基于大气的混沌特性,单一的确定性预报逐步向多值的不确定性概率预报转化已成为一种趋势.本文系统地评述了概率天气预报产生的背景,介绍了概率预报的相关概念及国内外的研究状况,着重讨论了多模式集成的概率预报的两种集成方法,即贝叶斯模式平均(Bayesian model avera-ging,BMA)和多元高斯集合核拟合法(Gaussian ensemble kernel dressing,GEKD),并给出了两个例子的概率预报试验结果.利用BMA方法制作的概率预报的方差较小,减小了预报的不确定性,因此预报结果更接近大气的真实值.作为另一种多模式集成方法,多元高斯集合核拟合法回报的地面气温距平均值及趋势的概率预测结果与实测结果基本一致.利用此方法建立了地面气温年代际变化的概率多模式集合预测模型,并从中提取年代际气候变化特征,对东亚季风区年代际预测具有重要应用价值.
Advances in multimodel ensemble probabilistic prediction
Based on the chaotic characteristics of the atmosphere, it has become a tendency that the deterministic forecast gradually turns to be the probabilistic forecast with uncertainty.This paper systematically reviews the development background of probabilistic weather forecast, and introduces the concept, and advances of the probabilistic forecast research at home and abroad. Reviews are primarily focused on two kinds of multimodel ensemble probabilistic forecast methods, namely the Bayesian model averaging (BMA) and the multivariate Gaussian ensemble kernel dressing (GEKD). Cases of the BMA and GEKD probabilistic forecasts are shown. The deviation of the BMA probabilistic forecast becomes smaller, and it reduces the uncertainty of the forecast.Hence,the forecast is close to the real value of the atmosphere. As another multimodel ensemble probabilistic forecast approach, the GEKD probabilistic hindcast of the mean value of surface temperature anomaly and its trend basically coincides with the observed data.By using the GEKD approach,a multimodel ensemble probabilistic prediction model is established to predict the interdecadal variability of surface air temperature and to extract interdecadal climate variation features.It is of important application value for the interdecadal variability prediction over the East Asian monsoon region.
probability forecastinterdecadal predictionmultimodel ensembleBayesian model averaging (BMA)Gaussian ensemble kernel dressing (GEKD)
国家科技期刊平台
NSTL
万方数据
维普
