Multiresolution Data Assimilation for Auroral Energy Flux and Mean Energy Using DMSP SSUSI, THEMIS ASI, and An Empirical Model
Abstract We apply a multiresolution Gaussian process model (Lattice Kriging) to combine satellite observations, ground‐based observations, and an empirical auroral model, to produce the assimilation of auroral energy flux and mean energy over high‐latitude regions. Compared to a simple padding, the...
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Wiley
2022-09-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2022SW003146 |
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| author | Haonan Wu Xiyan Tan Qiong Zhang Whitney Huang Xian Lu Yukitoshi Nishimura Yongliang Zhang |
| author_facet | Haonan Wu Xiyan Tan Qiong Zhang Whitney Huang Xian Lu Yukitoshi Nishimura Yongliang Zhang |
| author_sort | Haonan Wu |
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| description | Abstract We apply a multiresolution Gaussian process model (Lattice Kriging) to combine satellite observations, ground‐based observations, and an empirical auroral model, to produce the assimilation of auroral energy flux and mean energy over high‐latitude regions. Compared to a simple padding, the assimilation coherently combines various data inputs leading to continuous transitions between different datasets. The multiresolution modeling capability is achieved by allocating multiple layers of basis functions with different resolutions. Higher‐resolution fitting results capture more mesoscale (10–100 s km) structures such as auroral arcs, than the low‐resolution ones and the empirical model. To better reconcile different datasets, two preprocessing steps, temporal interpolation of satellite data and spatial down‐sampling of low‐fidelity data, are implemented. The inherent smoothing effect of the fitting, which causes an unrealistic spreading of the aurora, is mitigated by a post processing step: the K Nearest Neighbor (KNN) algorithm. KNN identifies the probability of a region with significant aurora and thereby eliminates those regions with low values. Thereby, this methodology can be used to maintain realistic and mesoscale auroral structures without boundary issues. We then run the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM) driven by the high‐ and low‐resolution auroral assimilations and compare total electron contents (TECs). TIEGCM driven by data assimilation produces enhanced TECs by a factor of ∼2 than the one driven by the empirical aurora, and high‐resolution results show mesoscale structures. Our study shows the value of incorporating realistic auroral inputs via assimilation to drive ionosphere‐thermosphere models for better understanding the consequences of mesoscale phenomena. |
| format | Article |
| id | doaj-art-eb46275506754eb0b51e4aa85ccb5e9e |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2022-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-eb46275506754eb0b51e4aa85ccb5e9e2025-01-14T16:31:13ZengWileySpace Weather1542-73902022-09-01209n/an/a10.1029/2022SW003146Multiresolution Data Assimilation for Auroral Energy Flux and Mean Energy Using DMSP SSUSI, THEMIS ASI, and An Empirical ModelHaonan Wu0Xiyan Tan1Qiong Zhang2Whitney Huang3Xian Lu4Yukitoshi Nishimura5Yongliang Zhang6Department of Physics and Astronomy Clemson University Clemson SC USASchool of Mathematical and Statistical Sciences Clemson University Clemson SC USASchool of Mathematical and Statistical Sciences Clemson University Clemson SC USASchool of Mathematical and Statistical Sciences Clemson University Clemson SC USADepartment of Physics and Astronomy Clemson University Clemson SC USADepartment of Electrical and Computer Engineering Center for Space Physics Boston University Boston MA USAApplied Physics Laboratory Johns Hopkins University Laurel MD USAAbstract We apply a multiresolution Gaussian process model (Lattice Kriging) to combine satellite observations, ground‐based observations, and an empirical auroral model, to produce the assimilation of auroral energy flux and mean energy over high‐latitude regions. Compared to a simple padding, the assimilation coherently combines various data inputs leading to continuous transitions between different datasets. The multiresolution modeling capability is achieved by allocating multiple layers of basis functions with different resolutions. Higher‐resolution fitting results capture more mesoscale (10–100 s km) structures such as auroral arcs, than the low‐resolution ones and the empirical model. To better reconcile different datasets, two preprocessing steps, temporal interpolation of satellite data and spatial down‐sampling of low‐fidelity data, are implemented. The inherent smoothing effect of the fitting, which causes an unrealistic spreading of the aurora, is mitigated by a post processing step: the K Nearest Neighbor (KNN) algorithm. KNN identifies the probability of a region with significant aurora and thereby eliminates those regions with low values. Thereby, this methodology can be used to maintain realistic and mesoscale auroral structures without boundary issues. We then run the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM) driven by the high‐ and low‐resolution auroral assimilations and compare total electron contents (TECs). TIEGCM driven by data assimilation produces enhanced TECs by a factor of ∼2 than the one driven by the empirical aurora, and high‐resolution results show mesoscale structures. Our study shows the value of incorporating realistic auroral inputs via assimilation to drive ionosphere‐thermosphere models for better understanding the consequences of mesoscale phenomena.https://doi.org/10.1029/2022SW003146auroral data assimilationGaussian process model (Lattice Kriging)TIEGCMTHEMIS ASIDMSP SSUSImultiresolution assimilation |
| spellingShingle | Haonan Wu Xiyan Tan Qiong Zhang Whitney Huang Xian Lu Yukitoshi Nishimura Yongliang Zhang Multiresolution Data Assimilation for Auroral Energy Flux and Mean Energy Using DMSP SSUSI, THEMIS ASI, and An Empirical Model Space Weather auroral data assimilation Gaussian process model (Lattice Kriging) TIEGCM THEMIS ASI DMSP SSUSI multiresolution assimilation |
| title | Multiresolution Data Assimilation for Auroral Energy Flux and Mean Energy Using DMSP SSUSI, THEMIS ASI, and An Empirical Model |
| title_full | Multiresolution Data Assimilation for Auroral Energy Flux and Mean Energy Using DMSP SSUSI, THEMIS ASI, and An Empirical Model |
| title_fullStr | Multiresolution Data Assimilation for Auroral Energy Flux and Mean Energy Using DMSP SSUSI, THEMIS ASI, and An Empirical Model |
| title_full_unstemmed | Multiresolution Data Assimilation for Auroral Energy Flux and Mean Energy Using DMSP SSUSI, THEMIS ASI, and An Empirical Model |
| title_short | Multiresolution Data Assimilation for Auroral Energy Flux and Mean Energy Using DMSP SSUSI, THEMIS ASI, and An Empirical Model |
| title_sort | multiresolution data assimilation for auroral energy flux and mean energy using dmsp ssusi themis asi and an empirical model |
| topic | auroral data assimilation Gaussian process model (Lattice Kriging) TIEGCM THEMIS ASI DMSP SSUSI multiresolution assimilation |
| url | https://doi.org/10.1029/2022SW003146 |
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