An adjoint sensitivity-based data assimilation method and its comparison with existing variational methods
An adjoint sensitivity-based data assimilation (ASDA) method is proposed and applied to a heavy rainfall case over the Korean Peninsula. The heavy rainfall case, which occurred on 26 July 2006, caused torrential rainfall over the central part of the Korean Peninsula. The mesoscale convective system...
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| Format: | Article |
| Language: | English |
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Stockholm University Press
2014-01-01
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| Series: | Tellus: Series A, Dynamic Meteorology and Oceanography |
| Subjects: | |
| Online Access: | http://www.tellusa.net/index.php/tellusa/article/download/21584/pdf_1 |
| _version_ | 1811301192711536640 |
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| author | Yonghan Choi Gyu-HO Lim Dong-Kyou Lee Xiang-Yu Huang |
| author_facet | Yonghan Choi Gyu-HO Lim Dong-Kyou Lee Xiang-Yu Huang |
| author_sort | Yonghan Choi |
| collection | DOAJ |
| description | An adjoint sensitivity-based data assimilation (ASDA) method is proposed and applied to a heavy rainfall case over the Korean Peninsula. The heavy rainfall case, which occurred on 26 July 2006, caused torrential rainfall over the central part of the Korean Peninsula. The mesoscale convective system (MCS) related to the heavy rainfall was classified as training line/adjoining stratiform (TL/AS)-type for the earlier period, and back building (BB)-type for the later period. In the ASDA method, an adjoint model is run backwards with forecast-error gradient as input, and the adjoint sensitivity of the forecast error to the initial condition is scaled by an optimal scaling factor. The optimal scaling factor is determined by minimising the observational cost function of the four-dimensional variational (4D-Var) method, and the scaled sensitivity is added to the original first guess. Finally, the observations at the analysis time are assimilated using a 3D-Var method with the improved first guess. The simulated rainfall distribution is shifted northeastward compared to the observations when no radar data are assimilated or when radar data are assimilated using the 3D-Var method. The rainfall forecasts are improved when radar data are assimilated using the 4D-Var or ASDA method. Simulated atmospheric fields such as horizontal winds, temperature, and water vapour mixing ratio are also improved via the 4D-Var or ASDA method. Due to the improvement in the analysis, subsequent forecasts appropriately simulate the observed features of the TL/AS- and BB-type MCSs and the corresponding heavy rainfall. The computational cost associated with the ASDA method is significantly lower than that of the 4D-Var method. |
| first_indexed | 2024-04-13T07:04:30Z |
| format | Article |
| id | doaj.art-8b0a1ace327a4814ab54ff8477c1191b |
| institution | Directory Open Access Journal |
| issn | 0280-6495 1600-0870 |
| language | English |
| last_indexed | 2024-04-13T07:04:30Z |
| publishDate | 2014-01-01 |
| publisher | Stockholm University Press |
| record_format | Article |
| series | Tellus: Series A, Dynamic Meteorology and Oceanography |
| spelling | doaj.art-8b0a1ace327a4814ab54ff8477c1191b2022-12-22T02:57:02ZengStockholm University PressTellus: Series A, Dynamic Meteorology and Oceanography0280-64951600-08702014-01-0166011810.3402/tellusa.v66.2158421584An adjoint sensitivity-based data assimilation method and its comparison with existing variational methodsYonghan Choi0Gyu-HO Lim1Dong-Kyou Lee2Xiang-Yu Huang3School of Earth and Environmental Sciences, Seoul National University, Seoul, KoreaSchool of Earth and Environmental Sciences, Seoul National University, Seoul, KoreaSchool of Earth and Environmental Sciences, Seoul National University, Seoul, KoreaNational Center for Atmospheric Research, Boulder, CO, USAAn adjoint sensitivity-based data assimilation (ASDA) method is proposed and applied to a heavy rainfall case over the Korean Peninsula. The heavy rainfall case, which occurred on 26 July 2006, caused torrential rainfall over the central part of the Korean Peninsula. The mesoscale convective system (MCS) related to the heavy rainfall was classified as training line/adjoining stratiform (TL/AS)-type for the earlier period, and back building (BB)-type for the later period. In the ASDA method, an adjoint model is run backwards with forecast-error gradient as input, and the adjoint sensitivity of the forecast error to the initial condition is scaled by an optimal scaling factor. The optimal scaling factor is determined by minimising the observational cost function of the four-dimensional variational (4D-Var) method, and the scaled sensitivity is added to the original first guess. Finally, the observations at the analysis time are assimilated using a 3D-Var method with the improved first guess. The simulated rainfall distribution is shifted northeastward compared to the observations when no radar data are assimilated or when radar data are assimilated using the 3D-Var method. The rainfall forecasts are improved when radar data are assimilated using the 4D-Var or ASDA method. Simulated atmospheric fields such as horizontal winds, temperature, and water vapour mixing ratio are also improved via the 4D-Var or ASDA method. Due to the improvement in the analysis, subsequent forecasts appropriately simulate the observed features of the TL/AS- and BB-type MCSs and the corresponding heavy rainfall. The computational cost associated with the ASDA method is significantly lower than that of the 4D-Var method.www.tellusa.net/index.php/tellusa/article/download/21584/pdf_1ASDA method4D-Varradar dataheavy rainfallMCS |
| spellingShingle | Yonghan Choi Gyu-HO Lim Dong-Kyou Lee Xiang-Yu Huang An adjoint sensitivity-based data assimilation method and its comparison with existing variational methods Tellus: Series A, Dynamic Meteorology and Oceanography ASDA method 4D-Var radar data heavy rainfall MCS |
| title | An adjoint sensitivity-based data assimilation method and its comparison with existing variational methods |
| title_full | An adjoint sensitivity-based data assimilation method and its comparison with existing variational methods |
| title_fullStr | An adjoint sensitivity-based data assimilation method and its comparison with existing variational methods |
| title_full_unstemmed | An adjoint sensitivity-based data assimilation method and its comparison with existing variational methods |
| title_short | An adjoint sensitivity-based data assimilation method and its comparison with existing variational methods |
| title_sort | adjoint sensitivity based data assimilation method and its comparison with existing variational methods |
| topic | ASDA method 4D-Var radar data heavy rainfall MCS |
| url | http://www.tellusa.net/index.php/tellusa/article/download/21584/pdf_1 |
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