radiometrically complex land surfaces. However, ShARP uses a large database of input rainfall and output brightness temperatures, which might be undersampled. Furthermore, it is not capable to discriminate between solid and liquid phase of precipitation and specifically discriminate the background snow-cover emission and its contamination effects on the retrievals. We address these problems by extending it to a new Bayesian land-atmosphere retrieval framework (ShARP-L) that allows joint retrievals of atmospheric constituents and land surface physical properties. Using modern sparse approximation techniques, the database is reduced to atomic microwave signatures in a family of compact class consistent dictionaries. These dictionaries can efficiently represent the entire database and allow us to discriminate between different land-atmosphere states. First the algorithm makes use of the dictionaries to detect the phase of the precipitation and type of the land-cover and then it estimates the physical properties of precipitation and snow cover using an extended version of the Dantzig Selector, which is robust to non-Gaussian and correlated geophysical noise. Promising results are presented in retrievals of snowfall and snow-cover over coastal orographic features of North America's Coast Range and South America's Andes.

A new strategy for snow-cover mapping using remote sensing data and ensemble based systems techniques

NASA Astrophysics Data System (ADS)

Roberge, S.; Chokmani, K.; De Sève, D.

2012-04-01

The snow cover plays an important role in the hydrological cycle of Quebec (Eastern Canada). Consequently, evaluating its spatial extent interests the authorities responsible for the management of water resources, especially hydropower companies. The main objective of this study is the development of a snow-cover mapping strategy using remote sensing data and ensemble based systems techniques. Planned to be tested in a near real-time operational mode, this snow-cover mapping strategy has the advantage to provide the probability of a pixel to be snow covered and its uncertainty. Ensemble systems are made of two key components. First, a method is needed to build an ensemble of classifiers that is diverse as much as possible. Second, an approach is required to combine the outputs of individual classifiers that make up the ensemble in such a way that correct decisions are amplified, and incorrect ones are cancelled out. In this study, we demonstrate the potential of ensemble systems to snow-cover mapping using remote sensing data. The chosen classifier is a sequential thresholds algorithm using NOAA-AVHRR data adapted to conditions over Eastern Canada. Its special feature is the use of a combination of six sequential thresholds varying according to the day in the winter season. Two versions of the snow-cover mapping algorithm have been developed: one is specific for autumn (from October 1st to December 31st) and the other for spring (from March 16th to May 31st). In order to build the ensemble based system, different versions of the algorithm are created by varying randomly its parameters. One hundred of the versions are included in the ensemble. The probability of a pixel to be snow, no-snow or cloud covered corresponds to the amount of votes the pixel has been classified as such by all classifiers. The overall performance of ensemble based mapping is compared to the overall performance of the chosen classifier, and also with ground observations at meteorological stations.

Inventory of File sref_em.t03z.pgrb221.p1.f06.grib2

Science.gov Websites

surface WEASD 6 hour fcst Water Equivalent of Accumulated Snow Depth [kg/m^2] 016 surface APCP 0-6 hour surface WEASD 0-6 hour acc Water Equivalent of Accumulated Snow Depth [kg/m^2] 019 surface CSNOW 6 hour hour fcst Specific Humidity [kg/kg] 401 surface NCPCP 0-6 hour acc Large-Scale Precipitation (non

Inventory of File nam.t00z.awip2000.tm00.grib2

Science.gov Websites

analysis Pressure Reduced to MSL [Pa] 002 1 hybrid level RIME analysis Rime Factor [non-dim] 003 surface Temperature [K] 014 surface WEASD analysis Water Equivalent of Accumulated Snow Depth [kg/m^2] 015 2 m above ^2] 021 surface WEASD 0-0 day acc f Water Equivalent of Accumulated Snow Depth [kg/m^2] 022 surface

Mean wind patterns and snow depths in an alpine-subalpine ecosystem as measured by damage to coniferous trees

Treesearch

G. L. Wooldridge; R. C. Musselman; R. A. Sommerfeld; D. G. Fox; B. H. Connell

1996-01-01

1. Deformations of Engelmann spruce and subalpine fir trees were surveyed for the purpose of determining climatic wind speeds and directions and snow depths in the Glacier Lakes Ecosystem Experiments Site (GLEES) in the Snowy Range of southeastern Wyoming, USA. Tree deformations were recorded at 50- and 100-m grid intervals over areas of c. 30 ha and 300 ha,...

Influence of overstory on snow depth and density in hemlock-spruce stands: implications for management of deer habitat in Southeastern Alaska.

Treesearch

Thomas A. Hanley; Cathy L. Rose

1987-01-01

Snow depth and density were measured in 33 stands of western hemlock-Sitka spruce (Tsuga heterophylla [Rat] Sarg.-Picea sitchensis [Bong.] Carr.) over a 3-year period. The stands, near Juneau, Alaska, provided broad ranges of species composition, age, over-story canopy coverage, tree density, and wood volume. Stepwise multiple regression analyses indicated that both...

Modelling high Arctic deep permafrost temperature sensitivity in Northeast Greenland based on experimental and field observations

NASA Astrophysics Data System (ADS)

Rasmussen, Laura Helene; Zhang, Wenxin; Hollesen, Jørgen; Cable, Stefanie; Hvidtfeldt Christiansen, Hanne; Jansson, Per-Erik; Elberling, Bo

2017-04-01

Permafrost affected areas in Greenland are expected to experience a marked temperature increase within decades. Most studies have considered near-surface permafrost sensitivity, whereas permafrost temperatures below the depths of zero annual amplitude is less studied despite being closely related to changes in near-surface conditions, such as changes in active layer thermal properties, soil moisture and snow depth. In this study, we measured the sensitivity of thermal conductivity (TC) to gravimetric water content (GWC) in frozen and thawed permafrost sediments from fine-sandy and gravelly deltaic and fine-sandy alluvial deposits in the Zackenberg valley, NE Greenland. We further calibrated a coupled heat and water transfer model, the "CoupModel", for one central delta sediment site with average snow depth and further forced it with meteorology from a nearby delta sediment site with a topographic snow accumulation. With the calibrated model, we simulated deep permafrost thermal dynamics in four 20-year scenarios with changes in surface temperature and active layer (AL) soil moisture: a) 3 °C warming and AL water table at 0.5 m depth; b) 3 °C warming and AL water table at 0.1 m depth; c) 6 °C warming and AL water table at 0.5 m depth and d) 6 °C warming and AL water table at 0.1 m depth. Our results indicate that frozen sediments have higher TC than thawed sediments. All sediments show a positive linear relation between TC and soil moisture when frozen, and a logarithmic one when thawed. Gravelly delta sediments were highly sensitive, but never reached above 12 % GWC, indicating a field effect of water retention capacity. Alluvial sediments are less sensitive to soil moisture than deltaic (fine and coarse) sediments, indicating the importance of unfrozen water in frozen sediment. The deltaic site with snow accumulation had 1 °C higher mean annual ground temperature than the average snow depth site. Permafrost temperature at the depth of 18 m increased with 1.5 °C and 3.5 °C in the scenarios with 3 °C and 6 °C warming, respectively. Increasing the soil moisture had no important additional effect to warming, although an increase in thermal offset was indicated. We conclude that below-ground sediment properties affect the sensitivity of TC to GWC, that surface temperature changes can influence the deep permafrost within a short time scale, and that differences in snow depth affect surface temperatures. Sediment type and the type of precipitation should thus be considered when estimating future High Arctic deep permafrost sensitivity.

Spatiotemporal Variability of Great Lakes Basin Snow Cover Ablation Events

NASA Astrophysics Data System (ADS)

Suriano, Z. J.; Leathers, D. J.

2017-12-01

In the Great Lakes basin of North America, annual runoff is dominated by snowmelt. This snowmelt-induced runoff plays an important role within the hydrologic cycle of the basin, influencing soil moisture availability and driving the seasonal cycle of spring and summer Lake levels. Despite this, relatively little is understood about the patterns and trends of snow ablation event frequency and magnitude within the Great Lakes basin. This study uses a gridded dataset of Canadian and United States surface snow depth observations to develop a regional climatology of snow ablation events from 1960-2009. An ablation event is defined as an inter-diurnal snow depth decrease within an individual grid cell. A clear seasonal cycle in ablation event frequency exists within the basin and peak ablation event frequency is latitudinally dependent. Most of the basin experiences peak ablation frequency in March, while the northern and southern regions of the basin experience respective peaks in April and February. An investigation into the inter-annual frequency of ablation events reveals ablation events significantly decrease within the northeastern and northwestern Lake Superior drainage basins and significantly increase within the eastern Lake Huron and Georgian Bay drainage basins. In the eastern Lake Huron and Georgian Bay drainage basins, larger ablation events are occurring more frequently, and a larger impact to the hydrology can be expected. Trends in ablation events are attributed primarily to changes in snowfall and snow depth across the region.

Seasonal frost conditions in different periglacial landforms in the Eastern Pyrenees from 2003 to 2015

NASA Astrophysics Data System (ADS)

Salvador-Franch, Ferran; Salvà-Catarineu, Montserrat; Oliva, Marc; Gómez-Ortiz, Antonio

2016-04-01

Glaciers shaped the headwaters and valley floors in the Eastern Pyrenees during the Last Glaciation at elevations above 2100-2200 m. Since the deglaciation of these areas, periglacial processes have generated a wide range of periglacial landforms, such as rock glaciers, patterned ground and debris slopes. The role of soil temperatures is decisive for the degree of activity of periglacial processes: cryoturbation, solifluction, frost weathering, etc. Nowadays, periglacial processes in the Eastern Pyrenees are driven by a seasonal frozen layer extending 5-7 months. In general, at 2100 m the seasonal frost reaches 20 cm depth, while at 2700 m reaches 50 cm depth. However, soil temperatures, and thus, periglacial processes are strongly controlled by the large interannual variability of the snow cover. With the purpose of understanding the rhythm and intensity of soil freezing/thawing in 2003 we set up several monitoring sites along a vertical transect from the valley floors (1100 m) to the high plateaus (2700 m) across the southern slope of the Puigpedrós massif (2914 m), in the Eastern Pyrenees. The monitoring of soil temperatures has been conducted from 2003 to 2015 in different periglacial landforms using UTL and Hobo loggers. These loggers were installed at depths of 5, 20 and 50 cm at five sites: Calmquerdós (2730 m), Malniu (2230 m), La Feixa (2150 m), Meranges (1600 m) and Das (1097 m). Air temperatures used as reference come from two automatic stations of the Catalan Meteorological Survey in Malniu and Das, and with two loggers installed in La Feixa and Meranges. No permafrost regime was detected in none of the sites. Data shows evidence of the control of snow cover on the depth of the frozen layer and on the number of freeze-thaw cycles. Air temperatures at 2000-2200 m show a mean of 150 freeze-thaw cycles per year. In La Feixa, with very thin snow cover, only 67 cycles are recorded at 5 cm depth and 5 cycles at 50 cm depth. In Malniu, located at a higher elevation showing a thicker and longer snow cover, only 17 freeze-thaw cycles per year are recorded at 5 cm depth, with no cycles recorded at 50 cm depth. Soils remain unfrozen during years with a very thick snow cover. The snow cover is also largely conditioned by the microtopography and exposure to the dominant winds. These factors condition the distribution, duration and intensity of the frozen ground and, thus, determine the intensity of periglacial processes in these areas.

Ground thermal conditions along a vertical transect with contrasted topography in a high mountain Mediterranean environment (Puigpedrós massif, eastern Pyrenees), from 2003 to 2014

NASA Astrophysics Data System (ADS)

Salvador-Franch, Ferran; Salvà-Catarineu, Montserrat; Oliva, Marc; Gómez-Ortiz, Antonio

2015-04-01

During the Last Glaciation glaciers shaped the headwaters and valley floors in the Eastern Pyrenees above 2100-2200 m. Since the deglaciation of these high mountain environments, periglacial processes have generated rock glaciers, patterned ground and debris slopes. The role of soil temperatures is decisive regarding the contemporary activity of several processes: cryoturbation, solifluction, frost weathering, etc. Nowadays, periglacial processes are driven by a seasonal frozen layer extending 4-5 months. At 2100 m the seasonal frost reaches 20 cm depth, while at 2700 m reaches 50 cm depth. However, soil temperatures, and thus, periglacial processes are strongly controlled by the large interannual variability of the snow cover. With the purpose of understanding the rhythm and intensity of soil freezing/thawing we have set up several monitoring sites along a vertical transect from the high plateaus (2700 m) to the valley floors (1100 m) across the southern slope of the Puigpedrós massif (2914 m), in the Eastern Pyrenees. The monitoring of soil temperatures extends from 2003 to 2014. TinyTalk, UTL and Hobo loggers have been used in this study. These loggers were installed at depths of -5, -20 and -50 cm at five sites: Calmquerdós (2730 m), Malniu (2230 m), La Feixa (2150 m), Meranges (1600 m) and Das (1097 m). Air temperatures used as reference come from two automatic stations of the Catalan Meteorological Survey (Malniu, Das) as well as from two loggers installed in La Feixa and Meranges. Data shows the control of snow cover on the depth of the frozen layer and on the number of freeze-thaw cycles. Air temperatures at 2000-2200 m show a mean of 150 freeze-thaw cycles per year. In La Feixa, with very thin snow cover, only 67 cycles are recorded at 5 cm depth and 5 cycles at 50 cm depth. In Malniu, located at a higher elevation showing a thicker and longer snow cover, only 17 freeze-thaw cycles per year are recorded at 5 cm depth, with no cycles recorded at 50 cm depth. Soils remain unfrozen during years with a very thick snow cover. The snow cover is also largely conditioned by the microtopography and exposure to the dominant winds. These factors condition the distribution, duration and intensity of the frozen ground and, thus, determine the intensity of periglacial processes in these areas.

The temporal dynamics of carbon dioxide under snow in a high elevation Rocky Mountain subalpine forest and meadow

Treesearch

R. C. Musselman; W. J. Massman; J. M. Frank; J. L. Korfmacher

2005-01-01

Carbon dioxide (CO2) concentration under snow was examined through two winter seasons at a 3100 m elevation subalpine site in the Snowy Range of Wyoming. CO2 was monitored every half hour at the soil/snow interface, and at about 25 cm soil depth the second year, in a meadow and in an adjacent forest. CO2 under snow in the meadow was significantly higher than that in...

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.

Improvements in AVHRR Daytime Cloud Detection Over the ARM NSA Site

NASA Technical Reports Server (NTRS)

Chakrapani, V.; Spangenberg, D. A.; Doelling, D. R.; Minnis, P.; Trepte, Q. Z.; Arduini, R. F.

2001-01-01

Clouds play an important role in the radiation budget over Arctic and Antarctic. Because of limited surface observing capabilities, it is necessary to detect clouds over large areas using satellite imagery. At low and mid-latitudes, satellite-observed visible (VIS; 0.65 micrometers) and infrared (IR; 11 micrometers) radiance data are used to derive cloud fraction, temperature, and optical depth. However, the extreme variability in the VIS surface albedo makes the detection of clouds from satellite a difficult process in polar regions. The IR data often show that the surface is nearly the same temperature or even colder than clouds, further complicating cloud detection. Also, the boundary layer can have large areas of haze, thin fog, or diamond dust that are not seen in standard satellite imagery. Other spectral radiances measured by satellite imagers provide additional information that can be used to more accurately discriminate clouds from snow and ice. Most techniques currently use a fixed reflectance or temperature threshold to decide between clouds and clear snow. Using a subjective approach, Minnis et al. (2001) found that the clear snow radiance signatures vary as a function of viewing and illumination conditions as well as snow condition. To routinely process satellite imagery over polar regions with an automated algorithm, it is necessary to account for this angular variability and the change in the background reflectance as snow melts, vegetation grows over land, and melt ponds form on pack ice. This paper documents the initial satellite-based cloud product over the Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) site at Barrow for use by the modeling community. Cloud amount and height are determined subjectively using an adaptation of the methodology of Minnis et al. (2001) and the radiation fields arc determined following the methods of Doelling et al. (2001) as applied to data taken during the Surface Heat and Energy Budget of the Arctic (SHEBA). The procedures and data produced in this empirically based analysis will also facilitate the development of the automated algorithm for future processing of satellite data over the ARM NSA domain. Results are presented for May, June, and July 1998. ARM surface data are use to partially validate the results taken directly over the ARM site.

SWEAT: Snow Water Equivalent with AlTimetry

NASA Astrophysics Data System (ADS)

Agten, Dries; Benninga, Harm-Jan; Diaz Schümmer, Carlos; Donnerer, Julia; Fischer, Georg; Henriksen, Marie; Hippert Ferrer, Alexandre; Jamali, Maryam; Marinaci, Stefano; Mould, Toby JD; Phelan, Liam; Rosker, Stephanie; Schrenker, Caroline; Schulze, Kerstin; Emanuel Telo Bordalo Monteiro, Jorge

2017-04-01

To study how the water cycle changes over time, satellite and airborne remote sensing missions are typically employed. Over the last 40 years of satellite missions, the measurement of true water inventories stored in sea and land ice within the cryosphere have been significantly hindered by uncertainties introduced by snow cover. Being able to determine the thickness of this snow cover would act to reduce such error, improving current estimations of hydrological and climate models, Earth's energy balance (albedo) calculations and flood predictions. Therefore, the target of the SWEAT (Snow Water Equivalent with AlTimetry) mission is to directly measure the surface Snow Water Equivalent (SWE) on sea and land ice within the polar regions above 60°and below -60° latitude. There are no other satellite missions currently capable of directly measuring SWE. In order to achieve this, the proposed mission will implement a novel combination of Ka- and Ku-band radioaltimeters (active microwave sensors), capable of penetrating into the snow microstructure. The Ka-band altimeter (λ ≈ 0.8 cm) provides a low maximum snow pack penetration depth of up to 20 cm for dry snow at 37 GHz, since the volume scattering of snow dominates over the scattering caused by the underlying ice surface. In contrast, the Ku-band altimeter (λ ≈ 2 cm) provides a high maximum snowpack penetration depth of up to 15 m in high latitudes regions with dry snow, as volume scattering is decreased by a factor of 55. The combined difference in Ka- and Ku-band signal penetration results will provide more accurate and direct determination of SWE. Therefore, the SWEAT mission aims to improve estimations of global SWE interpreted from passive microwave products, and improve the reliability of numerical snow and climate models.

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.

Inventory of File sref_nmb.t03z.pgrb221.p1.f06.grib2

Science.gov Websites

surface WEASD 6 hour fcst Water Equivalent of Accumulated Snow Depth [kg/m^2] 016 surface APCP 3-6 hour surface WEASD 3-6 hour acc Water Equivalent of Accumulated Snow Depth [kg/m^2] 019 surface CSNOW 6 hour surface NCPCP 3-6 hour acc Large-Scale Precipitation (non-convective) [kg/m^2] 404 surface SNOM 3-6 hour

Inventory of File sref_nmm.t03z.pgrb221.p1.f06.grib2

Science.gov Websites

surface WEASD 6 hour fcst Water Equivalent of Accumulated Snow Depth [kg/m^2] 016 surface APCP 3-6 hour surface WEASD 3-6 hour acc Water Equivalent of Accumulated Snow Depth [kg/m^2] 019 surface CSNOW 6 hour surface NCPCP 3-6 hour acc Large-Scale Precipitation (non-convective) [kg/m^2] 404 surface SNOM 3-6 hour

Snow cover and extreme winter warming events control flower abundance of some, but not all species in high arctic Svalbard

PubMed Central

Semenchuk, Philipp R; Elberling, Bo; Cooper, Elisabeth J

2013-01-01

Abstract The High Arctic winter is expected to be altered through ongoing and future climate change. Winter precipitation and snow depth are projected to increase and melt out dates change accordingly. Also, snow cover and depth will play an important role in protecting plant canopy from increasingly more frequent extreme winter warming events. Flower production of many Arctic plants is dependent on melt out timing, since season length determines resource availability for flower preformation. We erected snow fences to increase snow depth and shorten growing season, and counted flowers of six species over 5 years, during which we experienced two extreme winter warming events. Most species were resistant to snow cover increase, but two species reduced flower abundance due to shortened growing seasons. Cassiope tetragona responded strongly with fewer flowers in deep snow regimes during years without extreme events, while Stellaria crassipes responded partly. Snow pack thickness determined whether winter warming events had an effect on flower abundance of some species. Warming events clearly reduced flower abundance in shallow but not in deep snow regimes of Cassiope tetragona, but only marginally for Dryas octopetala. However, the affected species were resilient and individuals did not experience any long term effects. In the case of short or cold summers, a subset of species suffered reduced reproductive success, which may affect future plant composition through possible cascading competition effects. Extreme winter warming events were shown to expose the canopy to cold winter air. The following summer most of the overwintering flower buds could not produce flowers. Thus reproductive success is reduced if this occurs in subsequent years. We conclude that snow depth influences flower abundance by altering season length and by protecting or exposing flower buds to cold winter air, but most species studied are resistant to changes. Winter warming events, often occurring together with rain, can substantially remove snow cover and thereby expose plants to cold winter air. Depending on morphology, different parts of the plant can be directly exposed. On this picture, we see Dryas octopetala seed heads from the previous growing season protrude through the remaining ice layer after a warming event in early 2010. The rest of the plant, including meristems and flower primordia, are still somewhat protected by the ice. In the background we can see a patch of Cassiope tetragona protruding through the ice; in this case, the whole plant including flower primordia is exposed, which might be one reason why this species experienced a loss of flowers the following season. Photograph by Philipp Semenchuk. PMID:24567826

Snow-mediated ptarmigan browsing and shrub expansion in arctic Alaska

Treesearch

Ken D. Tape; Rachel Lord; Hans-Peter Marshall; Roger W. Ruess

2010-01-01

Large, late-winter ptarmigan migrations heavily impact the shoot, plant, and patch architecture of shrubs that remain above the snow surface. Ptarmigan browsing on arctic shrubs was assessed in the vicinity of Toolik Lake, on the north side of the Brooks Range in Alaska. Data were collected in early May 2007, at maximum snow depth, after the bulk of the ptarmigan...

Estimating snowpack density from Albedo measurement

Treesearch

James L. Smith; Howard G. Halverson

1979-01-01

Snow is a major source of water in Western United States. Data on snow depth and average snowpack density are used in mathematical models to predict water supply. In California, about 75 percent of the snow survey sites above 2750-meter elevation now used to collect data are in statutory wilderness areas. There is need for a method of estimating the water content of a...

Past and projected future changes in snowpack and soil frost at the Hubbard Brook Experimental Forest, New Hampshire, USA

USDA-ARS?s Scientific Manuscript database

Long-term data from the Hubbard Brook Experimental Forest in New Hampshire show that air temperature has increased by about 1 °C over the last half century. The warmer climate has caused significant declines in snow depth, snow water equivalent, and snow cover duration. Paradoxically, it has been su...

Seasonal Snow Extent and Snow Volume in South America Using SSM/I Passive Microwave Data

NASA Technical Reports Server (NTRS)

Foster, James L.; Chang, A. T. C.; Hall, D. K.; Kelly, R.; Houser, Paul (Technical Monitor)

2001-01-01

Seasonal snow cover in South America was examined in this study using passive microwave satellite data from the Special Sensor Microwave Imagers (SSM/I) on board Defense Meteorological Satellite Program (DMSP) satellites. For the period from 1992-1998, both snow cover extent and snow depth (snow mass) were investigated during the winter months (May-August) in the Patagonia region of Argentina. Since above normal temperatures in this region are typically above freezing, the coldest winter month was found to be not only the month having the most extensive snow cover but also the month having the deepest snows. For the seven-year period of this study, the average snow cover extent (May-August) was about 0.46 million sq km and the average monthly snow mass was about 1.18 x 10(exp 13) kg. July 1992 was the month having the greatest snow extent (nearly 0.8 million sq km) and snow mass (approximately 2.6 x 10(exp 13) kg).

Surface and Atmospheric Contributions to Passive Microwave Brightness Temperatures for Falling Snow Events

NASA Technical Reports Server (NTRS)

Skofronick-Jackson, Gail; Johnson, Benjamin T.

2011-01-01

Physically based passive microwave precipitation retrieval algorithms require a set of relationships between satellite -observed brightness temperatures (TBs) and the physical state of the underlying atmosphere and surface. These relationships are nonlinear, such that inversions are ill ]posed especially over variable land surfaces. In order to elucidate these relationships, this work presents a theoretical analysis using TB weighting functions to quantify the percentage influence of the TB resulting from absorption, emission, and/or reflection from the surface, as well as from frozen hydrometeors in clouds, from atmospheric water vapor, and from other contributors. The percentage analysis was also compared to Jacobians. The results are presented for frequencies from 10 to 874 GHz, for individual snow profiles, and for averages over three cloud-resolving model simulations of falling snow. The bulk structure (e.g., ice water path and cloud depth) of the underlying cloud scene was found to affect the resultant TB and percentages, producing different values for blizzard, lake effect, and synoptic snow events. The slant path at a 53 viewing angle increases the hydrometeor contributions relative to nadir viewing channels. Jacobians provide the magnitude and direction of change in the TB values due to a change in the underlying scene; however, the percentage analysis provides detailed information on how that change affected contributions to the TB from the surface, hydrometeors, and water vapor. The TB percentage information presented in this paper provides information about the relative contributions to the TB and supplies key pieces of information required to develop and improve precipitation retrievals over land surfaces.

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.

Putting the Capital 'A' in CoCoRAHS: A Pilot Program to Measure Albedo using the Community Collaborative Rain, Hail, and Snow (CoCoRaHS) Network

NASA Astrophysics Data System (ADS)

Burakowski, E. A.; Stampone, M. D.; Wake, C. P.; Dibb, J. E.

2012-12-01

The Community Collaborative Rain, Hail, and Snow (CoCoRaHS) Network, started in 1998 as a community-based network of volunteer weather observer in Colorado, is the single largest provider of daily precipitation observations in the United States. We embrace the CoCoRaHS mission to use low-cost measurement tools, provide training and education, and utilize an interactive website to collect high quality albedo data for research and education applications. We trained a select sub-set of CoCoRaHS's eighteen most enthusiastic, self-proclaimed 'weather nuts' in the state of New Hampshire to collect surface albedo, snow depth, and snow density measurements between 23-Nov-2011 and 15-Mar-2012. At less than 700 per observer, the low-cost albedo data falls within ±0.05 of albedo values collected from a First Class Kipp and Zonen Albedometer (CMA6) at local solar noon. CoCoRaHS albedo values range from 0.99 for fresh snow to 0.34 for shallow, aged snow. Snow-free albedo ranges from 0.09 to 0.39, depending on ground cover. Albedo is found to increase logarithmically with snow depth and decrease linearly with snow density. The latter relationship with snow density is inferred to be a proxy for increasing snow grain size as snowpack ages and compacts, supported by spectral albedo measurements collected with an ASD FieldSpec4 spectrometer. The newly established albedo network also serves as a development test bed for interactive online mapping and graphing applications for CoCoRaHS observers to investigate spatial and temporal patterns in albedo, snow depth, and snow density (www.cocorahs-albedo.org). The 2012-2013 field season will include low-cost infrared temperature guns (<40 each) to investigate the relationship between surface albedo and skin temperature. We have also recruited middle- and high-schools as volunteer observers and are working with the teachers to develop curriculum and lesson plans that utilize the low-cost measurement tools provided by CoCoRAHS. CoCoRAHS data will provide critical spatially distributed measurements of surface data that will be used to validate and improve land surface modeling of New Hampshire climate under different land cover scenarios. Building on the success of the first season, the newly established albedo network shows promise to put the capital 'A' in CoCoRAHS.Figure 1. (a) Map of Community Collaborative Rain, Hail, and Snow (CoCoRAHS) volunteers participating in the pilot albedo project, and (b) CoCoRAHS snow measurement kit.

Strategies for cloud-top phase determination: differentiation between thin cirrus clouds and snow in manual (ground truth) analyses

NASA Astrophysics Data System (ADS)

Hutchison, Keith D.; Etherton, Brian J.; Topping, Phillip C.

1996-12-01

Quantitative assessments on the performance of automated cloud analysis algorithms require the creation of highly accurate, manual cloud, no cloud (CNC) images from multispectral meteorological satellite data. In general, the methodology to create ground truth analyses for the evaluation of cloud detection algorithms is relatively straightforward. However, when focus shifts toward quantifying the performance of automated cloud classification algorithms, the task of creating ground truth images becomes much more complicated since these CNC analyses must differentiate between water and ice cloud tops while ensuring that inaccuracies in automated cloud detection are not propagated into the results of the cloud classification algorithm. The process of creating these ground truth CNC analyses may become particularly difficult when little or no spectral signature is evident between a cloud and its background, as appears to be the case when thin cirrus is present over snow-covered surfaces. In this paper, procedures are described that enhance the researcher's ability to manually interpret and differentiate between thin cirrus clouds and snow-covered surfaces in daytime AVHRR imagery. The methodology uses data in up to six AVHRR spectral bands, including an additional band derived from the daytime 3.7 micron channel, which has proven invaluable for the manual discrimination between thin cirrus clouds and snow. It is concluded that while the 1.6 micron channel remains essential to differentiate between thin ice clouds and snow. However, this capability that may be lost if the 3.7 micron data switches to a nighttime-only transmission with the launch of future NOAA satellites.

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.

Finland Validation of the New Blended Snow Product

NASA Technical Reports Server (NTRS)

Kim, E. J.; Casey, K. A.; Hallikainen, M. T.; Foster, J. L.; Hall, D. K.; Riggs, G. A.

2008-01-01

As part of an ongoing effort to validate satellite remote sensing snow products for the recentlydeveloped U.S. Air Force Weather Agency (AFWA) - NASA blended snow product, Satellite and in-situ data for snow extent and snow water equivalent (SWE) are evaluated in Finland for the 2006-2007 snow season Finnish Meteorological Institute (FMI) daily weather station data and Finnish Environment Institute (SYKE) bi-monthly snow course data are used as ground truth. Initial comparison results display positive agreement between the AFWA NASA Snow Algorithm (ANSA) snow extent and SWE maps and in situ data, with discrepancies in accordance with known AMSR-E and MODIS snow mapping limitations. Future ANSA product improvement plans include additional validation and inclusion of fractional snow cover in the ANSA data product. Furthermore, the AMSR-E 19 GHz (horizontal channel) with the difference between ascending and descending satellite passes (Diurnal Amplitude Variations, DAV) will be used to detect the onset of melt, and QuikSCAT scatterometer data (14 GHz) will be used to map areas of actively melting snow.

