{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:10:43Z","timestamp":1760137843429,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,26]],"date-time":"2022-02-26T00:00:00Z","timestamp":1645833600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Natural Science Foundation  of China","award":["41876014"],"award-info":[{"award-number":["41876014"]}]},{"name":"the Open Project of Tianjin Key Laboratory of Oceanic Meteorology","award":["2020TKLOMYB04"],"award-info":[{"award-number":["2020TKLOMYB04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this paper, a novel and simple statistical prediction model for sea surface multivariate is developed based on extended empirical orthogonal functions (referred to as the MEEOF model). This simple model embeds the temporal evolution information into the empirical orthogonal function spatial patterns, which effectively captures the spatial distribution of the sea surface variables and their evolution process over time, and can be used to improve the accuracy of intra-seasonal ocean forecasts. At the same time, it considers both the correlation between different spatial and temporal points and the dynamic balance between different sea surface variables. The performance of the MEEOF prediction model has been examined in the South China Sea (SCS) based on remote sensing satellite datasets. Experimental results demonstrate that this model has significant forecasting capability within the forecast window of 15\u201390 days, compared with the traditional persistence forecasts and optimal climatic normal (OCN) results. Furthermore, this model exhibits good forecast performance throughout the entire forecast window; the final prediction model (referred to as the fusion model) is established by obtaining the weighted average for the MEEOF forecasts and persistence forecast results. Numerical experimental results show that this fusion prediction model consistently outperforms the persistence model, the OCN model, and the linear regression model over the entire forecast window. A case study shows that the propagation of ocean waves and the coordination between different sea surface variables can be well predicted by this simple model, indicating that this fusion model has a potential advantage in intra-seasonal predictions of the ocean.<\/jats:p>","DOI":"10.3390\/rs14051162","type":"journal-article","created":{"date-parts":[[2022,2,27]],"date-time":"2022-02-27T20:48:33Z","timestamp":1645994913000},"page":"1162","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Simple Statistical Intra-Seasonal Prediction Model for Sea Surface Variables Utilizing Satellite Remote Sensing"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8059-5010","authenticated-orcid":false,"given":"Qi","family":"Shao","sequence":"first","affiliation":[{"name":"School of Marine Science and Technology, Tianjin University, Tianjin 300072, China"},{"name":"Tianjin Key Laboratory for Oceanic Meteorology, Tianjin 300074, China"}]},{"given":"Yanling","family":"Zhao","sequence":"additional","affiliation":[{"name":"The PLA 31010 Units, Beijing 100081, China"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Tianjin University, Tianjin 300072, China"},{"name":"Tianjin Key Laboratory for Oceanic Meteorology, Tianjin 300074, China"}]},{"given":"Guijun","family":"Han","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Tianjin University, Tianjin 300072, China"}]},{"given":"Guangchao","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Tianjin University, Tianjin 300072, China"}]},{"given":"Chaoliang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Tianjin University, Tianjin 300072, China"}]},{"given":"Siyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Tianjin University, Tianjin 300072, China"}]},{"given":"Yantian","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Tianjin University, Tianjin 300072, China"}]},{"given":"Hanyu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Tianjin University, Tianjin 300072, China"}]},{"given":"Ping","family":"Qu","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory for Oceanic Meteorology, Tianjin 300074, China"},{"name":"Tianjin Institute of Meteorological Sciences, Tianjin 300074, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Schiller, A., and Brassington, G.B. 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