Interactive Multisensor Snow and Ice Mapping System (IMS) refinement utilizing GHCN-D dataset

Abstract:

The objective of this research is to provide an alternative data sources for the Ice Mapping System (IMS) Blended Snow Depth product, which is provided by NOAA Satellite and Information Services (NESDIS) for National Centers for Environmental Predictions (NCEP) operational models for weather and climate forecasting. The current IMS Blended Snow Depth applies current algorithm provides in-situ data from NESDIS US Cooperative sites that are nearly 1 day old. The replacement of the day old cooperative sites with current National Climatic Data Center’s (NCDC) Global Historical Climatology Network-Daily (GHCN-D) product should help provide more accurate Snow Depth estimates and forecasts. In order to provide said results, refinements had to be done by extracting data from the GHCN-D product and to evaluate the changes in data source compared to the current baseline. Two winter time periods were evaluated for 2010 and 2015. The results demonstrated the use of GHCN-D in the IMS snow blending algorithm improved accuracy, particularly over the CONUS area.