A SNOW DEPTH ANALYSIS FOR THE NEXT GENERATION OF GLOBAL PREDICTION SYSTEMS
Journal Title: International journal of ecosystems and ecology science (IJEES) - Year 2018, Vol 8, Issue 2
Abstract
Information on snow depth is a primary input to NOAA’s operational numerical weather prediction (NWP) models. Current NOAA’s National Centers for Environmental Prediction (NCEP) operational NWP models rely on snow depth observational data for their land surface model initializations. A new snow depth analysis system based on optimal interpolation method is being developed for NCEP NWP models with improved spatial resolution and utilization of multiple sources of observational data. The analysis blends bias-corrected satellite snow depth from the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on board the Global Change Observation Mission 1st - Water (GCOM-W1) with an extended network of in-situ stations from the Global Historical Climatology Network (GHCN) to generate snow depth globally at 12 km resolution. A simplified snow accumulation and melt model driven by Global Forecast System (GFS)’s precipitation and temperature has been developed to estimate first guess snow depth fields. Details of the main components of the algorithm and evaluation results are presented.
Authors and Affiliations
Cezar Kongoli12, Peter Romanov42, Sean Helfrich2, Jiarui Dong3, Michael Ek3, Tomas Smith2
Keywords
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- EP ID EP278191
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