{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T07:39:54Z","timestamp":1774424394156,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T00:00:00Z","timestamp":1632960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2017YFA0604202"],"award-info":[{"award-number":["2017YFA0604202"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Grant nos. 41690124 and 41690120"],"award-info":[{"award-number":["Grant nos. 41690124 and 41690120"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory","award":["311020008"],"award-info":[{"award-number":["311020008"]}]},{"name":"Oceanic Sustainability based Marine Science and Technology Cooperation in Maritime Silk Road and Island Countries","award":["no grant nos."],"award-info":[{"award-number":["no grant nos."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Parameter estimation plays an important role in reducing model error and thus is of great significance to improve the simulation and prediction capabilities of the model. However, due to filtering divergence, parameter estimation by ensemble-based filters still faces great challenges. Previous studies have shown that a covariance inflation scheme could alleviate the filtering divergence problem by increasing the signal-to-noise ratio of the state-parameter covariance. In this study, we proposed a new inflation scheme based on a local ensemble transform Kalman filter (LETKF). With the new scheme, the Zebiak\u2013Cane (Z-C) model parameters were estimated by assimilating the sea surface temperature anomaly (SSTA) data. The effectiveness of the parameter estimation and its influence on El Ni\u00f1o\u2013Southern Oscillation (ENSO) prediction were evaluated in an observation system simulation experiments (OSSE) framework and real-world scenario, respectively. With the utilization of the OSSE framework, the results showed that the model parameters were successfully estimated. Parameter estimation reduced the model error when compared with only state estimation (onlySE); however, multiple parameter estimation (MPE) further improved the ENSO prediction skill by providing better initial conditions and parameter values than the single parameter estimation (SPE). Parameter estimation could thus alleviate the spring prediction barrier (SPB) phenomenon of ENSO to a certain extent. In real-world experiments, the optimized parameters significantly improved the ENSO forecasting skill, primarily in prediction of warm events. This study provides an effective parameter estimation strategy to improve climate models and further climate predictions in the real world.<\/jats:p>","DOI":"10.3390\/rs13193923","type":"journal-article","created":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T21:26:20Z","timestamp":1633728380000},"page":"3923","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Parameter Estimation Based on a Local Ensemble Transform Kalman Filter Applied to El Ni\u00f1o\u2013Southern Oscillation Ensemble Prediction"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4102-2188","authenticated-orcid":false,"given":"Yanqiu","family":"Gao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519082, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1158-6425","authenticated-orcid":false,"given":"Youmin","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Oceanography, Hohai University, Nanjing 210098, China"},{"name":"Environmental Science and Engineering, University of Northern British Columbia, Prince George, BC V2N 4Z9, Canada"}]},{"given":"Xunshu","family":"Song","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519082, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3457-0832","authenticated-orcid":false,"given":"Zheqi","family":"Shen","sequence":"additional","affiliation":[{"name":"Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519082, China"},{"name":"College of Oceanography, Hohai University, Nanjing 210098, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1038\/nature02439","article-title":"Predictability of El Ni\u00f1o over the past 148 years","volume":"428","author":"Chen","year":"2004","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1175\/JCLI-D-18-0189.1","article-title":"Coupled Data Assimilation and Ensemble Initialization with Application to Multiyear ENSO Prediction","volume":"32","author":"Sandery","year":"2019","journal-title":"J. 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