An Initial Field Intelligent Correcting Algorithm for Numerical Forecasting Based on Artificial Neural Networks under the Conditions of Limited Observations: Part I—Focusing on Ocean Temperature

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.

Keywords

• initial field intelligent correcting
• limited observations
• artificial neural networks
• ocean temperature forecasting
• data assimilation
• numerical models