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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.; Choudhury, Bhaskar

    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. Monitoring global snow cover

    NASA Technical Reports Server (NTRS)

    Armstrong, Richard; Hardman, Molly

    1991-01-01

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

  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. Blending satellite-based snow depth products with in situ observations for streamflow predictions in the Upper Colorado River Basin

    NASA Astrophysics Data System (ADS)

    Liu, Yuqiong; Peters-Lidard, Christa D.; Kumar, Sujay V.; Arsenault, Kristi R.; Mocko, David M.

    2015-02-01

    In snowmelt-driven river systems, it is critical to enable reliable predictions of the spatiotemporal variability in seasonal snowpack to support local and regional water management. Previous studies have shown that assimilating satellite-station blended snow depth data sets can lead to improved snow predictions, which however do not always translate into improved streamflow predictions, especially in complex mountain regions. In this study, we explore how an existing optimal interpolation-based blending strategy can be enhanced to reduce biases in satellite snow depth products for improving streamflow predictions. Two major new considerations are explored, including: (1) incorporating terrain aspect and (2) incorporating areal snow coverage information. The methodology is applied to the bias reduction of the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth estimates, which are then assimilated into the Noah land surface model via the ensemble Kalman Filtering (EnKF) for streamflow predictions in the Upper Colorado River Basin. Our results indicate that using only observations from low-elevation stations such as the Global Historical Climatology Network (GHCN) in the bias correction can lead to underestimation in streamflow, while using observations from high-elevation stations (e.g., the Snow Telemetry (SNOTEL) network) along with terrain aspect is critically important for achieving reliable streamflow predictions. Additionally incorporating areal snow coverage information from the Moderate Resolution Imaging Spectroradiometer (MODIS) can slightly improve the streamflow results further.

  5. Importance of snow to global precipitation

    NASA Astrophysics Data System (ADS)

    Field, P. R.; Heymsfield, A. J.

    2015-11-01

    Precipitation controls the availability of drinking water and viability of the land to support agriculture. Failure to accurately predict the location, magnitude, and frequency of precipitation impacts not only numerical weather forecasting but also climate modeling. It has been proposed that most rainfall events originate from ice that has melted to form rain. Here we use remote sensing from spaceborne cloud radar to quantify that idea. A new metric is constructed to quantify the fraction of rain events at the surface that are linked to snow melting at a higher altitude. CloudSat is used to show the global variation of the importance of snow in the precipitation process. In the tropics, subtropics, midlatitude and polar regions 0.3, 0.4, 0.8, and >0.9, respectively, of all precipitation events (>1 mm/d) are linked to the production of snow in clouds.

  6. 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.

  7. 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

  8. 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.

  9. Simulation of black carbon in snow and effects on snow albedo in the Canadian Global Climate Model

    NASA Astrophysics Data System (ADS)

    Namazi, M.; von Salzen, K.; Cole, J. N.

    2013-12-01

    Black carbon (BC) aerosol forms through the incomplete combustion of fossil fuels, biofuel, and biomass. BC plays an essential role in the Earth's climate through absorption of solar radiation in the air and by snow. We developed a new physically based parameterization of BC concentration in snow by considering deposition rates of black carbon and snow, properties of the snow layer, and scavenging of BC through melting. The parameterization was implemented in the latest version of the Canadian Global Climate Model, CanAM4. Simulated results for BC snow mixing ratio are in good agreement with measurements reported in recent studies. Furthermore, we investigate effects of BC in snow on snow albedo and radiative forcing. We validate and compare our model results with other studies.

  10. 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. PMID:17838886

  11. 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.

  12. 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.

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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. PMID:24533080

  19. Preliminary snow products from the Global Change Observation Mission (GCOM) AMSR2 instrument

    NASA Astrophysics Data System (ADS)

    Lee, Y.; Kongoli, C.; Key, J.

    2013-12-01

    Preliminary snow products will be presented based on the Advanced Microwave Scanning Radiometer 2 (AMSR2) measurements. The AMSR2 instrument was launched on May 18, 2012 onboard the Global Change Observation Mission 1st - Water "SHIZUKU" (GCOM-W1) satellite. It has the heritage of microwave measurements of the Scanning Multi-channel Microwave Radiometer (SMMR), the Special Sensor Microwave Imager (SSM/I), and the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) for snow measurements in near all sky conditions. This study will describe the suite of AMSR2 snow cover detection, snow depth, and snow water equivalent (SWE) algorithms being developed for operational use by NOAA. The snow cover detection algorithm utilizes the brightness temperature based decision tree approach (Grody, 1991; Grody and Basist, 1996) with additional climatology tests as enhancements. The snow depth algorithm is based on the dynamic empirical approach (Kelly, 2009). The SWE is calculated by applying the snow density lookup table (Brown and Mote, 2009) for the global seasonal snow classification (Sturm et al. 1995) to the snow depth.

  20. 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+).

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

    NASA Astrophysics Data System (ADS)

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

    2013-04-01

    Snow accumulation is critical for water availability in the Northern Hemisphere, raising concern that global warming could have important impacts on natural and human systems in snow-dependent regions. Although regional hydrologic changes have been observed (for example, refs , , , ), the time of emergence of extreme changes in snow accumulation and melt remains a key unknown for assessing climate-change impacts. 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 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.

  2. 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

  3. 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. PMID:24015153

  4. Snow characterization at a global scale with passive microwave satellite observations

    NASA Astrophysics Data System (ADS)

    Cordisco, E.; Prigent, C.; Aires, F.

    2006-10-01

    The sensitivity of passive microwave satellite observations to snow characteristics is evaluated, between 19 and 85 GHz, for a winter season, for the Northern Hemisphere. The surface emissivities derived from the Special Sensor Microwave/Imager measurements are systematically compared with in situ snow measurements at 2784 stations, in North America and Eurasia. In addition, coincident satellite responses from active microwave sensors (ERS scatterometer) and visible observations (AVHRR) are also analyzed. Vegetation interferes with the signal that is received by the satellites. Snow emissivities also react to scattering by the snow grain growth that is related to the snow metamorphism during the winter. This phenomenon increases with frequency and is already very sensitive at 37 GHz. Passive microwaves at high frequency (85 GHz) are very sensitive to the presence of snow on the ground, even for very low snow depth. None of the tested satellite measurements is well correlated to the snow depth at a global scale, making snow depth retrieval from these observations very difficult on a global basis. The sensitivity of the satellite observations to snow characteristics depends on local conditions. To partly alleviate these difficulties, a neural network inversion scheme based on local statistics is developed to combine satellite observations, in situ measurements, and land surface model outputs. The combination of different wavelengths partly limits the ambiguities related to the individual sensitivity of each satellite observation to the various sources of variabilities. The final retrieval algorithm is compatible with an assimilation strategy that would better constrain the behavior of surface models. Finally, a clustering algorithm is applied to the suite of satellite observations and clearly shows a strong sensitivity to the snow characteristics and metamorphism during the winter. Characterization of the snowpack using satellite observation classification can yield

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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…

  10. 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

  11. 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. PMID:27297721

  12. 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.

  13. 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.

  14. Beyond peak water storage? A global estimate of declining water storage in reservoirs and snow packs

    NASA Astrophysics Data System (ADS)

    Wisser, D.; Frolking, S.; Wada, Y.; Bierkens, M. F.

    2012-12-01

    Water storage is one of the primary mechanisms for coping with increasing variability of water supply and demand that can be expected with growing population and a changing climate. Man-made reservoirs can currently store about 15% of the global annual runoff. A similar amount of water is stored in one of the most important natural storage components - seasonal snow packs. The amount of water stored in each of those man-made and natural systems is roughly equivalent to the total annual anthropogenic water withdrawals. Storage in seasonal snow packs is declining as a result of climate-driven changes in snowfall and snowmelt. At the same time, reservoir storage is declining as a result of sedimentation and limited construction of new reservoirs. We use a global hydrological model, combined with a global data set of ~6000 large reservoirs to simulate changes in reservoir and snow pack water storage and analyze impacts of those changes on seasonal water availability using a set of scenarios for changing climate conditions. Reservoir sedimentation is simulated using global erosion and sedimentation data sets and validated with observed reservoir storage loss. Results indicate annual loss rates between 0.5 and 1.0% of the installed capacity for most reservoirs, outpacing the storage increases through the construction of new reservoirs for the last decades so that reservoir storage is declining globally. With most reservoirs being about 50 years old, these losses threaten the sustainability of reservoir operation and can pose significant challenges to water resources management. Similarly, seasonal snow storage is declining at about 0.5% per year for the last 20 years. Even without changes in the magnitude of total precipitation, there can be significant changes in basin hydrology if there are climate-driven changes in snowfall and snowmelt, potentially away from the period (summer) when demand for irrigation, water supply, or hydropower production is high. These shifts

  15. Theoretical Accuracy of Global Snow-Cover Mapping Using Satellite Data in the Earth Observing System (EOS) Era

    NASA Technical Reports Server (NTRS)

    Hall, D. K.; Foster, J. L.; Salomonson, V. V.; Klein, A. G.; Chien, J. Y. L.

    1998-01-01

    Following the launch of the Earth Observing System first morning (EOS-AM1) satellite, daily, global snow-cover mapping will be performed automatically at a spatial resolution of 500 m, cloud-cover permitting, using Moderate Resolution Imaging Spectroradiometer (MODIS) data. A technique to calculate theoretical accuracy of the MODIS-derived snow maps is presented. Field studies demonstrate that under cloud-free conditions when snow cover is complete, snow-mapping errors are small (less than 1%) in all land covers studied except forests where errors are greater and more variable. The theoretical accuracy of MODIS snow-cover maps is largely determined by percent forest cover north of the snowline. Using the 17-class International Geosphere-Biosphere Program (IGBP) land-cover maps of North America and Eurasia, the Northern Hemisphere is classified into seven land-cover classes and water. Snow-mapping errors estimated for each of the seven land-cover classes are extrapolated to the entire Northern Hemisphere for areas north of the average continental snowline for each month. Average monthly errors for the Northern Hemisphere are expected to range from 5 - 10%, and the theoretical accuracy of the future global snow-cover maps is 92% or higher. Error estimates will be refined after the first full year that MODIS data are available.

  16. 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. PMID:22054697

  17. 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.

