中文摘要：

公共天气服务是向向用户提供概率的天气预报的 trending，代替传统的确定的预报。概率的预报技术不断地正在被改进优化可得到的预报信息。预报(BPF ) 的贝叶斯的处理器，为概率的预报的一个新统计方法，能根据在那个预报系统产生的观察和预报之间的历史的统计关系把一张确定的预报转变成一张概率的预报。这种技术在确定说明一个确定的预报系统的典型预报性能预报无常。meta-Gaussian 可能性的模型对有单调可能性的比率的许多随机的依赖结构合适。收养这种可能性的模特儿的 meta-Gaussian BPF 能因此越过许多地被使用，包括气象学和水文学。有二个连续随机的变量和正常线性的 BPF 的 Bayes 定理简短被介绍。为用一个单个预言者的连续 predictand 的 meta-Gaussian BPF 然后被介绍并且讨论。meta-Gaussian BPF 的表演在一个初步的实验被测试。在在长沙和武汉车站的 0000 UTC 的每日的表面温度的控制预报被用作确定的预报数据。这些控制预报从整体预言被拿，一 96-h 铅时间由中国气象学的管理的国家气象学的中心产生了，中等范围的天气的欧洲中心预报，并且 US 公民为在 2008 年 1 月期间的环境预言集中。实验的结果证明 meta-Gaussian BPF 能从三整体预言中的任何一个把表面温度的一张确定的控制预报转变成表面温度的一张有用概率的预报。这些概率的预报确定控制预报的无常；因此，概率的预报的表演基于内在的确定的控制预报的来源不同。

英文摘要：

Public weather services are trending toward providing users with probabilistic weather forecasts, in place of traditional deterministic forecasts. Probabilistic forecasting techniques are continually being improved to optimize available forecasting information. The Bayesian Processor of Forecast （BPF）, a new statistical method for probabilistic forecast, can transform a deterministic forecast into a probabilistic forecast accord- ing to the historical statistical relationship between observations and forecasts generated by that forecasting system. This technique accounts for the typical forecasting performance of a deterministic forecasting sys- tem in quantifying the forecast uncertainty. The meta-Gaussian likelihood model is suitable for a variety of stochastic dependence structures with monotone likelihood ratios. The meta-Gaussian BPF adopting this kind of likelihood model can therefore be applied across many fields, including meteorology and hy- drology. The Bayes theorem with two continuous random variables and the normal-linear BPF are briefly introduced. The meta-Gaussian BPF for a continuous predictand using a single predictor is then presented and discussed. The performance of the meta-Gaussian BPF is tested in a preliminary experiment. Control forecasts of daily surface temperature at 0000 UTC at Changsha and Wuhan stations are used as the de- terministic forecast data. These control forecasts are taken from ensemble predictions with a 96-h lead time generated by the National Meteorological Center of the China Meteorological Administration, the European Centre for Medium-Range Weather Forecasts, and the US National Centers for Environmental Prediction during January 2008. The results of the experiment show that the meta-Gaussian BPF can transform a deterministic control forecast of surface temperature from any one of the three ensemble predictions into a useful probabilistic forecast of surface temperature. These probabilistic forecasts quantify the uncertainty of the control forecast; accordingly,