Local Variability in Firn Layering and Compaction Rates Using GPR Data, Depth-Density Profiles, and In-Situ Reflectors in the Dry Snow Zone Near Summit Station, Greenland

NASA Astrophysics Data System (ADS)

Lines, A.; Elliott, J.; Ray, L.; Albert, M. R.

2017-12-01

Understanding the surface mass balance (SMB) of the Greenland ice sheet is critical to evaluating its response to a changing climate. A key factor in translating satellite and airborne elevation measurements of the ice sheet to SMB is understanding natural variability of firn layer depth and the relative compaction rate of these layers. A site near Summit Station, Greenland was chosen to investigate the variation in layering across a 100m by 100m grid using a 900 MHz and a 2.6 GHz ground penetrating radar (GPR) antenna. These radargrams were ground truthed by taking depth density profiles of five 2m snow pits and five 5m firn cores within the 100m by 100m grid. Combining these measurements with the accumulation data from the nearby ICECAPS weekly bamboo forest measurements, it's possible to see how the snow deposition from individual storm events can vary over a small area. Five metal reflectors were also placed on the surface of the snow in the bounds of the grid to serve as reference reflectors for similar measurements that will be taken in the 2018 field season at Summit Station. This will assist in understanding how one year of accumulation in the dry snow zone impacts compaction and how this rate can vary over a small area.

Long-term deepened snow promotes tundra evergreen shrub growth and summertime ecosystem net CO2 gain but reduces soil carbon and nutrient pools.

PubMed

Christiansen, Casper T; Lafreniére, Melissa J; Henry, Gregory H R; Grogan, Paul

2018-02-07

Arctic climate warming will be primarily during winter, resulting in increased snowfall in many regions. Previous tundra research on the impacts of deepened snow has generally been of short duration. Here, we report relatively long-term (7-9 years) effects of experimentally deepened snow on plant community structure, net ecosystem CO 2 exchange (NEE), and soil biogeochemistry in Canadian Low Arctic mesic shrub tundra. The snowfence treatment enhanced snow depth from 0.3 to ~1 m, increasing winter soil temperatures by ~3°C, but with no effect on summer soil temperature, moisture, or thaw depth. Nevertheless, shoot biomass of the evergreen shrub Rhododendron subarcticum was near-doubled by the snowfences, leading to a 52% increase in aboveground vascular plant biomass. Additionally, summertime NEE rates, measured in collars containing similar plant biomass across treatments, were consistently reduced ~30% in the snowfenced plots due to decreased ecosystem respiration rather than increased gross photosynthesis. Phosphate in the organic soil layer (0-10 cm depth) and nitrate in the mineral soil layer (15-25 cm depth) were substantially reduced within the snowfences (47-70 and 43%-73% reductions, respectively, across sampling times). Finally, the snowfences tended (p = .08) to reduce mineral soil layer C% by 40%, but with considerable within- and among plot variation due to cryoturbation across the landscape. These results indicate that enhanced snow accumulation is likely to further increase dominance of R. subarcticum in its favored locations, and reduce summertime respiration and soil biogeochemical pools. Since evergreens are relatively slow growing and of low stature, their increased dominance may constrain vegetation-related feedbacks to climate change. We found no evidence that deepened snow promoted deciduous shrub growth in mesic tundra, and conclude that the relatively strong R. subarcticum response to snow accumulation may explain the extensive spatial variability in observed circumpolar patterns of evergreen and deciduous shrub growth over the past 30 years. © 2018 John Wiley & Sons Ltd.

Determining and validating the effective snow grain size and pollution amount from satellite measurements in polar regions

NASA Astrophysics Data System (ADS)

Heygster, Georg; Wiebe, Heidrun; Zege, Eleonora; Aoki, Teruo; Kokhanovsky, Alexander; Katsev, I. L.; Prikhach, Alexander; Malinka, A. V.; Grudo, J. O.

Sea ice is part of the cryosphere, besides the ice sheets, ice shelves, and glaciers. Compared to the other components, it is small in volume but large in area. Snow on top of the sea ice is even less in mass, but strongly influences the albedo of the sea ice, and thus the local radiative balance which plays an essential role for the albedo feedback process. The albedo of snow does not have a constant value, but depends on the grain size (smaller grains have higher albedo) and the amount of pollution like soot and in fewer cases dust which both lower the albedo significantly. Our retrievals are based on an algorithm that uses optical satellite observations to calculate the size of the snow grains and its pollution, the Snow Grain Size and Pollution amount (SGSP) algorithm (Zege et al. 2009) Here we present the algorithm and its operational implementation, based on MODIS data, to calculate the snow grain size and pollution amount in near real time, and a destriping procedure. The resulting data are used for a validation study by comparing them to in situ data taken at several places near Hokkaido (Japan), Barrow (Alaska, USA) between 2002 and 2005 and in Antarctica in 2003. While each single set of observations, in the Arctic and in the Antarctic, shows encouraging correlations, the regression lines between in situ and satellite retrievals of the snow grain size are quite different, with slopes of 1.01 (Arctic and Japan) and 0.44 (Antarctica). The discrepancy remains unresolved, emphasizing the need for more in situ observations for validation. Among the potential reasons for the discrepancy are the different kinds of in situ measured snow grain sizes. The crystal size was measured in the Arctic (Barrow) and Japan (Hokkaido) using a lens and optical methods have been used in Antarctica.

Mapping snow depth return levels: smooth spatial modeling versus station interpolation

NASA Astrophysics Data System (ADS)

Blanchet, J.; Lehning, M.

2010-12-01

For adequate risk management in mountainous countries, hazard maps for extreme snow events are needed. This requires the computation of spatial estimates of return levels. In this article we use recent developments in extreme value theory and compare two main approaches for mapping snow depth return levels from in situ measurements. The first one is based on the spatial interpolation of pointwise extremal distributions (the so-called Generalized Extreme Value distribution, GEV henceforth) computed at station locations. The second one is new and based on the direct estimation of a spatially smooth GEV distribution with the joint use of all stations. We compare and validate the different approaches for modeling annual maximum snow depth measured at 100 sites in Switzerland during winters 1965-1966 to 2007-2008. The results show a better performance of the smooth GEV distribution fitting, in particular where the station network is sparser. Smooth return level maps can be computed from the fitted model without any further interpolation. Their regional variability can be revealed by removing the altitudinal dependent covariates in the model. We show how return levels and their regional variability are linked to the main climatological patterns of Switzerland.

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.

Research of microwave scattering properties of snow fields

NASA Technical Reports Server (NTRS)

Angelakos, D. J.

1978-01-01

The results obtained in the research program of microwave scattering properties of snow fields are presented. Experimental results are presented showing backscatter dependence on frequency (5.8-8.0 GHz), angle of incidence (0-60 degrees), snow wetness (time of day), and frequency modulation (0-500 MHz). Theoretical studies are being made of the inverse scattering problem yielding some preliminary results concerning the determination of the dielectric constant of the snow layer. The experimental results lead to the following conclusions: snow layering affects backscatter, layer response is significant up to 45 degrees of incidence, wetness modifies snow layer effects, frequency modulation masks the layer response, and for the proper choice of probing frequency and for nominal snow depths, it appears to be possible to measure the effective dielectric constant and the corresponding water content of a snow pack.

Increased nitrogen leaching following soil freezing is due to decreased root uptake in a northern hardwood forest

Treesearch

John L. Campbell; Anne M. Socci; Pamela H. Templer

2014-01-01

The depth and duration of snow pack is declining in the northeastern United States as a result of warming air temperatures. Since snow insulates soil, a decreased snow pack can increase the frequency of soil freezing, which has been shown to have important biogeochemical implications. One of the most notable effects of soil freezing is increased inorganic nitrogen...

Forest influences on snow accumulation and snowmelt at the Hubbard Brook Experimental Forest, New Hampshire, USA

Treesearch

Colin A. Penn; Beverley C. Wemple; John L. Campbell

2012-01-01

Many factors influence snow depth, water content and duration in forest ecosystems. The effects of forest cover and canopy gap geometry on snow accumulation has been well documented in coniferous forests of western North America and other regions; however, few studies have evaluated these effects on snowpack dynamics in mixed deciduous forests of the northeastern USA....

Dynamics of glide avalanches and snow gliding

NASA Astrophysics Data System (ADS)

Ancey, Christophe; Bain, Vincent

2015-09-01

In recent years, due to warmer snow cover, there has been a significant increase in the number of cases of damage caused by gliding snowpacks and glide avalanches. On most occasions, these have been full-depth, wet-snow avalanches, and this led some people to express their surprise: how could low-speed masses of wet snow exert sufficiently high levels of pressure to severely damage engineered structures designed to carry heavy loads? This paper reviews the current state of knowledge about the formation of glide avalanches and the forces exerted on simple structures by a gliding mass of snow. One particular difficulty in reviewing the existing literature on gliding snow and on force calculations is that much of the theoretical and phenomenological analyses were presented in technical reports that date back to the earliest developments of avalanche science in the 1930s. Returning to these primary sources and attempting to put them into a contemporary perspective are vital. A detailed, modern analysis of them shows that the order of magnitude of the forces exerted by gliding snow can indeed be estimated correctly. The precise physical mechanisms remain elusive, however. We comment on the existing approaches in light of the most recent findings about related topics, including the physics of granular and plastic flows, and from field surveys of snow and avalanches (as well as glaciers and debris flows). Methods of calculating the forces exerted by glide avalanches are compared quantitatively on the basis of two case studies. This paper shows that if snow depth and density are known, then certain approaches can indeed predict the forces exerted on simple obstacles in the event of glide avalanches or gliding snow cover.

Uncertainty in Estimates of Net Seasonal Snow Accumulation on Glaciers from In Situ Measurements

NASA Astrophysics Data System (ADS)

Pulwicki, A.; Flowers, G. E.; Radic, V.

2017-12-01

Accurately estimating the net seasonal snow accumulation (or "winter balance") on glaciers is central to assessing glacier health and predicting glacier runoff. However, measuring and modeling snow distribution is inherently difficult in mountainous terrain, resulting in high uncertainties in estimates of winter balance. Our work focuses on uncertainty attribution within the process of converting direct measurements of snow depth and density to estimates of winter balance. We collected more than 9000 direct measurements of snow depth across three glaciers in the St. Elias Mountains, Yukon, Canada in May 2016. Linear regression (LR) and simple kriging (SK), combined with cross correlation and Bayesian model averaging, are used to interpolate estimates of snow water equivalent (SWE) from snow depth and density measurements. Snow distribution patterns are found to differ considerably between glaciers, highlighting strong inter- and intra-basin variability. Elevation is found to be the dominant control of the spatial distribution of SWE, but the relationship varies considerably between glaciers. A simple parameterization of wind redistribution is also a small but statistically significant predictor of SWE. The SWE estimated for one study glacier has a short range parameter (90 m) and both LR and SK estimate a winter balance of 0.6 m w.e. but are poor predictors of SWE at measurement locations. The other two glaciers have longer SWE range parameters ( 450 m) and due to differences in extrapolation, SK estimates are more than 0.1 m w.e. (up to 40%) lower than LR estimates. By using a Monte Carlo method to quantify the effects of various sources of uncertainty, we find that the interpolation of estimated values of SWE is a larger source of uncertainty than the assignment of snow density or than the representation of the SWE value within a terrain model grid cell. For our study glaciers, the total winter balance uncertainty ranges from 0.03 (8%) to 0.15 (54%) m w.e. depending primarily on the interpolation method. Despite the challenges associated with accurately and precisely estimating winter balance, our results are consistent with the previously reported regional accumulation gradient.

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.

Snow grain size and shape distributions in northern Canada

NASA Astrophysics Data System (ADS)

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

2016-12-01

Pioneer snow work in the 1970s and 1980s proposed new approaches to retrieve snow depth and water equivalent from space using passive microwave brightness temperatures. Numerous research work have led to the realization that microwave approaches depend strongly on snow grain morphology (size and shape), which was poorly parameterized since recently, leading to strong biases in the retrieval calculations. Related uncertainties from space retrievals and the development of complex thermodynamic multilayer snow and emission models motivated several research works on the development of new approaches to quantify snow grain metrics given the lack of field measurements arising from the sampling constraints of such variable. This presentation focuses on the unknown size distribution of snow grain sizes. Our group developed a new approach to the `traditional' measurements of snow grain metrics where micro-photographs of snow grains are taken under angular directional LED lighting. The projected shadows are digitized so that a 3D reconstruction of the snow grains is possible. This device has been used in several field campaigns and over the years a very large dataset was collected and is presented in this paper. A total of 588 snow photographs from 107 snowpits collected during the European Space Agency (ESA) Cold Regions Hydrology high-resolution Observatory (CoReH2O) mission concept field campaign, in Churchill, Manitoba Canada (January - April 2010). Each of the 588 photographs was classified as: depth hoar, rounded, facets and precipitation particles. A total of 162,516 snow grains were digitized across the 588 photographs, averaging 263 grains/photo. Results include distribution histograms for 5 `size' metrics (projected area, perimeter, equivalent optical diameter, minimum axis and maximum axis), and 2 `shape' metrics (eccentricity, major/minor axis ratio). Different cumulative histograms are found between the grain types, and proposed fits are presented with the Kernel distribution function. Finally, a comparison with the Specific Surface Area (SSA) derived from reflectance values using the Infrared Integrating Sphere (IRIS) highlight different power statistical fits for the 5 `size' metrics.

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.

A Full Snow Season in Yellowstone: A Database of Restored Aqua Band 6

NASA Technical Reports Server (NTRS)

Gladkova, Irina; Grossberg, Michael; Bonev, George; Romanov, Peter; Riggs, George; Hall, Dorothy

2013-01-01

The algorithms for estimating snow extent for the Moderate Resolution Imaging Spectroradiometer (MODIS) optimally use the 1.6- m channel which is unavailable for MODIS on Aqua due to detector damage. As a test bed to demonstrate that Aqua band 6 can be restored, we chose the area surrounding Yellowstone and Grand Teton national parks. In such rugged and difficult-to-access terrain, satellite images are particularly important for providing an estimation of snow-cover extent. For the full 2010-2011 snow season covering the Yellowstone region, we have used quantitative image restoration to create a database of restored Aqua band 6. The database includes restored radiances, normalized vegetation index, normalized snow index, thermal data, and band-6-based snow-map products. The restored Aqua-band-6 data have also been regridded and combined with Terra data to produce a snow-cover map that utilizes both Terra and Aqua snow maps. Using this database, we show that the restored Aqua-band-6-based snow-cover extent has a comparable performance with respect to ground stations to the one based on Terra. The result of a restored band 6 from Aqua is that we have an additional band-6 image of the Yellowstone region each day. This image can be used to mitigate cloud occlusion, using the same algorithms used for band 6 on Terra. We show an application of this database of restored band-6 images to illustrate the value of creating a cloud gap filling using the National Aeronautics and Space Administration s operational cloud masks and data from both Aqua and Terra.

A Comparison of Satellite-Derived Snow Maps with a Focus on Ephemeral Snow in North Carolina

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Fuhrmann, Christopher M.; Perry, L. Baker; Riggs, George A.; Robinson, David A.; Foster, James L.

2010-01-01

In this paper, we focus on the attributes and limitations of four commonly-used daily snowcover products with respect to their ability to map ephemeral snow in central and eastern North Carolina. We show that the Moderate-Resolution Imaging Spectroradiometer (MODIS) fractional snow-cover maps can delineate the snow-covered area very well through the use of a fully-automated algorithm, but suffer from the limitation that cloud cover precludes mapping some ephemeral snow. The semi-automated Interactive Multi-sensor Snow and ice mapping system (IMS) and Rutgers Global Snow Lab (GSL) snow maps are often able to capture ephemeral snow cover because ground-station data are employed to develop the snow maps, The Rutgers GSL maps are based on the IMS maps. Finally, the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) provides some good detail of snow-water equivalent especially in deeper snow, but may miss ephemeral snow cover because it is often very thin or wet; the AMSR-E maps also suffer from coarse spatial resolution. We conclude that the southeastern United States represents a good test region for validating the ability of satellite snow-cover maps to capture ephemeral snow cover,

High-resolution LIDAR and ground observations of snow cover in a complex forested terrain in the Sierra Nevada - implications for optical remote sensing of seasonal snow.

NASA Astrophysics Data System (ADS)

Kostadinov, T. S.; Harpold, A.; Hill, R.; McGwire, K.

2017-12-01

Seasonal snow cover is a key component of the hydrologic regime in many regions of the world, especially those in temperate latitudes with mountainous terrain and dry summers. Such regions support large human populations which depend on the mountain snowpack for their water supplies. It is thus important to quantify snow cover accurately and continuously in these regions. Optical remote-sensing methods are able to detect snow and leverage space-borne spectroradiometers with global coverage such as MODIS to produce global snow cover maps. However, snow is harder to detect accurately in mountainous forested terrain, where topography influences retrieval algorithms, and importantly - forest canopies complicate radiative transfer and obfuscate the snow. Current satellite snow cover algorithms assume that fractional snow-covered area (fSCA) under the canopy is the same as the fSCA in the visible portion of the pixel. In-situ observations and first principles considerations indicate otherwise, therefore there is a need for improvement of the under-canopy correction of snow cover. Here, we leverage multiple LIDAR overflights and in-situ observations with a distributed fiber-optic temperature sensor (DTS) to quantify snow cover under canopy as opposed to gap areas at the Sagehen Experimental Forest in the Northern Sierra Nevada, California, USA. Snow-off LIDAR overflights from 2014 are used to create a baseline high-resolution digital elevation model and classify pixels at 1 m resolution as canopy-covered or gap. Low canopy pixels are excluded from the analysis. Snow-on LIDAR overflights conducted by the Airborne Snow Observatory in 2016 are then used to classify all pixels as snow-covered or not and quantify fSCA under canopies vs. in gap areas over the Sagehen watershed. DTS observations are classified as snow-covered or not based on diel temperature fluctuations and used as validation for the LIDAR observations. LIDAR- and DTS-derived fSCA is also compared with retrievals from hyperspectral imaging spectroradiometer (AVIRIS) data. Initial evidence suggest fSCA was generally lower under canopy and that overall snow cover estimates were overestimated as a result. Implications for a canopy correction applicable to coarser-resolution sensors like MODIS are discussed, as are topography and view angle effects.

Past and projected future changes in snowpack and soil frost at the Hubbard Brook Experimental Forest, New Hampshire, USA

Treesearch

John L. Campbell; Scott V. Ollinger; Gerald N. Flerchinger; Haley Wicklein; Katharine Hayhoe; Amey S. Bailey

2010-01-01

Long-term data from the Hubbard Brook Experimental Forest in New Hampshire show that air temperature has increased by about 1 °C over the last half century. The warmer climate has caused significant declines in snow depth, snow water equivalent and snow cover duration. Paradoxically, it has been suggested that warmer air temperatures may result in colder soils...

Optimizing Observations of Sea Ice Thickness and Snow Depth in the Arctic

DTIC Science & Technology

2015-09-30

Region Research and Engineering Laboratory (CRREL), Naval Research Laboratory (NRL) and National Aeronautics and Space Administration ( NASA ) in...and results from this focused effort with data collected during related national and international activities (e.g. other NASA IceBridge sea ice...surface elevation of the snow or ice/air interface, and radar altimetry measurements of the snow/ice interface, taken by NASA IceBridge and NRL

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

A long-term assessment of the variability in winter use of dense conifer cover by female white-tailed deer.

PubMed

Delgiudice, Glenn D; Fieberg, John R; Sampson, Barry A

2013-01-01

Long-term studies allow capture of a wide breadth of environmental variability and a broader context within which to maximize our understanding of relationships to specific aspects of wildlife behavior. The goal of our study was to improve our understanding of the biological value of dense conifer cover to deer on winter range relative to snow depth and ambient temperature. We examined variation among deer in their use of dense conifer cover during a 12-year study period as potentially influenced by winter severity and cover availability. Female deer were fitted with a mixture of very high frequency (VHF, n = 267) and Global Positioning System (GPS, n = 24) collars for monitoring use of specific cover types at the population and individual levels, respectively. We developed habitat composites for four study sites. We fit multinomial response models to VHF (daytime) data to describe population-level use patterns as a function of snow depth, ambient temperature, and cover availability. To develop alternative hypotheses regarding expected spatio-temporal patterns in the use of dense conifer cover, we considered two sets of competing sub-hypotheses. The first set addressed whether or not dense conifer cover was limiting on the four study sites. The second set considered four alternative sub-hypotheses regarding the potential influence of snow depth and ambient temperature on space use patterns. Deer use of dense conifer cover increased the most with increasing snow depth and most abruptly on the two sites where it was most available, suggestive of an energy conservation strategy. Deer use of dense cover decreased the most with decreasing temperatures on the sites where it was most available. At all four sites deer made greater daytime use (55 to >80% probability of use) of open vegetation types at the lowest daily minimum temperatures indicating the importance of thermal benefits afforded from increased exposure to solar radiation. Date-time plots of GPS data (24 hr) allowed us to explore individual diurnal and seasonal patterns of habitat use relative to changes in snow depth. There was significant among-animal variability in their propensity to be found in three density classes of conifer cover and other open types, but little difference between diurnal and nocturnal patterns of habitat use. Consistent with our findings reported elsewhere that snow depth has a greater impact on deer survival than ambient temperature, herein our population-level results highlight the importance of dense conifer cover as snow shelter rather than thermal cover. Collectively, our findings suggest that maximizing availability of dense conifer cover in an energetically beneficial arrangement with quality feeding sites should be a prominent component of habitat management for deer.

A Long-Term Assessment of the Variability in Winter Use of Dense Conifer Cover by Female White-Tailed Deer

PubMed Central

DelGiudice, Glenn D.; Fieberg, John R.; Sampson, Barry A.

2013-01-01

Backgound Long-term studies allow capture of a wide breadth of environmental variability and a broader context within which to maximize our understanding of relationships to specific aspects of wildlife behavior. The goal of our study was to improve our understanding of the biological value of dense conifer cover to deer on winter range relative to snow depth and ambient temperature. Methodology/Principal Findings We examined variation among deer in their use of dense conifer cover during a 12-year study period as potentially influenced by winter severity and cover availability. Female deer were fitted with a mixture of very high frequency (VHF, n = 267) and Global Positioning System (GPS, n = 24) collars for monitoring use of specific cover types at the population and individual levels, respectively. We developed habitat composites for four study sites. We fit multinomial response models to VHF (daytime) data to describe population-level use patterns as a function of snow depth, ambient temperature, and cover availability. To develop alternative hypotheses regarding expected spatio-temporal patterns in the use of dense conifer cover, we considered two sets of competing sub-hypotheses. The first set addressed whether or not dense conifer cover was limiting on the four study sites. The second set considered four alternative sub-hypotheses regarding the potential influence of snow depth and ambient temperature on space use patterns. Deer use of dense conifer cover increased the most with increasing snow depth and most abruptly on the two sites where it was most available, suggestive of an energy conservation strategy. Deer use of dense cover decreased the most with decreasing temperatures on the sites where it was most available. At all four sites deer made greater daytime use (55 to >80% probability of use) of open vegetation types at the lowest daily minimum temperatures indicating the importance of thermal benefits afforded from increased exposure to solar radiation. Date-time plots of GPS data (24 hr) allowed us to explore individual diurnal and seasonal patterns of habitat use relative to changes in snow depth. There was significant among-animal variability in their propensity to be found in three density classes of conifer cover and other open types, but little difference between diurnal and nocturnal patterns of habitat use. Conclusions/Significance Consistent with our findings reported elsewhere that snow depth has a greater impact on deer survival than ambient temperature, herein our population-level results highlight the importance of dense conifer cover as snow shelter rather than thermal cover. Collectively, our findings suggest that maximizing availability of dense conifer cover in an energetically beneficial arrangement with quality feeding sites should be a prominent component of habitat management for deer. PMID:23785421

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.

A new approach to assess the skier additional stress within a multi-layered snowpack

NASA Astrophysics Data System (ADS)

Monti, Fabiano; Gaume, Johan; van Herwijnen, Alec; Schweizer, Jürg

2014-05-01

The physical and mechanical processes of dry-snow slab avalanche formation can be distinguished into two subsequent phases: failure initiation and crack propagation. Several approaches tried to quantify slab avalanche release probability in terms of failure initiation, based on a simple strength-of-material approach (strength vs. stress). Even if it is known that both weak layer and slab properties play a major role in avalanche release, apart from weak layer characteristics, often only the slab thickness and its average density were considered. For calculating the amount of additional stress (e.g. due to a skier) at the depth of the weak layer, the snow cover was often assumed to be a semi-infinite elastic half space in order to apply Boussinesq's theory. However, finite element (FE) calculations have shown that slab layering strongly influences the stress at depth. To avoid FE calculations, we suggest a new approach based on a simplification of multi-layered elasticity theory. It allows computing the additional stress due to a skier at the depth of the weak layer, taking into account the layering of the snow slab and the substratum. The proposed approach was first tested on simplified snow profiles and compared reasonably well with FE calculations. We then implemented the method to refine the classical skier stability index. Using manually observed snow profiles, classified in different stability classes using stability tests, we obtained a satisfactory discrimination power. Lastly, the refined skier stability index was implemented into the 1-D snow cover model SNOWPACK and presented on two case studies. In the future, it will be interesting to implement the proposed method for describing skier-induced stress within a multi-layered snowpack into more complex models which take into account not only failure initiation but also crack propagation.

Natural resources inventory and land evaluation in Switzerland

NASA Technical Reports Server (NTRS)

Haefner, H. (Principal Investigator)

1976-01-01

The author has identified the following significant results. Using MSS channels 5 and 7 and a supervised classification system with a PPD classification algorithm, it was possible to map the exact areal extent of the snow cover and of the transition zone with melting snow patches and snow free parts of various sizes over a large area under different aspects such as relief, exposure, shadows etc. A correlation of the data from ground control, areal underflights and earth resources satellites provided a very accurate interpretation of the melting procedure of snow in high mountains.

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.

The Snow Data System at NASA JPL

NASA Astrophysics Data System (ADS)

Horn, J.; Painter, T. H.; Bormann, K. J.; Rittger, K.; Brodzik, M. J.; Skiles, M.; Burgess, A. B.; Mattmann, C. A.; Ramirez, P.; Joyce, M.; Goodale, C. E.; McGibbney, L. J.; Zimdars, P.; Yaghoobi, R.

2017-12-01

The Snow Data System at NASA JPL includes data processing pipelines built with open source software, Apache 'Object Oriented Data Technology' (OODT). Processing is carried out in parallel across a high-powered computing cluster. The pipelines use input data from satellites such as MODIS, VIIRS and Landsat. They apply algorithms to the input data to produce a variety of outputs in GeoTIFF format. These outputs include daily data for SCAG (Snow Cover And Grain size) and DRFS (Dust Radiative Forcing in Snow), along with 8-day composites and MODICE annual minimum snow and ice calculations. This poster will describe the Snow Data System, its outputs and their uses and applications. It will also highlight recent advancements to the system and plans for the future.

The Snow Data System at NASA JPL

NASA Astrophysics Data System (ADS)

Joyce, M.; Laidlaw, R.; Painter, T. H.; Bormann, K. J.; Rittger, K.; Brodzik, M. J.; Skiles, M.; Burgess, A. B.; Mattmann, C. A.; Ramirez, P.; Goodale, C. E.; McGibbney, L. J.; Zimdars, P.; Yaghoobi, R.

2016-12-01

The Snow Data System at NASA JPL includes data processing pipelines built with open source software, Apache 'Object Oriented Data Technology' (OODT). Processing is carried out in parallel across a high-powered computing cluster. The pipelines use input data from satellites such as MODIS, VIIRS and Landsat. They apply algorithms to the input data to produce a variety of outputs in GeoTIFF format. These outputs include daily data for SCAG (Snow Cover And Grain size) and DRFS (Dust Radiative Forcing in Snow), along with 8-day composites and MODICE annual minimum snow and ice calculations. This poster will describe the Snow Data System, its outputs and their uses and applications. It will also highlight recent advancements to the system and plans for the future.

Projections for Changes in Natural and Technical Snow Reliability of a Major Turkish Ski Resort by Using RegCM4.3.5

NASA Astrophysics Data System (ADS)

Ozturk, Tugba; Cenk Demiroglu, O.; Tufan Turp, M.; Türkeş, Murat; Kurnaz, M. Levent

2014-05-01

Climate change has been and increasingly will be a major threat to the ski tourism industry whose survival is highly dependent on existence of snow cover of sufficient depth and duration. The common knowledge requires that in order for a ski resort to be viable, it has to perform operations for at least 100 days in seven out of ten winters. For this matter, it is now even more usual for the ski resorts to adapt to this issue by technical snowmaking. In this study, projected future changes for the period of 2010-2040, 2040-2070, and 2070-2100 in air temperature, relative humidity, and snow depth climatology and variability with respect to the control period of 1970-2000 were assessed for the domain of a major ski resort in Turkey. Regional Climate Model (RegCM4.3.5) of ICTP (International Centre for Theoretical Physics) was used for projections of future and present climate conditions. HadGEM2 global climate model of the Met Office Hadley Centre, MPI-ESM-MR of the Max Planck Institute for Meteorology, GFDL-ESM2M of the National Oceanic and Atmospheric Administration Geophysical Fluid Dynamics Laboratory were downscaled to 10 km for the resort and its surrounding region. Both the projections and the downscaling were realized according to the RCP4.5 and the RCP8.5 emission scenarios of the IPCC. The outputs on snow depth were used for a count of the changes on snow cover duration sufficient for skiing actitivies, signaling natural snow-reliability, whereas the outputs on air temperature and relative humidity were utilized for determination of wet-bulb temperatures. The latter measure was used to interpret the changes in the snowmaking capacity, in other words; technical snow-reliability, of the resort. This work was supported by the BU Reasearch Fund under the project number 7362. One of the authors (MLK) was partially supported by Mercator-IPC Fellowship Program.

