Assessment of Snow Cover Using MIRS, MSPPS, and IMS Measurements over Mountainous Terrain and Forested Areas

Jessica Chiu, Stephany Paredes, Tarendra Lakhankar, Reza M. Khanbilvardi

NOAA-CESSRST, The City College of New York, New York, NY 10031 USA

This study is being pursued to assess the accuracy of MIRS, MSPPS, and IMS products in mountainous terrain and forested areas in the United States of America. Microwave instruments use brightness temperatures in order to determine near real time surface and precipitation products, such as MIRS and MSPPS products. Since vegetation emits microwave radiation of its own, the satellite may sense an increase in brightness temperature. Vegetation may also cover existing snow, hindering data collection via microwaves. Mountainous terrain presents a culmination of possible errors; redistribution of snow by wind and avalanches, inconsistency in the snow water equivalent (SWE), and inaccurate snow albedo. All daily global snow products generated by the MIRS and MSPPS system is being acquired from the NOAA Comprehensive Large Array-data Stewardship System (CLASS). To assess the snow extent accuracy from these products, snow extent from the NOAA Interactive Multisensor Snow and Ice Mapping System (IMS) will be used. The U.S. Elevation Products and the Woodland Tint will be acquired from the National Geospatial Program of the U.S. Geological Survey (USGS.) Comparative plots of MIRS vs IMS and MSPPS vs IMS for three years (2014-2016) were programmed using Matlab to compare the true positives and negatives and false positives and negatives of both products pixel by pixel over the range of a year. These comparative plots are being analyzed over a contour map of United States terrain to visually evaluate the quality of the products over a mountainous terrain. The same comparative plots are being discussed over a contour map of North American land cover to visually evaluate the quality of the products over forested areas. The expected outcome from this study is that the assessment of product discrepancies or errors should correlate with the mountainous terrain and forested areas of the United States. This assessment can help future SWE models mitigate errors or discrepancies with data collection.