Abstract
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 15–90 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.
Highlights
Introduction published maps and institutional affilOcean forecasting is based on the understanding of the past and present state of the ocean and its evolutionary laws, using numerical models, observational data, data assimilation schemes, and other statistical methods to analyze, judge, and forecast ocean processes and phenomena [1]
Considering the above, we develop a simple statistical prediction model based on the multivariate extended empirical orthogonal function (EEOF) decomposition, which is referred to as the MEEOF prediction model in this study
26-year period in the South China Sea (SCS): in each experiment, the datasets with 25 years are used as the samples for EEOF decomposition; another independent year is used for prediction validation
Summary
Ocean forecasting is based on the understanding of the past and present state of the ocean and its evolutionary laws, using numerical models, observational data, data assimilation schemes, and other statistical methods to analyze, judge, and forecast ocean processes and phenomena [1]. It is well known that accurate predictions of the ocean state at different spatial and temporal scales are necessary [2]. Mesoscale phenomena are widespread throughout the ocean, where mesoscale eddies are the most energetic forms of motion prevailing in the ocean [3]. The SCS has abundant water exchange channels and is vulnerable to the large-scale circulation of the Sulu Sea and the Pacific Ocean, showing strong seasonal signals and inter-annual oscillations [5]; on the other hand, the unique geographical environment of the SCS makes it extremely abundant in oceanic eddies, iations
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