Snow cover detection algorithm using dynamic time warping method and reflectances of MODIS solar spectrum channels

NASA Astrophysics Data System (ADS)

Lee, Kyeong-sang; Choi, Sungwon; Seo, Minji; Lee, Chang suk; Seong, Noh-hun; Han, Kyung-Soo

2016-10-01

Snow cover is biggest single component of cryosphere. The Snow is covering the ground in the Northern Hemisphere approximately 50% in winter season and is one of climate factors that affects Earth's energy budget because it has higher reflectance than other land types. Also, snow cover has an important role about hydrological modeling and water resource management. For this reason, accurate detection of snow cover acts as an essential element for regional water resource management. Snow cover detection using satellite-based data have some advantages such as obtaining wide spatial range data and time-series observations periodically. In the case of snow cover detection using satellite data, the discrimination of snow and cloud is very important. Typically, Misclassified cloud and snow pixel can lead directly to error factor for retrieval of satellite-based surface products. However, classification of snow and cloud is difficult because cloud and snow have similar optical characteristics and are composed of water or ice. But cloud and snow has different reflectance in 1.5 1.7 μm wavelength because cloud has lower grain size and moisture content than snow. So, cloud and snow shows difference reflectance patterns change according to wavelength. Therefore, in this study, we perform algorithm for classifying snow cover and cloud with satellite-based data using Dynamic Time Warping (DTW) method which is one of commonly used pattern analysis such as speech and fingerprint recognitions and reflectance spectral library of snow and cloud. Reflectance spectral library is constructed in advance using MOD21km (MODIS Level1 swath 1km) data that their reflectance is six channels including 3 (0.466μm), 4 (0.554μm), 1 (0.647μm), 2 (0.857μm), 26 (1.382μm) and 6 (1.629μm). We validate our result using MODIS RGB image and MOD10 L2 swath (MODIS swath snow cover product). And we use PA (Producer's Accuracy), UA (User's Accuracy) and CI (Comparison Index) as validation criteria. The result of our study detect as snow cover in the several regions which are did not detected as snow in MOD10 L2 and detected as snow cover in MODIS RGB image. The result of our study can improve accuracy of other surface product such as land surface reflectance and land surface emissivity. Also it can use input data of hydrological modeling.

Ground-Truthing a Next Generation Snow Radar

NASA Astrophysics Data System (ADS)

Yan, S.; Brozena, J. M.; Gogineni, P. S.; Abelev, A.; Gardner, J. M.; Ball, D.; Liang, R.; Newman, T.

2016-12-01

During the early spring of 2016 the Naval Research Laboratory (NRL) performed a test of a next generation airborne snow radar over ground truth data collected on several areas of fast ice near Barrow, AK. The radar was developed by the Center for Remote Sensing of Ice Sheets (CReSIS) at the University of Kansas, and includes several improvements compared to their previous snow radar. The new unit combines the earlier Ku-band and snow radars into a single unit with an operating frequency spanning the entire 2-18 GHz, an enormous bandwidth which provides the possibility of snow depth measurements with 1.5 cm range resolution. Additionally, the radar transmits on dual polarizations (H and V), and receives the signal through two orthogonally polarized Vivaldi arrays, each with 128 phase centers. The 8 sets of along-track phase centers are combined in hardware to improve SNR and narrow the beamwidth in the along-track, resulting in 8 cross-track effective phase centers which are separately digitized to allow for beam sharpening and forming in post-processing. Tilting the receive arrays 30 degrees from the horizontal also allows the formation of SAR images and the potential for estimating snow-water equivalent (SWE). Ground truth data (snow depth, density, salinity and SWE) were collected over several 60 m wide swaths that were subsequently overflown with the snow radar mounted on a Twin Otter. The radar could be operated in nadir (by beam steering the receive antennas to point beneath the aircraft) or side-looking modes. Results from the comparisons will be shown.

Impact of the springtime Himalayan-Tibetan Plateau on the onset on the Indian summer monsoon in coupled forecasts

NASA Astrophysics Data System (ADS)

Orsolini, Yvan; Senan, Retish; Weisheimer, Antje; Vitart, Frederic; Balsamo, Gianpaolo; Doblas-Reyes, Francisco; Stockdale, Timothy; Dutra, Emanuel

2016-04-01

The springtime snowpack over the Himalayan-Tibetan Plateau (HTP) region has long been suggested to be an influential factor on the onset of the Indian summer monsoon. In the frame of the SPECS project, we have assessed the impact of realistic snow initialization in springtime over HTP on the onset of the Indian summer monsoon. We examine a suite of coupled ocean-atmosphere 4-month ensemble reforecasts made at the European Centre for Medium-Range Weather Forecasts (ECMWF), using the Seasonal Forecasting System 4. The reforecasts were initialized on 1 April every year for the period 1981-2010. In these seasonal reforecasts, the snow is initialized "realistically" with ERA-Interim/Land Reanalysis. In addition, we carried out an additional set of forecasts, identical in all aspects except that initial conditions for snow-related land surface variables over the HTP region are randomized. We show that high snow depth over HTP influences the meridional tropospheric temperature gradient reversal that marks the monsoon onset. Composite difference based on a normalized HTP snow index reveal that, in high snow years, (i) the onset is delayed by about 8 days, and (ii) negative precipitation anomalies and warm surface conditions prevail over India. We show that about half of this delay can be attributed to the realistic initialization of snow over the HTP region. We further demonstrate that high April snow depths over HTP are not uniquely influenced by either the El Nino-Southern Oscillation, the Indian Ocean Dipole or the North Atlantic Oscillation.

Uncertainty in solid precipitation and snow depth prediction for Siberia using the Noah and Noah-MP land surface models

NASA Astrophysics Data System (ADS)

Suzuki, Kazuyoshi; Zupanski, Milija

2018-01-01

In this study, we investigate the uncertainties associated with land surface processes in an ensemble predication context. Specifically, we compare the uncertainties produced by a coupled atmosphere-land modeling system with two different land surface models, the Noah- MP land surface model (LSM) and the Noah LSM, by using the Maximum Likelihood Ensemble Filter (MLEF) data assimilation system as a platform for ensemble prediction. We carried out 24-hour prediction simulations in Siberia with 32 ensemble members beginning at 00:00 UTC on 5 March 2013. We then compared the model prediction uncertainty of snow depth and solid precipitation with observation-based research products and evaluated the standard deviation of the ensemble spread. The prediction skill and ensemble spread exhibited high positive correlation for both LSMs, indicating a realistic uncertainty estimation. The inclusion of a multiple snowlayer model in the Noah-MP LSM was beneficial for reducing the uncertainties of snow depth and snow depth change compared to the Noah LSM, but the uncertainty in daily solid precipitation showed minimal difference between the two LSMs. The impact of LSM choice in reducing temperature uncertainty was limited to surface layers of the atmosphere. In summary, we found that the more sophisticated Noah-MP LSM reduces uncertainties associated with land surface processes compared to the Noah LSM. Thus, using prediction models with improved skill implies improved predictability and greater certainty of prediction.

Plasma lactate concentrations in free-ranging moose (Alces alces) immobilized with etorphine.

PubMed

Haga, Henning A; Wenger, Sandra; Hvarnes, Silje; Os, Oystein; Rolandsen, Christer M; Solberg, Erling J

2009-11-01

To investigate plasma lactate concentrations of etorphine-immobilized moose in relation to environmental, temporal and physiological parameters. Prospective clinical study. Fourteen female and five male moose (Alces alces), estimated age range 1-7 years. The moose were darted from a helicopter with 7.5 mg etorphine per animal using projectile syringes and a dart gun. Once immobilized, the moose were approached, a venous blood sample was obtained and vital signs including pulse oximetry were recorded. Diprenorphine was administered to reverse the effects of etorphine. Timing of events, ambient temperature and snow depth were recorded. Blood samples were cooled and centrifuged before plasma was harvested and frozen. The plasma was thawed later and lactate analysed. Data were analysed using descriptive statistics and regression analysis. All animals recovered uneventfully and were alive 12 weeks after immobilization. Mean +/- SD plasma lactate was found to be 9.2 +/- 2.1 mmol L(-1). Plasma lactate concentrations were related positively to snow depth and negatively to time from induction of immobilization to blood sampling. The model that best described the variability in plasma lactate concentrations used induction time (time from firing the dart to the moose being immobilized). The second best model included induction time and snow depth. Plasma lactate concentrations in these etorphine-immobilized moose were in the range reported for other immobilized wild ruminants. Decreasing induction time, which may be related to a more profound etorphine effect, and increasing snow depth possibly may increase plasma lactate concentrations in etorphine-immobilized moose.

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.

A statistical estimation of Snow Water Equivalent coupling ground data and MODIS images

NASA Astrophysics Data System (ADS)

Bavera, D.; Bocchiola, D.; de Michele, C.

2007-12-01

The Snow Water Equivalent (SWE) is an important component of the hydrologic balance of mountain basins and snow fed areas in general. The total cumulated snow water equivalent at the end of the accumulation season represents the water availability at melt. Here, a statistical methodology to estimate the Snow Water Equivalent, at April 1st, is developed coupling ground data (snow depth and snow density measurements) and MODIS images. The methodology is applied to the Mallero river basin (about 320 km²) located in the Central Alps, northern Italy, where are available 11 snow gauges and a lot of sparse snow density measurements. The application covers 7 years from 2001 to 2007. The analysis has identified some problems in the MODIS information due to the cloud cover and misclassification for orographic shadow. The study is performed in the framework of AWARE (A tool for monitoring and forecasting Available WAter REsource in mountain environment) EU-project, a STREP Project in the VI F.P., GMES Initiative.

How Can Polarization States of Reflected Light from Snow Surfaces Inform Us on Surface Normals and Ultimately Snow Grain Size Measurements?

NASA Astrophysics Data System (ADS)

Schneider, A. M.; Flanner, M.; Yang, P.; Yi, B.; Huang, X.; Feldman, D.

2016-12-01

The Snow Grain Size and Pollution (SGSP) algorithm is a method applied to Moderate Resolution Imaging Spectroradiometer data to estimate snow grain size from space-borne measurements. Previous studies validate and quantify potential sources of error in this method, but because it assumes flat snow surfaces, however, large scale variations in surface normals can cause biases in its estimates due to its dependence on solar and observation zenith angles. To address these variations, we apply the Monte Carlo method for photon transport using data containing the single scattering properties of different ice crystals to calculate polarization states of reflected monochromatic light at 1500nm from modeled snow surfaces. We evaluate the dependence of these polarization states on solar and observation geometry at 1500nm because multiple scattering is generally a mechanism for depolarization and the ice crystals are relatively absorptive at this wavelength. Using 1500nm thus results in a higher number of reflected photons undergoing fewer scattering events, increasing the likelihood of reflected light having higher degrees of polarization. In evaluating the validity of the model, we find agreement with previous studies pertaining to near-infrared spectral directional hemispherical reflectance (i.e. black-sky albedo) and similarities in measured bidirectional reflectance factors, but few studies exist modeling polarization states of reflected light from snow surfaces. Here, we present novel results pertaining to calculated polarization states and compare dependences on solar and observation geometry for different idealized snow surfaces. If these dependencies are consistent across different ice particle shapes and sizes, then these findings could inform the SGSP algorithm by providing useful relationships between measurable physical quantities and solar and observation geometry to better understand variations in snow surface normals from remote sensing observations.

The effect of frozen soil on snowmelt runoff at Sleepers River, Vermont

USGS Publications Warehouse

Shanley, J.B.; Chalmers, A.

1999-01-01

Soil frost depth has been monitored at the Sleepers River Research Watershed in northeastern Vermont since 1984. Soil frost develops every winter, particularly in open fields, but its depth varies from year to year in inverse relation to snow depth. During the 15 years of record at a benchmark mid-elevation open site, the annual maximum frost depth varied from 70 to 390 mm. We empirically tested the hypothesis that frozen soil prevents infiltration and recharge, thereby causing an increased runoff ratio (streamflow/(rain + snowmelt)) during the snowmelt hydrograph rise and a decreased runoff ratio during snowmelt recession. The hypothesis was not supported at the 111 km2 W-5 catchment; there was no significant correlation of the runoff ratio with the seasonal maximum frost depth for either the pre-peak or post-peak period. In an analysis of four events, however, the presence of frost promoted a large and somewhat quicker response to rainfall relative to the no-frost condition, although snow cover caused a much greater time-to-peak regardless of frost status. For six years of flow and frost depth measured at the 59 ha agricultural basin W-2, the hypothesis appeared to be supported. The enhancement of runoff due to soil frost is evident on small plots and in extreme events, such as rain on frozen snow-free soil. In the northeastern USA and eastern Canada, the effect is often masked in larger catchments by several confounding factors, including storage of meltwater in the snowpack, variability in snowmelt timing due to elevational and aspect differences, interspersed forested land where frost may be absent, and the timing of soil thawing relative to the runoff peak.Soil frost depth has been monitored at the Sleepers River Research Watershed in northeastern Vermont since 1984. Soil frost develops every winter, particularly in open fields, but its depth varies greatly from year to year in inverse relation to snow depth. During the 15 years of record at a benchmark mid-elevation open site, the annual maximum frost depth varied from 70 to 390 mm. We empirically tested the hypothesis that frozen soil prevents infiltration and recharge, thereby causing an increased runoff ratio (streamflow/(rain + snowmelt)) during the snowmelt hydrograph rise and a decreased runoff ratio during snowmelt recession. The hypothesis was not supported at the 111 km2 W-5 catchment; there was no significant correlation of the runoff ratio with the seasonal maximum frost depth for either the pre-peak or post-peak period. In an analysis of four events, however, the presence of frost promoted a large and somewhat quicker response to rainfall relative to the no-frost condition, although snow cover caused a much greater time-to-peak regardless of frost status. For six years of flow and frost depth measured at the 59 ha agricultural basin W-2, the hypothesis appeared to be supported. The enhancement of runoff due to soil frost is evident on small plots and in extreme events, such as rain of frozen snow-free soil. In the northeastern USA and eastern Canada, the effect is often masked in larger catchments by several confounding factors, including storage of meltwater in the snowpack, variability in snowmelt timing due to elevational and aspect differences, interspersed forested land where frost may be absent, and the timing of soil thawing relative to the runoff peak.

Introducing two Random Forest based methods for cloud detection in remote sensing images

NASA Astrophysics Data System (ADS)

Ghasemian, Nafiseh; Akhoondzadeh, Mehdi

2018-07-01

Cloud detection is a necessary phase in satellite images processing to retrieve the atmospheric and lithospheric parameters. Currently, some cloud detection methods based on Random Forest (RF) model have been proposed but they do not consider both spectral and textural characteristics of the image. Furthermore, they have not been tested in the presence of snow/ice. In this paper, we introduce two RF based algorithms, Feature Level Fusion Random Forest (FLFRF) and Decision Level Fusion Random Forest (DLFRF) to incorporate visible, infrared (IR) and thermal spectral and textural features (FLFRF) including Gray Level Co-occurrence Matrix (GLCM) and Robust Extended Local Binary Pattern (RELBP_CI) or visible, IR and thermal classifiers (DLFRF) for highly accurate cloud detection on remote sensing images. FLFRF first fuses visible, IR and thermal features. Thereafter, it uses the RF model to classify pixels to cloud, snow/ice and background or thick cloud, thin cloud and background. DLFRF considers visible, IR and thermal features (both spectral and textural) separately and inserts each set of features to RF model. Then, it holds vote matrix of each run of the model. Finally, it fuses the classifiers using the majority vote method. To demonstrate the effectiveness of the proposed algorithms, 10 Terra MODIS and 15 Landsat 8 OLI/TIRS images with different spatial resolutions are used in this paper. Quantitative analyses are based on manually selected ground truth data. Results show that after adding RELBP_CI to input feature set cloud detection accuracy improves. Also, the average cloud kappa values of FLFRF and DLFRF on MODIS images (1 and 0.99) are higher than other machine learning methods, Linear Discriminate Analysis (LDA), Classification And Regression Tree (CART), K Nearest Neighbor (KNN) and Support Vector Machine (SVM) (0.96). The average snow/ice kappa values of FLFRF and DLFRF on MODIS images (1 and 0.85) are higher than other traditional methods. The quantitative values on Landsat 8 images show similar trend. Consequently, while SVM and K-nearest neighbor show overestimation in predicting cloud and snow/ice pixels, our Random Forest (RF) based models can achieve higher cloud, snow/ice kappa values on MODIS and thin cloud, thick cloud and snow/ice kappa values on Landsat 8 images. Our algorithms predict both thin and thick cloud on Landsat 8 images while the existing cloud detection algorithm, Fmask cannot discriminate them. Compared to the state-of-the-art methods, our algorithms have acquired higher average cloud and snow/ice kappa values for different spatial resolutions.

Downscaling of snow depth and river discharge in Japan by the Pseudo-Global-Warming Method

NASA Astrophysics Data System (ADS)

Kimura, F.; Ma, X.; Hara, M.; Advanced Atmosphere-Ocean-Land Modeling Program

2010-12-01

Although a heavy snowfall often brings disaster, snow cover is one of the major water resources in Japan. Even during the winter, the monthly mean of the surface air temperature often exceeds 0 deg. in large parts of the heavy snow areas along the Sea of Japan. Thus, snow cover may be seriously reduced in these areas as a result of global warming, which is caused by an increase in greenhouse gases. This study estimates the impact of global warming on the snow depth in Japan during early winter. Some dynamical downscaling experiments are conducted by the Pseudo-Global-Warming method for the future projection of snow cover. By the hindcast runs, precipitation, snow depth, and surface air temperature show good agreement with the AMeDAS station data observed in a High-Snow-Cover (HSC) year and a Low-Snow-Cover (LSC) yea. Pseudo-Global-Warming runs for these years indicate that the decreasing ratios of the snow water are more significant in the areas whose altitude is less than 1500 m. The increase of the air temperature is one of the major factors for the decrease in snow water, since the present mean air temperature in most of these areas is near 0 deg. even in winter. On the other hand, the change in the aerial-mean precipitation due to global warming is less than 15% in both years. To evaluate the impact of the reduction of snow cover to water resource, a hydrological simulation is also made for the Agano River basin, which locates in Niigata and Fukushima Prefectures. The Agano River drains into the Sea of Japan and is the second largest river in Japan with annual discharge of about 12.9 billion m3. A hind cast experiment is carried out for the two decades from 1980 to 1999. The average correlation coefficient of 0.79 for the monthly mean discharge in the winter season indicates that the interannual variation of the river discharge could be reproduced and that the method is useful for climate change study. Then the hydrological response to the future global warming in the 2070s is investigated. Assuming the reference present climate period of 1990s, the monthly mean discharge for the 2070s is projected to increase by approximately 43% in January and 55% in February, but to decrease by approximately 38% in April and 32% in May. The flood peak in the hydrograph will shift to approximately one month earlier, i.e., from April in the 1990s to March in the 2070s. Furthermore, the 10-year average of snowfall amount is projected to be approximately 49.5% lower in the 2070s than that in the 1990s. Acknowledgment: This work was supported by the Global Environment Research Fund (S-5-3) of the Ministry of the Environment, Japan. References 1. Ma, X., T. Yoshikane, M. Hara, Y. Wakazuki, H. G Takahashi, and F. Kimura, 2010: Hydrological response to future climate change in the Agano River basin, Japan, Hydrological Research Letters, 4, 25-29 2. Hara,M., T.Yoshikane, H.Kawase and F.Kimura 2008:Impact of the Estimation of Global Warming on Snow Depth in Japan by the Pseudo-Global-Warming Method. Hydrological Research Letters 2 61-64.

Statistical downscaling of regional climate scenarios for the French Alps : Impacts on snow cover

NASA Astrophysics Data System (ADS)

Rousselot, M.; Durand, Y.; Giraud, G.; Mérindol, L.; Déqué, M.; Sanchez, E.; Pagé, C.; Hasan, A.

2010-12-01

Mountain areas are particularly vulnerable to climate change. Owing to the complexity of mountain terrain, climate research at scales relevant for impacts studies and decisive for stakeholders is challenging. A possible way to bridge the gap between these fine scales and those of the general circulation models (GCMs) consists of combining high-resolution simulations of Regional Climate Models (RCMs) to statistical downscaling methods. The present work is based on such an approach. It aims at investigating the impacts of climate change on snow cover in the French Alps for the periods 2021-2050 and 2071-2100 under several IPCC hypotheses. An analogue method based on high resolution atmospheric fields from various RCMs and climate reanalyses is used to simulate local climate scenarios. These scenarios, which provide meteorological parameters relevant for snowpack evolution, subsequently feed the CROCUS snow model. In these simulations, various sources of uncertainties are thus considered (several greenhouse gases emission scenarios and RCMs). Results are obtained for different regions of the French Alps at various altitudes. For all scenarios, temperature increase is relatively uniform over the Alps. This regional warming is larger than that generally modeled at the global scale (IPCC, 2007), and particularly strong in summer. Annual precipitation amounts seem to decrease, mainly as a result of decreasing precipitation trends in summer and fall. As a result of these climatic evolutions, there is a general decrease of the mean winter snow depth and seasonal snow duration for all massifs. Winter snow depths are particularly reduced in the Northern Alps. However, the impact on seasonal snow duration is more significant in the Southern and Extreme Southern Alps, since these regions are already characterized by small winter snow depths at low elevations. Reference : IPCC (2007a). Climate change 2007 : The physical science basis. Contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate Change. In : Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor, and H.L. Miller (eds.). Cambridge University Press, Cambridge, UK and New York, NY, USA. This work is performed in the framework of the SCAMPEI ANR (French research project).

The growth of shrubs on high Arctic tundra at Bylot Island: impact on snow physical properties and permafrost thermal regime

NASA Astrophysics Data System (ADS)

Domine, Florent; Barrere, Mathieu; Morin, Samuel

2016-12-01

With climate warming, shrubs have been observed to grow on Arctic tundra. Their presence is known to increase snow height and is expected to increase the thermal insulating effect of the snowpack. An important consequence would be the warming of the ground, which will accelerate permafrost thaw, providing an important positive feedback to warming. At Bylot Island (73° N, 80° W) in the Canadian high Arctic where bushes of willows (Salix richardsonii Hook) are growing, we have observed the snow stratigraphy and measured the vertical profiles of snow density, thermal conductivity and specific surface area (SSA) in over 20 sites of high Arctic tundra and in willow bushes 20 to 40 cm high. We find that shrubs increase snow height, but only up to their own height. In shrubs, snow density, thermal conductivity and SSA are all significantly lower than on herb tundra. In shrubs, depth hoar which has a low thermal conductivity was observed to grow up to shrub height, while on herb tundra, depth hoar only developed to 5 to 10 cm high. The thermal resistance of the snowpack was in general higher in shrubs than on herb tundra. More signs of melting were observed in shrubs, presumably because stems absorb radiation and provide hotspots that initiate melting. When melting was extensive, thermal conductivity was increased and thermal resistance was reduced, counteracting the observed effect of shrubs in the absence of melting. Simulations of the effect of shrubs on snow properties and on the ground thermal regime were made with the Crocus snow physics model and the ISBA (Interactions between Soil-Biosphere-Atmosphere) land surface scheme, driven by in situ and reanalysis meteorological data. These simulations did not take into account the summer impact of shrubs. They predict that the ground at 5 cm depth at Bylot Island during the 2014-2015 winter would be up to 13 °C warmer in the presence of shrubs. Such warming may however be mitigated by summer effects.

21st century projections of snowfall and winter severity across central-eastern North America

NASA Astrophysics Data System (ADS)

Notaro, M.; Lorenz, D. J.; Hoving, C.; Schummer, M.

2014-12-01

Statistically downscaled climate projections from nine global climate models (GCMs) are used to force a snow accumulation and ablation model (SNOW-17) across the central-eastern North American Landscape Conservation Cooperatives (LCCs) to develop high-resolution projections of snowfall, snow depth, and winter severity index (WSI) by the mid- and late 21st century. Here, we use projections of a cumulative WSI (CWSI) known to influence autumn-winter waterfowl migration to demonstrate the utility of SNOW-17 results. The application of statistically downscaled climate data and a snow model leads to a better representation of lake processes in the Great Lakes Basin, topographic effects in the Appalachian Mountains, and spatial patterns of climatological snowfall, compared to the original GCMs. Annual mean snowfall is simulated to decline across the region, particularly in early winter (December-January), leading to a delay in the mean onset of the snow season. Due to a warming-induced acceleration of snowmelt, the percentage loss in snow depth exceeds that of snowfall. Across the Plains and Prairie Potholes LCC and Upper Midwest and Great Lakes LCC, daily snowfall events are projected to become less common, but more intense. The greatest reductions in the number of days per year with a present snowpack are expected close to the historical position of the -5°C isotherm in DJFM, around 44°N. The CWSI is projected to decline substantially during December-January, leading to increased likelihood of delays in timing and intensity of autumn-winter waterfowl migrations.

A Refined Methodology for Modelling Climate Change Impacts on Snow Sports Tourism

NASA Astrophysics Data System (ADS)

Demiroglu, O. Cenk; Turp, M. Tufan; Ozturk, Tugba; An, Nazan; Kurnaz, M. Levent

2015-04-01

Nature-based tourism is one of the most vulnerable sectors of the economy against climate change. Among its types, winter tourism stands out as the most critical due to the relatively high exposure and sensitivity of snow cover to the anthropogenic warming trends. In this study, we aim at improving previous works by Ozturk et al. where snow reliability of ski resorts have been examined through projections based on regional climate model outputs downscaled from various GCMs. Major improvements to these studies will be related to increasing the resolution, obtaining snow depth values from snow-water equivalent outputs, and hourly, instead of the daily, calculations of wet bulb temperatures. Daily snow depth values will be utilized for 100-days rule that looks for at least 100 days of snow cover at a minimum of 30 cm in order for a ski resort to be viable, whereas the wet bulb temperatures below -7 oC will indicate the snowmaking capacity. The domain of analysis will be the Balkans, the Middle East and the Caucasus. Therefore the spatial gap in the mostly Euro- and Amero-centric literature will also be improved. The domain will be modelled through RegCM 4.4.2 of the International Centre for Theoretical Physics basing its resolution on MPI-ESM-MR of Max Planck Institut für Meteorologie and the concentration scenario RCP 4.5 for a realistic tourism development future of 2020-2050.

Seismic spectrograms analysis applying the Hough transform to estimate the front speed of mass movements: Application to snow avalanches

NASA Astrophysics Data System (ADS)

Flores-Marquez, L.; Suriñach-Cornet, E., Sr.

2017-12-01

Seismic signals generated by snow avalanches and other mass movements are analyzed in their spectrogram representation. Spectrogram displays the evolution in time of the frequency content of the signals. The spectrogram of a seismic signal of a station to which a sliding mass, such as a snow avalanche, approaches, exhibits a triangular time / frequency signature. This increase in its higher frequency content over time is a consequence of the attenuation of the waves propagating in a media. Recognition of characteristic footprints in a spectrogram could help to identify and characterize diverse mass movement events such as landslides or snow avalanches. In order to recognize spectrogram features of seismic signals of Alpine snow avalanches, we propose an algorithm based on the Hough transform. The proposed algorithm is applied on an edge representation image of the seismic spectrogram obtained after fixing a threshold filter to the spectrogram, which enhances the most interesting frequencies of the seismogram that appear over time. This enables us to identify parameters (slopes) that correspond to the speeds associated with the type of snow avalanches, such as, powder, dense or transitional snow avalanches. The data analyzed in this work correspond to twenty different seismic signals generated by snow avalanches artificially released in the experimental site of Vallée de la Sionne (VDLS, SLF, Switzerland). The shape of the signal spectrograms are linked to the flow regimes previously identified. Our findings show that some ranges of speeds are inherent to the type of avalanche.

Winter range arrival and departure of white-tailed deer in northeastern Minnesota

USGS Publications Warehouse

Nelson, M.E.

1995-01-01

I analyzed 364 spring and 239 fall migrations by 194 white-tailed deer (Odocoileus virginianus) from 1975 to 1993 in northeastern Minnesota to determine the proximate cause of arrivals on and departures from winter ranges. The first autumn temperatures below -7?C initiated fall migrations for 14% (95% confidence interval (CI) = 0-30) of female deer prior to snowfall in three autumns, but only 2% remained on winter ranges. During 14 autumns, the first temperatures below -7?C coincidental with snowfalls elicited migration in 45% (95% CI = 34-57) of females, and 91 % remained on winter ranges. Arrival dates failed to correlate with independent variables of temperature and snow depth, precluding predictive modeling of arrival on winter ranges. During 13 years, a mean of 80% of females permanently arrived on winter ranges by 31 December. Mean departure dates from winter ranges varied annually (19 March - 4 May) and between winter ranges (14 days) and according to snow depth (15-cm differences). Only 15 - 41 % of deer departed when snow depths were> 30 cm but 80% had done so by the time of lO-cm depths. Mean weekly snow depths in March (18-85 cm) and mean temperature in April (0.3 -8.1 ?c) explained most of the variation in mean departure dates from two winter ranges (Ely, R2 = 0.87, P < 0.0005, n = 19 springs; Isabella, R2 = 0.85, P = 0.0001, n = 12 springs). Mean differences between observed mean departure dates and mean departure dates predicted from equations ranged from 3 days (predictions within the study area) to 8 days (predictions for winter ranges 100-440 km distant).

Snowfall Rate Retrieval using NPP ATMS Passive Microwave Measurements

NASA Technical Reports Server (NTRS)

Meng, Huan; Ferraro, Ralph; Kongoli, Cezar; Wang, Nai-Yu; Dong, Jun; Zavodsky, Bradley; Yan, Banghua; Zhao, Limin

2014-01-01

Passive microwave measurements at certain high frequencies are sensitive to the scattering effect of snow particles and can be utilized to retrieve snowfall properties. Some of the microwave sensors with snowfall sensitive channels are Advanced Microwave Sounding Unit (AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow-on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has been developed recently. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. The model employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derive the probability of snowfall (Kongoli et al., 2014). In addition, a set of NWP model based filters is also employed to improve the accuracy of snowfall detection. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two-stream radiative transfer model (Yan et al., 2008). A method developed by Heymsfield and Westbrook (2010) is adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. The ATMS SFR product is validated against radar and gauge snowfall data and shows that the ATMS algorithm outperforms the AMSU/MHS SFR.

