Impact of optimizing both model parameters and states simultaneously on ENSO prediction using adaptive hybrid inflation EAKF algorithm within an intermediate coupled model
文献类型：期刊论文
| 作者 | Gong, Mengmeng2; Zhang, Liang2; Zhang, Shaoqing3,4; Gao, Chuan1; Chen, Xingrong5; Zhang, Xuefeng2 |
| 刊名 | CLIMATE DYNAMICS
 |
| 出版日期 | 2025-08-14 |
| 卷号 | 63期号:8页码:14 |
| 关键词 | Parameter estimation Hybrid adaptive covariance inflation ICM ENSO prediction |
| ISSN号 | 0930-7575 |
| DOI | 10.1007/s00382-025-07787-5 |
| 通讯作者 | Zhang, Liang(168zhangliang2011@163.com) |
| 英文摘要 | In ensemble filters, limitations such as finite ensemble size and imperfect physical parameterizations exist, introducing sampling and model errors can cause background spread and covariances to be underestimated, leading to filter divergence. When applied to parameter estimation, such filtering can cause parameters to converge to inappropriate values, resulting in a poor performance of parameter estimation. To alleviate the problem, an inflation method is commonly employed in ensemble filters data assimilation to increase prior variances and mitigate filter divergence. In previous study, the adaptive covariance inflation algorithm called t-X has been successfully applied to state estimation of an intermediate coupled model (ICM). This study uses the t-X to conduct an in-depth investigation within an observation system simulation experiment (OSSE) framework using the ICM and the Ensemble Adjustment Kalman Filter (EAKF) for El Ni & ntilde;o and Southern Oscillation (ENSO) simulation and prediction. The goal is to develop a joint approach for optimizing both model parameters and states simultaneously. Results show optimizing parameters concurrently with model initial states further enhances the simulation and prediction capabilities of the model. The t-X shows a great advantage in optimizing the parameter with an error of only 0.07%. And it significantly reduces the prediction errors for the "Ni & ntilde;o 3.4" and "Ni & ntilde;o 1 + 2" zones by 89.31% and 93.31%, respectively. It can be seen that the advantages of the t-X are mainly in the equatorial eastern Pacific and south boundaries of the ICM. |
| WOS关键词 | DATA ASSIMILATION ; EL-NINO ; COVARIANCE INFLATION ; ERROR COVARIANCE ; PREDICTABILITY ; OPTIMIZATION |
| 资助项目 | National Key R&D Program of China[2023YFC2809104] ; National Key R&D Program of China[2024YFF0808900] ; National Key R&D Program of China[2023YFC3107901] ; National Natural Science Foundation of China[42376192] ; National Natural Science Foundation of China[42375143] ; National Natural Science Foundation of China[42176032] ; Natural Science Foundation of Tianjin[22JCYBJC01160] ; Open Project of Tianjin Key Laboratory of Oceanic Meteorology[2022TKLOM01] |
| WOS研究方向 | Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:001550967600004 |
| 出版者 | SPRINGER |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/203170]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 |
| 通讯作者 | Zhang, Liang |
| 作者单位 | 1.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Observat & Forecasting, Key Lab Ocean Circulat & Waves, Qingdao 266000, Peoples R China 2.Tianjin Univ, Sch Marine Sci & Technol, Tianjin 300100, Peoples R China 3.Ocean Univ China, Coll Ocean & Atmospher Sci, Qingdao, Peoples R China 4.Ocean Univ China, Inst Adv Ocean Study, Frontiers Sci Ctr Deep Ocean Multispheres & Earth, Key Lab Phys Oceanog,Minist Educ, Qingdao, Peoples R China 5.Natl Marine Environm Forecasting Ctr, Beijing 100080, Peoples R China |
| 推荐引用方式 GB/T 7714 | Gong, Mengmeng,Zhang, Liang,Zhang, Shaoqing,et al. Impact of optimizing both model parameters and states simultaneously on ENSO prediction using adaptive hybrid inflation EAKF algorithm within an intermediate coupled model[J]. CLIMATE DYNAMICS,2025,63(8):14. |
| APA | Gong, Mengmeng,Zhang, Liang,Zhang, Shaoqing,Gao, Chuan,Chen, Xingrong,&Zhang, Xuefeng.(2025).Impact of optimizing both model parameters and states simultaneously on ENSO prediction using adaptive hybrid inflation EAKF algorithm within an intermediate coupled model.CLIMATE DYNAMICS,63(8),14. |
| MLA | Gong, Mengmeng,et al."Impact of optimizing both model parameters and states simultaneously on ENSO prediction using adaptive hybrid inflation EAKF algorithm within an intermediate coupled model".CLIMATE DYNAMICS 63.8(2025):14. |
入库方式： OAI收割
来源：海洋研究所
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