Observing Array Designed for Improving the Short‐Term Prediction of Kuroshio Extension State Transition Processes
Abstract Given the essential implications of Kuroshio Extension (KE) bimodality on oceanic dynamical environment and climate, the present study investigates the targeted observation schemes, based on the conditional nonlinear optimal perturbation (CNOP) method and a reduced‐gravity shallow‐water mod...
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| Format: | Article |
| Language: | English |
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American Geophysical Union (AGU)
2024-11-01
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| Series: | Earth and Space Science |
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| Online Access: | https://doi.org/10.1029/2024EA003881 |
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| author | Yu Geng Qiang Wang Hong‐Li Ren Bo Dan Stefano Pierini Hui Zhang |
| author_facet | Yu Geng Qiang Wang Hong‐Li Ren Bo Dan Stefano Pierini Hui Zhang |
| author_sort | Yu Geng |
| collection | DOAJ |
| description | Abstract Given the essential implications of Kuroshio Extension (KE) bimodality on oceanic dynamical environment and climate, the present study investigates the targeted observation schemes, based on the conditional nonlinear optimal perturbation (CNOP) method and a reduced‐gravity shallow‐water model, to improve the forecast skills of transition processes of KE bimodal states. To obtain a suitable observing array, the observation schemes, with different numbers of observation sites and observation distances between two sites, are designed. Furthermore, to demonstrate the superiority of the observing networks in predicting KE transition processes, two existing observation schemes and six random observation schemes are compared with the CNOP‐determined observing array. Based on this, a relatively optimal observing array with three sites and observation distance of 90 km is established, which is mainly located between 31°N and 33°N in the south of Japan. This targeted observing network is universal for two KE transition processes. The removal of initial errors on this array results in the mean prediction improvements of about 9.2% and 22.5% for KE transition processes from the low‐ to the high‐energy state and from the high‐ to the low‐energy state, respectively. |
| format | Article |
| id | doaj-art-0f36def06e7741e9b34eb50f9793c8bb |
| institution | Kabale University |
| issn | 2333-5084 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Earth and Space Science |
| spelling | doaj-art-0f36def06e7741e9b34eb50f9793c8bb2024-12-14T03:25:53ZengAmerican Geophysical Union (AGU)Earth and Space Science2333-50842024-11-011111n/an/a10.1029/2024EA003881Observing Array Designed for Improving the Short‐Term Prediction of Kuroshio Extension State Transition ProcessesYu Geng0Qiang Wang1Hong‐Li Ren2Bo Dan3Stefano Pierini4Hui Zhang5State Key Laboratory of Severe Weather and Institute of Tibetan Plateau Meteorology Chinese Academy of Meteorological Sciences Beijing ChinaCollege of Oceanography Hohai University Nanjing ChinaState Key Laboratory of Severe Weather and Institute of Tibetan Plateau Meteorology Chinese Academy of Meteorological Sciences Beijing ChinaKey Laboratory of Ministry of Natural Resources for Marine Environmental Information Technology National Marine Data and Information Service Ministry of Natural Resources Tianjin ChinaDipartimento di Scienze e Tecnologie Università di Napoli Parthenope Naples ItalyCAS Key Laboratory of Ocean Circulation and Waves Institute of Oceanology Chinese Academy of Sciences Qingdao ChinaAbstract Given the essential implications of Kuroshio Extension (KE) bimodality on oceanic dynamical environment and climate, the present study investigates the targeted observation schemes, based on the conditional nonlinear optimal perturbation (CNOP) method and a reduced‐gravity shallow‐water model, to improve the forecast skills of transition processes of KE bimodal states. To obtain a suitable observing array, the observation schemes, with different numbers of observation sites and observation distances between two sites, are designed. Furthermore, to demonstrate the superiority of the observing networks in predicting KE transition processes, two existing observation schemes and six random observation schemes are compared with the CNOP‐determined observing array. Based on this, a relatively optimal observing array with three sites and observation distance of 90 km is established, which is mainly located between 31°N and 33°N in the south of Japan. This targeted observing network is universal for two KE transition processes. The removal of initial errors on this array results in the mean prediction improvements of about 9.2% and 22.5% for KE transition processes from the low‐ to the high‐energy state and from the high‐ to the low‐energy state, respectively.https://doi.org/10.1029/2024EA003881Kuroshio Extensionbimodalityshort‐term predictiontargeted observing array |
| spellingShingle | Yu Geng Qiang Wang Hong‐Li Ren Bo Dan Stefano Pierini Hui Zhang Observing Array Designed for Improving the Short‐Term Prediction of Kuroshio Extension State Transition Processes Earth and Space Science Kuroshio Extension bimodality short‐term prediction targeted observing array |
| title | Observing Array Designed for Improving the Short‐Term Prediction of Kuroshio Extension State Transition Processes |
| title_full | Observing Array Designed for Improving the Short‐Term Prediction of Kuroshio Extension State Transition Processes |
| title_fullStr | Observing Array Designed for Improving the Short‐Term Prediction of Kuroshio Extension State Transition Processes |
| title_full_unstemmed | Observing Array Designed for Improving the Short‐Term Prediction of Kuroshio Extension State Transition Processes |
| title_short | Observing Array Designed for Improving the Short‐Term Prediction of Kuroshio Extension State Transition Processes |
| title_sort | observing array designed for improving the short term prediction of kuroshio extension state transition processes |
| topic | Kuroshio Extension bimodality short‐term prediction targeted observing array |
| url | https://doi.org/10.1029/2024EA003881 |
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