中文摘要：

为用户提供概率天气预报信息是公共气象服务的发展趋势,概率天气预报技术的不断改进实现了概率天气预报信息的不断优化。在众多概率天气预报技术方法中,贝叶斯预报处理器是一种新近出现的、基于贝叶斯统计理论的概率预报技术;贝叶斯预报处理器可以根据一个确定性预报系统的预报值与观测值之间代表着这个系统预报性能的统计关系,借助于贝叶斯统计理论,把一个确定性预报转化为一个概率预报,从而实现对预报不确定性的定量化。由于亚高斯似然模型可以适用于多种单调似然比随机依赖结构,故采用该似然模型的亚高斯贝叶斯预报处理器,它在气象、水文等领域具有较强的适用性。在简要介绍了连续型二维随机变量情形下的贝叶斯定理及正态-线性贝叶斯预报处理器之后,详细论述了采用单一预报因子的连续型预报量亚高斯贝叶斯预报处理器,并以长沙站和武汉站2008年1月每日00时（世界时）地面气温（T2m）的中国国家气象中心、欧洲中期天气预报中心、美国国家环境预测中心集合预报中的控制预报资料（预报时效选为96h）作为确定性预报样本,对亚高斯贝叶斯预报处理器进行了初步试验。结果表明,亚高斯贝叶斯预报处理器可以将T2m各集合预报中的控制预报转化为能定量地表达各控制预报不确定性的T2m概率预报;源自不同控制预报的亚高斯贝叶斯预报处理器T2m概率预报的性能存在差异。

英文摘要：

One of the trend of the public weather service is to provide users with probabilistic weather forecasts.The continuous improvement of probabilistic forecasting techniques realizes the constant optimization of probabilistic forecast information.Among numerous techniques and methods for probabilistic forecasting,the Bayesian Processor of Forecast（BPF） is a new statistical probabilistic forecasting technique based on the Bayesian statistical theory.The BPF can,based on the Bayesian statisical theory,transform a deterministic forecast from a deterministic forecasting system into a probabilistic forecast according to the statistical relationship between historical observations and forecasts from that system that is able to denote the forecasting performance of that deterministic system to quantify the forecasting uncertainty of that deterministic forecast.The meta-Gaussian likelihood model is suitable for several kinds of stochastic dependence structures with monotone likelihood ratio,so the meta-Gaussian BPF adopting this kind of likelihood model can be flexibly applied in many fields,such as meteorology,and hydrology.After the Bayes theorem with two continuous random variables and the normal-linear BPF are briefly introduced,this paper discusses the meta-Gaussian BPF for a continuous predictand using one single predictor.In order to test its performance,a preliminary experiment of the meta-Gaussian BPF is carried out,using daily control forecasts（with a leadtime of 96 hours） from the NMC,ECMWF and NCEP ensemble predictions of surface temperature（T2m） at 00：00 UTC at Changsha station and Wuhan station during January 2008 as the deterministic forecasting data.The analysis of experiment results shows that the meta-Gaussian BPF can transform a control forecast of T2m from any one of the three ensemble predictions into a probabilistic forecast of T2m,which quantifies the forecasting uncertainty of that control forecast;the performances of the T2m probabilistic forecasts obtained from different control forec