Performances of the snow accumulation melting model SAMM: results in the Northern Apennines test area

NASA Astrophysics Data System (ADS)

Lagomarsino, Daniela; Martelloni, Gianluca; Segoni, Samuele; Catani, Filippo; Fanti, Riccardo

2013-04-01

In this work we propose a snow accumulation-melting model (SAMM) to forecast the snowpack height and we compare the results with a simple temperature index model and an improved version of the latter.For this purpose we used rainfall, temperature and snowpack thickness 5-years data series from 7 weather stations in the Northern Apennines (Emilia Romagna Region, Italy). SAMM is based on two modules modelling the snow accumulation and the snowmelt processes. Each module is composed by two equations: a mass conservation equation is solved to model snowpack thickness and an empirical equation is used for the snow density. The processes linked to the accumulation/depletion of the snowpack (e.g. compression of the snowpack due to newly fallen snow and effects of rainfall) are modelled identifying limiting and inhibitory factors according to a kinetic approach. The model depends on 13 empirical parameters, whose optimal values were defined with an optimization algorithm (simplex flexible) using calibration measures of snowpack thickness. From an operational point of view, SAMM uses as input data only temperature and rainfall measurements, bringing the additional advantage of a relatively easy implementation. In order to verify the improvement of SAMM with respect to a temperature-index model, the latter was applied considering, for the amount of snow melt, the following equation: M = fm(T-T0), where M is hourly melt, fm is the melting factor and T0 is a threshold temperature. In this case the calculation of the depth of the snowpack requires the use of 3 parameters: fm, T0 and ?0 (the mean density of the snowpack). We also performed a simulation by replacing the SAMM melting module with the above equation and leaving unchanged the accumulation module: in this way we obtained a model with 9 parameters. The simulations results suggest that any further extension of the simple temperature index model brings some improvements with a consequent decrease of the mean error between model and experimental data of the snowpack thickness.

A lee-side eddy and its influence on snow accumulation

NASA Astrophysics Data System (ADS)

Gerber, Franziska; Mott, Rebecca; Hoch, Sebastian W.; Lehning, Michael

2016-04-01

Knowledge of changes in seasonal mountain snow water resources is essential for e.g. hydropower companies. To successfully predict these changes a fundamental understanding of precipitation patterns and their changes in mountainous terrain is needed. Both, snow accumulation and ablation need to be investigated to make precise predictions of the amount of water stored in seasonal snow cover. Only if the processes governing snow accumulation and ablation are understood with sufficient quantitative accuracy, the evolution of snow water resources under a changing climate can be addressed. Additionally, knowledge of detailed snow accumulation patterns is essential to assess avalanche danger. In alpine terrain, snow accumulation is strongly dependent on the local wind field. Based on the concept of preferential deposition, reduced snow accumulation is expected on the upper windward slope of a mountain due to updrafts, while enhanced snow accumulation should occur through blocking at the windward foot or due to flow separation on the leeward side. However, the understanding of these processes is mainly based on numerical simulations, as they are hard to measure. A LiDAR (Light Detection And Ranging) campaign was conducted in October 2015 in the Dischma valley (Davos, CH) to investigate the local flow field in the lee of the Sattelhorn during a one-day snowfall event. The flow field was monitored using a plane position indicator (PPI) scan at 25/28° and a range height indicator (RHI) scan across the Sattelhorn. Additionally, snow height change measurements on the leeward side of the Sattelhorn were performed by terrestrial laser scanning (TLS). Analyses of the flow field in the framework of preferential deposition are in agreement with the concept of flow separation and preferred snow deposition on leeward slopes. A very persistent eddy that formed over the leeward slope of the Sattelhorn detached from the main flow became evident from the retrievals of the RHI scans. An additional flow component around the eastern edge of Sattelhorn introduces a cross-loading component along the Sattelhorn ridge. Snow depth data is, however, only available for the slope and thus covers only the upper part of the eddy. Thus, this winter we will collect more complete snow depth data to reveal the overall influence of the eddy on snow accumulation.

Snowmelt in a High Latitude Mountain Catchment: Effect of Vegetation Cover and Elevation

NASA Astrophysics Data System (ADS)

Pomeroy, J. W.; Essery, R. L.; Ellis, C. R.; Hedstrom, N. R.; Janowicz, R.; Granger, R. J.

2004-12-01

The energetics and mass balance of snowpacks in the premelt and melt period were compared from three elevation bands in a high latitude mountain catchment, Wolf Creek Research Basin, Yukon. Elevation is strongly correlated with vegetation cover and in this case the three elevation bands (low, middle, high) correspond to mature spruce forest, dense shrub tundra and sparse tundra (alpine). Measurements of radiation, ground heat flux, snow depth, snowfall, air temperature, wind speed were made on a half-hourly basis at the three elevations for a 10 year period. Sondes provided vertical gradients of air temperature, humidity, wind speed and air pressure. Snow depth and density surveys were conducted monthly. Comparisons of wind speed, air temperature and humidity at three elevations show that the expected elevational gradients in the free atmosphere were slightly enhanced just above the surface canopies, but that the climate at the snow surface was further influenced by complex canopy effects. Premelt snow accumulation was strongly affected by intercepted snow in the forest and blowing snow sublimation in the sparse tundra but not by the small elevational gradients in snowfall. As a result the maximum premelt SWE was found in the mid-elevation shrub tundra and was roughly double that of the sparse tundra or forest. Minimum variability of SWE was observed in the forest and shrub tundra (CV=0.25) while in the sparse tundra variability doubled (CV=0.5). Snowmelt was influenced by differences in premelt accumulation as well as differences in the net energy fluxes to snow. Elevation had a strong effect on the initiation of melt with the forest melt starting on average 16 days before the shrub tundra and 19 days before the sparse tundra. Mean melt rates showed a maximum in middle elevations and increased from 860 kJ/day in the forest to 1460 kJ/day in the sparse tundra and 2730 kJ/day in the shrub tundra. The forest canopy reduced melt while the shrub canopy enhanced it relative to the sparsely vegetated tundra. Duration of melt was similar in the forest and shrub tundra at 20 days while the sparse tundra was shorter at 13 days; the differences due to differing snow accumulation and melt rates. The greatest variability in the timing and rate of melt was found in the shrub tundra, where the effect of the shrub canopy over snow depends on snow depth and insolation and is reduced in years with high snow accumulation or extensive cloudy periods in spring. The results show that it is necessary to consider the combination of elevation and vegetation effects on snow microclimate and melt processes in high latitude mountain catchments, but that weather patterns induce substantial variability on the effect these factors.

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.

Normalized-Difference Snow Index (NDSI)

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Riggs, George A.

2010-01-01

The Normalized-Difference Snow Index (NDSI) has a long history. 'The use of ratioing visible (VIS) and near-infrared (NIR) or short-wave infrared (SWIR) channels to separate snow and clouds was documented in the literature beginning in the mid-1970s. A considerable amount of work on this subject was conducted at, and published by, the Air Force Geophysics Laboratory (AFGL). The objective of the AFGL work was to discriminate snow cover from cloud cover using an automated algorithm to improve global cloud analyses. Later, automated methods that relied on the VIS/NIR ratio were refined substantially using satellite data In this section we provide a brief history of the use of the NDSI for mapping snow cover.

Snow Depth from Lidar: Challenges and New Technology for Measurements in Extreme Terrain

NASA Astrophysics Data System (ADS)

Berisford, D. F.; Kadatskiy, V.; Boardman, J. W.; Bormann, K.; Deems, J. S.; Goodale, C. E.; Mattmann, C. A.; Ramirez, P.; Richardson, M.; Painter, T. H.

2014-12-01

The Airborne Snow Observatory (ASO) uses an airborne LiDAR system to measure basin-wide snow depth with cm-scale accuracy at ~1m spatial resolution. This is accomplished by creating a Digital Elevation Model (DEM) over snow-free terrain in the summer, then repeating the flights again when the terrain is snow-covered and subtracting the elevations. Snow Water Equivalent (SWE) is then calculated by incorporating modeled snow density estimates, and when combined with coincident spectrometer albedo measurements, informs distributed hydrologic modeling and runoff prediction. This method provides SWE estimates of unprecedented accuracy and extent compared to traditional snow surveys and towers, and 24hr latency data products through the ASO processing pipeline using Apache Tika and OODT software. The timely ASO outputs support operational decision making by water/dam operators for optimal water management. The water-resource snowpack in the western US lies in remote mountainous terrain, spanning large areas containing steep faces at all aspects, often amongst tree canopy. This extreme terrain presents unusual challenges for LiDAR, and requires high altitude flights to achieve wide area coverage, high point density to capture small terrain features, and the ability to capture all slope aspects without shadowing. These challenges were met by the new state-of-the-art Riegl LMS-Q1560 LiDAR system, which incorporates two independent laser channels and a single rotating mirror. Both lasers and mirror are designed to provide forward, backward, and nadir look capability, which minimizes shadowing and ensures data capture even on very steep slopes. The system is capable of logging more than 10 simultaneous pulses in the air, which allows data collection at extremely high resolution while maintaining very high altitude which reduces complete region acquisition time significantly, and allows data collection over terrain with extreme elevation variation. Our experience to-date includes acquisition of data over terrain relief of more than 3500m, and ranges of up to 6000m in a single swath. We present data acquired during spring of 2013 and 2014 in western Colorado and the central Sierra Nevada, which demonstrates the capability of the new LiDAR technology and shows basin-wide measured snow depth and SWE results.

Applying High Resolution Imagery to Understand the Role of Dynamics in the Diminishing Arctic Sea Ice Cover

DTIC Science & Technology

2015-09-30

observations collected by the NASA Operation IceBridge (OIB) project, including high-resolution visible-band imagery (Onana et al., 2013), snow depth ( Newman et...2014; Farrell et al., 2015; Hutchings et al., 2015; Richter-Menge and Farrell, 2014), snow depth ( Newman et al., 2014; Webster et al., 2014), sea ice...with A. Mahoney , H. Eicken and C. Haas on an ONR-funded project "Mass balance of multi-year sea ice in the southern Beaufort Sea". This effort

A Vision for an International Multi-Sensor Snow Observing Mission

NASA Technical Reports Server (NTRS)

Kim, Edward

2015-01-01

Discussions within the international snow remote sensing community over the past two years have led to encouraging consensus regarding the broad outlines of a dedicated snow observing mission. The primary consensus - that since no single sensor type is satisfactory across all snow types and across all confounding factors, a multi-sensor approach is required - naturally leads to questions about the exact mix of sensors, required accuracies, and so on. In short, the natural next step is to collect such multi-sensor snow observations (with detailed ground truth) to enable trade studies of various possible mission concepts. Such trade studies must assess the strengths and limitations of heritage as well as newer measurement techniques with an eye toward natural sensitivity to desired parameters such as snow depth and/or snow water equivalent (SWE) in spite of confounding factors like clouds, lack of solar illumination, forest cover, and topography, measurement accuracy, temporal and spatial coverage, technological maturity, and cost.

Measured Two-Dimensional Ice-Wedge Polygon Thermal and Active Layer Dynamics

NASA Astrophysics Data System (ADS)

Cable, W.; Romanovsky, V. E.; Busey, R.

2016-12-01

Ice-wedge polygons are perhaps the most dominant permafrost related features in the arctic landscape. The microtopography of these features, that includes rims, troughs, and high and low polygon centers, alters the local hydrology. During winter, wind redistribution of snow leads to an increased snowpack depth in the low areas, while the slightly higher areas often have very thin snow cover, leading to differences across the landscape in vegetation communities and soil moisture between higher and lower areas. To investigate the effect of microtopographic caused variation in surface conditions on the ground thermal regime, we established temperature transects, composed of five vertical array thermistor probes (VATP), across four different development stages of ice-wedge polygons near Barrow, Alaska. Each VATP had 16 thermistors from the surface to a depth of 1.5 m, for a total of 80 temperature measurements per polygon. We found snow cover, timing and depth, and active layer soil moisture to be major controlling factors in the observed thermal regimes. In troughs and in the centers of low-centered polygons, the combined effect of typically saturated soils and increased snow accumulation resulted in the highest mean annual ground temperatures (MAGT) and latest freezeback dates. While the centers of high-centered polygons, with thinner snow cover and a dryer active layer, had the lowest MAGT, earliest freezeback dates, and shallowest active layer. Refreezing of the active layer initiated at nearly the same time for all locations and polygons however, we found large differences in the proportion of downward versus upward freezing and the length of time required to complete the refreezing process between polygon types and locations. Using our four polygon stages as a space for time substitution, we conclude that ice-wedge degradation resulting in surface subsidence and trough deepening can lead to overall drying of the active layer and increased skewedness of snow distribution. Which in turn leads to shallower active layers, earlier freezeback dates, and lower MAGT. We also find that the large variation in active layer dynamics (active layer depth, downward vs upward freezing, and freezeback date) are important considerations to understanding and scaling biological processes occurring in these landscapes.

Monitoring of snowpack dynamics in mountainous terrain by cosmic-ray neutron sensing compared to Terrestrial Laser Scanning observations

NASA Astrophysics Data System (ADS)

Schattan, P.; Baroni, G.; Schrön, M.; Köhli, M.; Oswald, S. E.; Huttenlau, M.; Achleitner, S.

2017-12-01

Monitoring a mountain snowpack in a representative domain of several hectares is challenging due to its high heterogeneity in time and space. Recent studies have suggested cosmic-ray neutron sensing (CRNS) as a promising method for monitoring snow representatively at these scales. Little is known however about the depth of sensitivity, the effects of fractional snow coverage in complex terrain or the influence of snow density profiles. Therefore, a field campaign in the Austrian Alps was conducted from March 2014 to June 2016. The main scope was to evaluate the characteristics of CRNS for monitoring a snowpack in a relatively wet and mountainous environment. During the experiment, the study site experienced a peak snow accumulation in terms of snow water equivalent (SWE) of up to 600 mm in the 2014/2015 winter season. Snow depth (SD) and SWE measurements from an automatic weather station were compared to CRNS neutron counts. Several spatially distributed Terrestrial Laser Scanning (TLS)-based SD and SWE maps were additionally used to cope with the spatial heterogeneity of the site. Furthermore, an URANOS neutron transport model was set up to provide additional insights into the response of CRNS to the presence of a complex snowpack. Therein, spatially distributed SWE scenarios and different snow density assumptions are used for hypothesis testing. The field measurements revealed an unexpectedly high potential of CRNS for monitoring heterogeneous snowpack dynamics beyond shallow snowpacks. A clear, nonlinear relation was found for both SD and SWE with neutron counts. In contrast to previous studies suggesting signal saturation at around 100 mm of SWE, complete signal saturation was observed only for SWE values beyond 500 to 600 mm. In addition, first modelling results highlight the effects of snow density profiles, small-scale changes in SWE, and the complex patterns of fractional snow cover on neutron counts. Understanding the interactions between neutrons and snow cover in complex terrain potentially improves the transferability of the results to other locations.

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.

UAS applications in high alpine, snow-covered terrain

NASA Astrophysics Data System (ADS)

Bühler, Y.; Stoffel, A.; Ginzler, C.

2017-12-01

Access to snow-covered, alpine terrain is often difficult and dangerous. Hence parameters such as snow depth or snow avalanche release and deposition zones are hard to map in situ with adequate spatial and temporal resolution and with spatial continuous coverage. These parameters are currently operationally measured at automated weather stations and by observer networks. However such isolated point measurements are not able to capture the information spatial continuous and to describe the high spatial variability present in complex mountain topography. Unmanned Aerial Systems (UAS) have the potential to fill this gap by frequently covering selected high alpine areas with high spatial resolution down to ground resolutions of even few millimeters. At the WSL Institute for Snow and Avalanche Research SLF we test different photogrammetric UAS with visual and near infrared bands. During the last three years we were able to gather experience in more than 100 flight missions in extreme terrain. By processing the imagery applying state-of-the-art structure from motion (SfM) software, we were able to accurately document several avalanche events and to photogrammetrically map snow depth with accuracies from 1 to 20 cm (dependent on the flight height above ground) compare to manual snow probe measurements. This was even possible on homogenous snow surfaces with very little texture. A key issue in alpine terrain is flight planning. We need to cover regions at high elevations with large altitude differences (up to 1 km) with high wind speeds (up to 20 m/s) and cold temperatures (down to - 25°C). Only a few UAS are able to cope with these environmental conditions. We will give an overview on our applications of UAS in high alpine terrain that demonstrate the big potential of such systems to acquire frequent, accurate and high spatial resolution geodata in high alpine, snow covered terrain that could be essential to answer longstanding questions in avalanche and snow hydrology research.

Alaska Division of Geological and Geophysical Surveys

Science.gov Websites

Name Title Gabriel Wolken, Ph.D. Program Manager Katreen Wikstrom Jones M.Sc. Geologist Research flood forecasting) rely on a quantitative assessment of distributed snow thickness and stored water . 2015. End-of-winter snow depth variability on glaciers in Alaska. Journal of Geophysical Research

43 CFR 36.11 - Special access.

Code of Federal Regulations, 2014 CFR

2014-10-01

... imprisonment in accordance with the penalty provisions applicable to the area. [51 FR 31629, Sept. 4, 1986; 51... term: (1) Area also includes public lands administered by the BLM and designated as wilderness study areas. (2) Adequate snow cover shall mean snow of sufficient depth, generally 6-12 inches or more, or a...

43 CFR 36.11 - Special access.

Code of Federal Regulations, 2011 CFR

2011-10-01

... imprisonment in accordance with the penalty provisions applicable to the area. [51 FR 31629, Sept. 4, 1986; 51... term: (1) Area also includes public lands administered by the BLM and designated as wilderness study areas. (2) Adequate snow cover shall mean snow of sufficient depth, generally 6-12 inches or more, or a...

43 CFR 36.11 - Special access.

Code of Federal Regulations, 2012 CFR

2012-10-01

... imprisonment in accordance with the penalty provisions applicable to the area. [51 FR 31629, Sept. 4, 1986; 51... term: (1) Area also includes public lands administered by the BLM and designated as wilderness study areas. (2) Adequate snow cover shall mean snow of sufficient depth, generally 6-12 inches or more, or a...

43 CFR 36.11 - Special access.

Code of Federal Regulations, 2010 CFR

2010-10-01

... imprisonment in accordance with the penalty provisions applicable to the area. [51 FR 31629, Sept. 4, 1986; 51... term: (1) Area also includes public lands administered by the BLM and designated as wilderness study areas. (2) Adequate snow cover shall mean snow of sufficient depth, generally 6-12 inches or more, or a...

43 CFR 36.11 - Special access.

Code of Federal Regulations, 2013 CFR

2013-10-01

... imprisonment in accordance with the penalty provisions applicable to the area. [51 FR 31629, Sept. 4, 1986; 51... term: (1) Area also includes public lands administered by the BLM and designated as wilderness study areas. (2) Adequate snow cover shall mean snow of sufficient depth, generally 6-12 inches or more, or a...

Snow Water Equivalent estimation based on satellite observation

NASA Astrophysics Data System (ADS)

Macchiavello, G.; Pesce, F.; Boni, G.; Gabellani, S.

2009-09-01

The availability of remotely sensed images and them analysis is a powerful tool for monitoring the extension and typology of snow cover over territory where the in situ measurements are often difficult. Information on snow are fundamental for monitoring and forecasting the available water above all in regions at mid latitudes as Mediterranean where snowmelt may cause floods. The hydrological model requirements and the daily acquisitions of MODIS (Moderate Resolution Imaging Spectroradiometer), drove, in previous research activities, to the development of a method to automatically map the snow cover from multi-spectral images. But, the major hydrological parameter related to the snow pack is the Snow Water Equivalent (SWE). This represents a direct measure of stored water in the basin. Because of it, the work was focused to the daily estimation of SWE from MODIS images. But, the complexity of this aim, based only on optical data, doesn’t find any information in literature. Since, from the spectral range of MODIS data it is not possible to extract a direct relation between spectral information and the SWE. Then a new method, respectful of the physic of the snow, was defined and developed. Reminding that the snow water equivalent is the product of the three factors as snow density, snow depth and the snow covered areas, the proposed approach works separately on each of these physical behaviors. Referring to the physical characteristic of snow, the snow density is function of the snow age, then it was studied a new method to evaluate this. Where, a module for snow age simulation from albedo information was developed. It activates an age counter updated by new snow information set to estimate snow age from zero accumulation status to the end of melting season. The height of the snow pack, can be retrieved by adopting relation between vegetation and snow depth distributions. This computes snow height distribution by the relation between snow cover fraction and the forest canopy density. Finally, the SWE has to be calculated for the snow covered areas, detected by means of a previously developed decision tree classifier able to classify snow cover by self selecting rules in a statistically optimum way. The advantages introduced from this work are many. Firstly, applying a suitable method with data features, it is possible to automatically obtain snow cover description with high frequency. Moreover, the advantages of the modularity in the proposed approach allows to improve the three factors estimation in an independent way. Limitations lie into clouds problem that affects results by obscuring the observed territory, that is bounded by fusing temporal and spatial information. Then the spatial resolution of data, satisfactory with the scale of hydrological models, mismatch with the available in situ point information, causing difficulties for a method validation or calibration. However this working flow results computationally cost-effectiveness, robust to the radiometric noise of the original data, provides spatially extended and frequent information.

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.

Efficient wetland surface water detection and monitoring via Landsat: Comparison with in situ data from the Everglades Depth Estimation Network

USGS Publications Warehouse

Jones, John W.

2015-01-01

The U.S. Geological Survey is developing new Landsat science products. One, named Dynamic Surface Water Extent (DSWE), is focused on the representation of ground surface inundation as detected in cloud-/shadow-/snow-free pixels for scenes collected over the U.S. and its territories. Characterization of DSWE uncertainty to facilitate its appropriate use in science and resource management is a primary objective. A unique evaluation dataset developed from data made publicly available through the Everglades Depth Estimation Network (EDEN) was used to evaluate one candidate DSWE algorithm that is relatively simple, requires no scene-based calibration data, and is intended to detect inundation in the presence of marshland vegetation. A conceptual model of expected algorithm performance in vegetated wetland environments was postulated, tested and revised. Agreement scores were calculated at the level of scenes and vegetation communities, vegetation index classes, water depths, and individual EDEN gage sites for a variety of temporal aggregations. Landsat Archive cloud cover attribution errors were documented. Cloud cover had some effect on model performance. Error rates increased with vegetation cover. Relatively low error rates for locations of little/no vegetation were unexpectedly dominated by omission errors due to variable substrates and mixed pixel effects. Examined discrepancies between satellite and in situ modeled inundation demonstrated the utility of such comparisons for EDEN database improvement. Importantly, there seems no trend or bias in candidate algorithm performance as a function of time or general hydrologic conditions, an important finding for long-term monitoring. The developed database and knowledge gained from this analysis will be used for improved evaluation of candidate DSWE algorithms as well as other measurements made on Everglades surface inundation, surface water heights and vegetation using radar, lidar and hyperspectral instruments. Although no other sites have such an extensive in situ network or long-term records, the broader applicability of this and other candidate DSWE algorithms is being evaluated in other wetlands using this work as a guide. Continued interaction among DSWE producers and potential users will help determine whether the measured accuracies are adequate for practical utility in resource management.

Enhancement of the MODIS Snow and Ice Product Suite Utilizing Image Segmentation

NASA Technical Reports Server (NTRS)

Tilton, James C.; Hall, Dorothy K.; Riggs, George A.

2006-01-01

A problem has been noticed with the current NODIS Snow and Ice Product in that fringes of certain snow fields are labeled as "cloud" whereas close inspection of the data indicates that the correct labeling is a non-cloud category such as snow or land. This occurs because the current MODIS Snow and Ice Product generation algorithm relies solely on the MODIS Cloud Mask Product for the labeling of image pixels as cloud. It is proposed here that information obtained from image segmentation can be used to determine when it is appropriate to override the cloud indication from the cloud mask product. Initial tests show that this approach can significantly reduce the cloud "fringing" in modified snow cover labeling. More comprehensive testing is required to determine whether or not this approach consistently improves the accuracy of the snow and ice product.

Dynamics of active layer in wooded palsas of northern Quebec

NASA Astrophysics Data System (ADS)

Jean, Mélanie; Payette, Serge

2014-02-01

Palsas are organic or mineral soil mounds having a permafrost core. Palsas are widespread in the circumpolar discontinuous permafrost zone. The annual dynamics and evolution of the active layer, which is the uppermost layer over the permafrost table and subjected to the annual freeze-thaw cycle, are influenced by organic layer thickness, snow depth, vegetation type, topography and exposure. This study examines the influence of vegetation types, with an emphasis on forest cover, on active layer dynamics of palsas in the Boniface River watershed (57°45‧ N, 76°00‧ W). In this area, palsas are often colonized by black spruce trees (Picea mariana (Mill.) B.S.P.). Thaw depth and active layer thickness were monitored on 11 wooded or non-wooded mineral and organic palsas in 2009, 2010 and 2011. Snow depth, organic layer thickness, and vegetation types were assessed. The mapping of a palsa covered by various vegetation types and a large range of organic layer thickness were used to identify the factors influencing the spatial patterns of thaw depth and active layer. The active layer was thinner and the thaw rate slower in wooded palsas, whereas it was the opposite in more exposed sites such as forest openings, shrubs and bare ground. Thicker organic layers were associated with thinner active layers and slower thaw rates. Snow depth was not an important factor influencing active layer dynamics. The topography of the mapped palsa was uneven, and the environmental factors such as organic layer, snow depth, and vegetation types were heterogeneously distributed. These factors explain a part of the spatial variation of the active layer. Over the 3-year long study, the area of one studied palsa decreased by 70%. In a context of widespread permafrost decay, increasing our understanding of factors that influence the dynamics of wooded and non-wooded palsas and understanding of the role of vegetation cover will help to define the response of discontinuous permafrost landforms to changing climatic conditions.

Integrated Airborne and In-Situ Measurements over Land-Fast Ice near Barrow, AK.

NASA Astrophysics Data System (ADS)

Brozena, J. M.; Gardner, J. M.; Liang, R.; Ball, D.; Richter-Menge, J.; Claffey, K. J.; Abelev, A.; Hebert, D. A.; Jones, K.

2014-12-01

During March of 2014, the Naval Research Laboratory and the Cold Regions Research and Engineering Laboratory collected an integrated set of airborne and in-situ measurements over two areas of floating, but land-fast ice near the coast of Barrow, AK. The near-shore site was just north of Point Barrow, and the "offshore" site was ~ 20 km east of Point Barrow. The in-situ data provided ground-truth for airborne measurements from a scanning LiDAR (Riegl Q 560i), digital photogrammetry (Applanix DSS-439) and a snow radar procured from the Center for Remote Sensing of Ice Sheets of the University of Kansas. The objective of the survey was to aid our understanding of the use of the airborne data to calibrate/validate Cryosat-2 data. Sampling size or "footprint" plays a critical role in the attempt to compare in-situ measurements with airborne (or satellite) measurements. Thus the in-situ data were arranged to minimize aliasing. Ground measurements were collected along transects at both sites consisting of a 2 km long profile of snow depth and ice thickness measurements with periodic boreholes. A 60 m x 400 m swath of snow depth measurements was centered on this profile. Airborne data were collected on five overflights of the two transect areas. The LiDAR measured total freeboard (ice + snow) referenced to leads in the ice, and produced swaths 200-300 m wide. The radar measured snow thickness. The freeboard and snow thickness measurements are used to estimate ice thickness via isostasy and density estimates. The central swath of in situ snow depth data allows examination of the effects of cross-track variations considering the relatively large footprint of the snow radar. Assuming a smooth, flat surface the radar range resolution in air is < 4 cm, but the along-track sampling distance is ~ 3 m after unfocussed SAR processing. The width of the footprint varies from ~ 9 m up to about 40 m (beam-limited) for uneven surfaces. However, the radar could not resolve snow thickness except in areas of relatively flat snow and ice. The LiDAR had a ground point spacing of ~25-50 cm (depending on survey altitude) and so easily encompassed all other data. Comparisons and processing methodology will be shown. The results of this ground-truth experiment will inform our analysis of grids of airborne data collected over areas of sea-ice illuminated by Cryosat-2.

Impacts of microtopographic snow redistribution and lateral subsurface processes on hydrologic and thermal states in an Arctic polygonal ground ecosystem: a case study using ELM-3D v1.0

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

Bisht, Gautam; Riley, William J.; Wainwright, Haruko M.

Microtopographic features, such as polygonal ground, are characteristic sources of landscape heterogeneity in the Alaskan Arctic coastal plain. In this study, we analyze the effects of snow redistribution (SR) and lateral subsurface processes on hydrologic and thermal states at a polygonal tundra site near Barrow, Alaska. We extended the land model integrated in the E3SM to redistribute incoming snow by accounting for microtopography and incorporated subsurface lateral transport of water and energy (ELM-3D v1.0). Multiple 10-year-long simulations were performed for a transect across a polygonal tundra landscape at the Barrow Environmental Observatory in Alaska to isolate the impact of SRmore » and subsurface process representation. When SR was included, model predictions better agreed (higher R 2, lower bias and RMSE) with observed differences in snow depth between polygonal rims and centers. The model was also able to accurately reproduce observed soil temperature vertical profiles in the polygon rims and centers (overall bias, RMSE, and R 2 of 0.59°C, 1.82°C, and 0.99, respectively). The spatial heterogeneity of snow depth during the winter due to SR generated surface soil temperature heterogeneity that propagated in depth and time and led to ~ 10 cm shallower and ~ 5 cm deeper maximum annual thaw depths under the polygon rims and centers, respectively. Additionally, SR led to spatial heterogeneity in surface energy fluxes and soil moisture during the summer. Excluding lateral subsurface hydrologic and thermal processes led to small effects on mean states but an overestimation of spatial variability in soil moisture and soil temperature as subsurface liquid pressure and thermal gradients were artificially prevented from spatially dissipating over time. The effect of lateral subsurface processes on maximum thaw depths was modest, with mean absolute differences of ~ 3 cm. Our integration of three-dimensional subsurface hydrologic and thermal subsurface dynamics in the E3SM land model will facilitate a wide range of analyses heretofore impossible in an ESM context.« less

Impacts of microtopographic snow-redistribution and lateral subsurface processeson hydrologic and thermal states in an Arctic polygonal ground ecosystem

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

Bisht, Gautam; Riley, William J.; Wainwright, Haruko M.

