Science.gov

Sample records for blended global snow

  1. 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.; hide

    2009-01-01

    A joint U.S. Air Force/NASA blended, global snow product that utilizes Earth Observation System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and QuikSCAT (Quick Scatterometer) (QSCAT) data has been developed. Existing snow products derived from these sensors have been blended into a single, global, daily, user-friendly product by employing a newly-developed Air Force Weather Agency (AFWA)/National Aeronautics and Space Administration (NASA) Snow Algorithm (ANSA). This initial blended-snow product uses minimal modeling to expeditiously yield improved snow products, which include snow cover extent, fractional snow cover, snow water equivalent (SWE), onset of snowmelt, and identification of actively melting snow cover. The blended snow products are currently 25-km resolution. These products are validated with data from the lower Great Lakes region of the U.S., from Colorado during the Cold Lands Processes Experiment (CLPX), and from Finland. The AMSR-E product is especially useful in detecting snow through clouds; however, passive microwave data miss snow in those regions where the snow cover is thin, along the margins of the continental snowline, and on the lee side of the Rocky Mountains, for instance. In these regions, the MODIS product can map shallow snow cover under cloud-free conditions. The confidence for mapping snow cover extent is greater with the MODIS product than with the microwave product when cloud-free MODIS observations are available. Therefore, the MODIS product is used as the default for detecting snow cover. The passive microwave product is used as the default only in those areas where MODIS data are not applicable due to the presence of clouds and darkness. The AMSR-E snow product is used in association with the difference between ascending and descending satellite passes or Diurnal Amplitude Variations (DAV) to detect the onset of melt, and a QSCAT product will be used to

  2. A blending snow cover data base on MODIS and AMSR-E snow cover in Qinghai-Tibet Plateau

    NASA Astrophysics Data System (ADS)

    Xiaohua, H.; Wang, J.; Che, T.; Dai, L. Y.

    2012-04-01

    The algorithms of MODIS Terra and MODIS Aqua versions of the snow products have been developed by the NASA National Snow and Ice Data Center (NSIDC). The MODIS global snow-cover products have been available through the NSIDC Distributed Active Archive Center (DAAC) since February 24, 2000 to Terra and July 4, 2002 to Aqua. The MODIS snow-cover maps represent a potential improvement relative to hemispheric-scale snow maps that are available today mainly because of the improved spatial resolution and snow/cloud discrimination capabilities of MODIS, and the frequent global coverage. In China, the snow distribution is different to other regions. Their accuracy on Qinghai-Tibet Plateau (QTP), however, has not yet been established. There are some drawbacks about NSIDC global snow cover products on QTP: 1. The characteristics of snow depth distribution on QTP: Thin, discontinuous. Our research indicated the MODIS snow-cover products underestimated the snow cover area in QTP. 2. The daily snow cover product from MODIS-Terra and Aqua can include the data gaps. 3. The snow products can separate snow from most obscuring clouds. However, there are still many cloud pixels in daily snow cover products. The study developed a new blending daily snow cover algorithm through improving the NSIDC snow algorithms and combining MODIS and AMSR-E data in QTP. The new snow cover products will provide daily snow cover at 500-m resolution in QTP. The new snow cover algorithm employs a grouped-criteria technique using the Normalized Difference Snow Index (NDSI) and other spectral threshold tests and image fusion technology to identify and classify snow on a pixel-by-pixel basis. The usefulness of the NDSI is based on the fact that snow and ice are considerably more reflective in the visible than in the shortwave IR part of the spectrum, and the reflectance of most clouds remains high in the short-wave IR, while the reflectance of snow is low. We propose a set of three steps, based on a

  3. 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.

  4. 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

  5. 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.

  6. Building a Cloud-based Global Snow Observatory

    NASA Astrophysics Data System (ADS)

    Li, X.; Coll, J. M.

    2016-12-01

    Snow covers some 40 percent of Earth's land masses year in and year out and constitutes a vitally important variable for the planet's climate, hydrology, and biosphere due to its high albedo and insulation. It affects atmospheric circulation patterns, permafrost, glacier mass balance, river discharge, and groundwater recharge (Dietz et al. 2015). Snow is also nature's igloo where species from microscopic fungi to 800-pound moose survive the winter each in its own way (Pauli et al. 2013; Petty et al. 2015). Many studies have found that snow in high elevation regions is particularly sensitive to global climate change and is considered as sentinel of change. For human beings, about one-sixth of the world's population depends on seasonal snow and glaciers for their water supply (Barnett et al. 2005) and more than 50% of mountainous areas have an essential or supportive role for downstream regions (Viviroli et al. 2007). Large snowstorms also have a major impact on society in terms of human life, economic loss, and disruption (Squires et al. 2014). Remote sensing provides a practical approach of monitoring global snow and ice cover change. Based on our comprehensive validation and assessment on MODIS snow products, we build a cloud-based Global Snow Observatory (GSO) using Google Earth Engine (GEE) to serve as a platform for global researchers and the general public to access, visualize, and analyze snow data and to build snowmelt runoff models for mountain watersheds. Specifically, we build the GSO to serve global MODIS daily snow cover data and their analyses through GEE on Google App Engine. The GSO provides users the functions of accessing and extracting cloud-gap-filled snow data and interactive snow cover change exploration. In addition to snow cover frequency (SCF), we also plan to develop several other snow cover parameters, including snow cover duration/days, snow cover onset dates, and snow cover melting dates, and to study the shift and trend of global snow

  7. Improving the Accuracy 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.; Kumar, Sujay; Chien, Janety Y. L.; Riggs, George A.

    2012-01-01

    The Air Force Weather Agency (AFWA) -- NASA blended snow-cover product, called ANSA, utilizes Earth Observing System 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 1500 National Climatic Data Center (NCDC) co-op stations in the Lower Great Lakes Basin. Compared to station data, the ANSA significantly underestimates SWE in densely-forested areas. We use two methods to remove some of the bias observed in forested areas to reduce the root-mean-square error (RMSE) between the ANSA- and station-derived SWE. First, we calculated a 5- year mean ANSA-derived SWE for the winters of 2005-06 through 2009-10, and developed a five-year mean bias-corrected SWE map for each month. For most of the months studied during the five-year period, the 5-year bias correction improved the agreement between the ANSA-derived and station-derived SWE. However, anomalous months such as when there was very little snow on the ground compared to the 5-year mean, or months in which the snow was much greater than the 5-year mean, showed poorer results (as expected). We also used a 7-day running mean (7DRM) bias correction method using days just prior to the day in question to correct the ANSA data. This method was more effective in reducing the RMSE between the ANSA- and co-op-derived SWE values, and in capturing the effects of anomalous snow conditions.

  8. 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.

  9. Estimation of global snow cover using passive microwave data

    NASA Astrophysics Data System (ADS)

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

    2003-04-01

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

  10. 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.

  11. Global snow cover: comparison of modeling results with satellite-derived snow cover maps

    NASA Astrophysics Data System (ADS)

    Bartolini, E.; Adam, J. C.; Claps, P.

    2010-12-01

    Snow processes play an important role in the hydrologic cycle. Snowpack accumulation and depletion not only influence annual water availability and runoff seasonality, but also the functioning of ecosystems and the interactions between human activities and surface water systems. Moreover, at the large scale, snow cover influences the climate system due to its high albedo which affects the surface energy balance. Therefore, a global-scale hydrologic model that is able to predict snow cover extent (SCE) and snow water equivalent (SWE) in response to changes in climate can provide crucial information that is needed to plan for changes in water resources availability but also on potential feedbacks to the climate system. In this study, snow depth time series are simulated for the period 1950-1999 using the Variable Infiltration Capacity (VIC) model. In order to evaluate the performance of the VIC model in simulating SCE, results from the VIC model over the northern hemisphere (NH) are compared with satellite-derived SCE from the National Snow and Ice Data Center (NSIDC) NH EASE-Grid Weekly Snow Cover and Sea Ice Extent database. The comparison is focused on both temporal and spatial agreement between model and satellite-derived SCE. Temporal agreement is assessed by comparing the number of days with snow cover for both annual and seasonal periods. Spatial agreement is assessed by comparing SCE for a few large river basins. We demonstrate that there is a reasonable agreement between model and satellite-derived SCE, particularly if a 1 cm snow depth threshold is used when designating each VIC grid cell as "snow covered". However, some consistent biases are evident, particularly during the snowmelt season when the VIC model predicts a faster ablation period than the satellite data. It is possible that biases may also be attributed to the satellite-data, particularly in regards to a coarser spatial resolution than the VIC model results as well as the processing of the

  12. Snow: a reliable indicator for global warming in the future?

    NASA Astrophysics Data System (ADS)

    Jacobi, H.-W.

    2012-03-01

    The cryosphere consists of water in the solid form at the Earth's surface and includes, among others, snow, sea ice, glaciers and ice sheets. Since the 1990s the cryosphere and its components have often been considered as indicators of global warming because rising temperatures can enhance the melting of solid water (e.g. Barry et al 1993, Goodison and Walker 1993, Armstrong and Brun 2008). Changes in the cryosphere are often easier to recognize than a global temperature rise of a couple of degrees: many locals and tourists have hands-on experience in changes in the extent of glaciers or the duration of winter snow cover on the Eurasian and North American continents. On a more scientific basis, the last IPCC report left no doubt: the amount of snow and ice on Earth is decreasing (Lemke et al 2007). Available data showed clearly decreasing trends in the sea ice and frozen ground extent of the Northern Hemisphere (NH) and the global glacier mass balance. However, the trend in the snow cover extent (SCE) of the NH was much more ambiguous; a result that has since been confirmed by the online available up-to-date analysis of the SCE performed by the Rutgers University Global Snow Lab (climate.rutgers.edu/snowcover/). The behavior of snow is not the result of a simple cause-and-effect relationship between air temperature and snow. It is instead related to a rather complex interplay between external meteorological parameters and internal processes in the snowpack. While air temperature is of course a crucial parameter for snow and its melting, precipitation and radiation are also important. Further physical properties like snow grain size and the amount of absorbing impurities in the snow determine the fraction of absorbed radiation. While all these parameters affect the energy budget of the snowpack, each of these variables can dominate depending on the season or, more generally, on environmental conditions. As a result, the reduction in SCE in spring and summer in the

  13. Global Annual Snow Accumulation by Months

    DTIC Science & Technology

    1988-02-01

    enormous mountain ranges to the west and north, by the great Gobi Desert, and by the semi- permanent Siberian High over Mongolia [111. This is the dry...accumulation is on the highest mountains , RAND’s 40 by 50 grid was too coarse to pick up the variations. However, not more tha.i two grid points were involved...the mountains slope gradually eastward to the coastal plain. During the snow season, or the period of the northeast monsoon from November to March

  14. The effect of eurasian snow cover on global climate.

    PubMed

    Barnett, T P; Dümenil, L; Schlese, U; Roeckner, E

    1988-01-29

    Numerical simulations with a global atmospheric circulation model suggest that largescale variations in the amount of snowfall over Eurasia in the springtime are linked to the subsequent strength of the Asian summer monsoon. Large-scale changes in Eurasian snow cover are coupled to larger scale changes in the global climate system. There is a large, strong teleconnection to the atmospheric field over North America. The model results also show snow cover effects to subsequently alter other climatic fields known to be intimately associated with the El Niño-Southern Oscillation (ENSO) phenomenon. Thus the model results seem to challenge the current dogma that the ENSO phenomenon is solely the result of close coupling between the atmosphere and ocean by suggesting that processes over continental land masses may also have to be considered.

  15. A Review of Global Satellite-Derived Snow Products

    NASA Technical Reports Server (NTRS)

    Frei, Allan; Tedesco, Marco; Lee, Shihyan; Foster, James; Hall, Dorothy K.; Kelly, Richard; Robinson, David A.

    2011-01-01

    Snow cover over the Northern Hemisphere plays a crucial role in the Earth s hydrology and surface energy balance, and modulates feedbacks that control variations of global climate. While many of these variations are associated with exchanges of energy and mass between the land surface and the atmosphere, other expected changes are likely to propagate downstream and affect oceanic processes in coastal zones. For example, a large component of the freshwater flux into the Arctic Ocean comes from snow melt. The timing and magnitude of this flux affects biological and thermodynamic processes in the Arctic Ocean, and potentially across the globe through their impact on North Atlantic Deep Water formation. Several recent global remotely sensed products provide information at unprecedented temporal, spatial, and spectral resolutions. In this article we review the theoretical underpinnings and characteristics of three key products. We also demonstrate the seasonal and spatial patterns of agreement and disagreement amongst them, and discuss current and future directions in their application and development. Though there is general agreement amongst these products, there can be disagreement over certain geographic regions and under conditions of ephemeral, patchy and melting snow

  16. A Review of Global Satellite-derived Snow Products

    NASA Technical Reports Server (NTRS)

    Frei, Allan; Tedesco, Marco; Lee, Shihyan; Foster, James; Hall, Dorothy K.; Kelly, Richard; Robinson, David A.

    2012-01-01

    Snow cover over the Northern Hemisphere plays a crucial role in the Earth's hydrology and surface energy balance, and modulates feedbacks that control variations of global climate. While many of these variations are associated with exchanges of energy and mass between the land surface and the atmosphere, other expected changes are likely to propagate downstream and affect oceanic processes in coastal zones. For example, a large component of the freshwater flux into the Arctic Ocean comes from snow melt. The timing and magnitude of this flux affects biological and thermodynamic processes in the Arctic Ocean, and potentially across the globe through their impact on North Atlantic Deep Water formation. Several recent global remotely sensed products provide information at unprecedented temporal, spatial, and spectral resolutions. In this article we review the theoretical underpinnings and characteristics of three key products. We also demonstrate the seasonal and spatial patterns of agreement and disagreement amongst them, and discuss current and future directions in their application and development. Though there is general agreement amongst these products, there can be disagreement over certain geographic regions and under conditions of ephemeral, patchy and melting snow.

  17. A Review of Global Satellite-Derived Snow Products

    NASA Technical Reports Server (NTRS)

    Frei, Allan; Tedesco, Marco; Lee, Shihyan; Foster, James; Hall, Dorothy K.; Kelly, Richard; Robinson, David A.

    2011-01-01

    Snow cover over the Northern Hemisphere plays a crucial role in the Earth's hydrology and surface energy balance, and modulates feedbacks that control variations of global climate. While many of these variations are associated with exchanges of energy and mass between the land surface and the atmosphere, other expected changes are likely to propagate downstream and affect oceanic processes in coastal zones. For example, a large component of the freshwater flux into the Arctic Ocean comes from snow melt. The timing and magnitude of this flux affects biological and thermodynamic processes in the Arctic Ocean, and potentially across the globe through their impact on North Atlantic Deep Water formation. Several recent global remotely sensed products provide information at unprecedented temporal, spatial, and spectral resolutions. In this article we review the theoretical underpinnings and characteristics of three key products. We also demonstrate the seasonal and spatial patterns of agreement and disagreement amongst them, and discuss current and future directions in their application and development. Though there is general agreement amongst these products, there can be disagreement over certain geographic regions and under conditions of ephemeral, patchy and melting snow.

  18. Using Spaceborne Ku-Band Scatterometer for Global Snow Cover Monitoring

    NASA Technical Reports Server (NTRS)

    Nghiem, S. V.; Tsai, W.-Y.

    1999-01-01

    We demonstrate for the first time the utility of spaceborne Ku-band scatterometer for global snow cover monitoring. Satellite radar data were collected over the globe by the NASA Scatterometer (NSCAT) operated at 14 GHz on board the Japanese ADEOS spacecraft from September 1996 to June 1997, spanning the 1997 seasonal snow season. First, we present backscatter signature of dry and wet snow to facilitate the interpretation of NSCAT backscatter evolution over snow cover regions. Surface field experiments indicated that dry snow backscatter at Ku band is approximately 40 times stronger than that at C band. Thus, Ku-band scatterometer measurements are sensitive to snow cover, which is typically transparent to C-band scatterometer returns. Furthermore, Ku-band backscatter does not saturate for most of natural snow depths as compared to radar responses at 19 GHz and 37 GHz or higher frequencies which have more limited penetration depths into snow. Ku-band backscatter is also sensitive to wetness in snow, which is appropriate to detect early snow melt conditions. Using the snow backscatter characteristics, we investigate NSCAT backscatter evolution over global snow cover regions throughout the 1997 snow season. The results reveal detail delineations between different regional snow areas. We show the correlation of these delineations with the boundaries of different global snow classes defined by the U.S. Army Cold Regions Research and Engineering Laboratory snow classification system. Using in-situ snow depth data from the U.S. National Climatic Data Center, we show that Ku-band backscatter corresponds very well to the trend of snow melt while snow mapping products (U.S. Climate Prediction Center gridded snow charts) from visible sensors does not reflect the fast snow melt trend. To illustrate the practical application of global snow monitoring with spaceborne Ku-band scatterometer, we present NSCAT backscatter response corresponding to the snow event leading to the 1997

  19. Use of Sentinels to aid the global monitoring of snow cover

    NASA Astrophysics Data System (ADS)

    Pulliainen, Jouni; Salminen, Miia; Luojus, Kari; Metsämäki, Sari; Lemmetyinen, Juha; Takala, Matias; Cohen, Juval; Böttcher, Kristine

    2014-05-01

    Earth observation instruments onboard Sentinel satellites provide a unique opportunity for the monitoring and investigation of global snow processes. The issue of the possible decay of seasonal snow cover is highly relevant for climate research. In addition to water cycle, the extent and amount of snow affects to surface albedo, and indirectly to carbon cycling. The latter issue includes snow-induced changes in permafrost regions (active layer characteristics), as well as the effect of snow (melt) to vegetation growth and soil respiration. Recent advances in ESA DUE GlobSnow project have shown that by combining data from optical satellite sensors and passive microwave instruments advanced Climate Data Records (CDR) on seasonal snow cover can be produced, extending to time periods of over 30 years. The combined snow cover products provide information both on Snow Extent (SE) and Snow Water Equivalent (SWE) on a daily basis. The applicable instruments providing historical data for CDR generation include such microwave radiometers as SMMR, AMSR and SSMI/I, and such optical sensors as AVHRR, AATSR and VIIRS. Sentinel 3, especially its SLSTR instrument, is a prominent tool for expanding the snow CDR for forthcoming years. The developed global snow cover monitoring methodology, demonstrated and discussed here, derives the SWE information from passive microwave data (accompanied with in situ observations of snow depth at synoptic weather stations). The snow extent and fractional snow cover (FSC) on ground is derived from optical satellite data, in order to accurately map the continental line of seasonal snow cover, and to map regions of ephemeral snow cover. An advanced feature in the developed methodology is the provision of uncertainty information on snow cover characteristics associated with each individual satellite data footprint on ground and moment of time. In addition to assisting the generation and extension of the global snow cover CDR, Sentinel missions provide

  20. The Effect of Eurasian Snow Cover on Global Climate

    NASA Astrophysics Data System (ADS)

    Barnett, T. P.; Dumenil, L.; Schlese, U.; Roeckner, E.

    1988-01-01

    Numerical simulations with a global atmospheric circulation model suggest that large-scale variations in the amount of snowfall over Eurasia in the springtime are linked to the subsequent strength of the Asian summer monsoon. Large-scale changes in Eurasian snow cover are coupled to larger scale changes in the global climate system. There is a large, strong teleconnection to the atmospheric field over North America. The model results also show cover effects to subsequently alter other climatic fields known to be intimately associated with the El Nino-Southern Oscillation (ENSO) phenomenon. Thus the model results seem to challenge the current dogma that the ENSO phenomenon is solely the result of close coupling between the atmosphere and ocean by suggesting that processes over continental land masses may also have to be considered.

  1. An Evaluation of Snow Initializations for NCEP Global and Regional Forecasting Models

    NASA Astrophysics Data System (ADS)

    Dawson, N.; Broxton, P. D.; Zeng, X.; Leuthold, M.; Barlage, M. J.; Holbrook, V. P.

    2015-12-01

    Snow plays a major role in land-atmosphere interactions, affecting the forecasting of weather, climate, and water resources. At the same time, the strong spatial heterogeneity in snow depth and snow water equivalent (SWE) makes it challenging to evaluate gridded snow quantities using in situ point measurements. First, we have developed a new method to upscale point measurements into gridded datasets. This method is found to be superior to three other methods. It is then used to generate daily snow depth and SWE datasets for water years 2012-2014 at eight 2° X 2° areas using in situ measurements from the COOP and SNOTEL networks. These areas encompass a variety of terrain characteristics over North America. These datasets are used to quantify the performance of daily snow depth and SWE initialization in the NCEP global forecasting models (GFS and CFS) and regional models (NAM and RAP). Model initializations which utilize AFWA snow depths (GFS, CFS, and NAM) are found to have a too shallow snow depth compared to our area averaged method. Across all areas and water years, our method averaged 0.58m (0.57m) of snow while the models averaged 0.18m (0.19m) with a mean absolute error of 0.42m (0.47m) for the global (regional) models utilizing AFWA data. These models also ended the snow season much too early on average (by more than a month). The RAP model, which cycles snow instead of initializing with AFWA snow depths, underestimates snow depth to a lesser degree and has a mean absolute error of 0.26m while ending the snow season about two weeks early on average. Compared with snow depth errors, SWE errors from GFS, CFS, and NAM are even larger because of their use of globally constant snow densities. Furthermore, we have evaluated the daily snow depth gridded data produced by the Canadian Meteorological Centre (CMC), which has been utilized as the best available ground truth in multiple studies. It is found that the CMC product underestimates snow depth and has a mean

  2. NOAA NESDIS global automated satellite-based snow mapping system and products

    NASA Astrophysics Data System (ADS)

    Romanov, Peter

    2016-05-01

    Accurate, timely and spatially detailed information on the snow cover distribution and on the snow pack properties is needed in various research and practical applications including numerical weather prediction, climate modeling, river runoff estimates and flood forecasts. Owing to the wide area coverage, high spatial resolution and short repeat cycle of observations satellites present one of the key components of the global snow and ice cover monitoring system. The Global Multisensor Automated Snow and Ice Mapping System (GMASI) has been developed at the request of NOAA National Weather Service (NWS) and NOAA National Ice Center (NIC) to facilitate NOAA operational monitoring of snow and ice cover and to provide information on snow and ice for use in NWP models. Since 2006 the system has been routinely generating daily snow and ice cover maps using combined observations in the visible/infrared and in the microwave from operational meteorological satellites. The output product provides continuous (gap free) characterization of the global snow and ice cover distribution at 4 km spatial resolution. The paper presents a basic description of the snow and ice mapping algorithms incorporated in the system as well as of the product generated with GMASI. It explains the approach used to validate the derived snow and ice maps and provides the results of their accuracy assessment.

  3. Prey Preferences of the Snow Leopard (Panthera uncia): Regional Diet Specificity Holds Global Significance for Conservation

    PubMed Central

    Lyngdoh, Salvador; Shrotriya, Shivam; Goyal, Surendra P.; Clements, Hayley; Hayward, Matthew W.; Habib, Bilal

    2014-01-01

    The endangered snow leopard is a large felid that is distributed over 1.83 million km2 globally. Throughout its range it relies on a limited number of prey species in some of the most inhospitable landscapes on the planet where high rates of human persecution exist for both predator and prey. We reviewed 14 published and 11 unpublished studies pertaining to snow leopard diet throughout its range. We calculated prey consumption in terms of frequency of occurrence and biomass consumed based on 1696 analysed scats from throughout the snow leopard's range. Prey biomass consumed was calculated based on the Ackerman's linear correction factor. We identified four distinct physiographic and snow leopard prey type zones, using cluster analysis that had unique prey assemblages and had key prey characteristics which supported snow leopard occurrence there. Levin's index showed the snow leopard had a specialized dietary niche breadth. The main prey of the snow leopard were Siberian ibex (Capra sibrica), blue sheep (Pseudois nayaur), Himalayan tahr (Hemitragus jemlahicus), argali (Ovis ammon) and marmots (Marmota spp). The significantly preferred prey species of snow leopard weighed 55±5 kg, while the preferred prey weight range of snow leopard was 36–76 kg with a significant preference for Siberian ibex and blue sheep. Our meta-analysis identified critical dietary resources for snow leopards throughout their distribution and illustrates the importance of understanding regional variation in species ecology; particularly prey species that have global implications for conservation. PMID:24533080

  4. Prey preferences of the snow leopard (Panthera uncia): regional diet specificity holds global significance for conservation.

    PubMed

    Lyngdoh, Salvador; Shrotriya, Shivam; Goyal, Surendra P; Clements, Hayley; Hayward, Matthew W; Habib, Bilal

    2014-01-01

    The endangered snow leopard is a large felid that is distributed over 1.83 million km(2) globally. Throughout its range it relies on a limited number of prey species in some of the most inhospitable landscapes on the planet where high rates of human persecution exist for both predator and prey. We reviewed 14 published and 11 unpublished studies pertaining to snow leopard diet throughout its range. We calculated prey consumption in terms of frequency of occurrence and biomass consumed based on 1696 analysed scats from throughout the snow leopard's range. Prey biomass consumed was calculated based on the Ackerman's linear correction factor. We identified four distinct physiographic and snow leopard prey type zones, using cluster analysis that had unique prey assemblages and had key prey characteristics which supported snow leopard occurrence there. Levin's index showed the snow leopard had a specialized dietary niche breadth. The main prey of the snow leopard were Siberian ibex (Capra sibrica), blue sheep (Pseudois nayaur), Himalayan tahr (Hemitragus jemlahicus), argali (Ovis ammon) and marmots (Marmota spp). The significantly preferred prey species of snow leopard weighed 55±5 kg, while the preferred prey weight range of snow leopard was 36-76 kg with a significant preference for Siberian ibex and blue sheep. Our meta-analysis identified critical dietary resources for snow leopards throughout their distribution and illustrates the importance of understanding regional variation in species ecology; particularly prey species that have global implications for conservation.

  5. 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+).

  6. 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+).

  7. Response of snow-dependent hydrologic extremes to continued global warming.

    PubMed

    Diffenbaugh, Noah S; Scherer, Martin; Ashfaq, Moetasim

    2013-04-01

    Snow accumulation is critical for water availability in the northern hemisphere (1,2), raising concern that global warming could have important impacts on natural and human systems in snow-dependent regions (1,3). Although regional hydrologic changes have been observed (e.g., (1,3-5)), the time of emergence of extreme changes in snow accumulation and melt remains a key unknown for assessing climate change impacts (3,6,7). We find that the CMIP5 global climate model ensemble exhibits an imminent shift towards low snow years in the northern hemisphere, with areas of western North America, northeastern Europe, and the Greater Himalaya showing the strongest emergence during the near-term decades and at 2°C global warming. The occurrence of extremely low snow years becomes widespread by the late-21(st) century, as do the occurrence of extremely high early-season snowmelt and runoff (implying increasing flood risk), and extremely low late-season snowmelt and runoff (implying increasing water stress). Our results suggest that many snow-dependent regions of the northern hemisphere are likely to experience increasing stress from low snow years within the next three decades, and from extreme changes in snow-dominated water resources if global warming exceeds 2°C above the pre-industrial baseline.

  8. Response of snow-dependent hydrologic extremes to continued global warming

    PubMed Central

    Diffenbaugh, Noah S.; Scherer, Martin; Ashfaq, Moetasim

    2013-01-01

    Snow accumulation is critical for water availability in the northern hemisphere 1,2, raising concern that global warming could have important impacts on natural and human systems in snow-dependent regions 1,3. Although regional hydrologic changes have been observed (e.g., 1,3–5), the time of emergence of extreme changes in snow accumulation and melt remains a key unknown for assessing climate change impacts 3,6,7. We find that the CMIP5 global climate model ensemble exhibits an imminent shift towards low snow years in the northern hemisphere, with areas of western North America, northeastern Europe, and the Greater Himalaya showing the strongest emergence during the near-term decades and at 2°C global warming. The occurrence of extremely low snow years becomes widespread by the late-21st century, as do the occurrence of extremely high early-season snowmelt and runoff (implying increasing flood risk), and extremely low late-season snowmelt and runoff (implying increasing water stress). Our results suggest that many snow-dependent regions of the northern hemisphere are likely to experience increasing stress from low snow years within the next three decades, and from extreme changes in snow-dominated water resources if global warming exceeds 2°C above the pre-industrial baseline. PMID:24015153

  9. Global Snow Cover Estimation with Microwave Brightness Temperature Measurements and One-Class in situ Observations

    NASA Astrophysics Data System (ADS)

    Xu, X.; Liu, X.; Li, X.; Shi, Q.

    2016-12-01

    Brightness temperature (BT), which is remotely sensed by the space-borne microwave radiometer, is widely used in snow cover monitoring for its long time series imaging capabilities in all-weather conditions. Traditional linear fitting and stand-alone methods are usually uncertain with respect to the spatial distribution and temporal variation of derived snow cover, as they rarely consider local conditions and scene characteristics but fit the model with static empirical coefficients. In this paper, a novel method utilizing daily ground in situ observations is proposed and evaluated, with the purpose for accurate estimation of long-term daily snow cover. To solve the challenge that ground snow-free records are insufficient, a one-class classifier, namely the presence and background learning (PBL) algorithm, is employed to identify daily global snow cover. Benefiting from daily ground in situ observations on a global scale, the proposed method is temporally and spatially dynamic such that estimation errors are globally independent during the entire study period. The proposed method is applied to the estimation of global daily snow cover from 1987 to 2010; the results are validated by ground in situ observations and compared with available optical-based and microwave-based snow cover products. Promising accuracy and model stability are achieved in daily, monthly and yearly validations as compared against ground observations (global omission error < 0.13, overall accuracy > 0.82 in China region, and keep stable in monthly and yearly averages). The comparison against the MODIS daily snow cover product (MOD10C1) shows good agreement under cloud-free conditions (Cohen's kappa = 0.715). The comparison against the NOAA daily Interactive Multisensor Snow and Ice Mapping System (IMS) dataset suggests promising agreement in the Northern Hemisphere. Another comparison against the AMSR-E daily SWE dataset (AE_DySno) demonstrates the efficiency of the proposed method regarding to

  10. Response of snow-dependent hydrologic extremes to continued global warming

    SciTech Connect

    Diffenbaugh, Noah; Scherer, Martin; Ashfaq, Moetasim

    2012-01-01

    Snow accumulation is critical for water availability in the Northern Hemisphere1,2, raising concern that global warming could have important impacts on natural and human systems in snow-dependent regions1,3. Although regional hydrologic changes have been observed (for example, refs 1,3 5), the time of emergence of extreme changes in snow accumulation and melt remains a key unknown for assessing climate- change impacts3,6,7. We find that the CMIP5 global climate model ensemble exhibits an imminent shift towards low snow years in the Northern Hemisphere, with areas of western North America, northeastern Europe and the Greater Himalaya showing the strongest emergence during the near- termdecadesandat2 Cglobalwarming.Theoccurrenceof extremely low snow years becomes widespread by the late twenty-first century, as do the occurrences of extremely high early-season snowmelt and runoff (implying increasing flood risk), and extremely low late-season snowmelt and runoff (implying increasing water stress). Our results suggest that many snow-dependent regions of the Northern Hemisphere are likely to experience increasing stress from low snow years within the next three decades, and from extreme changes in snow-dominated water resources if global warming exceeds 2 C above the pre-industrial baseline.

  11. Global Snow Extent Climate Data Records and Trends Derived from Satellite Passive Microwave and Visible Data

    NASA Astrophysics Data System (ADS)

    Brodzik, M. J.; Savoie, M. H.; Armstrong, R. L.

    2008-12-01

    The extent and variability of seasonal snow cover are important parameters in climate and hydrologic systems due to effects on energy and moisture budgets. Northern Hemisphere snow cover extent, comprising about 98 percent of global seasonal snow cover, is the largest single spatial component of the cryosphere, with a mean maximum extent of 47 million square kilometers, nearly 50 percent of the land surface area. During the past four decades much important information on Northern Hemisphere snow extent has been provided by the NOAA weekly snow extent charts derived from visible-wavelength polar-orbiting and geostationary satellite imagery. NSIDC distributes these data as the Northern Hemisphere EASE-Grid Weekly Snow Cover and Sea Ice Extent Version 3. Since 1978, satellite passive microwave sensors have provided an independent source for snow monitoring, with the ability to penetrate clouds, provide data during darkness and the potential to provide an index of snow water equivalent. The historic microwave record spans a thirty year period and data are available from NSIDC as the Global EASE-Grid Monthly Snow Water Equivalent Climatology Product. Both data sets have been updated through spring, 2008. Trend analysis on the passive microwave record is complicated by the short overlap period of SMMR and SSM/I in 1987. To derive a consistent map of passive microwave snow cover, we examined the temporally closest overpasses from each sensor at selected targets and derived regression equations to cross-calibrate the sensors. Passive microwave snow algorithms have also consistently overestimated snow cover on the Tibet Plateau. We attribute the overmeasure to the use of algorithms that have assumed a thick atmosphere. These algorithms overmeasure snow extent when applied to very high elevation surfaces. We have derived an atmospheric correction to compensate for the influence of the reduced atmospheric thickness on snow extent estimates. Using the latest improvements to

  12. Satellites - New global observing techniques for ice and snow

    NASA Technical Reports Server (NTRS)

    Gloersen, P.; Salomonson, V. V.

    1975-01-01

    The possibility that the variation in areal extent of the snow cover may be related by empirical means to the average monthly run-off in a given watershed was demonstrated by comparing run-off records from the Indus River Basin in south-east Asia with a series of snow-cover maps obtained from Nimbus-3 and 4 imagery. Similar studies using the higher spatial resolution available with ERTS-1 imagery were carried out for the Wind River Mountains watersheds in Wyoming, where it was found that the empirical relationship varied with mean elevation of the watershed. In addition, digital image enhancement techniques are shown to be useful for identifying glacier features thought to be related to extent of snow cover, moraine characteristics, debris coverage, and the like. Finally, longer wavelength observations using sensors on board the Nimbus-5 satellite are shown to be useful for indicating crystal size distributions and onset of melting on glacier snow cover.

  13. Global Snow-Cover Evolution from Twenty Years of Satellite Passive Microwave Data

    USGS Publications Warehouse

    Mognard, N.M.; Kouraev, A.V.; Josberger, E.G.

    2003-01-01

    Starting in 1979 with the SMMR (Scanning Multichannel Microwave Radiometer) instrument onboard the satellite NIMBUS-7 and continuing since 1987 with the SSMI (Special Sensor Microwave Imager) instrument on board the DMSP (Defence Meteorological Satellite Program) series, more then twenty years of satellite passive microwave data are now available. This dataset has been processed to analyse the evolution of the global snow cover. This work is part of the AICSEX project from the 5th Framework Programme of the European Community. The spatio-temporal evolution of the satellite-derived yearly snow maximum extent and the timing of the spring snow melt were estimated and analysed over the Northern Hemisphere. Significant differences between the evolution of the yearly maximum snow extent in Eurasia and in North America were found. A positive correlation between the maximum yearly snow cover extent and the ENSO index was obtained. High interannual spatio-temporal variability characterises the timing of snow melt in the spring. Twenty-year trends in the timing of spring snow melt have been computed and compared with spring air temperature trends for the same period and the same area. In most parts of Eurasia and in the central and western parts of North America the tendency has been for earlier snow melt. In northeastern Canada, a large area of positive trends, where snow melt timing starts later than in the early 1980s, corresponds to a region of positive trends of spring air temperature observed over the same period.

  14. Global Blended Learning Practices for Teaching and Learning, Leadership and Professional Development

    ERIC Educational Resources Information Center

    Hilliard, Ann Toler

    2015-01-01

    Blended learning is a combination of online and face-to-face activities for classroom instruction or other training modalities to help develop new knowledge and skills that can be transferred to the workplace environment. The use of blended learning is expanding globally (Vaughn, 2007). Blended learning is evident in professional development…

  15. 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.

  16. 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

  17. Snow fracture: From micro-cracking to global failure

    NASA Astrophysics Data System (ADS)

    Capelli, Achille; Reiweger, Ingrid; Schweizer, Jürg

    2017-04-01

    Slab avalanches are caused by a crack forming and propagating in a weak layer within the snow cover, which eventually causes the detachment of the overlying cohesive slab. The gradual damage process leading to the nucleation of the initial failure is still not entirely understood. Therefore, we studied the damage process preceding snow failure by analyzing the acoustic emissions (AE) generated by bond failure or micro-cracking. The AE allow studying the ongoing progressive failure in a non-destructive way. We performed fully load-controlled failure experiments on snow samples presenting a weak layer and recorded the generated AE. The size and frequency of the generated AE increased before failure revealing an acceleration of the damage process with increased size and frequency of damage and/or microscopic cracks. The AE energy was power-law distributed and the exponent (b-value) decreased approaching failure. The waiting time followed an exponential distribution with increasing exponential coefficient λ before failure. The decrease of the b-value and the increase of λ correspond to a change in the event distribution statistics indicating a transition from homogeneously distributed uncorrelated damage producing mostly small AE to localized damage, which cause larger correlated events which leads to brittle failure. We observed brittle failure for the fast experiment and a more ductile behavior for the slow experiments. This rate dependence was reflected also in the AE signature. In the slow experiments the b value and λ were almost constant, and the energy rate increase was moderate indicating that the damage process was in a stable state - suggesting the damage and healing processes to be balanced. On a shorter time scale, however, the AE parameters varied indicating that the damage process was not steady but consisted of a sum of small bursts. We assume that the bursts may have been generated by cascades of correlated micro-cracks caused by localization of

  18. Global Precipitation Measurement (GPM) Microwave Imager Falling Snow Retrieval Algorithm Performance

    NASA Astrophysics Data System (ADS)

    Skofronick Jackson, Gail; Munchak, Stephen J.; Johnson, Benjamin T.

    2015-04-01

    Retrievals of falling snow from space represent an important data set for understanding the Earth's atmospheric, hydrological, and energy cycles. 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 and uncertainties remaining. This work reports on the development and post-launch testing of retrieval algorithms for the NASA Global Precipitation Measurement (GPM) mission Core Observatory satellite launched in February 2014. In particular, we will report on GPM Microwave Imager (GMI) radiometer instrument algorithm performance with respect to falling snow detection and estimation. Since GPM's launch, the at-launch GMI precipitation algorithms, based on a Bayesian framework, have been used with the new GPM data. The at-launch database is generated using proxy satellite data merged with surface measurements (instead of models). One year after launch, the Bayesian database will begin to be replaced with the more realistic observational data from the GPM spacecraft radar retrievals and GMI data. It is expected that the observational database will be much more accurate for falling snow retrievals because that database will take full advantage of the 166 and 183 GHz snow-sensitive channels. Furthermore, much retrieval algorithm work has been done to improve GPM retrievals over land. The Bayesian framework for GMI retrievals is dependent on the a priori database used in the algorithm and how profiles are selected from that database. Thus, a land classification sorts land surfaces into ~15 different categories for surface-specific databases (radiometer brightness temperatures are quite dependent on surface characteristics). In addition, our work has shown that knowing if the land surface is snow-covered, or not, can improve the performance of the algorithm. Improvements were made to the algorithm that allow for daily inputs of ancillary snow cover

  19. Global and local-scale variation in bacterial community structure of snow from the Swiss and Australian Alps.

    PubMed

    Wunderlin, Tina; Ferrari, Belinda; Power, Michelle

    2016-09-01

    Seasonally, snow environments cover up to 50% of the land's surface, yet the microbial diversity and ecosystem functioning within snow, particularly from alpine regions are not well described. This study explores the bacterial diversity in snow using next-generation sequencing technology. Our data expand the global inventory of snow microbiomes by focusing on two understudied regions, the Swiss Alps and the Australian Alps. A total biomass similar to cell numbers in polar snow was detected, with 5.2 to 10.5 × 10(3) cells mL(-1) of snow. We found that microbial community structure of surface snow varied by country and site and along the altitudinal range (alpine and sub-alpine). The bacterial communities present were diverse, spanning 25 distinct phyla, but the six phyla Proteobacteria (Alpha- and Betaproteobacteria), Acidobacteria, Actinobacteria, Bacteroidetes, Cyanobacteria and Firmicutes, accounted for 72%-98% of the total relative abundance. Taxa such as Acidobacteriaceae and Methylocystaceae, associated with cold soils, may be part of the atmospherically sourced snow community, while families like Sphingomonadaceae were detected in every snow sample and are likely part of the common snow biome. © FEMS 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  20. Global Seasonal Climatologies of Ocean Chlorophyll: Blending In situ and Satellite Data for the CZCS Era

    NASA Technical Reports Server (NTRS)

    Gregg, Watson W.; Conkright, Margarita E.

    1999-01-01

    The historical archives of in situ (National Oceanographic Data Center) and satellite (Coastal Zone Color Scanner) chlorophyll data were combined using the blended analysis method of Reynolds [1988] in an attempt to construct an improved climatological seasonal representation of global chlorophyll distributions. The results of the blended analysis differed dramatically from the CZCS representation: global chlorophyll estimates increased 8-35% in the blended analysis depending upon season. Regional differences were even larger, up to 140% in the equatorial Indian Ocean in summer (during the southwest monsoon). Tropical Pacific chlorophyll values increased 25-41%. The results suggested that the CZCS generally underestimates chlorophyll. Regional and seasonal differences in the blended analysis were sufficiently large as to produce a different representation of global chlorophyll distributions than otherwise inferred from CZCS data alone. Analyses of primary production and biogeochemical cycles may be substantially impacted by these results.

  1. Global Precipitation Measurement Cold Season Precipitation Experiment (GCPEx): For Measurement Sake Let it Snow

    NASA Technical Reports Server (NTRS)

    Skofronick-Jackson, Gail; Hudak, David; Petersen, Walter; Nesbitt, Stephen W.; Chandrasekar, V.; Durden, Stephen; Gleicher, Kirstin J.; Huang, Gwo-Jong; Joe, Paul; Kollias, Pavlos; Reed, Kimberly A.; Schwaller, Mathew R.; Stewart, Ronald; Tanelli, Simone; Tokay, Ali; Wang, James R.; Wolde, Mengistu

    2014-01-01

    As a component of the Earth's hydrologic cycle, and especially at higher latitudes,falling snow creates snow pack accumulation that in turn provides a large proportion of the fresh water resources required by many communities throughout the world. To assess the relationships between remotely sensed snow measurements with in situ measurements, a winter field project, termed the Global Precipitation Measurement (GPM) mission Cold Season Precipitation Experiment (GCPEx), was carried out in the winter of 2011-2012 in Ontario, Canada. Its goal was to provide information on the precipitation microphysics and processes associated with cold season precipitation to support GPM snowfall retrieval algorithms that make use of a dual-frequency precipitation radar and a passive microwave imager on board the GPM core satellite,and radiometers on constellation member satellites. Multi-parameter methods are required to be able to relate changes in the microphysical character of the snow to measureable parameters from which precipitation detection and estimation can be based. The data collection strategy was coordinated, stacked, high-altitude and in-situ cloud aircraft missions with three research aircraft sampling within a broader surface network of five ground sites taking in-situ and volumetric observations. During the field campaign 25 events were identified and classified according to their varied precipitation type, synoptic context, and precipitation amount. Herein, the GCPEx fieldcampaign is described and three illustrative cases detailed.

  2. 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?

  3. The impact of organochlorines cycling in the cryosphere on global distributions and fate--2. Land ice and temporary snow cover.

    PubMed

    Hofmann, Lorenz; Stemmler, Irene; Lammel, Gerhard

    2012-03-01

    Global fate and transport of γ-HCH and DDT was studied using a global multicompartment chemistry-transport model, MPI-MCTM, with and without inclusion of land ice (in Antarctica and Greenland) or snow cover (dynamic). MPI-MCTM is based on coupled ocean and atmosphere general circulation models. After a decade of simulation 4.2% γ-HCH and 2.3% DDT are stored in land ice and snow. Neglection of land ice and snow in modelling would underestimate the total environmental residence time, τ(ov), of γ-HCH and overestimate τ(ov) for DDT, both on the order of 1% and depending on actual compartmental distribution. Volatilisation of DDT from boreal, seasonally snow covered land is enhanced throughout the year, while volatilisation of γ-HCH is only enhanced during the snow-free season. Including land ice and snow cover in modelling matters in particular for the Arctic, where higher burdens are predicted to be stored.

  4. An in situ-satellite blended analysis of global sea surface salinity

    NASA Astrophysics Data System (ADS)

    Xie, P.; Boyer, T.; Bayler, E.; Xue, Y.; Byrne, D.; Reagan, J.; Locarnini, R.; Sun, F.; Joyce, R.; Kumar, A.

    2014-09-01

    The blended monthly sea surface salinity (SSS) analysis, called the NOAA "Blended Analysis of Surface Salinity" (BASS), is constructed for the 4 year period from 2010 to 2013. Three data sets are employed as inputs to the blended analysis: in situ SSS measurements aggregated and quality controlled by NOAA/NODC, and passive microwave (PMW) retrievals from both the National Aeronautics and Space Administration's (NASA) Aquarius/SAC-D and the European Space Agency's (ESA) Soil Moisture-Ocean Salinity (SMOS) satellites. The blended analysis comprises two steps. First, the biases in the satellite retrievals are removed through probability distribution function (PDF) matching against temporally spatially colocated in situ measurements. The blended analysis is then achieved through optimal interpolation (OI), where the analysis for the previous time step is used as the first guess while the in situ measurements and bias-corrected satellite retrievals are employed as the observations to update the first guess. Cross validations illustrate improved quality of the blended analysis, with reduction in bias and random errors over most of the global oceans as compared to the individual inputs. Large uncertainty, however, remains in high-latitude oceans and coastal regions where the in situ networks are sparse and current-generation satellite retrievals have limitations. Our blended SSS analysis shows good agreements with the NODC in situ-based analysis over most of the tropical and subtropical oceans, but large differences are observed for high-latitude oceans and along coasts. In the tropical oceans, the BASS is shown to have coherent variability with precipitation and evaporation associated with the evolution of the El Niño-Southern Oscillation (ENSO).

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

    NASA Astrophysics Data System (ADS)

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

    2015-07-01

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

  6. Can snow save us from global warming? (Louis Agassiz Medal Lecture)

    NASA Astrophysics Data System (ADS)

    Dominé, Florent; Picard, Ghislain; Morin, Samuel; Krinner, Gerhard; Gouttevin, Isabelle; Menegoz, Martin; Gallet, Jean-Charles; Arnaud, Laurent; Dumont, Marie; Lafaysse, Matthieu; Brun, Eric

    2013-04-01

    How snow will interact with climate in the current warming context is an open issue. There is of course the well known snow-albedo feedback, whereby the replacement of snow by darker surfaces positively feedbacks on climate. But many other snow-climate feedbacks have been proposed recently, both positive and negative, so that predictions of future polar climate are rather uncertain. Warming will change precipitation and metamorphic conditions in the snowpack, affecting snow physical properties such as grain size, albedo and thermal conductivity. Their consequences are difficult to predict, with threshold effects between different regimes in snow metamorphism. A significant negative feedback between precipitation, snow albedo and climate has been detected in Antarctica, where increased precipitation increase albedo by bringing small grains to the surface, and this is not compensated by the warmer temperatures that accelerate grain growth. On the contrary, similar processes acting in the Arctic have led to a positive feedback. Changes in snow thermal conductivity can have important effects on the growth of sea ice, with spatially variable effects. In the Arctic, the warming-induced growth of higher vegetation such as shrubs can produce additional effects. Shrubs trap snow, increase snow depth and shield snow from wind erosion. Snow physical properties will change, with the likely partial replacement of wind slabs by layers of depth hoar of lower thermal conductivity. This may limit winter ground cooling, with effects on permafrost stability and on the release of greenhouse gases from thawing carbon stocks. Several examples will be detailed to illustrate the complexity of snow-climate interactions and to stress the need for a detailed description of snow physical properties in climate models, before we can conclude as to whether snow will slow down polar warming or on the contrary accelerate it, with possible catastrophic consequences.

  7. Blending Gauge Data with CMORPH for a Global Daily Precipitation Analysis

    NASA Astrophysics Data System (ADS)

    Wu, S.; Xie, P.

    2016-12-01

    A gauge - CMORPH blended analysis (GCB) of daily precipitation has been constructed on a 0.25olat/lon grid over the global land for an 18-year period from 1998 to the present. The GCB analysis is developed to combine the strength of gauge observations and satellite estimates of precipitation. The gauge data used here is the CPC unified daily gauge analysis which is created by interpolating gauge reports of daily precipitation from 30000 stations around the world with consideration of orographic effects. The input satellite estimates ae from the reprocessed, bias-corrected CMORPH. The CMORPH bias correction is performed through PDF calibration against temporally / spatially co-located gauge data. A processing system is developed to blend the CPC gauge analysis and the bias-corrected CMORPH, implementing the OI-based conceptual model described in Xie and Xiong (2011). The bias-corrected CMORPH is utilized as the first guess, while the gauge data play a role as the observations to refine the first guess over regions with reasonable gauge station coverage. Error structures are defined for the input gauge and CMORPH data to ensure optimal performance of the resulting blended analysis. As a result, over regions of dense gauge networks, the blended analysis is dominated by the gauge analysis while over places of sparse station coverage (e.g. equatorial Africa) the analysis is virtually the same as the first guess, the bias-corrected CMORPH satellite estimates. Over grid boxes where / when the current generation CMORPH does not provide coverage (e.g. beyond 60oS/N parallels) or presents poor detection skills (e.g. regions covered with snowfall), the blended analysis is simply defined as the same as the gauge-based analysis. Validation results showed improved performance of the gauge - CMORPH blended analysis upon individual inputs. Detailed results will be reported at the AGU meetings.

  8. Global wind patterns and associated snow anomalies over Eurasia: predictability and influence on large scale monsoon circulation.

    NASA Astrophysics Data System (ADS)

    Corti, S.; Molteni, F.; Brankovic, C.

    2003-04-01

    In this study we focus on (the relationship between): (i) the global long-lasting (persisting from winter to the early summer) upper tropospheric anomalous circulation; (ii) the tropical SST anomalies (which can determine the kind of flow (i)) (iii) the snow depth anomalies over Eurasia ( which can be determined by (ii) through (i)); (iv) and the large scale monsoon circulation in the following summer (related to (i), (ii) and (iii)). The dataset is the 40-year record (1958-98) of NCEP/NCAR re-analyses for sea surface temperatures and upper air fields, while, for snow depth fields, the Historical Soviet Daily Snow Depth dataset (based on observations at a series of 284 World Meteorological Organization (WMO) stations throughout the Former Soviet Union) is used. First the leading variability patterns of the atmospheric flow are searched for by calculating empirical orthogonal functions (EOFs) of seasonal anomalies. The Eurasian snow depth anomalies and SST anomalies associated with the leading circulation patterns are then identified by computing, for each season, the covariance between the principal components (associated with the EOFs) and the snow/SST anomaly time series. The relationship with the large scale monsoon circulation is evaluated through (lagged) correlations with the Webster and Yang index.

  9. Global Snow Mass Measurements and the Effect of Stratigraphic Detail on Inversion of Microwave Brightness Temperatures

    NASA Astrophysics Data System (ADS)

    Richardson, Mark; Davenport, Ian; Gurney, Robert

    2014-05-01

    Snow provides large seasonal storage of freshwater, and information about the distribution of snow mass as snow water equivalent (SWE) is important for hydrological planning and detecting climate change impacts. Large regional disagreements remain between estimates from reanalyses, remote sensing and modelling. Assimilating passive microwave information improves SWE estimates in many regions, but the assimilation must account for how microwave scattering depends on snow stratigraphy. Physical snow models can estimate snow stratigraphy, but users must consider the computational expense of model complexity versus acceptable errors. Using data from the National Aeronautics and Space Administration Cold Land Processes Experiment and the Helsinki University of Technology microwave emission model of layered snowpacks, it is shown that simulations of the brightness temperature difference between 19 and 37 GHz vertically polarised microwaves are consistent with advanced microwave scanning radiometer-earth observing system and special sensor microwave imager retrievals once known stratigraphic information is used. Simulated brightness temperature differences for an individual snow profile depend on the provided stratigraphic detail. Relative to a profile defined at the 10-cm resolution of density and temperature measurements, the error introduced by simplification to a single layer of average properties increases approximately linearly with snow mass. If this brightness temperature error is converted into SWE using a traditional retrieval method, then it is equivalent to ±13 mm SWE (7 % of total) at a depth of 100 cm. This error is reduced to ±5.6 mm SWE (3 % of total) for a two-layer model.

  10. Anthropogenic osmium in rain and snow reveals global-scale atmospheric contamination.

    PubMed

    Chen, Cynthia; Sedwick, Peter N; Sharma, Mukul

    2009-05-12

    Osmium is one of the rarer elements in seawater, with typical concentration of approximately 10 x 10(-15) g g(-1) (5.3 x 10(-14) mol kg(-1)). The osmium isotope composition ((187)Os/(188)Os ratio) of deep oceans is 1.05, reflecting a balance between inputs from continental crust (approximately 1.3) and mantle/cosmic dust (approximately 0.13). Here, we show that the (187)Os/(188)Os ratios measured in rain and snow collected around the world range from 0.16 to 0.48, much lower than expected (>1), but similar to the isotope composition of ores (approximately 0.2) that are processed to extract platinum and other metals to be used primarily in automobile catalytic converters. Present-day surface seawater has a lower (187)Os/(188)Os ratio (approximately 0.95) than deep waters, suggesting that human activities have altered the isotope composition of the world's oceans and impacted the global geochemical cycle of osmium. The contamination of the surface ocean is particularly remarkable given that osmium has few industrial uses. The pollution may increase with growing demand for platinum-based catalysts.

  11. Anthropogenic osmium in rain and snow reveals global-scale atmospheric contamination

    PubMed Central

    Chen, Cynthia; Sedwick, Peter N.; Sharma, Mukul

    2009-01-01

    Osmium is one of the rarer elements in seawater, with typical concentration of ≈10 × 10−15 g g−1 (5.3 × 10−14 mol kg−1). The osmium isotope composition (187Os/188Os ratio) of deep oceans is 1.05, reflecting a balance between inputs from continental crust (≈1.3) and mantle/cosmic dust (≈0.13). Here, we show that the 187Os/188Os ratios measured in rain and snow collected around the world range from 0.16 to 0.48, much lower than expected (>1), but similar to the isotope composition of ores (≈0.2) that are processed to extract platinum and other metals to be used primarily in automobile catalytic converters. Present-day surface seawater has a lower 187Os/188Os ratio (≈0.95) than deep waters, suggesting that human activities have altered the isotope composition of the world's oceans and impacted the global geochemical cycle of osmium. The contamination of the surface ocean is particularly remarkable given that osmium has few industrial uses. The pollution may increase with growing demand for platinum-based catalysts. PMID:19416862

  12. Global mountain snow and ice loss driven by dust and black carbon radiative forcing

    NASA Astrophysics Data System (ADS)

    Painter, T. H.

    2014-12-01

    Changes in mountain snow and glaciers have been our strongest indicators of the effects of changing climate. Earlier melt of snow and losses of glacier mass have perturbed regional water cycling, regional climate, and ecosystem dynamics, and contributed strongly to sea level rise. Recent studies however have revealed that in some regions, the reduction of albedo by light absorbing impurities in snow and ice such as dust and black carbon can be distinctly more powerful than regional warming at melting snow and ice. In the Rocky Mountains, dust deposition has increased 5 to 7 fold in the last 150 years, leading to ~3 weeks earlier loss of snow cover from forced melt. In absolute terms, in some years dust radiative forcing there can shorten snow cover duration by nearly two months. Remote sensing retrievals are beginning to reveal powerful dust and black carbon radiative forcing in the Hindu Kush through Himalaya. In light of recent ice cores that show pronounced increases in loading of dust and BC during the Anthropocene, these forcings may have contributed far more to glacier retreat than previously thought. For example, we have shown that the paradoxical end of the Little Ice Age in the European Alps beginning around 1850 (when glaciers began to retreat but temperatures continued to decline and precipitation was unchanged) very likely was driven by the massive increases in deposition to snow and ice of black carbon from industrialization in surrounding nations. A more robust understanding of changes in mountain snow and ice during the Anthropocene requires that we move past simplistic treatments (e.g. temperature-index modeling) to energy balance approaches that assess changes in the individual forcings such as the most powerful component for melt - net solar radiation. Remote sensing retrievals from imaging spectrometers and multispectral sensors are giving us more powerful insights into the time-space variation of snow and ice albedo.

  13. The Cycles of Snow Cover in Pyrenees Mountain and Mont Lebanon Analyzed Using the Global Modeling Technique.

    NASA Astrophysics Data System (ADS)

    Drapeau, L.; Mangiarotti, S.; Le Jean, F.; Gascoin, S.; Jarlan, L.

    2014-12-01

    The global modeling technique provides a way to obtain ordinary differential equations from single time series1. This technique, initiated in the 1990s, could be applied successfully to numerous theoretic and experimental systems. More recently it could be applied to environmental systems2,3. Here this technique is applied to seasonal snow cover area in the Pyrenees mountain (Europe) and Mont Lebanon (Mediterranean region). The snowpack evolution is complex because it results from combination of processes driven by physiography (elevation, slope, land cover...) and meteorological variables (precipitation, temperature, wind speed...), which are highly heterogeneous in such regions. Satellite observations in visible bands offer a powerful tool to monitor snow cover areas at global scale, with large resolutions range. Although this observable does not directly inform about snow water equivalent, its dynamical behavior strongly relies on it. Therefore, snow cover area is likely to be a good proxy of the global dynamics and global modeling technique a well adapted approach. The MOD10A2 product (500m) generated from MODIS by the NASA is used after a pretreatment is applied to minimize clouds effect. The global modeling technique is then applied using two packages4,5. The analysis is performed with two time series for the whole period (2000-2012) and year by year. Low-dimensional chaotic models are obtained in many cases. Such models provide a strong argument for chaos since involving the two necessary conditions in a synthetic way: determinism and strong sensitivity to initial conditions. The models comparison suggests important non-stationnarities at interannual scale which prevent from detecting long term changes. 1: Letellier et al 2009. Frequently asked questions about global modeling, Chaos, 19, 023103. 2: Maquet et al 2007. Global models from the Canadian lynx cycles as a direct evidence for chaos in real ecosystems. J. of Mathematical Biology, 55 (1), 21-39 3

  14. Scattering optics of snow.

    PubMed

    Kokhanovsky, Alexander A; Zege, Eleonora P

    2004-03-01

    Permanent snow and ice cover great portions of the Arctic and the Antarctic. It appears in winter months in northern parts of America, Asia, and Europe. Therefore snow is an important component of the hydrological cycle. Also, it is a main regulator of the seasonal variation of the planetary albedo. This seasonal change in albedo is determined largely by the snow cover. However, the presence of pollutants and the microstructure of snow (e.g., the size and shape of grains, which depend also on temperature and on the age of the snow) are also of importance in the variation of the snow's spectral albedo. The snow's spectral albedo and its bidirectional reflectance are studied theoretically. The albedo also determines the spectral absorptance of snow, which is of importance, e.g., in studies of the heating regime in snow. We investigate the influence of the nonspherical shape of grains and of close-packed effects on snow's reflectance in the visible and the near-infrared regions of the electromagnetic spectrum. The rate of the spectral transition from highly reflective snow in the visible to almost totally absorbing black snow in the infrared is governed largely by the snow's grain sizes and by the load of pollutants. Therefore both the characteristics of snow and its concentration of impurities can be monitored on a global scale by use of spectrometers and radiometers placed on orbiting satellites.

  15. Groundwater dynamics mediate low-flow response to global warming in snow-dominated alpine regions

    Treesearch

    Christina Tague; Gordon E. Grant

    2009-01-01

    In mountain environments, spatial and temporal patterns of snow accumulation and melt are dominant controls on hydrologic responses to climate change. In this paper, we develop a simple conceptual model that links the timing of peak snowmelt with geologically mediated differences in rate of streamflow recession. This model demonstrates that within the western United...

