Large-scale high resolution (1-4 KM) estimates of daily snowpack properties snow cover area (SCA), snow water equivalent (SWE) and snow depth (SD) - over seasonal snow covered land are needed for…

This cooperative agreement award from the National Oceanic and Atmospheric Administration will fund the development of global, high-resolution snowpack property datasets and analysis products at the University of Maryland, College Park from August 2021 to July 2023. The $205,000 award supports creating the first multi-decadal, 4-km resolution daily snow cover area, snow water equivalent, and snow depth dataset and analysis system under NOAA's Weather and Air Quality Research program (CFDA 11.459). The new global snow monitoring products aim to improve weather, climate and hydrological models by addressing limitations in coverage, consistency and resolution of existing snow analysis tools. Spatially and temporally comprehensive snowpack observation data are critical inputs for forecasting and research but are currently lacking over many regions. The University of Maryland will leverage its expertise to generate enhanced high-resolution snowpack characterization to advance atmospheric sciences and protection of life and property.

Large-scale high resolution (1-4 KM) estimates of daily snowpack properties snow cover area (SCA), snow water equivalent (SWE) and snow depth (SD) - over seasonal snow covered land are needed for detailed assessments of and inputs to weather, climate and hydrological prediction models especially over poorly monitored areas. Snow monitoring products that are most frequently used at NOAA, interactive multisensor snow and ICE mapping system (IMS) snow charts and USAF 557TH weather wing (WW)'S current snow depth analysis model (SNODEP) analysis, have critical shortcomings in temporal consistency, coverage and/or spatial resolution. High resolution analysis products such as noaas snow data assimilation system (1 KM ) is performed only over continental US, has a lifespan since 2000, and its SWE analysis depends (as in IMS and SNODEP) on the human analyst. The overall objective of the proposed research is to develop a first-of-its kind global 4-KM multi decadal SCA/SWE/SD dataset and…