Seasonal snow accumulation and melt have a critical role in global water and energy budgets, and the 2017 Earth Science Decadal Survey recommended snow water equivalent (SWE) as an “explorer priority” for future missions. In recent years, NASA SnowEx field campaigns have tested multiple remote sensing techniques as potential mission concepts by collecting ground and airborne observations of snow across a range of land cover and snow types. However, a single sensor will not be able to provide estimates of all snow types in all conditions globally; instead, an integrative approach is required, utilizing multiple observational sources and merging them with models for complete spatiotemporal coverage.
This talk will address the need for an observation-model merging environment for improving estimates of snow properties such as SWE, snow density, snow grain size, and albedo. Recent efforts with the NASA Land Information System (LIS) modeling environment improve snow modeling capabilities by incorporating new snow process models into the framework. Further, data assimilation experiments help demonstrate the utility of satellite observations from both current and proposed technologies.
The data integration approaches described here will be a crucial step in a future snow satellite mission. This presentation will include a discussion on how recent model-data fusion work can inform future plans for a snow satellite mission and how such efforts should meet NASA’s commitment to open science.