SET Awarded NASA SBIR Grant to improve operational density forecasting

SET Awarded NASA SBIR Grant to improve operational density forecasting

BOULDER, COLORADO. Space Environment Technologies (SET) has been awarded a NASA Small Business Innovation Research (SBIR) Phase I grant for improving thermosphere densities.

Under the direction of Principal Investigator and Director of SET’s Space Weather Division Shaylah Mustschler, SET will improve forecast solar and geomagnetic indices inputs into the U.S. Space Force’s (USSF) High Accuracy Satellite Drag Model (HASDM). HASDM is the thermospheric density model used for operations by the USSF for space object catalog maintenance and conjunction assessment.

The two space weather indices of interest in this project are the S10 index, which is a measure of extreme ultraviolet rays specifically between 26-34 nm (the primary EUV index in JB2008), and the Dst (Disturbance storm time) index, which indicates the strength of a geomagnetic storm in this measure of the magnetospheric ring current.

To improve the accuracy of the forecast S10 index, SET will explore integration of observation-based predictions from solar source surface maps from the Solar Indices Forecasting Tool (SIFT) S10 developed by the National Solar Observatory. The SIFT utilizes the solar near-side, i.e., the Earth-side window, magnetic field distribution estimated with the ADAPT flux transport model. The results provide an output for use by thermospheric density models. To improve Dst forecasts, the SET team will use machine learning (ML) models developed by A. Hu and B. Swiger, respectively, from University of Colorado Boulder under the direction of E. Camporeale, to improve short-term predictions out to 6 hours and longer-term predictions up to 7 days.

During Phase 1 of this project, Dr. Mutschler and her collaborators will validate the SIFT forecast S10 methodology, the short-term (Hu) and long-term (Swiger) Dst forecast ML models in comparison with SET’s current operational forecasting capabilities. The end result will allow the team to incorporate the SIFT S10 and ML Dst forecasts in operations.

This project is part of SET’s ongoing effort to improve safety and efficiency in characterizing the Low Earth Orbit (LEO) operational domain for Space Traffic Management.