    2014-12-01

    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. Here we employ global sensitivity analysis to explore how different error types (i.e., bias, random errors), different error distributions, and different error magnitudes influence physically based simulations of four snow variables (snow water equivalent, ablation rates, snow disappearance, and sublimation). We use Sobol' global sensitivity analysis, which is typically used for model parameters, but adapted here for testing model sensitivity to co-existing errors in all forcings. We quantify the Utah Energy Balance model's sensitivity to forcing errors with 1 520 000 Monte Carlo simulations across four sites and four different scenarios. Model outputs were generally (1) more sensitive to forcing biases than random errors, (2) less sensitive to forcing error distributions, and (3) sensitive to different forcings depending on the relative magnitude of errors. For typical error magnitudes, precipitation bias was the most important factor for snow water equivalent, ablation rates, and snow disappearance timing, but other forcings had a significant impact depending on forcing error magnitudes. 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.

  18. Performance of and Uncertainties in the Global Precipitation Measurement (GPM) Microwave Imager Retrieval Algorithm for Falling Snow Estimates

    NASA Astrophysics Data System (ADS)

    Skofronick Jackson, G.; Munchak, S. J.; Johnson, B. T.

    2014-12-01

    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 early post-launch testing of retrieval algorithms for the 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. Throughout 2014, the at-launch GMI precipitation algorithms, based on a Bayesian framework, have been used with the new GPM data. 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. 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 values and also updated Bayesian channel weights for various surface types. We will evaluate the algorithm that was released to the public in July 2014 and has already shown that it can detect and estimate falling snow. Performance factors to be investigated include the ability to detect falling snow at various rates, causes of errors, and performance for various surface types. A major source of ground validation data will be the NOAA NMQ dataset. We will also provide qualitative information on known uncertainties and errors associated with both the satellite retrievals and the ground validation measurements. We will report on the analysis of our falling snow validation completed by the time of the AGU conference.

  19. 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.

  20. 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.

  1. 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).

  2. 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

  3. 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.

  4. 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

  5. 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. PMID:15015542

  6. The impact of snow nitrate photolysis on boundary layer chemistry and the recycling and redistribution of reactive nitrogen across Antarctica and Greenland in a global chemical transport model

    NASA Astrophysics Data System (ADS)

    Zatko, Maria; Geng, Lei; Alexander, Becky; Sofen, Eric; Klein, Katarina

    2016-03-01

    The formation and recycling of reactive nitrogen (NO, NO2, HONO) at the air-snow interface has implications for air quality and the oxidation capacity of the atmosphere in snow-covered regions. Nitrate (NO3-) photolysis in snow provides a source of oxidants (e.g., hydroxyl radical) and oxidant precursors (e.g., nitrogen oxides) to the overlying boundary layer, and alters the concentration and isotopic (e.g., δ15N) signature of NO3- preserved in ice cores. We have incorporated an idealized snowpack with a NO3- photolysis parameterization into a global chemical transport model (Goddard Earth Observing System (GEOS) Chemistry model, GEOS-Chem) to examine the implications of snow NO3- photolysis for boundary layer chemistry, the recycling and redistribution of reactive nitrogen, and the preservation of ice-core NO3- in ice cores across Antarctica and Greenland, where observations of these parameters over large spatial scales are difficult to obtain. A major goal of this study is to examine the influence of meteorological parameters and chemical, optical, and physical snow properties on the magnitudes and spatial patterns of snow-sourced NOx fluxes and the recycling and redistribution of reactive nitrogen across Antarctica and Greenland. Snow-sourced NOx fluxes are most influenced by temperature-dependent quantum yields of NO3- photolysis, photolabile NO3- concentrations in snow, and concentrations of light-absorbing impurities (LAIs) in snow. Despite very different assumptions about snowpack properties, the range of model-calculated snow-sourced NOx fluxes are similar in Greenland (0.5-11 × 108 molec cm-2 s-1) and Antarctica (0.01-6.4 × 108 molec cm-2 s-1) due to the opposing effects of higher concentrations of both photolabile NO3- and LAIs in Greenland compared to Antarctica. Despite the similarity in snow-sourced NOx fluxes, these fluxes lead to smaller factor increases in mean austral summer boundary layer mixing ratios of total nitrate (HNO3+ NO3-), NOx, OH

  7. Snow and glacier change in koshi Basin Himalaya and its response to global warming

    NASA Astrophysics Data System (ADS)

    Gao, Y.; Yang, X.; Yao, T.; Yufeng, D.

    2010-12-01

    Recently, the argument that Himalayan glaciers will completely melt is rather controversial and the U.N.'s leading panel on climate change has apologized for misleading data published in a 2007 report that warned Himalayan glaciers could melt by 2035. Why the gradual melting of Himalayan glaciers makes most of the major media headlines? This is because Himalayan glacier is the headstream of major rivers in South Asia and Southeast Asia and more than 1/6 people live there. If mass of the glaciers melt or even disappear, people who rely on those rivers will be at risk. After this dispute, we need to realize that:”Although the melting rate still need to further study, the Himalayan glaciers are indeed melting. And in these areas, there are more uncertainties to affect water resource, such as snow fall, precipitation, regional temperature changes and so on”. Koshi Basin Himalaya, located in the boundary between China and Nepal, consist of three rivers i.e. Sun Koshi, Arun river (the headwaters of arun river in China called Pengqu) and Tamur. All of them converge to India Ganga River. The total area of Koshi Basin is about ~57,870 km2 and elevation ranges from 21 m (plain) to 8825m (Mountain glacier). This basin has the typical vertical zonation of Himalaya, so we choose it as the study area. Based on the snow cover data observed by the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Terra spacecraft from 2000-2010, the spatial-temporal distribution and variation of snow cover over the koshi basin are statistical analyzed. Glacier changes are also detected from Landsat images in 2000, 2005 and 2010. It is found that snow cover areas are mainly concentrated in the Ridge of Himalaya Mountain. And there are more persistently snow covered areas and glaciers in the South Slope of Himalaya Mountain with aspect to the North Slope, although the mean elevation of the North Slope is higher than south slope. During the decade of 2000-2010, a slight decreasing

  8. A Distributed Snow Evolution Modeling System (SnowModel)

    NASA Astrophysics Data System (ADS)

    Liston, G. E.; Elder, K.

    2004-12-01

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

  9. 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.

  10. 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

    2016-01-01

    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

  11. 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

  12. 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

  13. The impact of snow nitrate photolysis on boundary layer chemistry and the recycling and redistribution of reactive nitrogen across Antarctica in a global chemical transport model

    NASA Astrophysics Data System (ADS)

    Zatko, M. C.; Geng, L.; Alexander, B.; Sofen, E. D.; Klein, K.

    2015-07-01

    The formation and recycling of reactive nitrogen (NO, NO2, HONO) at the air-snow interface has implications for air quality and the oxidation capacity of the atmosphere in snow-covered regions. Nitrate(NO3-) photolysis in snow provides a source of oxidants (e.g., hydroxyl radical, ozone) and oxidant precursors (e.g., nitrogen oxides) to the overlying boundary layer, and disturbs the preservation of NO3- in ice cores. We have incorporated the photolysis of Antarctic snow NO3- into a global chemical transport model (GEOS-Chem) to examine the implications of snow NO3- photolysis for boundary layer chemistry, the recycling and redistribution of reactive nitrogen across the Antarctic continent, and the preservation of ice-core NO3- in Antarctic ice cores. The calculated potential flux of snow-sourced NOx in Antarctica (0.5-7.8 × 108 molec cm-2 s-1) and calculated e-folding depths of UV actinic flux in snowpack (24-69 cm) are comparable to observations. Snow-sourced NOx increases mean austral summer boundary layer mixing ratios of total nitrate (HNO3 + NO3-), NOx, OH, and O3 in Antarctica by a factor of up to 32, 38, 7, and 2, respectively, in the model. Model results also suggest that NO3- can be recycled between the air and snow multiple times and that NO3- can remain in the snow photic zone for at least 7.5 years on the East Antarctic plateau. The fraction of photolysis-driven loss of NO3- from the snow is ∼ 0.99 on the East Antarctic plateau, while areas of wind convergence (e.g., over the Ronne Ice Shelf) have a net gain of NO3- due to redistribution of snow-sourced reactive nitrogen across the Antarctic continent. The modeled enrichment in ice-core δ 15N(NO3-) due to photolysis-driven loss of snow NO3- ranges from 0 to 363 ‰ and the magnitudes of the spatial trends are consistent with δ 15N(NO3-) observations, suggesting that the spatial variability in snow δ 15N(NO3-) across the Antarctic continent is determined mainly by the degree of photolysis

  14. 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

  15. Appalachia Snow

    Atmospheric Science Data Center

    2014-05-15

    ... on December 4 and 5, 2002, also brought the season's first snow to parts of the south and southern Appalachia. The extent of snow cover over central Kentucky, eastern Tennessee, western North Carolina and ...

  16. 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.

  17. Snow surface temperature, radiative forcing and snow depth as determinants of snow density

    NASA Astrophysics Data System (ADS)

    Kirchner, P. B.; Painter, T. H.; Skiles, M.; Deems, J. S.

    2014-12-01

    Watershed scale observations of snow water equivalence (SWE) are becoming increasingly important globally as the quantity and timing of snowmelt has become less predictable. In the Colorado River watershed, where dust deposition can hasten snowmelt by several weeks, the need for these observations is critical. While advances in measuring snow depth and albedo from the NASA Airborne Snow Observatory have greatly improved our ability to constrain snow depth and radiative forcing, we have yet to develop a method for remotely observing snow density, which is required for calculating SWE. We evaluate measured and modeled variables of snow- infrared surface temperature, radiative forcing and snow depth as predictors of snow density. We use 10 seasons of in situ measured snow surface temperature, cumulative modeled dust in snow radiative forcing, snow depth and manually measured snow density from locations in the Rocky Mountains of southwestern Colorado. We also use measured snow depth and SWE from the 2013 and 2014 water years, from 23-35 locations stratified by modeled downwelling short wave radiation, and evaluate them as predictors of snow density. Our analysis shows that daily mean snow surface temperature (R2 0.61, p = <0.001) and cumulative radiative forcing (R2 0.54, p = <0.001) individually have significant coefficients of determination whereas snow depth alone was not significant. Multiple regression with all three variables (R2 0.84, p = <0.001) was the best predictor of density. Furthermore, when snowpack conditions were isothermal at 0° C, the diurnal coefficient of variation, of measured hourly surface temperature, exhibited consistently high variance. In 2013 we found significant correlations between spatially distributed measurements of snow density (R2 0.33, p = <0.001) and modeled downwelling short wave radiation. However, in 2014 the correlation was very low, supporting our hypothesis that seasonal differences in dust driven radiative forcing are also

  18. 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.