  16. Predicting global population connectivity and targeting conservation action for snow leopard across its range

    Treesearch

    Philip Riordan; Samuel A. Cushman; David Mallon; Kun Shi; Joelene Hughes

    2016-01-01

    Movements of individuals within and among populations help to maintain genetic variability and population viability. Therefore, understanding landscape connectivity is vital for effective species conservation. The snow leopard is endemic to mountainous areas of central Asia and occurs within 12 countries. We assess potential connectivity across the species’...

  17. Satellites: New global observing techniques for ice and snow. [using erts-1 and nimbus 5 satellite

    NASA Technical Reports Server (NTRS)

    Gloersen, P.; Salomonson, V. V.

    1974-01-01

    The relation of aereal extent of snow cover to the average monthly runoff in a given watershed was investigated by comparing runoff records with a series of snowcover maps. Studies using the high spatial resolution available with ERTS-1 imagery were carried out for the Wind River Mountains watersheds in Wyoming, where it was found that the empirical relationship varied with mean elevation of the watershed. In addition, digital image enhancement techniques are shown to be useful for identifying glacier features related to extent of snowcover, moraine characteristics, and debris average. Longer wavelength observations using sensors on board the Nimbus 5 Satellite are shown to be useful for indicating crystal size distributions and onset of melting on glacier snow cover.

  18. Global near real-time precipitation estimates by optimally blending gauge, satellite, and model data

    NASA Astrophysics Data System (ADS)

    Beck, H.; De Roo, A. P. J.; Pappenberger, F.; van Dijk, A.; Levizzani, V.; Wood, E. F.; Huffman, G. J.

    2016-12-01

    Accurate and timely precipitation data are essential for many scientific and operational applications. Yet, a fully global near real-time (NRT) product simultaneously providing good performance in densely-gauged, tropical convection-, and snow-dominated regions is still lacking. We developed a NRT variant of the retrospective Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset. MSWEP-NRT provides gap-free, fully global precipitation estimates with three-hourly temporal and 0.25° spatial resolution, by merging seven NRT data sources; one based solely on gauge observations (CPC Unified), four on satellite remote sensing (CMORPH, GSMaP, IMERG, and TMPA 3B42RT), and two on weather forecast models (GDAS and JRA-55). The product has a latency of approximately four hours. To account for latency differences among data sources and for potential disruptions in input data availability, MSWEP-NRT data less than seven days old are progressively upgraded to include any new data as they become available. To ensure the reliability necessary for operational use, the product is produced at two independent locations and distributed using two independent data providers. MSWEP-NRT can be used to more or less seamlessly extend the retrospective MSWEP dataset until the present, since both use the same bias correction factors and similar merging techniques. The product is accessible via THREDDS and FTP at www.gloh2o.org.

  19. Global transcriptome changes in perennial ryegrass during early infection by pink snow mould

    PubMed Central

    Kovi, Mallikarjuna Rao; Abdelhalim, Mohamed; Kunapareddy, Anil; Ergon, Åshild; Tronsmo, Anne Marte; Brurberg, May Bente; Hofgaard, Ingerd Skow; Asp, Torben; Rognli, Odd Arne

    2016-01-01

    Lack of resistance to pink snow mould (Microdochium nivale) is a major constraint for adaptation of perennial ryegrass (Lolium perenne L.) to continental regions with long-lasting snow cover at higher latitudes. Almost all investigations of genetic variation in resistance have been performed using cold acclimated plants. However, there may be variation in resistance mechanisms that are functioning independently of cold acclimation. In this study our aim was to identify candidate genes involved in such resistance mechanisms. We first characterized variation in resistance to M. nivale among non-acclimated genotypes from the Norwegian cultivar ‘Fagerlin’ based on relative regrowth and fungal quantification by real-time qPCR. One resistant and one susceptible genotype were selected for transcriptome analysis using paired-end sequencing by Illumina Hiseq 2000. Transcriptome profiles, GO enrichment and KEGG pathway analysis indicate that defense response related genes are differentially expressed between the resistant and the susceptible genotype. A significant up-regulation of defense related genes, as well as genes involved in cell wall cellulose metabolic processes and aryl-alcohol dehydrogenase (NADP+) activity, was observed in the resistant genotype. The candidate genes identified in this study might be potential molecular marker resources for breeding perennial ryegrass cultivars with improved resistance to pink snow mould. PMID:27346054

  20. Global transcriptome changes in perennial ryegrass during early infection by pink snow mould.

    PubMed

    Kovi, Mallikarjuna Rao; Abdelhalim, Mohamed; Kunapareddy, Anil; Ergon, Åshild; Tronsmo, Anne Marte; Brurberg, May Bente; Hofgaard, Ingerd Skow; Asp, Torben; Rognli, Odd Arne

    2016-06-27

    Lack of resistance to pink snow mould (Microdochium nivale) is a major constraint for adaptation of perennial ryegrass (Lolium perenne L.) to continental regions with long-lasting snow cover at higher latitudes. Almost all investigations of genetic variation in resistance have been performed using cold acclimated plants. However, there may be variation in resistance mechanisms that are functioning independently of cold acclimation. In this study our aim was to identify candidate genes involved in such resistance mechanisms. We first characterized variation in resistance to M. nivale among non-acclimated genotypes from the Norwegian cultivar 'Fagerlin' based on relative regrowth and fungal quantification by real-time qPCR. One resistant and one susceptible genotype were selected for transcriptome analysis using paired-end sequencing by Illumina Hiseq 2000. Transcriptome profiles, GO enrichment and KEGG pathway analysis indicate that defense response related genes are differentially expressed between the resistant and the susceptible genotype. A significant up-regulation of defense related genes, as well as genes involved in cell wall cellulose metabolic processes and aryl-alcohol dehydrogenase (NADP+) activity, was observed in the resistant genotype. The candidate genes identified in this study might be potential molecular marker resources for breeding perennial ryegrass cultivars with improved resistance to pink snow mould.

  1. Deeper winter snow reduces ecosystem C losses but increases the global warming potential of Arctic tussock tundra over the growing season.

    NASA Astrophysics Data System (ADS)

    Blanc-Betes, E.; Welker, J. M.; Gomez-Casanovas, N.; Gonzalez-Meler, M. A.

    2015-12-01

    Arctic winter precipitation is projected to increase globally over the next decades, spatial variability encompassing areas with increases and decreases in winter snow. Changes in winter precipitation strongly affect C dynamics in Arctic systems and may lead to major positive climate forcing feedbacks. However, impacts of predicted changes in snowfall and accumulation on the rate and form of C fluxes (CO2 and CH4) and associated forcing feedbacks from Arctic tundra remain uncertain. We investigated how changes in winter precipitation affect net ecosystem CO2 and CH4 fluxes and budgets of moist acidic tundra in an 18-yrs snow fence experiment over a complete growing season at Toolik Lake, AK. Arctic tundra under ambient winter precipitation (CTL) was a net source of CO2 and CH4, yielding net C losses over the growing season. Reduced snow (-15-30% snow depth; RS) switched the system to a net CO2 sink mostly by limiting SOC decomposition within colder soils. Snow additions progressively reduced net ecosystem CO2 losses compared to CTL, switching the system into a weaker net CO2 source with medium additions (+20-45% snow depth; MS) and into a small net CO2 sink with high additions (+70-100% snow depth; HS). Increasingly wetter soils with snow additions constrained the temperature sensitivity of aerobic decomposition and favored the anaerobic metabolism, buffering ecosystem CO2 losses despite substantial soil warming. Accordingly, Arctic tundra switched from a sustained CH4 sink at RS site to an increasingly stronger CH4 source with snow additions. Accounting for both CO2 and CH4, the RS site became a net C sink over the growing season, overall reducing the global warming potential (CO2 equiv.; GWP) of the system relative to CTL. Snow additions progressively reduced net C losses at the MS site compared to CTL and the system transitioned into a net C sink at HS plots, partly due to the slower metabolism of anaerobic decomposition. However, given the greater radiative

  2. Long-term Analysis of Effects of Global Warming on Snow Water Equivalent at the Tedori River Basin

    NASA Astrophysics Data System (ADS)

    Noto, Fumikazu; Maruyama, Toshisuke; Hayase, Yoshio; Takimoto, Hiroshi; Nakamura, Kimihito

    The snow water equivalent(SWE) was estimated using hydrological data of 31 years(July 1976-June 2007) at the Tedori river basin, and the decrease in SWE due to global warming was estimated using RCM20. The main results were as follows: (1) During the 31 years, the SWE varied considerably from 400mm to 1,500mm, with a tendency to decreaseby an average of 11mm per year. (2) The SWE predicted for July 2081-June 2100 afterconsidering the effects of global warming was estimated to be an average of 157mm, which is about one-fifth of the current value; this indicatesa very severe shortage on irrigation water in early spring. (3) The SWE estimated using the data ofthe mountainous observation site(Maruyama) was closely related to the SWE estimated forthe basin, with a coefficient of determination of 0.80. (4) The average temperature from December to February at Kanazawa during 2081-2100 was also predicted to increase by 2.92°C and the rainfall was predicted to decrease by 13.2%; these would critically affectthe SWE.

  3. 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

  4. Evidence of global-scale As, Mo, Sb, and Tl atmospheric pollution in the antarctic snow.

    PubMed

    Hong, Sungmin; Soyol-Erdene, Tseren-Ochir; Hwang, Hee Jin; Hong, Sang Bum; Hur, Soon Do; Motoyama, Hidaeki

    2012-11-06

    We report the first comprehensive and reliable time series for As, Mo, Sb, and Tl in the snowpack from Dome Fuji in the central East Antarctic Plateau. Our results show significant enrichment of these elements due to either anthropogenic activities or large volcanic eruptions during the past 50 years. With respect to the values reported from 1960 to 1964, we observed the maximum increases in crustal enrichment factors (EFs) for As (a factor of ~15), Mo (~4), Sb (~4), and Tl (~2) during the period between the 1970s and 1990s, reflecting the global dispersion of anthropogenic pollutants of these elements, even to the most remote areas on Earth. Such enrichments are likely related to emissions of trace elements from nonferrous metal smelting and fossil fuel combustion processes in South America, especially in Chile. A drastic decrease in the As concentration and its EF values was observed after the year 2000 in response to the introduction of environmental regulations in the 1990s to reduce As emissions from the copper industry, primarily in Chile. The observed decrease suggests that governmental regulations for pollution control are effective in reducing air pollution at both the regional and global level.

  5. NOAA's National Snow Analyses

    NASA Astrophysics Data System (ADS)

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

    2005-12-01

    . The NOHRSC NSA products are used operationally by NOAA's National Weather Service field offices when issuing hydrologic forecasts and warnings including river and flood forecasts, water supply forecasts, and spring flood outlooks for the nation. Additionally, the NOHRSC NSA products are used by a wide variety of federal, state, local, municipal, private-sector, and general-public end-users with a requirement for real-time snowpack information. The paper discusses, in detail, the techniques and procedures used to create the NOHRSC NSA products and gives a number of examples of the real-time snow products generated and distributed over the NOHRSC web site (www.nohrsc.noaa.gov). Also discussed are major limitations of the approach, the most notable being deficiencies in observation of snow water equivalent. Snow observation networks generally lack the consistency and coverage needed to significantly improve confidence in snow model states through updating. Many regions of the world simply lack snow water equivalent observations altogether, a significant constraint on global application of the NSA approach.

  6. A Multi-sensor Assimilation Framework for Global Snow: Towards A Combined, Radiometric and Gravimetric Data Assimilation Approach

    NASA Astrophysics Data System (ADS)

    Forman, B. A.; Wang, J.

    2016-12-01

    A novel, multi-sensor data assimilation framework is presented that assimilates passive microwave (PMW) brightness temperature (Tb) observations in conjunction with gravimetric retrievals of terrestrial water storage (TWS) into an advanced land surface model for the purpose of improving snow depth and snow water equivalent (SWE) estimates. The multi-frequency, multi-polarization, multi-instrument framework employs machine learning to predict PMW Tb along with a relatively simple, linear transformation to predict TWS as a function of land surface model state information. Next, an ensemble Kalman filter (EnKF) is used­ to simultaneously merge the land surface model with observed PMW Tb from the Advanced Microwave Scanning Radiometer (AMSR-E) and observed TWS anomalies from the Gravity Recovery and Climate Experiment (GRACE). A synthetic case study is presented for select locations in North America that compares model results with and without assimilation against synthetic observations of snow depth and SWE. It is shown that the data assimilation framework improves modeled estimates of snow depth and SWE during both the accumulation and ablation phases of the snow season. The data assimilation routine produces a conditioned (updated) estimate that is more accurate and contains less uncertainty than the model without assimilation. This study establishes a dual (multi-sensor) assimilation framework for both radiometric and gravimetric observations in order to improve modeled estimates of snow depth and SWE across regional- and continental-scales.

  7. 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.

  8. 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.

  9. Snow on Antarctic sea ice

    NASA Astrophysics Data System (ADS)

    Massom, Robert A.; Eicken, Hajo; Hass, Christian; Jeffries, Martin O.; Drinkwater, Mark R.; Sturm, Matthew; Worby, Anthony P.; Wu, Xingren; Lytle, Victoria I.; Ushio, Shuki; Morris, Kim; Reid, Phillip A.; Warren, Stephen G.; Allison, Ian

    2001-08-01

    Snow on Antarctic sea ice plays a complex and highly variable role in air-sea-ice interaction processes and the Earth's climate system. Using data collected mostly during the past 10 years, this paper reviews the following topics: snow thickness and snow type and their geographical and seasonal variations; snow grain size, density, and salinity; frequency of occurrence of slush; thermal conductivity, snow surface temperature, and temperature gradients within snow; and the effect of snow thickness on albedo. Major findings include large regional and seasonal differences in snow properties and thicknesses; the consequences of thicker snow and thinner ice in the Antarctic relative to the Arctic (e.g., the importance of flooding and snow-ice formation); the potential impact of increasing snowfall resulting from global climate change; lower observed values of snow thermal conductivity than those typically used in models; periodic large-scale melt in winter; and the contrast in summer melt processes between the Arctic and the Antarctic. Both climate modeling and remote sensing would benefit by taking account of the differences between the two polar regions.

  10. MODIS Snow and Ice Production

    NASA Technical Reports Server (NTRS)

    Hall, Dorthoy K.; Hoser, Paul (Technical Monitor)

    2002-01-01

    Daily, global snow cover maps, and sea ice cover and sea ice surface temperature (IST) maps are derived from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS), are available at no cost through the National Snow and Ice Data Center (NSIDC). Included on this CD-ROM are samples of the MODIS snow and ice products. In addition, an animation, done by the Scientific Visualization studio at Goddard Space Flight Center, is also included.

  11. Snow Art

    ERIC Educational Resources Information Center

    Kraus, Nicole

    2012-01-01

    It was nearing the end of a very long, rough winter with a lot of snow and too little time to play outside. The snow had formed small hills and valleys over the bushes and this was at the perfect height for the students to paint. In this article, the author describes how her transitional first-grade students created snow art paintings. (Contains 1…

  12. Snow Art

    ERIC Educational Resources Information Center

    Kraus, Nicole

    2012-01-01

    It was nearing the end of a very long, rough winter with a lot of snow and too little time to play outside. The snow had formed small hills and valleys over the bushes and this was at the perfect height for the students to paint. In this article, the author describes how her transitional first-grade students created snow art paintings. (Contains 1…

  13. 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

    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.

  14. Evaluation of the global MODIS 30 arc-second spatially and temporally complete snow-free land surface albedo and reflectance anisotropy dataset

    NASA Astrophysics Data System (ADS)

    Sun, Qingsong; Wang, Zhuosen; Li, Zhan; Erb, Angela; Schaaf, Crystal B.

    2017-06-01

    Land surface albedo is an essential variable for surface energy and climate modeling as it describes the proportion of incident solar radiant flux that is reflected from the Earth's surface. To capture the temporal variability and spatial heterogeneity of the land surface, satellite remote sensing must be used to monitor albedo accurately at a global scale. However, large data gaps caused by cloud or ephemeral snow have slowed the adoption of satellite albedo products by the climate modeling community. To address the needs of this community, we used a number of temporal and spatial gap-filling strategies to improve the spatial and temporal coverage of the global land surface MODIS BRDF, albedo and NBAR products. A rigorous evaluation of the gap-filled values shows good agreement with original high quality data (RMSE = 0.027 for the NIR band albedo, 0.020 for the red band albedo). This global snow-free and cloud-free MODIS BRDF and albedo dataset (established from 2001 to 2015) offers unique opportunities to monitor and assess the impact of the changes on the Earth's land surface.

  15. Hemispheric-scale Snow Cover Climatologies Derived From Satellite Remote Sensing

    NASA Astrophysics Data System (ADS)

    Armstrong, R. L.; Brodzik, M.; Savoie, M. H.

    2002-12-01

    During the past thirty-five years much important information on Northern Hemisphere snow cover has been provided by the NOAA weekly snow extent charts derived from visible-band polar orbiting and geo-stationary satellite imagery. This product represents the longest single time series of any geophysical product obtained from satellite and is available from NSIDC as the Northern Hemisphere EASE-Grid Weekly Snow Cover and Sea Ice Extent Version 2. Because of the ability to penetrate clouds, provide data during darkness and the potential to provide an index of snow depth or water equivalent, passive microwave satellite remote sensing can greatly enhance snow measurements based on visible data alone. It is now possible to monitor the global fluctuation of snow cover over a twenty-three year period using passive microwave data (Scanning Multichannel Microwave Radiometer (SMMR) 1978-1987 and Special Sensor Microwave/Imager (SSM/I), 1987-present). We present a recent NSIDC prototype data set of microwave-derived mean monthly snow water equivalent. While other satellite-derived snow extent data are available, they are regional in scale, limited in time period and often do not represent consistent data processing techniques. The launch of the NASA EOS platforms of Terra in December 1999 and Aqua in May 2002 provide new and enhanced opportunities for mapping of snow at the global scale. Both Terra and Aqua carry a MODIS (Moderate Resolution Imaging Spectroradiometer) which provides snow maps at an unprecedented 500 m resolution. The Aqua platform also carries the AMSR-E (Advanced Microwave Scanning Radiometer-EOS) which has approximately double the spatial resolution of SSM/I and will be providing passive microwave-derived snow water equivalent at the global scale beginning in 2003. Because there are clear advantages, and corresponding disadvantages, in applying only visible or passive microwave methods to snow mapping, we are currently developing a blended product which

  16. Black carbon aerosol size in snow

    PubMed Central

    Schwarz, J. P.; Gao, R. S.; Perring, A. E.; Spackman, J. R.; Fahey, D. W.

    2013-01-01

    The effect of anthropogenic black carbon (BC) aerosol on snow is of enduring interest due to its consequences for climate forcing. Until now, too little attention has been focused on BC's size in snow, an important parameter affecting BC light absorption in snow. Here we present first observations of this parameter, revealing that BC can be shifted to larger sizes in snow than are typically seen in the atmosphere, in part due to the processes associated with BC removal from the atmosphere. Mie theory analysis indicates a corresponding reduction in BC absorption in snow of 40%, making BC size in snow the dominant source of uncertainty in BC's absorption properties for calculations of BC's snow albedo climate forcing. The shift reduces estimated BC global mean snow forcing by 30%, and has scientific implications for our understanding of snow albedo and the processing of atmospheric BC aerosol in snowfall. PMID:23449011

  17. Black carbon aerosol size in snow.

    PubMed

    Schwarz, J P; Gao, R S; Perring, A E; Spackman, J R; Fahey, D W

    2013-01-01

    The effect of anthropogenic black carbon (BC) aerosol on snow is of enduring interest due to its consequences for climate forcing. Until now, too little attention has been focused on BC's size in snow, an important parameter affecting BC light absorption in snow. Here we present first observations of this parameter, revealing that BC can be shifted to larger sizes in snow than are typically seen in the atmosphere, in part due to the processes associated with BC removal from the atmosphere. Mie theory analysis indicates a corresponding reduction in BC absorption in snow of 40%, making BC size in snow the dominant source of uncertainty in BC's absorption properties for calculations of BC's snow albedo climate forcing. The shift reduces estimated BC global mean snow forcing by 30%, and has scientific implications for our understanding of snow albedo and the processing of atmospheric BC aerosol in snowfall.

  18. 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.

  19. MODIS Snow-Cover Products

    NASA Astrophysics Data System (ADS)

    Hall, D. K.; Riggs, G. A.; Salomonson, V. V.; Barton, J. S.

    2001-12-01

    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, available globally and at up to 500-m resolution, are derived from automated algorithms. This means that a consistent data set is generated for long-term climate 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 ~5.6-km spatial resolution, with both daily and eight-day composite products. Recent improvements to the algorithm include the addition of a "thermal mask" that eliminates pixels from the snow maps that had contained "false snow" in the original algorithm. The origin of the false snow detection is from a variety of sources, including some clouds, large cities and atmospheric aerosols. The thermal mask reduces the snow-mapping errors associated with the MODIS snow-cover products. Cloud masking had been done using the 'cloud obscuration flag' from the cloud mask. However, that technique resulted in a cloud mask that was too conservative. Selective use of the cloud spectral tests from the MODIS cloud mask will be used to allow an improved determination of snow-covered area. Algorithm improvements will be implemented during the 2001-02 snow year in the Northern Hemisphere, however it is anticipated that in the future, all MODIS snow and ice products will be reprocessed so that consistent products will be

  20. Appalachia Snow

    Atmospheric Science Data Center

    2014-05-15

    ... by the Blue Ridge mountain belt along the east and the Appalachian Plateau along the west. Valleys and ridges between the higher ... Snow location:  United States region:  Eastern United States Order:  4 ...

  1. Global Metabolic Regulation of the Snow Alga Chlamydomonas nivalis in Response to Nitrate or Phosphate Deprivation by a Metabolome Profile Analysis

    PubMed Central

    Lu, Na; Chen, Jun-Hui; Wei, Dong; Chen, Feng; Chen, Gu

    2016-01-01

    In the present work, Chlamydomonas nivalis, a model species of snow algae, was used to illustrate the metabolic regulation mechanism of microalgae under nutrient deprivation stress. The seed culture was inoculated into the medium without nitrate or phosphate to reveal the cell responses by a metabolome profile analysis using gas chromatography time-of-flight mass spectrometry (GC/TOF-MS). One hundred and seventy-one of the identified metabolites clustered into five groups by the orthogonal partial least squares discriminant analysis (OPLS-DA) model. Among them, thirty of the metabolites in the nitrate-deprived group and thirty-nine of the metabolites in the phosphate-deprived group were selected and identified as “responding biomarkers” by this metabolomic approach. A significant change in the abundance of biomarkers indicated that the enhanced biosynthesis of carbohydrates and fatty acids coupled with the decreased biosynthesis of amino acids, N-compounds and organic acids in all the stress groups. The up- or down-regulation of these biomarkers in the metabolic network provides new insights into the global metabolic regulation and internal relationships within amino acid and fatty acid synthesis, glycolysis, the tricarboxylic acid cycle (TCA) and the Calvin cycle in the snow alga under nitrate or phosphate deprivation stress. PMID:27171077

  2. Multi-Sensor Approach to Mapping Snow Cover Using Data From NASA's EOS Aqua and Terra Spacecraft

    NASA Astrophysics Data System (ADS)

    Armstrong, R. L.; Brodzik, M. J.

    2003-12-01

    Snow cover is an important variable for climate and hydrologic models due to its effects on energy and moisture budgets. Over the past several decades both optical and passive microwave satellite data have been utilized for snow mapping at the regional to global scale. For the period 1978 to 2002, we have shown earlier that both passive microwave and visible data sets indicate a similar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are, depending on season, less than those provided by the visible satellite data and the visible data typically show higher monthly variability. Snow mapping using optical data is based on the magnitude of the surface reflectance while microwave data can be used to identify snow cover because the microwave energy emitted by the underlying soil is scattered by the snow grains resulting in a sharp decrease in brightness temperature and a characteristic negative spectral gradient. Our previous work has defined the respective advantages and disadvantages of these two types of satellite data for snow cover mapping and it is clear that a blended product is optimal. We present a multi-sensor approach to snow mapping based both on historical data as well as data from current NASA EOS sensors. 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. An example of validation at the brightness temperature level is provided through the comparison of AMSR-E with data from the well-calibrated heritage SSM/I sensor over a large homogeneous snow-covered surface (Dome C, Antarctica). Prototype snow cover maps from AMSR-E compare well with maps derived from SSM/I. Our current blended product is being developed in the 25 km EASE-Grid while the MODIS data being used are in the Climate Modelers Grid (CMG) at approximately 5 km

  3. Volcanic Snow

    NASA Image and Video Library

    2015-03-04

    Remember this? Since its first observation in 2009, the volcanic vent complex to the northeast of Rachmaninoff basin has rewarded us with remarkable views of its explosive history. Portions of the vent are blanketed in a layer of very fine-grained material thought to be composed of pyroclastic particles, and when we last saw this landform at very high resolution we could appreciate just how fine that texture is. Now, with a resolution almost four times greater than that last image, we can see how the pyroclastic deposit softens the form of adjacent impact craters - almost like snow. Fiery, hot, angry snow. http://photojournal.jpl.nasa.gov/catalog/PIA19230

  4. Snow complexity representation and GCM climate

    NASA Astrophysics Data System (ADS)

    Dutra, Emanuel; Viterbo, Pedro; Miranda, Pedro M. A.; Balsamo, Gianpaolo

    2010-05-01

    Accurate simulations of the snow cover strongly impact on the quality of weather and climate predictions as the solar radiation absorption at land-atmosphere interface is modified by a factor up to 4 in response to snow presence (albedo effect). In Northern latitudes and Mountainous regions snow acts also as an important energy and water reservoir and a correct representation of snow mass and snow density is crucial for temperature predictions at all time-scales, with direct consequences for soil hydrology (thermal insulation effect). Three different complexity snow schemes implemented in the ECMWF land surface scheme HTESSEL are tested within the EC-EARTH framework. The snow schemes are: 1) OLD, the original HTESSEL single bulk layer snow scheme (same as in the ERA-40 and ERA-Interim reanalysis); 2) OPER, a new snow scheme in operations since September 2009, with a liquid water reservoir and revised formulations of snow density, fractional cover and snow albedo; and 3) ML3, a multi-layer version of OPER. All three snow schemes in HTESSEL are energy- and mass- balance models. The multi-layer snow scheme, ML3, was validated in offline mode covering several spatial and temporal scales: (i) site simulations for several observation locations from the Snow Models intercomparison project-2 (SnowMip2) and (ii) global simulations driven by the meteorological forcing from the Global Soil Wetness Project-2 (GSWP2) and the ECMWF ERA-Interim re-analysis. On point locations ML3 improve snow mass simulations, while on a global scale the impacts are residual pointing to the need of coupled atmosphere simulations. The 3 schemes are compared in the framework of the atmospheric model of EC-EARTH, based on the current seasonal forecast system of ECMWF. The standard configuration runs at T159 horizontal spectral resolution with 62 vertical levels. Three member ensembles of 30 years (1979-2008) simulations, with prescribed SSTs and sea ice, were performed for each of the snow schemes

  5. Snow geese

    USGS Publications Warehouse

    Hupp, Jerry W.; Robertson, Donna G.; Brackney, Alan W.; Douglas, David C.; Reynolds, Patricia E.; Rhode, E.B.

    2002-01-01

    Part of the coastal plain of the Arctic National Wildlife Refuge, Alaska, is used as an autumn staging area by lesser snow geese (Chen caerulescens caerulescens) from the Western Canadian Arctic population (hereafter called the Western Arctic population). There were approximately 200,000 breeding adults in the Western Arctic population through the mid-1980s (Johnson and Herter 1989), but the population has recently increased to about 500,000 breeding adults (Kerbes et al. 1999).Early in their autumn migration, adult and juvenile snow geese from the Western Arctic population feed intensively while staging on the Beaufort Sea coastal plain in Canada and Alaska to build fat reserves needed for migration. Aerial censuses from 1973 to 1985 indicated that up to 600,000 adult and juvenile snow geese used the coastal plain for 2-4 weeks in late August until mid-September (Oates et al. 1987).We studied annual variation in numbers and spatial distribution of snow geese that staged on the coastal plain of the Arctic Refuge.

  6. 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.

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

    NASA Technical Reports Server (NTRS)

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

    2000-01-01

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

  8. 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.

  9. 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.

  10. The History of Winter and the Global Snowflake Network, Engaging Teachers and Students in Science Field Research in Snow and Ice

    NASA Astrophysics Data System (ADS)

    Bender, K. J.; Wasilewski, P. J.; Gabrys, R. E.

    2006-05-01

    A weeklong Professional development/"Teacher as scientist" Cryosphere science training camp held annually in February in Lake Placid, NY, the History of Winter program (HOW) has been serving teachers in the NASA Goddard Space Flight Center service area since 2000. Currently, HOW participants include university faculty interested in enhancing their pre-service science education programs, in-service teachers and pre-service education students. HOW utilizes a stratified professional development approach to science content mastery and delivery while involving participants in scientific field research. Each year program components and resources are added to HOW to provide continued, sustainable interest in the program and to support participants as they continue their HOW experience. An offshoot of the HOW Program, the Global Snowflake Network (GSN) launched in the winter of 2006 engages an international audience including both formal and informal education groups. The goal is to provide an interactive online data resource in science and education for the characterization of snowfall and related weather systems. The Global Snowflake Network has been accepted as an education outreach proposal for the International Polar Year. Collaborations with other agencies and universities also with IPY-accepted proposals are now underway. HOW and the GSN are endorsed by the NASA Goddard Education Office and many of the Goddard Snow and Ice scientists. Together these programs offer a unique, sustainable, and proven outreach for the Cryosphere research program.

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

    NASA Technical Reports Server (NTRS)

    Yasunari, Teppei

    2012-01-01

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

  12. The influence of sea ice on Antarctic ice core sulfur chemistry and on the future evolution of Arctic snow depth: Investigations using global models

    NASA Astrophysics Data System (ADS)

    Hezel, Paul J.

    SO2-4 deposition to differences between the modern and LGM climates, including sea ice extent, sea surface temperatures, oxidant concentrations, and meteorological conditions. We are unable to find a mechanism whereby MSA deposition fluxes are higher than nss SO2-4 deposition fluxes on the East Antarctic Plateau in the LGM compared the modern period. We conclude that the observed differences between MSA and nss SO2-4 on glacial-interglacial time scales are due to post-depositional processes that affect the ice core MSA concentrations. We can not rule out the possibility of increased DMS emissions in the LGM compared to the modern day. If oceanic DMS production and ocean-to-air fluxes in the sea ice zone are significantly enhanced by the presence of sea ice as indicated by observations, we suggest that the potentially larger amplitude of the seasonal cycle in sea ice extent in the LGM implies a more important role for sea ice in modulating the sulfur cycle during the LGM compared to the modern period. We then shift our focus to study the evolution of snow depth on sea ice in global climate model simulations of the 20th and 21st centuries from the Coupled Model Intercomparison Project 5 (CMIP5). Two competing processes, decreasing sea ice extent and increasing precipitation, will affect snow accumulation on sea ice in the future, and it is not known a priori which will dominate. The decline in Arctic sea ice extent is a well-studied problem in future scenarios of climate change. Moisture convergence into the Arctic is also expected to increase in a warmer world, which may result in increasing snowfall rates. We show that the accumulated snow depth on sea ice in the spring declines as a result of decreased ice extent in the early autumn, in spite of increased winter snowfall rates. The ringed seal (Phoca hispida ) depends on accumulated snow in the spring to build subnivean birth lairs, and provides one of the motivations for this study. Using an empirical threshold of

  13. Linking snowfall and snow accumulation to generate spatial maps of SWE and snow depth

    NASA Astrophysics Data System (ADS)

    Broxton, Patrick D.; Dawson, Nicholas; Zeng, Xubin

    2016-06-01

    It is critically important but challenging to estimate the amount of snow on the ground over large areas due to its strong spatial variability. Point snow data are used to generate or improve (i.e., blend with) gridded estimates of snow water equivalent (SWE) by using various forms of interpolation; however, the interpolation methodologies often overlook the physical mechanisms for the snow being there in the first place. Using data from the Snow Telemetry and Cooperative Observer networks in the western United States, we show that four methods for the spatial interpolation of peak of winter snow water equivalent (SWE) and snow depth based on distance and elevation can result in large errors. These errors are reduced substantially by our new method, i.e., the spatial interpolation of these quantities normalized by accumulated snowfall from the current or previous water years. Our method results in significant improvement in SWE estimates over interpolation techniques that do not consider snowfall, regardless of the number of stations used for the interpolation. Furthermore, it can be used along with gridded precipitation and temperature data to produce daily maps of SWE over the western United States that are comparable to existing estimates (which are based on the assimilation of much more data). Our results also show that not honoring the constraint between SWE and snowfall when blending in situ data with gridded data can lead to the development and propagation of unrealistic errors.

  14. Blending type spline constructions: A brief overview

    NASA Astrophysics Data System (ADS)

    Pedersen, Aleksander; Bang, Børre

    2015-11-01

    In this paper we are presenting a brief overview of research on blending splines from 2004-2015. We discuss some of the properties which can be interesting to investigate when blending splines are used both for finite element analysis and geometry. Blending splines are constructions where local geometry is blended together by a blending function to create global geometry. The different basis functions has different properties, which can be related to different application areas. Example application areas where blending splines are utilized is listed, together with a focus on the basis and future work towards utilizing parts of blending splines in an isogeometric analysis(IGA) context.

  15. Detecting Falling Snow from Space

    NASA Technical Reports Server (NTRS)

    Jackson, Gail Skofronick; Johnson, Ben; Munchak, Joe

    2012-01-01

    There is an increased interest in detecting and estimating the amount of falling snow reaching the Earth's surface in order to fully capture the atmospheric water cycle. An initial step toward global spaceborne falling snow algorithms includes determining the thresholds of detection for various active and passive sensor channel configurations, snow event cloud structures and microphysics, snowflake particle electromagnetic properties, and surface types. In this work, cloud resolving model simulations of a lake effect and synoptic snow event were used to determine the minimum amount of snow (threshold) that could be detected by the following instruments: the W -band radar of CloudSat, Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) Ku and Ka band, and the GPM Microwave Imager (GMI) channels from 10 to 183 plus or minus 7 GHz. Eleven different snowflake shapes were used to compute radar reflectivities and passive brightness temperatures. Notable results include: (1) the W-Band radar has detection thresholds more than an order of magnitude lower than the future GPM sensors, (2) the cloud structure macrophysics influences the thresholds of detection for passive channels, (3) the snowflake microphysics plays a large role in the detection threshold for active and passive instruments, (4) with reasonable assumptions, "the passive 166 GHz channel has detection threshold values comparable to the GPM DPR Ku and Ka band radars with approximately 0.05 g per cubic meter detected at the surface, or an approximately 0.5-1 millimeter per hr. melted snow rate (equivalent to 0.5-2 centimeters per hr. solid fluffy snowflake rate). With detection levels of falling snow known, we can focus current and future retrieval efforts on detectable storms and concentrate advances on achievable results. We will also have an understanding of the light snowfall events missed by the sensors and not captured in the global estimates.

  16. City snow's physicochemical property affects snow disposal

    NASA Astrophysics Data System (ADS)

    Dovbysh, V. O.; Sharukha, A. V.; Evtin, P. V.; Vershinina, S. V.

    2015-10-01

    At the present day the industrial cities run into severe problem: fallen snow in a city it's a concentrator of pollutants and their quantity is constantly increasing by technology development. Pollution of snow increases because of emission of gases to the atmosphere by cars and factories. Large accumulation of polluted snow engenders many vexed ecological problems. That's why we need a new, non-polluting, scientifically based method of snow disposal. This paper investigates polluted snow's physicochemical property effects on snow melting. A distinctive feature of the ion accelerators with self-magnetically insulated diode is that there.

  17. Snow Radiance Assimilation Studies

    NASA Astrophysics Data System (ADS)

    Kim, E. J.; Durand, M. T.; Toure, A.; Margulis, S. A.; Goita, K.; Royer, A.; Lu, H.

    2009-12-01

    Passive microwave-based retrievals of terrestrial snow parameters from satellite observations form a 30-year global record which will continue for the forseeable future. So far, these snow retrievals have been generated primarily by regression-based empirical “inversion” methods based on snapshots in time, and are limited to footprints around 25 km in diameter. Assimilation of microwave radiances into physical land surface models may be used to create a retrieval framework that is inherently self-consistent with respect to model physics as well as a more physically-based approach vs. legacy retrieval/inversion methods. This radiance assimilation approach has been used for years for atmospheric parameters by the operational weather forecasting community with great success, and represents one motivation for our work. A radiance assimilation scheme for snow requires a snowpack land surface model (LSM) coupled to a radiative transfer model (RTM). In previous local-scale studies, Durand, Kim, & Margulis (2008) explored the requirements on LSM model fidelity (i.e., snowpack state information) required in order for the RTM to produce brightness temperatures suitable for radiance assimilation purposes at a local scale, using the well-known Microwave Emission Model for Layered Snowpacks (MEMLS) as the RTM and a combination of Simple SIB (SSiB) and Snow Atmosphere (SAST) as the LSM. They also demonstrated improvement of simulated snow depth through the use of an ensemble Kalman filter scheme at this local scale (2009). This modeling framework reflects another motivation—namely, possibilities for downscaling. Our focus at this stage has been at the local scale where high-quality ground truth data is available in order to evaluate radiance assimilation under a “best case scenario.” The quantitative results then form a benchmark for future assessment of effects such as sparse forcing data, upscaling/downscaling, forest attenuation, and model details. Field data from

  18. Yeah!!! A Snow Day!

    ERIC Educational Resources Information Center

    Cone, Theresa Purcell; Cone, Stephen L.

    2006-01-01

    As children see the first snowflake fall from the sky, they are filled with anticipation of playing in the snow. The snowy environment presents a wonderful opportunity for presenting interdisciplinary activities that connect snow play, snow formation, and snow stories with manipulative activities, gymnastic balances, and dance sequences. In this…

  19. Yeah!!! A Snow Day!

    ERIC Educational Resources Information Center

    Cone, Theresa Purcell; Cone, Stephen L.

    2006-01-01

    As children see the first snowflake fall from the sky, they are filled with anticipation of playing in the snow. The snowy environment presents a wonderful opportunity for presenting interdisciplinary activities that connect snow play, snow formation, and snow stories with manipulative activities, gymnastic balances, and dance sequences. In this…

  20. 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,

  1. New nitrogen uptake strategy: specialized snow roots.

    PubMed

    Onipchenko, Vladimir G; Makarov, Mikhail I; van Logtestijn, Richard S P; Ivanov, Viktor B; Akhmetzhanova, Assem A; Tekeev, Dzhamal K; Ermak, Anton A; Salpagarova, Fatima S; Kozhevnikova, Anna D; Cornelissen, Johannes H C

    2009-08-01

    The evolution of plants has yielded a wealth of adaptations for the acquisition of key mineral nutrients. These include the structure, physiology and positioning of root systems. We report the discovery of specialized snow roots as a plant strategy to cope with the very short season for nutrient uptake and growth in alpine snow-beds, i.e. patches in the landscape that remain snow-covered well into the summer. We provide anatomical, chemical and experimental (15)N isotope tracking evidence that the Caucasian snow-bed plant Corydalis conorhiza forms extensive networks of specialized above-ground roots, which grow against gravity to acquire nitrogen directly from within snow packs. Snow roots capture nitrogen that would otherwise partly run off down-slope over a frozen surface, thereby helping to nourish these alpine ecosystems. Climate warming is changing and will change mountain snow regimes, while large-scale anthropogenic N deposition has increased snow N contents. These global changes are likely to impact on the distribution, abundance and functional significance of snow roots.

  2. Polymer blends

    DOEpatents

    Allen, Scott D.; Naik, Sanjeev

    2017-08-22

    The present invention provides, among other things, extruded blends of aliphatic polycarbonates and polyolefins. In one aspect, provided blends comprise aliphatic polycarbonates such as poly(propylene carbonate) and a lesser amount of a crystalline or semicrystalline polymer. In certain embodiments, provided blends are characterized in that they exhibit unexpected improvements in their elongation properties. In another aspect, the invention provides methods of making such materials and applications of the materials in applications such as the manufacture of consumer packaging materials.

  3. Modeling of falling snow properties for snowpack models: towards a better link between falling snow and snow on the ground

    NASA Astrophysics Data System (ADS)

    Vionnet, Vincent; Milbrandt, Jason; Yamaguchi, Satoru; Morrison, Hugh

    2017-04-01

    Falling snow is made of different types of solid hydrometeors including single ice crystals, aggregates at different riming stages and graupel. This variability results from complex in-cloud processes (deposition, riming and aggregation) and sub-cloud processes (melting and sublimation). It has consequences for the properties of fresh fallen snow accumulating on the ground including density and specific surface area (SSA). These properties strongly affect the evolution of snow on the ground by modifying for example the albedo and the thermal conductivity. Detailed snowpack models such as Crocus or SNOWPACK use near-surface wind speed, air temperature and humidity to compute the density and SSA of falling snow. The shape, size and degree of riming of falling solid hydrometeors are not directly taken into account, which limit the accurate determination of properties of fresh fallen snow. An alternative is offered by new bulk cloud microphysics schemes implemented in numerical weather prediction system that can be used to drive snowpack model. In particular, the scheme P3 (Predicted Particle Properties) proposes an innovative representation of all ice-phase particles by predicting several physical properties (e.g. size, rime fraction, rime density …). In this study, we first theoretically analyze the strengths and limitations of P3 to represent the density and SSA of falling snow. Parameterizations are then proposed to derive density and SSA from P3 output. Finally, P3 implemented in the Canadian Global Environmental Multiscale (GEM) weather prediction model is used to simulate snowfall events observed at the Falling Snow Observatory (Nagoaka, Japan). Model predictions are compared with (i) observations of the type of falling snow particles derived from disdrometer data and (ii) manual measurements of fresh fallen snow density and SSA. This study is a first step towards the development of a more integrated approach between falling snow and snow on the ground.

  4. Mapping "At Risk" Snow in the Pacific Northwest

    NASA Astrophysics Data System (ADS)

    Nolin, A. W.; Daly, C.

    2005-12-01

    One of the most visible and widely felt impacts of climate change is the change (mostly loss) of low elevation snow cover in the mid-latitudes. Snow cover that accumulates at temperatures close to the ice-water phase transition is at greater risk to climate warming than cold climate snow packs because it affects both precipitation phase and ablation rates. Changes in such climatologically sensitive snow packs can impact hydropower generation, reservoir storage, rain-on-snow floods, and winter recreation. Using a climatologically based global snow cover classification (Sturm et al., 1995) "at risk" snow is defined as lower elevation maritime and alpine snow classes. This original classification was produced globally at 0.5-degree resolution and used monthly means of temperature and precipitation as well as vegetation cover to map snow climates. In this work, the classification is updated for the Pacific Northwest region using fields of temperature and precipitation from PRISM as well as MODIS-derived global maps of vegetation cover. This new classification has significantly improved grid resolution (4 km x 4 km) and is able to clearly identify regions of ephemeral and lower elevation maritime and alpine snow that are thought to be at risk in a climate warming scenario. Results indicate that the economic impacts of this shift from snow- to rain-dominated winter precipitation that lower elevation ski areas in the region would experience significant negative impacts.

  5. Satellite Snow-Cover Mapping: A Brief Review

    NASA Technical Reports Server (NTRS)

    Hall, Dorothy K.

    1995-01-01

    Satellite snow mapping has been accomplished since 1966, initially using data from the reflective part of the electromagnetic spectrum, and now also employing data from the microwave part of the spectrum. Visible and near-infrared sensors can provide excellent spatial resolution from space enabling detailed snow mapping. When digital elevation models are also used, snow mapping can provide realistic measurements of snow extent even in mountainous areas. Passive-microwave satellite data permit global snow cover to be mapped on a near-daily basis and estimates of snow depth to be made, but with relatively poor spatial resolution (approximately 25 km). Dense forest cover limits both techniques and optical remote sensing is limited further by cloudcover conditions. Satellite remote sensing of snow cover with imaging radars is still in the early stages of research, but shows promise at least for mapping wet or melting snow using C-band (5.3 GHz) synthetic aperture radar (SAR) data. Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) data beginning with the launch of the first EOS platform in 1998. Digital maps will be produced that will provide daily, and maximum weekly global snow, sea ice and lake ice cover at 1-km spatial resolution. Statistics will be generated on the extent and persistence of snow or ice cover in each pixel for each weekly map, cloudcover permitting. It will also be possible to generate snow- and ice-cover maps using MODIS data at 250- and 500-m resolution, and to study and map snow and ice characteristics such as albedo. been under development. Passive-microwave data offer the potential for determining not only snow cover, but snow water equivalent, depth and wetness under all sky conditions. A number of algorithms have been developed to utilize passive-microwave brightness temperatures to provide information on snow cover and water equivalent. The variability of vegetative Algorithms are being developed to map global snow

  6. Blended Learning

    ERIC Educational Resources Information Center

    Halan, Deepak

    2005-01-01

    Blended learning basically refers to using several methods for teaching. It can be thought to be a learning program where more than one delivery mode is being used with the ultimate goal of optimizing the learning result and cost of program delivery. Examples of blended learning could be the combination of technology-based resources and…

  7. Blended Learning

    ERIC Educational Resources Information Center

    Imbriale, Ryan

    2013-01-01

    Teachers always have been and always will be the essential element in the classroom. They can create magic inside four walls, but they have never been able to create learning environments outside the classroom like they can today, thanks to blended learning. Blended learning allows students and teachers to break free of the isolation of the…

  8. Blended Learning

    ERIC Educational Resources Information Center

    Tucker, Catlin; Umphrey, Jan

    2013-01-01

    Catlin Tucker, author of "Blended Learning in Grades 4-12," is an English language arts teacher at Windsor High School in Sonoma County, CA. In this conversation with "Principal Leadership," she defines blended learning as a formal education program in which a student is engaged in active learning in part online where they…

  9. Blended Learning

    ERIC Educational Resources Information Center

    Imbriale, Ryan

    2013-01-01

    Teachers always have been and always will be the essential element in the classroom. They can create magic inside four walls, but they have never been able to create learning environments outside the classroom like they can today, thanks to blended learning. Blended learning allows students and teachers to break free of the isolation of the…

  10. Blended Learning

    ERIC Educational Resources Information Center

    Tucker, Catlin; Umphrey, Jan

    2013-01-01

    Catlin Tucker, author of "Blended Learning in Grades 4-12," is an English language arts teacher at Windsor High School in Sonoma County, CA. In this conversation with "Principal Leadership," she defines blended learning as a formal education program in which a student is engaged in active learning in part online where they…

  11. Snow depth on Arctic and Antarctic sea ice derived from autonomous (Snow Buoy) measurements

    NASA Astrophysics Data System (ADS)

    Nicolaus, Marcel; Arndt, Stefanie; Hendricks, Stefan; Heygster, Georg; Huntemann, Marcus; Katlein, Christian; Langevin, Danielle; Rossmann, Leonard; Schwegmann, Sandra

    2016-04-01

    The snow cover on sea ice received more and more attention in recent sea ice studies and model simulations, because its physical properties dominate many sea ice and upper ocean processes. In particular; the temporal and spatial distribution of snow depth is of crucial importance for the energy and mass budgets of sea ice, as well as for the interaction with the atmosphere and the oceanic freshwater budget. Snow depth is also a crucial parameter for sea ice thickness retrieval algorithms from satellite altimetry data. Recent time series of Arctic sea ice volume only use monthly snow depth climatology, which cannot take into account annual changes of the snow depth and its properties. For Antarctic sea ice, no such climatology is available. With a few exceptions, snow depth on sea ice is determined from manual in-situ measurements with very limited coverage of space and time. Hence the need for more consistent observational data sets of snow depth on sea ice is frequently highlighted. Here, we present time series measurements of snow depths on Antarctic and Arctic sea ice, recorded by an innovative and affordable platform. This Snow Buoy is optimized to autonomously monitor the evolution of snow depth on sea ice and will allow new insights into its seasonality. In addition, the instruments report air temperature and atmospheric pressure directly into different international networks, e.g. the Global Telecommunication System (GTS) and the International Arctic Buoy Programme (IABP). We introduce the Snow Buoy concept together with technical specifications and results on data quality, reliability, and performance of the units. We highlight the findings from four buoys, which simultaneously drifted through the Weddell Sea for more than 1.5 years, revealing unique information on characteristic regional and seasonal differences. Finally, results from seven snow buoys co-deployed on Arctic sea ice throughout the winter season 2015/16 suggest the great importance of local

  12. Water and life from snow: A trillion dollar science question

    NASA Astrophysics Data System (ADS)

    Sturm, Matthew; Goldstein, Michael A.; Parr, Charles

    2017-05-01

    Snow provides essential resources/services in the form of water for human use, and climate regulation in the form of enhanced cooling of the Earth. In addition, it supports a thriving winter outdoor recreation industry. To date, the financial evaluation of the importance of snow is incomplete and hence the need for accelerated snow research is not as clear as it could be. With snow cover changing worldwide in several worrisome ways, there is pressing need to determine global, regional, and local rates of snow cover change, and to link these to financial analyses that allow for rational decision making, as risks related to those decisions involve trillions of dollars.

  13. Characteristics, dynamics and significance of marine snow

    NASA Astrophysics Data System (ADS)

    Alldredge, Alice L.; Silver, Mary W.

    Macroscopic aggregates of detritus, living organisms and inorganic matter known as marine snow, have significance in the ocean both as unique, partially isolated microenvironments and as transport agents: much of surface-derived matter in the ocean fluxes to the ocean interior and the sea floor as marine snow. As microhabitats, marine snow aggregates contain enriched microbial communities and chemical gradients within which processes of photosynthesis, decomposition, and nutrient regeneration occur at highly elevated levels. Microbial communities associated with marine snow undergo complex successional changes on time scales of hours to days which significantly alter the chemical and biological properties of the particles. Marine snow can be produced either de novo by living plants and animals especially as mucus feeding webs of zooplankton, or by the biologically-enhanced physical aggregation of smaller particles. By the latter pathway, microaggregates, phytoplankton, fecal pellets, organic debris and clay-mineral particles collide by differential settlement or physical shear and adhere by the action of various, biologically-generated, organic compounds. Diatom flocculation is a poorly understood source of marine snow of potential global significance. Rates of snow production and breakdown are not known but are critical to predicting flux and to understanding biological community structure and transformations of matter and energy in the water column. The greatest challenge to the study of marine snow at present is the development of appropriate technology to measure abundances and characteristics of aggregates in situ.

  14. 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...

  15. Remote sensing of global snowpack energy and mass balance: In-situ measurements on the snow of interior and Arctic Alaska

    NASA Technical Reports Server (NTRS)

    Benson, Carl S.

    1989-01-01

    Observations led to a study of the physical properties of snow and the processes which operate on it. These observations included microwave brightness temperatures in interior Alaska which revealed: (1) up to three times more variability from one cell (1/2 degree latitude x 1/2 degree longitude) to the next in winter than in summer (5 to 15 K in winter and about 5 K in summer); (2) the overall range of temperature from week to week is about seven times greater in winter than in summer; (3) the microwave brightness temperature is about 25 K less than air temperature during summer but 35 to 60 K less during winter; and (4) the presence of snow cover appears to contribute to increasing the difference between air temperature and brightness temperature. The role of irregular substrate under the snow in enhancing convection has been studied with particular attention to variations in snow cover on water surfaces and in forested regions. LANDSAT imagery has been obtained to prepare a classification of ground surface types of the area. The extreme conditions of the 1988 to 1989 winter are discussed with respect to comparing the microwave data sets from 1985, and before, up to the present. The use of the Mt. Wrangell area as aerial photogrammetric controls for glacier measurements is given attention.

  16. Remote sensing of global snowpack energy and mass balance: In-situ measurements on the snow of interior and Arctic Alaska

    NASA Technical Reports Server (NTRS)

    Benson, Carl S.

    1994-01-01

    This project is continuing along the lines of the semiannual report dated January 1993. Four major tasks have been addressed: analysis of variability in the seasonal snow of interior and arctic Alaska, the interpretation of microwave brightness temperature across Alaska on transects from south to north, study of nonclimatic controls which affect glaciers, and the location of glacier facies boundaries.

  17. The Winter Environment: Snow

    ERIC Educational Resources Information Center

    Murphy, James E.

    1974-01-01

    Discusses the structure and formation of snow crystals, outlines the history of snow removal, and describes techniques that can be used by students for studying snowflakes and relating their structure to the conditions under which they were formed. (JR)

  18. Camping in the Snow.

    ERIC Educational Resources Information Center

    Brown, Constance

    1979-01-01

    Describes the experience of winter snow camping. Provides suggestions for shelter, snow kitchens, fires and stoves, cooking, latrines, sleeping warm, dehydration prevention, and clothing. Illustrated with full color photographs. (MA)

  19. MISAWA Snow Accumulation Study

    DTIC Science & Technology

    1991-02-01

    3 1.6 The Snow Forecasting Decision Tree ..................................................................... ...4 2. D AT A AN D...3 Figure 4. Snow Forecasting Decision Tree for Use as a Guide at...Calculation for the Best Misawa Snow Forecast Decision Tree ................................................................................... 11 Fi.gure 8

  20. Modelling of snow exceedances

    NASA Astrophysics Data System (ADS)

    Jordanova, Pavlina K.; Sadovský, Zoltán; Stehlík, Milan

    2017-07-01

    Modelling of snow exceedances is of great importance and interest for ecology, civil engineering and general public. We suggest the favorable fit for exceedances related to the exceptional snow loads from Slovakia, assuming that the data is driven by Generalised Pareto Distribution or Generalized Extreme Value Distribution. Further, the statistical dependence between the maximal snow loads and the corresponding altitudes is studied.

  1. Snow Roads and Runways

    DTIC Science & Technology

    1990-11-01

    9 Effect of time and tem perature...ice strength .......................................................................... 13 19. Effect of time on the strength of processed snow as a...function of temperature ......... 14 20. Effect of time on the strength of processed snow as a function of snow density ........ 14 21. Combined

  2. Snow algae-microbe-mineral interactions and implications for snow algae growth

    NASA Astrophysics Data System (ADS)

    Tschauner, O. D.; Harrold, Z.; Hausrath, E.; Garcia, A. H.; Murray, A. E.; Raymond, J. A.; Bartlett, C. L.

    2016-12-01

    Snow algae, which can reach densities of millions of cells per mL [1], can accelerate the melting of snow and ice fields by significantly lowering their albedo [2-4]. Studies have even suggested the effect of snow algae on albedo should be considered in quantitative albedo models. One of the factors controlling snow algae growth is nutrient availability. Previous observations of minerals and microbes attached to the cell walls of snow algae, and the preferential growth of snow algae in dusty snow, have suggested that snow algae-microbe-mineral interactions may help snow algae meet their trace nutrient needs. Understanding how snow algae are able to reach such high concentrations in a low nutrient snow environment is critical for predicting the extent to which snow algae blooms can impact snow albedo, snow and ice melt rate, and global climate change. We use synchrotron X-ray fluorescence (XRF), X-ray diffraction (XRD) and X-ray absorption near edge structure (XANES) to study the interactions between snow algae, microbes and minerals in both field and laboratory samples. Field samples were collected from Mt. Anderson Ridge, CA, and prepared using a Percoll density separation technique to isolate algae cells from bulk dust. Cell and mineral fractions were analyzed using synchrotron micro-XRF, micro-XRD and XANES. Results show the presence of ferric material similar to ferrihydrite surrounding snow alga. Growth experiments of xenic Chloromonas brevispina cultures incubated with Fe-bearing minerals, including nontronite, goethite, pyrite and olivine, suggest Fe-bearing minerals can support snow algae growth. Synchrotron XRF, XRD and XANES analyses of Cr. brevispinaalgae cell communities indicate the formation of cell-associated Fe-bearing mineral phases not present in the unreacted minerals. The sample preparation and synchrotron techniques described herein provide an approach for investigating a wide range of microbe-mineral interactions and their impacts on microbial

  3. Sensitivity of Passive Microwave Snow Depth Retrievals to Weather Effects and Snow Evolution

    NASA Technical Reports Server (NTRS)

    Markus, Thorsten; Powell, Dylan C.; Wang, James R.

    2006-01-01

    Snow fall and snow accumulation are key climate parameters due to the snow's high albedo, its thermal insulation, and its importance to the global water cycle. Satellite passive microwave radiometers currently provide the only means for the retrieval of snow depth and/or snow water equivalent (SWE) over land as well as over sea ice from space. All algorithms make use of the frequency-dependent amount of scattering of snow over a high-emissivity surface. Specifically, the difference between 37- and 19-GHz brightness temperatures is used to determine the depth of the snow or the SWE. With the availability of the Advanced Microwave Scanning Radiometer (AMSR-E) on the National Aeronautics and Space Administration's Earth Observing System Aqua satellite (launched in May 2002), a wider range of frequencies can be utilized. In this study we investigate, using model simulations, how snow depth retrievals are affected by the evolution of the physical properties of the snow (mainly grain size growth and densification), how they are affected by variations in atmospheric conditions and, finally, how the additional channels may help to reduce errors in passive microwave snow retrievals. The sensitivity of snow depth retrievals to atmospheric water vapor is confirmed through the comparison with precipitable water retrievals from the National Oceanic and Atmospheric Administration's Advanced Microwave Sounding Unit (AMSU-B). The results suggest that a combination of the 10-, 19-, 37-, and 89-GHz channels may significantly improve retrieval accuracy. Additionally, the development of a multisensor algorithm utilizing AMSR-E and AMSU-B data may help to obtain weather-corrected snow retrievals.

  4. The Role of Snow and Ice in the Climate System

    ScienceCinema

    Barry, Roger G.

    2016-07-12

    Global snow and ice cover (the 'cryosphere') plays a major role in global climate and hydrology through a range of complex interactions and feedbacks, the best known of which is the ice - albedo feedback. Snow and ice cover undergo marked seasonal and long term changes in extent and thickness. The perennial elements - the major ice sheets and permafrost - play a role in present-day regional and local climate and hydrology, but the large seasonal variations in snow cover and sea ice are of importance on continental to hemispheric scales. The characteristics of these variations, especially in the Northern Hemisphere, and evidence for recent trends in snow and ice extent are discussed.

  5. The Role of Snow and Ice in the Climate System

    SciTech Connect

    Barry, Roger

    2007-12-19

    Global snow and ice cover (the 'cryosphere') plays a major role in global climate and hydrology through a range of complex interactions and feedbacks, the best known of which is the ice - albedo feedback. Snow and ice cover undergo marked seasonal and long term changes in extent and thickness. The perennial elements - the major ice sheets and permafrost - play a role in present-day regional and local climate and hydrology, but the large seasonal variations in snow cover and sea ice are of importance on continental to hemispheric scales. The characteristics of these variations, especially in the Northern Hemisphere, and evidence for recent trends in snow and ice extent are discussed.

  6. A real-time snow water equivalent interpolation system using wireless sensor networks and historical remotely sensed data

    NASA Astrophysics Data System (ADS)

    Zheng, Z.; Zhang, Z.; Molotch, N. P.; Glaser, S.; Bales, R. C.; Conklin, M. H.