Microtopographic features, such as polygonal ground, are characteristic sources of landscape heterogeneity in the Alaskan Arctic coastal plain. We analyze the effects of snow redistribution (SR) and lateral subsurface processes on hydrologic and thermal states at a polygonal tundra site near Barrow, Alaska. We extended the land model integrated in the ACME Earth System Model (ESM) to redistribute incoming snow by accounting for microtopography and incorporated subsurface lateral transport of water and energy (ALMv0-3D). Three 10-years long simulations were performed for a transect across polygonal tundra landscape at the Barrow Environmental Observatory in Alaska to isolate the impact of SRmore » and subsurface process representation. When SR was included, model results show a better agreement (higher R 2 with lower bias and RMSE) for the observed differences in snow depth between polygonal rims and centers. The model was also able to accurately reproduce observed soil temperature vertical profiles in the polygon rims and centers (overall bias, RMSE, and R 2 of 0.59°C, 1.82°C, and 0.99, respectively). The spatial heterogeneity of snow depth during the winter due to SR generated surface soil temperature heterogeneity that propagated in depth and time and led to ~10 cm shallower and ~5 cm deeper maximum annual thaw depths under the polygon rims and centers, respectively. Additionally, SR led to spatial heterogeneity in surface energy fluxes and soil moisture during the summer. Excluding lateral subsurface hydrologic and thermal processes led to small effects on mean states but an overestimation of spatial variability in soil moisture and soil temperature as subsurface liquid pressure and thermal gradients were artificially prevented from spatially dissipating over time. The effect of lateral subsurface processes on active layer depths was modest with mean absolute difference of ~3 cm. Finally, our integration of three-dimensional subsurface hydrologic and thermal subsurface dynamics in the ACME land model will facilitate a wide range of analyses heretofore impossible in an ESM context.« less

Impacts of microtopographic snow redistribution and lateral subsurface processes on hydrologic and thermal states in an Arctic polygonal ground ecosystem: a case study using ELM-3D v1.0

DOE PAGES

Bisht, Gautam; Riley, William J.; Wainwright, Haruko M.; ...

2018-01-08

Microtopographic features, such as polygonal ground, are characteristic sources of landscape heterogeneity in the Alaskan Arctic coastal plain. In this study, we analyze the effects of snow redistribution (SR) and lateral subsurface processes on hydrologic and thermal states at a polygonal tundra site near Barrow, Alaska. We extended the land model integrated in the E3SM to redistribute incoming snow by accounting for microtopography and incorporated subsurface lateral transport of water and energy (ELM-3D v1.0). Multiple 10-year-long simulations were performed for a transect across a polygonal tundra landscape at the Barrow Environmental Observatory in Alaska to isolate the impact of SRmore » and subsurface process representation. When SR was included, model predictions better agreed (higher R 2, lower bias and RMSE) with observed differences in snow depth between polygonal rims and centers. The model was also able to accurately reproduce observed soil temperature vertical profiles in the polygon rims and centers (overall bias, RMSE, and R 2 of 0.59°C, 1.82°C, and 0.99, respectively). The spatial heterogeneity of snow depth during the winter due to SR generated surface soil temperature heterogeneity that propagated in depth and time and led to ~ 10 cm shallower and ~ 5 cm deeper maximum annual thaw depths under the polygon rims and centers, respectively. Additionally, SR led to spatial heterogeneity in surface energy fluxes and soil moisture during the summer. Excluding lateral subsurface hydrologic and thermal processes led to small effects on mean states but an overestimation of spatial variability in soil moisture and soil temperature as subsurface liquid pressure and thermal gradients were artificially prevented from spatially dissipating over time. The effect of lateral subsurface processes on maximum thaw depths was modest, with mean absolute differences of ~ 3 cm. Our integration of three-dimensional subsurface hydrologic and thermal subsurface dynamics in the E3SM land model will facilitate a wide range of analyses heretofore impossible in an ESM context.« less

Impacts of microtopographic snow-redistribution and lateral subsurface processeson hydrologic and thermal states in an Arctic polygonal ground ecosystem

DOE PAGES

Bisht, Gautam; Riley, William J.; Wainwright, Haruko M.; ...

2018-01-08

Microtopographic features, such as polygonal ground, are characteristic sources of landscape heterogeneity in the Alaskan Arctic coastal plain. We analyze the effects of snow redistribution (SR) and lateral subsurface processes on hydrologic and thermal states at a polygonal tundra site near Barrow, Alaska. We extended the land model integrated in the ACME Earth System Model (ESM) to redistribute incoming snow by accounting for microtopography and incorporated subsurface lateral transport of water and energy (ALMv0-3D). Three 10-years long simulations were performed for a transect across polygonal tundra landscape at the Barrow Environmental Observatory in Alaska to isolate the impact of SRmore » and subsurface process representation. When SR was included, model results show a better agreement (higher R 2 with lower bias and RMSE) for the observed differences in snow depth between polygonal rims and centers. The model was also able to accurately reproduce observed soil temperature vertical profiles in the polygon rims and centers (overall bias, RMSE, and R 2 of 0.59°C, 1.82°C, and 0.99, respectively). The spatial heterogeneity of snow depth during the winter due to SR generated surface soil temperature heterogeneity that propagated in depth and time and led to ~10 cm shallower and ~5 cm deeper maximum annual thaw depths under the polygon rims and centers, respectively. Additionally, SR led to spatial heterogeneity in surface energy fluxes and soil moisture during the summer. Excluding lateral subsurface hydrologic and thermal processes led to small effects on mean states but an overestimation of spatial variability in soil moisture and soil temperature as subsurface liquid pressure and thermal gradients were artificially prevented from spatially dissipating over time. The effect of lateral subsurface processes on active layer depths was modest with mean absolute difference of ~3 cm. Finally, our integration of three-dimensional subsurface hydrologic and thermal subsurface dynamics in the ACME land model will facilitate a wide range of analyses heretofore impossible in an ESM context.« less

Impacts of microtopographic snow redistribution and lateral subsurface processes on hydrologic and thermal states in an Arctic polygonal ground ecosystem: a case study using ELM-3D v1.0

NASA Astrophysics Data System (ADS)

Bisht, Gautam; Riley, William J.; Wainwright, Haruko M.; Dafflon, Baptiste; Yuan, Fengming; Romanovsky, Vladimir E.

2018-01-01

Microtopographic features, such as polygonal ground, are characteristic sources of landscape heterogeneity in the Alaskan Arctic coastal plain. Here, we analyze the effects of snow redistribution (SR) and lateral subsurface processes on hydrologic and thermal states at a polygonal tundra site near Barrow, Alaska. We extended the land model integrated in the E3SM to redistribute incoming snow by accounting for microtopography and incorporated subsurface lateral transport of water and energy (ELM-3D v1.0). Multiple 10-year-long simulations were performed for a transect across a polygonal tundra landscape at the Barrow Environmental Observatory in Alaska to isolate the impact of SR and subsurface process representation. When SR was included, model predictions better agreed (higher R2, lower bias and RMSE) with observed differences in snow depth between polygonal rims and centers. The model was also able to accurately reproduce observed soil temperature vertical profiles in the polygon rims and centers (overall bias, RMSE, and R2 of 0.59 °C, 1.82 °C, and 0.99, respectively). The spatial heterogeneity of snow depth during the winter due to SR generated surface soil temperature heterogeneity that propagated in depth and time and led to ˜ 10 cm shallower and ˜ 5 cm deeper maximum annual thaw depths under the polygon rims and centers, respectively. Additionally, SR led to spatial heterogeneity in surface energy fluxes and soil moisture during the summer. Excluding lateral subsurface hydrologic and thermal processes led to small effects on mean states but an overestimation of spatial variability in soil moisture and soil temperature as subsurface liquid pressure and thermal gradients were artificially prevented from spatially dissipating over time. The effect of lateral subsurface processes on maximum thaw depths was modest, with mean absolute differences of ˜ 3 cm. Our integration of three-dimensional subsurface hydrologic and thermal subsurface dynamics in the E3SM land model will facilitate a wide range of analyses heretofore impossible in an ESM context.

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.

Long-term erythemal UV doses at Sodankylä estimated using total ozone, sunshine duration, and snow depth

NASA Astrophysics Data System (ADS)

Lindfors, A. V.; Arola, A.; Kaurola, J.; Taalas, P.; SvenøE, T.

2003-08-01

A method for estimating daily erythemal UV doses using total ozone, sunshine duration, and snow depth has been developed. The method consists of three steps: (1) daily clear-sky UV doses were simulated using the UVSPEC radiative transfer program, with daily values of total ozone as input data, (2) an empirical relationship was sought between the simulated clear-sky UV doses, the measured UV doses, and the duration of bright sunshine, and (3) daily erythemal UV doses were estimated using this relationship. The method accounts for the varying surface albedo by dividing the period of interest into winter and summer days, depending on the snow depth. Using this method, the daily erythemal UV doses at Sodankylä were estimated for the period 1950-1999. This was done using Tromsø's total ozone together with Sodankylä's own sunshine duration and snow depth as input data. Although the method is fairly simple, the results are in good agreement, even on the daily scale, with the UV radiation measured with the Brewer spectrophotometer at Sodankylä. Over the period 1950-1999 a statistically significant increasing trend of 3.9% per decade in erythemal UV doses was found for March. The fact that this trend is much more pronounced during the latter part of the period, which is also the case for April, suggests a connection to the stratospheric ozone depletion. For July, on the other hand, a significant decreasing trend of 3.3% per decade, supported by the changes in both total ozone and sunshine duration, was found.

Long-term erythemal UV doses at Sodankylä estimated using total ozone, sunshine duration and snow depth

NASA Astrophysics Data System (ADS)

Lindfors, A. V.; Arola, A.; Kaurola, J.; Taalas, P.; Svenøe, T.

2003-04-01

A method for estimating daily erythemal UV doses using total ozone, sunshine duration and snow depth has been developed. The method consists of three steps: (1) daily clear-sky UV doses were simulated using the UVSPEC radiative transfer program, with daily values of total ozone as input data, (2) an empirical relationship was sought between the simulated clear-sky UV doses, the measured UV doses and the duration of bright sunshine, (3) daily erythemal UV doses were estimated using this relationship. The method accounts for the varying surface albedo by dividing the period of interest into winter and summer days, depending on the snow depth. Using this method, the daily erythemal UV doses at Sodankylä were estimated for the period 1950--99. This was done using Tromsø's total ozone together with Sodankylä's own sunshine duration and snow depth as input data. Although the method is fairly simple, the results are in good agreement, even on the daily scale, with the UV radiation measured with the Brewer spectrophotometer at Sodankylä. Statistically significant increasing trends in erythemal UV doses of a few percents per decade over the period 1950--99 were found for March and April, suggesting a connection to the stratospheric ozone depletion. For July, on the other hand, a significant decreasing trend of about 3% per decade, supported by the changes in both total ozone and sunshine duration, was found. The produced data set of erythemal UV doses is the longest time series of estimated UV known to the authors.

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.

NPP ATMS Snowfall Rate Product

NASA Technical Reports Server (NTRS)

Meng, Huan; Ferraro, Ralph; Kongoli, Cezar; Wang, Nai-Yu; Dong, Jun; Zavodsky, Bradley; Yan, Banghua

2015-01-01

Passive microwave measurements at certain high frequencies are sensitive to the scattering effect of snow particles and can be utilized to retrieve snowfall properties. Some of the microwave sensors with snowfall sensitive channels are Advanced Microwave Sounding Unit (AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow-on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has been developed recently. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. The model employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derive the probability of snowfall (Kongoli et al., 2015). In addition, a set of NWP model based filters is also employed to improve the accuracy of snowfall detection. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two-stream radiative transfer model (Yan et al., 2008). A method developed by Heymsfield and Westbrook (2010) is adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. NCEP CMORPH analysis has shown that integration of ATMS SFR has improved the performance of CMORPH-Snow. The ATMS SFR product is also being assessed at several NWS Weather Forecast Offices for its usefulness in weather forecast.

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.

MODIS Snow-Cover Products

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Riggs, George A.; Salomonson, Vinvent V.; DiGirolamo, Nicolo; Bayr, Klaus J.; Houser, Paul (Technical Monitor)

2001-01-01

On December 18, 1999, the Terra satellite was launched with a complement of five instruments including the Moderate Resolution Imaging Spectroradiometer (MODIS). Many geophysical products are derived from MODIS data including global snow-cover products. These products have been available through the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) since September 13, 2000. MODIS snow-cover products represent potential improvement to the currently available operation products mainly because the MODIS products are global and 500-m resolution, and have the capability to separate most snow and clouds. Also the snow-mapping algorithms are automated which means that a consistent data set is generated for long-term climates studies that require snow-cover information. Extensive quality assurance (QA) information is stored with the product. The snow product suite starts with a 500-m resolution swath snow-cover map which is gridded to the Integerized Sinusoidal Grid to produce daily and eight-day composite tile products. The sequence then proceeds to a climate-modeling grid product at 5-km spatial resolution, with both daily and eight-day composite products. A case study from March 6, 2000, involving MODIS data and field and aircraft measurements, is presented. Near-term enhancements include daily snow albedo and fractional snow cover.

Energy expenditure and clearing snow: a comparison of shovel and snow pusher.

PubMed

Smolander, J; Louhevaara, V; Ahonen, E; Polari, J; Klen, T

1995-04-01

In order to assess the energy demands of manual clearing of snow, nine men did snow clearing work for 15 min with a shovel and a snow pusher. The depth of the snowcover was 400-600 mm representing a very heavy snowfall. Heart rate (HR), oxygen consumption (VO2), pulmonary ventilation (VE), respiratory exchange ratio (R), and rating of perceived exertion (RPE) were determined during the work tasks. HR, VE, R, and RPE were not significantly different between the shovel and snow pusher. HR averaged (+/- SD) 141 +/- 20 b min-1 with the shovel, and 142 +/- 19 beats.min-1 with the snow pusher. VO2 was 2.1 +/- 0.41.min-1 (63 +/- 12%VO2 max) in shovelling and 2.6 +/- 0.51.min-1 (75 +/- 14%VO2max) in snow pushing (p < 0.001). In conclusion manual clearing of snow in conditions representing heavy snowfalls was found to be strenuous physical work, not suitable for persons with cardiac risk factors, but which may serve as a mode of physical training in healthy adults.

Facilitating the exploitation of ERTS imagery using snow enhancement techniques

NASA Technical Reports Server (NTRS)

Wobber, F. J. (Principal Investigator); Martin, K. R.; Sheffield, C.; Russell, O.; Amato, R. V.

1973-01-01

The author has identified the following significant results. EarthSat has established an effective mail-based method for obtaining timely ground truth (snow depth) information over an extensive area. The method is both efficient and inexpensive compared with the cost of a similarly scaled direct field checking effort. Additional geological information has been acquired which is not shown in geological maps in the area. Excellent quality snow-free ERTS-1 transparencies of the test areas have been received and are being analyzed.

Evaluating Interannual Variability of Accumulation Gradients on the Juneau Icefield

NASA Astrophysics Data System (ADS)

Koncewicz, E.; Bollen, K.; Burkhart, A.; Cabrera, V.; Rovzar, T.; Truax, O.; McNeil, C.; Nicholson, L. I.; O'Neel, S.

2016-12-01

The Juneau Icefield Research Program has collected mass balance data over the last 70 years on the Taku and Lemon Creek glaciers. We analyze data from 2004-2016 to investigate the interannual variability in the accumulation gradients of these two glaciers from ground penetrating radar (GPR), probing, and snow pits. Understanding interannual variability of accumulation gradients on the Juneau Icefield will help us to interpret its long-term mass balance record. The Lemon Creek Glacier is a small valley glacier on the southwest edge of the Icefield. GPR data was collected over the glacier surface in March 2015 and 2016. In July of 2014 and 2016, the accumulation area was probed for snow depth, and two snow pits were dug for snow depth and density. The accumulation gradients resulting from each method are compared between years to assess the interannnual variability of the accumulation gradient and the resulting glacier wide mass balance. The Taku Glacier is the largest outlet glacier on the Juneau Icefield. We use three snow pits dug each year along the longitudinal profile of the glacier between 1000m and 1115m, the region that typically reflects the ELA. In 2004, 2005, 2010, 2011, and 2016, snow probing was continued in the central region of the Taku and the resulting gradients are compared to each other and to the gradients derived from the snow pits. We assess the resulting impact on glacier wide mass balance furthering our understanding of the state of these two well-monitored glaciers on the Juneau Icefield.

Habitat characteristics at marten subnivean access sites

USGS Publications Warehouse

Corn, Janelle G.; Raphael, Martin G.

1992-01-01

The occurrence of coarse woody debris (CWD) at sites of subnivean (under snow) access by martens (Martes americana) has not been quantified adequately, and must be better understood to provide suitable winter habitat management for the species. Consequently, we studied subnivean activity of martens in a subalpine forest in southern Wyoming to determine how subnivean space was accessed, and to examine microhabitat characteristics around entry sites. Martens used existing openings in snow, created primarily by logs at low snow depths and by small live spruce and fir trees at greater snow depths. Sites of marten subnivean entry had greater percent cover (P ≤ 0.01) and total volume of CWD (P ≤ 0.01), greater numbers of log layers (all P ≤ 0.02), greater volume of undecayed (P ≤ 0.05) and moderately decayed logs (P ≤ 0.02), less volume of very decayed logs (P ≤ 0.001), and fewer small root masses (P ≤ 0.001) than surrounding forest stands. Provision of sufficient CWD in winter habitat of martens may require specific effort, particularly in managed forests of the central Rocky Mountains.

Winter precipitation and snow accumulation drive the methane sink or source strength of Arctic tussock tundra.

PubMed

Blanc-Betes, Elena; Welker, Jeffrey M; Sturchio, Neil C; Chanton, Jeffrey P; Gonzalez-Meler, Miquel A

2016-08-01

Arctic winter precipitation is projected to increase with global warming, but some areas will experience decreases in snow accumulation. Although Arctic CH4 emissions may represent a significant climate forcing feedback, long-term impacts of changes in snow accumulation on CH4 fluxes remain uncertain. We measured ecosystem CH4 fluxes and soil CH4 and CO2 concentrations and (13) C composition to investigate the metabolic pathways and transport mechanisms driving moist acidic tundra CH4 flux over the growing season (Jun-Aug) after 18 years of experimental snow depth increases and decreases. Deeper snow increased soil wetness and warming, reducing soil %O2 levels and increasing thaw depth. Soil moisture, through changes in soil %O2 saturation, determined predominance of methanotrophy or methanogenesis, with soil temperature regulating the ecosystem CH4 sink or source strength. Reduced snow (RS) increased the fraction of oxidized CH4 (Fox) by 75-120% compared to Ambient, switching the system from a small source to a net CH4 sink (21 ± 2 and -31 ± 1 mg CH4  m(-2)  season(-1) at Ambient and RS). Deeper snow reduced Fox by 35-40% and 90-100% in medium- (MS) and high- (HS) snow additions relative to Ambient, contributing to increasing the CH4 source strength of moist acidic tundra (464 ± 15 and 3561 ± 97 mg CH4  m(-2)  season(-1) at MS and HS). Decreases in Fox with deeper snow were partly due to increases in plant-mediated CH4 transport associated with the expansion of tall graminoids. Deeper snow enhanced CH4 production within newly thawed soils, responding mainly to soil warming rather than to increases in acetate fermentation expected from thaw-induced increases in SOC availability. Our results suggest that increased winter precipitation will increase the CH4 source strength of Arctic tundra, but the resulting positive feedback on climate change will depend on the balance between areas with more or less snow accumulation than they are currently facing. © 2016 John Wiley & Sons Ltd.

Spatiotemporal Variability and in Snow Phenology over Eurasian Continent druing 1966-2012

NASA Astrophysics Data System (ADS)

Zhong, X.; Zhang, T.; Wang, K.; Zheng, L.; Wang, H.

2016-12-01

Snow cover is a key part of the cryosphere, which is a critical component of the global climate system. Snow cover phenology critically effects on the surface energy budget, the surface albedo and hydrological processes. In this study, the climatology and spatiotemporal variability of snow cover phenology were investigated using the long-term (1966-2012) ground-based measurements of daily snow depth from 1103 stations across the Eurasian Continent. The results showed that the distributions of the first date, last date, snow cover duration and number of snow cover days generally represented the latitudinal zonality over the Eurasian Continent, and there were significant elevation gradient patterns in the Tibetan Plateau. The first date of snow cover delayed by about 1.2 day decade-1, the last date of snow cover advanced with the rate of -1.2 day decade-1, snow cover duration and number of snow cover days shortened by about 2.7and 0.6 day decade-1, respectively, from 1966 through 2012. Compared with precipitation, the correlation between snow cover phenology and air temperature was more significant. The changes in snow cover duration were mainly controlled by the changes of air temperature in autumn and spring. The shortened number of snow cover days was affected by rising temperature during the cold season except for the air temperature in autumn and spring.

Validation and Uncertainty Estimates for MODIS Collection 6 "Deep Blue" Aerosol Data

NASA Technical Reports Server (NTRS)

Sayer, A. M.; Hsu, N. C.; Bettenhausen, C.; Jeong, M.-J.

2013-01-01

The "Deep Blue" aerosol optical depth (AOD) retrieval algorithm was introduced in Collection 5 of the Moderate Resolution Imaging Spectroradiometer (MODIS) product suite, and complemented the existing "Dark Target" land and ocean algorithms by retrieving AOD over bright arid land surfaces, such as deserts. The forthcoming Collection 6 of MODIS products will include a "second generation" Deep Blue algorithm, expanding coverage to all cloud-free and snow-free land surfaces. The Deep Blue dataset will also provide an estimate of the absolute uncertainty on AOD at 550 nm for each retrieval. This study describes the validation of Deep Blue Collection 6 AOD at 550 nm (Tau(sub M)) from MODIS Aqua against Aerosol Robotic Network (AERONET) data from 60 sites to quantify these uncertainties. The highest quality (denoted quality assurance flag value 3) data are shown to have an absolute uncertainty of approximately (0.086+0.56Tau(sub M))/AMF, where AMF is the geometric air mass factor. For a typical AMF of 2.8, this is approximately 0.03+0.20Tau(sub M), comparable in quality to other satellite AOD datasets. Regional variability of retrieval performance and comparisons against Collection 5 results are also discussed.

LANDSAT-D investigations in snow hydrology

NASA Technical Reports Server (NTRS)

Dozier, J.

1983-01-01

Progress on the registration of TM data to digital topographic data; on comparison of TM, MSS and NOAA meteorological satellite data for snowcover mapping; and on radiative transfer models for atmospheric correction is reported. Some methods for analyzing spatial contiguity of snow within the snow covered area were selected. The methods are based on a two-channel version of the grey level co-occurence matrix, combined with edge detection derived from an algorithm for computing slopes and exposures from digital terrain data.

How much can we save? Impact of different emission scenarios on future snow cover in the Alps

NASA Astrophysics Data System (ADS)

Marty, Christoph; Schlögl, Sebastian; Bavay, Mathias; Lehning, Michael

2017-02-01

This study focuses on an assessment of the future snow depth for two larger Alpine catchments. Automatic weather station data from two diverse regions in the Swiss Alps have been used as input for the Alpine3D surface process model to compute the snow cover at a 200 m horizontal resolution for the reference period (1999-2012). Future temperature and precipitation changes have been computed from 20 downscaled GCM-RCM chains for three different emission scenarios, including one intervention scenario (2 °C target) and for three future time periods (2020-2049, 2045-2074, 2070-2099). By applying simple daily change values to measured time series of temperature and precipitation, small-scale climate scenarios have been calculated for the median estimate and extreme changes. The projections reveal a decrease in snow depth for all elevations, time periods and emission scenarios. The non-intervention scenarios demonstrate a decrease of about 50 % even for elevations above 3000 m. The most affected elevation zone for climate change is located below 1200 m, where the simulations show almost no snow towards the end of the century. Depending on the emission scenario and elevation zone the winter season starts half a month to 1 month later and ends 1 to 3 months earlier in this last scenario period. The resulting snow cover changes may be roughly equivalent to an elevation shift of 500-800 or 700-1000 m for the two non-intervention emission scenarios. At the end of the century the number of snow days may be more than halved at an elevation of around 1500 m and only 0-2 snow days are predicted in the lowlands. The results for the intervention scenario reveal no differences for the first scenario period but clearly demonstrate a stabilization thereafter, comprising much lower snow cover reductions towards the end of the century (ca. 30 % instead of 70 %).

Idiosyncratic Responses of High Arctic Plants to Changing Snow Regimes

PubMed Central

Rumpf, Sabine B.; Semenchuk, Philipp R.; Dullinger, Stefan; Cooper, Elisabeth J.

2014-01-01

The Arctic is one of the ecosystems most affected by climate change; in particular, winter temperatures and precipitation are predicted to increase with consequent changes to snow cover depth and duration. Whether the snow-free period will be shortened or prolonged depends on the extent and temporal patterns of the temperature and precipitation rise; resulting changes will likely affect plant growth with cascading effects throughout the ecosystem. We experimentally manipulated snow regimes using snow fences and shoveling and assessed aboveground size of eight common high arctic plant species weekly throughout the summer. We demonstrated that plant growth responded to snow regime, and that air temperature sum during the snow free period was the best predictor for plant size. The majority of our studied species showed periodic growth; increases in plant size stopped after certain cumulative temperatures were obtained. Plants in early snow-free treatments without additional spring warming were smaller than controls. Response to deeper snow with later melt-out varied between species and categorizing responses by growth forms or habitat associations did not reveal generic trends. We therefore stress the importance of examining responses at the species level, since generalized predictions of aboveground growth responses to changing snow regimes cannot be made. PMID:24523859

Idiosyncratic responses of high Arctic plants to changing snow regimes.

PubMed

Rumpf, Sabine B; Semenchuk, Philipp R; Dullinger, Stefan; Cooper, Elisabeth J

2014-01-01

The Arctic is one of the ecosystems most affected by climate change; in particular, winter temperatures and precipitation are predicted to increase with consequent changes to snow cover depth and duration. Whether the snow-free period will be shortened or prolonged depends on the extent and temporal patterns of the temperature and precipitation rise; resulting changes will likely affect plant growth with cascading effects throughout the ecosystem. We experimentally manipulated snow regimes using snow fences and shoveling and assessed aboveground size of eight common high arctic plant species weekly throughout the summer. We demonstrated that plant growth responded to snow regime, and that air temperature sum during the snow free period was the best predictor for plant size. The majority of our studied species showed periodic growth; increases in plant size stopped after certain cumulative temperatures were obtained. Plants in early snow-free treatments without additional spring warming were smaller than controls. Response to deeper snow with later melt-out varied between species and categorizing responses by growth forms or habitat associations did not reveal generic trends. We therefore stress the importance of examining responses at the species level, since generalized predictions of aboveground growth responses to changing snow regimes cannot be made.

Winter CO2 efflux from cold semiarid sagebrush shrublands distributed across the rain-to-snow transition zone

NASA Astrophysics Data System (ADS)

Fellows, A.; Flerchinger, G. N.; Lohse, K. A.; Seyfried, M. S.

2017-12-01

Predicting winter CO2 efflux across the rain-to-snow transition zone is challenging in the cold semiarid northern Great Basin, USA, complicated by steep environmental gradients and marked heterogeneity in ecosystem properties. We therefore examined winter CO2 efflux over 9 site-years using 4 eddy covariance towers located in the Reynolds Creek Critical Zone Observatory. The sites were sagebrush shrublands located at 1425, 1680, 2098, and 2111 m, and spanned a large part of the rain-to-snow transition zone. We focused on two objectives. First, we quantified winter CO2 efflux at the sites, and considered how these varied with elevation. Second, we used a within-site and cross-site analysis to examine the biological and physical factors that impact winter CO2 efflux. Winter conditions were identified using temperature, snow depth, and CO2 exchange measurements and included 12,922 observations. The duration of winter conditions increased from 90 to 180 days with elevation. Peak snow depth increased from < 30 to > 100 cm with elevation. Cumulative winter CO2 efflux accounted for > 10% of the total annual CO2 efflux, increased with elevation, and was a key component of net ecosystem production at some sites in some years. The importance of winter CO2 efflux was accentuated by the region's long winters and also dry summers that decreased water availability and decomposition during non-winter periods. Preliminary regressions examining air temperature, soil temperature, wind speed, snow depth, and gross carbon uptake indicated some of these factors control the rate of winter CO2 efflux and require consideration, but that additional work is needed to disentangle co-linearity and assess the importance of these factors within and between sites. These findings suggest a consideration of winter CO2 efflux is warranted in cold winter-wet semiarid ecosystems, particularly where winters are long and non-winter CO2 efflux is strongly limited by water availability.

Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) Global Snow-Cover Maps

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Riggs, George A.; Salomonson, Vincent V.; Scharfen, Greg R.

2000-01-01

Following the 1999 launch of the Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS), the capability exists to produce global snow-cover maps on a daily basis at 500-m resolution. Eight-day composite snow-cover maps will also be available. MODIS snow-cover products are produced at Goddard Space Flight Center and archived and distributed by the National Snow and Ice Data Center (NSIDC) in Boulder, Colorado. The products are available in both orbital and gridded formats. An online search and order tool and user-services staff will be available at NSIDC to assist users with the snow products. The snow maps are available at a spatial resolution of 500 m, and 1/4 degree x 1/4 degree spatial resolution, and provide information on sub-pixel (fractional) snow cover. Pre-launch validation work has shown that the MODIS snow-mapping algorithms perform best under conditions of continuous snow cover in low vegetation areas, but can also map snow cover in dense forests. Post-launch validation activities will be performed using field and aircraft measurements from a February 2000 validation mission, as well as from existing satellite-derived snow-cover maps from NOAA and Landsat-7 Enhanced Thematic Mapper Plus (ETM+).

Quantifying the effects of mountain pine beetle infestation on water and biogeochemical cycles at multiple spatial and temporal scales

NASA Astrophysics Data System (ADS)

Brooks, P. D.; Harpold, A. A.; Somor, A. J.; Troch, P. A.; Gochis, D. J.; Ewers, B. E.; Pendall, E.; Biederman, J. A.; Reed, D.; Barnard, H. R.; Whitehouse, F.; Aston, T.; Borkhuu, B.

