Sea surface temperature (SST) significantly affects the processes of air–sea interactions, and, thus, forms an important indicator of climate changes. In SST predictions, the approach of artificial neural networks (ANNs) is data-driven, unlike that of the numerical models, which are physics-based. In this letter, Operational SST and Ice Analysis (OSTIA) data set were used for training ANN models and verifying prediction results. Typically, time series SST data of a certain period were directly used for ANN’s training. To reduce the prediction errors caused by SST variations, the authors propose to separate SST time series data into climatological monthly mean and monthly anomaly data sets and construct two neural network models. The combination of these two models gives the final SST prediction results. This method was used for 12-month lead time SST prediction in the South China Sea. The average bias and standard deviation between the predicted SST and OSTIA SST are −0.16 °C and 0.37 °C, respectively. The percentage of SST difference between the predicted SST and OSTIA SST, within ±0.5 °C and ±1 °C, is 71.24% and 95.22%, respectively. The results indicate that the proposed training method gives good prediction accuracy.

中文翻译：

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基于人工神经网络的南海海面温度预测

海面温度（SST）显着影响海气相互作用的过程，因此是气候变化的重要指标。在 SST 预测中，人工神经网络 (ANN) 的方法是数据驱动的，与基于物理的数值模型不同。在这封信中，Operational SST 和 Ice Analysis (OSTIA) 数据集用于训练 ANN 模型和验证预测结果。通常，一定时期的时间序列 SST 数据直接用于 ANN 的训练。为了减少海温变化带来的预测误差，作者提出将海温时间序列数据分为气候月均值和月异常数据集，构建两个神经网络模型。这两个模型的组合给出了最终的 SST 预测结果。该方法用于南海12个月超前海温预报。预测的 SST 和 OSTIA SST 之间的平均偏差和标准偏差分别为 -0.16 °C 和 0.37 °C。在±0.5 °C 和±1 °C 内，预测的SST 和OSTIA SST 之间的SST 差异百分比分别为71.24% 和95.22%。结果表明，所提出的训练方法给出了良好的预测精度。