    2015-12-01

    Over 100 wireless sensors for monitoring real-time snow conditions were deployed in ten clusters distributed across the headwaters of the American River Basin. The sensors are strategically placed to measure snow depth across elevation gradients and local differences in slope, aspect and canopy coverage. The sensors provided near-real-time snow-depth readings during the 2014 and 2015 snow seasons. Also, time series of snow water equivalent (SWE) maps were reconstructed for 2012-2014 using energy-balance modeling with modeled energy forcings (NLDAS), terrain corrections for solar radiation (TOPORAD), and fractional snow cover data (MODIS). We blended the real-time snow-depth readings with the historical SWE reconstructions to interpolate real-time SWE conditions across the basin. Snow-depth readings from all sensors for selected dates in 2014 were converted into SWE estimates using density values from snow-pillow sites in the basin. SWE values for pixels where sensors were located were extracted from the reconstructed 2012-2013 data to develop a time-series array. Using a Nearest-Neighbor algorithm we searched the array for the closest conditions that matched the sensor data, interpolated the residuals between reconstructed versus measured SWE across the basin, and added the interpolated values to the reconstructed SWE. We also blended the sensor measurements with the 2014 reconstruction results that were from the same dates. We evaluated both the historical SWE blending results and the concurrent SWE blending results with the operational networks measurements, finding that the concurrent SWE blending has a slightly lower RMSE compared to that of historical SWE blending. Since the reconstruction results could only be estimated after the end of the season, concurrent SWE blending is not applicable to the real time SWE interpolation even though it has a more accurate estimation. However, the small difference of RMSE between the two approaches informs us that the

  7. Multi-scale evaluation of high-resolution multi-sensor blended global precipitation products over the Yangtze River

    NASA Astrophysics Data System (ADS)

    Li, Zhe; Yang, Dawen; Hong, Yang

    2013-09-01

    In the present study, four high-resolution multi-sensor blended precipitation products, TRMM Multisatellite Precipitation Analysis (TMPA) research product (3B42 V7) and near real-time product (3B42 RT), Climate Prediction Center MORPHing technique (CMORPH) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), are evaluated over the Yangtze River basin from April 2008 to March 2012 using the gauge data. This regional evaluation is performed at temporal scales ranging from annual to daily, based on a number of diagnostic statistics. Gauge adjustment greatly reduces the bias in 3B42 V7, a post real-time research product. Additionally, it helps the product maintain a stable skill level in winter. When additional indicators such as spatial correlation, Root Mean Square Error (RMSE), and Probability of Detection (POD) are considered, 3B42 V7 is not always superior to other products (especially CMORPH) at the daily scale. Among the near real-time datasets, 3B42 RT overestimates annual rainfall over the basin; CMORPH and PERSIANN underestimate it. In particular, the upper Yangtze always suffers from positive bias (>1 mm day-1) in the 3B42 RT dataset and negative bias (-0.2 to -1 mm day-1) in the CMORPH dataset. When seasonal scales are considered, CMORPH exhibits negative bias, mainly introduced during cold periods. The correlation between CMORPH and gauge data is the highest. On the contrary, the correlation between 3B42 RT and gauge data is more scattered; statistically, this results in lower bias. Finally, investigation of the probability distribution functions (PDFs) suggests that 3B42 V7 and 3B42 RT are consistently better at retrieving the PDFs in high-intensity events. Overall, this study provides useful information about the error characteristics associated with the four mainstream satellite precipitation products and their implications regarding hydrological applications over the Yangtze River basin.

  8. Black Carbon concentration in snow and its effect on snow albedo: measurements from two snow seasons in Changbai Mountain, North East China

    NASA Astrophysics Data System (ADS)

    Gallet, J.; Pedersen, C.; Zhang, X.; Wang, Z.; Berntsen, T.; Strom, J.

    2012-12-01

    Black Carbon (BC) atmospheric particles originate from incomplete combustion of fossil fuel and biomass. When deposited on the surface, even small amounts of BC can reduce the snow albedo. However, the lack of observations and poor process understanding makes estimates of its climate impact uncertain. We have conducted measurements of semi-continuous BC concentrations in snow surface and snow spectral albedo at CAS Research Station of Changbai Mountain Forest Ecosystem in North East China during two snow seasons 2009/10 and 2010/11. Our measurements show BC concentrations in snow surface ranging from 200 to 1000 ppbw for the first snow season. For the second snow season we see that the amount of BC is constantly increasing from November until end of February with values above 1000 ppbw in February 2011. While the first snow season presents several precipitation events during the winter, the second is much dryer during wintertime. We observe that the snow spectral albedo is strongly affected by the BC content, but that this effect can be enhanced by the meteorological conditions, such as the snow physical properties (fresh or old snow) and the number of precipitations event during the winter. A quantitative study is difficult to assert because the number of processes involved. However, this study shows that the association of dry winter and high BC level affects even more the albedo of snow and should be taken into account in global modeling studies. The study area is particularly interesting because of the high BC levels in snow, its surface area and the global impact on Earth energy budget, in combination with the very low number of existing measurements from this region.

  9. Modelling high-resolution snow cover precipitation supply for German river catchments with SNOW 4

    NASA Astrophysics Data System (ADS)

    Böhm, Uwe; Reich, Thomas; Schneider, Gerold; Fiedler, Anett

    2013-04-01

    Formation of snow cover causes a delayed response of surface to precipitation. Both melting of snow and release of liquid water retained within the snow cover form precipitation supply which contributes to runoff and infiltration. The model SNOW 4 is developed to simulate snow cover accumulation and depletion and the resulting precipitation supply on a regular grid. The core of the model is formed by a set of equations which describe the snow cover energy and mass balance. The snow surface energy balance is calculated as a result of the radiation balance and the heat fluxes between atmosphere, soil and snow cover. The available melting heat enters the mass balance computation part of the model and melting of snow or freezing of liquid water within the snow layer takes place depending on its sign. Retention, aging and snow cover regeneration are taken into consideration. The model runs operationally 4 times a day and provides both a snow cover and precipitation supply analysis for the last 30 hours and a forecast for up to 72 hours. For the 30-hour analysis, regionalised observations are used both to define the initial state and force the model. Hourly measurements of air temperature, water vapour pressure, wind speed, global radiation or sunshine duration and precipitation are interpolated to the model grid. For the forecast period, SNOW 4 obtains the required input data from the operational products of the COSMO-EU weather forecast model. The size of a grid box is 1km2. The model area covers a region of 1100x1000km2 and includes the catchments of the German rivers completely. The internal time step is set to 1 hour. Once a day, the compliance between model and regionalized snow cover data is assessed. If discrepancies exceed certain thresholds, the model must be adjusted by a weighted approach towards the observations. The model simulations are updated every six hours based on the most recent observations and weather forecasts. The model works operationally since

  10. 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

  11. Distributed calibrating snow models using remotely sensed snow cover information

    NASA Astrophysics Data System (ADS)

    Li, H.

    2015-12-01

    Distributed calibrating snow models using remotely sensed snow cover information Hongyi Li1, Tao Che1, Xin Li1, Jian Wang11. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China For improving the simulation accuracy of snow model, remotely sensed snow cover data are used to calibrate spatial parameters of snow model. A physically based snow model is developed and snow parameters including snow surface roughness, new snow density and critical threshold temperature distinguishing snowfall from precipitation, are spatially calibrated in this study. The study region, Babaohe basin, located in northwestern China, have seasonal snow cover and with complex terrain. The results indicates that the spatially calibration of snow model parameters make the simulation results more reasonable, and the simulated snow accumulation days, plot-scale snow depth are more better than lumped calibration.

  12. Transport of intercepted snow from trees during snow storms

    Treesearch

    David H. Miller

    1966-01-01

    Five principal processes by which intercepted snow in trees is removed during snow storms are described and evaluated as far as data permit: vapor flux from melt water, vapor flux from bodies of snow, stem flow and dripping of melt water, sliding of bodies of intercepted snow from branches, and wind erosion and transport of intercepted snow. Further research is...

  13. Unusually Low Snow Cover in the U.S.

    NASA Technical Reports Server (NTRS)

    2002-01-01

    New maps of snow cover produced by NASA's Terra satellite show that this year's snow line stayed farther north than normal. When combined with land surface temperature measurements, the observations confirm earlier National Oceanic and Atmospheric Administration reports that the United States was unusually warm and dry this past winter. The above map shows snow cover over the continental United States from February 2002 and is based on data acquired by the Moderate-Resolution Imaging Spectroradiometer (MODIS). The amount of land covered by snow during this period was much lower than usual. With the exception of the western mountain ranges and the Great Lakes region, the country was mostly snow free. The solid red line marks the average location of the monthly snow extent; white areas are snow-covered ground. Snow was mapped at approximately 5 kilometer pixel resolution on a daily basis and then combined, or composited, every eight days. If a pixel was at least 50 percent snow covered during all of the eight-day periods that month, it was mapped as snow covered for the whole month. For more information, images, and animations, read: Terra Satellite Data Confirm Unusually Warm, Dry U.S. Winter Image by Robert Simmon, based on data from the MODIS Snow/Ice Global Mapping Project

  14. 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.

  15. A study of stable isotopic variations of Antarctic snow by albedo differences

    NASA Astrophysics Data System (ADS)

    Lee, Jeonghoon; Han, Yeongcheol; Ham, Ji-Young; Kim, Young-Hee; Kim, Songyi; Kim, Hyerin; Na, Un-Sung

    2015-04-01

    Snow's albedo can be decreased if there are any impurities on the snow surface other than snow itself. Due to the decrease of albedo of snow, melting rates of surface snow can be enhanced, which is very crucial in climate change and hydrogeology in many parts of the world. Anthropogenic black carbons caused by the incomplete combustion of fossil fuel affect on snow and tephra particles generated by geologic volcanic activities reduce snow albedo. In this study, we investigated isotopic compositions between snow covered by tephra particles and clean snow. Isotopic compositions of snow with tephra statistically shows more enriched than those of clean snow (p<0.02). This can be explained by the fact that snow becomes enriched in 18O or D relative to meltwater as melting rates are increased. In addition, the slopes of the linear regression between oxygen and hydrogen for snow with tephra and clean snow are 6.7 and 8, respectively, and the latter is similar to that of the global meteoric water line of 8. Therefore, we can conclude that snow impurities control the isotopic compositions of snow, which is very crucial in the study of climate change and hydrogeology. To quantitatively explain these observations, melting experiments and numerical approaches are required.

  16. Assessment of Northern Hemisphere Snow Water Equivalent Datasets in ESA SnowPEx project

    NASA Astrophysics Data System (ADS)

    Luojus, Kari; Pulliainen, Jouni; Cohen, Juval; Ikonen, Jaakko; Derksen, Chris; Mudryk, Lawrence; Nagler, Thomas; Bojkov, Bojan

    2016-04-01

    Reliable information on snow cover across the Northern Hemisphere and Arctic and sub-Arctic regions is needed for climate monitoring, for understanding the Arctic climate system, and for the evaluation of the role of snow cover and its feedback in climate models. In addition to being of significant interest for climatological investigations, reliable information on snow cover is of high value for the purpose of hydrological forecasting and numerical weather prediction. Terrestrial snow covers up to 50 million km² of the Northern Hemisphere in winter and is characterized by high spatial and temporal variability. Therefore satellite observations provide the best means for timely and complete observations of the global snow cover. There are a number of independent SWE products available that describe the snow conditions on multi-decadal and global scales. Some products are derived using satellite-based information while others rely on meteorological observations and modelling. What is common to practically all the existing hemispheric SWE products, is that their retrieval performance on hemispherical and multi-decadal scales are not accurately known. The purpose of the ESA funded SnowPEx project is to obtain a quantitative understanding of the uncertainty in satellite- as well as model-based SWE products through an internationally coordinated and consistent evaluation exercise. The currently available Northern Hemisphere wide satellite-based SWE datasets which were assessed include 1) the GlobSnow SWE, 2) the NASA Standard SWE, 3) NASA prototype and 4) NSIDC-SSM/I SWE products. The model-based datasets include: 5) the Global Land Data Assimilation System Version 2 (GLDAS-2) product 6) the European Centre for Medium-Range Forecasts Interim Land Reanalysis (ERA-I-Land) which uses a simple snow scheme 7) the Modern Era Retrospective Analysis for Research and Applications (MERRA) which uses an intermediate complexity snow scheme; and 8) SWE from the Crocus snow scheme, a

  17. Microwave emissions from snow

    NASA Technical Reports Server (NTRS)

    Chang, A. T. C.

    1984-01-01

    The radiation emitted from dry and wet snowpack in the microwave region (1 to 100 GHz) is discussed and related to ground observations. Results from theoretical model calculations match the brightness temperatures obtained by truck mounted, airborne and spaceborne microwave sensor systems. Snow wetness and internal layer structure complicate the snow parameter retrieval algorithm. Further understanding of electromagnetic interaction with snowpack may eventually provide a technique to probe the internal snow properties

  18. Snow [Chapter 10

    Treesearch

    R. A. Sommerfeld

    1994-01-01

    Generally, the annual snowpack at GLEES is established in November and lasts into July. Figure 10.1 is the 1987-91 recession curve of the snow-covered area fraction versus degree days. About 20% of the area consists of rocks, which are usually blown clear of snow, and trees. The trees may hide some of the snow in the aerial photographs that were used to develop the...

  19. Role of Tibetan Buddhist monasteries in snow leopard conservation.

    PubMed

    Li, Juan; Wang, Dajun; Yin, Hang; Zhaxi, Duojie; Jiagong, Zhala; Schaller, George B; Mishra, Charudutt; McCarthy, Thomas M; Wang, Hao; Wu, Lan; Xiao, Lingyun; Basang, Lamao; Zhang, Yuguang; Zhou, Yunyun; Lu, Zhi

    2014-02-01

    The snow leopard (Panthera uncia) inhabits the rugged mountains in 12 countries of Central Asia, including the Tibetan Plateau. Due to poaching, decreased abundance of prey, and habitat degradation, it was listed as endangered by the International Union for Conservation of Nature in 1972. Current conservation strategies, including nature reserves and incentive programs, have limited capacities to protect snow leopards. We investigated the role of Tibetan Buddhist monasteries in snow leopard conservation in the Sanjiangyuan region in China's Qinghai Province on the Tibetan Plateau. From 2009 to 2011, we systematically surveyed snow leopards in the Sanjiangyuan region. We used the MaxEnt model to determine the relation of their presence to environmental variables (e.g., elevation, ruggedness) and to predict snow leopard distribution. Model results showed 89,602 km(2) of snow leopard habitat in the Sanjiangyuan region, of which 7674 km(2) lay within Sanjiangyuan Nature Reserve's core zones. We analyzed the spatial relation between snow leopard habitat and Buddhist monasteries and found that 46% of monasteries were located in snow leopard habitat and 90% were within 5 km of snow leopard habitat. The 336 monasteries in the Sanjiangyuan region could protect more snow leopard habitat (8342 km(2) ) through social norms and active patrols than the nature reserve's core zones. We conducted 144 household interviews to identify local herders' attitudes and behavior toward snow leopards and other wildlife. Most local herders claimed that they did not kill wildlife, and 42% said they did not kill wildlife because it was a sin in Buddhism. Our results indicate monasteries play an important role in snow leopard conservation. Monastery-based snow leopard conservation could be extended to other Tibetan Buddhist regions that in total would encompass about 80% of the global range of snow leopards.

  20. "Let It Snow, Let It Snow, Let It Snow!"

    ERIC Educational Resources Information Center

    Pangbourne, Laura

    2010-01-01

    Winter in the UK has, in recent years, brought a significant amount of snow and cold weather. This was the case while the author was a trainee teacher on placement at a rural primary school in Dartmoor early in 2010. The day started promisingly with the class looking at the weather forecast on the interactive whiteboard and having a short…

  1. "Let It Snow, Let It Snow, Let It Snow!"

    ERIC Educational Resources Information Center

    Pangbourne, Laura

    2010-01-01

    Winter in the UK has, in recent years, brought a significant amount of snow and cold weather. This was the case while the author was a trainee teacher on placement at a rural primary school in Dartmoor early in 2010. The day started promisingly with the class looking at the weather forecast on the interactive whiteboard and having a short…

  2. Snow avalanche formation

    NASA Astrophysics Data System (ADS)

    Schweizer, Jürg; Bruce Jamieson, J.; Schneebeli, Martin

    2003-12-01

    Snow avalanches are a major natural hazard, endangering human life and infrastructure in mountainous areas throughout the world. In many countries with seasonally snow-covered mountains, avalanche-forecasting services reliably warn the public by issuing occurrence probabilities for a certain region. However, at present, a single avalanche event cannot be predicted in time and space. Much about the release process remains unknown, mainly because of the highly variable, layered character of the snowpack, a highly porous material that exists close to its melting point. The complex interaction between terrain, snowpack, and meteorological conditions leading to avalanche release is commonly described as avalanche formation. It is relevant to hazard mapping and essential to short-term forecasting, which involves weighting many contributory factors. Alternatively, the release process can be studied and modeled. This approach relies heavily on snow mechanics and snow properties, including texture. While the effect of meteorological conditions or changes on the deformational behavior of snow is known in qualitative or semiquantitative manner, the knowledge of the quantitative relation between snow texture and mechanical properties is limited, but promising developments are under way. Fracture mechanical models have been applied to explain the fracture propagation, and micromechanical models including the two competing processes (damage and sintering) have been applied to explain snow failure. There are knowledge gaps between the sequence of processes that lead to the release of the snow slab: snow deformation and failure, damage accumulation, fracture initiation, and fracture propagation. Simultaneously, the spatial variability that affects damage, fracture initiation, and fracture propagation has to be considered. This review focuses on dry snow slab avalanches and shows that dealing with a highly porous media close to its melting point and processes covering several

  3. Eurasian snow depth in long-term climate reanalyses

    NASA Astrophysics Data System (ADS)

    Wegmann, Martin; Orsolini, Yvan; Dutra, Emanuel; Bulygina, Olga; Sterin, Alexander; Brönnimann, Stefan

    2017-04-01

    Snow cover variability has significant effects on local and global climate evolution. By changing surface energy fluxes and hydrological conditions, changes in snow cover can alter atmospheric circulation and lead to remote climate effects. To document such multi-scale climate effects, atmospheric reanalysis and derived products offer the opportunity to analyze snow variability in great detail far back to the early 20th century. So far only little is know about their quality. Comparing snow depth in four long-term reanalysis datasets with Russian in situ snow depth data, we find a moderately high daily correlation (around 0.6-0.7), which is comparable to correlations for the recent era (1981-2010), and a good representation of sub-decadal variability. However, the representation of pre-1950 inter-decadal snow variability is questionable, since reanalysis products divert towards different base states. Limited availability of independent long-term snow data makes it difficult to assess the exact cause for this bifurcation in snow states, but initial investigations point towards representation of the atmosphere rather than differences in assimilated data or snow schemes. This study demonstrates the ability of long-term reanalysis to reproduce snow variability accordingly.

  4. Snow cover changes in the Hindu-Kush Karakoram Himalaya

    NASA Astrophysics Data System (ADS)

    Terzago, Silvia; Von Hardenberg, Jost; Palazzi, Elisa; Provenzale, Antonello

    2013-04-01

    Snow cover plays a key role in high-altitude environments, and changes in the snow spatial/temporal distribution and thickness affect energy, radiation and water budgets at the Earth's surface. In particular, a reduction in the snow amount has a direct effect on the availability and seasonal distribution of water resources. This is especially true in areas such as the Hindu-Kush Karakoram Himalaya (HKKH) region, which provides water to about 1.5 Billion peoples in India, Nepal, Pakistan and China. Despite its importance, knowledge on snow dynamics in the HKKH region is still incomplete, owing also to sparse and sporadic surface observations. In this work, we used simulations from Global Climate Models (GCMs) to gain information on snowpack characteristics and climatology in the HKKH region. We selected a set of GCM snow depth datasets from the CMIP5 ensemble, esploring snow abundance and distribution at monthly scale. In order to investigate how well Global Climate Models represent the snow climatology, we compared the results with the ERA-Interim reanalysis, used as an approximation to the real conditions. After exploring the average snow conditions in the last decades, we analyzed the effects of climate change in the HKKH region by using an ensemble of future snow projections obtained from different GCMs and in different climate change scenarios.

  5. Satellite sensor estimates of Northern Hemisphere snow volume

    NASA Technical Reports Server (NTRS)

    Chang, A. T. C.; Foster, J. L.; Hall, D. K.

    1990-01-01

    In the Northern Hemisphere the mean monthly snow-covered area ranges from about 7 percent of the land area in summer to over 40 percent in winter, thus making snow one of the most rapidly varying natural surface features. The mean monthly snow volume ranges from about 1.5 x 10 to the 16th g in summer to about 3.0 x 10 to the 18th g in winter. Currently several algorithms utilizing passive microwave brightness temperatures are available to estimate snow cover and depth. The algorithm presented here uses the difference between the 37-GHz channel and the 18-GHz channel of the SMMR on the Nimbus-7 satellite to derive estimates of snow volume. Even though satellite sensor snow records are currently too short to reveal trends, continued monitoring over about the next 10 years should make it possible to establish whether incipient or current trends are significant in the context of global climate change.

  6. Monitoring and projecting snow on Hawaii Island

    NASA Astrophysics Data System (ADS)

    Zhang, Chunxi; Hamilton, Kevin; Wang, Yuqing

    2017-05-01

    The highest mountain peaks on Hawaii Island are snow covered for part of almost every year. This snow has aesthetic and recreational value as well as cultural significance for residents and visitors. Thus far there have been almost no systematic observations of snowfall, snow cover, or snow depth in Hawaii. Here we use satellite observations to construct a daily index of Hawaii Island snow cover starting from 2000. The seasonal mean of our index displays large interannual variations that are correlated with the seasonal mean freezing level and frequency of trade wind inversions as determined from nearby balloon soundings. Our snow cover index provides a diagnostic for monitoring climate variability and trends within the extensive area of the globe dominated by the North Pacific trade wind meteorological regime. We have also conducted simulations of the Hawaii climate with a regional atmospheric model. Retrospective simulations for 1990-2015 were run with boundary conditions prescribed from gridded observational analyses. Simulations for the end of 21st century employed boundary conditions based on global climate model projections that included standard scenarios for anticipated anthropogenic climate forcing. The future projections indicate that snowfall will nearly disappear by the end of the current century.

  7. Loropetalum chinense 'Snow Panda'

    USDA-ARS?s Scientific Manuscript database

    A new Loropetalum chinense, ‘Snow Panda’, developed at the U.S. National Arboretum is described. ‘Snow Panda’ (NA75507, PI660659) originated from seeds collected near Yan Chi He, Hubei, China in 1994 by the North America-China Plant Exploration Consortium (NACPEC). Several seedlings from this trip w...

  8. Snow and Ice.

    ERIC Educational Resources Information Center

    Minneapolis Independent School District 275, Minn.

    This experimental edition provides a number of activities useful for investigating snow and ice with elementary school children. Commencing with games with ice cubes, the activities lead through studies of snowflakes, snowdrifts, effects of wind and obstacles on the shape and formation of drifts, to a study of animals living under snow. The…

  9. Snow Bank Detectives

    ERIC Educational Resources Information Center

    Olson, Eric A.; Rule, Audrey C.; Dehm, Janet

    2005-01-01

    In the city where the authors live, located on the shore of Lake Ontario, children have ample opportunity to interact with snow. Water vapor rising from the relatively warm lake surface produces tremendous "lake effect" snowfalls when frigid winter winds blow. Snow piles along roadways after each passing storm, creating impressive snow…

  10. Snow Bank Detectives

    ERIC Educational Resources Information Center

    Olson, Eric A.; Rule, Audrey C.; Dehm, Janet

    2005-01-01

    In the city where the authors live, located on the shore of Lake Ontario, children have ample opportunity to interact with snow. Water vapor rising from the relatively warm lake surface produces tremendous "lake effect" snowfalls when frigid winter winds blow. Snow piles along roadways after each passing storm, creating impressive snow…

  11. Let It Snow!

    NASA Astrophysics Data System (ADS)

    Williams, Kathryn R.

    2000-02-01

    The February 1930 issue of JCE contains an article, "Calcium Chloride for Snow Removal", by Lionel Richardson. The author presents a photograph and personal observations of an experimental truck/trailer combination for spreading CaCl2 on Brooklyn's streets after a heavy snowstorm. The From Past Issues story summarizes the 1930 paper and directs readers to additional library resources on snow removal.

  12. GulfSnow Peach

    USDA-ARS?s Scientific Manuscript database

    GulfSnow peach is jointly released for grower trial by the U.S. Department of Agriculture, Agricultural Research Service (Byron, GA), Georgia Agricultural Experiment Station and Florida Agricultural Experiment Station. GulfSnow was previously tested as AP06-09W and originated from a cross of AP98-3...

  13. Evaluation of snow cover and snow depth on the Qinghai-Tibetan Plateau derived from passive microwave remote sensing

    NASA Astrophysics Data System (ADS)

    Dai, Liyun; Che, Tao; Ding, Yongjian; Hao, Xiaohua

    2017-08-01

    Snow cover on the Qinghai-Tibetan Plateau (QTP) plays a significant role in the global climate system and is an important water resource for rivers in the high-elevation region of Asia. At present, passive microwave (PMW) remote sensing data are the only efficient way to monitor temporal and spatial variations in snow depth at large scale. However, existing snow depth products show the largest uncertainties across the QTP. In this study, MODIS fractional snow cover product, point, line and intense sampling data are synthesized to evaluate the accuracy of snow cover and snow depth derived from PMW remote sensing data and to analyze the possible causes of uncertainties. The results show that the accuracy of snow cover extents varies spatially and depends on the fraction of snow cover. Based on the assumption that grids with MODIS snow cover fraction > 10 % are regarded as snow cover, the overall accuracy in snow cover is 66.7 %, overestimation error is 56.1 %, underestimation error is 21.1 %, commission error is 27.6 % and omission error is 47.4 %. The commission and overestimation errors of snow cover primarily occur in the northwest and southeast areas with low ground temperature. Omission error primarily occurs in cold desert areas with shallow snow, and underestimation error mainly occurs in glacier and lake areas. With the increase of snow cover fraction, the overestimation error decreases and the omission error increases. A comparison between snow depths measured in field experiments, measured at meteorological stations and estimated across the QTP shows that agreement between observation and retrieval improves with an increasing number of observation points in a PMW grid. The misclassification and errors between observed and retrieved snow depth are associated with the relatively coarse resolution of PMW remote sensing, ground temperature, snow characteristics and topography. To accurately understand the variation in snow depth across the QTP, new algorithms

  14. Snow and ice ecosystems: not so extreme.

    PubMed

    Maccario, Lorrie; Sanguino, Laura; Vogel, Timothy M; Larose, Catherine

    2015-12-01

    Snow and ice environments cover up to 21% of the Earth's surface. They have been regarded as extreme environments because of their low temperatures, high UV irradiation, low nutrients and low water availability, and thus, their microbial activity has not been considered relevant from a global microbial ecology viewpoint. In this review, we focus on why snow and ice habitats might not be extreme from a microbiological perspective. Microorganisms interact closely with the abiotic conditions imposed by snow and ice habitats by having diverse adaptations, that include genetic resistance mechanisms, to different types of stresses in addition to inhabiting various niches where these potential stresses might be reduced. The microbial communities inhabiting snow and ice are not only abundant and taxonomically diverse, but complex in terms of their interactions. Altogether, snow and ice seem to be true ecosystems with a role in global biogeochemical cycles that has likely been underestimated. Future work should expand past resistance studies to understanding the function of these ecosystems.

  15. GATOR-GCMM: A global- through urban-scale air pollution and weather forecast model, 1. Model design and treatment of subgrid soil, vegetation, roads, rooftops, water, sea ice, and snow

    NASA Astrophysics Data System (ADS)

    Jacobson, Mark Z.

    2001-03-01

    A model that treats nesting of gas, size- and composition-resolved aerosol, radiative, and meteorological parameters from the global through urban scales (<5-km grid spacing) was developed. The model treats multiple one-way-nested layers and multiple air quality and meteorological domains in each layer between the global and the urban scales. This latter feature allows forecast of air pollution and weather at several urban or regional sites during the same simulation. Regardless of the number of domains used during a single continuous simulation, the central memory required never exceeds 1.5 times and 2.1 times that of the largest domain for gas and gas/aerosol simulations, respectively. A submodule was developed for all domains to treat ground temperatures, latent heat fluxes, and sensible heat fluxes over subgrid soil types (with and without vegetation), water, sea ice, and urban areas. Urban areas are divided into road surfaces, rooftops, vegetation, and bare soil. Snow is treated over all surface types. The global-through-urban model is applied in a companion paper to study elevated ozone, ozone in national parks, and weather during a field campaign in northern and central California.

  16. 50 years of snow stratigraphy observations

    NASA Astrophysics Data System (ADS)

    Johansson, C.; Pohjola, V.; Jonasson, C.; Challagan, T. V.

    2012-04-01

    With start in autumn 1961 the Abisko Scientific Research Station (ASRS) located in the Swedish sub Arctic has performed snow stratigraphy observations, resulting in a unique 50 year long time series of data. The data set contains grain size, snow layer hardness, grain compactness and snow layer dryness, observed every second week during the winter season. In general snow and snow cover are important factors for the global radiation budget, and the earth's climate. On a more local scale the layered snowpack creates a relatively mild microclimate for Arctic plants and animals, and it also determines the water content of the snowpack (snow water equivalent) important for e.g. hydrological applications. Analysis of the snow stratigraphy data, divided into three consecutive time periods, show that there has been a change in the last time period. The variable most affected is the snow layer hardness, which shows an increase in hardness of the snowpack. The number of observations with a very hard snow layer/ice at ground level increased three-fold between the first two time periods and the last time period. The thickness of the bottom layer in the snowpack is also highly affected. There has been a 60% increase in layers thinner than 10 cm in the last time period, resulting in a mean reduction in the thickness of the bottom layer from 14 cm to 11 cm. Hence the living conditions for plants and animals at the ground surface have been highly changed. The changes in the snowpack are correlated to an increased mean winter air temperature. Thus, continued increasing, or temperatures within the same ranges as in the last time period, is likely to create harder snow condition in the future. These changes are likely to affect animals that live under the snow such as lemmings and voles or animals that graze sub-Arctic vegetation in winter (e.g. reindeer that would potentially require increased supplementary feeding that incurs financial costs to Sami reindeer herders). Any decrease

  17. GPM Sees Nor'easter Dump Snow on New England

    NASA Image and Video Library

    At 5:05 p.m. EST Monday, Jan. 26, 2015, the Global Precipitation Measurement mission's Core Observatory flew over the Nor'easter that dumped snow on New England. This satellite image shows the rate...

  18. 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).

  19. Snow and ice: Chapter 3

    USGS Publications Warehouse

    Littell, Jeremy; McAfee, Stephanie A.; O'Neel, Shad; Sass, Louis; Burgess, Evan; Colt, Steve; Clark, Paul; Hayward, Gregory D.; Colt, Steve; McTeague, Monica L.; Hollingsworth, Teresa N.

    2017-01-01

    Temperature and precipitation are key determinants of snowpack levels. Therefore, climate change is likely to affect the role of snow and ice in the landscapes and hydrology of the Chugach National Forest region.Downscaled climate projections developed by Scenarios Network for Alaska and Arctic Planning (SNAP) are useful for examining projected changes in snow at relatively fine resolution using a variable called “snowday fraction (SDF),” the percentage of days with precipitation falling as snow.We summarized SNAP monthly SDF from five different global climate models for the Chugach region by 500 m elevation bands, and compared historical (1971–2000) and future (2030–2059) SDF. We found that:Snow-day fraction and snow-water equivalent (SWE) are projected to decline most in late autumn (October to November) and at lower elevations.Snow-day fraction is projected to decrease 23 percent (averaged across five climate models) from October to March, between sea level and 500 m. Between sea level and 1000 m, SDF is projected to decrease by 17 percent between October and March.Snow-water equivalent is projected to decrease most in autumn (October and November) and at lower elevations (below 1500 m), an average of -26 percent for the 2030–2059 period compared to 1971– 2000. Averaged across the cool season and the entire domain, SWE is projected to decrease at elevations below 1000 m because of increased temperature, but increase at higher elevations because of increased precipitation.Compared to 1971–2000, the percentage of the landscape that is snowdominant in 2030–2059 is projected to decrease, and the percentage in which rain and snow are co-dominant (transient hydrology) is projected to increase from 27 to 37 percent. Most of this change is at lower elevations.Glaciers on the Chugach National Forest are currently losing about 6 km3 of ice per year; half of this loss comes from Columbia Glacier (Berthier et al. 2010).Over the past decade, almost all

  20. Make Your Own Snow Day!

    ERIC Educational Resources Information Center

    Robeck, Edward

    2011-01-01

    Children love snow days, even when they come during the warmest weather. In this lesson the snow isn't falling outside, it's in the classroom--thanks to "Snowflake Bentley" (Briggs Martin 1998) and several models of snowflakes. A lesson on snow demonstrates several principles of practice for using models in elementary science. Focusing on snow was…

  1. Make Your Own Snow Day!

    ERIC Educational Resources Information Center

    Robeck, Edward

    2011-01-01

    Children love snow days, even when they come during the warmest weather. In this lesson the snow isn't falling outside, it's in the classroom--thanks to "Snowflake Bentley" (Briggs Martin 1998) and several models of snowflakes. A lesson on snow demonstrates several principles of practice for using models in elementary science. Focusing on snow was…

  2. Assessment of methods for mapping snow cover from MODIS

    NASA Astrophysics Data System (ADS)

    Rittger, Karl; Painter, Thomas H.; Dozier, Jeff

    2013-01-01

    Characterization of snow is critical for understanding Earth’s water and energy cycles. Maps of snow from MODIS have seen growing use in investigations of climate, hydrology, and glaciology, but the lack of rigorous validation of different snow mapping methods compromises these studies. We examine three widely used MODIS snow products: the “binary” (i.e., snow yes/no) global snow maps that were among the initial MODIS standard products; a more recent standard MODIS fractional snow product; and another fractional snow product, MODSCAG, based on spectral mixture analysis. We compare them to maps of snow obtained from Landsat ETM+ data, whose 30 m spatial resolution provides nearly 300 samples within a 500 m MODIS nadir pixel. The assessment uses 172 images spanning a range of snow and vegetation conditions, including the Colorado Rocky Mountains, the Upper Rio Grande, California’s Sierra Nevada, and the Nepal Himalaya. MOD10A1 binary and fractional fail to retrieve snow in the transitional periods during accumulation and melt while MODSCAG consistently maintains its retrieval ability during these periods. Averaged over all regions, the RMSE for MOD10A1 fractional is 0.23, whereas the MODSCAG RMSE is 0.10. MODSCAG performs the most consistently through accumulation, mid-winter and melt, with median differences ranging from -0.16 to 0.04 while differences for MOD10A1 fractional range from -0.34 to 0.35. MODSCAG maintains its performance over all land cover classes and throughout a larger range of land surface properties. Characterizing snow cover by spectral mixing is more accurate than empirical methods based on the normalized difference snow index, both for identifying where snow is and is not and for estimating the fractional snow cover within a sensor’s instantaneous field-of-view. Determining the fractional value is particularly important during spring and summer melt in mountainous terrain, where large variations in snow, vegetation and soil occur over

  3. The Snow Must Go On: Ground Ice Encasement, Snow Compaction and Absence of Snow Differently Cause Soil Hypoxia, CO2 Accumulation and Tree Seedling Damage in Boreal Forest.

    PubMed

    Martz, Françoise; Vuosku, Jaana; Ovaskainen, Anu; Stark, Sari; Rautio, Pasi

    2016-01-01

    At high latitudes, the climate has warmed at twice the rate of the global average with most changes observed in autumn, winter and spring. Increasing winter temperatures and wide temperature fluctuations are leading to more frequent rain-on-snow events and freeze-thaw cycles causing snow compaction and formation of ice layers in the snowpack, thus creating ice encasement (IE). By decreasing the snowpack insulation capacity and restricting soil-atmosphere gas exchange, modification of the snow properties may lead to colder soil but also to hypoxia and accumulation of trace gases in the subnivean environment. To test the effects of these overwintering conditions changes on plant winter survival and growth, we established a snow manipulation experiment in a coniferous forest in Northern Finland with Norway spruce and Scots pine seedlings. In addition to ambient conditions and prevention of IE, we applied three snow manipulation levels: IE created by artificial rain-on-snow events, snow compaction and complete snow removal. Snow removal led to deeper soil frost during winter, but no clear effect of IE or snow compaction done in early winter was observed on soil temperature. Hypoxia and accumulation of CO2 were highest in the IE plots but, more importantly, the duration of CO2 concentration above 5% was 17 days in IE plots compared to 0 days in ambient plots. IE was the most damaging winter condition for both species, decreasing the proportion of healthy seedlings by 47% for spruce and 76% for pine compared to ambient conditions. Seedlings in all three treatments tended to grow less than seedlings in ambient conditions but only IE had a significant effect on spruce growth. Our results demonstrate a negative impact of winter climate change on boreal forest regeneration and productivity. Changing snow conditions may thus partially mitigate the positive effect of increasing growing season temperatures on boreal forest productivity.

  4. The Snow Must Go On: Ground Ice Encasement, Snow Compaction and Absence of Snow Differently Cause Soil Hypoxia, CO2 Accumulation and Tree Seedling Damage in Boreal Forest

    PubMed Central

    Vuosku, Jaana; Ovaskainen, Anu; Stark, Sari; Rautio, Pasi

    2016-01-01

    At high latitudes, the climate has warmed at twice the rate of the global average with most changes observed in autumn, winter and spring. Increasing winter temperatures and wide temperature fluctuations are leading to more frequent rain-on-snow events and freeze-thaw cycles causing snow compaction and formation of ice layers in the snowpack, thus creating ice encasement (IE). By decreasing the snowpack insulation capacity and restricting soil-atmosphere gas exchange, modification of the snow properties may lead to colder soil but also to hypoxia and accumulation of trace gases in the subnivean environment. To test the effects of these overwintering conditions changes on plant winter survival and growth, we established a snow manipulation experiment in a coniferous forest in Northern Finland with Norway spruce and Scots pine seedlings. In addition to ambient conditions and prevention of IE, we applied three snow manipulation levels: IE created by artificial rain-on-snow events, snow compaction and complete snow removal. Snow removal led to deeper soil frost during winter, but no clear effect of IE or snow compaction done in early winter was observed on soil temperature. Hypoxia and accumulation of CO2 were highest in the IE plots but, more importantly, the duration of CO2 concentration above 5% was 17 days in IE plots compared to 0 days in ambient plots. IE was the most damaging winter condition for both species, decreasing the proportion of healthy seedlings by 47% for spruce and 76% for pine compared to ambient conditions. Seedlings in all three treatments tended to grow less than seedlings in ambient conditions but only IE had a significant effect on spruce growth. Our results demonstrate a negative impact of winter climate change on boreal forest regeneration and productivity. Changing snow conditions may thus partially mitigate the positive effect of increasing growing season temperatures on boreal forest productivity. PMID:27254100

  5. Simulating Snow Over Sea Ice In Climate Models

    NASA Technical Reports Server (NTRS)

    Arnold, James E. (Technical Monitor); Marshall, Susan; Oglesby, Robert J.; Drobot, Sheldon; Anderson, Mark

    2002-01-01

    We have evaluated two methods of simulating the seasonal cycle of snow over sea ice in and around the Arctic: The NCAR global climate model CCM3, with its standard snow hydrology, and the snow pack model SNTHERM, forced with hourly atmospheric output from CCM3. A new dataset providing dates for the onset of snow melt over Arctic sea ice provides a means for assessing basin-wide how well the models simulate melt onset, but contains no information on how long it then takes for all the snow to melt. Use of data from the SHEBA site provides very detailed information on the behavior of the snow before and during the melt season, but only for a very limited area. Russian drift data provide climatological data on the seasonal cycle of snow water equivalent and snow density, over multi-year sea ice in the central Arctic basin. These datasets are used to compare the two modeling methods, and to see if use of the more physically-realistic SNTHERM provides any significant improvements. Conclusions obtained so far include: 1. Both CCM3 and CCM3/SNTHERM do a good job overall of matching the onset of snow melt dataset; although CCM3/SNTHERM consistently trends to underestimate the date and CCM3 to overestimate it. 2. SHEBA and ice drift data for the Arctic show that CCM3/ SNTHERM does a better job than CCM3 at simulating the total melt period. 3. Ice drift snow density and accumulation data suggest that while providing superior results, CCM3/SNTHERM may still suffer from overly vigorous melting. 4. Both the large-scale atmospheric forcing and snow pack physical processes are important in proper simulation of the snow seasonal cycle. Ongoing work includes further diagnosis of CCM3/SNTHERM, use of more observational datasets, especially from marginal seas in the pan-Arctic, and full coupling of SNTHERM into CCM3 (work to date has all been off-line simulations).

  6. Simulating Snow Over Sea Ice In Climate Models

    NASA Technical Reports Server (NTRS)

    Arnold, James E. (Technical Monitor); Marshall, Susan; Oglesby, Robert J.; Drobot, Sheldon; Anderson, Mark

    2002-01-01

    We have evaluated two methods of simulating the seasonal cycle of snow over sea ice in and around the Arctic: The NCAR global climate model CCM3, with its standard snow hydrology, and the snow pack model SNTHERM, forced with hourly atmospheric output from CCM3. A new dataset providing dates for the onset of snow melt over Arctic sea ice provides a means for assessing basin-wide how well the models simulate melt onset, but contains no information on how long it then takes for all the snow to melt. Use of data from the SHEBA site provides very detailed information on the behavior of the snow before and during the melt season, but only for a very limited area. Russian drift data provide climatological data on the seasonal cycle of snow water equivalent and snow density, over multi-year sea ice in the central Arctic basin. These datasets are used to compare the two modeling methods, and to see if use of the more physically-realistic SNTHERM provides any significant improvements. Conclusions obtained so far include: 1. Both CCM3 and CCM3/SNTHERM do a good job overall of matching the onset of snow melt dataset; although CCM3/SNTHERM consistently trends to underestimate the date and CCM3 to overestimate it. 2. SHEBA and ice drift data for the Arctic show that CCM3/ SNTHERM does a better job than CCM3 at simulating the total melt period. 3. Ice drift snow density and accumulation data suggest that while providing superior results, CCM3/SNTHERM may still suffer from overly vigorous melting. 4. Both the large-scale atmospheric forcing and snow pack physical processes are important in proper simulation of the snow seasonal cycle. Ongoing work includes further diagnosis of CCM3/SNTHERM, use of more observational datasets, especially from marginal seas in the pan-Arctic, and full coupling of SNTHERM into CCM3 (work to date has all been off-line simulations).

  7. Integration of snow management practices into a detailed snow pack model

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

  8. Snow In the Sahara

    NASA Image and Video Library

    2017-09-28

    In December 2016, snow fell in the Sahara for the first time since 1979. In 1984, the charitable supergroup Band Aid sang: “There won’t be snow in Africa this Christmas time.” In fact, it does snow in Africa at high elevations. Kilimanjaro has long had a cap of snow and ice, though it has been shrinking. Skiiers travel for natural and manufactured snow in the Atlas Mountains of Morocco and Algeria, as well as a few spots in South Africa and Lesotho. Nonetheless, snow on the edge of the Sahara Desert is rare. On December 19, 2016, snow fell on the Algerian town of Ain Sefra, which is sometimes referred to as the “gateway to the desert.” The town of roughly 35,000 people sits between the Atlas Mountains and the northern edge of the Sahara. The last recorded snowfall in Ain Sefra occurred in February 1979. The Enhanced Thematic Mapper Plus (ETM+) on the Landsat 7 satellite acquired this natural-color image of snow in North Africa on December 19, 2016. This scene shows an area near the border of Morocco and Algeria, south of the city of Bouarfa and southwest of Ain Sefra. Though the news has been dominated by snow in the Saharan city, a review of several years of satellite data suggests that snow is also pretty rare in this section of the Atlas range. Read more: go.nasa.gov/2hIH4Xe NASA Earth Observatory image by Joshua Stevens, using Landsat data from the U.S. Geological Survey. Caption by Mike Carlowicz. b>NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

  9. State of the Earth’s cryosphere at the beginning of the 21st century : glaciers, global snow cover, floating ice, and permafrost and periglacial environments: Chapter A in Satellite image atlas of glaciers of the world

    USGS Publications Warehouse

    Williams, Richard S.; Ferrigno, Jane G.; Williams, Richard S., Jr.; Ferrigno, Jane G.

    2012-01-01

    This chapter is the tenth in a series of 11 book-length chapters, collectively referred to as “this volume,” in the series U.S. Geological Survey Professional Paper 1386, Satellite Image Atlas of Glaciers of the World. In the other 10 chapters, each of which concerns a specific glacierized region of Earth, the authors used remotely sensed images, primarily from the Landsat 1, 2, and 3 series of spacecraft, in order to analyze that glacierized region and to monitor changes in its glaciers. Landsat images, acquired primarily during the period 1972 through 1981, were used by an international team of glaciologists and other scientists to study the various glacierized regions and (or) to discuss related glaciological topics. In each glacierized region, the present distribution of glaciers within its geographic area is compared, wherever possible, with historical information about their past areal extent. The atlas provides an accurate regional inventory of the areal extent of glacier ice on our planet during the 1970s as part of an expanding international scientific effort to measure global environmental change on the Earth’s surface. However, this chapter differs from the other 10 in its discussion of observed changes in all four elements of the Earth’s cryosphere (glaciers, snow cover, floating ice, and permafrost) in the context of documented changes in all components of the Earth System. Human impact on the planet at the beginning of the 21st century is pervasive. The focus of Chapter A is on changes in the cryosphere and the importance of long-term monitoring by a variety of sensors carried on Earth-orbiting satellites or by a ground-based network of observatories in the case of permafrost. The chapter consists of five parts. The first part provides an introduction to the Earth System, including the interrelationships of the geosphere (cryosphere, hydrosphere, lithosphere, and atmosphere), the biosphere, climate processes, biogeochemical cycles, and the

  10. 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).

  11. 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.

  12. Politics of Snow

    NASA Astrophysics Data System (ADS)

    Burko, D.

    2012-12-01

    In a 2010 catalog introduction for my exhibition titled: POLITICS OF SNOW, Eileen Claussen, President of the Pew Center on Global Climate Change wrote the following: "Climate change has been taken over by politics…We are awash in talking points, briefing papers, scientific studies, and communiqués from national governments… Diane Burko's paintings remind us that all these words can often obscure or even obstruct our view of what is truly happening …..There is only so much you can do with words. People need to see that the world is changing before our eyes. When we look at Diane's images of the effects of climate change, we connect to something much deeper and more profound (and more moving) than the latest political pitch from one side or another in this debate…These paintings also connect us to something else. Even as Diane documents how things are changing, she also reminds us of the stunning beauty of nature - and, in turn, the urgency of doing everything in our power to protect it." The creation of this body of work was made possible because of the collaboration of many glacial geologists and scientists who continually share their visual data with me. Since 2006 I've been gathering repeats from people like Bruce Molnia (USGS) and Tad Pfeffer of Alaskan glaciers, from Daniel Fagre (USGS) of Glacier National Park and Lonnie Thompson and Jason Box (Ohio University's Byrd Polar Center) about Kilimanjaro, Qori Kalis and Petermann glaciers as well as from photographer David Breashears on the disappearing Himalayan glaciers. In my practice, I acknowledge the photographers, or archive agencies, such as USGS, NASA or Snow and Ice Center, in the title and all printed material. As a landscape painter and photographer my intent is to not reproduce those images but rather use them as inspiration. At first I used the documentary evidence in sets of diptychs or triptychs. Since 2010 I have incorporated geological charts of recessional lines, graphs, symbols and

  13. Nordic Snow Radar Experiment

    NASA Astrophysics Data System (ADS)

    Lemmetyinen, Juha; Kontu, Anna; Pulliainen, Jouni; Vehviläinen, Juho; Rautiainen, Kimmo; Wiesmann, Andreas; Mätzler, Christian; Werner, Charles; Rott, Helmut; Nagler, Thomas; Schneebeli, Martin; Proksch, Martin; Schüttemeyer, Dirk; Kern, Michael; Davidson, Malcolm W. J.

    2016-09-01

    The objective of the Nordic Snow Radar Experiment (NoSREx) campaign was to provide a continuous time series of active and passive microwave observations of snow cover at a representative location of the Arctic boreal forest area, covering a whole winter season. The activity was a part of Phase A studies for the ESA Earth Explorer 7 candidate mission CoReH2O (Cold Regions Hydrology High-resolution Observatory). The NoSREx campaign, conducted at the Finnish Meteorological Institute Arctic Research Centre (FMI-ARC) in Sodankylä, Finland, hosted a frequency scanning scatterometer operating at frequencies from X- to Ku-band. The radar observations were complemented by a microwave dual-polarization radiometer system operating from X- to W-bands. In situ measurements consisted of manual snow pit measurements at the main test site as well as extensive automated measurements on snow, ground and meteorological parameters. This study provides a summary of the obtained data, detailing measurement protocols for each microwave instrument and in situ reference data. A first analysis of the microwave signatures against snow parameters is given, also comparing observed radar backscattering and microwave emission to predictions of an active/passive forward model. All data, including the raw data observations, are available for research purposes through the European Space Agency and the Finnish Meteorological Institute. A consolidated dataset of observations, comprising the key microwave and in situ observations, is provided through the ESA campaign data portal to enable easy access to the data.

  14. A spotlight on snow leopard conservation in China.

    PubMed

    Alexander, Justine S; Zhang, Chengcheng; Shi, Kun; Riordan, Philip

    2016-07-01

    China holds the greatest proportion of the snow leopard's (Panthera uncia) global range and is central to their conservation. The country is also undergoing unprecedented economic growth, which increases both the threats to the snow leopard and the opportunities for its conservation. In this paper we aim to review published literature (from 1950 to 2014) in English and Mandarin on snow leopard ecology and conservation in China in order to identify thematic and geographic research gaps and propose research priorities. We first retrieved all published items that considered snow leopards in China (n = 106). We extracted from these papers 274 reports of snow leopard presence in China. We then reviewed a subset of papers (n = 33) of this literature, which specifically focused on snow leopard ecology and conservation within China. We introduced a thematic framework that allows a structured and comprehensive assessment of findings. This framework recognizes 4 critical and interrelated topics underpinning snow leopard ecology and conservation: habitat (distribution and protected area coverage); prey (distribution and abundance, predator-prey relationships); human interactions (hunting and trade, livestock interactions and conflicts); and the underlying policy context. Significant gains in knowledge as well as research gaps and priorities are discussed with reference to our framework. The modest quantity and limited scope of published research on the snow leopard in China calls for a continued and intensified effort to inform and support national conservation policies. © 2016 International Society of Zoological Sciences, Institute of Zoology/Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.

  15. Effective UV surface albedo of seasonally snow-covered lands

    NASA Astrophysics Data System (ADS)

    Tanskanen, A.; Manninen, T.

    2007-05-01

    At ultraviolet wavelengths the albedo of most natural surfaces is small with the striking exception of snow and ice. Therefore, snow cover is a major challenge for various applications based on radiative transfer modelling. The aim of this work was to determine the characteristic effective UV range surface albedo of various land cover types when covered by snow. First we selected 1 by 1 degree sample regions that met three criteria: the sample region contained dominantly subpixels of only one land cover type according to the 8 km global land cover classification product from the University of Maryland; the average slope of the sample region was less than 2 degrees according to the USGS's HYDRO1K slope data; the sample region had snow cover in March according to the NSIDC Northern Hemisphere weekly snow cover data. Next we generated 1 by 1 degree gridded 360 nm surface albedo data from the Nimbus-7 TOMS Lambertian equivalent reflectivity data, and used them to construct characteristic effective surface albedo distributions for each land cover type. The resulting distributions showed that each land cover type experiences a characteristic range of surface albedo values when covered by snow. The result is explained by the vegetation that extends upward beyond the snow cover and masks the bright snow covered surface.

  16. Effective UV surface albedo of seasonally snow-covered lands

    NASA Astrophysics Data System (ADS)

    Tanskanen, A.; Manninen, T.

    2007-02-01

    At ultraviolet wavelengths the albedo of most natural surfaces is small with the striking exception of snow and ice. Therefore, snow cover is a major challenge for various applications based on radiative transfer modelling. The aim of this work was to determine the characteristic effective UV range surface albedo of various land cover types when covered by snow. First we selected 1 by 1 degree sample regions that met three criteria: the sample region contained dominantly subpixels of only one land cover type according to the 8 km global land cover classification product from the University of Maryland; the average slope of the sample region was less than 2 degrees according to the USGS's HYDRO1K slope data; the sample region had snow cover in March according to the NSIDC Northern Hemisphere weekly snow cover data. Next we generated 1 by 1 degree gridded 360 nm surface albedo data from the Nimbus-7 TOMS Lambertian equivalent reflectivity data, and used them to construct characteristic effective surface albedo distributions for each land cover type. The resulting distributions showed that each land cover type experiences a characteristic range of surface albedo values when covered by snow. The result is explained by the vegetation that extends upward beyond the snow cover and masks the bright snow covered surface.

  17. Distribution and characteristic of PAHs in snow of Fildes Peninsula.

    PubMed

    Na, Guangshui; Liu, Chunyang; Wang, Zhen; Ge, Linke; Ma, Xindong; Yao, Ziwei

    2011-01-01

    Polycyclic aromatic hydrocarbons (PAHs) investigation in different matrices has been reported largely, whereas reports on snow samples were limited. Snow, as the main matrix in the polar region, has an important study meaning. PAHs in snow samples were analyzed to investigate the distribution and contamination status of them in the Antarctic, as well as to provide some references for global migration of PAHs. Snow samples collected in Fildes Peninsula were enriched and separated by solid-phase membrane disks and eluted by methylene dichloride, then quantified by gas chromatography/mass spectrometry. All types of PAHs were detected except for Benzo(a)pyrene. Principal component analysis method was applied to characterize them. Three factors (Naphthalene, Fluorene and Phenanthrene) accounted for 60.57%, 21.61% and 9.80%, respectively. The results showed that the major PAHs sources maybe the atmospheric transportation, and the combustion of fuel in Fildes Peninsula. The comparison of concentration and types of PAHs between accumulated snow and fresh snow showed that the main compound concentrations in accumulated snow samples were higher than those in fresh ones. The risk assessment indicated that the amount of PAHs in the snow samples would not lead to ecological risk.

  18. Early results from NASA's SnowEx campaign

    NASA Astrophysics Data System (ADS)

    Kim, Edward; Gatebe, Charles; Hall, Dorothy; Misakonis, Amy; Elder, Kelly; Marshall, Hans Peter; Hiemstra, Chris; Brucker, Ludovic; Crawford, Chris; Kang, Do Hyuk; De Marco, Eugenia; Beckley, Matt; Entin, Jared

    2017-04-01

    SnowEx is a multi-year airborne snow campaign with the primary goal of addressing the question: How much water is stored in Earth's terrestrial snow-covered regions? Year 1 (2016-17) focuses on the distribution of snow-water equivalent (SWE) and the snow energy balance in a forested environment. The year 1 primary site is Grand Mesa and the secondary site is the Senator Beck Basin, both in western, Colorado, USA. Ten core sensors on four core aircraft will make observations using a broad suite of airborne sensors including active and passive microwave, and active and passive optical/infrared sensing techniques to determine the sensitivity and accuracy of these potential satellite remote sensing techniques, along with models, to measure snow under a range of forest conditions. SnowEx also includes an extensive range of ground truth measurements—in-situ samples, snow pits, ground based remote sensing measurements, and sophisticated new techniques. A detailed description of the data collected will be given and some early results will be presented. Seasonal snow cover is the largest single component of the cryosphere in areal extent (covering an average of 46M km2 of Earth's surface (31 % of land areas) each year). This seasonal snow has major societal impacts in the areas of water resources, natural hazards (floods and droughts), water security, and weather and climate. The only practical way to estimate the quantity of snow on a consistent global basis is through satellites. Yet, current space-based techniques underestimate storage of snow water equivalent (SWE) by as much as 50%, and model-based estimates can differ greatly vs. estimates based on remotely-sensed observations. At peak coverage, as much as half of snow-covered terrestrial areas involve forested areas, so quantifying the challenge represented by forests is important to plan any future snow mission. Single-sensor approaches may work for certain snow types and certain conditions, but not for others

  19. Rocky Mountain Snow

    NASA Image and Video Library

    2017-09-27

    NASA image acquired December 19, 2012 In time for the 2012 winter solstice, a storm dropped snow over most of the Rocky Mountains in the United States. On December 20, the National Weather Service reported snow depths exceeding 100 centimeters (39 inches) in some places—the result of the recent snowfall plus accumulation from earlier storms. The Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua satellite captured this natural-color image on December 19, 2012. Clouds had mostly cleared from the region, though some cloud cover lingered over parts of the Pacific Northwest and Colorado. Showing more distinct contours than the clouds, the snow cover stretched across the Rocky Mountains and the surrounding region, from Idaho to Arizona and from California to Colorado. Snowfall did not stop in Colorado, as the storm continued moving eastward across the Midwest. By December 20, 2012, a combination of heavy snow and strong winds had closed schools, iced roads, and delayed flights, complicating plans for holiday travelers. Though troublesome for travel, the snow brought much-needed moisture; multiple cities had set new records for consecutive days without measurable snow, CBS news reported. As of December 18, the U.S. Drought Monitor stated that a substantial portion of the continental United States continued to suffer from drought, and “exceptional” drought conditions extended from South Dakota to southern Texas. NASA image courtesy Jeff Schmaltz, LANCE MODIS Rapid Response. Caption by Michon Scott. Instrument: Aqua - MODIS To read more go to: earthobservatory.nasa.gov/IOTD/view.php?id=80035 Credit: NASA Earth Observatory NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission

  20. Bacterial diversity in snow on North Pole ice floes.

    PubMed

    Hauptmann, Aviaja L; Stibal, Marek; Bælum, Jacob; Sicheritz-Pontén, Thomas; Brunak, Søren; Bowman, Jeff S; Hansen, Lars H; Jacobsen, Carsten S; Blom, Nikolaj

    2014-11-01

    The microbial abundance and diversity in snow on ice floes at three sites near the North Pole was assessed using quantitative PCR and 454 pyrosequencing. Abundance of 16S rRNA genes in the samples ranged between 43 and 248 gene copies per millilitre of melted snow. A total of 291,331 sequences were obtained through 454 pyrosequencing of 16S rRNA genes, resulting in 984 OTUs at 97 % identity. Two sites were dominated by Cyanobacteria (72 and 61 %, respectively), including chloroplasts. The third site differed by consisting of 95 % Proteobacteria. Principal component analysis showed that the three sites clustered together when compared to the underlying environments of sea ice and ocean water. The Shannon indices ranged from 2.226 to 3.758, and the Chao1 indices showed species richness between 293 and 353 for the three samples. The relatively low abundances and diversity found in the samples indicate a lower rate of microbial input to this snow habitat compared to snow in the proximity of terrestrial and anthropogenic sources of microorganisms. The differences in species composition and diversity between the sites show that apparently similar snow habitats contain a large variation in biodiversity, although the differences were smaller than the differences to the underlying environment. The results support the idea that a globally distributed community exists in snow and that the global snow community can in part be attributed to microbial input from the atmosphere.

  1. Monitoring and modelling snow avalanches in Svalbard

    NASA Astrophysics Data System (ADS)

    Humlum, O.; Christiansen, H.; Neumann, U.; Eckerstorfer, M.; Sjöblom, A.; Stalsberg, K.; Rubensdotter, L.

    2009-04-01

    Monitoring and modelling snow avalanches in Svalbard Ole Humlum 1,3, Hanne H. Christiansen 1, Ulrich Neumann 1, Markus Eckerstorfer 1, Anna Sjöblom 1, Knut Stalsberg 2 and Lena Rubensdotter 2. 1: The University Centre in Svalbard (UNIS). 2: Geological Survey of Norway (NGU) 3: University of Oslo Ground based transportation in Svalbard landscape all takes place across mountainous terrain affected by different geomorphological slope processes. Traffic in and around the Svalbard settlements is increasing, and at the same time global climate models project substantial increases in temperature and precipitation in northern high latitudes for coming century. Therefore improved knowledge on the effect of climatic changes on slope processes in such high arctic landscapes is becoming increasingly important. Motivated by this, the CRYOSLOPE Svalbard research project since 2007 has carried out field observations on snow avalanche frequency and associated meteorological conditions. Snow avalanches are important geomorphic agents of erosion and deposition, and have long been a source of natural disasters in many mid-latitude mountain areas. Avalanches as a natural hazard has thereby been familiar to inhabitants of the Alps and Scandinavia for centuries, while it is a more recent experience in high arctic Svalbard. In addition, overall climate, topography and especially high winter wind speeds makes it difficult to apply snow avalanche models (numerical or empirical) developed for use at lower latitudes, e.g. in central Europe. In the presentation we examplify results from the ongoing (since winter 2006-07) monitoring of snow avalanches in Svalbard along a 70 km long observational route in the mountains. In addition, we present observations on the geomorphological impact of avalanches, with special reference to the formation of rock glaciers. Finally, we also present some initial results from numerical attempts of snow avalanche risk modelling within the study area.

