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A Monolithic Shift from the Monolith: Towards a Smallsat Constellation Configuration for Global Snow Mass Characterization 
Friday, 31 January 2020, 11:00 - 12:30
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Presented by:

Bart Forman

Associate Professor

The Deborah J. Goodings Professor in Engineering for Global Sustainability

Department of Civil and Environmental Engineering

University of Maryland

The global snow science community has reached consensus on few topics but is in overall

agreement that no single measurement device – be it ground-based, airborne, or space-borne – can

accurately measure snow mass at all places on Earth and at all times of the snow season. However,

many snow science discussions revolve around the idea of building a singular, monolithic, spaceborne

sensor in order to quantify terrestrial snow mass. This talk is an attempt to move away from

that discussion and toward the notion of a distributed sets of sensors installed on “smallsats” (e.g.,

cubesats, nanosats) where each sensor has its own strengths (and weaknesses) but where the

collective sum of all of the sensors yields the most “bang for our buck” in terms of global snow

mass characterization during all times of the year. In an effort to find this optimal, hypothetical

satellite configuration (while adhering to a desired budget), the study presented here employs

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 space-based

sensors. For a given orbital configuration and mix of sensors, these simulations help quantify how

much of the global seasonal snow 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 terms of

accuracy or precision. The results of the simulations will help make progress toward accurate global

snow products.

Location Conference Hall, Network Building


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