2010-12-01

Unprecedented levels of bark beetle infestation over the last decade have radically altered forest structure across millions of hectares of Western U.S. montane environments. The widespread extent of this disturbance presents a major challenge for governments and resource managers who lack a predictive understanding of how water and biogeochemical cycles will respond to this disturbance over various temporal and spatial scales. There is a widespread perception, largely based on hydrological responses to fire or logging, that a reduction in both transpiration and interception following tree death will increase soil water availability and catchment water yield. However, few studies have directly addressed the effects of insect-induced forest decline on water and biogeochemical cycling. We address this knowledge gap using observations and modeling at scales from 100 to 109 m2 across study sites in CO and WY that vary in the intensity and timing of beetle infestation and tree death. Our focus on multiple sites with different levels of impact allows us to address two broad, organizing questions: How do changes in vegetation structure associated with MPB alter the partitioning of energy and water? And How do these changes in energy and water availability affect local to regional scale water and biogeochemical cycles? This presentation will focus primarily on energy balance and water partitioning, providing context for ongoing biogeochemical work. During the growing season, stand-scale transpiration declines rapidly and soil moisture increases following infestation, consistent with streamflow data from regional catchments that shows an increase in baseflow following widespread attack. During the winter and spring, stand scale snow surveys and continuous snow depth sensors suggested that the variability in snow cover decreased as the severity of beetle impact increases, but there were no significant stand-scale differences in snow depth among levels of impact. This is due both to an increase in snow under the canopies of dead trees and a decrease in snow cover in canopy gaps. For example, mean snow depth under the canopy was 86cm (CV 0.02) in unimpacted sites and 95cm (CV 0.05) in heavily impacted sites. In canopy gaps however, mean snow depth was 117cm (CV 0.11) in unimpacted sites but only 93cm (CV 0.07) in heavily impacted sites. At the watershed scale, bark beetle infestation was more likely to decrease the amount of both snowmelt and annual runoff, suggesting that the opening of the canopy increases sublimation and evaporation of the snow cover. These data suggest that the disturbance due to bark beetle infestation is both quantitatively and qualitatively different than either fire or logging. Using these observations, we develop a conceptual model for evaluating how biotic and abiotic processes couple water and biogeochemical cycles in forest ecosystems.

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.

JPSS Cryosphere Algorithms: Integration and Testing in Algorithm Development Library (ADL)

NASA Astrophysics Data System (ADS)

Tsidulko, M.; Mahoney, R. L.; Meade, P.; Baldwin, D.; Tschudi, M. A.; Das, B.; Mikles, V. J.; Chen, W.; Tang, Y.; Sprietzer, K.; Zhao, Y.; Wolf, W.; Key, J.

2014-12-01

JPSS is a next generation satellite system that is planned to be launched in 2017. The satellites will carry a suite of sensors that are already on board the Suomi National Polar-orbiting Partnership (S-NPP) satellite. The NOAA/NESDIS/STAR Algorithm Integration Team (AIT) works within the Algorithm Development Library (ADL) framework which mimics the operational JPSS Interface Data Processing Segment (IDPS). The AIT contributes in development, integration and testing of scientific algorithms employed in the IDPS. This presentation discusses cryosphere related activities performed in ADL. The addition of a new ancillary data set - NOAA Global Multisensor Automated Snow/Ice data (GMASI) - with ADL code modifications is described. Preliminary GMASI impact on the gridded Snow/Ice product is estimated. Several modifications to the Ice Age algorithm that demonstrates mis-classification of ice type for certain areas/time periods are tested in the ADL. Sensitivity runs for day time, night time and terminator zone are performed and presented. Comparisons between the original and modified versions of the Ice Age algorithm are also presented.

BOREAS HYD-4 Areal Snow Course Data

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Knapp, David E. (Editor); Metcalfe, John R.; Goodison, Barry E.; Walker, Anne; Smith, David E. (Technical Monitor)

2000-01-01

The Boreal Ecosystem-Atmosphere Study (BOREAS) Hydrology (HYD)-4 team focused on collecting data during the 1994 winter focused field campaign (FFCW) to improve the understanding of winter processes within the boreal forest. Knowledge of snow cover and its variability in the boreal forest is fundamental if BOREAS is to achieve its goals of understanding the processes and states involved in the exchange of energy and water. The development and validation of remote sensing algorithms will provide the means to extend the knowledge of these processes and states from the local to the regional scale. A specific thrust of the hydrology research is the development and validation of snow cover algorithms from airborne passive microwave measurements. Airborne remote sensing data (gamma, passive microwave) were acquired along a series of flight lines established in the vicinity of the BOREAS study areas. Ground snow surveys were conducted along selected sections of these aircraft flight lines. These calibration segments were typically 10-20 km in length, and ground data were collected at one to two kilometer intervals. The data are provided in tabular ASCII files. The HYD-04 areal snow course data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

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.

Sentinels for snow science

NASA Astrophysics Data System (ADS)

Gascoin, S.; Grizonnet, M.; Baba, W. M.; Hagolle, O.; Fayad, A.; Mermoz, S.; Kinnard, C.; Fatima, K.; Jarlan, L.; Hanich, L.

2017-12-01

Current spaceborne sensors do not allow retrieving the snow water equivalent in mountain regions, "the most important unsolved problem in snow hydrology" (Dozier, 2016). While the NASA is operating an airborne mission to survey the SWE in the western USA, elsewhere, however, snow scientists and water managers do not have access to routine SWE measurements at the scale of a mountain range. In this presentation we suggest that the advent of the Copernicus Earth Observation programme opens new perspectives to address this issue in mountain regions worldwide. The Sentinel-2 mission will provide global-scale multispectral observations at 20 m resolution every 5-days (cloud permitting). The Sentinel-1 mission is already imaging the global land surface with a C-band radar at 10 m resolution every 6 days. These observations are unprecedented in terms of spatial and temporal resolution. However, the nature of the observation (radiometry, wavelength) is in the continuity of previous and ongoing missions. As a result, it is relatively straightforward to re-use algorithms that were developed by the remote sensing community over the last decades. For instance, Sentinel-2 data can be used to derive maps of the snow cover extent from the normalized difference snow index, which was initially proposed for Landsat. In addition, the 5-days repeat cycle allows the application of gap-filling algorithms, which were developed for MODIS based on the temporal dimension. The Sentinel-1 data can be used to detect the wet snow cover and track melting areas as proposed for ERS in the early 1990's. Eventually, we show an example where Sentinel-2-like data improved the simulation of the SWE in the data-scarce region of the High Atlas in Morocco through assimilation in a distributed snowpack model. We encourage snow scientists to embrace Sentinel-1 and Sentinel-2 data to enhance our knowledge on the snow cover dynamics in mountain regions.

[Effects of seasonal snow cover on soil nitrogen transformation in alpine ecosystem: a review].

PubMed

Liu, Lin; Wu, Yan; He, Yi-xin; Wu, Ning; Sun, Geng; Zhang, Lin; Xu, Jun-jun

2011-08-01

Seasonal snow cover has pronounced effects on the soil nitrogen concentration and transformation in alpine ecosystem. Snowfall is an important form of nitrogen deposition, which directly affects the content of soil available nitrogen. Different depths and different duration of snow cover caused by snowfall may lead the heterogeneity of abiotic factors (soil temperature and moisture) and biotic factors (soil microbes, alpine plants, and alpine animals), and further, produce complicated effects on the mineralization and immobilization of soil nitrogen. This paper introduced in emphasis the inherent mechanisms of soil nitrogen mineralization and leaching under the effects of frequent freeze-thaw events during the durative melting of snow cover, and summarized the main research results of field in situ experiments about the effects of seasonal snow cover on soil nitrogen in alpine ecosystem based on the possible changes in snow cover in the future. Some suggestions with regard to the effects of seasonal snow cover on soil nitrogen were put forward.

Ensemble Mean Density and its Connection to Other Microphysical Properties of Falling Snow as Observed in Southern Finland

NASA Technical Reports Server (NTRS)

Tiira, Jussi; Moisseev, Dmitri N.; Lerber, Annakaisa von; Ori, Davide; Tokay, Ali; Bliven, Larry F.; Petersen, Walter

2016-01-01

In this study measurements collected during winters 2013/2014 and 2014/2015 at the University of Helsinki measurement station in Hyytiala are used to investigate connections between ensemble mean snow density, particle fall velocity and parameters of the particle size distribution (PSD). The density of snow is derived from measurements of particle fall velocity and PSD, provided by a particle video imager, and weighing gauge measurements of precipitation rate. Validity of the retrieved density values is checked against snow depth measurements. A relation retrieved for the ensemble mean snow density and median volume diameter is in general agreement with previous studies, but it is observed to vary significantly from one winter to the other. From these observations, characteristic mass- dimensional relations of snow are retrieved. For snow rates more than 0.2mm/h, a correlation between the intercept parameter of normalized gamma PSD and median volume diameter was observed.

Experimental Investigation of Concrete Runway Snow Melting Utilizing Heat Pipe Technology

PubMed Central

Su, Xin; Ye, Qing; Fu, Jianfeng

2018-01-01

A full scale snow melting system with heat pipe technology is built in this work, which avoids the negative effects on concrete structure and environment caused by traditional deicing chemicals. The snow melting, ice-freezing performance and temperature distribution characteristics of heat pipe concrete runway were discussed by the outdoor experiments. The results show that the temperature of the concrete pavement is greatly improved with the heat pipe system. The environment temperature and embedded depth of heat pipe play a dominant role among the decision variables of the snow melting system. Heat pipe snow melting pavement melts the snow completely and avoids freezing at any time when the environment temperature is below freezing point, which is secure enough for planes take-off and landing. Besides, the exportation and recovery of geothermal energy indicate that this system can run for a long time. This paper will be useful for the design and application of the heat pipe used in the runway snow melting. PMID:29551957

Experimental Investigation of Concrete Runway Snow Melting Utilizing Heat Pipe Technology.

PubMed

Chen, Fengchen; Su, Xin; Ye, Qing; Fu, Jianfeng

2018-01-01

A full scale snow melting system with heat pipe technology is built in this work, which avoids the negative effects on concrete structure and environment caused by traditional deicing chemicals. The snow melting, ice-freezing performance and temperature distribution characteristics of heat pipe concrete runway were discussed by the outdoor experiments. The results show that the temperature of the concrete pavement is greatly improved with the heat pipe system. The environment temperature and embedded depth of heat pipe play a dominant role among the decision variables of the snow melting system. Heat pipe snow melting pavement melts the snow completely and avoids freezing at any time when the environment temperature is below freezing point, which is secure enough for planes take-off and landing. Besides, the exportation and recovery of geothermal energy indicate that this system can run for a long time. This paper will be useful for the design and application of the heat pipe used in the runway snow melting.

Wind Tunnel Experiments: Influence of Erosion and Deposition on Wind-Packing of New Snow

NASA Astrophysics Data System (ADS)

Sommer, C.; Fierz, C. G.; Lehning, M.

2017-12-01

We observed the formation of wind crusts in wind tunnel experiments. A SnowMicroPen was used to measure the hardness profile of the snow and a Microsoft Kinect provided distributed snow depth data. Earlier experiments showed that no crust forms without saltation and that the dynamics of erosion and deposition may be a key factor to explain wind-packing. The Kinect data could be used to quantify spatial erosion and deposition patterns and the combination with the SnowMicroPen data allowed to study the effect of erosion and deposition on wind-hardening. We found that erosion had no hardening effect on fresh snow and that deposition is a necessary but not sufficient condition for wind crust formation. Deposited snow was only hardened in wind-exposed areas. The Kinect data was used to calculate the wind-exposure parameter Sx. We observed no significant hardening for Sx>0.25. The variability of resulting wind crust hardnesses at Sx<0.25 was still large, however.

Ensemble mean density and its connection to other microphysical properties of falling snow as observed in Southern Finland

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

Tiira, Jussi; Moisseev, Dmitri N.; von Lerber, Annakaisa

In this study measurements collected during winters 2013/2014 and 2014/2015 at the University of Helsinki measurement station in Hyytiala are used to investigate connections between ensemble mean snow density, particle fall velocity and parameters of the particle size distribution (PSD). The density of snow is derived from measurements of particle fall velocity and PSD, provided by a particle video imager, and weighing gauge measurements of precipitation rate. Validity of the retrieved density values is checked against snow depth measurements. Here, a relation retrieved for the ensemble mean snow density and median volume diameter is in general agreement with previous studies,more » but it is observed to vary significantly from one winter to the other. From these observations, characteristic mass–dimensional relations of snow are retrieved. For snow rates more than 0.2 mm h -1, a correlation between the intercept parameter of normalized gamma PSD and median volume diameter was observed.« less

Ensemble mean density and its connection to other microphysical properties of falling snow as observed in Southern Finland

DOE PAGES

Tiira, Jussi; Moisseev, Dmitri N.; von Lerber, Annakaisa; ...

2016-09-28

In this study measurements collected during winters 2013/2014 and 2014/2015 at the University of Helsinki measurement station in Hyytiala are used to investigate connections between ensemble mean snow density, particle fall velocity and parameters of the particle size distribution (PSD). The density of snow is derived from measurements of particle fall velocity and PSD, provided by a particle video imager, and weighing gauge measurements of precipitation rate. Validity of the retrieved density values is checked against snow depth measurements. Here, a relation retrieved for the ensemble mean snow density and median volume diameter is in general agreement with previous studies,more » but it is observed to vary significantly from one winter to the other. From these observations, characteristic mass–dimensional relations of snow are retrieved. For snow rates more than 0.2 mm h -1, a correlation between the intercept parameter of normalized gamma PSD and median volume diameter was observed.« less

Monitoring snow cover and its effect on runoff regime in the Jizera Mountains

NASA Astrophysics Data System (ADS)

Kulasova, Alena

2015-04-01

The Jizera Mountains in the northern Bohemia are known by its rich snow cover. Winter precipitation represents usually a half of the precipitation in the hydrological year. Gradual snow accumulation and melt depends on the course of the particular winter period, the topography of the catchments and the type of vegetation. During winter the snow depth, and especially the snow water equivalent, are affected by the changing character of the falling precipitation, air and soil temperatures and the wind. More rapid snowmelt occurs more on the slopes without forest oriented to the South, while a gradual snowmelt occurs on the locations turned to the North and in forest. Melting snow recharges groundwater and affects water quality in an important way. In case of extreme situation the snowmelt monitoring is important from the point of view of flood protection of communities and property. Therefore the immediate information on the amount of water in snow is necessary. The way to get this information is the continuous monitoring of the snow depth and snow water equivalent. In the Jizera Mountains a regular monitoring of snow cover has been going on since the end of the 19th century. In the 80s of the last century the Jizera Mountains were affected by the increased fallout of pollutants in the air. There followed a gradual dieback of the forest cover and cutting down the upper part of the ridges. In order to get data for the quantification of runoff regime changes in the changing natural environment, the Czech Hydrometeorological Institute (CHMI) founded in the upper part of the Mountains several experimental catchments. One of the activities of the employees of the experimental basis is the regular measurement of snow cover at selected sites from 1982 up to now. At the same time snow cover is being observed using snow pillows, where its mass is monitored with the help of pressure sensors. In order to improve the reliability of the continuous measurement of the snow water equivalent the LDSMS (Libor Danes Snow Measurement System) which uses weighing sensors has been developed. The system contains a novel device precluding the snowbridging (protected by a utility model). Data from manual and automatic measurements are transmitted to the central forecasting service of CHMI in Prague - Komorany (CPP) and to regional forecasting branches (RPP), where they are one of the inputs of the hydrological forecasting models. The contribution deals with the results of the manual snow measurement during the dieback of forest and now, in comparison with the automatic snow measurement in the experimental catchments of CHMI Uhlirska, Jezdecka and with the effect of snowmelt on the water level in the streams.

Seasonal Snow Extent and Snow Mass in South America Using SMMR and SSM/I Passive Microwave Data (1979-2003)

NASA Technical Reports Server (NTRS)

Foster, J. L.; Hall, D. K.; Chiu, L.; Kelly, R. E.; Powell, H.; Chiu, L.

2007-01-01

Seasonal snow cover in South America was examined in this study using passive microwave satellite data from the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-satellite and the Special Sensor Microwave Imagers (SSM/I) on board Defense Meteorological Satellite Program (DMSP) satellites. For the period from 1979-2003, both snow cover extent and snow depth (snow mass) were investigated during coldest months (May-September), primarily in the Patagonia area of Argentina and in Chile. Most of the seasonal snow in South America is in the Patagonia region of Argentina. Since winter temperatures in this region are often above freezing, the coldest winter month was found to be the month having the most extensive snow cover and also usually the month having the deepest snow cover as well. Sharp year-to-year differences were recorded using the passive microwave observations. The average snow cover extent for July, the month with the greatest average snow extent during the 25-year period of record, is 320,700 km(exp 2). In July of 1984, the average monthly snow cover was 701,250 km(exp 2) - the most extensive coverage observed between 1979 and 2003. However, in July of 1989, snow cover extent was only 120 km(exp 2). The 25-year period of record shows a sinusoidal like pattern, though there appears to be no obvious trend in either increasing or decreasing snow extent or snow mass between 1979 and 2003.

Building a Snow Data Management System using Open Source Software (and IDL)

NASA Astrophysics Data System (ADS)

Goodale, C. E.; Mattmann, C. A.; Ramirez, P.; Hart, A. F.; Painter, T.; Zimdars, P. A.; Bryant, A.; Brodzik, M.; Skiles, M.; Seidel, F. C.; Rittger, K. E.

2012-12-01

At NASA's Jet Propulsion Laboratory free and open source software is used everyday to support a wide range of projects, from planetary to climate to research and development. In this abstract I will discuss the key role that open source software has played in building a robust science data processing pipeline for snow hydrology research, and how the system is also able to leverage programs written in IDL, making JPL's Snow Data System a hybrid of open source and proprietary software. Main Points: - The Design of the Snow Data System (illustrate how the collection of sub-systems are combined to create a complete data processing pipeline) - Discuss the Challenges of moving from a single algorithm on a laptop, to running 100's of parallel algorithms on a cluster of servers (lesson's learned) - Code changes - Software license related challenges - Storage Requirements - System Evolution (from data archiving, to data processing, to data on a map, to near-real-time products and maps) - Road map for the next 6 months (including how easily we re-used the snowDS code base to support the Airborne Snow Observatory Mission) Software in Use and their Software Licenses: IDL - Used for pre and post processing of data. Licensed under a proprietary software license held by Excelis. Apache OODT - Used for data management and workflow processing. Licensed under the Apache License Version 2. GDAL - Geospatial Data processing library used for data re-projection currently. Licensed under the X/MIT license. GeoServer - WMS Server. Licensed under the General Public License Version 2.0 Leaflet.js - Javascript web mapping library. Licensed under the Berkeley Software Distribution License. Python - Glue code and miscellaneous data processing support. Licensed under the Python Software Foundation License. Perl - Script wrapper for running the SCAG algorithm. Licensed under the General Public License Version 3. PHP - Front-end web application programming. Licensed under the PHP License Version 3.01

Can GRACE detect winter snows in Japan?

NASA Astrophysics Data System (ADS)

Heki, Kosuke

2010-05-01

Current spatial resolution of the GRACE (Gravity Recovery and Climate Experiment) satellites is 300-400 km, and so its hydrological applications have been limited to continents and large islands. The Japanese Islands have width slightly smaller than this spatial resolution, but are known to show large amplitude seasonal changes in surface masses due mainly to winter snow. Such loads are responsible for seasonal crustal deformation observed with GEONET, a dense array of GPS (Global Positioning System) receivers in Japan (Heki, 2001). There is also a dense network of surface meteorological sensors for, e.g. snow depths, atmospheric pressures, etc. Heki (2004) showed that combined effects of surface loads, i.e. snow (predominant), atmosphere, soil moisture, dam impoundment, can explain seasonal crustal deformation observed by GPS to a large extent. The total weight of the winter snow in the Japanese Islands in its peak season may reach ~50 Gt. This is comparable to the annual loss of mountain glaciers in the Asian high mountains (Matsuo & Heki, 2010), and is above the detection level of GRACE. In this study, I use GRACE Level-2 Release-4 data from CSR, Univ. Texas, up to 2009 November, and evaluated seasonal changes in surface loads in and around the Japanese Islands. After applying a 350 km Gaussian filter and a de-striping filter, the peak-to-peak change of the water depth becomes ~4 cm in northern Japan. The maximum value is achieved in February-March. The region of large winter load spans from Hokkaido, Japan, to northeastern Honshu, which roughly coincides with the region of deep snow in Japan. Next I compiled snow depth data from surface meteorological observations, and converted them to loads using time-dependent snow density due to compaction. By applying the same spatial filter as the GRACE data, its spatial pattern becomes similar to the GRACE results. The present study suggests that GRACE is capable of detecting seasonal mass changes in an island arc not wider than a few hundreds of kilometers. References: Heki, K., Seasonal modulation of interseismic strain buildup in Northeastern Japan driven by snow loads, Science, 293, 89-92, 2001. Heki, K., Dense GPS array as a new sensor of seasonal changes of surface loads, AGU Monograph, 150, 177-196, 2004. Matsuo, K. and K. Heki, Time-variable ice loss in Asian high mountains from satellite gravimetry, Earth Planet. Sci. Lett., doi:10.1016/j.epsl.2009.11.053, 2010.

Continuous monitoring of a mountain snowpack in the Austrian Alps by above-ground neutron sensing

NASA Astrophysics Data System (ADS)

Schattan, Paul; Baroni, Gabriele; Oswald, Sascha E.; Schöber, Johannes; Fey, Christine; Francke, Till; Huttenlau, Matthias; Achleitner, Stefan

2017-04-01

In alpine catchments the knowledge of the spatially and temporally heterogeneous dynamics of snow accumulation and depletion is crucial for modelling and managing water resources. While snow covered area can be retrieved operationally from remote sensing data, continuous measurements of other snow state variables like snow depth (SD) or snow water equivalent (SWE) remain challenging. Existing methods of retrieving both variables in alpine terrain face severe issues like a lack of spatial representativeness, labour-intensity or discontinuity in time. Recently, promising new measurement techniques combining a larger support with low maintenance cost like above-ground gamma-ray scintillators, GPS interferometric reflectometry or above-ground cosmic-ray neutron sensors (CRNS) have been suggested. While CRNS has proven its potential for monitoring soil moisture in a wide range of environments and applications, the empirical knowledge of using CRNS for snowpack monitoring is still very limited and restricted to shallow snowpacks with rather uniform evolution. The characteristics of an above-ground cosmic-ray neutron sensor (CRNS) were therefore evaluated for monitoring a mountain snowpack in the Austrian Alps (Kaunertal, Tyrol) during three winter seasons. The measurement campaign included a number of measurements during the period from 03/2014 to 06/2016: (i) neutron count measurements by CRNS, (ii) continuous point-scale SD and SWE measurements from an automatic weather station and (iii) 17 Terrestrial Laser Scanning (TLS) with simultaneous SD and SWE surveys. The highest accumulation in terms of SWE was found in 04/2014 with 600 mm. Neutron counts were compared to all available snow data. While previous studies suggested a signal saturation at around 100 mm of SWE, no complete signal saturation was found. A strong non-linear relation was found for both SD and SWE with best fits for spatially distributed TLS based snow data. Initially slightly different shapes were found for accumulation and melting season conditions but this could be resolved by accounting for the limited measurement depth. This depth limit is in the range of 200 mm of SWE for dense snowpacks with high liquid water contents and associated snow density values around 450 kg m-3 and above. Furthermore, the results prove that for medium to high snowpack the inter-annual transferability of the results is very high regardless of pre-snowfall soil moisture conditions. These results underline the high potential of CRNS for closing the gap between point-scale measurements, hydrological models and remote sensing in snow hydrology and alpine terrain.

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.

A Prototype MODI- SSM/I Snow Mapping Algorithm

NASA Technical Reports Server (NTRS)

Tait, Andrew B.; Barton, Jonathan S.; Hall, Dorothy K.

1999-01-01

Data in the wavelength range 0.545 - 1.652 microns from the Moderate Resolution Imaging Spectroradiometer (MODIS), to be launched aboard the Earth Observing System (EOS) Terra in the fall of 1999, will be used to map daily global snow cover at 500m resolution. However, during darkness, or when the satellite's view of the surface is obscured by cloud, snow cover cannot be mapped using MODIS data. We show that during these conditions, it is possible to supplement the MODIS product by mapping the snow cover using passive microwave data from the Special Sensor Microwave Imager (SSM/I), albeit with much poorer resolution. For a 7-day time period in March 1999, a prototype MODIS snow-cover product was compared with a prototype MODIS-SSM/I product for the same area in the mid-western United States. The combined MODIS-SSM/I product mapped 9% more snow cover than the MODIS-only product.

Impacts of Synoptic Weather Patterns on Snow Albedo at Sites in New England

NASA Astrophysics Data System (ADS)

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

2015-12-01

Winter snow in the northeastern United States has changed over the last several decades, resulting in shallower snow packs, fewer days of snow cover and increasing precipitation falling as rain in the winter. In addition to these changes which cause reductions in surface albedo, increasing winter temperatures also lead to more rapid snow grain growth, resulting in decreased snow reflectivity. We present in-situ measurements and analyses to test the sensitivity of seasonal snow albedo to varying weather conditions at sites in New England. In particular, we investigate the impact of temperature on snow albedo through melt and grain growth, the impact of precipitation event frequency on albedo through snow "freshening," and the impact of storm path on snow structure and snow albedo. Over three winter seasons between 2013 and 2015, in-situ snow characterization measurements were made at three non-forested sites across New Hampshire. These near-daily measurements include spectrally resolved albedo, snow optical grain size determined through contact spectroscopy, snow depth, snow density and local meteorological parameters. Combining this information with storm tracks derived from HYSPLIT modeling, we quantify the current sensitivity of northeastern US snow albedo to temperature as well as precipitation type, frequency and path. Our analysis shows that southerly winter storms result in snow with a significantly lower albedo than storms which come from across the continental US or the Atlantic Ocean. Interannual variability in temperature and statewide spatial variability in snowfall rates at our sites show the relative importance of snowfall amount and temperatures in albedo evolution over the course of the winter.

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.

Wind and ecosystem response at the GLEES

Treesearch

Robert C. Musselman; Gene L. Wooldridge; William J. Massman; Richard A. Sommerfeld

1995-01-01

Research was conducted to determine wind patterns and snow deposition at a high elevation alpine/subalpine ecotone site using deformation response of trees to prevailing winds. The research has provided detailed maps of wind speed, wind direction, and snow depth as determined from tree deformation. The effects of prevailing wind on tree blowdown at the site have also...

International Conference on Snow Hydrology: The Integration of Physical, Chemical, and Biological Systems Held in Brownsville, Vermont on 6-9 October 1998

DTIC Science & Technology

1998-08-01

5 Estimating Snowmelt Infiltration into Frozen Soils L . Zhao and D .M . G ray...andY Matsuura ......................................................... 17 Effect of Subalpine Canopy Removal on Snowpack, Soil Solution, and Nutrient...Tonnessen, and K. Heuer ..................................................................... 26 Snow Depth. Soil Frost, and Nutrient Loss in a Northern

Relation between mountain goats and their habitat in southeastern Alaska.

Treesearch

J.L. Fox; C.A. Smith; J.W. Schoen

1989-01-01

Mountain goats in southeastern Alaska occupy habitats providing abundant areas of highquality forage during summer but only limited feeding areas during winter because of deep snow. Winter is a period of severe utritional deprivation, and goats converge into areas with available forage, often within old-growth forest where relatively low snow depths and litterfall...

Wheels and Tracks in Snow. Validation Study of the CRREL Shallow Snow Mobility Model

DTIC Science & Technology

1990-11-01

define g. an insignificant effect on T we plotted all of the for a vehicle by traction data for the wheels/ fracks vehicles, and for the CIV, against...divided by the number of depth, structure, water content, temperature, and contact points and the average contact area of these even type and strength

Ultra-Wideband Radar Measurements of Thickness of Snow Over Sea Ice

NASA Technical Reports Server (NTRS)

Kanagaratnam, P.; Markus, T.; Lytle, V.; Heavey, B.; Jansen, P.; Prescott, G.; Gogineni, S.

2007-01-01

An accurate knowledge of snow thickness and its variability over sea ice is crucial for determining the overall polar heat and freshwater budget, which influences the global climate. Recently, algorithms have been developed to extract snow thicknesses from passive microwave satellite data. However, validation of these data over the large footprint of the passive microwave sensor has been a challenge. The only method used thus far has been with meter sticks during ship cruises. To address this problem, we developed an ultra wideband frequency-modulated continuous-wave (FM-CW) radar to measure snow thickness over sea ice. We made snow-thickness measurements over Antarctic sea ice by operating the radar from a sled during September and October, 2003. We performed radar measurements over 11 stations with varying snow thickness between 4 and 85 cm. We observed excellent agreement between radar estimates of snow thickness with physical measurements, achieving a correlation coefficient of 0.95 and a vertical resolution of about 3 cm.

