MSIS‐UQ: Calibrated and Enhanced NRLMSIS 2.0 Model With Uncertainty Quantification. Issue 11 (8th November 2022)

• Record Type: Journal Article
• Authors: Licata, Richard J.; Mehta, Piyush M.; Weimer, Daniel R.; Tobiska, W. Kent; Yoshii, Jean
• Is Part Of: Space weather. Volume 20:Issue 11(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-11-08
• Subjects: thermosphere -- machine learning -- uncertainty quantification -- overcooling -- exospheric temperature; Space environment -- Periodicals; 551.509992
• DOI: 10.1029/2022SW003267
• Languages: English
• ISSNs: 1542-7390

Abstract

Abstract: The 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. Plain Language Summary: Uncertainty quantification (UQ) in modeling can be thought of as providing a range of values that can contain a solution as opposed to only providing a point estimate. UQ is currently not common‐place when it comes to atmospheric modeling even while we use imperfect models. One of the most commonly used atmospheric model families is the Mass Spectrometer and Incoherent Scatter radar (MSIS) series. The current iteration in the series is the Naval Research Laboratory (NRL) MSIS 2.0 empirical atmosphere model. NRLMSIS 2.0 provides estimates of multiple atmospheric constituents and temperature which have a wide array of applications. Using data derived from satellite sensors, we develop a model called MSIS‐UQ that predicts a parameter (exospheric temperature) which can be input to NRLMSIS 2.0 to provide calibrated estimates. Furthermore, MSIS‐UQ predicts a distribution of temperatures as opposed to a point estimate. We show how the MSIS‐UQ temperature predictions improve NRLMSIS 2.0 estimates of density by 25% and explore the usefulness of the uncertainty estimation capabilities of the model. We also perform a study where the magnetic model drivers are varied to investigate evidence of atmospheric overcooling after strong storms. Key Points: A global exospheric temperature model with uncertainty estimation capabilities is developed for NRLMSIS 2.0—called MSIS‐UQ MSIS‐UQ provides an avenue to obtain calibrated uncertainty estimates from NRLMSIS 2.0 density predictions MSIS‐UQ allows for NRLMSIS 2.0 to capture effects of post‐storm thermospheric overcooling