  19. 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.

  20. 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.

  1. 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…

  2. 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.

  3. 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.

  4. SnowClim: Snow climate monitoring for Europe

    NASA Astrophysics Data System (ADS)

    Bissolli, P.; Maier, U.

    2009-09-01

    Snow cover, particularly its depth and its frequency, is a very essential climate element. It influences the earth's surface radiation budget considerably due to its reflectivity properties and also it has large impact on economy and daily life (e.g. traffic, tourism). Although a lot of research and many national activities of snow monitoring have been done, there are very few products describing an integrated snow monitoring for whole Europe. In the light of a foreseen future Regional Climate Centre on Climate Monitoring (RCC-CM), the German Meteorological Service (Deutscher Wetterdienst, DWD) has established some first operational snow climate monitoring activities for the WMO Region VI (Europe and the Middle East). First selected key elements are the number of snowdays with a snow cover > 1 cm, the mean and the maximum snow depth per month. Results are presented in form of monthly and climatological maps, tables and diagrams of time series starting in 1981. Data presently are taken from observations at synoptical stations, received by the Global Telecommunication System. A first quality control based on threshold tests has been developed. The snow climate monitoring products are currently under further development. New evaluations will be carried out also on a daily data basis, additional satellite data, and also the quality control procedure will be extended. Some operational SnowClim products are available on the DWD web site: www.dwd.de/snowclim

  5. 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

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

    PubMed

    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

  7. Challenges in simulation of snow microstructure and implications for remote sensing of snow mass

    NASA Astrophysics Data System (ADS)

    Sandells, M. J.; Essery, R.; Leppänen, L.; Lemmetyinen, J.; Rutter, N.

    2014-12-01

    One of the greatest challenges for global measurement of snow mass is quantification of the snow microstructure. Radiative transfer models are more sensitive to the snow structure metrics used than to snow depth, so microstructure must be well quantified in order to retrieve snow mass from satellite observations. Principles of physics have been used to simulate microstructure in many years of avalanche and climate research, although these have different accuracy requirements to remote sensing applications. Growth of snow crystals is dependent primarily on the snow temperature gradient, but also the temperature and density of the snow. Forced with the same meteorological data, different models simulate different snow temperatures. Even with the same grain growth assumptions, this leads to different rates of microstructure evolution. This must be taken into consideration if snow models are to be used to give the necessary parameters for retrieval of snow water equivalent. The JULES Investigation Model snow model (JIM) has a highly configurable structure that allows different layering assumptions to be used. It incorporates all major components from existing snow models, which enables the simulation of an ensemble of 1701 members. JIM was used to quantify the impact of different model parameterizations such as snow compaction and thermal conductivity on simulated microstructure (previously referred to as 'grain size') for each of four different grain size parameterizations from the Crocus, MOSES, SNICAR and SNTHERM models. The Helsinki University of Technology snow microwave emission model was then used to demonstrate the impact of different snow model assumptions on the simulation of microwave brightness temperature. This paper discusses potential snow mass retrieval errors due to uncertainties in snow parameters from snow evolution models, and how these may be mitigated through techniques such as data assimilation.

  8. 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

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. Respective influence of global pollution and volcanic eruptions on the past variations of the trace metals content of Antarctic snows since 1880's

    SciTech Connect

    Boutron, C.

    1980-01-01

    Forty-eight successive dated snow samples collected in central east Antarctica covering a continuous time sequence between 1880's and 1977 have been analysed for Na, Mg, K, Ca, Fe, Al, Mn, Pb, Cd, Cu, Zn, and Ag in ultraclean conditions. For all the elements, both the concentrations and the enrichment factors observed in 1977 are very close to the ones observed 100 years ago, then confirming that the influence of global pollution is actually negligible for these elements in the remote areas of the southern hemisphere. During the last 100 years, the variations of the enrichment factors of Pb and Zn are shown to parallel rather well those of the global volcanic activity, which suggests that the high atmospheric enrichments observed presently for these two metals are likely linked with volcanism. Between 1912 and 1916 (+ or -5 years), there is a very strong signal showing a definite peak for the concentrations of most of the elements, it could be linked with a major Antarctic volcanic eruption.

  14. 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.

  15. 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

  16. 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.

  17. 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.

  18. 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.

  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. Snow particle speeds in drifting snow

    NASA Astrophysics Data System (ADS)

    Nishimura, Kouichi; Yokoyama, Chika; Ito, Yoichi; Nemoto, Masaki; Naaim-Bouvet, Florence; Bellot, Hervé; Fujita, Koji

    2014-08-01

    Knowledge of snow particle speeds is necessary for deepening our understanding of the internal structures of drifting snow. In this study, we utilized a snow particle counter (SPC) developed to observe snow particle size distributions and snow mass flux. Using high-frequency signals from the SPC transducer, we obtained the sizes of individual particles and their durations in the sampling area. Measurements were first conducted in the field, with more precise measurements being obtained in a boundary layer established in a cold wind tunnel. The obtained results were compared with the results of a numerical analysis. Data on snow particle speeds, vertical velocity profiles, and their dependence on wind speed obtained in the field and in the wind tunnel experiments were in good agreement: both snow particle speed and wind speed increased with height, and the former was always 1 to 2 m s-1 less than the latter below a height of 1 m. Thus, we succeeded in obtaining snow particle speeds in drifting snow, as well as revealing the dependence of particle speed on both grain size and wind speed. The results were verified by similar trends observed using random flight simulations. However, the difference between the particle speed and the wind speed in the simulations was much greater than that observed under real conditions. Snow transport by wind is an aeolian process. Thus, the findings presented here should be also applicable to other geophysical processes relating to the aeolian transport of particles, such as blown sand and soil.

  2. 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.

  3. 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

  4. 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

  5. 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)

  6. 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)

  7. Remote sensing of snow

    NASA Technical Reports Server (NTRS)

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

    1987-01-01

    The snow parameters affecting sensor responses at different wavelengths are discussed. The effects of snow depth and background radiation on gamma ray sensors and of crystal size, contaminants, snow depth, liquid water, and surface roughness on visible and near-infrared sensors are considered. The influence of temperature, crystal size, and liquid water on thermal infrared sensors and of liquid water, crystal size, water equivalent depth, stratification, snow surface roughness, density, temperature, and soil condition on microwave sensors are addressed.

  8. 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 banks. When…

  9. 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.

  10. 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.

  11. Land-atmosphere coupling associated with snow cover

    NASA Astrophysics Data System (ADS)

    Dutra, Emanuel; Schär, Christoph; Viterbo, Pedro; Miranda, Pedro M. A.

    2011-08-01

    This study investigates the role of interannual snow cover variability in controlling the land-atmosphere coupling and its relation with near surface (T2M) and soil temperature (STL1). Global atmospheric simulations are carried out with the EC-EARTH climate model using climatological sea surface temperature and sea ice distributions. Snow climatology, derived from a control run (COUP), is used to replace snow evolution in the snow-uncoupled simulation (UNCOUP). The snow cover and depth variability explains almost 60% of the winter T2M variability in predominantly snow-covered regions. During spring the differences in interannual variability of T2M are more restricted to the snow line regions. The variability of soil temperature is also damped in UNCOUP. However, there are regions with a pronounced signal in STL1 with no counterpart in T2M. These regions are characterized by a significant interannual variability in snow depth, rather than snow cover (almost fully snow covered during winter). These results highlight the importance of both snow cover and snow depth in decoupling the soil temperature evolution from the overlying atmosphere.

  12. 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.

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

    ScienceCinema

    Barry, Roger G.

    2009-09-01

    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.

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

    SciTech Connect

    Barry, Roger G.

    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.

  15. 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…

  16. 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…

  17. 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

  18. 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

  19. [Hyperspectral remote sensing estimation models for snow grain size].

    PubMed

    Wang, Jian-Geng; Feng, Xue-Zhi; Xiao, Peng-Feng; Liang, Ji; Zhang, Xue-Liang; Li, Hai-Xing; Li, Yun

    2013-01-01

    Snow grain size is a key parameter not only to affect the energy budget of the global or local region but also characterizing the status of snow vapor transport and temperature gradient. It is significant to monitor and estimate the snow grain size in large area for global or local climate change and water resource management. Recently, remote sensing technology has become a useful tool for snow grain size monitoring and estimating. In the present paper, the estimate models were built based on simulating the snow surface spectral reflectance curve in visible-infrared region and the sensitive bands and snow indices for snow grain size were selected. These models help estimate snow grain size by hyperspectral remote sensing. Through validating with ground true data, the results show that these models have higher explorative accuracy using 1 030, 1 260 nm and normalized difference snow index (460 and 1 030 nm). In addition, the correlation slopes of estimated and observed valves are 1.37, 0.61 and 0.62, respectively. R2 are 0.82, 0.86 and 0.93 and RMSE are 55.65, 50.83 and 35.91 microm, respectively. The result can provide a scientific basis for snow grain size monitoring and estimating. PMID:23586251

  20. 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.

  1. 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

  2. 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

  3. 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.

  4. "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…

  5. 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. PMID:23992599

  6. Loropetalum chinense 'Snow Panda'

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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...

  7. GulfSnow Peach

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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...

  8. "Snowing" Core in Earth?

    NASA Astrophysics Data System (ADS)

    Li, J.; Chen, B.; Cormier, V.; Gao, L.; Gubbins, D.; Kharlamova, S. A.; He, K.; Yang, H.