Snowpack displacement measured by terrestrial radar interferometry as precursor for wet snow avalanches

NASA Astrophysics Data System (ADS)

Caduff, Rafael; Wiesmann, Andreas; Bühler, Yves

2016-04-01

Wet snow and full depth gliding avalanches commonly occur on slopes during springtime when air temperatures rise above 0°C for longer time. The increase in the liquid water content changes the mechanical properties of the snow pack. Until now, forecasts of wet snow avalanches are mainly done using weather data such as air and snow temperatures and incoming solar radiation. Even tough some wet snow avalanche events are indicated before the release by the formation of visible signs such as extension cracks or compressional bulges in the snow pack, a large number of wet snow avalanches are released without any previously visible signs. Continuous monitoring of critical slopes by terrestrial radar interferometry improves the scale of reception of differential movement into the range of millimetres per hour. Therefore, from a terrestrial and remote observation location, information on the mechanical state of the snow pack can be gathered on a slope wide scale. Recent campaigns in the Swiss Alps showed the potential of snow deformation measurements with a portable, interferometric real aperture radar operating at 17.2 GHz (1.76 cm wavelength). Common error sources for the radar interferometric measurement of snow pack displacements are decorrelation of the snow pack at different conditions, the influence of atmospheric disturbances on the interferometric phase and transition effects from cold/dry snow to warm/wet snow. Therefore, a critical assessment of those parameters has to be considered in order to reduce phase noise effects and retrieve accurate displacement measurements. The most recent campaign in spring 2015 took place in Davos Dorf/GR, Switzerland and its objective was to observe snow glide activity on the Dorfberg slope. A validation campaign using total station measurements showed good agreement to the radar interferometric line of sight displacement measurements in the range of 0.5 mm/h. The refinement of the method led to the detection of numerous gliding patches distributed over the entire slope. Typically, patches showing (full depth) snow gliding reach extensions from 5x10 metres up to 40x60 metres. Using a sampling interval of 1-3 minutes, the temporal displacement of such snow glide-hot spots can be tracked and thus revealing the individual signature of deformation. Nearly linear behaviour over several days, peaking in a final acceleration releasing an avalanche was observed as well characteristic acceleration and deceleration cycles during day and night could be captured. These cycles sometimes trigger an avalanche and sometimes reach a full stop of the differential snow glide movement. Findings of the different campaigns will be presented. We put them in the context for possible future campaigns that could be used to solve scientific questions regarding the mechanical properties of the snow pack. We evaluate the possibilities for the use of terrestrial radar interferometry for hazard management and avalanche forecast.

Spatial-temporal dynamics of chemical composition of surface snow in East Antarctica along the Progress station-Vostok station transect

NASA Astrophysics Data System (ADS)

Khodzher, T. V.; Golobokova, L. P.; Osipov, E. Yu.; Shibaev, Yu. A.; Lipenkov, V. Ya.; Osipova, O. P.; Petit, J. R.

2014-05-01

In January of 2008, during the 53rd Russian Antarctic Expedition, surface snow samples were taken from 13 shallow (0.7 to 1.5 m depth) snow pits along the first tractor traverse from Progress to Vostok stations, East Antarctica. Sub-surface snow/firn layers are dated from 2.1 to 18 yr. The total length of the coast to inland traverse is more than 1280 km. Here we analysed spatial variability of concentrations of sulphate ions and elements and their fluxes in the snow deposited within the 2006-2008 time interval. Anions were analysed by high-performance liquid chromatography (HPLC), and the determination of selected metals, including Na, K, Mg, Ca and Al, was carried out by mass spectroscopy with atomization by induced coupled plasma (ICP-MS). Surface snow concentration records were examined for trends versus distance inland, elevation, accumulation rate and slope gradient. Na shows a significant positive correlation with accumulation rate, which decreases as distance from the sea and altitude increase. K, Ca and Mg concentrations do not show any significant relationship either with distance inland or with elevation. Maximal concentrations of these elements with a prominent Al peak are revealed in the middle part of the traverse (500-600 km from the coast). Analysis of element correlations and atmospheric circulation patterns allow us to suggest their terrestrial origin (e.g. aluminosilicates carried as a continental dust) from the Antarctic nunatak areas. Sulphate concentrations show no significant relationship with distance inland, elevation, slope gradient and accumulation rate. Non-sea salt secondary sulphate is the most important contribution to the total sulphate budget along the traverse. Sulphate of volcanic origin attributed to the Pinatubo eruption (1991) was revealed in the snow pit at 1276 km (depth 120-130 cm).

Operational satellites and the global monitoring of snow and ice

NASA Technical Reports Server (NTRS)

Walsh, John E.

1991-01-01

The altitudinal dependence of the global warming projected by global climate models is at least partially attributable to the albedo-temperature feedback involving snow and ice, which must be regarded as key variables in the monitoring for global change. Statistical analyses of data from IR and microwave sensors monitoring the areal coverage and extent of sea ice have led to mixed conclusions about recent trends of hemisphere sea ice coverage. Seasonal snow cover has been mapped for over 20 years by NOAA/NESDIS on the basis of imagery from a variety of satellite sensors. Multichannel passive microwave data show some promise for the routine monitoring of snow depth over unforested land areas.

Experimental and ecosystem model approach to assessing the sensitivity of High arctic deep permafrost to changes in surface temperature and precipitation

NASA Astrophysics Data System (ADS)

Rasmussen, L. H.; Zhang, W.; Elberling, B.; Cable, S.

2016-12-01

Permafrost affected areas in Greenland are expected to experience large temperature increases within the 21st century. Most previous studies on permafrost consider near-surface soil, where changes will happen first. However, how sensitive the deep permafrost temperature is to near-surface conditions through changes in soil thermal properties, snow depth and soil moisture, is not known. In this study, we measured the sensitivity of thermal conductivity (TC) to gravimetric water content (GWC) in frozen and thawed deep permafrost sediments from deltaic, alluvial and fluvial depositional environments in the Zackenberg valley, NE Greenland. We also calibrated a coupled heat and water transfer model, the "CoupModel", for the two closely situated deltaic sites, one with average snow depth and the other with topographic snow accumulation. With the calibrated model, we simulated deep permafrost thermal dynamics in four scenarios with changes in surface forcing: a. 3 °C warming and 20 % increase in precipitation; b. 3 °C warming and 100 % increase in precipitation; c. 6 °C warming and 20 % increase in precipitation; d. 6 °C warming and 100 % increase in precipitation.Our results indicated that frozen sediments had higher TC than thawed sediments. All sediments showed a positive linear relation between TC and soil moisture when frozen, and a logarithmic one when thawed. Fluvial sediments had high sensitivity, but never reached above 12 % GWC, indicating a field effect of water retention capacity. Alluvial sediments were less sensitive to soil moisture than deltaic and fluvial sediments, indicating the importance of unfrozen water in frozen sediment. The deltaic site with snow accumulation had 1 °C higher annual mean ground temperature than the average snow site. The soil temperature at the depth of 18 m increased with 1.5 °C and 3.5 °C in the scenarios with 3 °C and 6 °C warming, respectively. Precipitation had no significant additional effect to warming. We conclude that below-ground sediment properties affect the sensitivity of TC to GWC, that surface temperature changes can significantly affect the deep permafrost within a short period, and that differences in snow depth affect surface temperatures. Geology, pedology and precipitation should thus be considered if estimating future High arctic deep permafrost sensitivity.

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.

Experimental investigation of drifting snow in a wind tunnel

NASA Astrophysics Data System (ADS)

Crivelli, Philip; Paterna, Enrico; Horender, Stefan; Lehning, Michael

2015-11-01

Drifting snow has a significant impact on snow distribution in mountains, prairies as well as on glaciers and polar regions. In all these environments, the local mass balance is highly influenced by drifting snow. Despite most of the model approaches still rely on the assumption of steady-state and equilibrium saltation, recent advances have proven the mass-transport of drifting snow events to be highly intermittent. A clear understanding of such high intermittency has not yet been achieved. Therefore in our contribution we investigate mass- and momentum fluxes during drifting snow events, in order to better understand that the link between snow cover erosion and deposition. Experiments were conducted in a cold wind tunnel, employing sensors for the momentum flux measurements, the mass flux measurement and for the snow depth estimation over a certain area upstream of the other devices. Preliminary results show that the mass flux is highly intermittent at scales ranging from eddy turnover time to much larger scales. The former scales are those that contribute the most to the overall intermittency and we observe a link between the turbulent flow structures and the mass flux of drifting snow at those scales. The role of varying snow properties in inducing drifting snow intermittency goes beyond such link and is expected to occur at much larger scales, caused by the physical snow properties such as density and cohesiveness.

MODIS Snow and Ice Products from the NSIDC DAAC

NASA Technical Reports Server (NTRS)

Scharfen, Greg R.; Hall, Dorothy K.; Riggs, George A.

1997-01-01

The National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) provides data and information on snow and ice processes, especially pertaining to interactions among snow, ice, atmosphere and ocean, in support of research on global change detection and model validation, and provides general data and information services to cryospheric and polar processes research community. The NSIDC DAAC is an integral part of the multi-agency-funded support for snow and ice data management services at NSIDC. The Moderate Resolution Imaging Spectroradiometer (MODIS) will be flown on the first Earth Observation System (EOS) platform (AM-1) in 1998. The MODIS Instrument Science Team is developing geophysical products from data collected by the MODIS instrument, including snow and ice products which will be archived and distributed by NSIDC DAAC. The MODIS snow and ice mapping algorithms will generate global snow, lake ice, and sea ice cover products on a daily basis. These products will augment the existing record of satellite-derived snow cover and sea ice products that began about 30 years ago. The characteristics of these products, their utility, and comparisons to other data set are discussed. Current developments and issues are summarized.

Machine learning based cloud mask algorithm driven by radiative transfer modeling

NASA Astrophysics Data System (ADS)

Chen, N.; Li, W.; Tanikawa, T.; Hori, M.; Shimada, R.; Stamnes, K. H.

2017-12-01

Cloud detection is a critically important first step required to derive many satellite data products. Traditional threshold based cloud mask algorithms require a complicated design process and fine tuning for each sensor, and have difficulty over snow/ice covered areas. With the advance of computational power and machine learning techniques, we have developed a new algorithm based on a neural network classifier driven by extensive radiative transfer modeling. Statistical validation results obtained by using collocated CALIOP and MODIS data show that its performance is consistent over different ecosystems and significantly better than the MODIS Cloud Mask (MOD35 C6) during the winter seasons over mid-latitude snow covered areas. Simulations using a reduced number of satellite channels also show satisfactory results, indicating its flexibility to be configured for different sensors.

Methods for mapping and monitoring global glaciovolcanism

NASA Astrophysics Data System (ADS)

Curtis, Aaron; Kyle, Philip

2017-03-01

The most deadly (Nevado del Ruiz, 1985) and the most costly (Eyjafjallajökull, 2010) eruptions of the last 100 years were both glaciovolcanic. Considering its great importance to studies of volcanic hazards, global climate, and even astrobiology, the global distribution of glaciovolcanism is insufficiently understood. We present and assess three algorithms for mapping, monitoring, and predicting likely centers of glaciovolcanic activity worldwide. Each algorithm intersects buffer zones representing known Holocene-active volcanic centers with existing datasets of snow, ice, and permafrost. Two detection algorithms, RGGA and PZGA, are simple spatial join operations computed from the Randolph Glacier Inventory and the Permafrost Zonation Index, respectively. The third, MDGA, is an algorithm run on all 15 available years of the MOD10A2 weekly snow cover product from the Terra MODIS satellite radiometer. Shortcomings and advantages of the three methods are discussed, including previously unreported blunders in the MOD10A2 dataset. Comparison of the results leads to an effective approach for integrating the three methods. We show that 20.4% of known Holocene volcanic centers host glaciers or areas of permanent snow. A further 10.9% potentially interact with permafrost. MDGA and PZGA do not rely on any human input, rendering them useful for investigations of change over time. An intermediate step in MDGA involves estimating the snow-covered area at every Holocene volcanic center. These estimations can be updated weekly with no human intervention. To investigate the feasibility of an automatic ice-loss alert system, we consider three examples of glaciovolcanism in the MDGA weekly dataset. We also discuss the potential use of PZGA to model past and future glaciovolcanism based on global circulation model outputs. Combined, the three algorithms provide an automated system for understanding the geographic and temporal patterns of global glaciovolcanism which should be of use for hazard assessment, the search for extreme microbiomes, climate models, and implementation of ice-cover-based volcano monitoring systems.

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.

Comparison of AMSR-E and SSM/I snow parameter retrievals over the Ob river basin

USGS Publications Warehouse

Mognard, N.M.; Grippa, M.; LeToan, T.; Kelly, R.E.J.; Chang, A.T.C.; Josberger, E.G.

2004-01-01

Passive microwave observations from the Advanced Microwave Scanning Radiometer - EOS (AMSR-E) and from the Special Sensor Microwave Imager (SSM/I) are used to analyse the evolution of the snow pack in the Ob river basin during the snow season of 2002-03. The Ob river is the biggest Russian river with respect to its watershed area (2 975 000 km2). The Ob originates in the Altai mountains and flows northward across the vast West Siberian lowland towards the Arctic Ocean. The majority of snow cover is contained in the lowlands rather than in mountainous regions and persists for six months or more. During the snow season, surface air temperatures are very cold. Therefore, the combination of cold dry snow and large areas of uniform topography is ideal for snowpack extent and water equivalent retrievals from passive microwave observations. The thermal gradient through the snow pack is estimated and used to model the growth of the snow grain size and to compute the evolution of the passive microwave derived snow depth over the region. A comparison between the AMSR-E and SSM/I estimates is performed and the differences between the snow parameters from the two satellite instruments are analysed.

Dominance of grain size impacts on seasonal snow albedo at open sites in New Hampshire

NASA Astrophysics Data System (ADS)

Adolph, Alden C.; Albert, Mary R.; Lazarcik, James; Dibb, Jack E.; Amante, Jacqueline M.; Price, Andrea

2017-01-01

Snow cover serves as a major control on the surface energy budget in temperate regions due to its high reflectivity compared to underlying surfaces. Winter in the northeastern United States has changed over the last several decades, resulting in shallower snowpacks, fewer days of snow cover, and increasing precipitation falling as rain in the winter. As these climatic changes occur, it is imperative that we understand current controls on the evolution of seasonal snow albedo in the region. Over three winter seasons between 2013 and 2015, snow characterization measurements were made at three open sites across New Hampshire. These near-daily measurements include spectral albedo, snow optical grain size determined through contact spectroscopy, snow depth, snow density, black carbon content, local meteorological parameters, and analysis of storm trajectories using the Hybrid Single-Particle Lagrangian Integrated Trajectory model. Using analysis of variance, we determine that land-based winter storms result in marginally higher albedo than coastal storms or storms from the Atlantic Ocean. Through multiple regression analysis, we determine that snow grain size is significantly more important in albedo reduction than black carbon content or snow density. And finally, we present a parameterization of albedo based on days since snowfall and temperature that accounts for 52% of variance in albedo over all three sites and years. Our improved understanding of current controls on snow albedo in the region will allow for better assessment of potential response of seasonal snow albedo and snow cover to changing climate.

IceChrono1: a probabilistic model to compute a common and optimal chronology for several ice cores

NASA Astrophysics Data System (ADS)

Parrenin, Frédéric; Bazin, Lucie; Capron, Emilie; Landais, Amaëlle; Lemieux-Dudon, Bénédicte; Masson-Delmotte, Valérie

2016-04-01

Polar ice cores provide exceptional archives of past environmental conditions. The dating of ice cores and the estimation of the age scale uncertainty are essential to interpret the climate and environmental records that they contain. It is however a complex problem which involves different methods. Here, we present IceChrono1, a new probabilistic model integrating various sources of chronological information to produce a common and optimized chronology for several ice cores, as well as its uncertainty. IceChrono1 is based on the inversion of three quantities: the surface accumulation rate, the Lock-In Depth (LID) of air bubbles and the thinning function. The chronological information integrated into the model are: models of the sedimentation process (accumulation of snow, densification of snow into ice and air trapping, ice flow), ice and air dated horizons, ice and air depth intervals with known durations, Δdepth observations (depth shift between synchronous events recorded in the ice and in the air) and finally air and ice stratigraphic links in between ice cores. The optimization is formulated as a least squares problem, implying that all densities of probabilities are assumed to be Gaussian. It is numerically solved using the Levenberg-Marquardt algorithm and a numerical evaluation of the model's Jacobian. IceChrono follows an approach similar to that of the Datice model which was recently used to produce the AICC2012 chronology for 4 Antarctic ice cores and 1 Greenland ice core. IceChrono1 provides improvements and simplifications with respect to Datice from the mathematical, numerical and programming point of views. The capabilities of IceChrono is demonstrated on a case study similar to the AICC2012 dating experiment. We find results similar to those of Datice, within a few centuries, which is a confirmation of both IceChrono and Datice codes. We also test new functionalities with respect to the original version of Datice: observations as ice intervals with known durations, correlated observations, observations as gas intervals with known durations and observations as mixed ice-air stratigraphic links. IceChrono1 is freely available under the GPL v3 open source license.

IceChrono1: a probabilistic model to compute a common and optimal chronology for several ice cores

NASA Astrophysics Data System (ADS)

Parrenin, F.; Bazin, L.; Capron, E.; Landais, A.; Lemieux-Dudon, B.; Masson-Delmotte, V.

2015-05-01

Polar ice cores provide exceptional archives of past environmental conditions. The dating of ice cores and the estimation of the age-scale uncertainty are essential to interpret the climate and environmental records that they contain. It is, however, a complex problem which involves different methods. Here, we present IceChrono1, a new probabilistic model integrating various sources of chronological information to produce a common and optimized chronology for several ice cores, as well as its uncertainty. IceChrono1 is based on the inversion of three quantities: the surface accumulation rate, the lock-in depth (LID) of air bubbles and the thinning function. The chronological information integrated into the model are models of the sedimentation process (accumulation of snow, densification of snow into ice and air trapping, ice flow), ice- and air-dated horizons, ice and air depth intervals with known durations, depth observations (depth shift between synchronous events recorded in the ice and in the air) and finally air and ice stratigraphic links in between ice cores. The optimization is formulated as a least squares problem, implying that all densities of probabilities are assumed to be Gaussian. It is numerically solved using the Levenberg-Marquardt algorithm and a numerical evaluation of the model's Jacobian. IceChrono follows an approach similar to that of the Datice model which was recently used to produce the AICC2012 (Antarctic ice core chronology) for four Antarctic ice cores and one Greenland ice core. IceChrono1 provides improvements and simplifications with respect to Datice from the mathematical, numerical and programming point of views. The capabilities of IceChrono1 are demonstrated on a case study similar to the AICC2012 dating experiment. We find results similar to those of Datice, within a few centuries, which is a confirmation of both IceChrono1 and Datice codes. We also test new functionalities with respect to the original version of Datice: observations as ice intervals with known durations, correlated observations, observations as air intervals with known durations and observations as mixed ice-air stratigraphic links. IceChrono1 is freely available under the General Public License v3 open source license.

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.

Sustained winter streamflow from groundmelt

Treesearch

C. Anthony Federer

1965-01-01

The watersheds of the Hubbard Brook Experimental Forest in the White Mountains of New Hampshire are among the few small gaged watersheds for which continuous winter streamflow records are obtained while deep snow covers the area. Records show that a remarkably steady flow of between 0.006 and 0.025 area-inch of water per day leaves the watershed in spite of snow depths...

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.

Influences and interactions of inundation, peat, and snow on active layer thickness: Modeling Archive

DOE Data Explorer

Scott Painter; Ethan Coon; Cathy Wilson; Dylan Harp; Adam Atchley

2016-04-21

This Modeling Archive is in support of an NGEE Arctic publication currently in review [4/2016]. The Advanced Terrestrial Simulator (ATS) was used to simulate thermal hydrological conditions across varied environmental conditions for an ensemble of 1D models of Arctic permafrost. The thickness of organic soil is varied from 2 to 40cm, snow depth is varied from approximately 0 to 1.2 meters, water table depth was varied from -51cm below the soil surface to 31 cm above the soil surface. A total of 15,960 ensemble members are included. Data produced includes the third and fourth simulation year: active layer thickness, time of deepest thaw depth, temperature of the unfrozen soil, and unfrozen liquid saturation, for each ensemble member. Input files used to run the ensemble are also included.

On the extraordinary snow on the sea ice off East Antarctica in late winter, 2012

NASA Astrophysics Data System (ADS)

Toyota, Takenobu; Massom, Robert; Lecomte, Olivier; Nomura, Daiki; Heil, Petra; Tamura, Takeshi; Fraser, Alexander D.

2016-09-01

In late winter-early spring 2012, the second Sea Ice Physics and Ecosystems Experiment (SIPEX II) was conducted off Wilkes Land, East Antarctica, onboard R/V Aurora Australis. The sea-ice conditions were characterized by significantly thick first-year ice and snow, trapping the ship for about 10 days in the near coastal region. The deep snow cover was particularly remarkable, in that its average value of 0.45 m was almost three times that observed between 1992 and 2007 in the region. To reveal factors responsible, we used in situ observations and ERA-Interim reanalysis (1990-2012) to examine the relative contribution of the different components of the local-regional snow mass balance equation i.e., snow accumulation on sea ice, precipitation minus evaporation (P-E), and loss by (i) snow-ice formation and (ii) entering into leads due to drifting snow. Results show no evidence for significantly high P-E in the winter of 2012. Ice core analysis has shown that although the snow-ice layer was relatively thin, indicating less transformation from snow to snow-ice in 2012 as compared to measurements from 2007, the difference was not enough to explain the extraordinarily deep snow. Based on these results, we deduce that lower loss of snow into leads was probably responsible for the extraordinary snow in 2012. Statistical analysis and satellite images suggest that the reduction in loss of snow into leads is attributed to rough ice surface associated with active deformation processes and larger floe size due to sea-ice expansion. This highlights the importance of snow-sea ice interaction in determining the mean snow depth on Antarctic sea ice.

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. 

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.

Analysis of passive microwave signatures over snow-covered mountainous area

NASA Astrophysics Data System (ADS)

Kim, R. S.; Durand, M. T.

2015-12-01

Accurate knowledge of snow distribution over mountainous area is critical for climate studies and the passive microwave(PM) measurements have been widely used and invested in order to obtain information about snowpack properties. Understanding and analyzing the signatures for the explicit inversion of the remote sensing data from land surfaces is required for successful using of passive microwave sensors but this task is often ambiguous due to the large variability of physical conditions and object types. In this paper, we discuss the pattern of measured brightness temperatures and emissivities at vertical and horizontal polarization over the frequency range of 10.7 to 89 GHz of land surfaces under various snow and vegetation conditions. The Multiband polarimetric Scanning Radiometer(PSR) imagery is used over NASA Cold Land Processes Field Experiment(CLPX) study area with ground-based measurements of snow depth and snow properties. Classification of snow under various conditions in mountainous area is implemented based on different patterns of microwave signatures.

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.

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.

The Kühtai data set: 25 years of lysimetric, snow pillow, and meteorological measurements

PubMed Central

Kirnbauer, R.; Parajka, J.; Schöber, J.; Blöschl, G.

2017-01-01

Abstract Snow measurements at the Kühtai station in Tirol, Austria, (1920 m.a.s.l.) are described. The data set includes snow water equivalent from a 10 m2 snow pillow, snow melt outflow from a 10 m2 snow lysimeter placed at the same location as the pillow, meteorological data (precipitation, incoming shortwave radiation, reflected shortwave radiation, air temperature, relative air humidity, and wind speed), and other data (snow depths, snow temperatures at seven heights) from the period October 1990 to May 2015. All data have been quality checked, and gaps in the meteorological data have been filled in. The data set is unique in that all data are available at a temporal resolution of 15 min over a period of 25 years with minimal changes in the experimental setup. The data set can therefore be used to analyze snow pack processes over a long‐time period, including their extremes and long‐term changes, in an Alpine climate. Analyses may benefit from the combined measurement of snow water equivalent, lysimeter outflow, and precipitation at a wind‐sheltered alpine site. An example use of data shows the temporal variability of daily and 1 April snow water equivalent observed at the Kühtai site. The results indicate that the snow water equivalent maximum varies between 200 and more than 500 mm w.e., but there is no statistically significant temporal trend in the period 1990–2015. PMID:28931957

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.

Noninvasive genetic population survey of snow leopards (Panthera uncia) in Kangchenjunga conservation area, Shey Phoksundo National Park and surrounding buffer zones of Nepal.

PubMed

Karmacharya, Dibesh B; Thapa, Kamal; Shrestha, Rinjan; Dhakal, Maheshwar; Janecka, Jan E

2011-11-28

The endangered snow leopard is found throughout major mountain ranges of Central Asia, including the remote Himalayas. However, because of their elusive behavior, sparse distribution, and poor access to their habitat, there is a lack of reliable information on their population status and demography, particularly in Nepal. Therefore, we utilized noninvasive genetic techniques to conduct a preliminary snow leopard survey in two protected areas of Nepal. A total of 71 putative snow leopard scats were collected and analyzed from two different areas; Shey Phoksundo National Park (SPNP) in the west and Kangchanjunga Conservation Area (KCA) in the east. Nineteen (27%) scats were genetically identified as snow leopards, and 10 (53%) of these were successfully genotyped at 6 microsatellite loci. Two samples showed identical genotype profiles indicating a total of 9 individual snow leopards. Four individual snow leopards were identified in SPNP (1 male and 3 females) and five (2 males and 3 females) in KCA. We were able to confirm the occurrence of snow leopards in both study areas and determine the minimum number present. This information can be used to design more in-depth population surveys that will enable estimation of snow leopard population abundance at these sites.

Noninvasive genetic population survey of snow leopards (Panthera uncia) in Kangchenjunga conservation area, Shey Phoksundo National Park and surrounding buffer zones of Nepal

PubMed Central

2011-01-01

Background The endangered snow leopard is found throughout major mountain ranges of Central Asia, including the remote Himalayas. However, because of their elusive behavior, sparse distribution, and poor access to their habitat, there is a lack of reliable information on their population status and demography, particularly in Nepal. Therefore, we utilized noninvasive genetic techniques to conduct a preliminary snow leopard survey in two protected areas of Nepal. Results A total of 71 putative snow leopard scats were collected and analyzed from two different areas; Shey Phoksundo National Park (SPNP) in the west and Kangchanjunga Conservation Area (KCA) in the east. Nineteen (27%) scats were genetically identified as snow leopards, and 10 (53%) of these were successfully genotyped at 6 microsatellite loci. Two samples showed identical genotype profiles indicating a total of 9 individual snow leopards. Four individual snow leopards were identified in SPNP (1 male and 3 females) and five (2 males and 3 females) in KCA. Conclusions We were able to confirm the occurrence of snow leopards in both study areas and determine the minimum number present. This information can be used to design more in-depth population surveys that will enable estimation of snow leopard population abundance at these sites. PMID:22117538

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.

NASA's Potential Contributions to Avalanche Forecasting Using Active and Passive Microwave Measurements

NASA Technical Reports Server (NTRS)

Blonski, Slawomir

2007-01-01

This Candidate Solution is based on using active and passive microwave measurements acquired from NASA satellites to improve USDA (U.S. Department of Agriculture) Forest Service forecasting of avalanche danger. Regional Avalanche Centers prepare avalanche forecasts using ground measurements of snowpack and mountain weather conditions. In this Solution, range of the in situ observations is extended by adding remote sensing measurements of snow depth, snow water equivalent, and snowfall rate acquired by satellite missions that include Aqua, CloudSat, future GPM (Global Precipitation Measurement), and the proposed SCLP (Snow and Cold Land Processes). Measurements of snowpack conditions and time evolution are improved by combining the in situ and satellite observations with a snow model. Recurring snow observations from NASA satellites increase accuracy of avalanche forecasting, which helps the public and the managers of public facilities make better avalanche safety decisions.

The effects of snowpack grain size on satellite passive microwave observations from the Upper Colorado River Basin

USGS Publications Warehouse

Josberger, E.G.; Gloersen, P.; Chang, A.; Rango, A.

1996-01-01

Understanding the passive microwave emissions of a snowpack, as observed by satellite sensors, requires knowledge of the snowpack properties: water equivalent, grain size, density, and stratigraphy. For the snowpack in the Upper Colorado River Basin, measurements of snow depth and water equivalent are routinely available from the U.S. Department of Agriculture, but extremely limited information is available for the other properties. To provide this information, a field program from 1984 to 1995 obtained profiles of snowpack grain size, density, and temperature near the time of maximum snow accumulation, at sites distributed across the basin. A synoptic basin-wide sampling program in 1985 showed that the snowpack exhibits consistent properties across large regions. Typically, the snowpack in the Wyoming region contains large amounts of depth hoar, with grain sizes up to 5 mm, while the snowpack in Colorado and Utah is dominated by rounded snow grains less than 2 mm in diameter. In the Wyoming region, large depth hoar crystals in shallow snowpacks yield the lowest emissivities or coldest brightness temperatures observed across the entire basin. Yearly differences in the average grain sizes result primarily from variations in the relative amount of depth hoar within the snowpack. The average grain size for the Colorado and Utah regions shows much less variation than do the grain sizes from the Wyoming region. Furthermore, the greatest amounts of depth hoar occur in the Wyoming region during 1987 and 1992, years with strong El Nin??o Southern Oscillation, but the Colorado and Utah regions do not show this behavior.

Addressing sub-scan variability of tundra snow properties in ground-based Ku- and X-band scatterometer observations

NASA Astrophysics Data System (ADS)

King, J. M.; Kasurak, A.; Kelly, R. E.; Duguay, C. R.; Derksen, C.; Rutter, N.; Sandells, M.; Watts, T.

2012-12-01

During the winter of 2010-2011 ground-based Ku- (17.2 GHz) and X-band (9.6 GHz) scatterometers were deployed near Churchill, Manitoba, Canada to evaluate the potential for dual-frequency observation of tundra snow properties. Field-based scatterometer observations when combined with in-situ snowpack properties and physically based models, provide the means necessary to develop and evaluate local scale property retrievals. To form meaningful analysis of the observed physical interaction space, potential sources of bias and error in the observed backscatter must be identified and quantified. This paper explores variation in observed Ku- and X-band backscatter in relation to the physical complexities of shallow tundra snow whose properties evolve at scales smaller than the observing instrument. The University of Waterloo scatterometer (UW-Scat) integrates observations over wide azimuth sweeps, several meters in length, to minimize errors resulting from radar fade and poor signal-to-noise ratios. Under ideal conditions, an assumption is made that the observed snow target is homogeneous. Despite an often-outward appearance of homogeneity, topographic elements of the Canadian open tundra produce significant local scale variability in snow properties, including snow water equivalent (SWE). Snow at open tundra sites observed during this campaign was found to vary by as much as 20 cm in depth and 40 mm in SWE within the scatterometer field of view. Previous studies suggest that changes in snow properties on this order will produce significant variation in backscatter, potentially introducing bias into products used for analysis. To assess the influence of sub-scan variability, extensive snow surveys were completed within the scatterometer field of view immediately after each scan at 32 sites. A standardized sampling protocol captured a grid of geo-located measurements, characterizing the horizontal variability of bulk properties including depth, density, and SWE. Based upon these measurements, continuous surfaces were generated to represent the observed snow target. Two snow pits were also completed within the field of view, quantifying vertical variability in density, permittivity, temperature, grain size, and stratigraphy. A new post-processing method is applied to divide the previously aggregated scatterometer observations into smaller sub-sets, which are then co-located with the physical snow observations. Sub-scan backscatter coefficients and their relationship to tundra snowpack parameters are then explored. The results presented here provide quantitative methods relevant to the radar observation science of snow and, therefore, to potential future space-borne missions such as the Cold Regions Hydrology High-resolution Observatory (CoReH2O), a candidate European Space Agency Earth Explorer mission. Moreover, this paper provides guidelines for future studies exploring ground-based scatterometer observations of tundra snow.

