Improved Neutral Density Predictions Through Machine Learning Enabled Exospheric Temperature Model
Abstract The community has leveraged satellite accelerometer data sets in previous years to estimate neutral mass density and exospheric temperatures. We utilize derived temperature data and optimize a nonlinear machine‐learned (ML) regression model to improve upon the performance of the linear EXos...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-12-01
|
| Series: | Space Weather |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2021SW002918 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841536435956482048 |
|---|---|
| author | Richard J. Licata Piyush M. Mehta Daniel R. Weimer W. Kent Tobiska |
| author_facet | Richard J. Licata Piyush M. Mehta Daniel R. Weimer W. Kent Tobiska |
| author_sort | Richard J. Licata |
| collection | DOAJ |
| description | Abstract The community has leveraged satellite accelerometer data sets in previous years to estimate neutral mass density and exospheric temperatures. We utilize derived temperature data and optimize a nonlinear machine‐learned (ML) regression model to improve upon the performance of the linear EXospheric TEMPeratures on a PoLyhedrAl gRid (EXTEMPLAR) model. The newly developed EXTEMPLAR‐ML model allows for exospheric temperature predictions at any location with one model and provides performance improvements over its predecessor. We achieve reductions in mean absolute error of 2 K on an independent test set while providing similar error standard deviation values. Comparing the performance of both EXTEMPLAR models and the Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar Extended model (NRLMSISE‐00) across different solar and geomagnetic activity levels shows that EXTEMPLAR‐ML has the lowest mean absolute error across 80% of conditions tested. A study for spatial errors demonstrated that at all grid locations, EXTEMPLAR‐ML has the lowest mean absolute error for over 60% of the polyhedral grid cells on the test set. Like EXTEMPLAR, our model's outputs can be utilized by NRLMSISE‐00 (exclusively) to more closely match satellite accelerometer‐derived densities. We conducted 10 case studies where we compare the accelerometer‐derived temperature and density estimates from four satellites to NRLMSISE‐00, EXTEMPLAR, and EXTEMPALR‐ML during major storm periods. These comparisons show that EXTEMPLAR‐ML generally has the best performance of the three models during storms. We use principal component analysis on EXTEMPLAR‐ML outputs to verify the physical response of the model to its drivers. |
| format | Article |
| id | doaj-art-c21b2920816045c1bdd646ff42702ff9 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2021-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-c21b2920816045c1bdd646ff42702ff92025-01-14T16:27:23ZengWileySpace Weather1542-73902021-12-011912n/an/a10.1029/2021SW002918Improved Neutral Density Predictions Through Machine Learning Enabled Exospheric Temperature ModelRichard J. Licata0Piyush M. Mehta1Daniel R. Weimer2W. Kent Tobiska3Department 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 USAAbstract The community has leveraged satellite accelerometer data sets in previous years to estimate neutral mass density and exospheric temperatures. We utilize derived temperature data and optimize a nonlinear machine‐learned (ML) regression model to improve upon the performance of the linear EXospheric TEMPeratures on a PoLyhedrAl gRid (EXTEMPLAR) model. The newly developed EXTEMPLAR‐ML model allows for exospheric temperature predictions at any location with one model and provides performance improvements over its predecessor. We achieve reductions in mean absolute error of 2 K on an independent test set while providing similar error standard deviation values. Comparing the performance of both EXTEMPLAR models and the Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar Extended model (NRLMSISE‐00) across different solar and geomagnetic activity levels shows that EXTEMPLAR‐ML has the lowest mean absolute error across 80% of conditions tested. A study for spatial errors demonstrated that at all grid locations, EXTEMPLAR‐ML has the lowest mean absolute error for over 60% of the polyhedral grid cells on the test set. Like EXTEMPLAR, our model's outputs can be utilized by NRLMSISE‐00 (exclusively) to more closely match satellite accelerometer‐derived densities. We conducted 10 case studies where we compare the accelerometer‐derived temperature and density estimates from four satellites to NRLMSISE‐00, EXTEMPLAR, and EXTEMPALR‐ML during major storm periods. These comparisons show that EXTEMPLAR‐ML generally has the best performance of the three models during storms. We use principal component analysis on EXTEMPLAR‐ML outputs to verify the physical response of the model to its drivers.https://doi.org/10.1029/2021SW002918machine learningmodel developmentexospheric temperaturethermosphere |
| spellingShingle | Richard J. Licata Piyush M. Mehta Daniel R. Weimer W. Kent Tobiska Improved Neutral Density Predictions Through Machine Learning Enabled Exospheric Temperature Model Space Weather machine learning model development exospheric temperature thermosphere |
| title | Improved Neutral Density Predictions Through Machine Learning Enabled Exospheric Temperature Model |
| title_full | Improved Neutral Density Predictions Through Machine Learning Enabled Exospheric Temperature Model |
| title_fullStr | Improved Neutral Density Predictions Through Machine Learning Enabled Exospheric Temperature Model |
| title_full_unstemmed | Improved Neutral Density Predictions Through Machine Learning Enabled Exospheric Temperature Model |
| title_short | Improved Neutral Density Predictions Through Machine Learning Enabled Exospheric Temperature Model |
| title_sort | improved neutral density predictions through machine learning enabled exospheric temperature model |
| topic | machine learning model development exospheric temperature thermosphere |
| url | https://doi.org/10.1029/2021SW002918 |
| work_keys_str_mv | AT richardjlicata improvedneutraldensitypredictionsthroughmachinelearningenabledexospherictemperaturemodel AT piyushmmehta improvedneutraldensitypredictionsthroughmachinelearningenabledexospherictemperaturemodel AT danielrweimer improvedneutraldensitypredictionsthroughmachinelearningenabledexospherictemperaturemodel AT wkenttobiska improvedneutraldensitypredictionsthroughmachinelearningenabledexospherictemperaturemodel |