基于ECMWF细网格产品的一种优化BP-MOS气温预报方法

摘要

基于ECMWF细网格模式输出产品,以一种优化的BP-MOS模型预测1～7 d日最高和最低气温,并对比该方法和ECMWF细网格的2 m温度输出产品以及线性MOS方法的预报效果.结果表明:在预报因子处理时,考虑云量、风、湿度等对气温变化的"过程"影响能有效提高预报准确率;ECMWF细网格2 m温度产品在短期3 d内均方根误差均在2 ℃以内,但中期时段预报效果明显低于MOS方法;由于线性MOS模型预报存在不稳定现象,而BP神经网络的非线性映射关系使其在容错性方面优势明显,因此优化的BP-MOS模型预测效果良好.%Based on the high resolution prediction field data of ECMWF(European centre for medium-range weather forecasts),an optimized BP-MOS model was used to forecast the maximum and minimum temperatures in the next 1-7 days in different seasons,and the results were compared with the forecast effects of ECMWF 2 m temperature products and conventional linear MOS method.The results show that it could effectively improve the accuracy of prediction effect when the process effects of cloud cover,wind and humidity on temperature were considered.The root mean square errors of ECMWF 2 m temperature product in the short term(less than three days)were 2 ℃ below,however the forecast effect for the medium-term was significantly poorer than that of the two MOS methods.The linear MOS model had some unstable phenomena,and the nonlinear mapping relation of BP neural network made it better in fault tolerance.Although there were seasonal differences,the accuracy of the optimized BP-MOS model could meet the needs of the businesses.