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New MAPP Research evaluates method to improve snow depth and snow cover estimations


A new study funded by CPO’s MAPP Program evaluates whether incorporating observational albedo information into models, through a process known as data assimilation, improves snow depth and extent estimates. Changes in land surface albedo (or the reflectivity of surfaces) directly impacts snow accumulation and the timing of snowmelt, which are both critical for water resource management. In the new paper, the authors evaluated the impact of assimilating surface albedo information from MODIS into a land surface model, called Noah-MP, on snow cover estimates over the continental United States from 2000 to 2017. They found that by assimilating albedo observations, model estimates of snow depth and snow cover significantly improved over the High Plains and parts of the Rocky Mountains. This is an area where accurate snow depth and extent estimates are critical for spring/summer water resource estimation, so these improvements could help water managers better understand and anticipate changes in the snow reservoir. Most improvements from assimilation are observed over locations with moderate vegetation and lower elevation. In addition, the authors found that the configuration that jointly incorporates surface albedo and fractional snow cover measurements provides the most beneficial improvements. Overall, this study points to the need for improving the albedo formulations in land surface models and incorporating observational uncertainties with data assimilation configurations that include improved descriptions of albedo. This research is being conducted under a NIDIS and MAPP-funded project titled “Operational Transition of Soil Moisture and Snow Data Assimilation in the North American Land Data Assimilation System.”

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