  2. Recent research in snow hydrology

    NASA Technical Reports Server (NTRS)

    Dozier, Jeff

    1987-01-01

    Recent work on snow-pack energy exchange has involved detailed investigations on snow albedo and attempts to integrate energy-balance calculations over drainage basins. Along with a better understanding of the EM properties of snow, research in remote sensing has become more focused toward estimation of snow-pack properties. In snow metamorphism, analyses of the physical processes must now be coupled to better descriptions of the geometry of the snow microstructure. The dilution method now appears to be the best direct technique for measuring the liquid water content of snow; work on EM methods continues. Increasing attention to the chemistry of the snow pack has come with the general focus on acid precipitation in hydrology.

  3. Secrets of Snow Liveshot Recap

    NASA Image and Video Library

    Research Physical Scientist and Deputy Project Scientist for GPM Gail Skofronick-Jackson answers questions about the importance of studying snow from space, the impact of not enough snow, and the f...

  4. Crystal growth of artificial snow

    NASA Technical Reports Server (NTRS)

    Kimura, S.; Oka, A.; Taki, M.; Kuwano, R.; Ono, H.; Nagura, R.; Narimatsu, Y.; Tanii, J.; Kamimiytat, Y.

    1984-01-01

    Snow crystals were grown onboard the space shuttle during STS-7 and STS-8 to facilitate the investigation of crystal growth under conditions of weightlessness. The experimental design and hardware are described. Space-grown snow crystals were polyhedrons looking like spheres, which were unlike snow crystals produced in experiments on Earth.

  5. Lead contamination of urban snow.

    PubMed

    Grandstaff, D E; Myer, G H

    1979-01-01

    Lead content of newly fallen snow in an urban area ranges from 34 to 56 ppb. After falling, snow may incorporate major additional amounts of lead by dry deposition of lead aerosols from local sources. The highest concentration found was 2,700 ppb. Ingestion of lead-contaminated snow might pose a health hazard to inner city children.

  6. Producing Snow Extent and Snow Water Equivalent Information for Climate Research Purposes - ESA DUE Globsnow Effort

    NASA Astrophysics Data System (ADS)

    Luojus, Kari; Pulliainen, Jouni; Rott, Helmut; Nagler, Thomas; Solberg, Rune; Wiesmann, Andreas; Derksen, Chris; Metsämäki, Sari; Malnes, Eirik; Bojkov, Bojan

    2010-05-01

    The European Space Agency (ESA) Data User Element (DUE) funded GlobSnow project aims at creating a global database of snow parameters for climate research purposes. The main objective is to create a long term dataset on two essential snow parameters. The project will provide information concerning the areal extent of snow (SE) on a global scale and snow water equivalent (SWE) for the Northern Hemisphere. Both products will include the end product derived from the satellite data along with accuracy information for each snow parameter. The temporal span of the SE product will be 15 years and the span for the SWE product will be 30 years. A key improvement of the snow products, when compared with the currently available data sets, will be the inclusion of a statistically derived accuracy estimate accompanying each SE or SWE estimate (on a pixel level). In addition to the SE and SWE time-series, an operational near-real time (NRT) snow information service will be implemented. The service will provide daily snow maps for hydrological, meteorological, and climate research purposes. The snow products will be based on data acquired from optical and passive microwave-based spaceborne sensors combined with ground-based weather station observations. The work was initiated in November 2008, and is being coordinated by the Finnish Meteorological Institute (FMI). Other project partners involved are NR (Norwegian Computing Centre), ENVEO IT GmbH, GAMMA Remote Sensing AG, Finnish Environment Institute (SYKE), Environment Canada (EC) and Northern Research Institute (Norut). Extensive algorithm evaluation efforts were carried out for the candidate SWE and SE algorithms during 2009 using ground truth data gathered from Canada, Scandinavia, Russia and the Alps. The acquired evaluation results have enabled the selection of the algorithms to be utilized for the GlobSnow SE and SWE products. The SWE product is derived using the FMI Algorithm and the SE product is a combination of NR and

  7. 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.

  8. Dry Snow Metamorphism

    DTIC Science & Technology

    2012-09-19

    grained ice. The P.I. is studying ice and firn cores from Greenland and Antarctica under NSF funding using both these methods and a recently...Firn”, Rachel W. Obbard, Ian Baker and Rachel W. Lomonaco, Encyclopedia of Snow, Ice and Glaciers , V. Singh, P. Singh, and U. K. Haritashya (eds

  9. Snow White 5 Trench

    NASA Technical Reports Server (NTRS)

    2008-01-01

    This image was acquired by NASA's Phoenix Mars Lander's Robotic Arm Camera on the 35th Martian day of the mission, or Sol 34 (June 29, 2008), after the May 25, 2008, landing. This image shows the trench informally called 'Snow White 5.' The trench is 4-to-5 centimeters (about 1.5-to-1.9 inches) deep, 24 centimeters (about 9 inches) wide and 33 centimeters (13 inches) long.

    Snow White 5 is Phoenix's current active digging area after additional trenching, grooming, and scraping by Phoenix's Robotic Arm in the last few sols to trenches informally called Snow White 1, 2, 3, and 4. Near the top center of the image is the Robotic Arm's Thermal and Electrical Conductivity Probe.

    Snow White 5 is located in a patch of Martian soil near the center of a polygonal surface feature, nicknamed 'Cheshire Cat.' The digging site has been named 'Wonderland.'

    This image has been enhanced to brighten shaded areas.

    The Phoenix Mission is led by the University of Arizona, Tucson, on behalf of NASA. Project management of the mission is by NASA's Jet Propulsion Laboratory, Pasadena, Calif. Spacecraft development is by Lockheed Martin Space Systems, Denver.

  10. Snow White 5 Trench

    NASA Technical Reports Server (NTRS)

    2008-01-01

    This image was acquired by NASA's Phoenix Mars Lander's Robotic Arm Camera on the 35th Martian day of the mission, or Sol 34 (June 29, 2008), after the May 25, 2008, landing. This image shows the trench informally called 'Snow White 5.' The trench is 4-to-5 centimeters (about 1.5-to-1.9 inches) deep, 24 centimeters (about 9 inches) wide and 33 centimeters (13 inches) long.

    Snow White 5 is Phoenix's current active digging area after additional trenching, grooming, and scraping by Phoenix's Robotic Arm in the last few sols to trenches informally called Snow White 1, 2, 3, and 4. Near the top center of the image is the Robotic Arm's Thermal and Electrical Conductivity Probe.

    Snow White 5 is located in a patch of Martian soil near the center of a polygonal surface feature, nicknamed 'Cheshire Cat.' The digging site has been named 'Wonderland.'

    This image has been enhanced to brighten shaded areas.

    The Phoenix Mission is led by the University of Arizona, Tucson, on behalf of NASA. Project management of the mission is by NASA's Jet Propulsion Laboratory, Pasadena, Calif. Spacecraft development is by Lockheed Martin Space Systems, Denver.

  11. 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

  12. Snow in Italy

    NASA Image and Video Library

    2017-09-28

    NASA image acquired February 24, 2012 By late February, 2012, the great European cold wave had begun to loosen its frigid grip, but significant snow still remained in the region. The Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite captured this true-color image of snow in Italy on February 24 at 12:35 UTC (1:30 p.m. local time). In the north of the image, bright white clouds blanket the region in a broad arc. Snow, which tends to be generally less bright that clouds, covers the Alps in the north of Italy. The Apennine Mountains, which form the backbone of the Italian peninsula, also carry a blanket of snow. Although clouds and snow can, at times, be distinguished visually in a true-color image, sometimes they can appear very similar. When it is important to clearly define snow from cloud, false color images are often helpful. Rome, which can be seen as a gray smudge on the southwestern coast of the peninsula, recorded highs of a spring-like 50°F the day this image was captured, but earlier in the month the temperatures dove as low as 26°F on February 5. During that cold snap a rare intense snowfall blanketed Rome, causing the closure of the Colosseum, the Roman Forum and the Palatine Hill due to concerns of the risk of icy footing for tourists, and roads became impassible. Further north, temperatures plummeted to −21 °C (−6 °F) on 7 February. On February 11, news media reported over 2 meters (6.5 feet) of snow had fallen in Urbino, a walled town situated on a high sloping hillside on the eastern side of the Apennine Mountains. That same snowfall cut access to many remote towns in the Apennines, blocking roads and trapping some people in the homes. Credit: NASA/GSFC/Jeff Schmaltz/MODIS Land Rapid Response Team NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA

  13. Snow in Earth System Models: Recent Progress and Future Challenges

    NASA Astrophysics Data System (ADS)

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

    2016-12-01

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

  14. 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.

  15. 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.

  16. 'Snow Queen' Animation

    NASA Technical Reports Server (NTRS)

    2008-01-01

    This animation consists of two close-up images of 'Snow Queen,' taken several days apart, by the Robotic Arm Camera (RAC) aboard NASA's Phoenix Mars Lander.

    Snow Queen is the informal name for a patch of bright-toned material underneath the lander.

    Thruster exhaust blew away surface soil covering Snow Queen when Phoenix landed on May 25, 2008, exposing this hard layer comprising several smooth rounded cavities beneath the lander. The RAC images show how Snow Queen visibly changed between June 15, 2008, the 21st Martian day, or sol, of the mission and July 9, 2008, the 44th sol.

    Cracks as long as 10 centimeters (about four inches) appeared. One such crack is visible at the left third and the upper third of the Sol 44 image. A seven millimeter (one-third inch) pebble or clod appears just above and slightly to the right of the crack in the Sol 44 image. Cracks also appear in the lower part of the left third of the image. Other pieces noticeably shift, and some smooth texture has subtly roughened.

    The Phoenix team carefully positioned and focused RAC the same way in both images. Each image is about 60 centimeters, or about two feet, wide. The object protruding in from the top on the right half of the images is Phoenix's thermal and electrical conductivity probe.

    Snow Queen and other ice exposed by Phoenix landing and trenching operations on northern polar Mars is the first time scientists have been able to monitor Martian ice at a place where temperatures are cold enough that the ice doesn't immediately sublimate, or vaporize, away.

    The Phoenix Mission is led by the University of Arizona, Tucson, on behalf of NASA. Project management of the mission is by NASA's Jet Propulsion Laboratory, Pasadena, Calif. Spacecraft development is by Lockheed Martin Space Systems, Denver.

  17. Warmer climate: less or more snow?

    NASA Astrophysics Data System (ADS)

    Räisänen, Jouni

    2008-02-01

    Changes in snow amount, as measured by the water equivalent of the snow pack (SWE), are studied using simulations of 21st century climate by 20 global climate models. Although the simulated warming makes snow season to shorten from its both ends in all of Eurasia and North America, SWE at the height of the winter generally increases in the coldest areas. Elsewhere, snow decreases throughout the winter. The average borderline between increasing and decreasing midwinter SWE coincides broadly with the -20°C isotherm in late 20th century November March mean temperature, although with some variability between different areas. On the colder side of this isotherm, an increase in total precipitation generally dominates over reduced fraction of solid precipitation and more efficient melting, and SWE therefore increases. On the warmer side, where the phase of winter precipitation and snowmelt are more sensitive to the simulated warming, the reverse happens. The strong temperature dependence of the simulated SWE changes suggests that projections of SWE change could be potentially improved by taking into account biases in simulated present-day winter temperatures. A probabilistic cross verification exercise supports this suggestion.

  18. Evaluation of SNODAS snow depth and GPS-measured snow depth in the Western United States

    NASA Astrophysics Data System (ADS)

    Boniface, K.; Braun, J.; McCreight, J. L.; Larson, K. M.

    2013-12-01

    Seasonal snowpack represents an important freshwater reservoir and is a significant contributor to global water and energy cycles. To evaluate and better understand gridded snow depth estimates from the Snow Data Assimilation System (SNODAS), we compare against snow depth observations from GPS Interferometric Reflectometry (GPS-IR). GPS-IR snow depth observations at roughly 100 Plate Boundary Observatory sites (originally intended to measure tectonic activity) provide an independent data set to contextualize SNODAS estimates across the Western US for water years 2010-2013 and into the future. Results from this study indicate that SNODAS and GPS-IR products generally agree. More than 80% of the GPS sites compared with SNODAS shown Root Mean Square Deviations (RMSD) of less than 15 cm with correlation coefficients greater than 0.6. Significant differences are found between GPS-IR and SNODAS for locations which are distant from other point measurements, located in complex terrain, or located in areas with strong vegetation heterogeneities. GPS-IR derived estimates of snow depth provide useful error characterization of SNODAS data products across much of the western United States and have potential as an additional data assimilation source which could improve SNODAS products.

  19. 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.

  20. Measurements of thermal infrared spectral reflectance of frost, snow, and ice

    NASA Technical Reports Server (NTRS)

    Salisbury, John W.; D'Aria, Dana M.; Wald, Andrew

    1994-01-01

    Because much of Earth's surface is covered by frost, snow, and ice, the spectral emissivities of these materials are a significant input to radiation balance calculations in global atmospheric circulation and climate change models. Until now, however, spectral emissivities of frost and snow have been calculated from the optical constants of ice. We have measured directional hemispherical reflectance spectra of frost, snow, and ice from which emissivities can be predicted using Kirchhoff's law (e = 1-R). These measured spectra show that contrary to conclusions about the emissivity of snow drawn from previously calculated spectra, snow emissivity departs significantly from blackbody behavior in the 8-14 micrometer region of the spectrum; snow emissivity decreases with both increasing particle size and increasing density due to packing or grain welding; while snow emissivity increases due to the presence of meltwater.

  1. Measurements of thermal infrared spectral reflectance of frost, snow, and ice

    NASA Technical Reports Server (NTRS)

    Salisbury, John W.; D'Aria, Dana M.; Wald, Andrew

    1994-01-01

    Because much of Earth's surface is covered by frost, snow, and ice, the spectral emissivities of these materials are a significant input to radiation balance calculations in global atmospheric circulation and climate change models. Until now, however, spectral emissivities of frost and snow have been calculated from the optical constants of ice. We have measured directional hemispherical reflectance spectra of frost, snow, and ice from which emissivities can be predicted using Kirchhoff's law (e = 1-R). These measured spectra show that contrary to conclusions about the emissivity of snow drawn from previously calculated spectra, snow emissivity departs significantly from blackbody behavior in the 8-14 micrometer region of the spectrum; snow emissivity decreases with both increasing particle size and increasing density due to packing or grain welding; while snow emissivity increases due to the presence of meltwater.

  2. Potential for obtaining optimal snow states estimation by assimilating space-borne passive microwave measurements into surface snow modeling

    NASA Astrophysics Data System (ADS)

    Li, D.; Durand, M. T.; Margulis, S. A.

    2011-12-01

    Though hampered by coarse spatial resolution, passive microwave remote sensing (PM) is still attractive for snow measurement due to its global continuous coverage, high sensitivity to snow and frequent revisit time. Physical snow evolution-emission models have been routinely used to simulate snowpack states and brightness temperature (Tb) with surface meteorological data. In recent decade, incorporating PM snow measurements into surface modeling by data assimilation systems has shown promise in raising the accuracy of snowpack characterization. However, so far most snow assimilation projects have been experimentally oriented. In this study, we conducted several preliminary experiments by inter-comparing the true snow states, modeled snow states, modeled Tb and space-borne observed Tb, to access the potential of assimilating space-borne PM measurements into surface snow models to attain better snow characterizations. Our study was carried out in the Kern River basin, southern Sierra Nevada, USA. The northern part of the Kern basin, which ranges from 36.25°N to 36.75°N, was selected because it is uniformly snow-covered, above the treeline, and contains four California Data Exchange Center (CDEC) gages. In the experiments, a three-layer energy-balance based Simple Snow-Atmosphere-Soil (SAST) transfer model was integrated into the Simplified Simple Biosphere (SSiB) model, named Simplified Simple Biosphere version 3 (SSiB3). Forced by meteorological data, SSiB3's outputs, which are snow states include snow depth, ground temperature, grain size, volumetric water content, and snow density, were further input into the Microwave Emission Model of Layered Snowpack (MEMLS) to simulate dual-polarization snow Tb at multiple frequencies. Our space-borne PM data were collected from AMSR-E Level2A, 36.5GHz measurements. A new weighted average data processing method processes AMSR-E observation in their native resolution (8km×14km at 36.5GHz), to enhance the PM data

  3. Influence of snow cover changes on surface radiation and heat balance based on the WRF model

    NASA Astrophysics Data System (ADS)

    Yu, Lingxue; Liu, Tingxiang; Bu, Kun; Yang, Jiuchun; Chang, Liping; Zhang, Shuwen

    2016-07-01

    The snow cover extent in mid-high latitude areas of the Northern Hemisphere has significantly declined corresponding to the global warming, especially since the 1970s. Snow-climate feedbacks play a critical role in regulating the global radiation balance and influencing surface heat flux exchange. However, the degree to which snow cover changes affect the radiation budget and energy balance on a regional scale and the difference between snow-climate and land use/cover change (LUCC)-climate feedbacks have been rarely studied. In this paper, we selected Heilongjiang Basin, where the snow cover has changed obviously, as our study area and used the WRF model to simulate the influences of snow cover changes on the surface radiation budget and heat balance. In the scenario simulation, the localized surface parameter data improved the accuracy by 10 % compared with the control group. The spatial and temporal analysis of the surface variables showed that the net surface radiation, sensible heat flux, Bowen ratio, temperature and percentage of snow cover were negatively correlated and that the ground heat flux and latent heat flux were positively correlated with the percentage of snow cover. The spatial analysis also showed that a significant relationship existed between the surface variables and land cover types, which was not obviously as that for snow cover changes. Finally, six typical study areas were selected to quantitatively analyse the influence of land cover types beneath the snow cover on heat absorption and transfer, which showed that when the land was snow covered, the conversion of forest to farmland can dramatically influence the net radiation and other surface variables, whereas the snow-free land showed significantly reduced influence. Furthermore, compared with typical land cover changes, e.g., the conversion of forest into farmland, the influence of snow cover changes on net radiation and sensible heat flux were 60 % higher than that of land cover changes

  4. NASA Airborne Snow Observatory: Measuring Spatial Distribution of Snow Water Equivalent and Snow Albedo

    NASA Astrophysics Data System (ADS)

    Joyce, M.; Painter, T. H.; Ramirez, P.; Laidlaw, R.; Boardman, J. W.; Bormann, K. J.; Skiles, S. M.; Deems, J. S.; Berisford, D. F.; Richardson, M.

    2016-12-01

    The two most critical properties for understanding snowmelt runoff and timing are the spatial and temporal distributions of snow water equivalent (SWE) and snow albedo. Despite their importance in controlling volume and timing of runoff, snowpack albedo and SWE are still largely unquantified in the US and not at all in most of the globe, leaving runoff models poorly constrained. NASA Jet Propulsion Laboratory, in partnership with the California Department of Water Resources, has developed the Airborne Snow Observatory (ASO), an imaging spectrometer and scanning LiDAR system, to quantify SWE and snow albedo, generate unprecedented knowledge of snow properties for cutting edge cryospheric science, and provide complete, robust inputs to water management models and systems of the future. This poster will describe the NASA Airborne Snow Observatory, its outputs and their uses and applications, along with recent advancements to the system and plans for the project's future. Specifically, we will look at how ASO uses its imaging spectrometer to quantify spectral albedo, broadband albedo, and radiative forcing by dust and black carbon in snow. Additionally, we'll see how the scanning LiDAR is used to determine snow depth against snow-free acquisitions and to quantify snow water equivalent when combined with in-situ constrained modeling of snow density.

  5. NASA Airborne Snow Observatory: Measuring Spatial Distribution of Snow Water Equivalent and Snow Albedo

    NASA Astrophysics Data System (ADS)

    Joyce, M.; Painter, T. H.; Mattmann, C. A.; Ramirez, P.; Laidlaw, R.; Bormann, K. J.; Skiles, M.; Richardson, M.; Berisford, D. F.

    2015-12-01

    The two most critical properties for understanding snowmelt runoff and timing are the spatial and temporal distributions of snow water equivalent (SWE) and snow albedo. Despite their importance in controlling volume and timing of runoff, snowpack albedo and SWE are still largely unquantified in the US and not at all in most of the globe, leaving runoff models poorly constrained. NASA Jet Propulsion Laboratory, in partnership with the California Department of Water Resources, has developed the Airborne Snow Observatory (ASO), an imaging spectrometer and scanning LiDAR system, to quantify SWE and snow albedo, generate unprecedented knowledge of snow properties for cutting edge cryospheric science, and provide complete, robust inputs to water management models and systems of the future. This poster will describe the NASA Airborne Snow Observatory, its outputs and their uses and applications, along with recent advancements to the system and plans for the project's future. Specifically, we will look at how ASO uses its imaging spectrometer to quantify spectral albedo, broadband albedo, and radiative forcing by dust and black carbon in snow. Additionally, we'll see how the scanning LiDAR is used to determine snow depth against snow-free acquisitions and to quantify snow water equivalent when combined with in-situ constrained modeling of snow density.

  6. Nye Lecture: Snow Crystals, Shrubs, and the Changing Climate of the Arctic

    NASA Astrophysics Data System (ADS)

    Sturm, M.

    2005-12-01

    At the peak of winter, snow covers more than 45 million km2 of the northern hemisphere. More than 90 percent of this snow will melt before the end of the following summer. In the southern part of this snow-covered area, the seasonal pack is ephemeral, lasting but a few short weeks, but with increasing latitude (or altitude), it lasts much longer. In arctic and alpine locations it can persist for 9 months of the year. In these more extreme locations, the snow is an essential element of the ecosystem, both acting upon, and being acted on, by the biota. For historical reasons, our understanding of snow cover and its interactions has come from two disparate scientific sources: geophysicists working on glaciers and avalanches who were trying to understand snow properties and to develop a physical basis for snow science, and ecologists who were trying to understand the impact of snow on plants, animals, and humans. With the recognition now that snow is both a passive and active agent, we are seeing an increasing number of studies wherein both of these traditional approaches are combined. Geophysicists are learning the Latin names of shrubs while botanist can now identify wind slab. A personal example that illustrates the necessity of this melding process has been our effort to understand the climatic implications of Arctic snow-shrub interactions. We have had to combine traditional snow geophysical studies (i.e., crystal growth, thermal processes, light reflection) with traditional ecological studies (i.e., competition, carbon and nitrogen cycling). Through this process we have discovered that snow-shrub interactions, or more broadly, snow-vegetation interactions, are helping to push the Arctic down a warming trajectory that has global implications. Soil microbes and snow crystals, wind-blown snow and shrubs, are all leading actors in a climate change drama whose outcome is of concern to us all.

  7. 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. 

  8. Evaporation characteristics of ETBE-blended gasoline.

    PubMed

    Okamoto, Katsuhiro; Hiramatsu, Muneyuki; Hino, Tomonori; Otake, Takuma; Okamoto, Takashi; Miyamoto, Hiroki; Honma, Masakatsu; Watanabe, Norimichi

    2015-04-28

    To reduce greenhouse gas emissions, which contribute to global warming, production of gasoline blended with ethyl tert-buthyl ether (ETBE) is increasing annually. The flash point of ETBE is higher than that of gasoline, and blending ETBE into gasoline will change the flash point and the vapor pressure. Therefore, it is expected that the fire hazard caused by ETBE-blended gasoline would differ from that caused by normal gasoline. The aim of this study was to acquire the knowledge required for estimating the fire hazard of ETBE-blended gasoline. Supposing that ETBE-blended gasoline was a two-component mixture of gasoline and ETBE, we developed a prediction model that describes the vapor pressure and flash point of ETBE-blended gasoline in an arbitrary ETBE blending ratio. We chose 8-component hydrocarbon mixture as a model gasoline, and defined the relation between molar mass of gasoline and mass loss fraction. We measured the changes in the vapor pressure and flash point of gasoline by blending ETBE and evaporation, and compared the predicted values with the measured values in order to verify the prediction model. The calculated values of vapor pressures and flash points corresponded well to the measured values. Thus, we confirmed that the change in the evaporation characteristics of ETBE-blended gasoline by evaporation could be predicted by the proposed model. Furthermore, the vapor pressure constants of ETBE-blended gasoline were obtained by the model, and then the distillation curves were developed. Copyright © 2015 Elsevier B.V. All rights reserved.

  9. Effective Blended Learning Techniques

    ERIC Educational Resources Information Center

    Gill, Deborah

    2009-01-01

    Blended learning is becoming more prevalent in higher education courses. Reasons for blending range from accommodating more students to improving the quality of courses offered. The purpose of this paper is twofold: (1) to discuss student attitudes towards blended courses versus face-to-face versus completely online courses, and (2) to consider…

  10. 'Snow White' Trench

    NASA Technical Reports Server (NTRS)

    2008-01-01

    This image was acquired by NASA's Phoenix Mars Lander's Surface Stereo Imager on Sol 43, the 43rd Martian day after landing (July 8, 2008). This image shows the trench informally called 'Snow White.'

    Two samples were delivered to the Wet Chemistry Laboratory, which is part of Phoenix's Microscopy, Electrochemistry, and Conductivity Analyzer (MECA). The first sample was taken from the surface area just left of the trench and informally named 'Rosy Red.' It was delivered to the Wet Chemistry Laboratory on Sol 30 (June 25, 2008). The second sample, informally named 'Sorceress,' was taken from the center of the 'Snow White' trench and delivered to the Wet Chemistry Laboratory on Sol 41 (July 6, 2008).

    The Phoenix Mission is led by the University of Arizona, Tucson, on behalf of NASA. Project management of the mission is by NASA's Jet Propulsion Laboratory, Pasadena, Calif. Spacecraft development is by Lockheed Martin Space Systems, Denver.

  11. Snow in southwestern Europe

    NASA Image and Video Library

    2017-09-28

    In February 2015, New England was not alone in dealing with the wrath of Old Man Winter. Thick snow blanketed mountain ranges in southwestern Europe after a winter storm pushed through the region in early February. The Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra satellite captured this true-color image of the snow-covered peaks of the Cantabrian Mountains, the Pyrenees, the Alps, and Massif Central on February 9, 2015. Credit: NASA/GSFC/Jeff Schmaltz/MODIS Land Rapid Response Team NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

  12. 'Snow White' Trench

    NASA Technical Reports Server (NTRS)

    2008-01-01

    This image was acquired by NASA's Phoenix Mars Lander's Surface Stereo Imager on Sol 43, the 43rd Martian day after landing (July 8, 2008). This image shows the trench informally called 'Snow White.'

    Two samples were delivered to the Wet Chemistry Laboratory, which is part of Phoenix's Microscopy, Electrochemistry, and Conductivity Analyzer (MECA). The first sample was taken from the surface area just left of the trench and informally named 'Rosy Red.' It was delivered to the Wet Chemistry Laboratory on Sol 30 (June 25, 2008). The second sample, informally named 'Sorceress,' was taken from the center of the 'Snow White' trench and delivered to the Wet Chemistry Laboratory on Sol 41 (July 6, 2008).

    The Phoenix Mission is led by the University of Arizona, Tucson, on behalf of NASA. Project management of the mission is by NASA's Jet Propulsion Laboratory, Pasadena, Calif. Spacecraft development is by Lockheed Martin Space Systems, Denver.

  13. Development of an Automatic Blowing Snow station

    NASA Astrophysics Data System (ADS)

    Nishimura, K.

    2010-12-01

    On the Antarctic ice sheet, strong katabatic winds blow throughout the year and a large but unknown fraction of the snow which falls on it is removed continuously. This constitutes a significant factor in mass and energy balance and is all the more important when predicting the likely effects of global climate change. Further, recent experimental work has indicated that the snowdrift sublimation can lead to significant mass losses during strong winds and can be also an important factor in the surface mass balance of the Antarctic ice sheets. Nishimura and Nemoto (2005) carried out the blowing snow observations at Mizuho station, Antarctica in 2000 with the snow particle counters (SPC) that can sense not only the number of snow particles but also their diameters. SPC worked properly and the data obtained revealed profiles of mass flux and particle size distributions as a function of the friction velocity. However, the SPC requires rather high power supply and the data is stored in PC; it is not always suitable for the unmanned observations under the severe Antarctic conditions. Thus, we have developed a simpler device by measuring the attenuation of the light intensity, which strongly depends on the blowing snow flux. A small wind turbine and a cold-proof buttery were utilized as a power source. Firstly, its performance was tested with comparing the SPC in a cold wind tunnel system and it proved adequately fit for practical use by combining the output of the anemometer. In 2009/2010 winter, three systems have been set at Ishikari, Col du Lac blanc in France, and S17 near Syowa station in Antarctica, and the tests are still continuing.

  14. Growth Processes of Snow.

    DTIC Science & Technology

    1983-05-01

    no change in concentration until 3300 sec. and 3900 sec. However, there 34 7600- 9 see running moon 2-26-80 7200- 0 S6800- 400 3000 4500-- 6000 1 23...ranging from 1.80 to 1.95. This suggests that during this phase of spectral evolution, the sum of the diameters of snow particles is a conservative...transition from the dominance of deposition to aggregation. During the deposition growth phase the particles are small and collisions are rare

  15. Study of aerosol effect on accelerated snow melting over the Tibetan Plateau during boreal spring

    NASA Astrophysics Data System (ADS)

    Lee, Woo-Seop; Bhawar, Rohini L.; Kim, Maeng-Ki; Sang, Jeong

    2013-08-01

    In the present study, a coupled atmosphere-ocean global climate model (CSIRO-Mk3.6) is used to investigate the role of aerosol forcing agents as drivers of snow melting trends in the Tibetan Plateau (TP) region. Anthropogenic aerosol-induced snow cover changes in a warming climate are calculated from the difference between historical run (HIST) and all forcing except anthropogenic aerosol (NoAA). Absorbing aerosols can influence snow cover by warming the atmosphere, reducing snow reflectance after deposition. The warming the rate of snow melt, exposing darker surfaces below to short-wave radiation sooner, and allowing them to heat up even faster in the Himalayas and TP. The results show a strong spring snow cover decrease over TP when absorbing anthropogenic aerosol forcing is considered, whereas snow cover fraction (SCF) trends in NoAA are weakly negative (but insignificant) during 1951-2005. The enhanced spring snow cover trends in HIST are due to overall effects of different forcing agents: When aerosol forcing (AERO) is considered, a significant reduction of SCF than average can be found over the western TP and Himalayas. The large decreasing trends in SCF over the TP, with the maximum reduction of SCF around 12-15% over the western TP and Himalayas slope. Also accelerated snow melting during spring is due to effects of aerosol on snow albedo, where aerosol deposition cause decreases snow albedo. However, the SCF change in the “NoAA” simulations was observed to be less.

  16. Development and Evaluation of the GCOM-W1 AMSR2 Snow Depth and Snow Water Equivalent Algorithm

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

    An evaluation is presented of snow depth (SD) and snow water equivalent (SWE) estimates from recent developments to the standard snow product algorithm for the Advanced Microwave Scanning Radiometer - 2 (AMSR2) aboard the Global Change Observation Mission - Water. AMSR2 is designed as a follow-on from the successful Advanced Microwave Scanning Radiometer - EOS that ceased formal operations in 2011. The standard SD product for AMSR2 has been updated in two ways. First, the detection algorithm identifies various observable geophysical targets that can confound SD / SWE estimation (water bodies [including freeze/thaw state], rainfall, high altitude plateau regions [e.g. Tibetan plateau]) before detecting moderate and shallow snow. Second, the implementation of the Dense Media Radiative Transfer model (DMRT) originally developed by Tsang et al. (2000) and more recently adapted by Picard et al. (2011) is used to estimate SWE and SD. The implementation combines snow grain size and density parameterizations originally developed by Kelly et al. (2003). Snow grain size is estimated from the tracking of estimated air temperatures that are used to drive an empirical grain growth model. Snow density is estimated from the Sturm et al. (2010) scheme. Efforts have been made to keep the approach tractable while reducing uncertainty in these input variables. Results are presented from the recent winter seasons since 2012 to illustrate the performance of the new approach in comparison with the initial AMSR2 algorithm.

  17. Impurities in Snow: Effects on Spectral Albedo of Prairie Snowpacks

    NASA Astrophysics Data System (ADS)

    Morris, J. N.; Klein, A. G.

    2007-12-01

    While extensive research on soot in snow has been done in the Polar Regions, there remains a lack of observations addressing the effect of soot on snow albedo in North American prairie snowpacks which causes uncertainty to the overall global effect that soot in snow has on climate. Measurements of snow impurities in freshly fallen prairie snowpacks in northwestern Iowa and central Texas collected from February 28 - March 5, 2007 and April 6, 2007, respectively. Two significant snowfall events occurred in northwestern Iowa during the study; the second snowfall event produced the most severe blizzard conditions in northwestern Iowa in the last thirty years. An unusual snowfall event in central Texas offered a unique sampling opportunity Several types of sites were sampled during the field campaign; this includes: frozen lakes with minimal human impact, agricultural fields impacted by agricultural dust, and human impacted sample sites. At twelve sites in northwestern Iowa samples were collected on multiple days and for both snow events to examine changes in snow impurities over time. At all site locations snow samples, temperature, density, and grain size were recorded. Snow reflectance and snow radiance was collected at a subset of the sites with an ASD VNIR Spectroradiometer (350 - 1500 nm). Snow impurities of light-absorbing particulate matter were measured by filtering the meltwater through a nuclepore 0.4 micrometer filter. Impurity concentration was determined by comparing the filters against a set of standards. A photometer will provide a more exact determination of snow impurities in the near future. Preliminary soot observations indicate prairie snow pack concentrations ranging from 1 ngC/g to 236 ngC/g with an average of 61.4 ngC/g. These measurements are within range of previously published values in the Arctic and can lower snow albedo. Differences in soot concentrations were observed between the two Iowa snowfall events. Impurity concentrations measured

  18. A Bayesian spatial assimilation scheme for snow coverage observations in a gridded snow model

    NASA Astrophysics Data System (ADS)

    Kolberg, S.; Rue, H.; Gottschalk, L.

    2006-06-01

    A method for assimilating remotely sensed snow covered area (SCA) into the snow subroutine of a grid distributed precipitation-runoff model (PRM) is presented. The PRM is assumed to simulate the snow state in each grid cell by a snow depletion curve (SDC), which relates that cell's SCA to its snow cover mass balance. The assimilation is based on Bayes' theorem, which requires a joint prior distribution of the SDC variables in all the grid cells. In this paper we propose a spatial model for this prior distribution, and include similarities and dependencies among the grid cells. Used to represent the PRM simulated snow cover state, our joint prior model regards two elevation gradients and a degree-day factor as global variables, rather than describing their effect separately for each cell. This transformation results in smooth normalised surfaces for the two related mass balance variables, supporting a strong inter-cell dependency in their joint prior model. The global features and spatial interdependency in the prior model cause each SCA observation to provide information for many grid cells. The spatial approach similarly facilitates the utilisation of observed discharge. Assimilation of SCA data using the proposed spatial model is evaluated in a 2400 km2 mountainous region in central Norway (61° N, 9° E), based on two Landsat 7 ETM+ images generalized to 1 km2 resolution. An image acquired on 11 May, a week before the peak flood, removes 78% of the variance in the remaining snow storage. Even an image from 4 May, less than a week after the melt onset, reduces this variance by 53%. These results are largely improved compared to a cell-by-cell independent assimilation routine previously reported. Including observed discharge in the updating information improves the 4 May results, but has weak effect on 11 May. Estimated elevation gradients are shown to be sensitive to informational deficits occurring at high altitude, where snowmelt has not started and the snow

  19. 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

  20. Snow White Trenches

    NASA Technical Reports Server (NTRS)

    2008-01-01

    This image was acquired by NASA's Phoenix Mars Lander's Surface Stereo Imager on the 25th Martian day of the mission, or Sol 24 (June 19, 2008), after the May 25, 2008, landing. This image shows the trenches informally called 'Snow White 1' (left) and 'Snow White 2' (right). The trench is about 5 centimeters (2 inches) deep and 30 centimeters (12 inches) long.

    'Snow White' is located in a patch of Martian soil near the center of a polygonal surface feature, nicknamed 'Cheshire Cat.' The 'dump pile' is located at the top of the trench, the side farthest away from the lander, and has been dubbed 'Croquet Ground.' The digging site has been named 'Wonderland.'

    This image has been enhanced to brighten shaded areas.

    The Phoenix Mission is led by the University of Arizona, Tucson, on behalf of NASA. Project management of the mission is by NASA's Jet Propulsion Laboratory, Pasadena, Calif. Spacecraft development is by Lockheed Martin Space Systems, Denver.

  1. Multidecadal climate and seasonal snow conditions in Svalbard

    NASA Astrophysics Data System (ADS)

    Pelt, W. J. J.; Kohler, J.; Liston, G. E.; Hagen, J. O.; Luks, B.; Reijmer, C. H.; Pohjola, V. A.

    2016-11-01

    Svalbard climate is undergoing amplified change with respect to the global mean. Changing climate conditions directly affect the evolution of the seasonal snowpack, through its impact on accumulation, melt, and moisture exchange. We analyze long-term trends and spatial patterns of seasonal snow conditions in Svalbard between 1961 and 2012. Downscaled regional climate model output is used to drive a snow modeling system (SnowModel), with coupled modules simulating the surface energy balance and snowpack evolution. The precipitation forcing is calibrated and validated against snow depth data on a set of glaciers around Svalbard. Climate trends reveal seasonally inhomogeneous warming and a weakly positive precipitation trend, with strongest changes in the north. In response to autumn warming the date of snow onset increased (2 days decade-1), whereas in spring/summer opposing effects cause a nonsignificant trend in the snow disappearance date. Maximum snow water equivalent (SWE) in winter/spring shows a modest increase (+0.01 meters water equivalent (mwe) decade-1), while the end-of-summer minimum snow area fraction declined strongly (from 48% to 36%). The equilibrium line altitude is highest in relatively dry inland regions, and time series show a clear positive trend (25 m decade-1) as a result of summer warming. Finally, rain-on-snow in the core winter season, affecting ground ice formation and limiting access of grazing animals to food supplies, peaks during specific years (1994, 1996, 2000, and 2012) and is found to be concentrated in the lower lying coastal regions in southwestern Svalbard.

  2. Modeling and Prediction of Snow Cover over the Tibetan Plateau

    NASA Astrophysics Data System (ADS)

    Jin, J.; Zhuo, H.; Liu, Y.; Wu, G.

    2012-12-01

    Snow-atmosphere interactions are very important to weather and climate predictions for the Tibetan Plateau (TP) and its downwind regions in eastern Asia. In this study, the next generation Weather Research and Forecasting (WRF) model coupled with the Community Land Model (CLM) version 3.5 was used to improve simulations of regional-scale snow processes and related cold-season hydroclimate for the TP. CLM physically describes mass and heat transfer within the snowpack using five snow layers that include liquid water and solid ice. Interactions among the snow, soil, and vegetation are characterized by the CLM mass and energy equations. A sophisticated surface albedo scheme is adopted to improve simulations of the surface energy balance. The introduction of a maximum of ten sub-cells within each CLM cell improves the accuracy of land surface characterization and the water and energy fluxes between the land surface and the atmosphere. The first set of simulations was carried out with the coupled WRF-CLM forced with the Climate Forecast System (CFS) Reanalysis data, and these simulations were evaluated against snow cover, temperature, and precipitation observations from satellites and meteorological stations located in the TP. Our model evaluation showed that WRF-CLM was able to reasonably reproduce the observed data. Snow cover on the TP was then predicted with a 10 km resolution WRF-CLM driven with the global forecasts made by the CFS version 2. Comparing these high-resolution snow predictions with observations and the simulations that were forced with the Reanalysis data demonstrated that the spatial distribution of snow cover was realistically predicted at both seasonal and interannual scales. The detailed physical processes that control snow forecasts for the TP were also examined, and the model uncertainties were quantified. From these modeling results, we can clearly see that the coupled WRF-CLM provides an important tool for weather and climate forecasts for the

  3. The role of terrestrial snow cover in the climate system

    NASA Astrophysics Data System (ADS)

    Vavrus, Steve

    2007-07-01

    Snow cover is known to exert a strong influence on climate, but quantifying its impact is difficult. This study investigates the global impact of terrestrial snow cover through a pair of GCM simulations run with prognostic snow cover and with all snow cover on land eliminated (NOSNOWCOVER). In this experiment all snowfall over land was converted into its liquid-water equivalent upon reaching the surface. Compared with the control run, NOSNOWCOVER produces mean-annual surface air temperatures up to 5 K higher over northern North America and Eurasia and 8-10 K greater during winter. The globally averaged warming of 0.8 K is one-third as large as the model’s response to 2 × CO2 forcing. The pronounced surface heating propagates throughout the troposphere, causing changes in surface and upper-air circulation patterns. Despite the large atmospheric warming, the absence of an insulating snow pack causes soil temperatures in NOSNOWCOVER to fall throughout northern Asia and Canada, including extreme wintertime cooling of over 20 K in Siberia and a 70% increase in permafrost area. The absence of snow melt water also affects extratropical surface hydrology, causing significantly drier upper-layer soils and dramatic changes in the annual cycle of runoff. Removing snow cover also drastically affects extreme weather. Extreme cold-air outbreaks (CAOs)—defined relative to the control climatology—essentially disappear in NOSNOWCOVER. The loss of CAOs appears to stem from both the local effects of eliminating snow cover in mid-latitudes and a remote effect over source regions in the Arctic, where -40°C air masses are no longer able to form.

  4. Influence of wet conditions on snow temperature diurnal variations: An East Antarctic sea-ice case study

    NASA Astrophysics Data System (ADS)

    Lecomte, O.; Toyota, T.

    2016-09-01

    A one-dimensional snow-sea-ice model is used to simulate the evolution of temperature profiles in dry and wet snow over a diurnal cycle, at locations where associated observations collected during the Sea Ice Physics and Ecosystem eXperiment (SIPEX-II) are available. The model is used at two sites, corresponding to two of the field campaign's sea-ice stations (2 and 6), and under two configurations: dry and wet snow conditions. In the wet snow model setups, liquid water may refreeze internally into the snow. At station 6, this releases latent heat to the snow and results in temperature changes at the base of the snow pack of a magnitude comparing to the model-observation difference (1 - 2 ° C). As the temperature gradient across the snow is in turn weakened, the associated conductive heat flux through snow decreases. At station 2, internal refreezing also occurs but colder air temperatures and the competing process of strengthened heat conduction in snow concurrent to snow densification maintain a steady temperature profile. However, both situations share a common feature and show that the conductive heat flux through the snow may significantly be affected (by 10-20% in our simulations) as a result of the liquid water refreezing in snow, either through thermal conductivity enhancement or direct temperature gradient alteration. This ultimately gives motivation for further investigating the impacts of these processes on the sea-ice mass balance in the framework of global scale model simulations.

  5. Snow Cover Changes over Northern Eurasia from in Situ Observations

    NASA Astrophysics Data System (ADS)

    Bulygina, O. N.; Razuvaev, V. N.; Groisman, P. Ya; Korshunova, N. N.

    2012-04-01

    . This approach provides a more uniform spatial field for averaging. Results. In the recent decades, the Russian territory has experienced an increase in snow depth, both winter average and maximum snow depths, against the background of global temperature rise and sea ice reduction in the Arctic Ocean. Generalized regional characteristics of maximum snow water equivalent show an increase in water supply in the north of the East European Plain (by 4.5%/10yr in the west and by 6%/10yr in the east). This characteristic also increases in the southern forest zone of West Siberia and in the Far East (by approximately 6%/10yr ) and in central Eastern Siberia (by 3.4%/10yr). Only in the southwest of the East European Plain, we found a tendency for decrease in water supply (by -6.4%/10yr) along the forested snow courses. Among the two competing factors that can cause a systematic change in the maximum and mean snowpack density over Northern Eurasia, increase in maximum snow depth and a decrease in number of days with snow cover, the second factor (that causes a decrease in snow density) appeared to be more significant during the past 43 years.

  6. European snow cover in a changing climate: An analysis of the EURO-CORDEX regional climate model ensemble

    NASA Astrophysics Data System (ADS)

    Kotlarski, Sven; Teichmann, Claas; Gobiet, Andreas

    2015-04-01

    Surface snow cover plays an important and interactive role in global and regional climate systems. For this reason, state-of-the-art climate models employ snow parameterization schemes of differing complexity that simulate the snow cover response to climate change and climate variability and that allow for an approximate representation of snow-atmosphere feedbacks. A dedicated validation of snow cover characteristics simulated by climate models can provide valuable insight in the accuracy of the feedback representation and in the origin of, for instance, near-surface temperature biases. The analysis of scenario simulations provides estimates of future snow cover changes on continental and sub-continental scales as a response to rising greenhouse gas concentrations, complementing smaller-scale snow cover scenarios obtained from dedicated cryospheric impact models. We here present a first analysis of surface snow cover characteristics in the recently established EURO-CORDEX regional climate model (RCM) ensemble, considering simulations with grid spacings of both 12 and 50 km. The analysis covers snow cover validation in ERA-Interim-driven hindcast simulations as well as the assessment of 21st century snow cover changes over different parts of Europe. A particular focus is on the European Alps, a region with a high economic vulnerability with respect to the anticipated snow cover reduction. Model evaluation against satellite-derived and surface-based observational datasets reveals an approximate reproduction of spatio-temporal snow cover variability over Europe by the RCMs. In the Alps, however, high-elevation snow mass can be considerably overestimated by individual models. This feature is likely connected to cold high-elevation temperature biases. 21st century snow cover scenarios show an almost complete loss of snow cover in low-elevation regions, largely confirming previous works. The rate of snow cover decrease strongly depends on the warming magnitude and

  7. Detection Thresholds of Falling Snow from Satellite-Borne Active and Passive Sensors

    NASA Technical Reports Server (NTRS)

    Skofronick-Jackson, Gail; Johnson, Benjamin T.; Munchak, S. Joseph

    2012-01-01

    Precipitation, including rain and snow, is a critical part of the Earth's energy and hydrology cycles. Precipitation impacts latent heating profiles locally while global circulation patterns distribute precipitation and energy from the equator to the poles. For the hydrological cycle, falling snow is a primary contributor in northern latitudes during the winter seasons. Falling snow is the source of snow pack accumulations that provide fresh water resources for many communities in the world. Furthermore, falling snow impacts society by causing transportation disruptions during severe snow events. In order to collect information on the complete global precipitation cycle, both liquid and frozen precipitation must be collected. The challenges of estimating falling snow from space still exist though progress is being made. These challenges include weak falling snow signatures with respect to background (surface, water vapor) signatures for passive sensors over land surfaces, unknowns about the spherical and non-spherical shapes of the snowflakes, their particle size distributions (PSDs) and how the assumptions about the unknowns impact observed brightness temperatures or radar reflectivities, differences in near surface snowfall and total column snow amounts, and limited ground truth to validate against. While these challenges remain, knowledge of their impact on expected retrieval results is an important key for understanding falling snow retrieval estimations. Since falling snow from space is the next precipitation measurement challenge from space, information must be determined in order to guide retrieval algorithm development for these current and future missions. This information includes thresholds of detection for various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types. For example, can a lake effect snow system with low (approx 2.5 km) cloud tops having an ice water content (IWC) at the

  8. How do patchy snow covers affect turbulent sensible heat fluxes? - Numerical analysis and experimental findings

    NASA Astrophysics Data System (ADS)

    Schlögl, Sebastian; Mott, Rebecca; Lehning, Michael

    2017-04-01

    The surface energy balance of a snow cover significantly changes once the snow cover gets patchy. The substantial progress in knowledge about the surface energy balance of patchy snow covers is a mandatory requirement to reduce biases in flux parameterizations in larger scale meteorological or climatological models. The aim of this project was to numerically improve energy balance calculations late in the melting season when the spatial variability of turbulent fluxes is especially high owing to the complex feedback between bare/snow-covered areas and the atmosphere above. In order to account for the feedback between the atmosphere and the patchy snow-cover we calculated three-dimensional air temperature and wind velocity fields with the non-hydrostatic atmospheric model ARPS for an idealized flat test site initialized with different snow distributions and atmospheric conditions. The physics-based surface process model Alpine3D has been forced with these atmospheric fields close to the snow surface in order to resolve the small-scale spatial variability. We further initialized the model with atmospheric fields above the blending height as a reference case. The numerical analysis shows that for simulations initialized with fully-resolved atmospheric fields below the blending height, turbulent sensible heat fluxes are up to 50 W/m2 larger than for calculations forced without resolved atmospheric fields. This difference in turbulent sensible heat fluxes over snow increase with increasing number of snow patches and decreasing snow-cover fraction. This is mainly attributed to an increase in the mean near-surface air temperature over snow due to horizontal and vertical exchange processes induced by the heterogeneous land-surface. We used flux footprint estimations to analyse turbulence data measured during three ablation periods in the Dischma valley (Switzerland). This fundamental theory was deployed for eddy-covariance measurements revealing the origin of the measured

  9. 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...

  10. Snow Water Equivalent for Tuolumne River Basin

    NASA Image and Video Library

    2013-05-02

    NASA Airborne Snow Observatory measurements of snow water equivalent top image and snow albedo, or reflectivity bottom image for the Tuolumne River Basin in California Sierra Nevada on April 21, 2013.

  11. Optical Properties of Snow

    DTIC Science & Technology

    1982-01-01

    basalt, and the most common volcanic ash is andesite (R. Cadle, personal communication, 1980). Both of these rocks have very similar optical proper...giving no color to the snow; but in order to mimic a given ties for short waves: mi., - I x 10-1, constant across the concentration of soot, the andesite ...refractive v.o8 0. r~soo, 02.o5 % •%•, "index is taken as that of andesite (mt = 1.47) from Pollack et 0o..o al. [1973]. The imaginary index mim (k) was

  12. Snow across the Midwest

    NASA Image and Video Library

    2017-09-28

    On Nov. 22, 2015 at 19:15 UTC the MODIS instrument aboard NASA's Aqua satellite captured this image of Snow across the Midwest. Credit: NASA Goddard MODIS Rapid Response Team NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

  13. Snow White Trench (Animation)

    NASA Technical Reports Server (NTRS)

    2008-01-01

    [figure removed for brevity, see original site] Click on image for animation

    This animation shows the evolution of the trench called 'Snow White' that NASA's Phoenix Mars Lander began digging on the 22nd Martian day of the mission after the May 25, 2008, landing.

    The Phoenix Mission is led by the University of Arizona, Tucson, on behalf of NASA. Project management of the mission is by NASA's Jet Propulsion Laboratory, Pasadena, Calif. Spacecraft development is by Lockheed Martin Space Systems, Denver.

  14. Phoenix's Snow White Trench

    NASA Technical Reports Server (NTRS)

    2008-01-01

    A soil sample taken from the informally named 'Snow White' trench at NASA's Phoenix Mars Lander work site produced minerals that indicate evidence of past interaction between the minerals and liquid water.

    This image was taken by the Surface Stereo Imager on Sol 103, the 103rd day since landing (Sept. 8, 2008).

    The trench is approximately 23 centimeters (9 inches) long.

    The Phoenix Mission is led by the University of Arizona, Tucson, on behalf of NASA. Project management of the mission is by JPL, Pasadena, Calif. Spacecraft development was by Lockheed Martin Space Systems, Denver.

  15. Snow White Trench (Animation)

    NASA Technical Reports Server (NTRS)

    2008-01-01

    [figure removed for brevity, see original site] Click on image for animation

    This animation shows the evolution of the trench called 'Snow White' that NASA's Phoenix Mars Lander began digging on the 22nd Martian day of the mission after the May 25, 2008, landing.

    The Phoenix Mission is led by the University of Arizona, Tucson, on behalf of NASA. Project management of the mission is by NASA's Jet Propulsion Laboratory, Pasadena, Calif. Spacecraft development is by Lockheed Martin Space Systems, Denver.

  16. Snow Water Equivalent estimation from AMSR-E data based on priori snow properties in Xinjiang province of China

    NASA Astrophysics Data System (ADS)

    Dai, L.; Che, T.

    2011-12-01

    which are very close to zero. Obviously, the new algorithm shows a large improvement over the algorithm adopted by NSIDC, and a slightly improvement over the algorithm adopted by WESTDC for estimating SWE in Xinjiang province. The main cause is that the new algorithm is developed based on more accurate priori information about snow properties such as the stratigraphic condition, snow grain size, density, and their variations spatiotemporally. Therefore, a priori information of local snow condition is very important to retrieve the SWE from passive microwave brightness temperature data.
    Table1 Error statistics of SWE retrieved based on the new, Che and Global algorithms according to the data at seven stations in Xinjiang province from 2003-2008

  17. Preparation for Snow Cover Monitoring Using Sentinel-1 and Sentinel-3 Data

    NASA Astrophysics Data System (ADS)

    Nagler, Thomas; Rott, Helmut; Bippus, Gabriele; Ripper, Elisabeth

    2013-04-01

    Seasonal snow is a key element of the water cycle in high and mid latitudes, characterized by high spatial and temporal variability. Melt water is an important water resource in many mountain areas and also in lowlands downstream. Accurate observations of snow extent and physical properties of snow are not only of interest for climate change research, but are of great socio-economic importance. The Sentinel satellite series, including SAR and multispectral optical satellite data enable to monitor the snow extent from regional to global scale with high temporal sampling. Automatic processing lines of multispectral optical satellite data including rectification, calibration, cloud masking and snow detection have been implemented for generation of snow information and tested with various satellite sensors. Ongoing work is related with adapting and optimizing the snow retrieval algorithm for Sentinel 3 SLSTR and OCLI, making use of the full spectral capabilities of these sensors for generating high quality snow maps. The algorithm for mapping snow makes use of the typical spectral signature of snow in the visible (VIS) and short wave infrared (SWIR) region of the spectrum, which enables a clear discrimination against other surfaces. The baseline products include binary snow extent maps derived from combinations of VIS and SWIR bands and maps of fractional snow extent. The preliminary version of the retrieval algorithm uses dual-sensor Sentinel-3 SLSTR and OCLI data for mapping the snow extent and applies the multi-spectral un-mixing method and cloud screening making use of the various spectral channels of the two sensors. Snow conditions (wet/dry) can be retrieved from SAR observations as provided by Sentinel-1. The algorithm builds on the multi-temporal change detection technique for mapping melting snow areas and improved to make use of the dual-polarisation acquisition capabilities of Sentinel-1. In the presentation we will show first examples of the improved

  18. "Journey to the Stars": Presenting What Stars Are to Global Planetarium Audiences by Blending Astrophysical Visualizations Into a Single Immersive Production at the American Museum of Natural History

    NASA Astrophysics Data System (ADS)

    Emmart, Carter; Mac Low, M.; Oppenheimer, B. R.; Kinzler, R.; Paglione, T. A. D.; Abbott, B. P.

    2010-01-01

    "Journey to the Stars" is the latest and fourth space show based on storytelling from data visualization at the Rose Center for Earth and Space at the American Museum of Natural History. This twenty five minute, full dome movie production presents to planetarium audiences what the stars are, where they come from, how they vary in type and over time, and why they are important to life of Earth. Over forty scientists from around the world contributed their research to what is visualized into roughly fifteen major scenes. How this production is directed into a consolidated immersive informal science experience with learning goals is an integrative process with many inputs and concerns for scientific accuracy. The goal is a seamless merger of visualizations at varying spatial and temporal scales with acuity toward depth perception, revealing unseen phenomena, and the layering of concepts together to build an understanding of stars; to blend our common experience of them in the sky with the uncommon meaning we have come to know through science. Scripted by Louise Gikow who has worked for Children's Television Workshop, narrated by Whoopie Goldberg, and musically scored by Robert Miller, this production strives to guide audiences through challenging scientific concepts by complimenting the natural beauty the subject matter presents with understandable prose and musical grandeur. "Journey to the Stars" was produced in cooperation with NASA's Science Mission Directorate, Heliophysics Division and is in release at major planetariums, worldwide.

  19. The Estimation of Snow Damages considering the Topographical Characteristics in South Korea

    NASA Astrophysics Data System (ADS)

    Chung, G.; Kwon, S. H.; Kim, J.; Yeongrock, O.

    2016-12-01

    The property damages and loss of lives caused by the natural disasters has been increased. The global warming and climate change are considered as one of the most accelerating factors. In South Korea, the most severe damages have been caused by heavy rainfall and/or typhoon. However, the heavy snow damages are also increased recently. From 1993 and 2002, 7.6% of the property damage caused by natural disasters was induced by snow. For example, in March 2004, the substantial property damages were occurred by heavy snow in Chungcheong province. And, gymnastic building was collapsed by heavy snow and 10 people were died in 2014. In the future, more severe damage could be happened under the climate change scenarios. Therefore, it is very important to prepare snow disaster and mitigate damages. However, it is difficult to estimate accurate damage due to the complexity of snow physics and the lack of data. Therefore, more reliable damage estimation technique is required for the more effective disaster response and management. In this study, the topographical characteristics of South Korea were analyzed with respect to the design code for the greenhouses, animal farm, and fish farm that are the most vulnerable buildings under the heavy snow condition. The property damage caused by snow was estimated using multiple regression analysis using the historical damage data and the topographical characteristics. The developed model could be applied to estimate the snow damage as early as the amount of snow is forecasted in a region to prepare the disaster response.

  20. Effects of subgrid-scale snow thickness variability on radiative transfer in sea ice

    NASA Astrophysics Data System (ADS)

    Abraham, Carsten; Steiner, Nadja; Monahan, Adam; Michel, Christine

    2015-08-01

    Snow is a principal factor in controlling heat and light fluxes through sea ice. With the goal of improving radiative and heat flux estimates through sea ice in regional and global models without the need of detailed snow property descriptions, a new parameterization including subgrid-scale snow thickness variability is presented. One-parameter snow thickness distributions depending only on the gridbox-mean snow thickness are introduced resulting in analytical solutions for the fluxes of heat and light through the snow layer. As the snowpack melts, these snow thickness distributions ensure a smooth seasonal transition of the light field under sea ice. Spatially homogenous melting applied to an inhomogeneous distribution of snow thicknesses allows the appearance of bare sea ice areas and melt ponds before all snow has melted. In comparison to uniform-thickness snow used in previous models, the bias in the under sea-ice light field is halved with this parameterization. Model results from a one-dimensional ocean turbulence model coupled with a thermodynamic sea ice model are compared to observations near Resolute in the Canadian High Arctic. The simulations show substantial improvements not only to the light field at the sea ice base which will affect ice algal growth but also to the sea ice and seasonal snowpack evolution. During melting periods, the snowpack can survive longer while sea ice thickness starts to reduce earlier.

  1. A New Operational Snow Retrieval Algorithm Applied to Historical AMSR-E Brightness Temperatures

    NASA Technical Reports Server (NTRS)

    Tedesco, Marco; Jeyaratnam, Jeyavinoth

    2016-01-01

    Snow is a key element of the water and energy cycles and the knowledge of spatio-temporal distribution of snow depth and snow water equivalent (SWE) is fundamental for hydrological and climatological applications. SWE and snow depth estimates can be obtained from spaceborne microwave brightness temperatures at global scale and high temporal resolution (daily). In this regard, the data recorded by the Advanced Microwave Scanning Radiometer-Earth Orbiting System (EOS) (AMSR-E) onboard the National Aeronautics and Space Administration's (NASA) AQUA spacecraft have been used to generate operational estimates of SWE and snow depth, complementing estimates generated with other microwave sensors flying on other platforms. In this study, we report the results concerning the development and assessment of a new operational algorithm applied to historical AMSR-E data. The new algorithm here proposed makes use of climatological data, electromagnetic modeling and artificial neural networks for estimating snow depth as well as a spatio-temporal dynamic density scheme to convert snow depth to SWE. The outputs of the new algorithm are compared with those of the current AMSR-E operational algorithm as well as in-situ measurements and other operational snow products, specifically the Canadian Meteorological Center (CMC) and GlobSnow datasets. Our results show that the AMSR-E algorithm here proposed generally performs better than the operational one and addresses some major issues identified in the spatial distribution of snow depth fields associated with the evolution of effective grain size.

