Towards Optimal Snow Information from Space: A Synthetic Comparison of Observation Constellation Configurations
Presenting Author: Barton Forman
Organization: University of Maryland
Co-Author(s): Barton A. Forman1, Sujay Kumar2, Jonathan P. Verville3, Joseph E. Gurganus3, Lizhao Wang1, Jongmin Park1, and Jawairia Ahmad1 1University of Maryland College Park, Department of Civil and Environmental Engineering 2NASA Goddard Space Flight Center, Hydrological Sciences Laboratory 3NASA Goddard Space Flight Center, Software Engineering Division
Snow is important: It is a significant source of freshwater for human consumption, agriculture, hydropower, and manufacturing. In addition, snow is a first-order control on the land surface energy balance, and as such, plays a key role in regional weather and climatology. Snow has played a key role in major floods and droughts. As the water-stressed regions of the world continue to increase due to increasing population, the importance of snow will also increase.
Obtaining an accurate, global picture of snow mass has proven to be a challenge. While observation-based snow products have existed for decades, they generally exclude important regions where observations or retrievals are difficult—for example, in regions of dense forest, optically-thick cloud cover, and complex (mountainous) terrain. Furthermore, no single type of observation or retrieval algorithm works for all types of snow under all conditions.
One solution is to combine different observation types – passive and active microwave along with passive and active optical – and to merge those observations with a land surface model in order to synthesize a global snow mass product. Sensors already in orbit, plus sensors planned to be launched in the future, can be merged to explore a multitude of different information mixes, including LiDAR, RADAR, and radiometry. Within this mix of different sensors is a complex tradespace involving swath widths, repeat intervals, orbit inclinations, footprint size, footprint spacing, observation accuracy, and error characteristics. Over a timespan of years, the mix will continue to change as missions come and go.
The study presented here utilizes NASA’s Land Information System (LIS) in conjunction with the Tradespace Analysis Tool for Constellations (TAT-C) to explore potential combinations of existing and future sensors. For a given orbital configuration and mix of sensors, these simulations help quantify how much of the seasonal snow world can be observed, how often, with what footprint size and spacing and with what swath width. Such information will be highly valuable for informing discussions on future snow mission concepts. It will also highlight where modeling efforts can provide the greatest impact and perhaps indicate the parameters needing the greatest improvements in accuracy or precision. The results of the simulations will help make progress toward accurate global snow products.