    2008-12-01

    As a planet cools, an initially molten core gradually solidifies. Solidification occurs at shallow depths in the form of "snow", if the liquidus temperature gradient of the core composition is smaller than the adiabatic temperature gradient in the core. Experimental data on the melting behavior of iron-sulfur binary system suggest that the cores of Mercury and Ganymede are probably snowing at the present time. The Martian core is predicted to snow in the future, provided that the sulfur content falls into the range of 10 to 14 weight percent. Is the Earth's core snowing? If so, what are the surface manifestations? If the Earth's core snowed in the past, how did it affect the formation of the solid inner core and the geodynamo? Here, we evaluate the likelihood and consequences of a snowing core throughout the Earth's history, on the basis of mineral physics data describing the melting behavior, equation-of-state, and thermodynamic properties of iron-rich alloys at high pressures. We discuss if snowing in the present-day Earth can reproduce the shallow gradients of compressional wave velocity above the inner-core boundary, and whether or not snowing in the early Earth may reconcile the apparent young age of the solid inner core with a long-lived geodynamo.

  9. 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

  10. 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. PMID:26408452

  11. 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).

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

    NASA Video Gallery

    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...

  13. 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…

  14. ESA Globsnow - Hemispherical Snow Extent and Snow Water Equivalent Records for Climate Research Purposes

    NASA Astrophysics Data System (ADS)

    Luojus, K.; Pulliainen, J. T.; Takala, M.; Lemmetyinen, J.; Derksen, C.; Bojkov, B. R.

    2011-12-01

    The efforts of the European Space Agency (ESA) Data User Element (DUE) funded GlobSnow project has resulted in two new global records of snow parameters intended for climate research purposes. The datasets contains satellite-retrieved information on snow extent (SE) and snow water equivalent (SWE) extending 15 and 30 years respectively. The dataset on snow extent is based on optical data of Envisat AATSR and ERS-2 ATSR-2 sensors covering Northern Hemisphere between years 1995 to 2010. The record on snow water equivalent is based on satellite-based radiometer measurements (SMMR, SSM/I and AMSR-E) combined with ground-based weather station data, starting from 1979 and extending to present day. The GlobSnow SWE product is the first satellite-based dataset of snow water equivalent information on a daily basis at a hemispherical scale for 30+ years. In addition to the SE and SWE time-series, an operational near-real time (NRT) snow information service has been implemented. The current data, including the prototype products and the used validation data are available for all interested parties through the GlobSnow www-pages (http://www.globsnow.info). Extensive algorithm evaluation efforts were carried out for the candidate SWE and SE algorithms using ground truth data gathered from Canada, Scandinavia, Russia and the Alps. The acquired evaluation results enabled the selection of the final algorithms to be utilized for the GlobSnow products. The SWE product is derived using an assimilation algorithm by FMI and the SE product is a combination of NR and SYKE developed algorithms utilizing optical data. Both algorithms showed enhanced estimation characteristics when compared with currently available existing products. Prototype SE and SWE products were released for user evaluation during November 2009 covering the years 2003-2008 for SWE and 2004-2006 for SE. The final SWE product covers the Northern Hemisphere, spanning 1979 - 2010. The SE product covers the Northern

  15. Evaluation of MODIS snow cover products in the Upper Rio Grande River Basin

    NASA Astrophysics Data System (ADS)

    Klein, A.; Barnett, A.; Lee, S.

    2003-04-01

    Snow cover is an important water resource for the Upper Rio Grande River Basin of Colorado and New Mexico. Global daily snow cover maps currently are produced from Moderate Resolution Imaging Spectroradiometer (MODIS) data and are freely distributed by the National Snow and Ice Data Center (NSIDC). The classification accuracy of these daily snow maps was assessed by comparing MODIS snow cover maps with operational snow cover maps produced by the National Operational Hydrologic Remote Sensing Center (NOHRSC) and against in situ SNOTEL (Snowpack Telemetry) measurements for the 2000-2001 snow season. For days during the 2000-2001 snow season where both MODIS and NOHRSC snow maps exist the overall snow classification agreement is 86 percent. Comparisons of both snow maps against in situ SNOTEL measurements for the same days indicate snow mapping accuracies of 94 percent and 76 percent for MODIS and NOHRSC, respectively. A lengthened comparison of MODIS against SNOTEL measurements which increases the number of comparisons for snow-free conditions indicates a slightly lower overall classification accuracy of 88 percent. In addition, assessment of the usefulness of these two snow cover products as inputs into the Snowmelt Runoff Model (SRM) for streamflow prediction in the Upper Rio Grande River Basin is ongoing.

  16. 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. PMID:27254100

  17. 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

  18. 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).

  19. 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

  20. 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).

  1. 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

  2. A Prototype MODI- SSM/I Snow Mapping Algorithm

    NASA Technical Reports Server (NTRS)

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

    1999-01-01

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

  3. 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., 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

  4. Comparison of Snow Mass Estimates from a Prototype Passive Microwave Snow Algorithm, a Revised Algorithm and a Snow Depth Climatology

    NASA Technical Reports Server (NTRS)

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

    1997-01-01

    While it is recognized that no single snow algorithm is capable of producing accurate global estimates of snow depth, for research purposes it is useful to test an algorithm's performance in different climatic areas in order to see how it responds to a variety of snow conditions. This study is one of the first to develop separate passive microwave snow algorithms for North America and Eurasia by including parameters that consider the effects of variations in forest cover and crystal size on microwave brightness temperature. A new algorithm (GSFC 1996) is compared to a prototype algorithm (Chang et al., 1987) and to a snow depth climatology (SDC), which for this study is considered to be a standard reference or baseline. It is shown that the GSFC 1996 algorithm compares much more favorably to the SDC than does the Chang et al. (1987) algorithm. For example, in North America in February there is a 15% difference between the GSFC 198-96 Algorithm and the SDC, but with the Chang et al. (1987) algorithm the difference is greater than 50%. In Eurasia, also in February, there is only a 1.3% difference between the GSFC 1996 algorithm and the SDC, whereas with the Chang et al. (1987) algorithm the difference is about 20%. As expected, differences tend to be less when the snow cover extent is greater, particularly for Eurasia. The GSFC 1996 algorithm performs better in North America in each month than dose the Chang et al. (1987) algorithm. This is also the case in Eurasia, except in April and May when the Chang et al.(1987) algorithms is in closer accord to the SDC than is GSFC 1996 algorithm.

  5. 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. PMID:27135283

  6. Potential for Using Satellite Lidar for Seasonal Snow Depth Estimation

    NASA Astrophysics Data System (ADS)

    Jasinski, M. F.; Stoll, J.; Harding, D. J.; Fassnacht, S. R.; Carabajal, C. C.; Markus, T.

    2013-12-01

    This study evaluates the potential for estimating snow depth in complex mountainous terrain using high resolution satellite lidar. For over three decades, satellite remote sensing of snow depth and water equivalent has relied primarily on passive microwave sensors with an approximately 25 km footprint. While successfully employed in many global water balance analyses, their large footprints, necessary to capture the natural emission of the surface, are too coarse to define the spatial heterogeneity of mountain watershed-scale snow due to variable topography and vegetation. In this study, the capability of satellite lidar altimetry for estimating snow depth was evaluated primarily using surface elevations observed by the Geoscience Laser Altimeter Sensor (GLAS) flown on board the Ice, Cloud, and land Elevation Satellite from 2003-2009, with a footprint size of ~70m. The evaluation includes the analysis of GLAS waveforms at near-repeat locations during snow-off and snow on conditions, using several snow depth estimation approaches, focusing on the Uinta Mountains of NE Utah. Also presented is the concept for the ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS), currently set to launch in July 2016, and its potential capability for characterizing snow depth. The opportunity for partnering through NASA's Early Adopter Program using prototype aircraft observations also is presented.

  7. Secrets of Snow Liveshot Recap

    NASA Video Gallery

    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...

  8. 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.

  9. 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. PMID:24951969

  10. 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.

  11. Improving WEPP snow simulation

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Snow simulation is essential to reliable prediction of runoff and erosion, particularly in high-latitude areas in the northern tier of states and forested watersheds at high elevations. The Water Erosion Prediction Project (WEPP) model is one of a few including a component for winter hydrology simul...

  12. Principles of Snow Hydrology

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Snow hydrology is a specialized field of hydrology that is of particular importance for high latitudes and mountainous terrain. In many parts of the world, river and groundwater supplies for domestic, irrigation, industrial and ecosystem needs are generated from snowmelt, and an in-depth understand...

  13. 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

  14. Recent increase in snow-melt area in the Greenland Ice sheet as an indicator of the effect of reduced surface albedo by snow impurities

    NASA Astrophysics Data System (ADS)

    Rikiishi, K.

    2008-12-01

    Recent rapid decline of cryosphere including mountain glaciers, sea ice, and seasonal snow cover tends to be associated with global warming. However, positive feedback is likely to operate between the cryosphere and air temperature, and then it may not be so simple to decide the cause-and-effect relation between them. The theory of heat budget for snow surface tells us that sensible heat transfer from the air to the snow by atmospheric warming by 1°C is about 10 W/m2, which is comparable with heat supply introduced by reduction of the snow surface albedo by only 0.02. Since snow impurities such as black carbon and soil- origin dusts have been accumulated every year on the snow surface in snow-melting season, it is very important to examine whether the snow-melting on the ice sheets, mountain glaciers, and sea ice is caused by global warming or by accumulated snow impurities originated from atmospheric pollutants. In this paper we analyze the dataset of snow-melt area in the Greenland ice sheet for the years 1979 - 2007 (available from the National Snow and Ice Data Center), which is reduced empirically from the satellite micro-wave observations by SMMR and SMM/I. It has been found that, seasonally, the snow-melt area extends most significantly from the second half of June to the first half of July when the sun is highest and sunshine duration is longest, while it doesn't extend any more from the second half of July to the first half of August when the air temperature is highest. This fact may imply that sensible heat required for snow-melting comes from the solar radiation rather than from the atmosphere. As for the interannual variation of snow-melt area, on the other hand, we have found that the growth rate of snow-melt area gradually increases from July, to August, and to the first half of September as the impurities come out to and accumulated at the snow surface. However, the growth rate is almost zero in June and the second half of September when fresh snow

  15. '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.

  16. 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

  17. 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.

  18. 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.

  19. 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.

  20. '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.