  2. A New Operational Snow Retrieval Algorithm Applied to Historical AMSR-E Brightness Temperatures

    NASA Technical Reports Server (NTRS)

    Tedesco, Marco; Jeyaratnam, Jeyavinoth

    2016-01-01

    Snow is a key element of the water and energy cycles and the knowledge of spatio-temporal distribution of snow depth and snow water equivalent (SWE) is fundamental for hydrological and climatological applications. SWE and snow depth estimates can be obtained from spaceborne microwave brightness temperatures at global scale and high temporal resolution (daily). In this regard, the data recorded by the Advanced Microwave Scanning Radiometer-Earth Orbiting System (EOS) (AMSR-E) onboard the National Aeronautics and Space Administration's (NASA) AQUA spacecraft have been used to generate operational estimates of SWE and snow depth, complementing estimates generated with other microwave sensors flying on other platforms. In this study, we report the results concerning the development and assessment of a new operational algorithm applied to historical AMSR-E data. The new algorithm here proposed makes use of climatological data, electromagnetic modeling and artificial neural networks for estimating snow depth as well as a spatio-temporal dynamic density scheme to convert snow depth to SWE. The outputs of the new algorithm are compared with those of the current AMSR-E operational algorithm as well as in-situ measurements and other operational snow products, specifically the Canadian Meteorological Center (CMC) and GlobSnow datasets. Our results show that the AMSR-E algorithm here proposed generally performs better than the operational one and addresses some major issues identified in the spatial distribution of snow depth fields associated with the evolution of effective grain size.

  3. Sub-grid Representation of Snow in Land Surface Models

    NASA Astrophysics Data System (ADS)

    Ganji, Arman; Sushama, Laxmi

    2017-04-01

    Snow depth and fraction in high-latitude landscapes play a key role in defining surface energy and moisture relationships. In light of the role that snow plays in influencing various processes, it is important that the land surface schemes used in weather and climate models accurately represent the spatial variation of snow depth and cover. In this paper, a new sub-grid snow parameterization is proposed for the Canadian Land Surface Scheme (CLASS), which is used in the Canadian regional and global climate models. The sub-grid scheme takes into account elevation, slope and aspect variations within a grid cell and uses a clustering approach to classify sub-grid cells based on elevation, slope and aspect values into groups. The impact of these modifications on the regional hydrology is assessed by comparing two offline simulations, performed with the original and modified versions of CLASS, driven by atmospheric forcing data from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-Interim), for the 1970-2013 period, over a northwest Canadian domain. Results suggest higher Snow Water Equivalent (SWE) in the simulation with modified CLASS compared to the original version. Also, the simulated SWE using the modified CLASS is in better agreement to SNODAS. Furthermore, the results show that the magnitude of streamflows is improved in the modified model. This study thus demonstrates the added value of sub-grid snow parameterization, as reflected in the realistic simulation of surface hydrologic variables.

  4. Observations and Processes Near the Snow-Air Interface: Insights Gained from New and Comparative Sensor Systems in View of Snow Surface Energy Balance Closure

    NASA Astrophysics Data System (ADS)

    Huwald, H.; Selker, J. S.; Calaf-Bracons, M.; Parlange, M. B.

    2007-12-01

    Global warming drastically affects the seasonal snow cover in high altitude regions. The thermodynamic evolution of the snow pack is mainly controlled by the surface energy balance, however, most studies to date fail to close this budget on short time scales when using measurements of all its components. Also dynamic processes such as air movement in the snow pack associated with air exchange and the snow-atmosphere interface have to be taken into account. To investigate snow-atmosphere interaction, measurements of radiative and turbulent heat fluxes, and other meteorological quantities were obtained over a snow-covered glacier in the Swiss Alps during winter 2007. Humidity, air, surface, and snow temperature - quantities required to calculate energy fluxes for the surface energy budget - were measured with different sensors and techniques. Data revealed significant discrepancies between individual measurements at a location and time mainly due to solar heating of the sensors. We show that even shielded sensors overestimate air temperature during the day when compared to a radiation-independent reference sensor (sonic anemometer). Subsurface heat flux was determined from snow internal temperature and density data. High resolution temperature profiles were measured in the snow using traditional (thermocouple) and novel fiber optic distributed temperature instrumentation. To better understand the rate of gas exchange with the atmosphere controlling latent heat transport in the snow associated to phase changes (sublimation/deposition), air movement in the snow was investigated with using a new in-situ carbon monoxide trace gas measurement system providing high-resolution observation of snow transport process without gas extraction.

  5. Snow catch by conifer crowns

    Treesearch

    Donald R. Satterlund; Harold F. Haupt

    1967-01-01

    Study of interception storage of snow by two species of sapling conifers in northern Idaho revealed that cumulative snow catch follows the classical law of autocatakinetic growth, or [equation - see PDF] where I, is interception storage, e is the interception storage capacity of the tree, e is the base of the natural logarithm, k is a constant expressing the rate of...

  6. Tyzzer's disease in snow leopards.

    PubMed

    Schmidt, R E; Eisenbrandt, D L; Hubbard, G B

    1984-01-01

    Tyzzer's disease was diagnosed histologically in 2 litters of newborn snow leopard kittens. The gross and histological lesions were similar to those reported in domestic cats and other animals. No signs of illness was noted in either of the snow leopard dams.

  7. Snow in northern Alaska

    NASA Image and Video Library

    2017-09-28

    As autumn colors moved across much of the lower forty-eight states in mid-October 2015, winter weather had already arrived in Alaska. The Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA’s Terra satellite captured this true-color image of the icy scene on October 16 as it passed over the region. Point Barrow, the northern-most location in the United States sits between the Chukchi Sea (west) and the Beaufort Sea on the east. The rugged peaks of the Brooks Range can be seen along the southern section of the image. North of the Brooks Range the land is almost entirely covered with snow; to the south the tan and browns visible between snow marks uncovered land. Sea ice lies over the waters near the coasts of much of Alaska’s North Slope, especially east of Point Barrow. White cloud banks are notable in the northeast and southeast sections of the image. Credit: NASA/GSFC/Jeff Schmaltz/MODIS Land Rapid Response Team NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

  8. [Psycrophilic organisms in snow and ice].

    PubMed

    Kohshima, S

    2000-12-01

    Psychrophilic and psycrotrophic organisms are important in global ecology as a large proportion of our planet is cold. Two-third of sea-water covering more than 70% of Earth is cold deep sea water with temperature around 2 degrees C, and more than 90% of freshwater is in polar ice-sheets and mountain glaciers. Though biological activity in snow and ice had been believed to be extremely limited, various specialized biotic communities were recently discovered at glaciers of various part of the world. The glacier is relatively simple and closed ecosystem with special biotic community containing various psychrophilic and psycrotrophic organisms. Since psychrophilic organisms was discovered in the deep ice-core recovered from the antarctic ice-sheet and a lake beneath it, snow and ice environments in Mars and Europa are attracting a great deal of scientific attention as possible extraterrestrial habitats of life. This paper briefly reviews the results of the studies on ecology of psychrophilic organisms living in snow and ice environments and their physiological and biochemical adaptation to low temperature.

  9. The value of snow cover

    NASA Astrophysics Data System (ADS)

    Sokratov, S. A.

    2009-04-01

    Snow is the natural resource, like soil and water. It has specific properties which allow its use not just for skiing but also for houses cooling in summer (Swedish experience), for air fields construction (Arctic and Antarctic), for dams (north of Russia), for buildings (not only snow-houses of some Polar peoples but artistic hotel attracting tourists in Sweden), and as art material (Sapporo snow festival, Finnish events), etc. "Adjustment" of snow distribution and amount is not only rather common practice (avalanche-protection constructions keeping snow on slopes) but also the practice with long history. So-called "snow irrigation" was used in Russia since XIX century to protect winter crop. What is now named "artificial snow production", is part of much larger pattern. What makes it special—it is unavoidable in present climate and economy situation. 5% of national income in Austria is winter tourism. 50% of the economy in Savoy relay on winter tourism. In terms of money this can be less, but in terms of jobs and income involved this would be even more considerable in Switzerland. As an example—the population of Davos is 14000 in Summer and 50000 in Winter. Skiing is growing business. In present time you can find ski slopes in Turkey and Lebanon. To keep a cite suitable for attracting tourists you need certain amount of sunny days and certain amount of snow. The snow cannons are often the only way to keep a place running. On the other hand, more artificial snow does not necessary attract more tourists, while heavy natural snowfall does attract them. Artificial snow making is costly and requires infrastructure (ponds and electric lines) with very narrow range of weather conditions. Related companies are searching for alternatives and one of them can be "weather regulation" by distribution of some chemical components in clouds. It did not happen yet, but can happen soon. The consequences of such interference in Nature is hardly known. The ski tourism is not the

  10. Snow reflectance from thematic mapper

    NASA Technical Reports Server (NTRS)

    Dozier, J.

    1983-01-01

    Calculations of snow reflectance in all 6 TM reflective bands (i.e., 1,2,3,4,5, and 7) using a delta Eddington model show that snow reflectance in bands 4,5, and 7 is sensitive to grain size. Efforts to interpret the surface optical grain size for the spectral extension of albedo are described. Results show the TM data include spectral channels suitable for snow/cloud discrimination and for snow albedo measurements that can be extended throughout the solar spectrum. Except for band 1, the dynamic range is large enough that saturation occurs only occasionally. The finer resolution gives much better detail on the snowcovered area and might make it possible to use textural information instead of the snowline as an index to the amount of snow melt runoff.

  11. ESA SnowLab project

    NASA Astrophysics Data System (ADS)

    Wiesmann, Andreas; Caduff, Rafael; Frey, Othmar; Werner, Charles

    2016-04-01

    Retrieval of the snow water equivalaent (SWE) from passive microwave observations dates back over three decades to initial studies made using the first operational radiometers in space. However, coarse spatial resolution (25 km) is an acknowledged limitation for the application of passive microwave measurements. The natural variability of snow cover itself is also notable; properties such as stratigraphy and snow microstructure change both spatially and over time, affecting the microwave signature. To overcome this deficit, the satellite mission COld REgions Hydrology High-resolution Observatory (CoReH2O) was proposed to the European Space Agency (ESA) in 2005 in response to the call for Earth Explorer 7 candidate missions. CoReH2O was a dual frequency (X- and Ku-band) SAR mission aimed to provide maps of SWE over land and snow accumulation on glaciers at a spatial resolution of 200 to 500 meters with an unprecedented accuracy. Within the frame of preparatory studies for CoReH2O Phase A, ESA undertook several research initiatives from 2009 to 2013 to study the mission concept and capabilities of the proposed sensor. These studies provided a wealth of information on emission and backscattering signatures of natural snow cover, which can be exploited to study new potential mission concepts for retrieval of snow cover properties and other elements of the cryosphere. Currently data related to multi-frequency, multi-polarisation, multitemporal of active and passive microwave measurements are still not available. In addition, new methods related to e.g. tomography are currently under development and need to be tested with real data. Also, the potential of interferometric and polarimetric measurements of the snow cover and its possible impact for novel mission/retrieval concepts must be assessed. . The objective of the SnowLab activity is to fill this gap and complement these datasets from earlier campaigns by acquiring a comprehensive multi-frequency, multi

  12. Mixing of anthropogenic dust and carbonaceous aerosols in seasonal snow on snow albedo reduction in 2014 China survey

    NASA Astrophysics Data System (ADS)

    Wang, Xin; Huang, Jianping; Pu, Wei

    2016-04-01

    Anthropogenic dusts produced from the affected by human activities derived from the industrial areas and carbonaceous aerosols (black carbon and organic carbon) deposited into snow or ice core via wet and dry deposition play key roles to the regional and global climate. Recently, a China survey was performed to measure the concentrations of insoluble light-absorbing particles (ILAP) in seasonal snow across northern China in January and February of 2014. The results indicate that the higher concentration of NO3- and SO42- and heavy metals of Zn, Pb, Cd, Ni, and Cu are likely to be attributed to enhanced local industrial emissions due to human activities. The emissions from fossil fuel combustion and biomass burning are likely to be important for the chemical elements in the seasonal snow with long-range transport, while medium enrichment factors of Mg, Ca, and Al were predominantly associated with soil dust, which is the most important natural source. There are large ranges of the BC and AD in seasonal snow over northeast China because of the anthropogenic emissions, which are caused by human activities. In addition, although the values of the snow albedo by model simulations are little higher in the visible to near-infrared wavelength than that during the China survey, the surface snow albedo by field campaign measurements have good agreement with the model simulations in the visible wavelength.

  13. Multi-RTM-based Radiance Assimilation to Improve Snow Estimates

    NASA Astrophysics Data System (ADS)

    Kwon, Y.; Zhao, L.; Hoar, T. J.; Yang, Z. L.; Toure, A. M.

    2015-12-01

    Data assimilation of microwave brightness temperature (TB) observations (i.e., radiance assimilation (RA)) has been proven to improve snowpack characterization at relatively small scales. However, large-scale applications of RA require a considerable amount of further efforts. Our objective in this study is to explore global-scale snow RA. In a RA scheme, a radiative transfer model (RTM) is an observational operator predicting TB; therefore, the quality of the assimilation results may strongly depend upon the RTM used as well as the land surface model (LSM). Several existing RTMs show different sensitivities to snowpack properties and thus they simulate significantly different TB. At the global scale, snow physical properties vary widely with local climate conditions. No single RTM has been shown to be able to accurately reproduce the observed TB for such a wide range of snow conditions. In this study, therefore, we hypothesize that snow estimates using a microwave RA scheme can be improved through the use of multiple RTMs (i.e., multi-RTM-based approaches). As a first step, here we use two snowpack RTMs, i.e., the Dense Media Radiative Transfer-Multi Layers model (DMRT-ML) and the Microwave Emission Model for Layered Snowpacks (MEMLS). The Community Land Model version 4 (CLM4) is used to simulate snow dynamics. The assimilation process is conducted by the Data Assimilation Research Testbed (DART), which is a community facility developed by the National Center for Atmospheric Research (NCAR) for ensemble-based data assimilation studies. In the RA experiments, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) TB at 18.7 and 36.5 GHz vertical polarization channels are assimilated into the RA system using the ensemble adjustment Kalman filter. The results are evaluated using the Canadian Meteorological Centre (CMC) daily snow depth, the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction, and in-situ snowpack and river

  14. Task Space Angular Velocity Blending for Real-Time Trajectory Generation

    NASA Technical Reports Server (NTRS)

    Volpe, Richard A. (Inventor)

    1997-01-01

    The invention is embodied in a method of controlling a robot manipulator moving toward a target frame F(sub 0) with a target velocity v(sub 0) including a linear target velocity v and an angular target velocity omega(sub 0) to smoothly and continuously divert the robot manipulator to a subsequent frame F(sub 1) by determining a global transition velocity v(sub 1), the global transition velocity including a linear transition velocity v(sub 1) and an angular transition velocity omega(sub 1), defining a blend time interval 2(tau)(sub 0) within which the global velocity of the robot manipulator is to be changed from a global target velocity v(sub 0) to the global transition velocity v(sub 1) and dividing the blend time interval 2(tau)(sub 0) into discrete time segments (delta)t. During each one of the discrete time segments delta t of the blend interval 2(tau)(sub 0), a blended global velocity v of the manipulator is computed as a blend of the global target velocity v(sub 0) and the global transition velocity v(sub 1), the blended global velocity v including a blended angular velocity omega and a blended linear velocity v, and then, the manipulator is rotated by an incremental rotation corresponding to an integration of the blended angular velocity omega over one discrete time segment (delta)t.

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

    NASA Astrophysics Data System (ADS)

    Weidinger, Johannes; Gerlitz, Lars; Böhner, Jürgen

    2017-04-01

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

  16. High fidelity remote sensing of snow properties from MODIS and the Airborne Snow Observatory: Snowflakes to Terabytes

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

    The response of the cryosphere to climate forcings largely determines Earth's climate sensitivity. However, our understanding of the strength of the simulated snow albedo feedback varies by a factor of three in the GCMs used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, mainly caused by uncertainties in snow extent and the albedo of snow-covered areas from imprecise remote sensing retrievals. Additionally, the Western US and other regions of the globe depend predominantly on snowmelt for their water supply to agriculture, industry and cities, hydroelectric power, and recreation, against rising demand from increasing population. In the mountains of the Upper Colorado River Basin, dust radiative forcing in snow shortens snow cover duration by 3-7 weeks. Extended to the entire upper basin, the 5-fold increase in dust load since the late-1800s results in a 3-week earlier peak runoff and a 5% annual loss of total runoff. The remotely sensed dynamics of snow cover duration and melt however have not been factored into hydrological modeling, operational forecasting, and policymaking. To address these deficiencies in our understanding of snow properties, we have developed and validated a suite of MODIS snow products that provide accurate fractional snow covered area and radiative forcing of dust and carbonaceous aerosols in snow. The MODIS Snow Covered Area and Grain size (MODSCAG) and MODIS Dust Radiative Forcing in Snow (MODDRFS) algorithms, developed and transferred from imaging spectroscopy techniques, leverage the complete MODIS surface reflectance spectrum. The two most critical properties for understanding snowmelt runoff and timing are the spatial and temporal distributions of snow water equivalent (SWE) and snow albedo. We have created the Airborne Snow Observatory (ASO), an imaging spectrometer and scanning LiDAR system, to quantify SWE and snow albedo, generate unprecedented knowledge of snow properties, and provide complete

  17. A New Approach to Measuring Precipitation over Snow Cover

    NASA Astrophysics Data System (ADS)

    Tian, Y.; Liu, Y.; Arsenault, K. R.; Behrangi, A.

    2013-12-01

    One of the great challenges for truly global precipitation measurement is the remote sensing of precipitation over snow cover. Due to the physical limitation in the current retrieval methodology, satellite-based measurements of precipitation over snow-covered areas are unreliable and largely unavailable. In this presentation, a new satellite-based approach to the estimation of precipitation over snow cover is proposed and tested. The method is based on the principle that precipitation can be inferred by the changes in the water content of the snowpack. During the EOS era operational remote sensing of snow water equivalent is available, with similar spatial and temporal resolutions of the precipitation-sensing passive microwave sensors. With these satellite-based snow water equivalent measurements, daily precipitation amounts can be derived. We tested the method for the Northern Hemisphere for three snow-accumulation seasons, with AMSR-E snow water equivalent data, and compared with existing datasets, including CPC gauge analysis and GPCP. The new precipitation estimates captured natural- and realistic-looking storm events over largely under-instrumented regions. The spatial distribution appeared more reasonable than existing global datasets over many boreal inland areas. The results indicate this approach is feasible and promising. Besides the capability to estimate precipitation over snow cover, this new approach has the following additional advantages over the conventional methods: 1. The relationship between precipitation and the observed variable (i.e., SWE) is more direct than the conventional methods, which have to rely on scattering signals from hydrometeors (passive microwave) or cloud top brightness temperatures (infrared) to infer precipitation; 2. Temporal sampling error is small. The method will not miss any precipitation amount even if there are no instantaneous satellite overpasses during the precipitation event. The memory of the snowpack stores the

  18. Estimating snow water equivalent for a slightly tilted snow-covered prairie grass field by GPS interferometric reflectometry

    NASA Astrophysics Data System (ADS)

    Jacobson, Mark D.

    2014-12-01

    Snow water equivalent (SWE) measurements are necessary for the management of water supply and flood control systems in seasonal snow-covered regions. SWE measurements quantify the amount of water stored in snowpack; it can be estimated by the product of snow depth and density. In this paper, snow depth and density are estimated by a nonlinear least squares fitting algorithm. The inputs to this algorithm are global positioning system (GPS) signals and a simple GPS interferometric reflectometry model (GPS-IR) that incorporates a slightly tilted surface (GPS-IRT). The elevation angles of interest at the GPS receiving antenna are between 5° and 30°. A 1-day experiment with a snow-covered prairie grass field using GPS satellites PRN 15 and PRN 18 shows potential for inferring snow water equivalent using GPS-IRT. For this case study, the average inferred snow depth (12.4 cm) from the two satellite tracks underestimates the in situ measurements (17.6 cm ± 1.5 cm). However, the average inferred snow density (0.085 g•cm-3) from the two satellite tracks is within the in situ measurement range (0.08 g•cm-3 ± 0.02 g•cm-3). Consequently, the average inferred SWE (1.05 g•cm-2) from the two satellite tracks is within the in situ calculation range (1.40 g•cm-2 ± 0.36 g•cm-2). These results are also compared with the GPS-IR model.

  19. Why on the snow? Winter emergence strategies of snow-active Chironomidae (Diptera) in Poland.

    PubMed

    Soszyńska-Maj, Agnieszka; Paasivirta, Lauri; Giłka, Wojciech

    2016-10-01

    A long-term study of adult non-biting midges (Chironomidae) active in winter on the snow in mountain areas and lowlands in Poland yielded 35 species. The lowland and mountain communities differed significantly in their specific composition. The mountain assemblage was found to be more diverse and abundant, with a substantial contribution from the subfamily Diamesinae, whereas Orthocladiinae predominated in the lowlands. Orthocladius wetterensis Brundin was the most characteristic and superdominant species in the winter-active chironomid communities in both areas. Only a few specimens and species of snow-active chironomids were recorded in late autumn and early winter. The abundance of chironomids peaked in late February in the mountain and lowland areas with an additional peak in the mountain areas in early April. However, this second peak of activity consisted mainly of Orthocladiinae, as Diamesinae emerged earliest in the season. Most snow-active species emerged in mid- and late winter, but their seasonal patterns differed between the 2 regions as a result of the different species composition and the duration of snow cover in these regions. Spearman's rank correlation coefficient tests yielded positive results between each season and the number of chironomid individuals recorded in the mountain area. A positive correlation between air temperature, rising to +3.5 °C, and the number of specimens recorded on the snow in the mountain community was statistically significant. The winter emergence and mate-searching strategies of chironomids are discussed in the light of global warming, and a brief compilation of most important published data on the phenomena studied is provided. © 2015 Institute of Zoology, Chinese Academy of Sciences.

  20. GALE improves snow forecasting

    NASA Astrophysics Data System (ADS)

    Scientific results from an intensive study of winter storms on the U.S. East Coast last year contributed to improved weather forecasts of two successive snowstorms that virtually closed down Washington, D.C., for several days in January 1987.In the Genesis of Atlantic Lows Experiment (GALE) field project, scientists took detailed measurements simultaneously from the atmosphere and the ocean to study how these features interact at various stages of an East Coast winter storm, according to project director Richard Dirks, who is with the National Center for Atmospheric Research (NCAR) in Boulder, Colo. “It's interesting that we actually had four storms [in the GALE study] that were of similar intensity to the two East Coast storms” in January 1987, Dirks said. “However, last year the temperatures were warmer, and the storm tracks were located somewhat further offshore and therefore did not significantly affect the northeast corridor with heavy snows.”

  1. Snow metamorphism: A fractal approach.

    PubMed

    Carbone, Anna; Chiaia, Bernardino M; Frigo, Barbara; Türk, Christian

    2010-09-01

    Snow is a porous disordered medium consisting of air and three water phases: ice, vapor, and liquid. The ice phase consists of an assemblage of grains, ice matrix, initially arranged over a random load bearing skeleton. The quantitative relationship between density and morphological characteristics of different snow microstructures is still an open issue. In this work, a three-dimensional fractal description of density corresponding to different snow microstructure is put forward. First, snow density is simulated in terms of a generalized Menger sponge model. Then, a fully three-dimensional compact stochastic fractal model is adopted. The latter approach yields a quantitative map of the randomness of the snow texture, which is described as a three-dimensional fractional Brownian field with the Hurst exponent H varying as continuous parameters. The Hurst exponent is found to be strongly dependent on snow morphology and density. The approach might be applied to all those cases where the morphological evolution of snow cover or ice sheets should be conveniently described at a quantitative level.

  2. Tuning the Blend

    ERIC Educational Resources Information Center

    Schaffhauser, Dian

    2012-01-01

    "Tuning the blend" is a phrase that educators hear a lot these days. It refers to finding the correct balance of online activities and face-to-face instruction in hybrid--or blended--courses. Finding a mix that meets the needs of both faculty and students requires experimentation, experience, and constant tweaking. And, as with coffee, the same…

  3. Tuning the Blend

    ERIC Educational Resources Information Center

    Schaffhauser, Dian

    2012-01-01

    "Tuning the blend" is a phrase that educators hear a lot these days. It refers to finding the correct balance of online activities and face-to-face instruction in hybrid--or blended--courses. Finding a mix that meets the needs of both faculty and students requires experimentation, experience, and constant tweaking. And, as with coffee, the same…

  4. NASA’s Sense of Snow: the Airborne Snow Observatory

    NASA Image and Video Library

    Water is a critical resource in the western U.S. NASA’s Airborne Snow Observatory is giving California water agencies the first complete measurements of the water available in the Sierra snowpack ...

  5. Truck loading rack blending

    SciTech Connect

    Boubenider, E.

    1995-12-01

    Blending, the combining of two or more components to make a single product, has become widely used in most loading rack applications. Blending should not be confused with additive injection, which is the injection of very small doses of enhancers, detergents and dyes into a product stream. Changes in the environmental protection laws in the early 90`s have put increasing demands on marketing terminals with regards to reformulated fuels and environmental protection concerns. As a result of these new mandates, terminals have turned to blending at the loading rack as an economical and convenient means in meeting these new requirements. This paper will discuss some of these mandates and how loading rack blending is used for different applications. Various types of blending will also be discussed along with considerations for each method.

  6. Remotely Sensed Snow Data Assimilation within Distributed Snow 17 Model

    NASA Astrophysics Data System (ADS)

    Dechant, C. M.; Leisenring, M.; Moradkhani, H.

    2009-12-01

    Accurate estimation of the quantity of water stored in seasonal snow cover, particularly in the mountainous Western United States, is an important tool for water resources management. Challenges in the estimation of Snow Water Equivalent (SWE) arise from uncertain model forcing data, model structure/parameter error, poor spatial resolution of in-situ measurements and uncertainties in remotely sensed observations. Currently, the best method for quantifying SWE is to integrate both modeled and remotely sensed estimates of snow by accounting for the relative uncertainties associated with each estimate. Data assimilation techniques account for observed and modeled errors by treating them as a stochastic variable and sequentially updating/resampling the state values. This study examines the effectiveness of three snow data assimilation techniques for creating a more accurate estimate of SWE. In this study, SWE, modeled with a distributed version of the National Weather Service’s SNOW-17 model, and model parameters in the Snow-17 model are updated with remotely sensed snow cover area (SCA). The SNOW-17 model takes precipitation and temperature as an input and estimates both SWE and SCA. Model forcing data was gathered from the North-American Land Data Assimilation (NLDAS) dataset. The SCA information used in this study is produced by the MODIS instrument flown on the NASA Terra satellite. The model runs at 1/8th degree and MODIS data is aggregated to this resolution from a 500m resolution. Remotely sensed SCA is used as the observation in three different data assimilation schemes: Ensemble Kalman Filter (EnKF), Ensemble Kalman Smoother (EnKS) and the Particle Filter. The EnKF and EnKS both use the same update equation, which assumes normally distributed errors. The Particle Filter takes a different approach that does not require an assumption about the error distribution. The accuracy and uncertainties associated with each of these assimilation techniques are compared

  7. Passive Microwave Remote Sensing of Falling Snow and Associated GPM Field Campaigns

    NASA Technical Reports Server (NTRS)

    Skofronick-Jackson, Gail

    2011-01-01

    Retrievals of falling snow from space represent one of the next important challenges for the atmospheric, hydrological, and energy budget scientific communities. Historically, retrievals of falling snow have been difficult due to the relative insensitivity of satellite rain-based channels as used in the past. We emphasize the use of high frequency passive microwave channels (85-200 GHz) since these are more sensitive to the ice in clouds and have been used to estimate falling snow from space. While satellite-based remote sensing provides global coverage of falling snow events and the science is relatively new, retrievals are still undergoing development with challenges remaining. There are several current satellite sensors, though not specifically designed for estimating falling snow, are capable of measuring snow from space. These include NOAA's AMSU-B, the MHS sensors, and CloudSat radar. They use high frequency (greater than 85 GHz) passive and active microwave and millimeter-wave channels that are sensitive to the scattering from ice and snow particles in the atmosphere. Sensors with water vapor channels near 183 GHz center line provide opaqueness to the Earth's surface features that can contaminate the falling snow signatures, especially over snow covered surface. In addition, the Global Precipitation Measurement (GPM) mission scheduled for launch in 2013 is specifically designed to measure both liquid rain and frozen snow precipitation. Since falling snow from space is the next precipitation measurement challenge from space, information must be determined in order to guide retrieval algorithm development for these current and future missions. This information includes thresholds of detection for various sensor channel configurations, snow event system characteristics, and surface types. For example, can a lake effect snow system with low cloud tops having an ice water content (IWC) at the surface of 1.0 gram per cubic meter be detected? If this information is

  8. An improved snow hydrology for GCMS. Part 1: Snow cover fraction, albedo, grain size, and age

    SciTech Connect

    Marshall, S.; Oglesby, R.J.

    1994-07-01

    A new, physically-based snow hydrology has been implemented into the NCAR CCM1. The snow albedo is based on snow depth, solar zenith angle, snow cover pollutants, cloudiness, and a new parameter, the snow grain size. Snow grain size in turn depends on temperature and snow age. An improved expression is used for fractional snow cover which relates it to surface roughness and to snow depth. Each component of the new snow hydrology was implemented separately and then combined to make a new control run integrated for ten seasonal cycles. With the new snow hydrology, springtime snow melt occurs more rapidly, leading to a more reasonable late spring and summer distribution of snow cover. Little impact is seen on winter snow cover, since the new hydrology affects snow melt directly, but snowfall only indirectly, if at all. The influence of the variable grain size appears more important when snow packs are relatively deep while variable fractional snow cover becomes increasingly important as the snow pack thins. Variable surface roughness affects the snow cover fraction directly, but shows little effect on the seasonal cycle of the snow line. As an application of the new snow hydrology, we have rerun simulations involving Antarctic and Northern Hemisphere glaciation. Relatively little difference is seen for Antarctica, but a profound difference occurs for the Northern Hemisphere. In particular, ice sheets computed using new snow accumulations from the GCM are more numerous and larger in extent with the new snow hydrology. The new snow hydrology leads to a better simulation of the seasonal cycle of snow cover, however, our primary goal in implementing it into the GCM is to improve the predictive capabilities of the model. Since the snow hydrology is based on fundamental physical processes, and has well-defined parameters. it should enable model simulations of climatic change in which we have increased confidence. 37 refs., 15 figs., 2 tabs.

  9. Snow economics and the NOHRSC Snow Information System (SNOW-INFO) for the United States

    NASA Astrophysics Data System (ADS)

    Carroll, T.; Cline, D.; Berkowitz, E.; Savage, D.

    2003-04-01

    The National Operational Hydrologic Remote Sensing Center (NOHRSC) in the National Weather Service (NWS), National Oceanic and Atmospheric Administration (NOAA), provides remotely sensed and modeled snow cover products and data sets to support river and flood forecasting in the United States and also to enhance the national economy. Nationwide, on average, about 16% of the total annual precipitation occurs as snowfall. Many sectors of the U.S. economy rely on surface water from snowfall for production, including manufacturing, mining, thermoelectric power, agriculture, and others. Snow contributes 1.7 trillion annually (16%) to the Nation's gross domestic product (GDP) of 10.5 trillion. Manufacturing is by far the largest contributor to the Nation's GDP and is also the Nation's largest surface-water user. The contribution of snow to manufacturing revenue totals 1.6 trillion annually for the Nation and ranges from just a few billion dollars in the southeastern U.S. to over 200 billion each in Michigan and New York. Hydropower supplies about 10% of the electricity used in the United States, enough to serve the needs of 28 million people. Annual hydroelectric power production exceeds 250 billion kilowatt-hours with the contribution from snow exceeding 6 billion in energy revenue each year (i.e., 30% of the Nation's annual hydroelectric production of 20 billion). Seasonal snowpacks are an essential component of agricultural water supplies throughout most of the U.S. and provide much of the surface water used to irrigate over 55 million acres of U.S. farmland each year. Agriculture net revenue supported by snowmelt exceeds 33 billion annually. Surface water supplies are essential for thermoelectric power generation by coal-fired, oil-fired, and nuclear power plants. Providing about 90% of the Nation's electricity supply, thermoelectric power revenues exceed 215 billion each year while water from snow contributes about 25 billion to this revenue annually. With 1

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

  11. Assessment of dynamic probabilistic methods for mapping snow cover in Québec Canada

    NASA Astrophysics Data System (ADS)

    De Seve, D.; Perreault, L.; Vachon, F.; Guay, F.; choquette, Y.

    2012-04-01

    Hydro-Quebec is the leader in electricity production in North America and uses hydraulic resources to generate 97% of its overall production where snow represents 30% of its annual energy reserve. Information on snow cover extent (SC) and snow water equivalent (SWE) is crucial for hydrological forecasting, particularly in Nordic regions where a majority of total precipitations falls as snow. Accurate estimation of the spatial distribution of snow cover variables is required to measure the extent of this resource but snow surveys are expensive due to inaccessibility factors and to the large extent nature of the Quebec geography. Consequently, the follow-up of snowmelt is particularly challenging for operational forecasting resulting in the need to develop a new approach to assist forecasters. For improved understanding of the dynamics of snow melting over watersheds and to generate optimized power production, Hydro-Québec's Research Institute (IREQ) has developed expertise in in-situ, remote sensing monitoring and statistical treatment of such data. The main goal of this Hydro-Quebec project is to develop an automatic and dynamic snow mapping system providing a daily snow map by merging remote sensing (AVHRR and SSMI) and in situ data. This paper focuses on the work accomplished on passive microwave SSM/I data to follow up snow cover. In our problematic, it is highly useful to classify snow, more specifically during the snowmelt period. The challenge is to be able to discriminate ground from wet snow as it will react as a black body, therefore, adding noise to global brightness temperature. Two dynamic snow classifiers were developed and tested. For this purpose, channels at 19 and 37 GHz in vertical polarization have been used to feed each model. SWE values from gamma ray in situ stations (GMON) and data snow depth from ultrasonic sensor (SR50) were used to validate the output models. The first algorithm is based on a standard K-mean clustering approach, combined

  12. Observation of Snow Cover Variations at Mt. Kilimanjaro Using Landsat TM and ETM+ Images

    NASA Astrophysics Data System (ADS)

    Park, S.; Jung, H.; Lee, M.; Jung, H.

    2012-12-01

    Since the industrial revolution began, CO2 levels have been increasing with climate change. The objectives of this study are to quantitatively analyze snow cover area and distribution according to height changes with respect to time and to statistically predict the date of snow cover disappearance using remote sensing data over Mt. Kilimanjaro, Tanzania. Total numbers of 23 Landsat-5 TM and Landsat-7 ETM+ images are used for observing the snow cover variation, spanning the 27 years from June 1984 to July 2011. For this observation of snow cover variations, the following steps are applied: 1) atmospheric correction is performed on each image using the cosine approximation (COST) atmospheric correction algorithm, 2) the snow cover area is extracted from the normalized difference snow index (NDSI) algorithm, 3) the minimum height is determined using SRTM DEM and extracted snow cover area, and 4) the date of snow cover disappearance is predicted using a linear regression model. Among 23 images, seventeen images of the dry season are used for analyzing snow cover changes. Results show that snow cover area for about 30 years are largely changed from 9.01 km2 to 2.54 km2, equivalent to a 73% reduction. The minimum height of snow cover increased by approximately 290 m, from 4,603 m to 4,893 m. Linear regression model result shows that the snow cover area decreased by about 0.34 km2/yr and the minimum height of snow cover increased by about 9.85 m/yr. Moreover, L-band synthetic aperture radar (SAR) images are used to analyze seasonal variation of snow cover from 2006 to 2011. The results indicate that snow cover area of Mt. Kilimanjaro has fast decreased according to global warming. *This work was researched by the supporting project to educate GIS experts; Distribution of elevations for snow-covered area: (a) 24 Jun. 1984 and (b) 21 Jul. 2011 ; Changes of snow-covered area with respect to surface elevation: (a) 24 Jun. 1984 and (b) 21 Jul. 2011

  13. About UV albedo of seasonal snow at Sodankyla including Arctic - Antarctic comparison aspects

    NASA Astrophysics Data System (ADS)

    Meinander, O.; Kazadzis, S.; Arola, A.; Kivi, R.; Kontu, A.; Suokanerva, H.; Kyrö, E.; Aaltonen, V.; Manninen, T.; Riihelä, A.; Roujean, J.-L.; Hautecoeur, O.

    2013-05-01

    Finland is especially advantageous for snow albedo studies, as it represents the European Arctic, the snow cover melts every year, we have five out of the six global snow classes, and the topography is flat, thus favorable to albedo studies. In 2007, new continuous broadband measurements on Arctic snow UV albedo at Sodankyla (67°22'N, 26°39'E, 179 m asl) were started by the Finnish Meteorological Institute as part of the IPY activities. Weekly snow samples have been collected for BC analyses at Sodankyla since 2009, and snow grain size data belongs to the snow time regular measurement procedures at Sodankyla as well. In literature, albedo values for clean snow in UV-VIS are 0.97-0.98, consistent with the extremely small absorption coefficient of ice in this spectral range. We have found that in case of intensively melting Arctic snow, with melt water surrounding the several millimeter snow grains, containing possibly BC up to 40 ppb and organic carbon up to 1734 ppb, and confirmed by three independent ancillary snow albedo measurements, the UV-VIS albedo of snow measured at an open snow covered field (surrounded by distant trees not shadowing the field during the measurement) can be around 0.5-0.7. For comparison, we have measured the clean Arctic Sea ice and snow at 87°N to have A = 0.91 - 0.92 both in the UV and VIS. Our experimental results on artificially sooted snow show that when albedo of natural southern Finnish snow was AVIS=0.92 and AUV=0.70, with surface EC=87 ppb, then introducing an amount of EC=4916 ppb soot on the surface of snow, decreased albedo immediately into A=0.28-0.29 in both the UV and VIS. We have also studied the SZA asymmetry of albedo found in the Arctic and Antarctic albedo data, and Radiative Transfer (RT) model calculations have been used to study e.g. the effect of the measured local albedo on radiative forcing.

  14. Snow density climatology across the former USSR

    NASA Astrophysics Data System (ADS)

    Zhong, X.; Zhang, T.; Wang, K.

    2014-04-01

    Snow density is one of the basic properties used to describe snow cover characteristics, and it is a key factor for linking snow depth and snow water equivalent, which are critical for water resources assessment and modeling inputs. In this study, we used long-term data from ground-based measurements to investigate snow density (bulk density) climatology and its spatiotemporal variations across the former Soviet Union (USSR) from 1966 to 2008. The results showed that the long-term monthly mean snow density was approximately 0.22 ± 0.05 g cm-3 over the study area. The maximum and minimum monthly mean snow density was about 0.33 g cm-3 in June, and 0.14 g cm-3 in October, respectively. Maritime and ephemeral snow had the highest monthly mean snow density, while taiga snow had the lowest. The higher values of monthly snow density were mainly located in the European regions of the former USSR, on the coast of Arctic Russia, and the Kamchatka Peninsula, while the lower snow density occurred in central Siberia. Significant increasing trends of snow density from September through June of the next year were observed, however, the rate of the increase varied with different snow classes. The long-term (1966-2008) monthly and annual mean snow densities had significant decreasing trends, especially during the autumn months. Spatially, significant positive trends in monthly mean snow density lay in the southwestern areas of the former USSR in November and December and gradually expanded in Russia from February through April. Significant negative trends mainly lay in the European Russia and the southern Russia. There was a high correlation of snow density with elevation for tundra snow and snow density was highly correlated with latitude for prairie snow.

  15. Fracture mechanics of snow avalanches

    NASA Astrophysics Data System (ADS)

    Åström, J. A.; Timonen, J.

    2001-07-01

    Dense snow avalanches are analyzed by modeling the snow slab as an elastic and brittle plate, attached by static friction to the underlying ground. The grade of heterogeneity in the local fracture (slip) thresholds, and the ratio of the average substrate slip threshold to the average slab fracture threshold, are the decisive parameters for avalanche dynamics. For a strong pack of snow there appears a stable precursor of local slips when the frictional contacts are weakened (equivalent to rising temperature), which eventually trigger a catastrophic crack growth that suddenly releases the entire slab. In the opposite limit of very high slip thresholds, the slab simply melts when the temperature is increased. In the intermediate regime, and for a homogeneous slab, the model display features typical of real snow avalanches. The model also suggests an explanation to why avalanches are impossible to forecast reliably based on precursor observations. This explanation may as well be applicable to other catastrophic rupture phenomena such as earthquakes.

  16. Fracture mechanics of snow avalanches.

    PubMed

    Aström, J A; Timonen, J

    2001-07-01

    Dense snow avalanches are analyzed by modeling the snow slab as an elastic and brittle plate, attached by static friction to the underlying ground. The grade of heterogeneity in the local fracture (slip) thresholds, and the ratio of the average substrate slip threshold to the average slab fracture threshold, are the decisive parameters for avalanche dynamics. For a strong pack of snow there appears a stable precursor of local slips when the frictional contacts are weakened (equivalent to rising temperature), which eventually trigger a catastrophic crack growth that suddenly releases the entire slab. In the opposite limit of very high slip thresholds, the slab simply melts when the temperature is increased. In the intermediate regime, and for a homogeneous slab, the model display features typical of real snow avalanches. The model also suggests an explanation to why avalanches are impossible to forecast reliably based on precursor observations. This explanation may as well be applicable to other catastrophic rupture phenomena such as earthquakes.

  17. Snow White Trench After Scraping

    NASA Image and Video Library

    2008-07-24

    This view from the Surface Stereo Imager on NASA Phoenix Mars Lander shows the trench informally named Snow White after a series of scrapings were done in preparation for collecting a sample for analysis from a hard subsurface layer.

  18. Using Snow to Teach Geology.

    ERIC Educational Resources Information Center

    Roth, Charles

    1991-01-01

    A lesson plan, directed at middle school students and older, describes using snow to study the geological processes of solidification of molten material, sedimentation, and metamorphosis. Provides background information on these geological processes. (MCO)

  19. Using Snow to Teach Geology.

    ERIC Educational Resources Information Center

    Roth, Charles

    1991-01-01

    A lesson plan, directed at middle school students and older, describes using snow to study the geological processes of solidification of molten material, sedimentation, and metamorphosis. Provides background information on these geological processes. (MCO)

  20. 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.

  1. Influence of temperature and precipitation variability on near-term snow trends

    NASA Astrophysics Data System (ADS)

    Mankin, Justin S.; Diffenbaugh, Noah S.

    2015-08-01

    Snow is a vital resource for a host of natural and human systems. Global warming is projected to drive widespread decreases in snow accumulation by the end of the century, potentially affecting water, food, and energy supplies, seasonal heat extremes, and wildfire risk. However, over the next few decades, when the planning and implementation of current adaptation responses are most relevant, the snow response is more uncertain, largely because of uncertainty in regional and local precipitation trends. We use a large (40-member) single-model ensemble climate model experiment to examine the influence of precipitation variability on the direction and magnitude of near-term Northern Hemisphere snow trends. We find that near-term uncertainty in the sign of regional precipitation change does not cascade into uncertainty in the sign of regional snow accumulation change. Rather, temperature increases drive statistically robust consistency in the sign of future near-term snow accumulation trends, with all regions exhibiting reductions in the fraction of precipitation falling as snow, along with mean decreases in late-season snow accumulation. However, internal variability does create uncertainty in the magnitude of hemispheric and regional snow changes, including uncertainty as large as 33 % of the baseline mean. In addition, within the 40-member ensemble, many mid-latitude grid points exhibit at least one realization with a statistically significant positive trend in net snow accumulation, and at least one realization with a statistically significant negative trend. These results suggest that the direction of near-term snow accumulation change is robust at the regional scale, but that internal variability can influence the magnitude and direction of snow accumulation changes at the local scale, even in areas that exhibit a high signal-to-noise ratio.

  2. Factors Controlling Black Carbon Deposition in Snow in the Arctic

    NASA Astrophysics Data System (ADS)

    Qi, L.; Li, Q.; He, C.; Li, Y.

    2015-12-01

    This study evaluates the sensitivity of black carbon (BC) concentration in snow in the Arctic to BC emissions, dry deposition and wet scavenging efficiency using a 3D global chemical transport model GEOS-Chem driven by meteorological field GEOS-5. With all improvements, simulated median BC concentration in snow agrees with observation (19.2 ng g-1) within 10%, down from -40% in the default GEOS-Chem. When the previously missed gas flaring emissions (mainly located in Russia) are included, the total BC emission in the Arctic increases by 70%. The simulated BC in snow increases by 1-7 ng g-1, with the largest improvement in Russia. The discrepancy of median BC in snow in the whole Arctic reduces from -40% to -20%. In addition, recent measurements of BC dry deposition velocity suggest that the constant deposition velocity of 0.03 cm s-1 over snow and ice used in the GEOS-Chem is too low. So we apply resistance-in-series method to calculate the dry deposition velocity over snow and ice and the resulted dry deposition velocity ranges from 0.03 to 0.24 cm s-1. However, the simulated total BC deposition flux in the Arctic and BC in snow does not change, because the increased dry deposition flux has been compensated by decreased wet deposition flux. However, the fraction of dry deposition to total deposition increases from 16% to 25%. This may affect the mixing of BC and snow particles and further affect the radative forcing of BC deposited in snow. Finally, we reduced the scavenging efficiency of BC in mixed-phase clouds to account for the effect of Wegener-Bergeron-Findeisen (WBF) process based on recent observations. The simulated BC concentration in snow increases by 10-100%, with the largest increase in Greenland (100%), Tromsø (50%), Alaska (40%), and Canadian Arctic (30%). Annual BC loading in the Arctic increases from 0.25 to 0.43 mg m-2 and the lifetime of BC increases from 9.2 to 16.3 days. This indicates that BC simulation in the Arctic is really sensitive to

  3. Deceleration of Projectiles in Snow,

    DTIC Science & Technology

    1982-08-01

    contents of this report are not to be used for advertising or promotional purposes. Citation of brand names does not constitute an official endorsement or...projectile are directly wired els were used in these tests. The snow targets were to recording equipment, and the target is not accel- prepared by sifting...the snow target are identified in The target box was placed in a rigid stand located the figure. The travel times between these impacts on a tangent to

  4. 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.

  5. Quantifying the crucial role of snow in supplying human water demand

    NASA Astrophysics Data System (ADS)

    Mankin, J. S.; Viviroli, D.; Mekonnen, M. M.; Hoekstra, A. Y. Y.; Diffenbaugh, N. S.

    2014-12-01

    Snow is considered essential to ecosystems, people, and climate, regulating water availability by mediating runoff and soil moisture throughout the year, and altering planetary radiative balance. Like canary in a coalmine, snow also serves as a kind of sentinel system, providing a benchmark by which we can measure the advance of global warming. Yet recent analyses reveal that the relationship between snow and warming is more complex: Despite warming, for example, the magnitude of internal climate variability suggests some Northern Hemisphere regions may experience snow increases for at least the next 50 years. While studies demonstrate that snow supply is vital and in critical danger, such assessments are based only on projections of annualized supply-side changes, such as the fraction of total annual runoff coming from snowfall. These measures do not consider a region's unique seasonal patterns of water supply and, in particular, water demand. We therefore do not know snow's relative importance to each region's water supply portfolio, and thus how great a risk global warming presents to regional water availability. Here we present the first quantification of snow's observed role in fulfilling monthly water demand for people in the Northern Hemisphere given each basin's unique sub-annual patterns of snow accumulation and melt. This quantification also reconciles the requirements of ecosystems and includes the buffering capacity of existing artificial storage, such as dams and reservoirs. Our results provide a meaningful baseline against which projected snow changes (from global warming) or demand changes (from population or land-use change) can be evaluated to identify regions at acute risk of disequilibria between future snow water supply and its demand.

  6. The Fallacy of Drifting Snow

    NASA Astrophysics Data System (ADS)

    Andreas, Edgar L.

    2011-12-01

    A common parametrization over snow-covered surfaces that are undergoing saltation is that the aerodynamic roughness length for wind speed ( z 0) scales as {α u_ast^2/g}, where u * is the friction velocity, g is the acceleration of gravity, and α is an empirical constant. Data analyses seem to support this scaling: many published plots of z 0 measured over snow demonstrate proportionality to {u_ast^2 }. In fact, I show similar plots here that are based on two large eddy-covariance datasets: one collected over snow-covered Arctic sea ice; another collected over snow-covered Antarctic sea ice. But in these and in most such plots from the literature, the independent variable, u *, was used to compute z 0 in the first place; the plots thus suffer from fictitious correlation that causes z 0 to unavoidably increase with u * without any intervening physics. For these two datasets, when I plot z 0 against u * derived from a bulk flux algorithm—and thus minimize the fictitious correlation— z 0 is independent of u * in the drifting snow region, u * ≥ 0.30 ms-1. I conclude that the relation {z_0 = α u_ast^2/g} when snow is drifting is a fallacy fostered by analyses that suffer from fictitious correlation.

  7. Establishing Transferable Sub-Pixel Relationships for Estimating Snow Depth from Remotely-Sensed Snow Covered Area and Terrain Variability

    NASA Astrophysics Data System (ADS)

    Schneider, D.; Molotch, N. P.

    2014-12-01

    Snowmelt is the primary water source in the Western United States and mountainous regions globally. Forecasts of streamflow and water supply rely heavily on snow measurements from sparse observation networks that may not provide adequate information during abnormal climatic conditions. In this regard, remote sensing can be used to monitor snow covered area (SCA), which we hypothesize can be used in conjunction with terrain information to estimate spatially explicit snow depth (SD). Small-scale terrain variability can be considered a proxy for the snow holding capacity of the ground. SCA should be more sensitive to changes in snow depth for smooth, or low variable terrain, and less sensitive to rougher terrain. To this end, we have developed a method that is not expected to depend on repeated climatic conditions because it accounts for the static accumulation capacity rather than dynamic processes. In preliminary investigations, a LiDaR dataset from 2010 from Green Lakes Valley, Colorado, USA (Harpold et al. 2014) was used to relate snow depth with fSCA and the sub-pixel terrain variability. Snow depth (dependent variable) and fSCA (independent variable) were aggregated from 1 meter to 30 meters from the LiDaR snow depth product while terrain variability metrics such as the coefficient of variation of elevation were calculated using the 900 1-meter elevation pixels inside each 30 meter pixel. Single linear regression of SD fit with fSCA explains 38% of the variability with a mean absolute error (MAE) of 0.36 m, but the goodness of fit increases to an average of 53% with MAE of 0.25 m as the data is binned by elev-cv; this may indicate that SD-SCA relationships vary by terrain type (slope, aspect, etc.). Further analysis of the scales at which these relationships are applicable and the viability with off-the-shelf DEM and fSCA products is needed. The utility of these relationships is such that snow depth could be estimated above treeline for any set of climatic

  8. Snow in the Northeast

    NASA Image and Video Library

    2017-09-27

    Clear, cold air following behind the winter storm of January 21, 2014 highlights the extent of snowfall in the Mid-Atlantic states. The frigid temperatures that remain entrenched over the central and eastern U.S. will begin to affect the southern states through the rest of the week. Winter Weather Advisories are posted for a portions of Texas for snow and freezing rain, while Hard Freeze Warnings extend from the Southeast states into northern Florida. This image was taken around 1840Z on January 22, 2014. Image credit: NOAA/NASA/Suomi NPP Credit: NASA/NOAA via NOAA Environmental Visualization Laboratory NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

  9. Snow and Ice Products from the Moderate Resolution Imaging Spectroradiometer

    NASA Technical Reports Server (NTRS)

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

    2003-01-01

    Snow and sea ice products, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, flown on the Terra and Aqua satellites, are or will be available through the National Snow and Ice Data Center Distributed Active Archive Center (DAAC). The algorithms that produce the products are automated, thus providing a consistent global data set that is suitable for climate studies. The suite of MODIS snow products begins with a 500-m resolution, 2330-km swath snow-cover map that is then projected onto a sinusoidal grid to produce daily and 8-day composite tile products. The sequence proceeds to daily and 8-day composite climate-modeling grid (CMG) products at 0.05 resolution. A daily snow albedo product will be available in early 2003 as a beta test product. The sequence of sea ice products begins with a swath product at 1-km resolution that provides sea ice extent and ice-surface temperature (IST). The sea ice swath products are then mapped onto the Lambert azimuthal equal area or EASE-Grid projection to create a daily and 8-day composite sea ice tile product, also at 1 -km resolution. Climate-Modeling Grid (CMG) sea ice products in the EASE-Grid projection at 4-km resolution are planned for early 2003.

  10. Increased snow facilitates plant invasion in mixedgrass prairie.

    PubMed

    Blumenthlal, D; Chimner, R A; Welker, J M; Morgan, J A

    2008-07-01

    Although global change is known to influence plant invasion, little is known about interactions between altered precipitation and invasion. In the North American mixedgrass prairie, invasive species are often abundant in wet and nitrogen (N)-rich areas, suggesting that predicted changes in precipitation and N deposition could exacerbate invasion. Here, this possibility was tested by seeding six invasive species into experimental plots of mixedgrass prairie treated with a factorial combination of increased snow, summer irrigation, and N addition. Without added snow, seeded invasive species were rarely observed. Snow addition increased average above-ground biomass of Centaurea diffusa from 0.026 to 66 g m(-2), of Gypsophila paniculata from 0.1 to 7.3 g m(-2), and of Linaria dalmatica from 5 to 101 g m(-2). Given added snow, summer irrigation increased the density of G. paniculata, and N addition increased the density and biomass of L. dalmatica. Plant density responses mirrored those of plant biomass, indicating that increases in biomass resulted, in part, from increases in recruitment. In contrast to seeded invasive species, resident species did not respond to snow addition. These results suggest that increases in snowfall or variability of snowfall may exacerbate forb invasion in the mixedgrass prairie.

  11. Climate Effects and Efficacy of Dust and Soot in Snow

    NASA Astrophysics Data System (ADS)

    Zender, C. S.; Flanner, M. G.; Randerson, J. T.; Mahowald, N. M.; Rasch, P. J.; Yoshioka, M.; Painter, T.

    2006-12-01

    Dust and industrial and biomass burning emissions from low and mid-latitudes dominate the absorbing impurities trapped in snow at mid- and high-latitudes. We study the effects of dust and smoke on global and regional climate using a general circulation model driven by observed and predicted aerosol emissions determined from satellite and in situ observations. The model has sophisticated treatments of aerosol and snowpack radiative and thermodynamic processes that compare well with observations of snow albedo evolution and impurity concentration. This presentation focuses on the individual and combined contributions of present day dust and soot to snow-albedo forcing and on the global temperature and snowpack responses. Results are emphasized near India and East Asia, where the anthropogenic aerosol forcing of surface albedo and hydrology is greatest. We find that dust and black carbon (BC) aerosols have climate change efficacies (surface temperature change per unit forcing) about 3--4 times greater than CO2, making them the most efficacious forcing agents known. We estimate present day dust and soot snowpack-forcing of ~ 0.050 W m-2 warms global climate by ~ 0.16 °K. Anthropogenic soot from fossil fuel sources causes more than 50% of this warming, and biomass burning can account for up to 30% in strong tropical or boreal burn years. The greatest forcings occur in the Tarim/Mongol region (due to dust), northeastern China (due to soot), and the Tibetan Plateau (both). Dirty springtime snow in these regions can darken albedo by more than 0.1 and increase surface absorption by more than 20 W m-2. These results have implications for the strength of the Asian Monsoon, which is negatively correlated with antecedent snow cover in non-ENSO years. Dust and soot have such strong efficacies because they increase spring melt rates thus reduce summer snow cover. In some regions and seasons, dirty snow reduces snowpack depth and cover by 50%, triggering strong snow and sea

  12. Snow density climatology across the former USSR

    NASA Astrophysics Data System (ADS)

    Zhong, X.; Zhang, T.; Wang, K.

    2013-07-01

    Snow density is one of the basic properties used to describe snow cover characteristics, and it is a key factor for retrieving snow depth and snow water equivalent, which are critical for water resources assessment and modeling inputs. In this study, we used long-term data from ground-based measurements to investigate snow density climatology and its spatiotemporal variations across the former Soviet Union (USSR) from 1966 to 2008. The results showed that the long-term monthly mean snow density was approximately 0.194 ± 0.046 g cm-3 over the study area. The maximum and minimum monthly mean snow density was ˜ 0.295 g cm-3 in June, and 0.135 g cm-3 in October, respectively. Maritime snow had the highest monthly mean snow density, while taiga snow had the lowest. The higher values of monthly snow density were mainly located in the European regions of the former USSR, in Arctic Russia, and in some regions of the Russian Far East, and the lower snow density occurred in central Siberia. Significant increasing trends of snow density from September through June of the next year were observed, however, the rate of the increase varied with different snow classes. The long-term (1966-2008) monthly and annual mean snow densities had significant decreasing trends, especially during the autumn months. Spatially, significant positive trends in monthly mean snow density lay in the southwestern areas of the former USSR in November and December and gradually expanded in Russia from February through April. Significant negative trends mainly lay in the European Russia and the southern Russia. Snow density decreased with elevation, at about 0.004 g cm-3 per 100 m increase in elevation. This same relationship existed for all snow classes except for maritime and ephemeral snow.

  13. Impact of the seasonal evolution of snow properties on microwave emission model performance

    NASA Astrophysics Data System (ADS)

    Fuller, M.; Derksen, C.; Lemmetyinen, J.; Yackel, J.

