利用人工神经网络模型预测西北太平洋热带气旋生成频数

通过对60年(1950~2009年)北半球夏、秋季(6~10月)热带气旋(TC)频数与春季(3~5月)大尺度环境变量的相关分析,挑选出8个相关性较高的前期预报因子建立人工神经网络(ANN)模型,对2010~2017年8年夏、秋季TC频数进行回报,并将回报结果与传统多元线性回归(MLR)方法所得结果进行对比分析。结果表明,ANN模型对60年历史数据的拟合精度高,相关系数高达0.99,平均绝对误差低至0.77。在8年回报中,ANN模型相关系数为0.80,平均绝对误差为1.97;而MLR模型相关系数仅为0.46,平均绝对误差为3.30。ANN模型在历史数据拟合和回报中的表现都明显优于MLR模型,未来可考虑应用于实际的业务预测中。

Prediction of Frequency of Tropical Cyclones Forming over the Western North Pacific Using An Artificial Neural Network Model

In this study, artificial neural network (ANN) model and the multiple linear regression (MLR) model are used to predict the numbers of tropical cyclones (TCs) forming over the western North Pacific from June to October. The correlations between the frequency of TCs and the large-scale environmental variables during boreal spring (March-May) were analyzed for a period of approximately six decades 1950-2009; subsequently eight highly correlated predictors were selected to predict the TC frequency from 2010 to 2017. A comparison between ANN and MLR models shows that ANN model exhibits better performance as compared to MLR model. Specifically, the correlation coefficient (R) reached 0.99 and the mean absolute error (MAE) was 0.77 during the historical data simulation. During the prediction period, R values of ANN and MLR models were 0.80 and 0.46, respectively. MAE values of ANN and MLR models were1.97 and 3.30, respectively, which further confirms that ANN model significantly outperforms MLR model in both simulation and prediction and has potential for application in operational forecast.