  1. Combining low-cost GPS receivers with upGPR to derive continuously liquid water content, snow height and snow water equivalent in Alpine snow covers

    NASA Astrophysics Data System (ADS)

    Koch, Franziska; Schmid, Lino; Prasch, Monika; Heilig, Achim; Eisen, Olaf; Schweizer, Jürg; Mauser, Wolfram

    2015-04-01

    The temporal evolution of Alpine snowpacks is important for assessing water supply, hydropower generation, flood predictions and avalanche forecasts. Especially in high mountain regions with an extremely varying topography, it is until now often difficult to derive continuous and non-destructive information on snow parameters. Since autumn 2012, we are running a new low-cost GPS (Global Positioning System) snow measurement experiment at the high alpine study site Weissfluhjoch (2450 m a.s.l.) in Switzerland. The globally and freely broadcasted GPS L1-band (1.57542 GHz) was continuously recorded with GPS antennas, which are installed at the ground surface underneath the snowpack. GPS raw data, containing carrier-to-noise power density ratio (C/N0) as well as elevation and azimuth angle information for each time step of 1 s, was stored and analyzed for all 32 GPS satellites. Since the dielectric permittivity of an overlying wet snowpack influences microwave radiation, the bulk volumetric liquid water content as well as daily melt-freeze cycles can be derived non-destructively from GPS signal strength losses and external snow height information. This liquid water content information is qualitatively in good accordance with meteorological and snow-hydrological data and quantitatively highly agrees with continuous data derived from an upward-looking ground-penetrating radar (upGPR) working in a similar frequency range. As a promising novelty, we combined the GPS signal strength data with upGPR travel-time information of active impulse radar rays to the snow surface and back from underneath the snow cover. This combination allows determining liquid water content, snow height and snow water equivalent from beneath the snow cover without using any other external information. The snow parameters derived by combining upGPR and GPS data are in good agreement with conventional sensors as e.g. laser distance gauges or snow pillows. As the GPS sensors are cheap, they can easily

  2. 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

  3. 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.

  4. 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

  5. [Research on hyperspectral remote sensing in monitoring snow contamination concentration].

    PubMed

    Tang, Xu-guang; Liu, Dian-wei; Zhang, Bai; Du, Jia; Lei, Xiao-chun; Zeng, Li-hong; Wang, Yuan-dong; Song, Kai-shan

    2011-05-01

    Contaminants in the snow can be used to reflect regional and global environmental pollution caused by human activities. However, so far, the research on space-time monitoring of snow contamination concentration for a wide range or areas difficult for human to reach is very scarce. In the present paper, based on the simulated atmospheric deposition experiments, the spectroscopy technique method was applied to analyze the effect of different contamination concentration on the snow reflectance spectra. Then an evaluation of snow contamination concentration (SCC) retrieval methods was conducted using characteristic index method (SDI), principal component analysis (PCA), BP neural network and RBF neural network method, and the estimate effects of four methods were compared. The results showed that the neural network model combined with hyperspectral remote sensing data could estimate the SCC well. PMID:21800591

  6. 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

  7. 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

  8. 'Snow White' in Color

    NASA Technical Reports Server (NTRS)

    2008-01-01

    This color image taken by the Surface Stereo Imager on NASA's Phoenix Mars Lander shows the trench dubbed 'Snow White,' after further digging on the 25th Martian day, or sol, of the mission (June 19, 2008). The lander's solar panel is casting a shadow over a portion of the trench.

    The trench is about 5 centimeters (2 inches) deep and 30 centimeters (12 inches) long.

    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.

  9. Quantifying Snow Transport Using Snow Fences and Sonic Sensors

    NASA Astrophysics Data System (ADS)

    Sturm, M.; Berezovskaya, S.; Hiemstra, C.; Gelvin, A.

    2010-12-01

    Accurate assessment of snow transport (T) during wind events is a prerequisite to reliable estimation of snow loads on infrastructure and prediction of avalanche danger in the mountains. It is also a critical term in the winter water balance, affecting snow sublimation. To assess T we constructed two snow fences in northern Alaska and used an existing municipal fence near Barrow, Alaska to trap the wind-blown flux of snow. On the leeward side of each fence we installed a line of SR50 sonic ranging sensors that could be used to track the increase in snow height with time. We also installed web-cameras to monitor changes in drift shape. Periodic snow surface elevation surveys using a DGPS system provided more detailed drift profiles during the winter. Wind speed and direction were monitored near the fences. The sonic sensor results have been combined with the DGPS surveys to produce a time series of drift cross-section from which the flux has been computed. We have related this flux to individual wind events in an effort to identify the optimal conditions for blowing snow transport and to derive an empirical expression for T from weather measurements.

  10. 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.

  11. Microtomography of macroscopic snow samples

    NASA Astrophysics Data System (ADS)

    Matzl, M.; Schneebeli, M.; Steinfeld, D.; Steiner, S.; Heggli, M.

    2010-12-01

    During the last 10 years X-ray microtomography (micro-CT) has proved to be the first successful method to measure the true three-dimensional (3-D) structure of snow on the ground. Micro-CT is used to reconstruct 3-D microstructures as a source for numerical simulations, to conduct long-term observations of metamorphism or the behavior of snow under stress and to derive macroscopic parameters describing the microstructure of snow like specific surface area or density. However, micro-CT was confined to small samples with a typically evaluated size of 5 x 5 x 5 mm3. One reason for the small size was the limited computational power, the other the sample preparation. Based on the replica method for 3-D micro-CT samples introduced by Heggli et al. (2009), we are now able to visualize snow samples up to 70 mm height, and about 10 mm diameter, with a resolution of 10 μm. Because inclusion of small air bubbles during the casting process can not be avoided, we make two scans, one before and one after sublimation, the two scans are then registered and subtracted. After image segmentation and morphological image processing the replica can be analysed in the same way as direct snow measurements. Based on such samples, we imaged highly fragile snow samples, like new snow, buried surface hoar and other weak layers. The samples show a fascinating new image of how complex snow layers are. Most samples show strong density gradients within a structurally similar layer. We think that this technique will improve our understanding of snow metamorphism and snow properties. Heggli, M.; Frei, E.; Schneebeli, M., 2009: Instruments and Methods. Snow Replica method for three-dimensional X-ray microtomographic imaging. J. Glaciol. 55, 192: 631-639.

  12. 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

  13. Parameterizations for Narrowband and Broadband Albedo of Pure Snow, and Snow Containing Mineral Dust and Black Carbon

    NASA Astrophysics Data System (ADS)

    Dang, C.; Brandt, R.; Warren, S. G.

    2014-12-01

    The reduction of snow spectral albedo by black carbon(BC) and mineral dust, both alone and in combination, is computed using radiative-transfer modeling. Several size distributions of BC are examined; one is chosen to represent ambient soot. Many measurements of absorption spectra of mineral dust are critically reviewed as a basis for specifying dust properties for modeling. Two standard solar spectra are used: clear sky with the global average(insolation-weighted) solar zenith angle ~50 degrees, and the spectrum under an overcast cloud of optical thickness 11. The primary variables for the parameterizations are the radiatively-effective snow grain radius (r) and BC mass mixing ratio (C) in snow (and/or dust mixing ratio). A change of solar zenith angle can be mimicked by changing the snow grain radius. Results are shown for all mass-mixing ratios (MMR) covering the full range from pure snow to pure soot and pure dust, and for snow grain radii from 5 to 2500 mm, to cover the range of possible grain sizes on planetary surfaces. To keep the parameterizations simple, only opaque homogeneous snowpacks are considered. Parameterizations are developed for three broad bands used in GCMs as well as several narrower bands; quadratic or cubic functions of log r and log C are generally adequate. The parameterizations are valid up to BC content of 10 ppm, which is needed for highly polluted snow, for example as found in northeast China. A given MMR of BC causes greater albedo reduction in coarse-grained snow; BC and r can be combined into a single variable to compute the reduction of albedo relative to pure snow. For allwave albedo or visible albedo, a twofold increase of C results in the same change in BC-caused albedo reduction as multiplying r by 2.6. The near-IR albedo is less sensitive to BC content; there a twofold increase of C can be mimicked by a fivefold increase of r.The albedo reduction by soot is less if the snow already contains dust, a common situation on

  14. 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

  15. 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.

  16. 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

  17. On the evolution of the Snow Line in Protoplanetary Discs

    NASA Astrophysics Data System (ADS)

    Martin, Rebecca G.; Livio, Mario

    2014-01-01

    We examine the evolution of the snow line in a protoplanetary disc. If the magneto-rotational instability (MRI) drives turbulence throughout the disc, there is a unique snow line outside of which the disc is icy. The snow line moves closer to the star as the infall accretion rate drops. Because the snow line moves inside the radius of the Earth's orbit, the formation of our water-devoid planet is difficult with this model. However, protoplanetary discs are not likely to be sufficiently ionised to be fully turbulent. A dead zone at the mid-plane slows the flow of material through the disc and a global steady state cannot be achieved. We model the evolution of the snow line also in a disc with a dead zone. As the mass is accumulating, the outer parts of the dead zone become self gravitating, heat the massive disc and thus the outer snow line does not come inside the radius of the Earth's orbit. With this model there is sufficient time and mass in the disc for the Earth to form from water-devoid planetesimals at a radius of 1AU. Furthermore, the additional inner icy region within the dead zone predicted by this model may allow for the formation of giant planets close to their host star without the need for much migration.

  18. Influence of climate change predictions on snow in Sierra Nevada Mountains (Spain)

    NASA Astrophysics Data System (ADS)

    José Pérez-Palazón, María; Pimentel, Rafael; Herrero, Javier; María Perales, José; José Polo, María

    2015-04-01

    Snow is a basic component in Earth's surface energy balance. Its importance is greater in mountainous areas and therefore, its study is crucial to obtain conclusion about water resources over these areas. Moreover these regions are more vulnerable to climate variations. Sierra Nevada National Park (Southern Spain), with altitudes range from 2000 to 3500 m.a.s.l., is part of the global climate change observatories network and a clear example of snow regions in a semiarid environment. This work estimates the impact of climate change on snow dynamics and its influence on mountain hydrology in this area. Precipitation and temperature datasets from three different scenarios (A1B, A2, B1) proposed by the Fifth Assessment Report of IPCC (Intergovernmental Panel on Climate Change) were used as forcing meteorological sequences to simulate selected snow variables (snow water equivalent, daily snow cover area, annual number of days with snow and annual snowmelt and evaporation volumes). The associated results were compared for each scenario, and the snow behavior and evolution over the 2046-2100 periods were assessed. The results point to a higher decrease on snow cover extension, than those estimated for other snow variables. As expected, this variation is greater from the worst of the scenarios analyzed (A2). Furthermore, the comparison of the driving meteorological datasets throughout the reference period (1960-2000) with the real observations has allowed the introduction of another term of uncertainty in the estimations, not considered in a simple scenario analysis.

