MSIS‐UQ: Calibrated and Enhanced NRLMSIS 2.0 Model With Uncertainty Quantification
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...
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| Format: | Article |
| Language: | English |
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Wiley
2022-11-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2022SW003267 |
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| author | Richard J. Licata Piyush M. Mehta Daniel R. Weimer W. Kent Tobiska Jean Yoshii |
| author_facet | Richard J. Licata Piyush M. Mehta Daniel R. Weimer W. Kent Tobiska Jean Yoshii |
| author_sort | Richard J. Licata |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0e506a06924c414bbea6b710470bf3d8 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2022-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-0e506a06924c414bbea6b710470bf3d82025-01-14T16:35:33ZengWileySpace Weather1542-73902022-11-012011n/an/a10.1029/2022SW003267MSIS‐UQ: Calibrated and Enhanced NRLMSIS 2.0 Model With Uncertainty QuantificationRichard J. Licata0Piyush M. Mehta1Daniel R. Weimer2W. Kent Tobiska3Jean Yoshii4Department of Mechanical and Aerospace Enginering West Virginia University Morgantown WV USADepartment of Mechanical and Aerospace Enginering West Virginia University Morgantown WV USACenter for Space Science and Engineering Research Virginia Tech Blacksburg VA USASpace Environment Technologies Pacific Palisades CA USASpace Environment Technologies Pacific Palisades CA USAAbstract 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.https://doi.org/10.1029/2022SW003267thermospheremachine learninguncertainty quantificationovercoolingexospheric temperature |
| spellingShingle | Richard J. Licata Piyush M. Mehta Daniel R. Weimer W. Kent Tobiska Jean Yoshii MSIS‐UQ: Calibrated and Enhanced NRLMSIS 2.0 Model With Uncertainty Quantification Space Weather thermosphere machine learning uncertainty quantification overcooling exospheric temperature |
| title | MSIS‐UQ: Calibrated and Enhanced NRLMSIS 2.0 Model With Uncertainty Quantification |
| title_full | MSIS‐UQ: Calibrated and Enhanced NRLMSIS 2.0 Model With Uncertainty Quantification |
| title_fullStr | MSIS‐UQ: Calibrated and Enhanced NRLMSIS 2.0 Model With Uncertainty Quantification |
| title_full_unstemmed | MSIS‐UQ: Calibrated and Enhanced NRLMSIS 2.0 Model With Uncertainty Quantification |
| title_short | MSIS‐UQ: Calibrated and Enhanced NRLMSIS 2.0 Model With Uncertainty Quantification |
| title_sort | msis uq calibrated and enhanced nrlmsis 2 0 model with uncertainty quantification |
| topic | thermosphere machine learning uncertainty quantification overcooling exospheric temperature |
| url | https://doi.org/10.1029/2022SW003267 |
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