Abstract
For the numerical forecasting of ocean temperature, the effective fusion of observations and the initial field under the conditions of limited observations has always been a significant problem. Traditional data assimilation methods cannot make full use of limited observations to correct the initial field. In order to obtain an optimal initial field with limited observations, this study proposed an intelligent correcting (IC) algorithm based on artificial neural networks (ANNs). The IC algorithm can fully mine the correlation laws between the grid points using historical data, and this process essentially replaces the estimation of background error covariance in traditional data assimilation methods. Experimental results show that the IC algorithm can lead to superior forecasting accuracy, with a lower root mean square error (around 0.7 °C) and higher coefficient of determination (0.9934) relative to the optimal interpolation method. Through the IC algorithm, the largest reduction in mean forecasting error can reach around −0.5 °C and the maximum percentage decline in mean forecasting error can reach 30% compared with the original numerical forecasting results. Therefore, the experiments validate that the IC algorithm can effectively correct the initial field under the conditions of limited observations.
Highlights
Ocean temperature has important impacts on sonar detection accuracy, hydroacoustic communication efficiency, submarine navigation safety and so on
In order to address the restrictions of limited observations in ocean temperature forecasting, this study proposes an intelligent correcting (IC) algorithm based on artificial neural networks (ANNs) that are used for obtaining an optimal initial field
The feasibility of the IC algorithm based on ANNs is preliminarily demonstrated by ocean temperature forecasting
Summary
Ocean temperature has important impacts on sonar detection accuracy, hydroacoustic communication efficiency, submarine navigation safety and so on. Accurate ocean temperature forecasting is beneficial to marine activities. The approaches for ocean temperature forecasting can be divided into two categories, including numerical models and data-driven models. Numerical models are based on dynamic equations, while data-driven models are based on statistical laws of historical data. The forecasting approach of data-driven models has seen rapid progress in recent years [1,2,3,4,5,6]. Numerical models are still the mainstream approach for ocean– atmosphere forecasting [7,8,9,10,11]
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