  19. 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…

  20. 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.

  1. 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

  2. The Snows of Enceladus

    NASA Astrophysics Data System (ADS)

    Schenk, P.; Schmidt, J.; White, O.

    2011-10-01

    The icy south polar plumes of Enceladus make for a spectacular effect in the Saturn system (e.g., the Ering), but also profoundly alter the surface of Enceladus itself. Recent models of the plume particle dynamics predict that the heavier particles will reaccrete, effectively "snowing" fine-grained debris back onto the surface in discrete patterns [1], depending on the actual distribution of ejection sites. The densest fallout pattern is dominated by two scytheshaped lobes extending northward from the South-Polar-Terrains along the 40 and 220W longitudes. Recent color mapping of Enceladus demonstrates that IR/UV color asymmetries across the surface match these predicted patterns astonishingly well [2]. Theory and observation therefore confirm the apparent formation of a blanket of very small particles covering most of the surface of Enceladus to different depths, depending on location and plume source changes.

  3. 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

  4. Eurasian snow cover and Indian monsoon : A new episode of a debated relationship

    NASA Astrophysics Data System (ADS)

    Peings, Y.; Douville, H.

    2009-04-01

    Since the pioneering works of Blanford at the end of the 19th century, suggesting that Indian monsoon rainfall could be sensitive to snow conditions over Himalaya, many studies have been devoted to a better understanding of the possible teleconnection between winter/spring Eurasian snow cover and the following Indian monsoon. This issue has been recently revisited at CNRM using a maximum covariance analysis. This statistical tool has been applied on both observations (summer precipitation over India on the one hand, satellite data of snow cover or in situ measurements of snow depth on the other hand) and a subset of global coupled ocean-atmosphere simulations from the CMIP3 database. In line with former studies, the observations suggest a link between an east-west snow dipole over Eurasia and the Indian summer monsoon precipitation. However, our results indicate that this relationship is neither statistically significant nor stationary over the last forty years. Moreover, the strongest signal appears over eastern Eurasia and is not consistent with the Blanford hypothesis whereby more snow should lead to a weaker monsoon. The 20th century CMIP3 simulations provide longer timeseries to look for robust snow-monsoon relationships. Some models do show an apparent influence of the Eurasian snow cover on the Indian summer monsoon precipitation, but the snow patterns are model-dependent and not the same as in the observations. Moreover, the apparent snow-monsoon relationship generally denotes a too strong ENSO (El Niño Southern Oscillation) influence on both winter snow cover and summer monsoon rainfall rather than a direct effect of the Eurasian snow cover on the Indian monsoon. New sensitivity studies with the ARPEGE-Climat model are needed to assess the potential impact of snow anomalies on the monsoon, using climatological sea surface temperature to get rid of the oceanic variability.

  5. Improved snow depth retrieval by combining SSM/I and MODIS data with SMO regression

    NASA Astrophysics Data System (ADS)

    Liang, J.; Huang, K.; Liu, X.; Li, X.

    2013-12-01

    The snow depth retrieval using passive microwave remote sensing heavily depends on the spatial and temporal snow characteristics. The acquisition of the snow parameters usually requires costly field work or complicated modeling. Given that snow characteristics can also be retrieved by visible/infrared sensors, the integration of microwave and visible/infrared data has the potential for improving the retrieval accuracy. The snow depth retrieval is performed by using microwave brightness temperature at 19 and 37 GHz from the Special Sensor Microwave / Imager (SSM/I) and surface visible/infrared reflectance from Moderate Resolution Imaging Spectroadiometer (MODIS) products. The microwave brightness temperature difference could help to estimate snow volume and the visible/infrared surface reflectance contains the information of snow grain size. With these integrated remote sensing data, snow depth is retrieved by a nonlinear data mining technique, the modified sequential minimal optimization (SMO) algorithm for support vector machine (SVM) regression. We tested the proposed method by using about 25000 records of snow depth measurements from 53 meteorological stations in Xinjiang from 2000 to 2010. The RMSE of SVM retrieved snow depth of our method is 6.49 cm and 0.73 respectively. They are better than those of using brightness temperature data solely (6.90 cm and 0.66), the traditional spectral polarization difference (SPD) algorithm (12.06 cm and 0.36), the modified Chang algorithm in WESTDC (8.51 cm and 0.57), and the multilayer perceptron classifier of artificial neural network (ANN) (7.08 cm and 0.63). The regional comparisons of the proposed method and the global snow products (GLOBSNOW and AMSR-E daily snow) are also presented. The study has shown that combining MODIS surface reflectance data and SSM/I brightness temperature with SVM regression can provide more accurate snow depth retrieval results. Experimental result of the proposed method in 2004

  6. 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. PMID:21230135

  7. The Role of Terrestrial Snow Cover in the Climate System

    NASA Astrophysics Data System (ADS)

    Vavrus, S. J.

    2005-12-01

    Snow cover is known to exert a strong influence on the overlying atmosphere and underlying soil, but quantifying this impact is difficult. Besides its well-accepted ability to cool locally, snow cover can also force climate remotely in complex ways by inducing changes in the atmospheric circulation. Most research on the impact of snow cover has focused on the regional rather than global scale. By contrast, this study investigates the global impact of terrestrial snow cover in the present climate by comparing a pair of Community Climate System Model (CCSM3) simulations run with prognostic snow cover (control case) and with all snow cover on land eliminated (NOSNOWCOVER). In this experiment all snowfall over land was converted into 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 to 9 K greater in these regions during winter. The global-mean warming of 0.8 K in NOSNOWCOVER is nearly 1/3 as large as the simulated 2 x CO2 response. This pronounced surface heating dramatically increases geopotential heights throughout the troposphere: annual increases of up to 50 m occur at the 250 hPa level, along with even larger inflations during winter. Despite the large surface 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 more than 20 K in Siberia and a 5 to 10o equatorward expansion of simulated permafrost. The absence of local melt-water percolation causes significantly drier soils over northern boreal regions and a consequent decrease in cloudiness. The removal of snow cover also drastically affects extreme weather in middle latitudes. Extreme cold-air outbreaks (CAOs), defined relative to the control simulation, essentially disappear in NOSNOWCOVER. The loss of CAOs appears to stem from both the local

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

    NASA Video Gallery

    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 ...

  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. "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.

  11. 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)

  12. 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

  13. 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.

  14. 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.

  15. 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

  16. 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

  17. 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.

  18. 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.

  19. Skill of remote sensing snow products for distributed runoff prediction

    NASA Astrophysics Data System (ADS)

    Berezowski, Tomasz; Chormański, Jarosław; Batelaan, Okke

    2015-05-01

    With increasing availability of remote sensing snow cover products we aim to evaluate the skill of these datasets with regard to hydrological discharge simulation. In this paper ten model variants using different snow cover data (MOD10A1, IMS, AMSR-E SWE, GLOBSNOW SWE and observed in situ snow depth) and two different model structures for snow accumulation and snowmelt switching (based on snow cover data time series or temperature time series) are calibrated with a global optimisation algorithm. The simulated discharge is subjected to five criteria for validation, while the GLUE methodology is used for uncertainty analysis of the ten model variants. The skill of the datasets is tested for the Biebrza River catchment, which has a hydrological regime dominated by snowmelt. The discharge simulations are conducted with the distributed rainfall-runoff model WetSpa. MOD10A1 was the only data source which improved the validation Nash-Sutcliffe (NS) scores in reference to a standard model. However, other evaluation measures indicate that the following data sources performed better than the standard model: MOD10A1, observed snow depth and GLOBSNOW for Kling-Gupta efficiency and for high flows; IMS and MOD10A1 for bias; GLOBSNOW and MOD10A1 for coefficient of determination. MOD10A1 has the highest spatial resolution of all analysed data sources which might contribute to the high skill of this data. The use of the data-based switching model structure generally narrowed the behavioural parameter sets during the uncertainty analysis when compared to the temperature-based switching. However, no clear relation was observed between the prediction confidence interval and the two model structures. It is concluded that the skill of the remote sensing snow cover data for the model is positive, although, strongly varying with the data source used.

  20. 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.

  1. 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.

  2. 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. PMID:19086291

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. 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

  9. 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

  10. 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

  11. Merging the MODIS and NESDIS Monthly Snow-Cover Records to Study Decade-Scale Changes in Northern Hemisphere Snow Cover

    NASA Technical Reports Server (NTRS)

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

    2004-01-01

    A decade-scale record of Northern Hemisphere snow cover has been available from the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite Data and Information Service (NESDIS) and has been reconstructed and validated by Rutgers University following adjustments for inconsistencies that were discovered in the early years of the data set. This record provides weekly, monthly (and, in recent years, daily) snow cover from 1966 to the present for the Northern Hemisphere. With the December 1999 launch of NASA's Earth observing System (EOS) Terra satellite, snow maps are being produced globally, using automated algorithms, on a daily, weekly and monthly basis from the Moderate-Resolution Imaging Spectroradiometer (MODIS) instrument. The resolution of the MODIS monthly snow maps (0.05deg or about 5 km) is an improvement over that of the NESDIS-derived monthly snow maps (>approx.10 km) the maps, it is necessary to study the datasets carefully to determine if it is possible to merge the datasets into a continuous record. The months in which data are available for both the NESDIS and MODIS maps (March 2000 to the present) will be compared quantitatively to analyze differences in North American and Eurasian snow cover. Results from the NESDIS monthly maps show that for North America (including all 12 months), there is a trend toward slightly less snow cover in each succeeding decade. Interannual snow-cover extent has varied significantly since 2000 as seen in both the NESDIS and MODIS maps. As the length of the satellite record increases through the MODIS era, and into the National Polar-orbiting Environmental Satellite System (NPOESS) era, it should become easier to identify trends in areal extent of snow cover, if present, that may have climatic significance. Thus it is necessary to analyze the validity of merging the NESDIS and MODIS, and, in the future, the NPOESS datasets for determination of long-term continuity in measurement of Northern

  12. Snow Micro-Structure Model

    Energy Science and Technology Software Center (ESTSC)

    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 implementedmore » 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.« less

  13. 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.