    2010-12-01

    Snow cover exhibits great spatio-temporal variability, and is dynamically coupled with global hydrological and climatological processes. Accounting for snowpack evolution related to snow accumulation, metamorphosis, and melt are essential for both modeling and remote sensing applications. Microwave emission has frequency dependant relationships with snow water equivalent (SWE), but snow grain-size, snowpack layering, and snow liquid-water content can confuse the estimation of snow parameters with empirical stand-alone algorithms. This work presents an overview of seasonal snow and multi-frequency dual-polarization microwave emission measurements collected during the 2009-2010 winter season at a network of sites near Churchill, Manitoba, Canada. These observations were used to parameterize and evaluate model simulations of microwave snow emission using the multiple-layer version of the Helsinki University of Technology (HUT) microwave emission model. The HUT model is utilized in the European Space Agency’s (ESA) GlobSnow global snow monitoring service, applied to SWE and snow depth (SD) retrievals for the Northern Hemisphere. The HUT model used for forward brightness temperature simulations in the GlobSnow retrieval scheme is currently limited to one layer which necessitates idealizing physical properties of the entire snow pack. In this study, we explore the performance of simulations with the addition of a depth hoar layer and, when appropriate, an ice lens. Simulations for forest, lake, and open environments were synthesized through a scene simulation formulation of the HUT model to produce output suitable for comparison with measured brightness temperatures from the Advanced Microwave Scanning Radiometer (AMSR-E). While the multi-layer model better represents the vertical complexities of grain size and layering, implementation of a multi-layer approach remains a challenge due to model sensitivity with regard to the method of generalization of a complex snow

  14. Retrievals of Falling Snow from Satellite-borne Active and Passive Sensors

    NASA Astrophysics Data System (ADS)

    Jackson, Gail; Munchak, S. Joseph; Johnson, Benjamin

    2014-05-01

    Precipitation, including rain and snow, is a critical part of the Earth's energy and hydrology cycles. Precipitation impacts latent heating profiles locally while global circulation patterns distribute precipitation and energy from the equator to the poles. For the hydrological cycle, falling snow is a primary contributor in northern latitudes during the winter seasons. Falling snow is the source of snow pack accumulations that provide fresh water resources for many communities in the world. Furthermore, falling snow impacts society by causing transportation disruptions during severe snow events. In order to collect information on the complete global precipitation cycle, both liquid and frozen precipitation must be collected. The Global Precipitation Measurement (GPM) mission's Core satellite, scheduled for launch in February 2014, is well designed to detect and estimate falling snow. The GPM core carries a passive radiometer with frequencies (10-183 GHz) and an active radar with Ku- and Ka-band frequencies. Combined with the 65o inclination of the GPM Core satellite, these instruments allow for the GPM Core to sense and retrieve information about falling snow and light rain in regions of the earth where snow is common. The GPM Core's comprehensive active and passive channel set will also allow it to serve as a unifying reference for GPM constellation radiometer satellites. Since falling snow from space is the next precipitation measurement challenge from space, information is needed to guide retrieval algorithm development for these current and future missions. This information includes thresholds of detection for various sensor channel configurations, sensitivity to macroscale snow event system characteristics, and sensitivity to microscale snowflake particle characteristics. While the work in this area will continue for many years to come, our group has made substantial progress in this area by identifying minimum detectable melted rates of ~0.5 mm hr-1. Results

  15. 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

  16. Wind tunnel observations of drifting snow

    NASA Astrophysics Data System (ADS)

    Paterna, Enrico; Crivelli, Philip; Lehning, Michael

    2016-04-01

    Drifting snow has a significant impact on snow redistribution in mountains, prairies as well as on glaciers, ice shelves, and sea ice. In all these environments, the local mass balance is highly influenced by drifting snow. Understanding the dynamic of snow saltation is crucial to the accurate description of the process. We applied digital shadowgraphy in a cold wind tunnel to measure drifting snow over natural snow covers. The acquisition and evaluation of time-resolved shadowgraphy images allowed us to resolve a large part of the saltation layer. The technique has been successfully compared to the measurements obtained from a Snow Particle Counter, considered the most robust technique for snow mass-flux measurements so far. The streamwise snow transport is dominated by large-scale events. The vertical snow transport has a more equal distribution of energy across the scales, similarly to what is observed for the flow turbulence velocities. It is hypothesized that the vertical snow transport is a quantity that reflects the local entrainment of the snow crystals into the saltation layer while the streamwise snow transport results from the streamwise development of the trajectories of the snow particles once entrained, and therefore is rather a non-local quantity.

  17. Sodankylä manual snow survey program

    NASA Astrophysics Data System (ADS)

    Leppänen, L.; Kontu, A.; Hannula, H.-R.; Sjöblom, H.; Pulliainen, J.

    2015-12-01

    The manual snow survey program of the Arctic Research Centre of Finnish Meteorological Institute (FMI-ARC) consists of numerous observations of natural seasonal taiga snowpack in Sodankylä, northern Finland. The easily accessible measurement areas represent the typical forest and soil types in the boreal forest zone. Systematic snow measurements began in 1909 with snow depth (SD) and snow water equivalent (SWE); however some older records of the snow and ice cover exists. In 2006 the manual snow survey program expanded to cover snow macro- and microstructure from regular snow pits at several sites using both traditional and novel measurement techniques. Present-day measurements include observations of SD, SWE, temperature, density, horizontal layers of snow, grain size, specific surface area (SSA), and liquid water content (LWC). Regular snow pit measurements are performed weekly during the snow season. Extensive time series of manual snow measurements are important for the monitoring of temporal and spatial changes in seasonal snowpack. This snow survey program is an excellent base for the future research of snow properties.

  18. Uncertainty analysis of the optical satellite data-derived snow products

    NASA Astrophysics Data System (ADS)

    Salminen, Miia; Pulliainen, Jouni; Metsämäki, Sari; Luojus, Kari; Böttcher, Kristin; Hannula, Henna-Reetta

    2014-05-01

    The behavior of the global snow cover can be effectively estimated using optical Earth Observation (EO) data, in particular during the end of the melting season. In addition to successful dry and continuous 100% (full) snow cover mapping, optical methods perform well over snowmelt regions with patchy wet snow. Long decadal scale time series of satellite data estimates on global Snow Extent (SE) or Fractional Snow Cover (FSC) and albedo are needed for constructing Climate Data Records (CDR). CDRs have a high relevance in climate research e.g. in climate monitoring including trend analysis and verification of climate models. Currently, the available optical satellite data records for hemispherical snow monitoring reach back for several decades, e.g. AVHRR (since ca 1980), ATSR (since ca 1990), AATSR and MODIS (since ca 2000). Also, the current VIIRS (since 2011) and the future Sentinel-3 both provide very potential data for global snow monitoring. It is fundamental to generate extensive CDRs with quality/estimation error information attached to each snow estimate, as the usefulness of the EO-based snow estimate is highly dependent on the quality of the interpretation. The objective of this work is to establish and develop a methodology to determine a dynamic retrieval error estimate for the optical satellite-retrieved FSC. This is performed by applying an error propagation analysis for the consideration of the statistical error of FSC estimation. The procedure is demonstrated here by using the SCAmod algoritm, which is suited for global snow detection and able to perform well also in forested regions. Apart from determining the statistical (random) error, we outline the procedure for the evaluation of the systematic error (biases) of FSC products, both of which are essential for the generation of snow cover CDR. As we focus here on determining the statistical random error, it is crucial to know the variability of the different factors affecting the satellite

  19. Why Blended Will Win.

    ERIC Educational Resources Information Center

    Zenger, Jack; Uehlein, Curt

    2001-01-01

    Electronic learning and traditional learning not only can coexist, but can merge to create something far better. A blended solution has the following characteristics: integrated instructional design, consistent framework and nomenclature, each method delivering its best, flexibility, and variety. (JOW)

  20. Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

    NASA Astrophysics Data System (ADS)

    Stigter, Emmy E.; Wanders, Niko; Saloranta, Tuomo M.; Shea, Joseph M.; Bierkens, Marc F. P.; Immerzeel, Walter W.

    2017-07-01

    Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of) SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF). Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.

  1. Future Global Cryosphere: Impacts of Global Warming

    NASA Astrophysics Data System (ADS)

    Gan, T. Y.; Barry, R. G.

    2014-12-01

    In recent years, the Earth is undergoing potentially rapid changes in all cryospheric components, including Arctic sea ice shrinkage, mountain glacier recession, thawing permafrost, diminishing snow cover, and accelerated melting of the Greenland ice sheet. This has significant implications for global climate, hydrology, water resources, and global sea level. Physical evidences of changes observed in the cryosphere are: (a) Duration of ice cover of rivers and lakes in high latitudes of N. H. decreased by about two weeks over the 20th Century; (b) Significant retreat of glaciers world wide during the 20th Century; (c) Thinning of Arctic sea-ice extent and thickness by about 40% in late summer in recent decades, with the minimum sea ice concentration mapped by the SSM/I sensor of NASA in 2007; (d) Snow cover decreased in area by about 10% since global observations by satellites began in the late 1960s, in various places of the Northern Hemisphere; (e) In North America, snow water equivalent decreased by about 10mm since observations by passive microwave sensors began in the late 1970s; (f) Degradations of permafrost have been detected in some parts of the polar and sub-polar regions, and (g) The total 20th Century global average sea level rise was about 0.17m, likely due to decline in glaciers, snow, ice sheets, and losses from Greenland and Antarctica ice. Next, projected changes to the Cryosphere: northern hemisphere snow cover, avalanches, land ice, permafrost, freshwater ice, and sea ice changes, are presented.

  2. Snow Micro-Structure Model

    SciTech Connect

    Micah Johnson, Andrew Slaughter

    2014-06-25

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

  3. Snow in the Middle East

    NASA Image and Video Library

    2017-09-27

    This image from the Aqua satellite's MODIS instrument taken at 11:10 UTC on December 16, 2013 shows areas of snow in Syria, Jordan, Israel and the Palestinian Territory. Snow storms in the Middle East are not frequent but not uncommon either. However, this one was unusually early in the winter and more intense than normal. The storm paralyzed Jerusalem with 30 to 50 centimeters (12 to 20 inches) of snow, knocking out power for roughly 15,000 households. The snow closed mountain roads leading into the city, effectively cutting Jerusalem off. Amman, Jordan, received about 45 cm (18 inches) of snow, and Lebanon and Syria also were unusually cold and snowy. Lower elevations near the coast received torrential rain during the storm, resulting in flooding. Some 40,000 people were forced to evacuate flooded areas in Gaza, according to the Associated Press. The floods are not visible at this scale, but tan and green plumes of sediment are visible along the Mediterranean Sea coast. Such plumes can be caused by floods and run off, though stormy, turbid waters may also bring sediment to the surface. NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

  4. Snow Micro-Structure Model

    SciTech Connect

    Micah Johnson, Andrew Slaughter

    2014-06-25

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

  5. Northern Hemisphere Snow Extent Trends Derived From Visible and Microwave Satellite Data

    NASA Astrophysics Data System (ADS)

    Brodzik, M.; Armstrong, R. L.

    2002-12-01

    The extent and variability of seasonal snow cover are important parameters in climate and hydrologic systems due to effects on energy and moisture budgets. In terms of spatial extent, snow cover is the largest single component of the cryosphere with a mean maximum extent in the Northern Hemisphere of approximately 47 million km2, or nearly 50 percent of the land surface area. About 98 percent of the global seasonal snow cover is located in the Northern Hemisphere. During the past four decades much important information on Northern Hemisphere snow extent has been provided by the NOAA weekly snow extent charts derived from visible-band polar orbiting and geo-stationary satellite imagery. This product is available from NSIDC as the Northern Hemisphere EASE-Grid Weekly Snow Cover and Sea Ice Extent Version 2. Because of the ability to penetrate clouds, provide data during darkness and the potential to provide an index of snow depth or water equivalent, passive microwave satellite remote sensing offers an attractive alternative for hemispheric scale snow monitoring given the availability of a twenty-three year data record (Scanning Multichannel Microwave Radiometer (SMMR) 1978-1987 and Special Sensor Microwave/Imager (SSM/I) 1987-present). We evaluate trends in Northern Hemisphere snow extent using both the visible (NOAA) and the passive microwave (SMMR and SSM/I) data. The visible data have been manually interpreted while a single numerical algorithm is applied to the microwave data throughout the times series for each sensor. The visible data show higher magnitude departures from the monthly means while the passive microwave data indicate less snow-covered area during fall when the snow is shallow. The two data sets show comparable inter-annual variability with similar decreasing hemispheric trends of approximately 2 percent per decade. Individual trends for North America and Eurasia as well as monthly trends are presented.

  6. Scavenging insoluble light-absorbing particulates (ILAP) in seasonal snow over northern China

    NASA Astrophysics Data System (ADS)

    Wang, X.; Huang, J.

    2013-12-01

    It has been long believed that Black Carbon (BC) from biomass burning, fossil fuel and biofuel plays an important role in the earth's system through its climate effect. Compared with other insoluble light-absorbing particulates (ILAP), BC is a main component of the most effective light-absorbing particulates, which can dominates the absorption of solar radiation at the visible wavelengths. Furthermore, once deposits on snow, it could significant reduces the snow reflectance and accelerate the snow melting, therefore, it is considered as the second most important component as the CO2 to affect the globe warming. Although several experiments have already been performed for collecting and measuring the scavenging BC in snow on a global scale. Little attention has been given to the quantitative measurements of ILAP deposit on the snowpack at mid-latitude regions in Asia, especially over Northern China. Recently, there are two field campaigns were conducted in January and February 2010 and 2012 to measuring the ILAP in snow across northern China. About 700 snow samples were collected at 84 sites in seven provinces. The BC mass fractions in seasonal snow across northern Xinjiang have a median value of 70 ng g-1, and the concentrations of BC were in the remote northeast on the border of Siberia, with a median concentration in surface snow of 120 ng g-1. South of this, in the industrial northeast, the median snow BC concentration was 1200 ng g-1. In the northeast, snow particulate light absorption was dominated by BC. Across the grassland of Inner Mongolia, OC, likely mostly from local soil, dominates light absorption, with median BC concentrations of 340 ng g-1 responsible for only about one third of total particulate light absorption. In the Qilian Mountains, at the northern boundary of the Tibetan Plateau, snow particulate light absorption is dominated by local soil and desert dust.

  7. Effects of seasonal snow on the growing season of temperate vegetation in China.

    PubMed

    Yu, Zhen; Liu, Shirong; Wang, Jingxin; Sun, Pengsen; Liu, Weiguo; Hartley, Damon S

    2013-07-01

    Variations in seasonal snowfall regulate regional and global climatic systems and vegetation growth by changing energy budgets of the lower atmosphere and land surface. We investigated the effects of snow on the start of growing season (SGS) of temperate vegetation in China. Across the entire temperate region in China, the winter snow depth increased at a rate of 0.15 cm yr(-1) (P = 0.07) during the period 1982-1998, and decreased at a rate of 0.36 cm yr(-1) (P = 0.09) during the period 1998-2005. Correspondingly, the SGS advanced at a rate of 0.68 day yr(-1) (P < 0.01) during 1982-1998, and delayed at a rate of 2.13 day yr(-1) (P = 0.07) during 1998-2005, against a warming trend throughout the entire study period of 1982-2005. Spring air temperature strongly regulated the SGS of both deciduous broad-leaf and coniferous forests, whereas the winter snow had a greater impact on the SGS of grassland and shrubs. Snow depth variation combined with air temperature contributed to the variability in the SGS of grassland and shrubs, as snow acted as an insulator and modulated the underground thermal conditions. In addition, differences were seen between the impacts of winter snow depth and spring snow depth on the SGS; as snow depths increased, the effect associated went from delaying SGS to advancing SGS. The observed thresholds for these effects were snow depths of 6.8 cm (winter) and 4.0 cm (spring). The results of this study suggest that the response of the vegetation's SGS to seasonal snow change may be attributed to the coupling effects of air temperature and snow depth associated with the underground thermal conditions.

  8. Miscible polymer blend dynamics

    NASA Astrophysics Data System (ADS)

    Pathak, Jai Avinash

    The segmental and terminal dynamics of miscible polymer blends have been systematically investigated with pointed experiments to test dichotomous literature ideas on the origin of dynamic heterogeneity in these systems. Segmental dynamics have been studied by dielectric spectroscopy, while terminal dynamics have been studied by oscillatory shear rheology. It has been found that when composition fluctuations are suppressed, dynamic heterogeneities, such as the failure of time-temperature superposition (tTS), are also suppressed. This observation lends credence to the ideas of Fischer and Kumar that spontaneous composition fluctuations in miscible blends profoundly affect their segmental dynamics. In addition, data acquired in this study on two model weakly-interacting miscible polyolefin blends, were combined with literature data to show that breakdown of tTS worsens with increasing dynamic asymmetry (intrinsic differences in component dynamics) in weakly-interacting miscible blends. This observation is adduced as evidence for the role of dynamic asymmetry in miscible blend dynamics, in addition to the role of composition fluctuations. Finally, attempts were made to use information on component segmental dynamics, as obtained from the composition fluctuation model of Kumar, to predict terminal dynamics in miscible blends. In this regard, the composition fluctuation model was first used to model segmental dynamics in a model weakly-interacting blend. Then, experimental segmental and terminal dynamics data were used to identify a possible segmental time-scale which may control terminal relaxation of a chain in a blend. This timescale was found to lie on the long-time end of the distribution of segmental relaxation times for each component. It was calculated from the segmental relaxation time distribution for each component of a miscible blend as the average-longest segmental time experienced by the monomers of a given chain. Using the Doi-Edwards tube model, the

  9. Sodankylä manual snow survey program

    NASA Astrophysics Data System (ADS)

    Leppänen, Leena; Kontu, Anna; Hannula, Henna-Reetta; Sjöblom, Heidi; Pulliainen, Jouni

    2016-05-01

    The manual snow survey program of the Arctic Research Centre of the Finnish Meteorological Institute (FMI-ARC) consists of numerous observations of natural seasonal taiga snowpack in Sodankylä, northern Finland. The easily accessible measurement areas represent the typical forest and soil types in the boreal forest zone. Systematic snow measurements began in 1909 with snow depth (HS) and snow water equivalent (SWE). In 2006 the manual snow survey program expanded to cover snow macro- and microstructure from regular snow pits at several sites using both traditional and novel measurement techniques. Present-day snow pit measurements include observations of HS, SWE, temperature, density, stratigraphy, grain size, specific surface area (SSA) and liquid water content (LWC). Regular snow pit measurements are performed weekly during the snow season. Extensive time series of manual snow measurements are important for the monitoring of temporal and spatial changes in seasonal snowpack. This snow survey program is an excellent base for the future research of snow properties.

  10. Metagenomic and satellite analyses of red snow in the Russian Arctic

    PubMed Central

    Hisakawa, Nao; Quistad, Steven D.; Hester, Eric R.; Martynova, Daria; Sala, Enric; Gavrilo, Maria V.

    2015-01-01

    Cryophilic algae thrive in liquid water within snow and ice in alpine and polar regions worldwide. Blooms of these algae lower albedo (reflection of sunlight), thereby altering melting patterns (Kohshima, Seko & Yoshimura, 1993; Lutz et al., 2014; Thomas & Duval, 1995). Here metagenomic DNA analysis and satellite imaging were used to investigate red snow in Franz Josef Land in the Russian Arctic. Franz Josef Land red snow metagenomes confirmed that the communities are composed of the autotroph Chlamydomonas nivalis that is supporting a complex viral and heterotrophic bacterial community. Comparisons with white snow communities from other sites suggest that white snow and ice are initially colonized by fungal-dominated communities and then succeeded by the more complex C. nivalis-heterotroph red snow. Satellite image analysis showed that red snow covers up to 80% of the surface of snow and ice fields in Franz Josef Land and globally. Together these results show that C. nivalis supports a local food web that is on the rise as temperatures warm, with potential widespread impacts on alpine and polar environments worldwide. PMID:26713242

  11. Snow occurrence changes over the central and eastern United States under future warming scenarios.

    PubMed

    Ning, Liang; Bradley, Raymond S

    2015-11-20

    Changes of snow occurrence across the central and eastern United States under future warming for the late 21(st) century are investigated by applying an empirical hyperbolic tangent function to both observed and downscaled high spatial resolution (~12.5 km) daily temperature and precipitation, to compare the historical (1981-2000) and future (2081-2100) snow occurrence. The observed distributions of snow frequency show that snow-rain transition zones are mainly zonally distributed, since they are largely determined by temperature, with slight shifts to the south over the Appalachian Mountains. The snow-rain transition zone is located around 38-46°N for November and March, and 32-42°N for winter months (DJF). These observed patterns are reproduced well for the historical period by an ensemble average of multiple general circulation models (GCMs). The probabilistic projections show that the snow-rain transition zone will shift to the north under the background of global warming at magnitudes of 2-6 °C, indicating that large areas will experience a partial, or even a very large, loss of snow occurrence in the future. The northward shifts are about 2° latitude under the representative concentration pathways 4.5 (RCP4.5) scenario and 4° latitude under the RCP8.5 scenario. The percentages of the area losing snow occurrence are also assessed.

  12. Metagenomic and satellite analyses of red snow in the Russian Arctic.

    PubMed

    Hisakawa, Nao; Quistad, Steven D; Hester, Eric R; Martynova, Daria; Maughan, Heather; Sala, Enric; Gavrilo, Maria V; Rohwer, Forest

    2015-01-01

    Cryophilic algae thrive in liquid water within snow and ice in alpine and polar regions worldwide. Blooms of these algae lower albedo (reflection of sunlight), thereby altering melting patterns (Kohshima, Seko & Yoshimura, 1993; Lutz et al., 2014; Thomas & Duval, 1995). Here metagenomic DNA analysis and satellite imaging were used to investigate red snow in Franz Josef Land in the Russian Arctic. Franz Josef Land red snow metagenomes confirmed that the communities are composed of the autotroph Chlamydomonas nivalis that is supporting a complex viral and heterotrophic bacterial community. Comparisons with white snow communities from other sites suggest that white snow and ice are initially colonized by fungal-dominated communities and then succeeded by the more complex C. nivalis-heterotroph red snow. Satellite image analysis showed that red snow covers up to 80% of the surface of snow and ice fields in Franz Josef Land and globally. Together these results show that C. nivalis supports a local food web that is on the rise as temperatures warm, with potential widespread impacts on alpine and polar environments worldwide.

  13. Lake Effect Snow Covers Buffalo

    NASA Technical Reports Server (NTRS)

    2002-01-01

    An average of one foot of snow per day has fallen on Buffalo, New York, since Christmas Eve, resulting in a total of up to 5 feet from December 24-28. The snow fell very heavily, with accumulations of up to 3 inches per hour. Cold winds blowing along the surface of Lake Erie pick up warmth and moisture, which falls as snow as the warm air rises. This image was acquired by the Geostationary Operational Environmental Satellite (GOES), operated by NOAA, on December 27, 2001, at 12:32 p.m. EST. The scene shows thick bands of clouds extending from the eastern tip of Lake Erie and over Buffalo. The arrows show the wind direction, which is blowing down the length of the lake. Image and animation by Robert Simmon, based on data from the NASA GOES Project Science Office.

  14. Lake Effect Snow Covers Buffalo

    NASA Technical Reports Server (NTRS)

    2002-01-01

    An average of one foot of snow per day has fallen on Buffalo, New York, since Christmas Eve, resulting in a total of up to 5 feet from December 24-28. The snow fell very heavily, with accumulations of up to 3 inches per hour. Cold winds blowing along the surface of Lake Erie pick up warmth and moisture, which falls as snow as the warm air rises. This image was acquired by the Geostationary Operational Environmental Satellite (GOES), operated by NOAA, on December 27, 2001, at 12:32 p.m. EST. The scene shows thick bands of clouds extending from the eastern tip of Lake Erie and over Buffalo. The arrows show the wind direction, which is blowing down the length of the lake. Image and animation by Robert Simmon, based on data from the NASA GOES Project Science Office.

  15. Radar spectral observations of snow

    NASA Technical Reports Server (NTRS)

    Stiles, W. H.; Ulaby, F. T.; Fung, A. K.; Aslam, A.

    1981-01-01

    Radar remote sensing experiments have been conducted at test sites in Kansas, Colorado, and South Dakota over the last six years to examine backscatter coefficient response to snowcovered terrain. Truck-mounted 1-35 GHz scatterometers were employed in conjunction with detailed ground-truth measurements. From these experiments and associated modeling efforts, most of the fundamental questions concerning backscatter behavior in response to important snow parameters have been, at least qualitatively, answered. The optimum angular range seems to be between 20 and 50 deg and, for these angles, the results indicate that the radar backscatter generally: (1) increases with increasing water equivalent, (2) decreases with increasing liquid water, (3) increases with increasing crystal size, (4) is insensitive to surface roughness for dry snow conditions, and (5) can be sensitive to soil state if the snowcover is dry. This paper gives a summary of these results, along with empirical and theoretical models for describing the backscatter from snow.

  16. Correlated declines in Pacific arctic snow and sea ice cover

    USGS Publications Warehouse

    Stone, Robert P.; Douglas, David C.; Belchansky, Gennady I.; Drobot, Sheldon

    2005-01-01

    Simulations of future climate suggest that global warming will reduce Arctic snow and ice cover, resulting in decreased surface albedo (reflectivity). Lowering of the surface albedo leads to further warming by increasing solar absorption at the surface. This phenomenon is referred to as “temperature–albedo feedback.” Anticipation of such a feedback is one reason why scientists look to the Arctic for early indications of global warming. Much of the Arctic has warmed significantly. Northern Hemisphere snow cover has decreased, and sea ice has diminished in area and thickness. As reported in the Arctic Climate Impact Assessment in 2004, the trends are considered to be outside the range of natural variability, implicating global warming as an underlying cause. Changing climatic conditions in the high northern latitudes have influenced biogeochemical cycles on a broad scale. Warming has already affected the sea ice, the tundra, the plants, the animals, and the indigenous populations that depend on them. Changing annual cycles of snow and sea ice also affect sources and sinks of important greenhouse gases (such as carbon dioxide and methane), further complicating feedbacks involving the global budgets of these important constituents. For instance, thawing permafrost increases the extent of tundra wetlands and lakes, releasing greater amounts of methane into the atmosphere. Variable sea ice cover may affect the hemispheric carbon budget by altering the ocean–atmosphere exchange of carbon dioxide. There is growing concern that amplification of global warming in the Arctic will have far-reaching effects on lower latitude climate through these feedback mechanisms. Despite the diverse and convincing observational evidence that the Arctic environment is changing, it remains unclear whether these changes are anthropogenically forced or result from natural variations of the climate system. A better understanding of what controls the seasonal distributions of snow and ice

  17. Retrievals of Falling Snow from Satellite-borne Active and Passive Sensors

    NASA Astrophysics Data System (ADS)

    Skofronick-Jackson, Gail; Munchak, S. Joseph; Johnson, Benjamin

    2013-04-01

    Precipitation, including rain and snow, is a critical part of the Earth's energy and hydrology cycles. Precipitation impacts latent heating profiles locally while global circulation patterns distribute precipitation and energy from the equator to the poles. For the hydrological cycle, falling snow is a primary contributor in northern latitudes during the winter seasons. Falling snow is the source of snow pack accumulations that provide fresh water resources for many communities in the world. Furthermore, falling snow impacts society by causing transportation disruptions during severe snow events. In order to collect information on the complete global precipitation cycle, both liquid and frozen precipitation must be collected. The Global Precipitation Measurement (GPM) mission's Core satellite, scheduled for launch in 2014, is well designed to detect and estimate falling snow. The GPM core carries a passive radiometer with frequencies (10-183 GHz) and an active radar with Ku- and Ka-band frequencies. Combined with the 65 degree inclination of the GPM Core satellite, these instruments allow for the GPM Core to sense and retrieve information about falling snow and light rain in regions of the earth where snow is common. The GPM Core's comprehensive active and passive channel set will also allow it to serve as a unifying reference for GPM constellation radiometer satellites. Since falling snow from space is the next precipitation measurement challenge from space, information is needed to guide retrieval algorithm development for these current and future missions. This information includes thresholds of detection for various sensor channel configurations, sensitivity to macroscale snow event system characteristics, and sensitivity to microscale snowflake particle characteristics. While the work in this area will continue for many years to come, our group has made substantial progress in this area by identifying minimum detectable melted rates of ~0.5 mm/hr. Results will

  18. Cold, Ice, and Snow Safety (For Parents)

    MedlinePlus

    ... for TV, Video Games, and the Internet Cold, Ice, and Snow Safety KidsHealth > For Parents > Cold, Ice, ... the top layer gets wet from snow or freezing rain, they can peel off some clothes down ...

  19. Snow wetness measurements for melt forecasting

    NASA Technical Reports Server (NTRS)

    Linlor, W. I.; Clapp, F. D.; Meier, M. F.; Smith, J. L.

    1975-01-01

    A microwave technique for directly measuring snow pack wetness in remote installations is described. The technique, which uses satellite telemetry for data gathering, is based on the attenuation of a microwave beam in transmission through snow.

  20. New Energy-efficient Snow production

    NASA Astrophysics Data System (ADS)

    Rhyner, H.

    2009-04-01

    Artificial snow making is widely used in the Alps, mainly to compensate for missing snow cover. Since snow production requires both water and energy, it is necessary to develop new technologies in this field that optimise the production process. In particular in terms of energy consumption, new technologies are developed to minimize the use of energy and costs. The aims of this paper are to model the process of artificial snow making in the Swiss Alps. Several field and laboratory campaigns will be presented. The actual process of snow produciton, as it exits the snow canons and snow hoses and acummulates on the ground is modelled and validated with field and laboratory experiments. Amongst other techniques, infra-red meausurements show detailed temperature distributions. Techniques are demonstrated on how snow-making can be optimised.

  1. A Test of Snow Fortifications.

    DTIC Science & Technology

    1979-10-01

    140). A comparison with the broaching from sand, phase I tests at Camp Riple \\ in a 30-cm-deep snow cla, and water described b\\ Lewandowski (1970...sprinkled with water during the construction of the trench. fortification construction in cold regions is contained In loose snow and in the absence of...das are required to dig one kilometer of tren~h. Sur~h an c\\- Test preparations penditure ot labor." Before the test at Camp Riple \\ , a rehearsalI \\k

  2. Periodontal status in snow leopards.

    PubMed

    Cook, R A; Stoller, N H

    1986-11-01

    Periodontal examinations were performed on ten 1- to 22-year-old snow leopards (6 males and 4 females), using dentistry methods for determining the plaque and gingival indices. All tooth surfaces were probed, and alveolar bone attachment loss was determined. After subgingival plaque removal, plaque specimens were examined for differential bacterial morphotypes. The small number of leopards evaluated precluded definitive statistical analysis. However, the progression from gingival health to gingivitis to periodontitis was similar to that seen in man. Therefore, the use of plaque index, gingival index, alveolar bone attachment loss, and differential bacterial morphotypes can be used to determine the dental health of snow leopards.

  3. Use of AMSR-E microwave satellite data for land surface characteristics and snow cover variation.

    PubMed

    Boori, Mukesh Singh; Ferraro, Ralph R; Choudhary, Komal; Kupriyanov, Alexander

    2016-12-01

    This data article contains data related to the research article entitled "Global land cover classification based on microwave polarization and gradient ratio (MPGR)" [1] and "Microwave polarization and gradient ratio (MPGR) for global land surface phenology" [2]. This data article presents land surface characteristics and snow cover variation information from sensors like EOS Advanced Microwave Scanning Radiometer (AMSR-E). This data article use the HDF Explorer, Matlab, and ArcGIS software to process the pixel latitude, longitude, snow water equivalent (SWE), digital elevation model (DEM) and Brightness Temperature (BT) information from AMSR-E satellite data to provide land surface characteristics and snow cover variation data in all-weather condition at any time. This data information is useful to discriminate different land surface cover types and snow cover variation, which is turn, will help to improve monitoring of weather, climate and natural disasters.

  4. 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

  5. Correlation and prediction of snow water equivalent from snow sensors

    Treesearch

    Bruce J. McGurk; David L. Azuma

    1992-01-01

    Since 1982, under an agreement between the California Department of Water Resources and the USDA Forest Service, snow sensors have been installed and operated in Forest Service-administered wilderness areas in the Sierra Nevada of California. The sensors are to be removed by 2005 because of the premise that sufficient data will have been collected to allow "...

  6. Millimetre-Wave Propagation Through Snow

    NASA Astrophysics Data System (ADS)

    Wallace, H. Bruce

    1983-09-01

    During the SNOW-ONE and SNOW-ONE-A exercises at Camp Ethan Allen, Vermont, measurements of attenuation through falling snow were made at 35, 95, 140, and 217 GHz over a two-way path length of one kilometre. Attenuations measured appeared to be a function of crystal size, free water content and snow mass concentration. Instrumentation design and the results of measurements are presented with some general observations of turbulent phenomena.

  7. [Snow cover pollution monitoring in Ufa].

    PubMed

    Daukaev, R A; Suleĭmanov, R A

    2008-01-01

    The paper presents the results of examining the snow cover polluted with heavy metals in the large industrial town of Ufa. The level of man-caused burden on the snow cover of the conventional parts of the town was estimated and compared upon exposure to a wide range of snow cover pollutants. The priority snow cover pollutants were identified among the test heavy metals.

  8. Physical and Optical Properties of Falling Snow

    DTIC Science & Technology

    1989-07-01

    thefallvelocity. They found that Me~lor (1966) and Rosinski et al. (1983) observed the snow fall velocities generally ranged between the fall velocities of snow...mass (after Rosinski et al. 1983). nally designed to measure rain rates (Wang et al. 1980), was modified for operation in snow. The Locatelli and...is partially collimated. Snow and measuring the melted droplet diameter. particles falling through the sample area break the Rosinski et al. (1983

  9. Microwave scattering properties of snow fields

    NASA Technical Reports Server (NTRS)

    Angelakos, D. J.

    1977-01-01

    Experimental results were presented showing backscatter dependence on frequency, angle of incidence, snow wetness, and frequency modulation. Theoretical studies were 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: (1) snow layering affects backscatter; (2) layer response was significant up to 45 degrees of incidence; (3) wetness modifies snow layer effects; and (4) frequency modulation masks the layer response.

  10. Highway Snow Control Research in Japan

    DTIC Science & Technology

    1990-09-01

    addition, it was found that the vane shear strength can be expressed as a power function of the snow (* 2nsity. A28 Masao Takeuchi and Shin - etsu Kamada...region, and Yamashita (1983 [HI81), for the Kan- etsu Expressway. lchihara (1970 [H 191) discussed general problems of con- struction and maintenance...142. Yamashita, M. (1983) Snow removal operation in heavy Tamura, A. and Y. Shioda (1982) Snow control for the snow areas of the Kan- etsu Expressway

  11. Advances in RUC LSM snow component to address cold biases in snow-covered regions in RAP and HRRR

    NASA Astrophysics Data System (ADS)

    Smirnova, T. G.; Benjamin, S.; Brown, J. M.

    2016-12-01

    RUC Land-Surface Model (LSM), a Weather Research and Forecast (WRF) LSM option, is used as a land surface component in the operational Rapid Refresh (RAP) over North America domain and in the High-Resolution Rapid Refresh (HRRR) over CONUS domain. It was also added to the land-surface model suite available in NASA Land Information System (LIS), and work has been started to implement it in the Next Generation Global Prediction System (NGGPS) as part of the RAP/HRRR physics suite. The RUC LSM performance has been evaluated for almost two decades within the real-time operational weather prediction systems focused on storm-scale predictions for severe weather and safer aviation. And in the recent couple of years it has been more and more extensively utilized by the WRF community in different parts of the world, including Arctic regions, and for different applications. Valuable feedback from the National Weather Prediction forecast offices and the WRF community has motivated further advances towards better representation of processes in snow-covered regions. The new treatment has been implemented for grid cells partially covered with snow. It considers snow-covered and non-snow-covered portions of a grid cell independently, and independently determined surface fluxes are aggregated to feed back into the surface-layer scheme at the end of each time step. This new "mosaic" approach removes the constraint of keeping skin temperature of partially covered with snow grid cells at or below the freezing point, and helps to reduce cold biases in these regions. Comparison results from experiments with the new and old approaches will be presented at the meeting. Also, techniques impemented in RAP/HRRR for optimal initialization of snow cover on the ground will be presented.

  12. The influence of snow cover thickness on the thermal regime of Tête Rousse Glacier (Mont Blanc range, 3200 m a.s.l.): consequences for water storage, outburst flood hazards and glacier response to global warming

    NASA Astrophysics Data System (ADS)

    Gilbert, A.; Vincent, C.; Wagnon, P.; Thibert, E.; Rabatel, A.

    2012-04-01

    Tête Rousse Glacier (French Alps) was responsible for an outburst flood in 1892 that devastated the village of St Gervais-Le Fayet close to Chamonix, causing 175 fatalities. In 2010, geophysical surveys of this glacier revealed a subglacial lake that was subsequently drained artificially. The processes controlling the thermal regime of the glacier have been investigated on the basis of measurements and snow cover and heat flow models using meteorological data covering the last 200 years. Temperature measurements show a polythermal structure with subglacial water trapped by the cold lowest part of the glacier (-2°C). The modeling approach shows that the polythermal structure results mainly from changes in the depth of the snow cover with time at the glacier surface. Paradoxically, periods with negative mass balances, associated with warmer air temperature, tend to cool the glacier because the warmer temperatures reduce the snowpack depth and extent, thereby decreasing the insulation of the glacier from the cold and the amount of latent heat introduced by meltwater refreezing. Conversely years with colder temperatures, associated with positive mass balances, tend to increase the glacier temperature by maintaining a thick snowpack all year round at the glacier surface. The thermal effect of the subglacial lake is evaluated and suggests that the lake existed before 1980. Modeling shows that the glacier will cool again in the future. This study provides insight into the thermal processes responsible for water storage inside a small static glacier which can lead to catastrophic outburst floods such as the 1892 event or potentially dangerous situations as in 2010.

  13. Utilizing Multiple Datasets for Snow Cover Mapping

    NASA Technical Reports Server (NTRS)

    Tait, Andrew B.; Hall, Dorothy K.; Foster, James L.; Armstrong, Richard L.

    1999-01-01

    Snow-cover maps generated from surface data are based on direct measurements, however they are prone to interpolation errors where climate stations are sparsely distributed. Snow cover is clearly discernable using satellite-attained optical data because of the high albedo of snow, yet the surface is often obscured by cloud cover. Passive microwave (PM) data is unaffected by clouds, however, the snow-cover signature is significantly affected by melting snow and the microwaves may be transparent to thin snow (less than 3cm). Both optical and microwave sensors have problems discerning snow beneath forest canopies. This paper describes a method that combines ground and satellite data to produce a Multiple-Dataset Snow-Cover Product (MDSCP). Comparisons with current snow-cover products show that the MDSCP draws together the advantages of each of its component products while minimizing their potential errors. Improved estimates of the snow-covered area are derived through the addition of two snow-cover classes ("thin or patchy" and "high elevation" snow cover) and from the analysis of the climate station data within each class. The compatibility of this method for use with Moderate Resolution Imaging Spectroradiometer (MODIS) data, which will be available in 2000, is also discussed. With the assimilation of these data, the resolution of the MDSCP would be improved both spatially and temporally and the analysis would become completely automated.

  14. Snow Mold Investigations in Eastern Washington

    Treesearch

    T. H. Filer; A. G. Law

    1961-01-01

    "Snow mold of turf" in the Pacific Northwest must include both Fusarium Patch caused by Calonectria graminicola (Berk and Br.) (conidial stage Fusarium nivale (Fr. ) CES.), and Gray snow mold caused by Typhula itoana Imai, which occur together to give a disease complex. Snow mold of turf is the most...

  15. 44 CFR 206.227 - Snow assistance.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... 44 Emergency Management and Assistance 1 2012-10-01 2011-10-01 true Snow assistance. 206.227 Section 206.227 Emergency Management and Assistance FEDERAL EMERGENCY MANAGEMENT AGENCY, DEPARTMENT OF... Snow assistance. Emergency or major disaster declarations based on snow or blizzard conditions will...

  16. 44 CFR 206.227 - Snow assistance.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 44 Emergency Management and Assistance 1 2011-10-01 2011-10-01 false Snow assistance. 206.227 Section 206.227 Emergency Management and Assistance FEDERAL EMERGENCY MANAGEMENT AGENCY, DEPARTMENT OF... Snow assistance. Emergency or major disaster declarations based on snow or blizzard conditions will...

  17. 44 CFR 206.227 - Snow assistance.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... 44 Emergency Management and Assistance 1 2014-10-01 2014-10-01 false Snow assistance. 206.227 Section 206.227 Emergency Management and Assistance FEDERAL EMERGENCY MANAGEMENT AGENCY, DEPARTMENT OF... Snow assistance. Emergency or major disaster declarations based on snow or blizzard conditions will...

  18. 44 CFR 206.227 - Snow assistance.

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... 44 Emergency Management and Assistance 1 2013-10-01 2013-10-01 false Snow assistance. 206.227 Section 206.227 Emergency Management and Assistance FEDERAL EMERGENCY MANAGEMENT AGENCY, DEPARTMENT OF... Snow assistance. Emergency or major disaster declarations based on snow or blizzard conditions will...

  19. Shallow Snow Model for Predicting Vehicle Performance

    DTIC Science & Technology

    1981-10-01

    promotional purposes. Cita - tion of brand names does not constitute an official endorsement or approval of the use of such commercial products. Ac eeson...vehicles. 18 ’A I I Table 7. Mechanical properties of shallow snow. Snow A ir p Snow temp temp p ca c W co (critical) type (SC) ( 0C) (g/cm 3) ( APa

  20. A blended land emissivity product from the Inter-Comparison of different Land Surface Emissivity Estimates

    NASA Astrophysics Data System (ADS)

    Norouzi, H.; Temimi, M.; Khanbilvardi, R.

    2012-12-01

    Passive microwave observations are routinely used to estimate rain rate, cloud liquid water, and total precipitable water. In order to have accurate estimations from microwave, the contribution of the surface should be accounted for. Over land, due to the complex interaction between the microwave signal and the soil surface, retrieval of land surface emissivity and other surface and subsurface parameters is not straightforward. Several microwave emissivity products from various microwave sensors have been proposed. However, lack of ground truth measurements makes the validation of these products difficult. This study aims to inter-compare several available emissivity products over land and ultimately proposes a unique blended product that overcomes the flaws of each individual product. The selected products are based on observations from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), the Special Sensor Microwave Imager (SSM/I), the Advanced Microwave Sounding unit (AMSU), and the Special Sensor Microwave Imager/Sounder (SSMIS). In retrieval of emissivities from these sensors different methods and ancillary data have been used. Some inherent discrepancies between the selected products can be introduced by as the difference in geometry in terms of incident angle, spectral response, and the foot print size which can affect the estimations. Moreover, ancillary data especially skin temperature and cloud mask cover can cause significant discrepancies between various estimations. The time series and correlation between emissivity maps are explored to assess the consistency of emissivity variations with geophysical variable such as snow, precipitation and drought. Preliminary results reveal that inconsistency between products varies based on land cover type due to penetration depth effect and ancillary data. Six years of estimations are employed in this research study, and a global blended emissivity estimations based on all product with minimal discrepancies

  1. Seasonal variations of snow depth on Mars.

    PubMed

    Smith, D E; Zuber, M T; Neumann, G A

    2001-12-07

    Using topography collected over one martian year from the Mars Orbiter Laser Altimeter on the Mars Global Surveyor (MGS) spacecraft, we have measured temporal changes in the elevation of the martian surface that correlate with the seasonal cycle of carbon dioxide exchange between the surface and atmosphere. The greatest elevation change (1.5 to 2 meters) occurs at high latitudes ( above 80 degrees ), whereas the bulk of the mass exchange occurs at lower latitudes (below 75 degrees N and below 73 degrees S). An unexpected period of sublimation was observed during northern hemisphere autumn, coincident with dust storms in the southern hemisphere. Analysis of MGS Doppler tracking residuals revealed temporal variations in the flattening of Mars that correlate with elevation changes. The combined changes in gravity and elevation constrain the average density of seasonally deposited carbon dioxide to be 910 +/- 230 kilograms per cubic meter, which is considerably denser than terrestrial snow.

  2. Communicators' perspective on snow avalanche risk communication

    NASA Astrophysics Data System (ADS)

    Charriere, M. K. M.; Bogaard, T.; Mostert, E.

    2014-12-01

    Among all the natural hazards, snow avalanches are the only ones for which a public danger scale is globally used. It consists of 5 levels of danger displayed with a given number and colour and for each of them, behavioural advices are provided. Even though this is standardized in most of the countries affected by this natural hazard, the tools (usually websites or smartphone applications) with which the information is disseminated to the general pubic differs, particularly in terms of target audience and level of details. This study aims at gathering the perspectives of several communicators that are responsible for these communication practices. The survey was created to assess how and why choices were made in the design process of the communication tools and to determine how their effectiveness is evaluated. Along with a review of existing avalanche risk communication tools, this study provides guidelines for communication and the evaluation of its effectiveness.

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

    NASA Technical Reports Server (NTRS)

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

    2000-01-01

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

  4. Seasonal snow cover and glacier change impact on water and energy cycle of Central Asia Endorheic Basin

    NASA Astrophysics Data System (ADS)

    Eisen, Vladimir; Eisen, Elena

    2010-05-01

    High mountains of Central Asia Endorheic Basin (CAEB) hold one of the greatest in the World concentration of snow and glacier ice water resources at mid- latitudes thousands of miles from the oceans providing up to 80% of total river runoff. The total external atmospheric moisture flow over the CAEB comprises approximately 200 billion cubic meters per year. The glaciers of CAEB receive and retain annually up to 10% of moisture transferred over the mountains. However, the area of seasonal snow and glaciers has declining rapidly as result of recent climatic change causes by increase in air temperature and precipitation partitioning between snow and rain, and evaporation fluxes. Based on remote sensing data CAEB glaciers shrunk by 5% between the middle of 1940th and 1970th and 10% during the next 30 years. Evaluation of seasonal snow cover for the same period revealed 20% seasonal snow covered area reduction. During the last thirty years, the duration of snow melt reduced by 30 days from the date of maximum snow cover to the date of its disappearance. Further decrease in seasonal snow cover will be accelerated due to increase of rainfall instead of snowfall in early spring months at high elevations, and consequently a lesser heat expenditure for snowmelt. At high mountains, about 40% of snow ablated during the penultimate 10 days of snow cover. During ablation season, the amount of energy used to melt snow and glacier ice is in the same order as the combination of other components of the heat budget (e.g., heat associated with atmospheric advection, radiation balance and turbulent heat exchange). Heating of the air would have been 3 times higher if snow and glacier ice melt had not occurred. Analysis of shallow ice-cores from high elevation snow/ice fields of CAEB has helped determining the climatic processes controlling hydrological regimes via the changes in global and regional atmospheric circulation patterns and simulates impact of these changes on water and

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

    NASA Technical Reports Server (NTRS)

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

    2000-01-01

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

  6. Supporting School Leaders in Blended Learning with Blended Learning

    ERIC Educational Resources Information Center

    Acree, Lauren; Gibson, Theresa; Mangum, Nancy; Wolf, Mary Ann; Kellogg, Shaun; Branon, Suzanne

    2017-01-01

    This study provides a mixed-methods case-study design evaluation of the Leadership in Blended Learning (LBL) program. The LBL program uses blended approaches, including face-to-face and online, to prepare school leaders to implement blended learning initiatives in their schools. This evaluation found that the program designers effectively…

  7. Snow cover and snow goose Anser caerulescens caerulescens distribution during spring migration

    USGS Publications Warehouse

    Hupp, Jerry W.; Zacheis, Amy B.; Anthony, R. Michael; Robertson, Donna G.; Erickson, Wallace P.; Palacios, Kelly C.

    2001-01-01

    Arctic geese often use spring migration stopover areas when feeding habitats are partially snow covered. Melting of snow during the stopover period causes spatial and temporal variability in distribution and abundance of feeding habitat. We recorded changes in snow cover and lesser snow goose Anser caerulescens caerulescens distribution on a spring migration stopover area in south-central Alaska during aerial surveys in 1993-1994. Our objectives were to determine whether geese selected among areas with different amounts of snow cover and to assess how temporal changes in snow cover affected goose distribution. We also measured temporal changes in chemical composition of forage species after snow melt. We divided an Arc/Info coverage of the approximately 210 km2 coastal stopover area into 2-km2 cells, and measured snow cover and snow goose use of cells. Cells that had 10-49.9% snow cover were selected by snow geese, whereas cells that lacked snow cover were avoided. In both years, snow cover diminished along the coast between mid-April and early May. Flock distribution changed as snow geese abandoned snow-free areas in favour of cells where snow patches were interspersed with bare ground. Snow-free areas may have been less attractive to geese because available forage had been quickly exploited as bare ground was exposed, and because soils became drier making extraction of underground forage more difficult. Fiber content of two forage species increased whereas non-structural carbohydrate concentrations of forage plants appeared to diminish after snow melt, but changes in nutrient concentrations likely occurred too slowly to account for abandonment of snow-free areas by snow geese.

  8. Changes in Snow Albedo Resulting from Snow Darkening Caused by Black Carbon

    NASA Astrophysics Data System (ADS)

    Engels, J.; Kloster, S.; Bourgeois, Q.

    2014-12-01

    We investigate the potential impact of snow darkening caused by pre-industrial and present-day black carbon (BC) emissions on snow albedo and subsequently climate. To assess this impact, we implemented the effect of snow darkening caused by BC emitted from natural as well as anthropogenic sources into the Max Planck Institute for Meteorology Earth System Model (MPI-M ESM). Considerable amounts of BC are emitted e.g. from fires and are transported through the atmosphere for several days before being removed by rain or snow precipitation in snow covered regions. Already very small quantities of BC reduce the snow reflectance significantly, with consequences for snow melting and snow spatial coverage. We implemented the snow albedo reduction caused by BC contamination and snow aging in the one layer land surface component (JSBACH) of the atmospheric general circulation model ECHAM6, developed at MPI-M. For this we used the single-layer simulator of the SNow, Ice, and Aerosol Radiation (SNICAR-Online (Flanner et al., 2007); http://snow.engin.umich.edu) model to derive snow albedo values for BC in snow concentrations ranging between 0 and 1500 ng(BC)/g(snow) for different snow grain sizes for the visible (0.3 - 0.7 μm) and near infrared range (0.7 - 1.5 μm). As snow grains grow over time, we assign different snow ages to different snow grain sizes (50, 150, 500, and 1000 μm). Here, a radius of 50 μm corresponds to new snow, whereas a radius of 1000 μm corresponds to old snow. The deposition rates of BC on snow are prescribed from previous ECHAM6-HAM simulations for two time periods, pre-industrial (1880-1889) and present-day (2000-2009), respectively. We perform a sensitivity study regarding the scavenging of BC by snow melt. To evaluate the newly implemented albedo scheme we will compare the modeled black carbon in snow concentrations to observed ones. Moreover, we will show the impact of the BC contamination and snow aging on the simulated snow albedo. The

  9. Photopolarimetric Retrievals of Snow Properties

    NASA Technical Reports Server (NTRS)

    Ottaviani, M.; van Diedenhoven, B.; Cairns, B.

    2015-01-01

    Polarimetric observations of snow surfaces, obtained in the 410-2264 nm range with the Research Scanning Polarimeter onboard the NASA ER-2 high-altitude aircraft, are analyzed and presented. These novel measurements are of interest to the remote sensing community because the overwhelming brightness of snow plagues aerosol and cloud retrievals based on airborne and spaceborne total reflection measurements. The spectral signatures of the polarized reflectance of snow are therefore worthwhile investigating in order to provide guidance for the adaptation of algorithms currently employed for the retrieval of aerosol properties over soil and vegetated surfaces. At the same time, the increased information content of polarimetric measurements allows for a meaningful characterization of the snow medium. In our case, the grains are modeled as hexagonal prisms of variable aspect ratios and microscale roughness, yielding retrievals of the grains' scattering asymmetry parameter, shape and size. The results agree with our previous findings based on a more limited data set, with the majority of retrievals leading to moderately rough crystals of extreme aspect ratios, for each scene corresponding to a single value of the asymmetry parameter.

  10. Autumn snow across the Midwest

    NASA Image and Video Library

    2017-09-27

    An autumn storm brought the first snow of the season to the Upper Mississippi River Valley and the Midwestern United States in early November, 2013. The Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA’s Terra satellite captured this true color image on November 6 just as the storm was clearing. A long band of snow stretching from Colorado in the southwest to Wisconsin in the northeast marked the path of the blowing storm. According to WeatherBug, up to 10 inches blanketed Gordon, Nebraska and Pipestone, Minnesota. Most snow totals in the Central and Northern Plains and Upper Mississippi Valley ranged from 2-5 inches, while Minneapolis-St. Paul metro area picked up 1-2 inches of new snow from the event. Credit: NASA/GSFC/Jeff Schmaltz/MODIS Land Rapid Response Team NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

  11. Identification of mineral dust layers in high alpine snow packs

    NASA Astrophysics Data System (ADS)

    Greilinger, Marion; Kau, Daniela; Schauer, Gerhard; Kasper-Giebl, Anne

    2017-04-01

    Deserts serve as a major source for aerosols in the atmosphere with mineral dust as a main contributor to primary aerosol mass. Especially the Sahara, the largest desert in the world, contributes roughly half of the primarily emitted aerosol mass found in the atmosphere [1]. The eroded Saharan dust is episodically transported over thousands of kilometers with synoptic wind patterns towards Europe [2] and reaches Austria about 20 to 30 days per year. Once the Saharan dust is removed from the atmosphere via dry or wet deposition processes, the chemical composition of the precipitation or the affected environment is significantly changed. Saharan dust serves on the one hand as high ionic input leading to an increase of ionic species such as calcium, magnesium or sulfate. On the other hand Saharan dust provides a high alkaline input neutralizing acidic components and causing the pH to increase [3]. Based on these changes in the ion composition, the pH and cross plots of the ion and conductivity balance [4] we tried to develop a method to identify Saharan dust layers in high alpine snow packs. We investigated seasonal snow packs of two high alpine sampling sites situated on the surrounding glaciers of the meteorological Sonnblick observatory serving as a global GAW (Global Atmospheric Watch) station located in the National Park Hohe Tauern in the Austrian Alps. Samples with 10 cm resolution representing the whole winter accumulation period were taken just prior to the start of snow melt at the end of April 2016. In both snow packs two layers with clearly different chemical behavior were observed. In comparison with the aerosol data from the Sonnblick observatory, these layers could be clearly identified as Saharan dust layers. Identified Saharan dust layers in the snow pack allow calculations of the ecological impact of deposited ions, with and without Saharan dust, during snow melt. Furthermore the chemical characteristics for the identification of Saharan dust layers

  12. Quantifying forest mortality with the remote sensing of snow

    NASA Astrophysics Data System (ADS)

    Baker, Emily Hewitt

    Greenhouse gas emissions have altered global climate significantly, increasing the frequency of drought, fire, and pest-related mortality in forests across the western United States, with increasing area affected each year. Associated changes in forests are of great concern for the public, land managers, and the broader scientific community. These increased stresses have resulted in a widespread, spatially heterogeneous decline of forest canopies, which in turn exerts strong controls on the accumulation and melt of the snowpack, and changes forest-atmosphere exchanges of carbon, water, and energy. Most satellite-based retrievals of summer-season forest data are insufficient to quantify canopy, as opposed to the combination of canopy and undergrowth, since the signals of the two types of vegetation greenness have proven persistently difficult to distinguish. To overcome this issue, this research develops a method to quantify forest canopy cover using winter-season fractional snow covered area (FSCA) data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) snow covered area and grain size (MODSCAG) algorithm. In areas where the ground surface and undergrowth are completely snow-covered, a pixel comprises only forest canopy and snow. Following a snowfall event, FSCA initially rises, as snow is intercepted in the canopy, and then falls, as snow unloads. A select set of local minima in a winter F SCA timeseries form a threshold where canopy is snow-free, but forest understory is snow-covered. This serves as a spatially-explicit measurement of forest canopy, and viewable gap fraction (VGF) on a yearly basis. Using this method, we determine that MODIS-observed VGF is significantly correlated with an independent product of yearly crown mortality derived from spectral analysis of Landsat imagery at 25 high-mortality sites in northern Colorado. (r =0.96 +/-0.03, p =0.03). Additionally, we determine the lag timing between green-stage tree mortality and

  13. 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.

  14. 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.

  15. The Effect of Black Carbon and Snow Grain Size on Snow Surface Albedo

    NASA Astrophysics Data System (ADS)

    Hadley, O. L.; Kirchstetter, T.; Flanner, M.

    2009-12-01

    Black carbon (BC) has been measured in snow and ice cores at levels that climate models predict are high enough to be the second leading cause in arctic ice melt and glacial retreat after greenhouse gas warming. BC deposited on snow reduces the snow surface albedo; however, in addition to BC content, snow albedo also depends on sky cover, solar angle, snow grain size and shape, surface roughness, and depth. Quantifying the albedo reduction due to BC separately from these other variables is difficult to achieve in field measurements. We are conducting laboratory experiments that isolate the effect of BC and snow grain size on snow albedo. Snow is made by spraying and freezing drops of water; BC contaminated snow is made from BC hydrosol. Snow albedo is measured with a spectrometer equipped with an integrating sphere over the entire visible spectrum (400-1000 nm). Snow grain size distribution and shape are characterized using a digital microscope to calculate the effective radius of the snow. Measured snow albedo is compared to that predicted using the Snow, Ice, and Aerosol Radiative Model. Preliminary results indicate good agreement between measured and modeled albedo for pure and BC contaminated snow.

  16. Heavy snow loads in Finnish forests respond regionally asymmetrically to projected climate change

    NASA Astrophysics Data System (ADS)

    Lehtonen, Ilari; Kämäräinen, Matti; Gregow, Hilppa; Venäläinen, Ari; Peltola, Heli

    2016-10-01

    This study examined the impacts of projected climate change on heavy snow loads on Finnish forests, where snow-induced forest damage occurs frequently. For snow-load calculations, we used daily data from five global climate models under representative concentration pathway (RCP) scenarios RCP4.5 and RCP8.5, statistically downscaled onto a high-resolution grid using a quantile-mapping method. Our results suggest that projected climate warming results in regionally asymmetric response on heavy snow loads in Finnish forests. In eastern and northern Finland, the annual maximum snow loads on tree crowns were projected to increase during the present century, as opposed to southern and western parts of the country. The change was rather similar both for heavy rime loads and wet snow loads, as well as for frozen snow loads. Only the heaviest dry snow loads were projected to decrease over almost the whole of Finland. Our results are aligned with previous snowfall projections, typically indicating increasing heavy snowfalls over the areas with mean temperature below -8 °C. In spite of some uncertainties related to our results, we conclude that the risk for snow-induced forest damage is likely to increase in the future in the eastern and northern parts of Finland, i.e. in the areas experiencing the coldest winters in the country. The increase is partly due to the increase in wet snow hazards but also due to more favourable conditions for rime accumulation in a future climate that is more humid but still cold enough.

  17. The representation of snow in the EC-Earth climate model: the impact of horizontal resolution

    NASA Astrophysics Data System (ADS)

    Terzago, Silvia; Palazzi, Elisa; von Hardenberg, Jost

    2017-04-01

    The representation of the mountain cryosphere in climate models is critical owing to the scale mismatch between the snow-related processes, occurring at scales considerably smaller than 1 km, and the coarse grid of climate models, in the order of 10 and 100 km resolution. For instance, elevation gradients affect locally the air temperature which in turn controls the partition between solid and liquid precipitation, snowpack internal processes and snow melt at local scale. An adequate representation of the drivers of snow processes (e.g., temperature, snowfall), therefore, calls for high-resolution simulations. Moreover, a quantification of the uncertainty on snowpack estimates related to the coarse model resolution is of prime importance to correctly interpret the snow outputs of the large-scale models, e.g. those included in Coupled Models Intercomparison Project experiments. This study aims to quantify the impact of the horizontal resolution on the simulation of snow-related variables focusing on the Greater Alpine Region (4-19°E, 43-49°N). We exploit a set of 5 simulations performed with the Global Climate Model EC-Earth run at increasing spatial resolutions, from 125 to 16 km, and we assess the differences (i) in the climatologies of the drivers of snow processes (air temperature, total precipitation, snowfall) and (ii) in the climatologies of the snow water equivalent distribution. Preliminary results show that in the finest resolution runs a slightly higher amount of snow precipitation leads to significantly thicker snow depths. We also investigate the future expected changes of snow resources (mid 21st century, RCP8.5 scenario) and we quantify the discrepancies among the EC-Earth simulations run at the different horizontal resolutions. Finally we compare the results obtained with the EC-Earth model to those obtained in a previous study in which we considered the full ensemble of CMIP5 models.

  18. Heavy snow loads in Finnish forests respond regionally asymmetrically to projected climate change

    SciTech Connect

    Lehtonen, Ilari; Kamarainen, Matti; Gregow, Hilppa; Venalainen, Ari; Peltola, Heli

    2016-10-17

    This study examined the impacts of projected climate change on heavy snow loads on Finnish forests, where snow-induced forest damage occurs frequently. For snow-load calculations, we used daily data from five global climate models under representative concentration pathway (RCP) scenarios RCP4.5 and RCP8.5, statistically downscaled onto a high-resolution grid using a quantile-mapping method. Our results suggest that projected climate warming results in regionally asymmetric response on heavy snow loads in Finnish forests. In eastern and northern Finland, the annual maximum snow loads on tree crowns were projected to increase during the present century, as opposed to southern and western parts of the country. The change was rather similar both for heavy rime loads and wet snow loads, as well as for frozen snow loads. Only the heaviest dry snow loads were projected to decrease over almost the whole of Finland. Our results are aligned with previous snowfall projections, typically indicating increasing heavy snowfalls over the areas with mean temperature below -8 °C. In spite of some uncertainties related to our results, we conclude that the risk for snow-induced forest damage is likely to increase in the future in the eastern and northern parts of Finland, i.e. in the areas experiencing the coldest winters in the country. In conclusion, the increase is partly due to the increase in wet snow hazards but also due to more favourable conditions for rime accumulation in a future climate that is more humid but still cold enough.

