Analysis and of estimation snowpack properties using CLPX data
Amir E Azar, NOAA-CREST, New York, NY; and A. Powell, D. Seo, and R. Khanbilvardi
Snow and its properties estimations are among NOAA's priorities from two perspectives: global scale, and regional/continental scale. In a global scale snow high albedo plays a major role in earth's energy balance. Also, estimation of snowpack properties is significantly important in regional scale for various problems such as flood predictions and water resource management. Satellite passive microwave data from SSM/I, SMMR, and AMSR-E have shown potential for estimation of snowpack properties. Many algorithms have been introduced for SWE and snow depth estimations based on the statistical, empirical and physical analysis of snow and microwave data. However, non have been very successful in taking the variation and evolution of snow grain size into account. Snow grain size, besides snow depth and water equivalent, plays a major role in microwave radiation of snow. Then, a comprehensive method for estimation of snowpack properties need to take snow grain size into account. In this study, first, we focus on analysis of snow grain size behavior with respect to other snow parameters such as snow depth, density, and temperature. Then, we derive a pattern which can be used to approximate the range of grain size variations. Finally, we analyze the snow behavior with respect to microwave scatterings in SSM/I channels.
Data used in this research are from NASA Cold Land Processes Field Experiment (CLPX) in Colorado. This intensive field survey has been conducted in February and March of 2002 and 2003. The measurements include the grain size, density, and temperature in different layers of snowpack profile. The analysis of snowpack profile showed that the snow density usually tends to increase towards the bottom of the snow. Snow density is about 0.1 for fresh snow and increase up to 0.4 for the old snow, bottom layers. Snowpack temperature increases towards snow-ground interface ranging between -1 to 0. The very top layer temperature is usually very dependent to the air temperature in the area (-6 to -8 for our measurements). The grain size profile generally shows an increase in the snowpack profile. Fresh snow grains are around 0.1 which increase toward the lower layers of snow up to 2.5mm. It needs to be mentioned that this changes in grain size profile are not linear. In addition, there have been some cases of a layer of dense snow with large grain size in between snow layers with smaller grain size which can be explained by melt and refreeze during the season. Overall, the snow grain size variation is highly correlated with both snow density, and temperature but the correlation is generally higher between snow grain size and snowpack temperature as compared with grain size and density. In order to better re-analyze evolution of snowpack properties, we excluded the very top layer of the fresh snow from the analysis. The results showed an increase in correlation between snowpack temperature and grain size. On the other hand, snowpack temperature profile might be estimated by a function (possibly linear) having the top and bottom temperature. The slope of the regression line indicates the dependency of the regression slop to the snow depth. The surface and body temperature of the snowpack can be obtained from NCDC and microwave channels. Using snowpack temperature, the grain size evolution can be approximated.
Extended Abstract (236K)
Session 12, Advances in Remote Sensing and Data Assimilation in Hydrology, Part IV
Thursday, 24 January 2008, 3:30 PM-5:00 PM, 223
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