87th AMS Annual Meeting

Monday, 15 January 2007
Regional Validation of Existing Algorithms for SWE Estimations Using AMSR-E data
Exhibit Hall C (Henry B. Gonzalez Convention Center)
Narges Shahroudi, NOAA/CREST/CUNY, New York, NY
This study describes the validation of existing algorithms for estimating Snow Water Equivalent (SWE) and snow depth from spaceborne microwave instruments. Two different regions are selected due to their land cover and topography characteristics. First, Great Plains located between 48N-41N and 116W-92W, including north and south Dakotas, Wyoming, and Montana. Second, is the north eastern of United States which is located between 45N-40N and 74W-70W, including New York and New England and it is covered by grass land, and Forest.

The satellite data were collected by the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-EOS) sensor abroad Aqua. AMSR-E is a six frequency total-power microwave radiometer system with dual polarization capability for all frequency bands. The frequency bands include 6.925, 10.65, 18.7, 23.8, 36.5, and 89.0 GHz. The ground truth data are collected from National Climate Data Center (NCDC) and The National Operational Hydrologic Remote Sensing Center (NOHRSC). NCDC is the world's largest active archive of weather data. NOHRSC provides comprehensive snow observations, analyses, data sets and map products for the United States.

Three different algorithms were examined to determine SWE in the study areas. These algorithms have been originally developed for SSM/I and SMMR passive microwave data. Algorithms suggested by Goodison-Walker and Chang use the scattering signature between 37GHz and 19GHz. These two algorithms use vertical and horizontal polarizations consecutively. The other algorithm utilizes 85GHz for shallow snow and uses Normalized Difference Vegetation Index (NDVI) to take into account the vegetation effect. The preliminary results of the study revealed that in the Northeast of the United States the third algorithm has a better performance.

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