  19. Heavy snow loads in Finnish forests respond regionally asymmetrically to projected climate change

    DOE PAGES

    Lehtonen, Ilari; Kamarainen, Matti; Gregow, Hilppa; ...

    2016-10-17

    This study examined the impacts of projected climate change on heavy snow loads on Finnish forests, where snow-induced forest damage occurs frequently. For snow-load calculations, we used daily data from five global climate models under representative concentration pathway (RCP) scenarios RCP4.5 and RCP8.5, statistically downscaled onto a high-resolution grid using a quantile-mapping method. Our results suggest that projected climate warming results in regionally asymmetric response on heavy snow loads in Finnish forests. In eastern and northern Finland, the annual maximum snow loads on tree crowns were projected to increase during the present century, as opposed to southern and western parts ofmore » the country. The change was rather similar both for heavy rime loads and wet snow loads, as well as for frozen snow loads. Only the heaviest dry snow loads were projected to decrease over almost the whole of Finland. Our results are aligned with previous snowfall projections, typically indicating increasing heavy snowfalls over the areas with mean temperature below -8 °C. In spite of some uncertainties related to our results, we conclude that the risk for snow-induced forest damage is likely to increase in the future in the eastern and northern parts of Finland, i.e. in the areas experiencing the coldest winters in the country. In conclusion, the increase is partly due to the increase in wet snow hazards but also due to more favourable conditions for rime accumulation in a future climate that is more humid but still cold enough.« less

  20. Statistical modelling of the snow depth distribution in open alpine terrain

    NASA Astrophysics Data System (ADS)

    Grünewald, T.; Stötter, J.; Pomeroy, J. W.; Dadic, R.; Moreno Baños, I.; Marturià, J.; Spross, M.; Hopkinson, C.; Burlando, P.; Lehning, M.

    2013-08-01

    The spatial distribution of alpine snow covers is characterised by large variability. Taking this variability into account is important for many tasks including hydrology, glaciology, ecology or natural hazards. Statistical modelling is frequently applied to assess the spatial variability of the snow cover. For this study, we assembled seven data sets of high-resolution snow-depth measurements from different mountain regions around the world. All data were obtained from airborne laser scanning near the time of maximum seasonal snow accumulation. Topographic parameters were used to model the snow depth distribution on the catchment-scale by applying multiple linear regressions. We found that by averaging out the substantial spatial heterogeneity at the metre scales, i.e. individual drifts and aggregating snow accumulation at the landscape or hydrological response unit scale (cell size 400 m), that 30 to 91% of the snow depth variability can be explained by models that are calibrated to local conditions at the single study areas. As all sites were sparsely vegetated, only a few topographic variables were included as explanatory variables, including elevation, slope, the deviation of the aspect from north (northing), and a wind sheltering parameter. In most cases, elevation, slope and northing are very good predictors of snow distribution. A comparison of the models showed that importance of parameters and their coefficients differed among the catchments. A "global" model, combining all the data from all areas investigated, could only explain 23% of the variability. It appears that local statistical models cannot be transferred to different regions. However, models developed on one peak snow season are good predictors for other peak snow seasons.

  1. Population genetic analysis and origin discrimination of snow crab (Chionoecetes opilio) using microsatellite markers.

    PubMed

    Kang, Jung-Ha; Park, Jung-Youn; Kim, Eun-Mi; Ko, Hyun-Sook

    2013-10-01

    Major habitats for the snow crab Chionoecetes opilio are mostly found within the northwest Atlantic and North Pacific Oceans. However, the East Sea populations of C. opilio, along with its relative the red snow crab (C. japonicas), are two of the most important commercial crustacean species for fisheries on the east coast of the Korean Peninsula. The East Sea populations of C. opilio are facing declining resources due to overfishing and global climate change. Thus, an analysis of population structure is necessary for future management. Five Korean and one Russian group of C. opilio were analyzed using nine microsatellite markers that were recently developed using next-generation sequencing. No linkage disequilibrium was found between any pair of loci, indicating that the markers were independent. The number of alleles per locus varied from 4 to 18 with a mean of 12, and allelic richness per locus ranged from 4.0 to 17.1 across all populations with a mean of 9.7. The Hardy-Weinberg equilibrium test revealed significant deviation in three out of nine loci in some populations after sequential Bonferroni correction and all of them had higher expected heterozygosity than observed heterozygosity. Null alleles were presumed in four loci, which explained the homozygosity in three loci. The pairwise fixation index (F ST ) values among the five Korean snow crab populations did not differ significantly, but all of the pairwise F ST values between each of the Korean snow crab populations and the Russian snow crab population differed significantly. An UPGMA dendrogram revealed clear separation of the Russian snow crab population from the Korean snow crab populations. Assignment tests based on the allele distribution discriminated between Korean and Russian origins with 93 % accuracy. Therefore, the snow crab populations around the Korean Peninsula need to be managed separately from the populations in Bering Sea in global scale resource management. Also, this information can be

  2. 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.

  3. Historical Snow Cover Variability Data Reconstructed from AVHRR and MODIS over High Asia

    NASA Astrophysics Data System (ADS)

    Zhou, H.; Aizen, E.; Aizen, V. B.

    2010-12-01

    Seasonal snow cover (SSC) contributes up to 60% of river runoff in Central Asia (CA) river basins. Decrease in SSC area is one of the major consequences of CA low lands desertification. The dynamics of seasonal snow that covered huge territory of CA has strong teleconnection with global and regional atmospheric processes. Accurate, high resolution SSC data over CA, from Mongolia (113°E) to Caspian Sea (51°E) and from Western Siberia (56°N) to Tibetan Plateau (32°N) for the last 25 years reconstructed from AVHRR and MODIS data may have very large scope of applications in developing different scale hydrological models and climate study. In our research we present a 24 years (1986-2009) SSC area product computed from AVHRR and MODIS that demonstrate the variability of SSC areas over CA. Daily and 8-day cloud free snow cover product in 500m spatial resolution have been generated from existing MODIS snow cover products since March 2000. Level 1 AVHRR swath data has been used to generate snow cover with 1km spatial resolution since 1986. The georeferencing accuracy of snow cover product derived from AVHRR is better than 1/3 of one pixel, which is achieved by using the new GCP image correction method and automated image matching technique. A new aggregated rating snow detection scheme has been designed to work over CA for each AVHRR image. It makes use of the spectral properties of AVHRR, as well as the surface skin temperature from the NCEP reanalysis dataset. Maximum snow cover composite strategy has been used to generate the 8-day composite product. Validation against MODIS snow cover product suggests the performance of AVHRR 8-day composite snow cover product is similar to MODIS 8-day snow cover. This product is a valuable asset for our further climate and hydrological simulations in CA. Using newly developed product the following information has been calculated: SSC area, SSC days and SSC index, maximum SSC area and day of maximum SSC areas, snow cover onset

  4. [Effects of snow pack removal on soil microbial biomass carbon and nitrogen and the number of soil culturable microorganisms during wintertime in alpine Abies faxoniana forest of western Sichuan, Southwest China].

    PubMed

    Yang, Yu-Lian; Wu, Fu-Zhong; He, Zhen-Hua; Xu, Zhen-Feng; Liu, Yang; Yang, Wan-Qin; Tan, Bo

    2012-07-01

    To understand the effects of the lack of snow pack under global warming on the characteristics of soil microorganisms during wintertime, a snow-shading experiment was conducted in a primary fir (Abies faxoniana) forest after snow pack removal, with the soil microbial biomass carbon (MBC) and nitrogen (MBN) and soil culturable microorganisms (bacteria and fungi) at the stages of snow forming, snow covering, and snow melting investigated. Snow pack removal had significant effects on the soil MBC and MBN and the number of soil culturable bacteria and fungi, but the responses of the culturable microorganisms differed with the stages of snow-shading. Under the condition of snow pack removal, the MBC and MBN in soil organic layer decreased significantly at the early stages of snow forming and snow melting but increased significantly at snow covering stage and at the later stage of snow melting, and the number of culturable bacteria decreased significantly from the early stage of snow forming to the stage of snow covering while that of culturable fungi had a significant increase from the early stage of snow forming to the stage of snow melting. After snow melting, the MBC and the number of culturable fungi in soil organic layer had a significant decrease, the number of cultural bacteria was in adverse, but the MBN had less change. The MBC and MBN and the number of culturable microorganisms in soil mineral layer had the similar variation trends as those in soil organic layer, but the fluctuations were smaller. It was suggested that snow pack removal changed the ratio of culturable bacteria to culturable fungi, showing positive effects on the number of soil culturable fungi during wintertime in alpine Abies faxoniana forest of western Sichuan.

  5. 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

  6. Characteristics of Heavy Snowfall and Snow Crystal Habits in the ESSAY (Experiment on Snow Storms At Yeongdong) Campaign in Korea

    NASA Astrophysics Data System (ADS)

    Seong, D. K.; Seok, S. W.; Eun, S. H.; Kim, B. G.; Reum, K. A.; Lee, K. M.; Jeon, H. R.; Byoung Choel, C.; Park, Y. S.

    2015-12-01

    Characteristics of heavy snowfall and snow crystal habits have been investigated in the campaign of Experiment on Snow Storms At Yeongdong (ESSAY) using radiosonde soundings, Global Navigation Satellite System (GNSS), and a digital camera with a magnifier for taking a photograph of snowfall crystals. The analysis period is mainly both winters of 2014 and 2015. The synoptic situations are similar to those of the previous studies such as the Low pressure system passing by the far South of the Korean peninsula along with the Siberian High extending to northern Japan, which eventually results in the northeasterly or easterly flows and the long-lasting snowfall episodes in the Yeongdong region. The snow crystal habits observed in the ESSAY campaign were mainly dendrite, consisting of 70% of the entire habits. The rimed habits were frequently captured when two-layered clouds were observed, probably through the process of freezing of super-cooled droplets on the ice particles. The homogeneous habit such as dendrite was shown in case of shallow clouds with its thickness of below 500 m whereas various habits were captured such as graupel, dendrites, rimed dendrites, aggregates of dendrites, plates, rimed plates, etc in the thick cloud with its thickness greater than 1.5 km. The dendrites appeared to be dominant in the condition of cloud top temperature specifically ranging -12~-16℃. Interestingly temporal evolutions of snow crystal habits were consistently shown for several snowfall events such as changes from rimed particles to dendrites(or aggregated dendrites). The association of snow crystal habits with temperature and super-saturation in the cloud will be in detail examined. However, better understandings of characteristics of snow crystal habits would contribute to preventing breakdown accidents such as a greenhouse destruction and collapse of a temporary building due to heavy snowfall, and traffic accidents due to snow-slippery road condition, providing a higher

  7. Application of a distributed snow model for the assessment of past snow cover changes in Austria

    NASA Astrophysics Data System (ADS)

    Marke, Thomas; Hanzer, Florian; Siegmann, Marcel; Strasser, Ulrich

    2017-04-01

    Snow depth and snow cover duration in mountain regions are subject to high spatial and temporal variability. Information on this natural variability and climate change induced modifications in snow conditions is a prerequisite for science and stakeholders to understand past and present snow conditions, but also for the interpretation of future snow projections. While instrumental time series of the relevant meteorological and snow cover variables are essential for climate studies, only few long-term climate and snow observation time series are available, and their spatial representativity is strongly limited. This study applies the hydroclimatological model AMUNDSEN to improve the spatial density of snow information in Austria by continuously simulating daily snow accumulation and ablation at a spatial resolution of 1 x 1 km for all of Austria and the period 1948-2009. The model is driven with homogenized and quality-checked meteorological station observations of daily temperature (minimum and maximum) and precipitation. Model output comprises daily maps of snow water equivalent, snow depth, and freshly fallen snow. Following a thorough validation of the snow model through comparison of simulations to snow observations (point scale) as well as to remotely sensed snow cover patterns, changes in the Austrian snow cover are elaborated and presented in this study. Beside the illustration of changes in the spatial distribution of snow in Austria for the 1 x 1 km raster, temporal trends are elaborated by statistical analyses of mean snow cover change for different elevation ranges providing more detailed insights in the temporal variability and changes in snow cover conditions.

  8. A stable snow-atmosphere coupled mode

    NASA Astrophysics Data System (ADS)

    Zhao, Liang; Zhu, Yuxiang; Liu, Haiwen; Liu, Zhongfang; Liu, Yanju; Li, Xiuping; Chen, Zhou

    2016-10-01

    Snow is both an important lower boundary forcing of the atmosphere and a response to atmospheric forcing in the extratropics. It is still unclear whether a stable snow-atmosphere coupled mode exists in the extratropics, like the ENSO in the tropics. Using Sliding Correlation analysis over Any Window, the present study quantitatively evaluates the stability of coupling relationships between the major modes of winter snow over the Northern Hemisphere and the winter atmospheric Arctic Oscillation (AO), the Antarctic Oscillation (AAO) and the Siberian High over the period 1872-2010, and discusses their possible relationships for different seasons. Results show that the first mode of the winter snow cover fraction and the winter AO together constitute a stable snow-atmosphere coupled mode, the SNAO. The coupled mode is stronger during recent decades than before. The snow anomaly over Europe is one key factor of the SNAO mode due to the high stability there, and the polar vortex anomaly in the atmosphere is its other key factor. The continuity of signals in the SNAO between autumn and winter is weaker than that between winter and spring. The second winter snow mode is generally stably correlated with the winter AAO and was more stable before the 1970s. The AAO signal with boreal snow has a strong continuity in seasonal transition. Generally, through these coupled modes, snow and atmosphere can interact in the same season or between different seasons: autumn snow can influence the winter atmosphere; the winter atmosphere can influence spring snow.

  9. Rabi, Snow, and "The Two Cultures"

    NASA Astrophysics Data System (ADS)

    Day, Michael A.

    2003-04-01

    John Rigden in his biography of I. I. Rabi, "Rabi: Scientist and Citizen" (1987, 2000 with a new preface) includes an intriguing footnote concerning Rabi's influence on C. P. Snow. According to the footnote, when Snow and his son were visiting the Rabis in New York City, Rabi's wife heard Snow tell his son that Rabi was "the man who gave me [Snow] the idea for the two cultures." In this talk, after a brief overview of Rabi's views on science and society, the mutual influence between Rabi and Snow is explored. On the basis of chronology and an interpretation of Rabi's works (published and unpublished) as well as letters between Rabi and Snow, a case is made that Rabi could very well have been the man who gave Snow the idea for "The Two Cultures."

  10. Blending the Basics.

    ERIC Educational Resources Information Center

    McCampbell, Bill

    2001-01-01

    Blended e-learning approaches combine the power of the Internet with existing class events or assignments. Inexpensive tools include electronic mail, keyboarding instruction, online research, streamed video or audio clips, message boards, chat rooms, scanned class reading assignments, and online assessments. IT buzzwords are decoded. (MLH)

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

    NASA Astrophysics Data System (ADS)

    Räisänen, Petri; Makkonen, Risto; Kirkevåg, Alf

    2017-04-01

    Snow grains are non-spherical and generally irregular in shape. Still, in radiative transfer calculations, they are often treated as spheres. This also applies to the computation of snow albedo in the Snow, Ice, and Aerosol Radiation (SNICAR) model and in the Los Alamos sea ice model, version 4 (CICE4), both of which are employed in the Community Earth System Model and in the Norwegian Earth System Model (NorESM). In this work, we evaluate the effect of snow grain shape on climate simulated by NorESM in a slab ocean configuration of the model. An experiment with spherical snow grains (SPH) is compared with another (NONSPH) in which the snow shortwave single-scattering properties are based on a combination of non-spherical snow grain shapes optimized using measurements of angular scattering by blowing snow. The key difference between these treatments is that the asymmetry parameter is smaller in the non-spherical case (≈ 0.78 in the visible region) than in the spherical case (≈ 0.89). Therefore, for a given snow grain size, the use of non-spherical snow grains yields a higher snow broadband albedo, typically by ≈0.03. Consequently, considering the spherical case as the baseline, the use of non-spherical snow grains results in a negative radiative forcing (RF), with a global-mean top-of-the-model value of ≈ -0.22 W m-2. Although this global-mean RF is modest, it has a rather substantial impact on the climate simulated by NoRESM. In particular, the global annual-mean 2-m air temperature in NONSPH is 1.17 K lower than in SPH, with substantially larger differences at high latitudes. The climatic response is amplified by strong snow and sea ice feedbacks. It is further found that the difference between NONSPH and SPH could be largely "tuned away" by adjusting the snow grain size in the NONSPH experiment by ≈ 70%. The impact of snow grain shape on the radiative effect (RE) of absorbing aerosols in snow (black carbon and mineral dust) is also discussed. For an

  12. Snow hydrology in a general circulation model

    SciTech Connect

    Marshall, S. ); Roads, J.O. ); Glatzmaier, G. )

    1994-08-01

    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. A 3-year GCM simulation with this 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. 52 refs., 13 figs., 5 tabs.

  13. Arctic moisture source for Eurasian snow cover variations in autumn

    NASA Astrophysics Data System (ADS)

    Wegmann, Martin; Orsolini, Yvan; Vázquez Dominguez, Marta; Gimeno Presa, Luis; Nieto, Raquel; Buligyna, Olga; Jaiser, Ralf; Handorf, Dörthe; Rinke, Anette; Dethloff, Klaus; Sterin, Alexander; Brönnimann, Stefan

    2015-04-01

    Global warming is enhanced at high northern latitudes where the Arctic surface air temperature has risen at twice the rate of the global average in recent decades - a feature called Arctic amplification. This recent Arctic warming signal likely results from several factors such as the albedo feedback due to a diminishing cryosphere, enhanced poleward atmospheric and oceanic transport, and change in humidity. The reduction in Arctic sea ice is without doubt substantial and a key factor. Arctic summer sea-ice extent has declined by more than 10% per decade since the start of the satellite era (e.g. Stroeve et al., 2012), culminating in a new record low in September 2012, with the long-term trend largely attributed to anthropogenic global warming. Eurasian snow cover changes have been suggested as a driver for changes in the Arctic Oscillation and might provide a link between sea ice decline in the Arctic during summer and atmospheric circulation in the following winter. However, the mechanism connecting snow cover in Eurasia to sea ice decline in autumn is still under debate. Our analysis focuses at sea ice decline in the Barents-Kara Sea region, which allows us to specify regions of interest for FLEXPART forward and backwards moisture trajectories. Based on Eularian and Lagrangian diagnostics from ERA-INTERIM, we can address the origin and cause of late autumn snow depth variations in a dense (snow observations from 820 land stations), unutilized observational datasets over the Commonwealth of Independent States. Open waters in the Barents and Kara Sea have been shown to increase the diabatic heating of the atmosphere, which amplifies baroclinic cyclones and might induce a remote atmospheric response by triggering stationary Rossby waves (Honda et al. 2009). In agreement with these studies, our results show enhanced storm activity originating at the Barents and Kara with disturbances entering the continent through a small sector from the Barents and Kara Seas

  14. Apparatus for blending small particles

    DOEpatents

    Bradley, R.A.; Reese, C.R.; Sease, J.D.

    1975-08-26

    An apparatus is described for blending small particles and uniformly loading the blended particles in a receptacle. Measured volumes of various particles are simultaneously fed into a funnel to accomplish radial blending and then directed onto the apex of a conical splitter which collects the blended particles in a multiplicity of equal subvolumes. Thereafter the apparatus sequentially discharges the subvolumes for loading in a receptacle. A system for blending nuclear fuel particles and loading them into fuel rod molds is described in a preferred embodiment. (auth)

  15. 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.

  16. Experimental manipulations of snow-depth: Effects on nutrient content of caribou forage

    USGS Publications Warehouse

    Walsh, Noreen E.; McCabe, Thomas R.; Welker, J.M.; Parsons, A.N.

    1997-01-01

    We investigated the potential effects of global climate change on arctic tundra vegetation used as caribou forage. A total of 96 experimental plots was established at six sites on the coastal plain of the Arctic National Wildlife Refuge, Alaska, in 1993 and 1994. We erected snow-fences to increase the amount of snow deposition, and therefore delay the date of the snowmelt on 48 plots (referred to as increased snow/late melting plots). We used black mesh netting on the surface of the snow to increase the rate of melting on 24 plots; the remaining 24 plots served as controls. In July 1994, we collected green leaves from Eriophorum vaginatum, Salix planifolia, and Betula nana and analysed these samples for total carbon and total nitrogen content. Ratios of carbon to nitrogen differed among treatments for all three species. Generally, C:N ratios for B. nana and E. vaginatum on increased snow/late melting plots were lower than on control plots. C:N ratios for S. planifolia on increased snow/late melting plots did not differ from controls, but were lower than on plots which started to melt early. These results may be due to the timing of nitrogen translocation from leaf and stem tissue into storage organs, or due to an increase in available nitrogen input to the system. Further sampling is needed to adequately determine the mechanism responsible for increased nitrogen content of caribou forage in areas with increased amount of snow and delayed snowmelt. ?? 1997 Blackwell Science Ltd.

  17. Feasibility of Estimating Snow Depth in Complex Terrain Using Satellite Lidar Altimetry

    NASA Technical Reports Server (NTRS)

    Jasinski, Michael F.; Stoll, Jeremy

    2012-01-01

    Satellite retrievals of snow depth and water equivalent (SWE) are critical for monitoring watershed scale processes around the world. However, the problem is especially challenging in mountainous regions where complex heterogeneities limit the utility of low resolution satellite sensors. The Geoscience Laser Altimeter Sensor (GLAS) aboard the Ice, Cloud, and land Elevation Satellite (ICESat) collected surface elevation data along near-repeat reference transects over land areas from 2003-2009. Although intended for monitoring ice caps and sea ice, the seven year global GLAS data base has provided unprecedented opportunity to test the capability of satellite lidar technology for estimating snow depth over land. GLAS single track and low repeat frequency does not provide data sufficient for operational estimates. However, its comparatively small footprint size of -65 m and its database of seasonal repeat observations during both snow and no-snow conditions have been sufficient to evaluate the potential of spacebased lidar altimetry for estimating snow depth. Recent analysis of ICESat elevations in the Uinta Mountains in NE Utah provide encouraging results for watershed scale estimates of snow depth. Research reported here focuses on the sensitivity of several versions of an ICESat snow depth algorithm to a range of landscape types defined by vegetation cover, slope and roughness. Results are compared to available SNOTEL data.

  18. Spatially continuous mapping of snow depth in high alpine catchments using digital photogrammetry

    NASA Astrophysics Data System (ADS)

    Bühler, Y.; Marty, M.; Egli, L.; Veitinger, J.; Jonas, T.; Thee, P.; Ginzler, C.

    2014-06-01

    Information on snow depth and its spatial distribution is crucial for many applications in snow and avalanche research as well as in hydrology and ecology. Today snow depth distributions are usually estimated using point measurements performed by automated weather stations and observers in the field combined with interpolation algorithms. However, these methodologies are not able to capture the high spatial variability of the snow depth distribution present in alpine terrain. Continuous and accurate snow depth mapping has been done using laser scanning but this method can only cover limited areas and is expensive. We use the airborne ADS80 opto-electronic scanner with 0.25 m spatial resolution to derive digital surface models (DSMs) of winter and summer terrains in the neighborhood of Davos, Switzerland. The DSMs are generated using photogrammetric image correlation techniques based on the multispectral nadir and backward looking sensor data. We compare these products with the following independent datasets acquired simultaneously: (a) manually measured snow depth plots (b) differential Global Navigation Satellite System (dGNSS) points (c) Terrestrial Laser Scanning (TLS) and (d) Ground Penetrating Radar (GPR) datasets, to assess the accuracy of the photogrammetric products. The results of this investigation demonstrate the potential of optical scanners for wide-area, continuous and high spatial resolution snow-depth mapping over alpine catchments above tree line.

  19. Newly Implemented Snow-Vegetation Representation in the Community Land Model

    NASA Astrophysics Data System (ADS)

    Perket, J.; Flanner, M.; Clark, M. P.; Lawrence, D. M.

    2014-12-01

    Boreal forests are a major source of surface albedo feedback spread in CMIP5 models. We've incorporated improvements into the Community Land Model (CLM) vegetation canopy snow treatment in order to more realistically represent boreal forest canopy albedo. Current CLM hydrology does not differentiate the phase of precipitation intercepted by vegetation. To represent canopy snow, there is a sharp temperature-dependent switch in canopy albedo parameters at 0 K. Snow immediately ceases to exist when vegetation temperature rises above freezing. We've separated phases in the CLM vegetation hydrology, allowing snow to have its own storage maximum and interception treatment. Wind and melt based unloading terms have also been incorporated to more accurately simulate canopy processes, creating a path for canopy snow loss in freezing temperatures. To evaluate the new treatment, we compared singe-point CLM 4.5 simulations with accumulated canopy snow mass measurements from Umpqua Forest, Oregon. Additionally, we have considered the effects of modifying snow cover fraction and latent heat fluxes from phase changes. Global CLM simulations evaluate the climatic differences between existing CLM and CLM with the new implementations for boreal forests.

  20. Assessing the Effects of Dust Loading of Snow on Regional Hydroclimatology Using an Improved Regional Climate Model

    NASA Astrophysics Data System (ADS)

    Oaida, C. M.; Xue, Y.; Painter, T.; Flanner, M. G.; De Sales, F.

    2012-12-01

    Radiative processes play an important role on both global and regional scales. This study focuses on their effects over snow-covered surfaces due to dust loading. Studies have shown that dust emissions from the Colorado Plateau have increased 5-7 fold in the last century and a half due to grazing and agricultural practices, which decreases snow albedo and enhances solar radiation absorption. In an offline study, Painter et al. (2007) have shown that snow cover was shortened by 18 to 35 days due to dust radiative forcing in snow in the San Juan Mountains, Colorado, USA. Our present study will quantitatively assess dust's influence on radiative forcing and runoff timing in mountain snow packs using a physically comprehensive regional climate model. For this study, we employ NCAR's WRF ARW model, which is coupled with a land surface model, Simplified Simple Biosphere version 3 (SSiB3). We have modified the original WRF-SSiB3 framework to include a snow-radiative transfer model, Snow, Ice, and Aerosol Radiative (SNICAR) model. SNICAR considers the effects of snow grain size and aerosol on snow albedo evolution. Snow grain size and growth is important in snow albedo feedbacks, especially when aerosols in snow are considered, because larger snow grains decrease snow albedo, and in the presence of dust, grain growth rates increase, decreasing snow reflectance even further than if the snow was pure. In the original WRF-SSiB3, albedo was empirically adjusted during snow melt. Implementing SNICAR allows us to have a more physically based process to represent changes in albedo due to snow metamorphism as well as those due to impurities in snow, which makes the regional climate model capable of realistically simulating radiative forcing on snow covered areas with aerosol loading. The model was further modified to account for the presence of aerosols in snow in terms of the distribution of these impurities as well as their scavenging by melt water throughout the snow layers We

  1. The Effect of Blending HRM Transformational Leadership Style with HRM ICT Expertise Leadership Style on Creating New HRM Strategy That Enable National Companies to Go Global. Evidence from Jordan: Sayegh Group and Hikma Pharmaceutical Corporation

    ERIC Educational Resources Information Center

    Khudeir, Hamzeh

    2016-01-01

    There is no single HRM strategy that can cure all HRM problems and complicated issues. Thus, most companies go by the book in this regard; however, the majorities have their own policies and strategies they use to achieve their objectives in general and competitive advantage in particular. One of the strategies used in HRM is blending HRM…

  2. Detection Thresholds of Falling Snow from Satellite-Borne Active and Passive Sensors

    NASA Technical Reports Server (NTRS)

    Jackson, Gail

    2012-01-01

    Precipitation, including rain and snow, is a critical part of the Earth's energy and hydrology cycles. In order to collect information on the complete global precipitation cycle and to understand the energy budget in terms of precipitation, uniform global estimates of both liquid and frozen precipitation must be collected. Active observations of falling snow are somewhat easier to estimate since the radar will detect the precipitation particles and one only needs to know surface temperature to determine if it is liquid rain or snow. The challenges of estimating falling snow from passive spaceborne observations still exist though progress is being made. While these challenges are still being addressed, knowledge of their impact on expected retrieval results is an important key for understanding falling snow retrieval estimations. Important information to assess falling snow retrievals includes knowing thresholds of detection for active and passive sensors, various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types. For example, can a lake effect snow system with low (2.5 km) cloud tops having an ice water content (Iwe) at the surface of 0.25 g m-3 and dendrite snowflakes be detected? If this information is known, we can focus retrieval efforts on detectable storms and concentrate advances on achievable results. Here, the focus is to determine thresholds of detection for falling snow for various snow conditions over land and lake surfaces. The analysis relies on simulated Weather Research Forecasting (WRF) simulations of falling snow cases since simulations provide all the information to determine the measurements from space and the ground truth. Results are presented for active radar at Ku, Ka, and W-band and for passive radiometer channels from 10 to 183 GHz (Skofronick-Jackson, et al. submitted to IEEE TGRS, April 2012). The notable results show: (1) the W-Band radar has detection thresholds more

  3. Impacts of Snow Darkening by Absorbing Aerosols on Eurasian Climate

    NASA Technical Reports Server (NTRS)

    Kim, Kyu-Myong; Lau, William K M.; Yasunari, Teppei J.; Kim, Maeng-Ki; Koster, Randal D.

    2016-01-01

    The deposition of absorbing aerosols on snow surfaces reduces snow-albedo and allows snowpack to absorb more sunlight. This so-called snow darkening effect (SDE) accelerates snow melting and leads to surface warming in spring. To examine the impact of SDE on weather and climate during late spring and early summer, two sets of NASA GEOS-5 model simulations with and without SDE are conducted. Results show that SDE-induced surface heating is particularly pronounced in Eurasian regions where significant depositions of dust transported from the North African deserts, and black carbon from biomass burning from Asia and Europe occur. In these regions, the surface heating due to SDE increases surface skin temperature by 3-6 degrees Kelvin near the snowline in spring. Surface energy budget analysis indicates that SDE-induced excess heating is associated with a large increase in surface evaporation, subsequently leading to a significant reduction in soil moisture, and increased risks of drought and heat waves in late spring to early summer. Overall, we find that rainfall deficit combined with SDE-induced dry soil in spring provide favorable condition for summertime heat waves over large regions of Eurasia. Increased frequency of summer heat waves with SDE and the region of maximum increase in heat-wave frequency are found along the snow line, providing evidence that early snowmelt by SDE may increase the risks of extreme summer heat wave. Our results suggest that climate models that do not include SDE may significantly underestimate the effect of global warming over extra-tropical continental regions.

  4. On charging of snow particles in blizzard

    NASA Technical Reports Server (NTRS)

    Shio, Hisashi

    1991-01-01

    The causes of the charge polarity on the blizzard, which consisted of fractured snow crystals and ice particles, were investigated. As a result, the charging phenomena showed that the characteristics of the blizzard are as follows: (1) In the case of the blizzard with snowfall, the fractured snow particles drifting near the surface of snow field (lower area: height 0.3 m) had positive charge, while those drifting at higher area (height 2 m) from the surface of snow field had negative charge. However, during the series of blizzards two kinds of particles positively and negatively charged were collected in equal amounts in a Faraday Cage. It may be considered that snow crystals with electrically neutral properties were separated into two kinds of snow flakes (charged positively and negatively) by destruction of the snow crystals. (2) In the case of the blizzard which consisted of irregularly formed ice drops (generated by peeling off the hardened snow field), the charge polarity of these ice drops salting over the snow field was particularly controlled by the crystallographic characteristics of the surface of the snow field hardened by the powerful wind pressure.

  5. Snow mapping from space platforms

    NASA Technical Reports Server (NTRS)

    Itten, K. I.

    1980-01-01

    The paper considers problems of optimum resolution, periodicity, and wavelength bands used for snow mapping. Analog and digital methods were used for application of satellite data; techniques were developed for producing steamflow forecasts, hydroelectric power generation regulation data, irrigation potentials, and information on the availability of drinking water supplies. Future systems will utilize improved spectral band selection, new spectral regions, higher repetition rates, and more rapid access to satellite data.

  6. Dirty snow after nuclear war

    NASA Technical Reports Server (NTRS)

    Warren, S. G.; Wiscombe, W. J.

    1985-01-01

    It is shown that smoke from fires started by nuclear explosions could continue to cause significant disruption even after it has fallen from the atmosphere, by lowering the reflectivity of snow and sea ice surfaces, with possible effects on climate in northern latitudes caused by enhanced absorption of sunlight. The reduced reflectivity could persist for several years on Arctic sea ice and on the ablation area of the Greenland ice sheet.

  7. Snow in northeastern United States

    NASA Image and Video Library

    2017-09-27

    Snow covered the northeastern United States on last day of meteorological winter, 2014. Climatologists and meteorologists break seasons down into three-month groups, based on annual temperature and our calendar. This method is helpful for weather observing and forecasting, and for planning consistent agricultural dates, such as expected first frosts or best planting date. Meteorological winter – the season where temperatures are, on average, coldest and when snow is most likely to fall – runs from December 1 to February 28 in the United States and Canada. Winter can also be defined by the astronomical calendar, which is based on the rotation of the Earth around the sun. In this method, the seasons are defined by two solstices (times when the sun’s path is furthest from the Earth’s equator) and two equinoxes (the times when the sun passes directly above the equator). In the Northern Hemisphere, winter begins on the winter solstice, which falls on or around December 22 and ends on or around March 21, at the vernal (spring) equinox. On February 28, 2014, the Moderate Resolution Imaging Spectroradiometer aboard NASA’s Aqua satellite captured this true-color image of a sunny winter day in the northeastern United States. Snow stretches from Maine west to Indiana and south along the ridges of the Appalachian Mountains well into West Virginia. In Canada, the landscape appears greener, primarily because snow lies on conifers (evergreen) trees in the boreal forest regions. The Great Lakes, with the exception of Lake Ontario, are almost completely covered with ice. Credit: NASA/GSFC/Jeff Schmaltz/MODIS Land Rapid Response Team NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on

  8. The Inverted Snow Globe Shadow

    NASA Astrophysics Data System (ADS)

    Ribeiro, Jair Lúcio Prados

    2015-01-01

    Our high school optics course finishes with an assignment that students usually appreciate. They must take pictures of everyday situations representing optical phenomena such as reflection, refraction, or dispersion, and post them on Instagram.1 When the photos were presented to the class, one student revealed an intriguing photo, similar to Fig. 1, showing a snow globe exposed to sunlight and its inverted shadow. This paper offers an explanation of the problem, which occurs due to light refraction from the globe.

  9. Dirty snow after nuclear war

    NASA Technical Reports Server (NTRS)

    Warren, S. G.; Wiscombe, W. J.

    1985-01-01

    It is shown that smoke from fires started by nuclear explosions could continue to cause significant disruption even after it has fallen from the atmosphere, by lowering the reflectivity of snow and sea ice surfaces, with possible effects on climate in northern latitudes caused by enhanced absorption of sunlight. The reduced reflectivity could persist for several years on Arctic sea ice and on the ablation area of the Greenland ice sheet.

  10. 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.

  11. Spatiotemporal analysis of snow trends in Austria

    NASA Astrophysics Data System (ADS)

    Koch, Roland; Schöner, Wolfgang

    2015-04-01

    This study presents the spatiotemporal analysis of Austrian snow observations. A set of consistent and reliable long-term time series of snow depth on a daily scale from selected meteorological sites across Austria is used. The time series were collected by the Central Institute for Meteorology and Geodynamics (ZAMG) and the Hydrographical Central Bureau of Austria (HZB). The data cover a time period from the late nineteenth century until today. In the first part of the study spatiotemporal characteristics of seasonal snow depth observations were investigated by the method of principal component analysis (PCA). Furthermore, the spatial patterns of variability have been used for a regionalisation, identifying regions with similar conditions during the base period 1961 to 2010. The results show a clear separation of four major regions including various sub-regions. However, the regionalisation was limited due to sparse data coverage. The non-parametric Mann-Kendall statistical test had been used to assess the significance of trends in snow indices, e.g. snow depth, maximum snow depth, snow cover duration, at monthly and seasonal time scales. In order to remove the influence of the lag-1 serial correlation from the snow data, the trend-free pre-whitening approach was applied. In the monthly and seasonal time series during the period 1961-2010, negative trends in snow indices were significant at the 95% confidence level primarily at stations in the Western and Southern part of Austria. In addition, the correlation between snow observations and gridded HISTALP winter temperature and precipitation fields was investigated. The analysis has shown an increased temperature and decreased precipitation during the 1990s, yielding a pronounced reduction in snow depth and duration. As a matter of fact, the results indicate major shifts of the snow depth and snow cover duration around the 1970s and especially the 1990s, which are predominantly responsible for trends.

  12. Measuring snow liquid water content with low-cost GPS receivers.

    PubMed

    Koch, Franziska; Prasch, Monika; Schmid, Lino; Schweizer, Jürg; Mauser, Wolfram

    2014-11-06

    The amount of liquid water in snow characterizes the wetness of a snowpack. Its temporal evolution plays an important role for wet-snow avalanche prediction, as well as the onset of meltwater release and water availability estimations within a river basin. However, it is still a challenge and a not yet satisfyingly solved issue to measure the liquid water content (LWC) in snow with conventional in situ and remote sensing techniques. We propose a new approach based on the attenuation of microwave radiation in the L-band emitted by the satellites of the Global Positioning System (GPS). For this purpose, we performed a continuous low-cost GPS measurement experiment at the Weissfluhjoch test site in Switzerland, during the snow melt period in 2013. As a measure of signal strength, we analyzed the carrier-to-noise power density ratio (C/N0) and developed a procedure to normalize these data. The bulk volumetric LWC was determined based on assumptions for attenuation, reflection and refraction of radiation in wet snow. The onset of melt, as well as daily melt-freeze cycles were clearly detected. The temporal evolution of the LWC was closely related to the meteorological and snow-hydrological data. Due to its non-destructive setup, its cost-efficiency and global availability, this approach has the potential to be implemented in distributed sensor networks for avalanche prediction or basin-wide melt onset measurements.

  13. Measuring Snow Liquid Water Content with Low-Cost GPS Receivers

    PubMed Central

    Koch, Franziska; Prasch, Monika; Schmid, Lino; Schweizer, Jürg; Mauser, Wolfram

    2014-01-01

    The amount of liquid water in snow characterizes the wetness of a snowpack. Its temporal evolution plays an important role for wet-snow avalanche prediction, as well as the onset of meltwater release and water availability estimations within a river basin. However, it is still a challenge and a not yet satisfyingly solved issue to measure the liquid water content (LWC) in snow with conventional in situ and remote sensing techniques. We propose a new approach based on the attenuation of microwave radiation in the L-band emitted by the satellites of the Global Positioning System (GPS). For this purpose, we performed a continuous low-cost GPS measurement experiment at the Weissfluhjoch test site in Switzerland, during the snow melt period in 2013. As a measure of signal strength, we analyzed the carrier-to-noise power density ratio (C/N0) and developed a procedure to normalize these data. The bulk volumetric LWC was determined based on assumptions for attenuation, reflection and refraction of radiation in wet snow. The onset of melt, as well as daily melt-freeze cycles were clearly detected. The temporal evolution of the LWC was closely related to the meteorological and snow-hydrological data. Due to its non-destructive setup, its cost-efficiency and global availability, this approach has the potential to be implemented in distributed sensor networks for avalanche prediction or basin-wide melt onset measurements. PMID:25384007

  14. A multi-dataset approach to developing time series of Arctic and sub-Arctic snow extent and snow water equivalent (Invited)

    NASA Astrophysics Data System (ADS)

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

    2010-12-01

    , and to each other. Monthly averaged (October through June) Arctic snow water equivalent (SWE) time series were derived for 1978 through 2009. Satellite derived datasets available for this analysis include an empirical tundra-specific passive microwave algorithm, and an assimilation technique which weighs passive microwave data combined with a semi-empirical radiative transfer model, with snow information from ground measurements. SWE estimates were also derived from the Canadian Meteorological Centre (CMC) daily gridded global snow depth analysis, and ERA-40 atmospheric reanalysis. The individual datasets exhibited considerable spread in the absolute mean monthly SWE estimates (particularly in the autumn), however the anomaly series showed significant agreement from January through April, particularly from 1998 onwards. The SWE time series indicate that increases in winter snowfall are contributing to an increase in peak pre-melt SWE in spite of the trend toward earlier snow melt and a shorter snow cover season.

  15. Nitrate photolysis in salty snow

    NASA Astrophysics Data System (ADS)

    Donaldson, D. J.; Morenz, K.; Shi, Q.; Murphy, J. G.

    2016-12-01

    Nitrate photolysis from snow can have a significant impact on the oxidative capacity of the local atmosphere, but the factors affecting the release of gas phase products are not well understood. Here, we report the first systematic study of the amounts of NO, NO2, and total nitrogen oxides (NOy) emitted from illuminated snow samples as a function of both nitrate and total salt (NaCl and Instant Ocean) concentration. We show that the release of nitrogen oxides to the gas phase is directly related to the expected nitrate concentration in the brine at the surface of the snow crystals, increasing to a plateau value with increasing nitrate, and generally decreasing with increasing NaCl or Instant Ocean (I.O.). In frozen mixed nitrate (25 mM) - salt (0-500 mM) solutions, there is an increase in gas phase NO2 seen at low added salt amounts: NO2 production is enhanced by 35% at low prefreezing [NaCl] and by 70% at similar prefreezing [I.O.]. Raman microscopy of frozen nitrate-salt solutions shows evidence of stronger nitrate exclusion to the air interface in the presence of I.O. than with added NaCl. The enhancement in nitrogen oxides emission in the presence of salts may prove to be important to the atmospheric oxidative capacity in polar regions.

  16. Digging of 'Snow White' Begins

    NASA Technical Reports Server (NTRS)

    2008-01-01

    NASA's Phoenix Mars Lander began excavating a new trench, dubbed 'Snow White,' in a patch of Martian soil located near the center of a polygonal surface feature, nicknamed 'Cheshire Cat.' The trench is about 2 centimeters (.8 inches) deep and 30 centimeters (about 12 inches) long. The 'dump pile' is located at the top of the trench, the side farthest away from the lander, and has been dubbed 'Croquet Ground.' The digging site has been named 'Wonderland.'

    At this early stage of digging, the Phoenix team did not expect to find any of the white material seen in the first trench, now called 'Dodo-Goldilocks.' That trench showed white material at a depth of about 5 centimeters (2 inches). More digging of Snow White is planned for coming sols, or Martian days.

    The dark portion of this image is the shadow of the lander's solar panel; the bright areas within this region are not in shadow.

    Snow White was dug on Sol 22 (June 17, 2008) with Phoenix's Robotic Arm. This picture was acquired on the same day by the lander's Surface Stereo Imager. This image has been enhanced to brighten shaded areas.

    The Phoenix Mission is led by the University of Arizona, Tucson, on behalf of NASA. Project management of the mission is by NASA's Jet Propulsion Laboratory, Pasadena, Calif. Spacecraft development is by Lockheed Martin Space Systems, Denver.

  17. Enhanced Solar Energy Absorption by Internally-mixed Black Carbon in Snow Grains

    SciTech Connect

    Flanner, M. G.; Liu, Xiaohong; Zhou, Cheng; Penner, Joyce E.; Jiao, C.

    2012-05-30

    Here we explore light absorption by snowpack containing black carbon (BC) particles residing within ice grains. Basic considerations of particle volumes and BC/snow mass concentrations show that there are generally 0:05-109 BC particles for each ice grain. This suggests that internal BC is likely distributed as multiple inclusions within ice grains, and thus the dynamic effective medium approximation (DEMA) (Chylek and Srivastava, 1983) is a more appropriate optical representation for BC/ice composites than coated-sphere or standard mixing approximations. DEMA calculations show that the 460 nm absorption cross-section of BC/ice composites, normalized to the mass of BC, is typically enhanced by factors of 1.8-2.1 relative to interstitial BC. BC effective radius is the dominant cause of variation in this enhancement, compared with ice grain size and BC volume fraction. We apply two atmospheric aerosol models that simulate interstitial and within-hydrometeor BC lifecycles. Although only {approx}2% of the atmospheric BC burden is cloud-borne, 71-83% of the BC deposited to global snow and sea-ice surfaces occurs within hydrometeors. Key processes responsible for within-snow BC deposition are development of hydrophilic coatings on BC, activation of liquid droplets, and subsequent snow formation through riming or ice nucleation by other species and aggregation/accretion of ice particles. Applying deposition fields from these aerosol models in offline snow and sea-ice simulations, we calculate that 32-73% of BC in global surface snow resides within ice grains. This fraction is smaller than the within-hydrometeor deposition fraction because meltwater flux preferentially removes internal BC, while sublimation and freezing within snowpack expose internal BC. Incorporating the DEMA into a global climate model, we simulate increases in BC/snow radiative forcing of 43-86%, relative to scenarios that apply external optical properties to all BC. We show that snow metamorphism

  18. Mercury distribution and deposition in glacier snow over western China.

    PubMed

    Zhang, Qianggong; Huang, Jie; Wang, Feiyue; Mark, Loewen; Xu, Jianzhong; Armstrong, Debbie; Li, Chaoliu; Zhang, Yulan; Kang, Shichang

    2012-05-15

    Western China is home to the largest aggregate of glaciers outside the polar regions, yet little is known about how the glaciers in this area affect the transport and cycling of mercury (Hg) regionally and globally. From 2005 to 2010, extensive glacier snow sampling campaigns were carried out in 14 snowpits from 9 glaciers over western China, and the vertical distribution profiles of Hg were obtained. The Total Hg (THg) concentrations in the glacier snow ranged from <1 to 43.6 ng L(-1), and exhibited clear seasonal variations with lower values in summer than in winter. Spatially, higher THg concentrations were typically observed in glacier snows from the northern region where atmospheric particulate loading is comparably high. Glacier snowpit Hg was largely dependent on particulate matters and was associated with particulate Hg, which is less prone to postdepositional changes, thus providing a valuable record of atmospheric Hg deposition. Estimated atmospheric Hg depositional fluxes ranged from 0.74 to 7.89 μg m(-2) yr(-1), agreeing very well with the global natural values, but are one to two orders of magnitude lower than that of the neighboring East Asia. Elevated Hg concentrations were observed in refrozen ice layers in several snowpits subjected to intense melt, indicating that Hg can be potentially released to meltwater.

  19. Spatial and Temporal Variability of Surface Snow Accumulation and Snow Chemistry at East Antarctic Ice Sheet

    NASA Astrophysics Data System (ADS)

    Motoyama, H.; Ito, K.; Hirabayashi, M.

    2014-12-01

    Snow stakes along the traverse routes have been observed for long term monitoring program 'the variation of ice sheet surface mass balance' from the 1960's by the Japanese Antarctic Research Expedition in Shirase glacier drainage basin, East Antarctica. During the traverse route between coastal S16 point (69 02'S, 40 03'E, 580m a.s.l.) to inland Dome Fuji (77 22'S, 39 42'E, 3,810m a.s.l.), the snow stake observations every 2 km have been carried out from 1993. Yearly net snow accumulations from S16 to Dome Fuji were calculated. Heavy, modern and light snow events were observed. They were different in way accumulating spatial pattern depending on places. The yearly accumulation rates were compared with seasonal change of AAO-index (SAM). As a result, yearly accumulation rate and AAO-index showed the positive correlation.Surface snow samplings were conducted every 10km along the traverse route. Generally, the snow surface features are classified into three regions. (1) the coastal region: smooth surface, high snow accumulation (2) the katabatic slope region: rough sastrugi surface and smooth glazed surface(3) the high plateau region: smooth surface, little snow accumulation The chemistry of surface snow changes from the coast to inland. Furthermore, the chemical properties of snow are different for each surface at the same area. We can classify the surface snow with fresh drifting snow, deposited drift snow, soft and hard surface snow, sustrugi, surface hoar and so on. The value of each isotope ration and ion concentration greatly varied. Sometimes, snow might deposit thick equally. But the deposited snow was redistributed by the wind. When the snowstorm occurred, the blowing snow started to deposit in a certain opportunity. As for it, the area was not the uniform. It is necessary to discuss inhomogeneity of the depositional condition quantitatively.

  20. Storing snow for the next winter: Two case studies on the application of snow farming.

    NASA Astrophysics Data System (ADS)

    Grünewald, Thomas; Wolfsperger, Fabian

    2016-04-01

    Snow farming is the conservation of snow during the warm half-year. This means that large piles of snow are formed in spring in order to be conserved over the summer season. Well-insulating materials such as chipped wood are added as surface cover to reduce melting. The aim of snow farming is to provide a "snow guaranty" for autumn or early winter - this means that a specific amount of snow will definitively be available, independent of the weather conditions. The conserved snow can then be used as basis for the preparation of winter sports grounds such as cross-country tracks or ski runs. This helps in the organization of early winter season sport events such as World Cup races or to provide appropriate training conditions for athletes. We present a study on two snow farming projects, one in Davos (Switzerland) and one in the Martell valley of South Tyrol. At both places snow farming has been used for several years. For the summer season 2015, we monitored both snow piles in order to assess the amount of snow conserved. High resolution terrestrial laser scanning was performed to measure snow volumes of the piles at the beginning and at the end of the summer period. Results showed that only 20% to 30 % of the snow mass was lost due to ablation. This mass loss was surprisingly low considering the extremely warm and dry summer. In order to identify the most relevant drivers of snow melt we also present simulations with the sophisticated snow cover models SNOWPACK and Alpine3D. The simulations are driven by meteorological input data recorded in the vicinity of the piles and enable a detailed analysis of the relevant processes controlling the energy balance. The models can be applied to optimize settings for snow farming and to examine the suitability of new locations, configurations or cover material for future snow farming projects.

  1. [The research of the relationship between snow properties and the bidirectional polarized reflectance from snow surface].

    PubMed

    Sun, Zhong-Qiu; Wu, Zheng-Fang; Zhao, Yun-Sheng

    2014-10-01

    In the context of remote sensing, the reflectance of snow is a key factor for accurate inversion for snow properties, such as snow grain size, albedo, because of it is influenced by the change of snow properties. The polarized reflectance is a general phenomenon during the reflected progress in natural incident light In this paper, based on the correct measurements for the multiple-angle reflected property of snow field in visible and near infrared wavelength (from 350 to 2,500 nm), the influence of snow grain size and wet snow on the bidirectional polarized property of snow was measured and analyzed. Combining the results measured in the field and previous conclusions confirms that the relation between polarization and snow grain size is obvious in infrared wavelength (at about 1,500 nm), which means the degree of polarization increasing with an increase of snow grain size in the forward scattering direction, it is because the strong absorption of ice near 1,500 nm leads to the single scattering light contributes to the reflection information obtained by the sensor; in other word, the larger grain size, the more absorption accompanying the larger polarization in forward scattering direction; we can illustrate that the change from dry snow to wet snow also influences the polarization property of snow, because of the water on the surface of snow particle adheres the adjacent particles, that means the wet snow grain size is larger than the dry snow grain size. Therefore, combining the multiple-angle polarization with reflectance will provide solid method and theoretical basis for inversion of snow properties.

  2. Snow depth and snow persistence patterns in the Arctic from analysis of the entire Landsat archive

    NASA Astrophysics Data System (ADS)

    Macander, M. J.; Swingley, C. S.; Parr, C.; Sturm, M.; Selkowitz, D.; Larsen, C.

    2016-12-01

    The entire archive of Landsat 5 TM, Landat 7 ETM+, and Landsat 8 OLI imagery collected between March 1 and August 31, 1999-2016 was analyzed to map the presence or absence of snow, with consideration given to clouds, cloud shadows, terrain shadows, and canopy cover. Google Earth Engine was utilized to rapidly classify and summarize the entire time-series. The time-series of observations were then pooled across all the years and a binary classification tree determined the day of year that based split the observations into a snow-covered and a snow-free season. The analysis was completed for arctic Alaska and covers approximately one million square kilometers. The snow persistence product was validated using SNOTEL sites and MODIS time series metrics. The snow persistence patterns are highly correlated with end of winter snow depth patterns. We compared the Landsat snow persistence to the normal snow depth from repeat LIDAR surveys and field snow depth measurements and applied the results to estimate snow distribution over much larger regions. A nonlinear relationship between normal snow-free day of year and mean end of winter snow depth was observed. A polynomial model (r-squared = 0.95) was developed and was extrapolated to the surrounding area to produce a regional map of modeled mean end of winter snow depth.

  3. Comparative Analysis of Hyperspectral and Multispectral Data for Mapping Snow Cover and Snow Grain Size

    NASA Astrophysics Data System (ADS)

    Anul Haq, M.

    2014-11-01

    The present study demonstrates the potential of imaging spectroscopy to produce the snow cover maps and estimation of snow grain size in the Himalayan region. Snow cover maps and snow grain size produce from imaging spectroscopy data were also compared with multispectral imagery (i.e. Landsat 8 and ASTER). Snow grain size was estimated using the snow grain index and compared with the asymptotic radiative transfer (ART) theory method. The overall matching area was 78.29 % among different snow grain size classes using grain index Method and ART method. An attempt has been made to derive the snow grain size using Landsat 8 and ASTER data for the same area. It was found that grain size derived from Landsat 8 and ASTER data show correlation of 81.67 % and 86.34 % respectively. The snow cover maps were produced using Normalized Difference Snow Index (NDSI). Snow cover maps were also produced using ASTER imagery for the same area and compared with Hyperion snow cover maps. The correlation between both snow cover maps were show 91 % correlation.

  4. Soot climate forcing via snow and ice albedos.

    PubMed

    Hansen, James; Nazarenko, Larissa

    2004-01-13

    Plausible estimates for the effect of soot on snow and ice albedos (1.5% in the Arctic and 3% in Northern Hemisphere land areas) yield a climate forcing of +0.3 W/m(2) in the Northern Hemisphere. The "efficacy" of this forcing is approximately 2, i.e., for a given forcing it is twice as effective as CO(2) in altering global surface air temperature. This indirect soot forcing may have contributed to global warming of the past century, including the trend toward early springs in the Northern Hemisphere, thinning Arctic sea ice, and melting land ice and permafrost. If, as we suggest, melting ice and sea level rise define the level of dangerous anthropogenic interference with the climate system, then reducing soot emissions, thus restoring snow albedos to pristine high values, would have the double benefit of reducing global warming and raising the global temperature level at which dangerous anthropogenic interference occurs. However, soot contributions to climate change do not alter the conclusion that anthropogenic greenhouse gases have been the main cause of recent global warming and will be the predominant climate forcing in the future.

  5. Soot climate forcing via snow and ice albedos

    PubMed Central

    Hansen, James; Nazarenko, Larissa

    2004-01-01

    Plausible estimates for the effect of soot on snow and ice albedos (1.5% in the Arctic and 3% in Northern Hemisphere land areas) yield a climate forcing of +0.3 W/m2 in the Northern Hemisphere. The “efficacy” of this forcing is ∼2, i.e., for a given forcing it is twice as effective as CO2 in altering global surface air temperature. This indirect soot forcing may have contributed to global warming of the past century, including the trend toward early springs in the Northern Hemisphere, thinning Arctic sea ice, and melting land ice and permafrost. If, as we suggest, melting ice and sea level rise define the level of dangerous anthropogenic interference with the climate system, then reducing soot emissions, thus restoring snow albedos to pristine high values, would have the double benefit of reducing global warming and raising the global temperature level at which dangerous anthropogenic interference occurs. However, soot contributions to climate change do not alter the conclusion that anthropogenic greenhouse gases have been the main cause of recent global warming and will be the predominant climate forcing in the future. PMID:14699053

  6. Blended learning in ethics education: a survey of nursing students.

    PubMed

    Hsu, Li-Ling

    2011-05-01

    Nurses are experiencing new ethical issues as a result of global developments and changes in health care. With health care becoming increasingly sophisticated, and countries facing challenges of graying population, ethical issues involved in health care are bound to expand in quantity and in depth. Blended learning rather as a combination of multiple delivery media designed to promote meaningful learning. Specifically, this study was focused on two questions: (1) the students' satisfaction and attitudes as members of a scenario-based learning process in a blended learning environment; (2) the relationship between students' satisfaction ratings of nursing ethics course and their attitudes in the blended learning environment. In total, 99 senior undergraduate nursing students currently studying at a public nursing college in Taiwan were invited to participate in this study. A cross-sectional survey design was adopted in this study. The participants were asked to fill out two Likert-scale questionnaire surveys: CAAS (Case Analysis Attitude Scale), and BLSS (Blended Learning Satisfaction Scale). The results showed what students felt about their blended learning experiences - mostly items ranged from 3.27-3.76 (the highest score is 5). Another self-assessment of scenario analysis instrument revealed the mean scores ranged from 2.87-4.19. Nearly 57.8% of the participants rated the course 'extremely helpful' or 'very helpful.' This study showed statistically significant correlations (r=0.43) between students' satisfaction with blended learning and case analysis attitudes. In addition, results testified to a potential of the blended learning model proposed in this study to bridge the gap between students and instructors and the one between students and their peers, which are typical of blended learning, and to create meaningful learning by employing blended pedagogical consideration in the course design. The use of scenario instruction enables students to develop critical

  7. From the clouds to the ground - snow precipitation patterns vs. snow accumulation patterns

    NASA Astrophysics Data System (ADS)

    Gerber, Franziska; Besic, Nikola; Mott, Rebecca; Gabella, Marco; Germann, Urs; Bühler, Yves; Marty, Mauro; Berne, Alexis; Lehning, Michael

    2017-04-01

    Knowledge about snow distribution and snow accumulation patterns is important and valuable for different applications such as the prediction of seasonal water resources or avalanche forecasting. Furthermore, accumulated snow on the ground is an important ground truth for validating meteorological and climatological model predictions of precipitation in high mountains and polar regions. Snow accumulation patterns are determined by many different processes from ice crystal nucleation in clouds to snow redistribution by wind and avalanches. In between, snow precipitation undergoes different dynamical and microphysical processes, such as ice crystal growth, aggregation and riming, which determine the growth of individual particles and thereby influence the intensity and structure of the snowfall event. In alpine terrain the interaction of different processes and the topography (e.g. lifting condensation and low level cloud formation, which may result in a seeder-feeder effect) may lead to orographic enhancement of precipitation. Furthermore, the redistribution of snow particles in the air by wind results in preferential deposition of precipitation. Even though orographic enhancement is addressed in numerous studies, the relative importance of micro-physical and dynamically induced mechanisms on local snowfall amounts and especially snow accumulation patterns is hardly known. To better understand the relative importance of different processes on snow precipitation and accumulation we analyze snowfall and snow accumulation between January and March 2016 in Davos (Switzerland). We compare MeteoSwiss operational weather radar measurements on Weissfluhgipfel to a spatially continuous snow accumulation map derived from airborne digital sensing (ADS) snow height for the area of Dischma valley in the vicinity of the weather radar. Additionally, we include snow height measurements from automatic snow stations close to the weather radar. Large-scale radar snow accumulation

  8. Snow depth mapping in high-alpine catchments using digital photogrammetry

    NASA Astrophysics Data System (ADS)

    Bühler, Y.; Marty, M.; Egli, L.; Veitinger, J.; Jonas, T.; Thee, P.; Ginzler, C.

