Quantifying Uncertainties in the Quiet‐Time Ionosphere‐Thermosphere Using WAM‐IPE
Abstract This study presents a data‐driven approach to quantify uncertainties in the ionosphere‐thermosphere (IT) system due to varying solar wind parameters (drivers) during quiet conditions (Kp < 4) and fixed solar radiation and lower atmospheric conditions representative of 16 March 2013. Ense...
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| Format: | Article |
| Language: | English |
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Wiley
2024-02-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2023SW003665 |
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| author | Weijia Zhan Alireza Doostan Eric Sutton Tzu‐Wei Fang |
| author_facet | Weijia Zhan Alireza Doostan Eric Sutton Tzu‐Wei Fang |
| author_sort | Weijia Zhan |
| collection | DOAJ |
| description | Abstract This study presents a data‐driven approach to quantify uncertainties in the ionosphere‐thermosphere (IT) system due to varying solar wind parameters (drivers) during quiet conditions (Kp < 4) and fixed solar radiation and lower atmospheric conditions representative of 16 March 2013. Ensemble simulations of the coupled Whole Atmosphere Model with Ionosphere Plasmasphere Electrodynamics (WAM‐IPE) driven by synthetic solar wind drivers generated through a multi‐channel variational autoencoder (MCVAE) model are obtained. Applying the polynomial chaos expansion (PCE) technique, it is possible to estimate the means and variances of the QoIs as well as the sensitivities of the QoIs with regard to the drivers. Our results highlight unique features of the IT system's uncertainty: (a) the uncertainty of the IT system is larger during nighttime; (b) the spatial distributions of the uncertainty for electron density and zonal drift at fixed local times present 4 peaks in the evening sector, which are associated with the low‐density regions of longitude structure of electron density; (c) the uncertainty of the equatorial electron density is highly correlated with the uncertainty of the zonal drift, especially in the evening sector, while it is weakly correlated with the vertical drift. A variance‐based global sensitivity analysis suggests that the IMF Bz plays a dominant role in the uncertainty of electron density. A further discussion shows that the uncertainty of the IT system is determined by the magnitudes and universal time variations of solar wind drivers. Its temporal and spatial distribution can be modulated by the average state of the IT system. |
| format | Article |
| id | doaj-art-0ee299064adc40d399563cfdffe3d4f1 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-0ee299064adc40d399563cfdffe3d4f12025-01-14T16:30:41ZengWileySpace Weather1542-73902024-02-01222n/an/a10.1029/2023SW003665Quantifying Uncertainties in the Quiet‐Time Ionosphere‐Thermosphere Using WAM‐IPEWeijia Zhan0Alireza Doostan1Eric Sutton2Tzu‐Wei Fang3Space Weather Technology Research and Education Center (SWx TREC) University of Colorado Boulder Boulder CO USAAnn and H.J. Smead Department of Aerospace Engineering Sciences University of Colorado Boulder Boulder CO USASpace Weather Technology Research and Education Center (SWx TREC) University of Colorado Boulder Boulder CO USANOAA Space Weather Prediction Center Boulder CO USAAbstract This study presents a data‐driven approach to quantify uncertainties in the ionosphere‐thermosphere (IT) system due to varying solar wind parameters (drivers) during quiet conditions (Kp < 4) and fixed solar radiation and lower atmospheric conditions representative of 16 March 2013. Ensemble simulations of the coupled Whole Atmosphere Model with Ionosphere Plasmasphere Electrodynamics (WAM‐IPE) driven by synthetic solar wind drivers generated through a multi‐channel variational autoencoder (MCVAE) model are obtained. Applying the polynomial chaos expansion (PCE) technique, it is possible to estimate the means and variances of the QoIs as well as the sensitivities of the QoIs with regard to the drivers. Our results highlight unique features of the IT system's uncertainty: (a) the uncertainty of the IT system is larger during nighttime; (b) the spatial distributions of the uncertainty for electron density and zonal drift at fixed local times present 4 peaks in the evening sector, which are associated with the low‐density regions of longitude structure of electron density; (c) the uncertainty of the equatorial electron density is highly correlated with the uncertainty of the zonal drift, especially in the evening sector, while it is weakly correlated with the vertical drift. A variance‐based global sensitivity analysis suggests that the IMF Bz plays a dominant role in the uncertainty of electron density. A further discussion shows that the uncertainty of the IT system is determined by the magnitudes and universal time variations of solar wind drivers. Its temporal and spatial distribution can be modulated by the average state of the IT system.https://doi.org/10.1029/2023SW003665uncertainty quantificationionosphere‐thermospheresolar windsensitivity analysis |
| spellingShingle | Weijia Zhan Alireza Doostan Eric Sutton Tzu‐Wei Fang Quantifying Uncertainties in the Quiet‐Time Ionosphere‐Thermosphere Using WAM‐IPE Space Weather uncertainty quantification ionosphere‐thermosphere solar wind sensitivity analysis |
| title | Quantifying Uncertainties in the Quiet‐Time Ionosphere‐Thermosphere Using WAM‐IPE |
| title_full | Quantifying Uncertainties in the Quiet‐Time Ionosphere‐Thermosphere Using WAM‐IPE |
| title_fullStr | Quantifying Uncertainties in the Quiet‐Time Ionosphere‐Thermosphere Using WAM‐IPE |
| title_full_unstemmed | Quantifying Uncertainties in the Quiet‐Time Ionosphere‐Thermosphere Using WAM‐IPE |
| title_short | Quantifying Uncertainties in the Quiet‐Time Ionosphere‐Thermosphere Using WAM‐IPE |
| title_sort | quantifying uncertainties in the quiet time ionosphere thermosphere using wam ipe |
| topic | uncertainty quantification ionosphere‐thermosphere solar wind sensitivity analysis |
| url | https://doi.org/10.1029/2023SW003665 |
| work_keys_str_mv | AT weijiazhan quantifyinguncertaintiesinthequiettimeionospherethermosphereusingwamipe AT alirezadoostan quantifyinguncertaintiesinthequiettimeionospherethermosphereusingwamipe AT ericsutton quantifyinguncertaintiesinthequiettimeionospherethermosphereusingwamipe AT tzuweifang quantifyinguncertaintiesinthequiettimeionospherethermosphereusingwamipe |