[A cloud detection algorithm for MODIS images combining Kmeans clustering and multi-spectral threshold method].

PubMed

Wang, Wei; Song, Wei-Guo; Liu, Shi-Xing; Zhang, Yong-Ming; Zheng, Hong-Yang; Tian, Wei

2011-04-01

An improved method for detecting cloud combining Kmeans clustering and the multi-spectral threshold approach is described. On the basis of landmark spectrum analysis, MODIS data is categorized into two major types initially by Kmeans method. The first class includes clouds, smoke and snow, and the second class includes vegetation, water and land. Then a multi-spectral threshold detection is applied to eliminate interference such as smoke and snow for the first class. The method is tested with MODIS data at different time under different underlying surface conditions. By visual method to test the performance of the algorithm, it was found that the algorithm can effectively detect smaller area of cloud pixels and exclude the interference of underlying surface, which provides a good foundation for the next fire detection approach.

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.

New estimates of changes in snow cover over Russia in recent decades

NASA Astrophysics Data System (ADS)

Bulygina, O.; Korshunova, N.; Razuvaev, V.; Groisman, P. Y.

2017-12-01

Snow covers plays critical roles in the energy and water balance of the Earth through its unique physical properties (high reflectivity and low thermal conductivity) and water storage. The main objective of this research is to monitoring snow cover change in Russia. The estimates of changes of major snow characteristics (snow cover duration, maximum winter snow depth, snow water equivalent) are described. Apart from the description of long-term averages of snow characteristics, the estimates of their change that are averaged over quasi-homogeneous climatic regions are derived and regional differences in the change of snow characteristics are studied. We used in our study daily snow observations for 820 Russian meteorological station from 1966 to 2017. All of these meteorological stations are of unprotected type. The water equivalent is analyzed from snow course survey data at 958 meteorological stations from 1966 to 2017. The time series are prepared by RIHMI-WDC. Regional analysis of snow cover data was carried out using quasi-homogeneous climatic regions. The area-averaging technique using station values converted to anomalies with respect to a common reference period (in this study, 1981-2010). Anomalies were arithmetically averaged first within 1°N x 2°E grid cells and thereafter by a weighted average value derived over the quasi-homogeneous climatic regions. This approach provides a more uniform spatial field for averaging. By using a denser network of meteorological stations, bringing into consideration snow course data and, we managed to specify changes in all observed major snow characteristics and to obtain estimates generalized for quasi-homogeneous climatic regions. The detected changes in the dates of the establishment and disappearance of the snow cover.

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.

Satellite Based Probabilistic Snow Cover Extent Mapping (SCE) at Hydro-Québec

NASA Astrophysics Data System (ADS)

Teasdale, Mylène; De Sève, Danielle; Angers, Jean-François; Perreault, Luc

2016-04-01

Over 40% of Canada's water resources are in Quebec and Hydro-Quebec has developed potential to become one of the largest producers of hydroelectricity in the world, with a total installed capacity of 36,643 MW. The Hydro-Québec fleet park includes 27 large reservoirs with a combined storage capacity of 176 TWh, and 668 dams and 98 controls. Thus, over 98% of all electricity used to supply the domestic market comes from water resources and the excess output is sold on the wholesale markets. In this perspective the efficient management of water resources is needed and it is based primarily on a good river flow estimation including appropriate hydrological data. Snow on ground is one of the significant variables representing 30% to 40% of its annual energy reserve. More specifically, information on snow cover extent (SCE) and snow water equivalent (SWE) is crucial for hydrological forecasting, particularly in northern regions since the snowmelt provides the water that fills the reservoirs and is subsequently used for hydropower generation. For several years Hydro Quebec's research institute ( IREQ) developed several algorithms to map SCE and SWE. So far all the methods were deterministic. However, given the need to maximize the efficient use of all resources while ensuring reliability, the electrical systems must now be managed taking into account all risks. Since snow cover estimation is based on limited spatial information, it is important to quantify and handle its uncertainty in the hydrological forecasting system. This paper presents the first results of a probabilistic algorithm for mapping SCE by combining Bayesian mixture of probability distributions and multiple logistic regression models applied to passive microwave data. This approach allows assigning for each grid point, probabilities to the set of the mutually exclusive discrete outcomes: "snow" and "no snow". Its performance was evaluated using the Brier score since it is particularly appropriate to measure the accuracy of probabilistic discrete predictions. The scores were measured by comparing the snow probabilities produced by our models with the Hydro-Québec's snow ground data.

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.

Assessment of the Relative Accuracy of Hemispheric-Scale Snow-Cover Maps

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Kelly, Richard E.; Riggs, George A.; Chang, Alfred T. C.; Foster, James L.; Houser, Paul (Technical Monitor)

2001-01-01

There are several hemispheric-scale satellite-derived snow-cover maps available, but none has been fully validated. For the period October 23 - December 25, 2000, we compare snow maps of North America derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the National Oceanic and Atmospheric Administration (NOAA) National Operational Hydrologic Remote Sensing Center (NOHRSC), which both rely on satellite data from the visible and near-infrared parts of the spectrum; we also compare MODIS and Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) passive-microwave snow maps. The maps derived from visible and near-infrared data are more accurate for mapping snow cover than are the passive-microwave-derived maps, however discrepancies exist as to the location and extent of the snow cover among those maps. The large (approx. 30 km) footprint of the SSM/I data and the difficulty in distinguishing wet and shallow snow from wet or snow-free ground, reveal differences up to 5.32 million sq km in the amount of snow mapped using MODIS versus SSM/I data. Algorithms that utilize both visible and passive-microwave data, which would take advantage of the all-weather mapping ability of the passive-microwave data, will be refined following the launch of the Advanced Microwave Scanning Radiometer (AMSR) in the fall of 2001.

A Blended Global Snow Product using Visible, Passive Microwave and Scatterometer Satellite Data

NASA Technical Reports Server (NTRS)

Foster, James L.; Hall, Dorothy K.; Eylander, John B.; Riggs, George A.; Nghiem, Son V.; Tedesco, Marco; Kim, Edward; Montesano, Paul M.; Kelly, Richard E. J.; Casey, Kimberly A.;

Continuity of MODIS and VIIRS Snow-Cover Maps during Snowmelt in the Catskill Mountains in New York

NASA Astrophysics Data System (ADS)

Hall, D. K.; Riggs, G. A., Jr.; Roman, M. O.; DiGirolamo, N. E.

2015-12-01

We investigate the local and regional differences and possible biases between the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible-Infrared Imager Radiometer Suite (VIIRS) snow-cover maps in the winter of 2012 during snowmelt conditions in the Catskill Mountains in New York using a time series of cloud-gap filled daily snow-cover maps. The MODIS Terra instrument has been providing daily global snow-cover maps since February 2000 (Riggs and Hall, 2015). Using the VIIRS instrument, launched in 2011, NASA snow products are being developed based on the heritage MODIS snow-mapping algorithms, and will soon be available to the science community. Continuity of the standard NASA MODIS and VIIRS snow-cover maps is essential to enable environmental-data records (EDR) to be developed for analysis of snow-cover trends using a consistent data record. For this work, we compare daily MODIS and VIIRS snow-cover maps of the Catskill Mountains from 29 February through 14 March 2012. The entire region was snow covered on 29 February and by 14 March the snow had melted; we therefore have a daily time series available to compare normalized difference snow index (NDSI), as an indicator of snow-cover fraction. The MODIS and VIIRS snow-cover maps have different spatial resolutions (500 m for MODIS and 375 m for VIIRS) and different nominal overpass times (10:30 AM for MODIS Terra and 2:30 PM for VIIRS) as well as different cloud masks. The results of this work will provide a quantitative assessment of the continuity of the snow-cover data records for use in development of an EDR of snow cover.http://modis-snow-ice.gsfc.nasa.gov/Riggs, G.A. and D.K. Hall, 2015: MODIS Snow Products User Guide to Collection 6, http://modis-snow-ice.gsfc.nasa.gov/?c=userguides

Evaluation of MODIS albedo product (MCD43A) over grassland, agriculture and forest surface types during dormant and snow-covered periods

Treesearch

Zhuosen Wang; Crystal B. Schaaf; Alan H. Strahler; Mark J. Chopping; Miguel O. Román; Yanmin Shuai; Curtis E. Woodcock; David Y. Hollinger; David R. Fitzjarrald

2014-01-01

This study assesses the Moderate-resolution Imaging Spectroradiometer (MODIS) BRDF/albedo 8 day standard product and products from the daily Direct Broadcast BRDF/albedo algorithm, and shows that these products agree well with ground-based albedo measurements during the more difficult periods of vegetation dormancy and snow cover. Cropland, grassland, deciduous and...

MODIS Snow-Cover Products

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Riggs, George A.; Salomonson, Vincent V.; DiGirolamo, Nicole E.; Bayr, Klaus J.; Houser, Paul R. (Technical Monitor)

2002-01-01

On December 18, 1999, the Terra satellite was launched with a complement of five instruments including the Moderate Resolution Imaging Spectroradiometer (MODIS). Many geophysical products are derived from MODIS data including global snow-cover products. MODIS snow and ice products have been available through the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) since September 13, 2000. MODIS snow-cover products represent potential improvement to or enhancement of the currently-available operational products mainly because the MODIS products are global and 500-m resolution, and have the capability to separate most snow and clouds. Also the snow-mapping algorithms are automated which means that a consistent data set may be generated for long-term climate studies that require snow-cover information. Extensive quality assurance (QA) information is stored with the products. The MODIS snow product suite begins with a 500-m resolution, 2330-km swath snow-cover map which is then gridded to an integerized sinusoidal grid to produce daily and 8-day composite tile products. The sequence proceeds to a climate-modeling grid (CMG) product at about 5.6-km spatial resolution, with both daily and 8-day composite products. Each pixel of the CMG contains fraction of snow cover from 40 - 100%. Measured errors of commission in the CMG are low, for example, on the continent of Australia in the spring, they vary from 0.02 - 0.10%. Near-term enhancements include daily snow albedo and fractional snow cover. A case study from March 6, 2000, involving MODIS data and field and aircraft measurements, is presented to show some early validation work.

Towards development of an operational snow on sea ice product

NASA Astrophysics Data System (ADS)

Stroeve, J.; Liston, G. E.; Barrett, A. P.; Tschudi, M. A.; Stewart, S.

2017-12-01

Sea ice has been visibly changing over the past couple of decades; most notably the annual minimum extent which has shown a distinct downward, and recently accelerating, trend. September mean sea ice extent was over 7×106 km2 in the 1980's, but has averaged less than 5×106 km2 in the last decade. Should this loss continue, there will be wide-ranging impacts on marine ecosystems, coastal communities, prospects for resource extraction and marine activity, and weather conditions in the Arctic and beyond. While changes in the spatial extent of sea ice have been routinely monitored since the 1970s, less is known about how the thickness of the ice cover has changed. While estimates of ice thickness across the Arctic Ocean have become available over the past 20 years based on data from ERS-1/2, Envisat, ICESat, CryoSat-2 satellites and Operation IceBridge aircraft campaigns, the variety of these different measurement approaches, sensor technologies and spatial coverage present formidable challenges. Key among these is that measurement techniques do not measure ice thickness directly - retrievals also require snow depth and density. Towards that end, a sophisticated snow accumulation model is tested in a Lagrangian framework to map daily snow depths across the Arctic sea ice cover using atmospheric reanalysis data as input. Accuracy of the snow accumulation is assessed through comparison with Operation IceBridge data and ice mass balance buoys (IMBs). Impacts on ice thickness retrievals are further discussed.

Physically Accurate Soil Freeze-Thaw Processes in a Global Land Surface Scheme

NASA Astrophysics Data System (ADS)

Cuntz, Matthias; Haverd, Vanessa

2018-01-01

The model Soil-Litter-Iso (SLI) calculates coupled heat and water transport in soil. It was recently implemented into the Australian land surface model CABLE, which is the land component of the Australian Community Climate and Earth System Simulator (ACCESS). Here we extended SLI to include accurate freeze-thaw processes in the soil and snow. SLI provides thence an implicit solution of the energy and water balances of soil and snow as a standalone model and within CABLE. The enhanced SLI was tested extensively against theoretical formulations, laboratory experiments, field data, and satellite retrievals. The model performed well for all experiments at wide-ranging temporal and spatial scales. SLI melts snow faster at the end of the cold season compared to observations though because there is no subgrid variability within SLI given by the implicit, coupled solution of energy and water. Combined CABLE-SLI shows very realistic dynamics and extent of permafrost on the Northern hemisphere. It illustrated, however, also the limits of possible comparisons between large-scale land surface models and local permafrost observations. CABLE-SLI exhibits the same patterns of snow depth and snow water equivalent on the Northern hemisphere compared to satellite-derived observations but quantitative comparisons depend largely on the given meteorological input fields. Further extension of CABLE-SLI with depth-dependence of soil carbon will allow realistic projections of the development of permafrost and frozen carbon stocks in a changing climate.

Separating local topography from snow effects on momentum roughness in mountain regions

NASA Astrophysics Data System (ADS)

Diebold, M.; Katul, G. G.; Calaf, M.; Lehning, M.; Parlange, M. B.

2013-12-01

Parametrization of momentum surface roughness length in mountainous regions continues to be an active research topic given its application to improved weather forecasting and sub-grid scale representation of mountainous regions in climate models. A field campaign was conducted in the Val Ferret watershed (Swiss Alps) to assess the role of topographic variability and snow cover on momentum roughness. To this end, turbulence measurements in a mountainous region with and without snow cover have been analyzed. A meteorological mast with four sonic anemometers together with temperature and humidity sensors was installed at an elevation of 2500 m and data were obtained from October 2011 until May 2012. Because of the long-term nature of these experiments, natural variability in mean wind direction allowed a wide range of terrain slopes and snow depths to be sampled. A theoretical framework that accounted only for topographically induced pressure perturbations in the mean momentum balance was used to diagnose the role of topography on the effective momentum roughness height as inferred from the log-law. Surface roughness depended systematically on wind direction but was not significantly influenced by the presence of snow depth variation. Moreover, the wind direction and so the surface roughness influenced the normalized turbulent kinetic energy, which in theory should not depend on these factors in the near-neutral atmospheric surface layer. The implications of those findings to modeling momentum roughness heights and turbulent kinetic energy (e.g. in conventional K-epsilon closure) in complex terrain are briefly discussed.

Real-Time Alpine Measurement System Using Wireless Sensor Networks

PubMed Central

2017-01-01

Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributed Wireless Sensor Network (WSN)-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra’s wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km2 network location. Combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape. PMID:29120376

Real-Time Alpine Measurement System Using Wireless Sensor Networks.

PubMed

Malek, Sami A; Avanzi, Francesco; Brun-Laguna, Keoma; Maurer, Tessa; Oroza, Carlos A; Hartsough, Peter C; Watteyne, Thomas; Glaser, Steven D

2017-11-09

Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributed Wireless Sensor Network (WSN)-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra's wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km 2 network location. Combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape.

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.

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.

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.

Winter warming as an important co-driver for Betula nana growth in western Greenland during the past century

PubMed Central

Hollesen, Jørgen; Buchwal, Agata; Rachlewicz, Grzegorz; Hansen, Birger U; Hansen, Marc O; Stecher, Ole; Elberling, Bo

2015-01-01

Growing season conditions are widely recognized as the main driver for tundra shrub radial growth, but the effects of winter warming and snow remain an open question. Here, we present a more than 100 years long Betula nana ring-width chronology from Disko Island in western Greenland that demonstrates a highly significant and positive growth response to both summer and winter air temperatures during the past century. The importance of winter temperatures for Betula nana growth is especially pronounced during the periods from 1910–1930 to 1990–2011 that were dominated by significant winter warming. To explain the strong winter importance on growth, we assessed the importance of different environmental factors using site-specific measurements from 1991 to 2011 of soil temperatures, sea ice coverage, precipitation and snow depths. The results show a strong positive growth response to the amount of thawing and growing degree-days as well as to winter and spring soil temperatures. In addition to these direct effects, a strong negative growth response to sea ice extent was identified, indicating a possible link between local sea ice conditions, local climate variations and Betula nana growth rates. Data also reveal a clear shift within the last 20 years from a period with thick snow depths (1991–1996) and a positive effect on Betula nana radial growth, to a period (1997–2011) with generally very shallow snow depths and no significant growth response towards snow. During this period, winter and spring soil temperatures have increased significantly suggesting that the most recent increase in Betula nana radial growth is primarily triggered by warmer winter and spring air temperatures causing earlier snowmelt that allows the soils to drain and warm quicker. The presented results may help to explain the recently observed ‘greening of the Arctic’ which may further accelerate in future years due to both direct and indirect effects of winter warming. PMID:25788025

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.

Winter warming as an important co-driver for Betula nana growth in western Greenland during the past century.

PubMed

Hollesen, Jørgen; Buchwal, Agata; Rachlewicz, Grzegorz; Hansen, Birger U; Hansen, Marc O; Stecher, Ole; Elberling, Bo

2015-06-01

Growing season conditions are widely recognized as the main driver for tundra shrub radial growth, but the effects of winter warming and snow remain an open question. Here, we present a more than 100 years long Betula nana ring-width chronology from Disko Island in western Greenland that demonstrates a highly significant and positive growth response to both summer and winter air temperatures during the past century. The importance of winter temperatures for Betula nana growth is especially pronounced during the periods from 1910-1930 to 1990-2011 that were dominated by significant winter warming. To explain the strong winter importance on growth, we assessed the importance of different environmental factors using site-specific measurements from 1991 to 2011 of soil temperatures, sea ice coverage, precipitation and snow depths. The results show a strong positive growth response to the amount of thawing and growing degree-days as well as to winter and spring soil temperatures. In addition to these direct effects, a strong negative growth response to sea ice extent was identified, indicating a possible link between local sea ice conditions, local climate variations and Betula nana growth rates. Data also reveal a clear shift within the last 20 years from a period with thick snow depths (1991-1996) and a positive effect on Betula nana radial growth, to a period (1997-2011) with generally very shallow snow depths and no significant growth response towards snow. During this period, winter and spring soil temperatures have increased significantly suggesting that the most recent increase in Betula nana radial growth is primarily triggered by warmer winter and spring air temperatures causing earlier snowmelt that allows the soils to drain and warm quicker. The presented results may help to explain the recently observed 'greening of the Arctic' which may further accelerate in future years due to both direct and indirect effects of winter warming. © 2015 John Wiley & Sons Ltd.

Obtaining sub-daily new snow density from automated measurements in high mountain regions

NASA Astrophysics Data System (ADS)

Helfricht, Kay; Hartl, Lea; Koch, Roland; Marty, Christoph; Olefs, Marc

2018-05-01

The density of new snow is operationally monitored by meteorological or hydrological services at daily time intervals, or occasionally measured in local field studies. However, meteorological conditions and thus settling of the freshly deposited snow rapidly alter the new snow density until measurement. Physically based snow models and nowcasting applications make use of hourly weather data to determine the water equivalent of the snowfall and snow depth. In previous studies, a number of empirical parameterizations were developed to approximate the new snow density by meteorological parameters. These parameterizations are largely based on new snow measurements derived from local in situ measurements. In this study a data set of automated snow measurements at four stations located in the European Alps is analysed for several winter seasons. Hourly new snow densities are calculated from the height of new snow and the water equivalent of snowfall. Considering the settling of the new snow and the old snowpack, the average hourly new snow density is 68 kg m-3, with a standard deviation of 9 kg m-3. Seven existing parameterizations for estimating new snow densities were tested against these data, and most calculations overestimate the hourly automated measurements. Two of the tested parameterizations were capable of simulating low new snow densities observed at sheltered inner-alpine stations. The observed variability in new snow density from the automated measurements could not be described with satisfactory statistical significance by any of the investigated parameterizations. Applying simple linear regressions between new snow density and wet bulb temperature based on the measurements' data resulted in significant relationships (r2 > 0.5 and p ≤ 0.05) for single periods at individual stations only. Higher new snow density was calculated for the highest elevated and most wind-exposed station location. Whereas snow measurements using ultrasonic devices and snow pillows are appropriate for calculating station mean new snow densities, we recommend instruments with higher accuracy e.g. optical devices for more reliable investigations of the variability of new snow densities at sub-daily intervals.

Modelling Ground Based X- and Ku-Band Observations of Tundra Snow

NASA Astrophysics Data System (ADS)

Kasurak, A.; King, J. M.; Kelly, R. E.

2012-12-01

As part of a radar-based remote sensing field experiment in Churchill, Manitoba ground based Ku- and X-band scatterometers were deployed to observe changing tundra snowpack conditions from November 2010 to March 2011. The research is part of the validation effort for the Cold Regions Hydrology High-resolution Observatory (CoReH2O) mission, a candidate in the European Space Agency's Earth Explorer program. This paper focuses on the local validation of the semi-empirical radiative transfer (sRT) model proposed for use in snow property retrievals as part of the CoReH2O mission. In this validation experiment, sRT was executed in the forward mode, simulating backscatter to assess the ability of the model. This is a necessary precursor to any inversion attempt. Two experiments are considered, both conducted in a hummocky tundra environment with shallow snow cover. In both cases, scatterometer observations were acquired over a field of view of approximately 10 by 20 meters. In the first experiment, radar observations were made of a snow field and then repeated after the snow had been removed. A ground-based scanning LiDAR system was used to characterize the spatial variability of snow depth through measurements of the snow and ground surface. Snow properties were determined in the field of view from two snow pits, 12 density core measurements, and Magnaprobe snow depth measurements. In the second experiment, a site was non-destructively observed from November through March, with snow properties measured out-of-scene, to characterize the snow evolution response. The model results from sRT fit the form of the observations from the two scatterometer field experiments but do not capture the backscatter magnitude. A constant offset for the season of 5 dB for X-band co- and cross-polarization response was required to match observations, in addition to a 3 dB X- and Ku-band co-polarization offset after the 6th of December. To explain these offsets, it is recognized that the two main physical processes represented by the model are snow volume scattering and ground surface reflectance. With a larger correction needed for X-band, where the ground portion of backscatter is expected to be larger, the contribution from the underlying soil is explored first. The ground contribution in sRT is computed using the semi-empirical Oh et al. (1992) model using permittivity from a temperate mineral soil based model. The ground response is tested against two observations of snow-removed tundra, and one observation of snow free tundra. A secondary analysis is completed using a modified sRT ground model, incorporating recent work on frozen organic permittivity by Mironov et al. (2010). Multi-scale surface roughness resulting from superimposed microtopography on regularly distributed hummocks is also addressed. These results demonstrate the applicability of microwave scattering models to tundra snowpacks underlain with peat, and demonstrate the applicability of the CoReH2O sRT model.

Estimation de l'equivalent en eau de la neige en milieu subarctique du Quebec par teledetection micro-ondes passives

NASA Astrophysics Data System (ADS)

Vachon, Francois

The snow cover (extent, depth and water equivalent) is an important factor in assessing the water balance of a territory. In a context of deregulation of electricity, better knowledge of the quantity of water resulting from snowmelt that will be available for hydroelectric power generation has become a major challenge for the managers of Hydro-Quebec's generating plant. In fact, the snow on the ground represents nearly one third of Hydro-Quebec's annual energy reserve and the proportion is even higher for northern watersheds. Snowcover knowledge would therefore help optimize the management of energy stocks. The issue is especially important when one considers that better management of water resources can lead to substantial economic benefits. The Research Institute of Hydro-Quebec (IREQ), our research partner, is currently attempting to optimize the streamfiow forecasts made by its hydrological models by improving the quality of the inputs. These include a parameter known as the snow water equivalent (SWE) which characterizes the properties of the snow cover. At the present time, SWE data is obtained from in situ measurements, which are both sporadic and scattered, and does not allow the temporal and spatial variability of SWE to be characterized adequately for the needs of hydrological models. This research project proposes to provide the Quebec utility's hydrological models with distributed SWE information about its northern watersheds. The targeted accuracy is 15% for the proposed period of analysis covering the winter months of January, February and March of 2001 to 2006. The methodology is based on the HUT snow emission model and uses the passive microwave remote sensing data acquired by the SSM/I sensor. Monitoring of the temporal and spatial variations in SWE is done by inversion of the model and benefits from the assimilation of in situ data to characterize the state of snow cover during the season. Experimental results show that the assimilation technique of in situ data (density and depth) can reproduce the temporal variations in SWE with a RMSE error of 15.9% (R2=0.76). The analysis of land cover within the SSMI pixels can reduce this error to 14.6% ( R2=0.66) for SWE values below 300 mm. Moreover, the results show that the fluctuations of SWE values are driven by changes in snow depths. Indeed, the use of a constant value for the density of snow is feasible and makes it possible to get as good if not better results. These results will allow IREQ to assess the suitability of using snow cover information provided by the remote sensing data in its forecasting models. This improvement in SWE characterization will meet the needs of IREQ for its work on optimization of the quality of hydrological simulations. The originality and relevance of this work are based primarily on the type of method used to quantify SWE and the site where it is applied. The proposed method focuses on the inversion of the HUT model from passive remote sensing data and assimilates in situ data. Moreover, this approach allows high SWE values (> 300 mm) to be quantified, which was impossible with previous methods. These high SWE values are encountered in areas with large amounts of snow such as northern Quebec. Keywords. remote sensing, microwave, snow water equivalent (SWE), model, retrieval, data assimilation, SWE monitoring, spatialization Complete reference. Vachon, F. (2009) Snow water equivalent retrieval in a subartic environment of Quebec using passive microwave remote sensing. Ph.D. Thesis, Sherbrooke University, Sherbrooke, 211 p.

An Innovative Network to Improve Sea Ice Prediction in a Changing Arctic

DTIC Science & Technology

2014-09-30

sea ice volume. The EXP ensemble is initialized with 1/5 of CNTL snow depths, thus resulting in a reduced snow cover and lower summer albedo ... Sea Ice - Albedo Feedback in Sea Ice Predictions is also about understanding sea ice predictability. REFERENCES Blanchard-Wrigglesworth, E., K...1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. An Innovative Network to Improve Sea Ice Prediction

Comparison of different methods to retrieve optical-equivalent snow grain size in central Antarctica

NASA Astrophysics Data System (ADS)

Carlsen, Tim; Birnbaum, Gerit; Ehrlich, André; Freitag, Johannes; Heygster, Georg; Istomina, Larysa; Kipfstuhl, Sepp; Orsi, Anaïs; Schäfer, Michael; Wendisch, Manfred

2017-11-01

The optical-equivalent snow grain size affects the reflectivity of snow surfaces and, thus, the local surface energy budget in particular in polar regions. Therefore, the specific surface area (SSA), from which the optical snow grain size is derived, was observed for a 2-month period in central Antarctica (Kohnen research station) during austral summer 2013/14. The data were retrieved on the basis of ground-based spectral surface albedo measurements collected by the COmpact RAdiation measurement System (CORAS) and airborne observations with the Spectral Modular Airborne Radiation measurement sysTem (SMART). The snow grain size and pollution amount (SGSP) algorithm, originally developed to analyze spaceborne reflectance measurements by the MODerate Resolution Imaging Spectroradiometer (MODIS), was modified in order to reduce the impact of the solar zenith angle on the retrieval results and to cover measurements in overcast conditions. Spectral ratios of surface albedo at 1280 and 1100 nm wavelength were used to reduce the retrieval uncertainty. The retrieval was applied to the ground-based and airborne observations and validated against optical in situ observations of SSA utilizing an IceCube device. The SSA retrieved from CORAS observations varied between 27 and 89 m2 kg-1. Snowfall events caused distinct relative maxima of the SSA which were followed by a gradual decrease in SSA due to snow metamorphism and wind-induced transport of freshly fallen ice crystals. The ability of the modified algorithm to include measurements in overcast conditions improved the data coverage, in particular at times when precipitation events occurred and the SSA changed quickly. SSA retrieved from measurements with CORAS and MODIS agree with the in situ observations within the ranges given by the measurement uncertainties. However, SSA retrieved from the airborne SMART data slightly underestimated the ground-based results.

Automated detection of snow avalanche deposits: segmentation and classification of optical remote sensing imagery

NASA Astrophysics Data System (ADS)

Lato, M. J.; Frauenfelder, R.; Bühler, Y.

2012-09-01

Snow avalanches in mountainous areas pose a significant threat to infrastructure (roads, railways, energy transmission corridors), personal property (homes) and recreational areas as well as for lives of people living and moving in alpine terrain. The impacts of snow avalanches range from delays and financial loss through road and railway closures, destruction of property and infrastructure, to loss of life. Avalanche warnings today are mainly based on meteorological information, snow pack information, field observations, historically recorded avalanche events as well as experience and expert knowledge. The ability to automatically identify snow avalanches using Very High Resolution (VHR) optical remote sensing imagery has the potential to assist in the development of accurate, spatially widespread, detailed maps of zones prone to avalanches as well as to build up data bases of past avalanche events in poorly accessible regions. This would provide decision makers with improved knowledge of the frequency and size distributions of avalanches in such areas. We used an object-oriented image interpretation approach, which employs segmentation and classification methodologies, to detect recent snow avalanche deposits within VHR panchromatic optical remote sensing imagery. This produces avalanche deposit maps, which can be integrated with other spatial mapping and terrain data. The object-oriented approach has been tested and validated against manually generated maps in which avalanches are visually recognized and digitized. The accuracy (both users and producers) are over 0.9 with errors of commission less than 0.05. Future research is directed to widespread testing of the algorithm on data generated by various sensors and improvement of the algorithm in high noise regions as well as the mapping of avalanche paths alongside their deposits.