  14. 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…

  15. A Blended Learning Experience

    ERIC Educational Resources Information Center

    Gecer, Aynur; Dag, Funda

    2012-01-01

    Blended (hybrid) learning is one of the approaches that is utilized to help students for meaningful learning via information and communication technologies in educational settings. In this study, Computer II Course which is taught in faculties of education was planned and implemented in the form of a blended learning environment. The data were…

  16. A Better Blend

    ERIC Educational Resources Information Center

    Demski, Jennifer

    2010-01-01

    In May 2009, the US Department of Education released a meta-analysis of effectiveness studies of online, face-to-face, and blended learning models. The analysis found that online learning produced better student outcomes than face-to-face classes, and that blended learning offered an even "larger advantage" over face-to-face. The hybrid approach…

  17. Blended Teaching & Learning

    ERIC Educational Resources Information Center

    Pape, Liz

    2010-01-01

    Blended learning is using online tools to communicate, collaborate and publish, to extend the school day or year and to develop the 21st-century skills students need. With blended learning, teachers can use online tools and resources as part of their daily classroom instruction. Using many of the online tools and resources students already are…

  18. 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.

  19. 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.

  20. 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.

  1. Snow contribution to springtime atmospheric predictability over the second half of the twentieth century

    NASA Astrophysics Data System (ADS)

    Peings, Yannick; Douville, H.; Alkama, R.; Decharme, B.

    2011-09-01

    A set of global atmospheric simulations has been performed with the ARPEGE-Climat model in order to quantify the contribution of realistic snow conditions to seasonal atmospheric predictability in addition to that of a perfect sea surface temperature (SST) forcing. The focus is on the springtime boreal hemisphere where the combination of a significant snow cover variability and an increasing solar radiation favour the potential snow influence on the surface energy budget. The study covers the whole 1950-2000 period through the use of an original snow mass reanalysis based on an off-line land surface model and possibly constrained by satellite snow cover observations. Two ensembles of 10-member AMIP-type experiments have been first performed with relaxed versus free snow boundary conditions. The nudging towards the monthly snow mass reanalysis significantly improves both potential and actual predictability of springtime surface air temperature over Central Europe and North America. Yet, the impact is confined to the lower troposphere and there is no clear improvement in the predictability of the large-scale atmospheric circulation. Further constraining the prescribed snow boundary conditions with satellite observations does not change much the results. Finally, using the snow reanalysis only for initializing the model on March 1st also leads to a positive impact on predicted low-level temperatures but with a weaker amplitude and persistence. A conditional skill approach as well as some selected case studies provide some guidelines for interpreting these results and suggest that an underestimated snow cover variability and a misrepresentation of ENSO teleconnections may hamper the benefit of an improved snow initialization in the ARPEGE-Climat model.

  2. Large scale snow water equivalent status monitoring: comparison of different snow water products in the upper Colorado Basin

    NASA Astrophysics Data System (ADS)

    Artan, G. A.; Verdin, J. P.; Lietzow, R.

    2013-12-01

    We illustrate the ability to monitor the status of snow water content over large areas by using a spatially distributed snow accumulation and ablation model that uses data from a weather forecast model in the upper Colorado Basin. The model was forced with precipitation fields from the National Weather Service (NWS) Multi-sensor Precipitation Estimator (MPE) and the Tropical Rainfall Measuring Mission (TRMM) data-sets; remaining meteorological model input data were from NOAA's Global Forecast System (GFS) model output fields. The simulated snow water equivalent (SWE) was compared to SWEs from the Snow Data Assimilation System (SNODAS) and SNOwpack TELemetry system (SNOTEL) over a region of the western US that covers parts of the upper Colorado Basin. We also compared the SWE product estimated from the special sensor microwave imager (SSM/I) and scanning multichannel microwave radiometer (SMMR) to the SNODAS and SNOTEL SWE data-sets. Agreement between the spatial distributions of the simulated SWE with MPE data was high with both SNODAS and SNOTEL. Model-simulated SWE with TRMM precipitation and SWE estimated from the passive microwave imagery were not significantly correlated spatially with either SNODAS or the SNOTEL SWE. Average basin-wide SWE simulated with the MPE and the TRMM data were highly correlated with both SNODAS (r = 0.94 and r = 0.64; d.f. = 14 - d.f. = degrees of freedom) and SNOTEL (r = 0.93 and r = 0.68; d.f. = 14). The SWE estimated from the passive microwave imagery was significantly correlated with the SNODAS SWE (r = 0.55, d.f. = 9, p = 0.05) but was not significantly correlated with the SNOTEL-reported SWE values (r = 0.45, d.f. = 9, p = 0.05).The results indicate the applicability of the snow energy balance model for monitoring snow water content at regional scales when coupled with meteorological data of acceptable quality. The two snow water contents from the microwave imagery (SMMR and SSM/I) and the Utah Energy Balance forced with the

  3. Interpretation of AMSU microwave measurements for the retrievals of snow water equivalent and snow depth

    NASA Astrophysics Data System (ADS)

    Kongoli, Cezar; Grody, Norman C.; Ferraro, Ralph R.

    2004-12-01

    The objective of this paper is to interpret microwave scattering signatures over snow cover as observed by the Advanced Microwave Sounding Unit (AMSU) for the retrievals of snow water equivalent and snow depth. A case study involving seasonal snow cover over the U.S. Great Plains was analyzed in detail. Area-wide analysis of the relationship between snow depth and the AMSU scattering signatures in the 23-150 GHz window region showed weak correlation, deteriorated by the dependence of these signatures on snow metamorphism. The lower frequency scattering index, computed as the difference in the brightness temperature between 23 and 31 GHz channels, was low and insensitive to fresh snow predominant in December, but increased later in the season, and thus was more sensitive to snow depth for older snow cover. However, this seasonal increase in microwave scattering was observed for every snow depth range, suggesting a strong dependence on snow metamorphism. In contrast, the 89 GHz scattering index responded to relatively shallow snow cover in December, but was less sensitive to snow depth variability later in the season. A snow hydrology model was applied at specific locations to estimate snow water equivalent (other than snow depth) for comparisons with the AMSU measurements. Overall, the lower frequency index was the best predictor of snow water equivalent and snow depth. However, correlation was higher for snow density and snow water equivalent. This was attributed to the response of this scattering index to the grain size evolution with time, which correlated better with the snow density and water equivalent changes in the snow cover than snow depth. Correlation between the snow water equivalent and the lower frequency index for fresh snow cover was significantly improved by switching to the higher frequency index at 89 GHz as predictor when the lower frequency index at 31 GHz was less than the 5 K threshold. Correlation further improved for fresh snow cover

  4. 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

  5. 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. PMID:26713242

  6. 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.

  7. 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

  8. Snow Hydrology in a General Circulation Model.

    NASA Astrophysics Data System (ADS)

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

    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 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 snow pack. 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.

  9. 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

  10. 44 CFR 206.227 - Snow assistance.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    .... Federal assistance will be provided for all costs eligible under 44 CFR 206.225 for a specified period of... 44 Emergency Management and Assistance 1 2010-10-01 2010-10-01 false Snow assistance. 206.227... Snow assistance. Emergency or major disaster declarations based on snow or blizzard conditions will...

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

    MedlinePlus

    ... to Know About Zika & Pregnancy Cold, Ice, and Snow Safety KidsHealth > For Parents > Cold, Ice, and Snow Safety Print A A A Text Size What's ... a few. Plus, someone has to shovel the snow, right? Once outdoors, however, take precautions to keep ...

  12. 44 CFR 206.227 - Snow assistance.

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    .... Federal assistance will be provided for all costs eligible under 44 CFR 206.225 for a specified period of... 44 Emergency Management and Assistance 1 2013-10-01 2013-10-01 false Snow assistance. 206.227... Snow assistance. Emergency or major disaster declarations based on snow or blizzard conditions will...

  13. 44 CFR 206.227 - Snow assistance.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    .... Federal assistance will be provided for all costs eligible under 44 CFR 206.225 for a specified period of... 44 Emergency Management and Assistance 1 2011-10-01 2011-10-01 false Snow assistance. 206.227... Snow assistance. Emergency or major disaster declarations based on snow or blizzard conditions will...

  14. 44 CFR 206.227 - Snow assistance.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    .... Federal assistance will be provided for all costs eligible under 44 CFR 206.225 for a specified period of... 44 Emergency Management and Assistance 1 2014-10-01 2014-10-01 false Snow assistance. 206.227... Snow assistance. Emergency or major disaster declarations based on snow or blizzard conditions will...

  15. 44 CFR 206.227 - Snow assistance.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    .... Federal assistance will be provided for all costs eligible under 44 CFR 206.225 for a specified period of... 44 Emergency Management and Assistance 1 2012-10-01 2011-10-01 true Snow assistance. 206.227... Snow assistance. Emergency or major disaster declarations based on snow or blizzard conditions will...

  16. Microwave emission from an irregular snow layer

    NASA Technical Reports Server (NTRS)

    Eom, H. J.; Lee, K. K.; Fung, A. K.

    1983-01-01

    Emission from an irregular snow layer is modeled by a layer of Mie scatterers using the radiative transfer method. Comparisons are made with measurements showing snow wetness effects and rough air-snow boundary effects. For convenience of reference, theoretical model behavior is also illustrated.

  17. 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.

  18. Elevation dependency of mountain snow depth

    NASA Astrophysics Data System (ADS)

    Grünewald, T.; Bühler, Y.; Lehning, M.

