A Model‐Independent Strategy for the Targeted Observation Analysis and Its Application in ENSO Prediction
Abstract The model‐dependency has been a challenging issue for traditional data assimilation‐based targeted observational method. This study developed a new strategy to address this challenge using multiple‐model prediction ensemble. It was found that while the ensemble size reaches a sufficiently l...
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| Format: | Article |
| Language: | English |
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American Geophysical Union (AGU)
2025-07-01
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| Series: | Journal of Advances in Modeling Earth Systems |
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| Online Access: | https://doi.org/10.1029/2024MS004742 |
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| author | Weixun Rao Youmin Tang Yanling Wu Xiaojing Li |
| author_facet | Weixun Rao Youmin Tang Yanling Wu Xiaojing Li |
| author_sort | Weixun Rao |
| collection | DOAJ |
| description | Abstract The model‐dependency has been a challenging issue for traditional data assimilation‐based targeted observational method. This study developed a new strategy to address this challenge using multiple‐model prediction ensemble. It was found that while the ensemble size reaches a sufficiently large number the optimal observational sites detected tend to stable and model‐independent. This new finding answers the long‐standing challenge question on the model dependence in targeted observational analysis, offering an efficient and objective way to identify optimal observational sites. With this strategy, we designed an optimal observational array in the tropical Pacific for the El Niño‐Southern Oscillation (ENSO) prediction using the multiple historical simulation data sets from Coupled Model Intercomparison Project Phase 6 and reanalysis data sets. Sensitive experiments show that while number of data sets reaches 12, a robust optimal observational array is obtained. The first 10 optimal observational sites, mostly located in the equatorial central eastern Pacific, can reduce initial uncertainties by 67%. This was further confirmed by the observation system simulation experiments, which is implemented by the Ensemble Adjustment Kalman Filter assimilation system developed in the Community Earth System Model. This newly developed model‐independent strategy makes it feasible to design a robust oceanic observational network for ENSO prediction even using the current targeted observational algorithm, well serving the goal of international Tropical Pacific Observation System 2020 project. |
| format | Article |
| id | doaj-art-e2a7da15c245444bbb13a3e704f282f7 |
| institution | DOAJ |
| issn | 1942-2466 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Journal of Advances in Modeling Earth Systems |
| spelling | doaj-art-e2a7da15c245444bbb13a3e704f282f72025-08-20T03:09:19ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662025-07-01177n/an/a10.1029/2024MS004742A Model‐Independent Strategy for the Targeted Observation Analysis and Its Application in ENSO PredictionWeixun Rao0Youmin Tang1Yanling Wu2Xiaojing Li3College of Oceanography Hohai University Nanjing ChinaCollege of Oceanography Hohai University Nanjing ChinaCollege of Oceanography Hohai University Nanjing ChinaState Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of Oceanography Hangzhou ChinaAbstract The model‐dependency has been a challenging issue for traditional data assimilation‐based targeted observational method. This study developed a new strategy to address this challenge using multiple‐model prediction ensemble. It was found that while the ensemble size reaches a sufficiently large number the optimal observational sites detected tend to stable and model‐independent. This new finding answers the long‐standing challenge question on the model dependence in targeted observational analysis, offering an efficient and objective way to identify optimal observational sites. With this strategy, we designed an optimal observational array in the tropical Pacific for the El Niño‐Southern Oscillation (ENSO) prediction using the multiple historical simulation data sets from Coupled Model Intercomparison Project Phase 6 and reanalysis data sets. Sensitive experiments show that while number of data sets reaches 12, a robust optimal observational array is obtained. The first 10 optimal observational sites, mostly located in the equatorial central eastern Pacific, can reduce initial uncertainties by 67%. This was further confirmed by the observation system simulation experiments, which is implemented by the Ensemble Adjustment Kalman Filter assimilation system developed in the Community Earth System Model. This newly developed model‐independent strategy makes it feasible to design a robust oceanic observational network for ENSO prediction even using the current targeted observational algorithm, well serving the goal of international Tropical Pacific Observation System 2020 project.https://doi.org/10.1029/2024MS004742targeted observationmodel‐dependencydata assimilation |
| spellingShingle | Weixun Rao Youmin Tang Yanling Wu Xiaojing Li A Model‐Independent Strategy for the Targeted Observation Analysis and Its Application in ENSO Prediction Journal of Advances in Modeling Earth Systems targeted observation model‐dependency data assimilation |
| title | A Model‐Independent Strategy for the Targeted Observation Analysis and Its Application in ENSO Prediction |
| title_full | A Model‐Independent Strategy for the Targeted Observation Analysis and Its Application in ENSO Prediction |
| title_fullStr | A Model‐Independent Strategy for the Targeted Observation Analysis and Its Application in ENSO Prediction |
| title_full_unstemmed | A Model‐Independent Strategy for the Targeted Observation Analysis and Its Application in ENSO Prediction |
| title_short | A Model‐Independent Strategy for the Targeted Observation Analysis and Its Application in ENSO Prediction |
| title_sort | model independent strategy for the targeted observation analysis and its application in enso prediction |
| topic | targeted observation model‐dependency data assimilation |
| url | https://doi.org/10.1029/2024MS004742 |
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