Integrating Modeling and Satellite Observations in a Novel Snow Observing Strategy

Seasonal snow has an important role in local, regional, and global water budgets, and springtime snowmelt is a major contributor to annual streamflow for many basins across the globe. Despite the importance of snow, one single sensor likely is not capable of observing all types of snow in all conditions. Therefore, seasonal snow is an ideal candidate for exploring observing strategies for integrating existing sensors and future mission concepts. Here we use the NASA Land Information System (LIS) modeling framework to explore a snow observing strategy for combining multi-sensor observations, modeling, and assimilation and to identify observational gaps and needs. We use the Missouri River Basin as a representative case study for testing and evaluating the observing strategy. We consider water year 2019, which was exceptional for the springtime rain-on-snow event that resulted in rapid snow melt and severe flooding along the Missouri River. We combine orbit tracks from the NASA Trade-space Analysis Tool for Designing Constellations (TAT-C) with synthetic observations generated in LIS to run assimilation experiments. These hypothetical scenarios allow us to evaluate how assimilating observations from existing and future sensors impacts modeled estimates of snow water equivalent and streamflow. We investigate how to integrate multiple snow observations, such as from passive microwave sensors and synthetic aperture radar, into the assimilation system. We also use this modeling environment to identify the need for additional observations, which can be used as a guideline for collecting observations from taskable assets such as commercial satellite platforms. The results from the hypothetical experiment presented here could be useful for designing a future global snow system that identifies the observational needs from multiple sensors and combines them into one assimilation framework.