    2014-12-01

    Elevation strongly affects quantity and distribution patterns of precipitation and snow. Positive elevation gradients were identified by many studies, usually based on data from sparse precipitation stations or snow depth measurements. We present a systematic evaluation of the elevation-snow depth relationship. We analyse areal snow depth data obtained by remote sensing for seven mountain sites near to the time of the maximum seasonal snow accumulation. Snow depths were averaged to 100 m elevation bands and then related to their respective elevation level. The assessment was performed at three scales: (i) the complete data sets (10 km scale), (ii) sub-catchments (km scale) and (iii) slope transects (100 m scale). We show that most elevation-snow depth curves at all scales are characterised through a single shape. Mean snow depths increase with elevation up to a certain level where they have a distinct peak followed by a decrease at the highest elevations. We explain this typical shape with a generally positive elevation gradient of snow fall that is modified by the interaction of snow cover and topography. These processes are preferential deposition of precipitation and redistribution of snow by wind, sloughing and avalanching. Furthermore, we show that the elevation level of the peak of mean snow depth correlates with the dominant elevation level of rocks (if present).

  19. 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.

  20. Seasonal variations of snow depth on Mars.

    PubMed

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

    2001-12-01

    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. PMID:11739951

  1. Seasonal variations of snow depth on Mars

    NASA Technical Reports Server (NTRS)

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

    2001-01-01

    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. Toward improving streamflow prediction in the Upper Colorado River Basin via assimilating bias-adjusted satellite snow depth retrievals

    NASA Astrophysics Data System (ADS)

    Liu, Y.; Peters-Lidard, C. D.; Kumar, S.; Arsenault, K. R.; Mocko, D. M.

    2014-12-01

    In snowmelt-driven river systems, it is critical to enable reliable predictions of the spatiotemporal variability in the seasonal snowpack in order to support local and regional water management. Previous studies have shown that improved snow predictions can be achieved by assimilating bias corrected snow depth retrievals from satellite-based passive microwave (PMW) sensors. However, improved snow predictions do not necessarily always translate into improved predictions of streamflow on which water management heavily relies. In this presentation, we explore how the existing bias correction strategy based on the optimal interpolation algorithm can be enhanced to produce an improved satellite-gauge blended snow depth product, which, when assimilated into a distributed snowmelt-runoff model, can lead to consistently improved streamflow predictions. The methodology is applied to the bias reduction of the snow depth estimates from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), which is then assimilated into the Noah land surface model via an ensemble Kalman Filter (EnKF) for streamflow prediction in the Upper Colorado River Basin. Our results indicate that using observations from high-elevation stations (e.g., the Snow Telemetry (SNOTEL) stations) and terrain aspect information in the bias correction process is critically important in achieving desirable streamflow predictions. Incorporating snow cover information (e.g., from the Moderate Resolution Imaging Spectroradiometer (MODIS)) into bias correction can further improve the streamflow results. However, increasing the spatial resolution of bias correction tends to have mixed results on streamflow prediction.

  3. 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.

  4. Photopolarimetric retrievals of snow properties

    NASA Astrophysics Data System (ADS)

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

    2015-10-01

    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.

  5. Photopolarimetric retrievals of snow properties

    NASA Astrophysics Data System (ADS)

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

    2015-05-01

    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 air- and space-borne 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 dataset, 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.

  6. 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

  7. 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.

  8. Comparison of Coordinated Satellite and Ground-based X-Band Radar Collections for the Retrieval of Snow Parameters

    NASA Astrophysics Data System (ADS)

    Deeb, E. J.; Marshall, H.; LeWinter, A. L.; Finnegan, D. C.; Deems, J. S.; Landry, C.

    2012-12-01

    In many regions of the world, snow is a major source of runoff contributing to human existence/sustenance, agriculture, and industry. The uncertainties in quantifying snow mass at both spatial and temporal scales have limited the vital management of this significant component to the global water cycle. With the sensitivity of radar backscatter to physical properties of snow at higher frequencies and the availability of high resolution commercial satellite imaging radars at X-Band frequencies (e.g. 9.6 GHz), snow experiments have been conducted to examine these relationships at finer spatial and temporal scales. For the past several winters, satellite radar acquisitions (at X-Band with co- and cross-polarizations) have been coordinated with ground-based radar collections within a well-instrumented southwestern Colorado basin exhibiting a wide range of snow conditions. Snow-free satellite radar collections (at X-Band with the same viewing geometry) have also been acquired to separate the backscatter contributions of the snow volume from the underlying background target. Ancillary data sets including ground-based LiDAR-derived snow depths and scientific snow pit sampling are also incorporated into the analysis. Despite the fact that it may not be possible to retrieve snow water equivalent from multi-polarization X-Band frequency alone, preliminary results of these comparisons are shown where the ground-based radar transects overlap the satellite radar coverage. Snow parameters such as saturated surface or internal snow layers, snow surface and stratigraphic roughness, and grain size variations may be of particular interest.

  9. 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.

  10. 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.

  11. 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

  12. 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

  13. 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

  14. 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.

  15. Elevation dependency of mountain snow depth

    NASA Astrophysics Data System (ADS)

    Grünewald, T.; Bühler, Y.; Lehning, M.

    2014-07-01

    Elevation strongly affects quantity and distribution of precipitation and snow. Positive elevation gradients were identified by many studies, usually based on data from sparse precipitation stations or snow depth measurements. We present a systematic evaluation of the elevation - snow depth relationship. We analyse areal snow depth data obtained by remote sensing for seven mountain sites. Snow depths were averaged to 100 m elevation bands and then related to their respective elevation level. The assessment was performed at three scales ranging from the complete data sets by km-scale sub-catchments to slope transects. We show that most elevation - snow depth curves at all scales are characterised through a single shape. Mean snow depths increase with elevation up to a certain level where they have a distinct peak followed by a decrease at the highest elevations. We explain this typical shape with a generally positive elevation gradient of snow fall that is modified by the interaction of snow cover and topography. These processes are preferential deposition of precipitation and redistribution of snow by wind, sloughing and avalanching. Furthermore we show that the elevation level of the peak of mean snow depth correlates with the dominant elevation level of rocks.

  16. 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

  17. Improving radiative transfer processes in snow-covered areas prone to dust loading using a regional climate model

    NASA Astrophysics Data System (ADS)

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

    2011-12-01

    Radiative processes play an important role on both global and regional scales. This study focuses on their effects over snow-covered surfaces, both clean and dust loaded. It is well understood that dust in snow enhances solar radiation absorption, leading to a decrease in snow albedo. However, the quantitative assessment of dust's influence on radiative forcing and runoff timing in mountain snow packs has only been recently investigated. 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. This dust largely originates from the Colorado Plateau with increases of 5-7 fold in the last century and a half due to grazing and agricultural practices. For this study, we employ NCAR's WRF ARW v3.3+ model, which is coupled with a land surface model, Simplified Simple Biosphere version 3 (SSiB3). We first investigate the impact of different atmospheric radiative transfer schemes in WRF3.3+-SSiB3 on the regional climate downscaling. After conducting simulations over North America for the period March through June, we found substantial differences in the downscaling skills with different atmospheric radiative schemes. These differences indicate the uncertainty due to the atmospheric radiative transfer parameterizations. To develop a regional climate model that is capable of realistically simulating radiative forcing on snow covered areas with aerosol loading, we coupled WRF3.3+-SSiB3 with 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. Our previous version of WRF3.3+-SSi

  18. 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

  19. Methaemoglobinaemia due to mephedrone ('snow').

    PubMed

    Ahmed, Noor; Hoy, Brent Philip Sew; McInerney, J

    2010-01-01

    Acquired methaemoglobinaemia is a serious complication caused by many oxidising drugs. It presents as cyanosis unresponsive to oxygen therapy. The case of 33-year-old male patient who presented in our department after noticing blue lips and fingers is presented. He had sniffed 1 g of 'snow' after buying it from a head shop. His oxygen saturation by pulse oximeter on room air at presentation was 90%, which did not improve with supplemental oxygen. Arterial blood gas analyses showed partial pressure of oxygen 37 kPa while on supplemental oxygen and a methaemoglobin concentration greater than 25%. The patient denied using any other recreational drugs and was not on regular treatment. Therefore, a diagnosis of methaemoglobinaemia due to mephedrone, which is the active ingredient of 'snow', was made. Treatment is with intravenous methylene blue. Our patient started to improve so methylene blue was not used and he was discharged after 8 h. PMID:22791577

  20. Snow Densification in Greenland (Invited)

    NASA Astrophysics Data System (ADS)

    Morris, E.; Wingham, D.

    2010-12-01

    As part of the calibration and validation experiments for the radar altimeter carried by the ESA Cryosat-2 satellite, snow density profiles have been measured along the EGIG line from Spring 2004 to Summer 2010. Repeated measurements at the same site allow the development of the characteristic stratigraphy of the dry snow zone to be observed and the rate of densification to be measured directly. Densification rates below the surface layer are found to be compatible with a physics-based grain boundary sliding model (Alley, 1987) but near-surface rates are enhanced. Alley, R.B. (1987). Firn densification by grain-boundary sliding: A First Model. J. de. Physique 48 (Coll. C1, Suppl. No.3) 249-256.

  1. 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.

  2. 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.

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

    USGS Publications Warehouse

    Walsh, N.E.; McCabe, T.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.

  4. 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.

  5. 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.

  6. 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.

  7. Analysis of climatic variability and snow cover in the Kaligandaki River Basin, Himalaya, Nepal

    NASA Astrophysics Data System (ADS)

    Mishra, Bhogendra; Babel, Mukand S.; Tripathi, Nitin K.

    2014-05-01

    Various remote sensing products and observed data sets were used to determine spatial and temporal trends in climatic variables and their relationship with snow cover area in the higher Himalayas, Nepal. The remote sensing techniques can detect spatial as well as temporal patterns in temperature and snow cover across the inaccessible terrain. Non-parametric methods (i.e. the Mann-Kendall method and Sen's slope) were used to identify trends in climatic variables. Increasing trends in temperature, approximately by 0.03 to 0.08 °C year-1 based on the station data in different season, and mixed trends in seasonal precipitation were found for the studied basin. The accuracy of MOD10A1 snow cover and fractional snow cover in the Kaligandaki Basin was assessed with respect to the Advanced Spaceborne Thermal Emission and Reflection Radiometer-based snow cover area. With increasing trends in winter and spring temperature and decreasing trends in precipitation, a significant negative trend in snow cover area during these seasons was also identified. Results indicate the possible impact of global warming on precipitation and snow cover area in the higher mountainous area. Similar investigations in other regions of Himalayas are warranted to further strengthen the understanding of impact of climate change on hydrology and water resources and extreme hydrologic events.

  8. 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.

  9. 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.

  10. 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.