A methodology for reduced order modeling and calibration of the upper atmosphere

Piyush M Mehta, Richard Linares

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Atmospheric drag is the largest source of uncertainty in accurately predicting the orbit of satellites in low Earth orbit (LEO). Accurately predicting drag for objects that traverse LEO is critical to Space Situational Awareness. Atmospheric models used for orbital drag calculations can be characterized either as empirical or physics-based (first principles based). Empirical models are fast to evaluate but offer limited real-time predictive/forecasting ability, while physics-based models offer greater predictive/forecasting ability but require dedicated parallel computational resources. Also, calibration with accurate data is required for either type of models. This paper presents a new methodology based on proper or-throgonal decomposition (POD) towards development of a quasi-physical, predictive, reduced order model that combines the speed of empirical and the predictive/forecasting capabilities of physics-based models. The methodology is developed to reduce the high-dimensionality of physics-based models while maintaining its capabilities. We develop the methodology using the Naval Research Lab’s MSIS model and show that the diurnal and seasonal variations can be captured using a small number of modes and parameters. We also present calibration of the reduced order model using the CHAMP and GRACE accelerometer-derived densities. Results show that the method performs well for modeling and calibration of the upper atmosphere.

Original languageEnglish (US)
Title of host publicationASTRODYNAMICS 2017
EditorsJohn H. Seago, Nathan J. Strange, Daniel J. Scheeres, Jeffrey S. Parker
PublisherUnivelt Inc.
Number of pages22
ISBN (Print)9780877036456
StatePublished - Jan 1 2018
EventAAS/AIAA Astrodynamics Specialist Conference, 2017 - Stevenson, United States
Duration: Aug 20 2017Aug 24 2017

Publication series

NameAdvances in the Astronautical Sciences
ISSN (Print)0065-3438


OtherAAS/AIAA Astrodynamics Specialist Conference, 2017
Country/TerritoryUnited States


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