基于BMA方法的地面气温概率预报研究

Probabilistic Forecast of Surface Air Temperature Using Bayesian Model Averaging

摘要: 利用欧洲中心欧洲中期天气预报中心(ECMWF)、美国国家环境预报中心(GFS)两个预报中心2018年6月1日至2018年11月30日地面2米气温0~4天预报资料，对地面气温进行贝叶斯模式平均(Bayesian Model Averaging, BMA)预报实验。结果表明，BMA方法不仅可以提供确定性结果预报而且可以提供全概率密度函数，定量描述预报不确定性大小。利用ACC、RMSE以及预报误差在1℃和2℃的预报准确率来看，BMA的预报结果相比于模式而言，有一定的提高。利用CRPS评分对BMA概率预报进行评估，发现BMA的概率预报技巧也有提高。另外对于BMA提供的概率预报，80%概率下温度预报可作为最高气温预报的界限。

Abstract: In this study, based on European Centre for Mediu-Range Weather Forecasts (ECMWF) and Global Forecast System (GFS), the probabilistic forecasts of surface air temperature during the period from 1 June to 31 November 2018 were conducted using Bayesian Model Averaging (BMA). The results showed that BMA method can not only provide deterministic result prediction, but also provide full probability density function to describe the uncertainty of prediction quantitatively. By using Analyzed the Correlation Coefficient (ACC), Root-mean-square error (RMSE) and the prediction accuracy of the prediction errors at 1˚C and 2˚C, the prediction results of BMA are better than models. The continuous ranked probability score (CRPS) is used to evaluate the probability prediction of BMA, and it is found that the probability prediction skills of BMA are also improved. In addition, for the probability forecast provided by BMA, the temperature forecast under 80% probability can be used as the limit of the maximum temperature forecast.