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MSIS-UQ: Calibrated and Enhanced NRLMSIS 2.0 Model With Uncertainty Quantification

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dc.contributor.authorLicata, Richard J.en
dc.contributor.authorMehta, Piyush M.en
dc.contributor.authorWeimer, Daniel R.en
dc.contributor.authorTobiska, W. Kenten
dc.contributor.authorYoshii, Jeanen
dc.date.accessioned2023-04-26T12:58:47Zen
dc.date.available2023-04-26T12:58:47Zen
dc.date.issued2022-11en
dc.description.abstractThe Mass Spectrometer and Incoherent Scatter radar (MSIS) model family has been developed and improved since the early 1970's. The most recent version of MSIS is the Naval Research Laboratory (NRL) MSIS 2.0 empirical atmospheric model. NRLMSIS 2.0 provides species density, mass density, and temperature estimates as function of location and space weather conditions. MSIS models have long been a popular choice of thermosphere model in the research and operations community alike, but-like many models-does not provide uncertainty estimates. In this work, we develop an exospheric temperature model based in machine learning that can be used with NRLMSIS 2.0 to calibrate it relative to high-fidelity satellite density estimates directly through the exospheric temperature parameter. Instead of providing point estimates, our model (called MSIS-UQ) outputs a distribution which is assessed using a metric called the calibration error score. We show that MSIS-UQ debiases NRLMSIS 2.0 resulting in reduced differences between model and satellite density of 25% and is 11% closer to satellite density than the Space Force's High Accuracy Satellite Drag Model. We also show the model's uncertainty estimation capabilities by generating altitude profiles for species density, mass density, and temperature. This explicitly demonstrates how exospheric temperature probabilities affect density and temperature profiles within NRLMSIS 2.0. Another study displays improved post-storm overcooling capabilities relative to NRLMSIS 2.0 alone, enhancing the phenomena that it can capture.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1029/2022SW003267en
dc.identifier.eissn1542-7390en
dc.identifier.issue11en
dc.identifier.othere2022SW003267en
dc.identifier.urihttp://hdl.handle.net/10919/114799en
dc.identifier.volume20en
dc.language.isoenen
dc.publisherAmerican Geophysical Unionen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectthermosphereen
dc.subjectmachine learningen
dc.subjectuncertainty quantificationen
dc.subjectovercoolingen
dc.subjectexospheric temperatureen
dc.titleMSIS-UQ: Calibrated and Enhanced NRLMSIS 2.0 Model With Uncertainty Quantificationen
dc.title.serialSpace Weather-The International Journal of Research and Applicationsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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