Improving Forecasting Ability of GITM Using Data‐Driven Model Refinement
Abstract At altitudes below about 600 km, satellite drag is one of the most important and variable forces acting on a satellite. Neutral mass density predictions in the upper atmosphere are therefore critical for (a) designing satellites; (b) performing adjustments to stay in an intended orbit; and...
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| Format: | Article |
| Language: | English |
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Wiley
2023-03-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2022SW003290 |
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| _version_ | 1841536472241405952 |
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| author | Brandon M. Ponder Aaron J. Ridley Ankit Goel D. S. Bernstein |
| author_facet | Brandon M. Ponder Aaron J. Ridley Ankit Goel D. S. Bernstein |
| author_sort | Brandon M. Ponder |
| collection | DOAJ |
| description | Abstract At altitudes below about 600 km, satellite drag is one of the most important and variable forces acting on a satellite. Neutral mass density predictions in the upper atmosphere are therefore critical for (a) designing satellites; (b) performing adjustments to stay in an intended orbit; and (c) collision avoidance maneuver planning. Density predictions have a great deal of uncertainty, including model biases and model misrepresentation of the atmospheric response to energy input. These may stem from inaccurate approximations of terms in the Navier‐Stokes equations, unmodeled physics, incorrect boundary conditions, or incorrect parameterizations. Two commonly parameterized source terms are the thermal conduction and eddy diffusion. Both are critical components in the transfer of the heat in the thermosphere. Determining how well the major constituents (N2, O2, and O) are as heat conductors will have effects on the temperature and mass density changes from a heat source. This work shows the effectiveness of using the retrospective cost model refinement (RCMR) technique at removing model bias caused by different sources within the Global Ionosphere Thermosphere Model. Numerical experiments, Challenging Minisatellite Payload and Gravity Recovery and Climate Experiment data during real events are used to show that RCMR can compensate for model bias caused by both inaccurate parameterizations and drivers. RCMR is used to show that eliminating model bias before a storm allows for more accurate predictions throughout the storm. |
| format | Article |
| id | doaj-art-382b6470cd1b4f3388fa0dc096e93360 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2023-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-382b6470cd1b4f3388fa0dc096e933602025-01-14T16:27:17ZengWileySpace Weather1542-73902023-03-01213n/an/a10.1029/2022SW003290Improving Forecasting Ability of GITM Using Data‐Driven Model RefinementBrandon M. Ponder0Aaron J. Ridley1Ankit Goel2D. S. Bernstein3Department of Climate and Space Sciences and Engineering University of Michigan Ann Arbor MI USADepartment of Climate and Space Sciences and Engineering University of Michigan Ann Arbor MI USADepartment of Mechanical Engineering University of Maryland, Baltimore County Baltimore MD USADepartment of Aerospace Engineering University of Michigan Ann Arbor MI USAAbstract At altitudes below about 600 km, satellite drag is one of the most important and variable forces acting on a satellite. Neutral mass density predictions in the upper atmosphere are therefore critical for (a) designing satellites; (b) performing adjustments to stay in an intended orbit; and (c) collision avoidance maneuver planning. Density predictions have a great deal of uncertainty, including model biases and model misrepresentation of the atmospheric response to energy input. These may stem from inaccurate approximations of terms in the Navier‐Stokes equations, unmodeled physics, incorrect boundary conditions, or incorrect parameterizations. Two commonly parameterized source terms are the thermal conduction and eddy diffusion. Both are critical components in the transfer of the heat in the thermosphere. Determining how well the major constituents (N2, O2, and O) are as heat conductors will have effects on the temperature and mass density changes from a heat source. This work shows the effectiveness of using the retrospective cost model refinement (RCMR) technique at removing model bias caused by different sources within the Global Ionosphere Thermosphere Model. Numerical experiments, Challenging Minisatellite Payload and Gravity Recovery and Climate Experiment data during real events are used to show that RCMR can compensate for model bias caused by both inaccurate parameterizations and drivers. RCMR is used to show that eliminating model bias before a storm allows for more accurate predictions throughout the storm.https://doi.org/10.1029/2022SW003290thermosphereionospherethermal conductivitymodel refinementstormforecasting |
| spellingShingle | Brandon M. Ponder Aaron J. Ridley Ankit Goel D. S. Bernstein Improving Forecasting Ability of GITM Using Data‐Driven Model Refinement Space Weather thermosphere ionosphere thermal conductivity model refinement storm forecasting |
| title | Improving Forecasting Ability of GITM Using Data‐Driven Model Refinement |
| title_full | Improving Forecasting Ability of GITM Using Data‐Driven Model Refinement |
| title_fullStr | Improving Forecasting Ability of GITM Using Data‐Driven Model Refinement |
| title_full_unstemmed | Improving Forecasting Ability of GITM Using Data‐Driven Model Refinement |
| title_short | Improving Forecasting Ability of GITM Using Data‐Driven Model Refinement |
| title_sort | improving forecasting ability of gitm using data driven model refinement |
| topic | thermosphere ionosphere thermal conductivity model refinement storm forecasting |
| url | https://doi.org/10.1029/2022SW003290 |
| work_keys_str_mv | AT brandonmponder improvingforecastingabilityofgitmusingdatadrivenmodelrefinement AT aaronjridley improvingforecastingabilityofgitmusingdatadrivenmodelrefinement AT ankitgoel improvingforecastingabilityofgitmusingdatadrivenmodelrefinement AT dsbernstein improvingforecastingabilityofgitmusingdatadrivenmodelrefinement |