    2015-02-01

    Information on snow depth and its spatial distribution is crucial for numerous applications in snow and avalanche research as well as in hydrology and ecology. Today, snow depth distributions are usually estimated using point measurements performed by automated weather stations and observers in the field combined with interpolation algorithms. However, these methodologies are not able to capture the high spatial variability of the snow depth distribution present in alpine terrain. Continuous and accurate snow depth mapping has been successfully performed using laser scanning but this method can only cover limited areas and is expensive. We use the airborne ADS80 optoelectronic scanner, acquiring stereo imagery with 0.25 m spatial resolution to derive digital surface models (DSMs) of winter and summer terrains in the neighborhood of Davos, Switzerland. The DSMs are generated using photogrammetric image correlation techniques based on the multispectral nadir and backward-looking sensor data. In order to assess the accuracy of the photogrammetric products, we compare these products with the following independent data sets acquired simultaneously: (a) manually measured snow depth plots; (b) differential Global Navigation Satellite System (dGNSS) points; (c) terrestrial laser scanning (TLS); and (d) ground-penetrating radar (GPR) data sets. We demonstrate that the method presented can be used to map snow depth at 2 m resolution with a vertical depth accuracy of ±30 cm (root mean square error) in the complex topography of the Alps. The snow depth maps presented have an average accuracy that is better than 15 % compared to the average snow depth of 2.2 m over the entire test site.

  9. Machine Learning on Images: Combining Passive Microwave and Optical Data to Estimate Snow Water Equivalent

    NASA Astrophysics Data System (ADS)

    Dozier, J.; Tolle, K.; Bair, N.

    2014-12-01

    We have a problem that may be a specific example of a generic one. The task is to estimate spatiotemporally distributed estimates of snow water equivalent (SWE) in snow-dominated mountain environments, including those that lack on-the-ground measurements. Several independent methods exist, but all are problematic. The remotely sensed date of disappearance of snow from each pixel can be combined with a calculation of melt to reconstruct the accumulated SWE for each day back to the last significant snowfall. Comparison with streamflow measurements in mountain ranges where such data are available shows this method to be accurate, but the big disadvantage is that SWE can only be calculated retroactively after snow disappears, and even then only for areas with little accumulation during the melt season. Passive microwave sensors offer real-time global SWE estimates but suffer from several issues, notably signal loss in wet snow or in forests, saturation in deep snow, subpixel variability in the mountains owing to the large (~25 km) pixel size, and SWE overestimation in the presence of large grains such as depth and surface hoar. Throughout the winter and spring, snow-covered area can be measured at sub-km spatial resolution with optical sensors, with accuracy and timeliness improved by interpolating and smoothing across multiple days. So the question is, how can we establish the relationship between Reconstruction—available only after the snow goes away—and passive microwave and optical data to accurately estimate SWE during the snow season, when the information can help forecast spring runoff? Linear regression provides one answer, but can modern machine learning techniques (used to persuade people to click on web advertisements) adapt to improve forecasts of floods and droughts in areas where more than one billion people depend on snowmelt for their water resources?

  10. Complex responses of spring alpine vegetation phenology to snow cover dynamics over the Tibetan Plateau, China.

    PubMed

    Wang, Siyuan; Wang, Xiaoyue; Chen, Guangsheng; Yang, Qichun; Wang, Bin; Ma, Yuanxu; Shen, Ming

    2017-09-01

    Snow cover dynamics are considered to play a key role on spring phenological shifts in the high-latitude, so investigating responses of spring phenology to snow cover dynamics is becoming an increasingly important way to identify and predict global ecosystem dynamics. In this study, we quantified the temporal trends and spatial variations of spring phenology and snow cover across the Tibetan Plateau by calibrating and analyzing time series of the NOAA AVHRR-derived normalized difference vegetation index (NDVI) during 1983-2012. We also examined how snow cover dynamics affect the spatio-temporal pattern of spring alpine vegetation phenology over the plateau. Our results indicated that 52.21% of the plateau experienced a significant advancing trend in the beginning of vegetation growing season (BGS) and 34.30% exhibited a delaying trend. Accordingly, the snow cover duration days (SCD) and snow cover melt date (SCM) showed similar patterns with a decreasing trend in the west and an increasing trend in the southeast, but the start date of snow cover (SCS) showed an opposite pattern. Meanwhile, the spatial patterns of the BGS, SCD, SCS and SCM varied in accordance with the gradients of temperature, precipitation and topography across the plateau. The response relationship of spring phenology to snow cover dynamics varied within different climate, terrain and alpine plant community zones, and the spatio-temporal response patterns were primarily controlled by the long-term local heat-water conditions and topographic conditions. Moreover, temperature and precipitation played a profound impact on diverse responses of spring phenology to snow cover dynamics. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. [Effects of snow pack removal on the dynamics of winter-time soil temperature, carbon, nitrogen, and phosphorus in alpine forests of west Sichuan].

    PubMed

    Tan, Bo; Wu, Fu-zhong; Yang, Wan-qin; Yang, Yu-lian; Wang, Ao; Kang, Li-na

    2011-10-01

    The dynamic changes of snow pack as affected by global warming might have strong effects on the ecological processes in alpine forests. To understand the responses of soil ecological processes in the alpine forests of west Sichuan to the decreasing snow pack under global warming, a snow-shading experiment was conducted in a primary fir forest from October 19, 2009 to May 18, 2010, with the effects of snow pack removal on the dynamics of soil temperature, carbon, nitrogen, and phosphorus investigated. The results showed that snow pack removal increased the diurnal variation amplitude of soil temperature and the frequency of freeze-thaw cycle, and advanced the time of soil frozen and melt as well as the peak time of soil dissolved carbon and nitrogen, available P, NH4(+)-N, and NO3(-)-N. Snow pack removal increased the concentrations of soil dissolved carbon and nitrogen and NO3(-)-N but decreased the concentrations of soil available P and NH4(+)-N, and changed the ratios of soil dissolved carbon and nitrogen, available P, NH4(+)-N, and NO3(-)-N in the period of snow cover and snow melt. The decreased snow pack in winter time in the alpine forests of west Sichuan as affected by global warming could alter the soil exterior environment, and further, affect the processes of soil carbon, nitrogen and phosphorus.

  12. Multi-model blending

    SciTech Connect

    Hamann, Hendrik F.; Hwang, Youngdeok; van Kessel, Theodore G.; Khabibrakhmanov, Ildar K.; Muralidhar, Ramachandran

    2016-10-18

    A method and a system to perform multi-model blending are described. The method includes obtaining one or more sets of predictions of historical conditions, the historical conditions corresponding with a time T that is historical in reference to current time, and the one or more sets of predictions of the historical conditions being output by one or more models. The method also includes obtaining actual historical conditions, the actual historical conditions being measured conditions at the time T, assembling a training data set including designating the two or more set of predictions of historical conditions as predictor variables and the actual historical conditions as response variables, and training a machine learning algorithm based on the training data set. The method further includes obtaining a blended model based on the machine learning algorithm.

  13. Arctic Moisture Source for Eurasian Snow Cover Variations in Autumn

    NASA Astrophysics Data System (ADS)

    Wegmann, M.

    2015-12-01

    Global warming is enhanced at high northern latitudes where the Arctic surface airtemperature has risen at twice the rate of the global average in recent decades - afeature called Arctic amplification. This recent Arctic warming signal likely resultsfrom several factors such as the albedo feedback due to a diminishing cryosphere,enhanced poleward atmospheric and oceanic transport, and change in humidity. Moreover, Arcticsummer sea-ice extent has declined by more than 10% per decade since the start ofthe satellite era (e.g. Stroeve et al., 2012), culminating in a new record low inSeptember 2012.Eurasian snow cover changes have been suggested as a driver for changes in theArctic Oscillation and might provide a link between sea ice decline in the Arcticduring summer and atmospheric circulation in the following winter. However, themechanism connecting snow cover in Eurasia to sea ice decline in autumn is stillunder debate. Our analysis focuses on sea ice decline in the Barents-Kara Sea region, which allowsus to specify regions of interest for FLEXPART forward and backwards moisturetrajectories. Based on Eularian and Lagrangian diagnostics from ERA-INTERIM, wecan address the origin and cause of late autumn snow depth variations in a dense(snow observations from 820 land stations), unutilized observational datasets over theCommonwealth of Independent States.Open waters in the Barents and Kara Sea have been shown to increase the diabaticheating of the atmosphere, which amplifies baroclinic cyclones and might induce aremote atmospheric response by triggering stationary Rossby waves (Honda et al.2009).In agreement with these studies, our results show enhanced storm activity originatingat the Barents and Kara with disturbances entering the continent through a smallsector from the Barents and Kara Seas. Maxima in storm activity trigger increasing uplift, oftenaccompanied by positive snowfall and snow depth anomalies.We show that declining sea ice in the Barents and Kara Seas

  14. Obscuration By Helicopter-Produced Snow Clouds

    NASA Astrophysics Data System (ADS)

    Ebersole, John F.

    1983-02-01

    Measurement data from a helicopter snow obscuration field test conducted at the SNOW ONE-A test at Camp Ethan Allen, Vermont, are discussed relative to temporal and spatial effects of helicopter-downwash-produced snow clouds on visible and infrared transmission. Two tests were conducted-one on December 15, 1981 and one on December 17, 1981. Intervening between these two dates was a snowstorm which left approximately 20 cm of new snow on the ground. During each test a helicopter performed several different flight patterns including hovering at fixed altitude, landing, rapid and slow descent, and forward motion flights, both perpendicular and parallel to the transmissometer line of sight. Application of the multispectral and temporal data for understanding transmission through helicopter-produced snow clouds is discussed. Also reported on briefly in this paper is the phenomenon of helicopter contrast enhancement resulting from obscuration of the (dark) background by the blowing snow cloud.

  15. Fragmentation of wind-blown snow crystals

    NASA Astrophysics Data System (ADS)

    Comola, Francesco; Kok, Jasper F.; Gaume, Johan; Paterna, Enrico; Lehning, Michael

    2017-05-01

    Understanding the dynamics driving the transformation of snowfall crystals into blowing snow particles is critical to correctly account for the energy and mass balances in polar and alpine regions. Here we propose a fragmentation theory of fractal snow crystals that explicitly links the size distribution of blowing snow particles to that of falling snow crystals. We use discrete element modeling of the fragmentation process to support the assumptions made in our theory. By combining this fragmentation model with a statistical mechanics model of blowing snow, we are able to reproduce the characteristic features of blowing snow size distributions measured in the field and in a wind tunnel. In particular, both model and measurements show the emergence of a self-similar scaling for large particle sizes and a systematic deviation from this scaling for small particle sizes.

  16. The Impact of Parameterising Light Penetration Into Snow on the Photochemical Production of NOx and OH Radicals in Snow

    NASA Astrophysics Data System (ADS)

    Frey, M. M.; Chan, H. G. V.; King, M. D.

    2015-12-01

    Snow photochemical processes drive production of chemical trace gases in snowpacks, including nitrogen oxides (NOx = NO + NO2) and hydrogen oxide radicals (HOx = OH + HO2), which are then released to the lower atmosphere. Coupled atmosphere-snow modelling of theses processes on global scales requires simple parameterisations of actinic flux in snow to reduce computational cost. The disagreement between a physical radiative-transfer model and a parameterisation based upon the e-folding depth of actinic flux in snow is evaluated. In particular, the photolysis of nitrate (NO3-), nitrite (NO2-) and hydrogen peroxide (H2O2) in snow and nitrogen dioxide (NO2) in the snowpack interstitial air are considered. The emission flux from the snowpack is estimated as the product of the depth-integrated photolysis rate coefficient and the concentration of photolysis precursors in the snow. The depth-integrated photolysis rate coefficient is calculated (a) explicitly with a radiative-transfer model (RT), and (b) with a simple parameterisation based on e-folding depth. The metric for the evaluation is based upon the deviation of the ratio of the depth-integrated photolysis rate coefficient (R) determined by the two methods from unity. R depends primarily on the position of the peak in the photolysis action spectrum of chemical species, solar zenith angle and physical properties of the snowpack, i.e. strong dependence on the light-scattering cross section and the mass ratio of the light-absorbing impurity (i.e. black carbon and HULIS), but only a weak dependence on density. For the photolysis of NO2, NO2-, NO3- and H2O2 the ratio R varies within the range of 0.82-1.35, 0.88-1.28, 0.93-1.27 and 0.91-1.28 respectively. The e-folding depth parameterisation underestimates for small solar zenith angles and overestimates at solar zenith angles around 60º compared to the RT method. A simple algorithm has been developed to improve the parameterisation which reduces values of R to 0

  17. 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.

  18. Evaluating snow models for hydrological applications

    NASA Astrophysics Data System (ADS)

    Jonas, T.; Magnusson, J.; Wever, N.; Essery, R.; Helbig, N.

    2014-12-01

    Much effort has been invested in developing snow models over several decades, resulting in a wide variety of empirical and physically-based snow models. Within the two categories, models are built on the same principles but mainly differ in choices of model simplifications and parameterizations describing individual processes. In this study, we demonstrate an informative method for evaluating a large range of snow model structures for hydrological applications using an existing multi-model energy-balance framework and data from two well-instrumented sites with a seasonal snow cover. We also include two temperature-index snow models and one physically-based multi-layer snow model in our analyses. Our results show that the ability of models to predict snowpack runoff is strongly related to the agreement of observed and modelled snow water equivalent whereas such relationship is not present for snow depth or snow surface temperature measurements. For snow water equivalent and runoff, the models seem transferable between our two study sites, a behaviour which is not observed for snow surface temperature predictions due to site-specificity of turbulent heat transfer formulations. Uncertainties in the input and validation data, rather than model formulation, appear to contribute most to low model performances in some winters. More importantly, we find that model complexity is not a determinant for predicting daily snow water equivalent and runoff reliably, but choosing an appropriate model structure is. Our study shows the usefulness of the multi-model framework for identifying appropriate models under given constraints such as data availability, properties of interest and computational cost.

  19. Some Optical Properties of Blowing Snow.

    DTIC Science & Technology

    1981-06-01

    1956, " Etudes de Glaclologle en Terre Adelie," Expeditions Polaires Francaises, Paris 5W. F. Budd, W. R. J. Oingle, and U. Radok, 1966, "The Byrd Snow...Snow Transport," Voprosy ispol’zovaniya snega, Institut Geografii Akademii Nauk SSSR, 106-119. ’H. Lister, 1960, "Glaciology I Solid Precipitation...1960, "Glaciology I Solid Precipitation and Drift Snow," T.A.E. Scientific Report No. 5, Trans-Antarctic Expedition Committee, London 13 9- where B

  20. Wideband Instrument for Snow Measurements (WISM)

    NASA Technical Reports Server (NTRS)

    Miranda, Felix A.

    2015-01-01

    This presentation provides a brief summary of the utility of a wideband active and passive (radar and radiometer, respectively) instrument (8-40 GHz) to support the snow science community. The effort seeks to improve snow measurements through advanced calibration and expanded frequency of active and passive sensors and to demonstrate their science utility through airborne retrievals of snow water equivalent (SWE). In addition the effort seeks to advance the technology readiness of broadband current sheet array (CSA) antenna technology for spaceflight applications.

  1. Improving Snow Roads and Airstrips in Antarctica

    DTIC Science & Technology

    1989-07-01

    in Antarctica Sung M. Lee, Wilbur M. Haas, Robert L. Brown and Albert F. Wuori -LECTE ALIG2 2 1989 Prepared for DIVISION OF POLAR PROGRAMS NATIONAL...Snow Roads and Airstrips in Antarctica 12. PERSONAL AUTHOR(S) Lee, Sung M., Haas, Wilbur M., Brown, Robert L. and Wuori, Albert F. 13a. TYPE OF REPORT...identify by block number) FIELD GROUP SUB-GROUP Aircraft skiway Snow roads Antarctica Snow runways 19. ABSTRACT (Continue on reverse if necessary and

  2. Cold-land Processes Pathfinder Mission (CLPP): Advanced Space-based Observation of Fresh Water Stored in Snow

    NASA Astrophysics Data System (ADS)

    Cline, D.; Davis, R. E.; Yueh, S.

    2005-12-01

    Fresh water stored in snow is an important component of the global water cycle. Across more than half of the Earth's land area, seasonal snowpacks function as dynamic fresh-water reservoirs by storing precipitation and delaying runoff. In many regions snowpacks are the dominant source of runoff, filling rivers and recharging aquifers that over a billion people depend on for their water resources. The importance of snow extends across many facets of science and society. Snow properties influence surface water and energy fluxes and other processes important to weather and climate. Snowpacks influence biogeochemical fluxes, permafrost and frozen soil characteristics, ecosystem dynamics, flooding, and even certain solid-earth hazards and dynamics such as landslides. The economic importance of snow water storage is very large, affecting many sectors of health and commerce. Accelerating shrinkage of seasonal snow packs due to our warming climate threatens water supplies, especially in semi-arid and arid regions where snow is the dominant source of runoff and fresh-water resources are already limited. Moreover, snow is strongly implicated in long-term climate-change hypotheses, which project widespread reduction of snow water storage in the future, affecting freshwater flows with severe adverse effects on biodiversity, regional food security and human health. It is a high priority to determine and understand the extent and causes of changes and variability in snow water storage in order to improve prediction. Advanced snow observations are needed to assess changes in snow water storage and to rigorously test models used to predict future changes. Our conventional ground and airborne snow observing-systems meet many specific local needs, but lack the consistency and coverage necessary for this larger purpose. Current and planned satellites do not have the necessary combination of frequencies and resolutions to measure snow water storage consistently across different

  3. Brief communication "Snow profile associated measurements (SPAM) - a new instrument for quick snow profile measurements"

    NASA Astrophysics Data System (ADS)

    Lahtinen, P.

    2011-06-01

    A new instrument concept (SPAM) for snow profile associated measurements is presented. The potential of the concept is demonstrated by presenting preliminary results obtained with the prototype instrument. With this concept it is possible to retrieve rapid snow profiles of e.g. light extinction, reflectance, temperature and snow layer structure with high vertical resolution. As a side-product, also snow depth is retrieved.

  4. Wideband Instrument for Snow Measurements (WISM)

    NASA Technical Reports Server (NTRS)

    Miranda, Felix A.; Lambert, Kevin M.; Romanofsky, Robert R.; Durham, Tim; Speed, Kerry; Lange, Robert; Olsen, Art; Smith, Brett; Taylor, Robert; Schmidt, Mark; hide

    2016-01-01

    This presentation discusses current efforts to develop a Wideband Instrument for Snow Measurements (WISM). The objective of the effort are as follows: to advance the utility of a wideband active and passive instrument (8-40 gigahertz) to support the snow science community; improve snow measurements through advanced calibration and expanded frequency of active and passive sensors; demonstrate science utility through airborne retrievals of snow water equivalent (SWE); and advance the technology readiness of broadband current sheet array (CSA) antenna technology for spaceflight applications.

  5. Microwave emission from dry and wet snow

    NASA Technical Reports Server (NTRS)

    Chang, T. C.; Gloersen, P.

    1975-01-01

    A microscopic model was developed to study the microwave emission from snow. In this model, the individual snow particles are considered to be the scattering centers. Mie scattering theory for spherical particles is then used to compute the volume scattering and extinction coefficients of the closely packed scattering spheres, which are assumed not to interact coherently. The results of the computations show significant volume scattering effects in the microwave region which result in low observed emissivities from cold, dry snow. In the case of wet snow, the microwave emissivities are increased considerably, in agreement with earlier experimental observations in which the brightness temperatures have increased significantly at the onset of melting.

  6. Interannual turbulence variability with seasonal snow

    NASA Astrophysics Data System (ADS)

    Diebold, M.; Weijs, S. V.; Higgins, C. W.; Lehning, M.; Parlange, M. B.

    2012-12-01

    Wind plays a crucial role in the spatial variability of snow in the mountains. It dictates where the snow is deposited, accumulated and eroded. To better understand the interactions between the snow and the wind field an experiment was deployed in the upper Val Ferret (Switzerland) during the winter 2011-2012. Poles and automated cameras were used to monitor the depth of the snowpack at various points around a small hill. A 10 meters meteorological tower with four sonic anemometers was deployed to obtain turbulence measurements data and regular snow profiles were undertaken. We present first results of this field campaign. The impact of the land surface on the wind characteristics is studied. These results suggest that the presence of snow at the land surface reduces the wind turbulent kinetic energy. The variability of snow from the windward to the lee side of the hill is as also presented. At our field site, the classic expected snow structures have been created by the wind. A large accumulation can be observed on the lee side of the hill, whereas the top is eroded and results in a snow depth twice smaller than the one in the accumulation area. The snowcover at the top of the hill is much more compact and typically melted slower than the snow in the accumulation zone.

  7. Snow Conditions Near Barrow in Spring 2012

    NASA Astrophysics Data System (ADS)

    Webster, M.; Rigor, I.; Nghiem, S. V.; Sturm, M.; Kurtz, N. T.; Farrell, S. L.; Gleason, E.; Lieb-Lappen, R.; Saiet, E.

    2012-12-01

    Snow has a dual role in the growth and decay of Arctic sea ice. It provides insulation from colder air temperatures during the winter, which hinders sea ice formation. Snow is highly reflective and, as a result, it delays the surface ice melt during the spring. Summer snow melt influences the formation and location of melt ponds on sea ice, which further modifies heat transport into sea ice and the underlying ocean. Identifying snow thickness and extent is of key importance in understanding the surface heat budget, particularly during the early spring when the maximum snowfall has surpassed, and surface melt has not yet occurred. Regarding Arctic atmospheric chemical processes, snow may sustain or terminate halogen chemical recycling and distribution, depending on the state of the snow cover. Therefore, an accurate assessment of the snow cover state in the changing Arctic is important to identify subsequent impacts of snow change on both physical and chemical processes in the Arctic environment. In this study, we assess the springtime snow conditions near Barrow, Alaska using coordinated airborne and in situ measurements taken during the NASA Operation IceBridge and BRomine, Ozone, and Mercury EXperiment (BROMEX) field campaigns in March 2012, and compare these to climatological records. Operation IceBridge was conceived to bridge the gap between satellite retrievals ice thickness by ICESat which ceased operating in 2009 and ICESat-2 which is planned for launch in 2016. As part of the IceBridge mission, snow depth may be estimated by taking the difference between the snow/air surface and the snow/ice interface measured by University of Kansas's snow radar installed on a P-3 Orion and the measurements have an approximate spatial resolution of 40 m along-track and 16 m across-track. The in situ snow depth measurements were measured by an Automatic Snow Depth Probe (Magnaprobe), which has an accuracy of 0.5 cm. Samples were taken every one-to-two meters at two sites

  8. Subpixel Snow-Covered-Area and Snow Grain Size From Mixture Analysis with AVIRIS Data

    NASA Technical Reports Server (NTRS)

    Green Robert O.; Painter, Thomas H.; Roberts, Dar A.; Dozier, Jeff

    1996-01-01

    Snow-covered-area (SCA) and snow grain size are crucial inputs to hydrologic and climatologic modeling of alpine and other seasonally snow-covered regions. SCA is necessary to parameterize energy budget calculations in climate models, to determine in which regions point snowmelt models are to be run for distributed snowmelt modeling efforts and to provide a basis from which estimates of snow water equivalent (SWE) may be made. Snow grain size, SWE and snow impurities determine the spectral albedo of snow, which controls the net solar flux at the snowpack surface. Snow albedo is of the utmost importance in snowmelt modeling, yet the difficulty with which grain size, SWE, and impurities are mapped has left the spatial distribution of snow albedo in alpine catchments poorly understood. The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) has been used to estimate sub-pixel snow-covered-area and snow grain size independently. In this paper we present a technique which improves estimates of both snow parameters by treating their mapping simultaneously.

  9. The Morphology of Polar Snow Surfaces: A Race Between Time and Snow Grain Properties

    NASA Astrophysics Data System (ADS)

    Filhol, S. V. P.; Sturm, M.

    2014-12-01

    Polar snow surfaces are rough, composed of multiple forms shaped by the interaction of snow grains and the wind. Based on the literature and new three-dimensional laser scanning data acquired in the Alaskan Arctic, we revisited the existing classifications of snow forms, and suggest a new genetic classification. Next we compared the morphology of aeolian snow features to analogous sand features, and then investigated the processes responsible for the differences. Although previous studies have suggested close similitudes between sand and snow features (barchan dunes, transverse dunes, etc.), we find significant differences, including: 1) snow features are smaller by a factor of a 100, 2) snow dunes are flatter, 3) snow dunes move four orders of magnitude faster than sand dunes, and 4) sand dunes last millennia, while snow dunes are by and large ephemeral. Coupling equations for dune age, propagation speed, snow flux, and wind speed, we find that the lower density of snow grains vs. sand (which should produce a higher flux) is balanced by sintering, which serves as a countdown timer, eventually bonding grains together, reducing material fluxes, and thereby limiting the growth and age of snow dunes.

  10. Operational snow mapping with simplified data assimilation using the seNorge snow model

    NASA Astrophysics Data System (ADS)

    Saloranta, Tuomo M.

    2016-07-01

    Frequently updated maps of snow conditions are useful for many applications, e.g., for avalanche and flood forecasting services, hydropower energy situation analysis, as well as for the general public. Numerical snow models are often applied in snow map production for operational hydrological services. However, inaccuracies in the simulated snow maps due to model uncertainties and the lack of suitable data assimilation techniques to correct them in near-real time may often reduce the usefulness of the snow maps in operational use. In this paper the revised seNorge snow model (v.1.1.1) for snow mapping is described, and a simplified data assimilation procedure is introduced to correct detected snow model biases in near real-time. The data assimilation procedure is theoretically based on the Bayesian updating paradigm and is meant to be pragmatic with modest computational and input data requirements. Moreover, it is flexible and can utilize both point-based snow depth and satellite-based areal snow-covered area observations, which are generally the most common data-sources of snow observations. The model and analysis codes as well as the "R" statistical software are freely available. All these features should help to lower the challenges and hurdles hampering the application of data-assimilation techniques in operational hydrological modeling. The steps of the data assimilation procedure (evaluation, sensitivity analysis, optimization) and their contribution to significantly increased accuracy of the snow maps are demonstrated with a case from eastern Norway in winter 2013/2014.

  11. Arrest of Avalanche Propagation by Discontinuities on Snow Cover

    NASA Astrophysics Data System (ADS)

    Frigo, B.; Chiaia, B.

    2009-04-01

    results are supported also by other investigations, which suggested that increased spatial variability in the snow cover leads to a lower release probability of snow avalanches. The above studies are based on very different approaches, such as cellular automata (Kronholm and Birkeland, 2005) and statistical renormalization (Chiaia and Frigo, 2009) models, but come to the same conclusion, i.e. that the presence of randomly distributed weak zones increase the global robustness and toughness of the snow slope. From a practical engineering viewpoint, results could be used towards a new idea of active avalanche protection, based on the presence of natural (e.g., trees) or artificial objects throughout the slope, able to create low deposition zones as discontinuities in the snow cover. Key words: snow avalanche, fracture mechanics, crack arrester. References Chiaia, B., Cornetti, P., Frigo, B., 2008. Triggering of dry snow slab avalanches: Stress versus fracture mechanical approach Cold Reg. Sci. Technol. 53 170-178. Chiaia, B., Frigo, B., 2009, A scale-invariant model for snow slab-avalanches, J. Stat. Phys., submitted Föhn, P.M.B., Camponovo, C., Krüsi, G., 1998. Mechanical and structural properties of weak snow layers measured in situ. Annals of Glaciology 26, 1-6. Jamieson, J.B., Johnston, C.D., 1992. A fracture-arrest model for unconfined dry slab avalanches. Canadian Geotechnical Journal, 29, 61-66. Jamieson, B., Johnston, C.D., 2001. Evaluation of shear frame test for weak snowpack layers. Annals of Glaciology 32, 59-69. Kirchner, H.O.K., Michot, G., Schweizer, J., 2002. Fracture toughness of snow in shear and tension. Scripta Materialia 46, 425-429. Kronholm, K., Birkeland, K. W., 2005. Integrating spatial patterns into a snow avalanche cellular automata model, Geophysical Research Letters 32, L19504. McClung, D. M. 1979. Shear fracture precipitated by strain softening as a mechanism of dry slab avalanche release, J. Geophys. Res. 84(B7) 3519-3526. Schweizer, J., 1999

  12. Simulations of Snow Redistribution in Marmot Creek with a Coupled Multiscale Atmospheric-Land Surface-Blowing Snow Model

    NASA Astrophysics Data System (ADS)

    Guzman, E. H.; Pomeroy, J. W.; Pietroniro, A.

    2009-12-01

    The hydrology of mountainous cold regions is significantly influenced by the development of the seasonal snowcover. However snow is often transported, sublimated and redistributed by wind before it can form a snowpack. Physical based hydrological models are well accepted methods to simulate snow processes. But even these sophisticated tools have a limited application with the lack of high (spatial and temporal) resolution forcing data. Economic and practical limitations normally make it impossible to have a sufficient spatial density of hydrometeorological observation stations to run snow redistribution models at high elevations. Therefore, a different method must be adopted in order to provide the forcing wind speeds, wind direction precipitation, temperature and humidity fields required. The objective of this study is to develop a multiscale modeling system capable of dynamically downscaling boundary layer atmospheric processes from the global model (~15 km) to the hydrological model spatial scale (~500 m). The methodology consists of coupling the atmospheric models: Global Environmental Multiscale Limited Area Model (GEM-LAM), the Mesoscale Compressible Community (MC2) model and the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface scheme with the hydrological model: Cold Regions Hydrological Model (CRHM). Each model is run at the scale for which it is most appropriate; GEM-LAM and ISBA at large scales (15 km), MC2 and CRHM at smaller scales (500 m). The atmospheric models are used on a non-interactive nesting configuration, where the variables chosen to validate the simulations correspond to the forcing data of CRHM set up for blowing snow simulations. Temperature, precipitation, relative humidity and the wind direction and speed were modelled and measured from eight hydrometeorological stations located in open environments from valley bottom to mountain ridgetop in Marmot Creek Research Basin (50° 57’ N, 115° 10’ W) in the Rocky

  13. Addressing frequency and magnitude of recent snow avalanches in Northern Iceland and Western Norway by using dendrogeomorphology

    NASA Astrophysics Data System (ADS)

    Decaulne, Armelle; Eggertsson, Ólafur; Laute, Katja; Sæmundsson, Şorsteinn; Beylich, Achim A.; Páll Jónsson, Helgi

    2010-05-01

    Snow avalanches are common in mountain areas of various kinds of cold environments. The more or less severity of wintry conditions determines the thickness, durability and stability of snow cover during the cold season. Winter conditions therefore influence the frequency and magnitude of snow avalanches. The aim of this research is (i) to use dendrogeomorphology as a proxy to extract the chronology of snow avalanches on colluvial surfaces (talus and cones) by analysing the tree-ring growth, and (ii) to study the various impacts snow avalanches on trees, i.e. the formation and dating of reaction wood. The study sites are located in Northern Iceland (Dalsmynni, Ljósavatnskarð and Fnjóskadalur valleys), and in Western Norway (Erdalen and Bødalen valleys). All sites are typical U-shaped valleys with important bedrock valley walls that develop downslope in slope accumulations, swept by numerous snow avalanches leaving geomorphological evidence of a significant activity. The currently investigated tree specie is Betula sp., birches being common in both areas. The results provide a temporal catalogue of snow-avalanche events during the last ± 100 years in areas with shortest historical records, and determine the changes in snow-avalanches regime during the same period. This can be correlated with snow-cover changes in the upper catchment areas. Such results are of interest for (i) the understanding of global changes on snow-avalanche activity in cold mountain areas, and (ii) getting a better knowledge of past frequency and magnitude of snow avalanches in areas of poor historical records, in relation with natural hazards.

  14. Weekly LiDAR snow depth mapping for operational snow hydrology - the NASA JPL Airborne Snow Observatory (Invited)

    NASA Astrophysics Data System (ADS)

    Deems, J. S.; Painter, T. H.; McGurk, B. J.

    2013-12-01

    Operational hydrologic simulation and forecasting in snowmelt-dominated watersheds currently relies on indices of snow accumulation and melt from measurements at a small number of point locations or geographically-limited manual surveys. These data sources cannot adequately characterize the spatial distribution of snow depth/water equivalent, which is the primary determinant of snowpack volume and runoff rates. The NASA JPL Airborne Snow Observatory's airborne laser scanning system maps snow depth at high spatial and temporal resolutions, providing an unprecedented snowpack monitoring capability and enabling a new operational paradigm. In the Spring of 2013, the ASO mapped snow depth in the Tuolumne River Basin in California's Yosemite National Park on a nominally weekly basis, and provided fast-turnaround spatial snow depth and water equivalent maps to the operators of Hetch Hetchy Reservoir, the water supply for 2.5 million people on the San Francisco peninsula. These products enabled more accurate runoff simulation and optimal reservoir management in a year of very low snow accumulation. We present the initial results from this new application of multi-temporal LiDAR mapping in operational snow hydrology.

  15. Digging in 'Snow White' Trench

    NASA Technical Reports Server (NTRS)

    2008-01-01

    This image was acquired by NASA's Phoenix Mars Lander's Surface Stereo Imager on the 44th Martian day of the mission, or Sol 43 (July 7, 2008), after the May 25, 2008, landing, showing the current sample scraping area in the trench informally called 'Snow White.'

    The Phoenix Mission is led by the University of Arizona, Tucson, on behalf of NASA. Project management of the mission is led by NASA's Jet Propulsion Laboratory, Pasadena, Calif. Spacecraft development is by Lockheed Martin Space Systems, Denver.

  16. Compressibility Characteristics of Compacted Snow

    DTIC Science & Technology

    1976-06-01

    Cornpressibility characteristics 7Jj i C’p of compacted snowifAG2� 004 t Cover: ~ ~ ~ ~ ~ ~ ~ ~ a - Thn***o htgrp fpoyrsaliekAmgife i ote rm...nwcmrse to7 asa 10 Phtgahb nhn Gow1 CRREL Report 76-21 Compressibility characteristics of compacted snow %i" Gunars Abele and Anthony J. Cow I ~ June 1976 A ...c , I fu. A AD,:j ly M3rs CORPS OF ENGINEERS, U.S. ARMY COLD REGIONS RESEARCH AND ENGINEERZ]NG LABORATORY HANOVER, NEW HAMPSHIRE Approved for public

  17. Water losses during technical snow production

    NASA Astrophysics Data System (ADS)

    Grünewald, Thomas; Wolfsperger, Fabian

    2017-04-01

    These days, the production of technical snow can be seen as a prerequisite for winter tourism. Huge amounts of water are used for technical snow production by ski resorts, especially in the beginning of the winter season. The aim is to guarantee an appropriate amount of snow to reliably provide optimal ski runs until the date of season opening in early December. Technical snow is generated by pumping pressurized water through the nozzles of a snow machine and dispersing the resulting spray of small water droplets which freeze during their travel to the ground. Cooling and freezing of the droplets can only happen if energy is emitted to the air mass surrounding the droplets. This heat transfer is happening through convective cooling and though evaporation and sublimation of water droplets and ice particles. This means that also mass is lost from the droplets and added in form of vapor to the air. It is important to note that not all water that is pumped through the snow machine is converted to snow distributed on the ground. Significant amounts of water are lost due to wind drift, sublimation and evaporation while droplets are traveling through the air or to draining of water which is not fully frozen when arriving at the ground. Studies addressing this question are sparse and the quantity of the water losses is still unclear. In order to assess this question in more detail, we obtained several systematic field observations at a test site near Davos, Switzerland. About a dozen of snow making tests had been performed during the last winter seasons. We compare the amount of water measured at the intake of the snow machine with the amount of snow accumulating at the ground during a night of snow production. The snow mass was calculated from highly detailed repeated terrestrial laser scanning measurements in combination with manually gathered snow densities. In addition a meteorological station had been set up in the vicinity observing all relevant meteorological

  18. Comparison of PALSAR-2 Interferometric Estimates of Snow Water Equivalent, Airborne Snow Observatory Snow Depths, and Results from a Distributed Energy Balance Snow Model (iSnobal)

    NASA Astrophysics Data System (ADS)

    Deeb, E. J.; Marshall, H. P.; Painter, T. H.; Marks, D. G.; Hedrick, A. R.; Havens, S.; Forster, R. R.; Siqueira, P.

    2016-12-01

    The interferometric approach to estimating snow water equivalent (SWE) leverages the fact that at relatively low frequencies ( 1 GHz, L-Band), differences in snow microstructure and layering do not significantly affect the radar backscatter of dry snow. At these frequencies, the main contribution of the radar backscatter is the snow/ground interface, and the difference in the timing of the radar propagation through the snowpack is controlled by snow depth, density and liquid water content. While engineering limitations prevent direct measurement of absolute radar travel-time, interferometric phase shift between acquisitions can be used to monitor changes in radar travel-time, caused by changes in snow properties. PALSAR-2 is a L-Band synthetic aperture radar (SAR) aboard the Japan Aerospace Exploration Agency's (JAXA) ALOS-2 satellite. Launched in 2014, PALSAR-2 interferometric pairs geographically and temporally overlap data collected by the NASA/JPL Airborne Snow Observatory (ASO) which provides spatial distribution of snow depths across basins (e.g. Tuolumne, CA and Grand Mesa, CO) identified as contributing significantly to NASA's multi-year airborne SnowEx campaign. As part of ASO's operational requirements, a spatially distributed energy balance snow model (iSnobal) is run over these domains estimating density (and other snow properties) and providing SWE products for water resource managers as well as other cryospheric science applications. This effort identifies PALSAR-2 satellite pairs closely coincident with ASO collections, processes interferometric products of coherence and phase change, and compares these results with the spatially distributed snow depths from ASO and modeled snow densities from iSnobal. Moreover, for satellite acquisitions not temporally matching the ASO collections, the modeled snow properties (depths and densities) from iSnobal are used for comparison with interferometric estimates of SWE. The integration of ground measurements

  19. Fiber Optic Distributed Temperature Sensing of Snow

    NASA Astrophysics Data System (ADS)

    Huwald, H.; Higgins, C. W.; Diebold, M.; Lehning, M.; Tyler, S. W.; Selker, J. S.; Parlange, M. B.

    2009-12-01

    Physical properties of seasonal and perennial snow covers can vary significantly on the order of a few meters with direct impact on snow dynamics, thermodynamics, temporal evolution, and ultimately on local snow water storage representing a challenge for measurement and modeling efforts. Detailed knowledge on small scale variability in snow internal temperature, density, and resulting subsurface heat fluxes is relatively limited, and pertinent snow cover internal data are also difficult to obtain. Uncertainty in the quantification of the components of the surface and snow internal energy budget is a consequence. From an experimental point of view, acquisition of distributed temperature data in the snow pack is non-trivial since accumulation, ablation, metamorphosis, etc., lead to continuous changes in the snow surface level. To provide better observational evidence of small scale variability and the associated snow physical processes we use fiber optic distributed temperature sensing (DTS), a rapidly emerging technology in environmental sensing, which provides high resolution temperature measurements in space (1 meter) and time (a few minutes) with a resolution better than 0.1C over distances of several kilometers. Innovative experimental designs such as 2D transects and high resolution vertical temperature profiles using fiber optic cables were deployed and tested at high altitude sites in the Swiss Alps. The results of the experiments yield both expertise in the application of the novel measurement systems and new insight in snow pack thermodynamics such as 2D conductive heat fluxes. Also, wind pumping processes were investigated with a complimentary experimental system of synchronized high frequency measurements of atmospheric turbulence and barometric pressure fluctuations in the snow.

  20. How to determine wet-snow instability

    NASA Astrophysics Data System (ADS)

    Reiweger, Ingrid; Mitterer, Christoph

    2017-04-01

    Processes leading to wet-snow instability are very complex and highly non-linear in time and space. Infiltrating water changes wet-snow strength and other mechanical properties. A high liquid water content presumably favors fracture propagation, which consequently has an influence on the formation of wet slab avalanches. The weakening of snow due to liquid water within the snowpack might be gradual (melt event) or sudden (rain-on-snow event). There are several feedback mechanisms between liquid water and snow stratigraphy, making the weakening process complex. We used modelled stability indices to determine periods with high wet-snow instability. These indices were either based on energy and mass balances indicating critical amounts of water within the snowpack or on simple hydro-mechanical relationships. In addition to the modelled indices, preliminary field studies investigated the fracture initiation and fracture propagation propensity within wet snowpacks. We therefore performed Rutschblock and propagation saw tests in faceted weak layers with different volumetric liquid water contents. Results of simulations and field experiments showed that a critical amount of liquid water combined with a pre-critical snow stratigraphy were relevant for wet-snow instability. The critical amount of water was assumed to drive both failure initiation and fracture propagation. The simulated indices and observed stability tests indicated a high wet-snow instability when the volumetric liquid water content within faceted weak layers exceeded 3. Within our propagation saw test measurements crack propagation propensity even slightly decreased at very low liquid water contents compared to completely dry conditions, presumably due to capillary forces. For liquid water contents higher than 3-4%, however, crack propagation propensity strongly increased, which we assume was due to the weakening of bonds between grains within the increasingly wet weak snow layer. Our results could be used

  1. Potential of a low-cost sensor network to understand the spatial and temporal dynamics of a mountain snow cover

    NASA Astrophysics Data System (ADS)

    Pohl, Stefan; Garvelmann, Jakob; Wawerla, Jens; Weiler, Markus

    2014-03-01

    The spatial and temporal dynamics of seasonal snow covers play a critical role for many hydrological, ecological, and climatic processes. This paper presents a new, innovative approach to continuously monitor these dynamics using numerous low-cost, standalone snow monitoring stations (SnoMoS). These stations provide snow and related meteorological data with a high temporal and spatial resolution. Data collected by SnoMoS include: snow depth, surface temperature, air temperature and humidity, total precipitation, global radiation, wind speed, and barometric pressure. A total of 99 sensors were placed over the winters 2010/2011 and 2011/2012 at multiple locations within three 40-180 km2 basins in the Black Forest region of Southern Germany. The locations were chosen to cover a wide range of slopes, elevations, and expositions in a stratified sampling design. Furthermore, "paired stations" located in close proximity to each other, one in the open and one underneath various forest canopies, were set up to investigate the influence of vegetation on snow dynamics. The results showed that considerable differences in snow depth and, therefore, snow water equivalent (SWE) are present within the study area despite its moderate temperatures and medium elevation range (400-1500 m). The relative impact of topographical factors like elevation, aspect, and of different types of forest vegetation were quantified continuously and were found to change considerably over the winter period. The recorded differences in SWE and snow cover duration were large enough that they should be considered in hydrologic and climate models.

  2. In-Situ, Model and Satellite-Derived Snow Water Equivalent Comparisons in Alaskan Permafrost Biosequestration Regions

    NASA Astrophysics Data System (ADS)

    Muskett, R. R.

    2012-04-01

    Permafrost stability and biosequestration are affected by changes of snow cover and changes of land-surface temperature. Satellite retrieval algorithms for estimates of snow water equivalent (SWE) have been performed since the NOAA NIMBUS-7 Scanning Multi-channel Microwave Radiometer beginning in October 1978. A key parameter of Advanced Microwave Scanning Radiometer for the Earth Observation System (AMSR-E) retrieval algorithm is snow density from surveys in Canada during 1946-1995 and Eurasia during 1966-1996. We compare in-situ measures of SWE model-derived and satellite-derived SWE in Alaska. On-average, AMSR-E underperforms (is less than) in-situ measured SWE. Snow density measurement along the Alaska permafrost transect in April 2009 and 2010 show a significant gradient, less dense snow in central Alaska to more dense snow near the Arctic coast of Prudhoe Bay. Air and land-surface temperatures show increases in the Arctic that are greater than the global average increase. We hypothesize that a factor in the AMSR-E SWE underperformance is caused by assumption of snow densities from the 1950s to 1990s that are no longer representative of Arctic snow packs due to effects from Arctic climate change, when other factors are negligible. Acknowledgements: GIPL SWE provided by Sergei Marchenko. Alaska Permafrost Transect data provided by Vladimir Romanovsky and William Cable, and Yukon Flats data provided by Matt Strum (CRREL) and Mark Waldrop (USGS). Funded by V. Romanovsky.

  3. Lasting effects of snow accumulation on summer performance of large herbivores in alpine ecosystems may not last.

    PubMed

    Mysterud, Atle; Austrheim, Gunnar

    2014-05-01

    One of the clearest predictions from the IPCC is that we can expect much less snow cover due to global warming in the 21st century, especially in the lower alpine areas. In alpine ecosystems, snow accumulation in depressions gives rise to distinct snow-bed vegetation types, assumed to play a key role in ecosystem function. A delayed plant phenology yields high-quality forage in late summer for wild and domestic herbivores. Yet, the mechanistic pathways for how declining snow may affect future performance of large herbivores beyond the effect of phenology remain poorly documented. Here, we link unique individual-based data on diet choice, habitat selection and performance of domestic sheep over a 10-year period to manually GPS-recorded spatial positions of snow cover in early summer (0.57% to 43.3% in snow beds on 1st of July) in an alpine ecosystem. Snowy winters gave a higher proportion of easily digestible herbs in the diet and a more variable use of snow-bed and meadow vegetation types resulting in faster growing lambs. These patterns were consistent between two density treatment levels although slightly more marked for diet at low density, suggesting that effects of simple mitigation efforts such as managing population numbers will be meagre. Our study thus yields novel insight into the strong impact of melting snow on ecosystem function in alpine habitats, which are likely to affect productivity of both domestic and wild ungulate populations.

  4. Vegetation and Variable Snow Cover: Spatial Patterns of Shrubland, and Grassland Snow

    NASA Astrophysics Data System (ADS)

    Liston, G. E.; Hiemstra, C. A.; Strack, J. E.

    2003-12-01

    Regions that experience long winters with snowfall and high winds frequently exhibit heterogeneous snow distribution patterns that arise from interactions among snow, wind, topography, and vegetation. Variable snow cover and resultant heterogeneities in albedo and growing season length can affect local weather patterns and energy budgets, and produce spatially co-variable ecosystem properties. While snow influences local atmospheric processes and ecosystems, an important and underappreciated feedback exists between vegetation and snow cover. Plant size, canopy density, and rigidity determine how much snow accumulates on the lee side of individual plants (e.g., shrubland vs. grassland). In addition, the canopy can also influence how much energy reaches the snowpack, thereby hindering or accelerating snowmelt. An overhanging canopy reduces incoming solar radiation while providing a source of turbulent sensible and longwave radiative energy. Historically, most snow vegetation interaction studies have been limited to areas that experience an abundance of snow (e.g., mountainous areas) where trees have a large influence on seasonal snow-cover. In contrast, snow cover patterns associated with shrublands and grasslands have received little attention, despite covering vast expanses (53%) of the seasonally snow-covered globe. In this study, snow depths were measured every two weeks from December through March in a small, 0.25 km2 study area located in North Park, Colorado. The study area possesses little topographic relief and consists of shrub patches, dominated by greasewood (Sarcobatus vermiculatus) and sagebrush (Artemisia tridentata), embedded in a matrix of graminoids (sedges, rushes, and grasses). Snow cover patterns and spatial statistics were dramatically different in graminoid-dominated cover compared with shrub cover. The graminoid snow cover was thinner, less variable, and more ephemeral than the shrub snow pack. Snow was readily eroded by wind from graminoid

  5. A satellite snow depth multi-year average derived from SSM/I for the high latitude regions

    USGS Publications Warehouse

    Biancamaria, S.; Mognard, N.M.; Boone, A.; Grippa, M.; Josberger, E.G.

    2008-01-01

    The hydrological cycle for high latitude regions is inherently linked with the seasonal snowpack. Thus, accurately monitoring the snow depth and the associated aerial coverage are critical issues for monitoring the global climate system. Passive microwave satellite measurements provide an optimal means to monitor the snowpack over the arctic region. While the temporal evolution of snow extent can be observed globally from microwave radiometers, the determination of the corresponding snow depth is more difficult. A dynamic algorithm that accounts for the dependence of the microwave scattering on the snow grain size has been developed to estimate snow depth from Special Sensor Microwave/Imager (SSM/I) brightness temperatures and was validated over the U.S. Great Plains and Western Siberia. The purpose of this study is to assess the dynamic algorithm performance over the entire high latitude (land) region by computing a snow depth multi-year field for the time period 1987-1995. This multi-year average is compared to the Global Soil Wetness Project-Phase2 (GSWP2) snow depth computed from several state-of-the-art land surface schemes and averaged over the same time period. The multi-year average obtained by the dynamic algorithm is in good agreement with the GSWP2 snow depth field (the correlation coefficient for January is 0.55). The static algorithm, which assumes a constant snow grain size in space and time does not correlate with the GSWP2 snow depth field (the correlation coefficient with GSWP2 data for January is - 0.03), but exhibits a very high anti-correlation with the NCEP average January air temperature field (correlation coefficient - 0.77), the deepest satellite snow pack being located in the coldest regions, where the snow grain size may be significantly larger than the average value used in the static algorithm. The dynamic algorithm performs better over Eurasia (with a correlation coefficient with GSWP2 snow depth equal to 0.65) than over North America

  6. Compatibilized Immiscible Polymer Blends for Gas Separations.

    PubMed

    Panapitiya, Nimanka; Wijenayake, Sumudu; Nguyen, Do; Karunaweera, Chamaal; Huang, Yu; Balkus, Kenneth; Musselman, Inga; Ferraris, John

    2016-07-30

    Membrane-based gas separation has attracted a great deal of attention recently due to the requirement for high purity gasses in industrial applications like fuel cells, and because of environment concerns, such as global warming. The current methods of cryogenic distillation and pressure swing adsorption are energy intensive and costly. Therefore, polymer membranes have emerged as a less energy intensive and cost effective candidate to separate gas mixtures. However, the use of polymeric membranes has a drawback known as the permeability-selectivity tradeoff. Many approaches have been used to overcome this limitation including the use of polymer blends. Polymer blending technology synergistically combines the favorable properties of different polymers like high gas permeability and high selectivity, which are difficult to attain with a single polymer. During polymer mixing, polymers tend to uncontrollably phase separate due to unfavorable thermodynamics, which limits the number of completely miscible polymer combinations for gas separations. Therefore, compatibilizers are used to control the phase separation and to obtain stable membrane morphologies, while improving the mechanical properties. In this review, we focus on immiscible polymer blends and the use of compatibilizers for gas separation applications.

  7. Compatibilized Immiscible Polymer Blends for Gas Separations

    PubMed Central

    Panapitiya, Nimanka; Wijenayake, Sumudu; Nguyen, Do; Karunaweera, Chamaal; Huang, Yu; Balkus, Kenneth; Musselman, Inga; Ferraris, John

    2016-01-01

    Membrane-based gas separation has attracted a great deal of attention recently due to the requirement for high purity gasses in industrial applications like fuel cells, and because of environment concerns, such as global warming. The current methods of cryogenic distillation and pressure swing adsorption are energy intensive and costly. Therefore, polymer membranes have emerged as a less energy intensive and cost effective candidate to separate gas mixtures. However, the use of polymeric membranes has a drawback known as the permeability-selectivity tradeoff. Many approaches have been used to overcome this limitation including the use of polymer blends. Polymer blending technology synergistically combines the favorable properties of different polymers like high gas permeability and high selectivity, which are difficult to attain with a single polymer. During polymer mixing, polymers tend to uncontrollably phase separate due to unfavorable thermodynamics, which limits the number of completely miscible polymer combinations for gas separations. Therefore, compatibilizers are used to control the phase separation and to obtain stable membrane morphologies, while improving the mechanical properties. In this review, we focus on immiscible polymer blends and the use of compatibilizers for gas separation applications. PMID:28773766

  8. A Blended Learning Framework for Curriculum Design and Professional Development

    ERIC Educational Resources Information Center

    Mirriahi, Negin; Alonzo, Dennis; Fox, Bob

    2015-01-01

    The need for flexibility in learning and the affordances of technology provided the impetus for the rise of blended learning (BL) globally across higher education institutions. However, the adoption of BL practices continues at a low pace due to academics' low digital fluency, various views and BL definitions, and limited standards-based tools to…

  9. A Blended Learning Framework for Curriculum Design and Professional Development

    ERIC Educational Resources Information Center

    Mirriahi, Negin; Alonzo, Dennis; Fox, Bob

    2015-01-01

    The need for flexibility in learning and the affordances of technology provided the impetus for the rise of blended learning (BL) globally across higher education institutions. However, the adoption of BL practices continues at a low pace due to academics' low digital fluency, various views and BL definitions, and limited standards-based tools to…

  10. Automated Propellant Blending

    NASA Technical Reports Server (NTRS)

    Hohmann, Carl W. (Inventor); Harrington, Douglas W. (Inventor); Dutton, Maureen L. (Inventor); Tipton, Billy Charles, Jr. (Inventor); Bacak, James W. (Inventor); Salazar, Frank (Inventor)

    2000-01-01

    An automated propellant blending apparatus and method that uses closely metered addition of countersolvent to a binder solution with propellant particles dispersed therein to precisely control binder precipitation and particle aggregation is discussed. A profile of binder precipitation versus countersolvent-solvent ratio is established empirically and used in a computer algorithm to establish countersolvent addition parameters near the cloud point for controlling the transition of properties of the binder during agglomeration and finishing of the propellant composition particles. The system is remotely operated by computer for safety, reliability and improved product properties, and also increases product output.

  11. Automated Propellant Blending

    NASA Technical Reports Server (NTRS)

    Hohmann, Carl W. (Inventor); Harrington, Douglas W. (Inventor); Dutton, Maureen L. (Inventor); Tipton, Billy Charles, Jr. (Inventor); Bacak, James W. (Inventor); Salazar, Frank (Inventor)

    1999-01-01

    An automated propellant blending apparatus and method uses closely metered addition of countersolvent to a binder solution with propellant particles dispersed therein to precisely control binder precipitation and particle aggregation. A profile of binder precipitation versus countersolvent-solvent ratio is established empirically and used in a computer algorithm to establish countersolvent addition parameters near the cloud point for controlling the transition of properties of the binder during agglomeration and finishing of the propellant composition particles. The system is remotely operated by computer for safety, reliability and improved product properties, and also increases product output.

  12. A new low-cost ultrasonic and meteorological sensor for observation of snow hydrological processes

    NASA Astrophysics Data System (ADS)

    Weiler, M.; Pohl, S.; Garvelmann, J.; Wawerla, J.

    2012-04-01

    The high spatial and temporal dynamics of snow accumulation and melt is generally difficult to capture. Instrumental methods have been developed to capture snow height in combination with meteorological variables, however, these stations are usually quite expensive and only few locations can be instrumented. In order to capture the dynamics due to different elevations, aspects, vegetation cover, and snow redistribution, a low-cost station network is needed that focuses on snow processes and can be set up in rugged environments. We developed a digital-based sensor with low power consumption that can be easily deployed and can collect data up to 6 month. Data collected by the sensors include: snow height, air temperature and humidity, surface (snow) temperature, liquid precipitation, global radiation, and wind speed. In addition, the sensor can be upgraded to take a digital picture of the environment for time-lapse photography. The bus system of the sensor is built to allow GPSR modem access in future. We successfully compared the system with standard, high-cost meteorological measurements and already deployed over 50 stations in three watersheds in the Black Forest, Germany. We also successfully use the sensor for water level measurements in streams and other applications are certainly possible.

  13. Microbes in High Arctic Snow and Implications for the Cold Biosphere ▿ †

    PubMed Central

    Harding, Tommy; Jungblut, Anne D.; Lovejoy, Connie; Vincent, Warwick F.

    2011-01-01

    We applied molecular, microscopic, and culture techniques to characterize the microbial communities in snow and air at remote sites in the Canadian High Arctic (Ward Hunt Island, Ellesmere Island, and Cornwallis Island, latitudes 74 to 83oN). Members of the Bacteria and Eukarya were prevalent in the snow, and their small subunit (SSU) rRNA gene signatures indicated strong local aerial transport within the region over the preceding 8 months of winter snowpack accumulation. Many of the operational taxonomic units (OTUs) were similar to previously reported SSU rRNA gene sequences from the Arctic Ocean, suggesting the importance of local aerial transport processes for marine microbiota. More than 47% of the cyanobacterial OTUs in the snow have been previously found in microbial mats in the region, indicating that this group was also substantially derived from local sources. Viable cyanobacteria isolated from the snow indicated free exchange between the snow and adjacent mat communities. Other sequences were most similar to those found outside the Canadian Arctic but were from snow, lake and sea ice, glaciers and permafrost, alpine regions, Antarctica, and other regions of the Arctic, supporting the concept of global distribution of microbial ecotypes throughout the cold biosphere. PMID:21460114

  14. A new snow parameterization for the Météo-France climate model

    NASA Astrophysics Data System (ADS)

    Douville, H.; Royer, J.-F.; Mahfouf, J.-F.

    1995-11-01

    Both observational and numerical studies demonstrate the sensitivity of the atmosphere to variations in the extent and mass of snow cover. There is therefore a need for simple but realistic snow parameterizations in forecast and climate models. A new snow hydrology scheme has recently been developed at Météo-France for use in the ARPEGE climate model and has been successfully tested against local field measurements in stand-alone experiments. This study describes the global validation of the parameterization in a 3-year integration for the present-day climate within the T42L30 version of ARPEGE. Results are compared with those from a control simulation and with available observed climatologies, in order to assess the impact of the new snow parameterization on the simulated surface climate. The seasonal cycle of the Northern Hemisphere snow cover is clearly improved when using the new scheme. The snow pack is still slightly overestimated in winter, but its poleward retreat is better reproduced during the melting season. As a consequence, the modified GCM performs well in simulating the springtime continental heating, which may play a strong role in the simulation of the Asian summer monsoon.

  15. Combining Passive Microwave and Optical Data to Estimate Snow Water Equivalent in Afghanistan's Hindu Kush

    NASA Astrophysics Data System (ADS)

    Dozier, J.; Bair, N.; Calfa, A. A.; Skalka, C.; Tolle, K.; Bongard, J.

    2015-12-01

    The task is to estimate spatiotemporally distributed estimates of snow water equivalent (SWE) in snow-dominated mountain environments, including those that lack on-the-ground measurements such as the Hindu Kush range in Afghanistan. During the snow season, we can use two measurements: (1) passive microwave estimates of SWE, which generally underestimate in the mountains; (2) fractional snow-covered area from MODIS. Once the snow has melted, we can reconstruct the accumulated SWE back to the last significant snowfall by calculating the energy used in melt. The reconstructed SWE values provide a training set for predictions from the passive microwave SWE and snow-covered area. We examine several machine learning methods—regression-boosted decision trees, bagged trees, neural networks, and genetic programming—to estimate SWE. All methods work reasonably well, with R2 values greater than 0.8. Predictors built with multiple years of data reduce the bias that usually appears if we predict one year from just one other year's training set. Genetic programming tends to produce results that additionally provide physical insight. Adding precipitation estimates from the Global Precipitation Measurements mission is in progress.

  16. The Potential for Snow to Supply Human Water Demand in the Present and Future

    NASA Technical Reports Server (NTRS)

    Mankin, Justin S.; Viviroli, Daniel; Singh, Deepti; Hoekstra, Arjen Y.; Diffenbaugh, Noah S.

    2015-01-01

    Runoff from snowmelt is regarded as a vital water source for people and ecosystems throughout the Northern Hemisphere (NH). Numerous studies point to the threat global warming poses to the timing and magnitude of snow accumulation and melt. But analyses focused on snow supply do not show where changes to snowmelt runoff are likely to present the most pressing adaptation challenges, given sub-annual patterns of human water consumption and water availability from rainfall. We identify the NH basins where present spring and summer snowmelt has the greatest potential to supply the human water demand that would otherwise be unmet by instantaneous rainfall runoff. Using a multi-model ensemble of climate change projections, we find that these basins - which together have a present population of approx. 2 billion people - are exposed to a 67% risk of decreased snow supply this coming century. Further, in the multi-model mean, 68 basins (with a present population of more than 300 million people) transition from having sufficient rainfall runoff to meet all present human water demand to having insufficient rainfall runoff. However, internal climate variability creates irreducible uncertainty in the project