针对贝叶斯概率预报模型(Bayesian processor of forecasts,BPF)中输入数据的正态转换问题,探讨了Meta-Gaussian模型(MG)和Box-Cox变换(BC)对BPF模型性能的影响。首先利用MG和BC分别对BPF模型输入数据进行正态转换,然后分别建立BPF-MG和...针对贝叶斯概率预报模型(Bayesian processor of forecasts,BPF)中输入数据的正态转换问题,探讨了Meta-Gaussian模型(MG)和Box-Cox变换(BC)对BPF模型性能的影响。首先利用MG和BC分别对BPF模型输入数据进行正态转换,然后分别建立BPF-MG和BPF-BC模型进行概率预报,最后对BPF-MG和BPF-BC在不同预见期和不同数据样本条件下的预报能力进行了分析。结果表明,当数据样本较少时,BPF-MG具有较高的稳定性,但BC转换比MG更简单,BC变换系数非常敏感;当数据样本增多后,BC变换的转换系数稳定,BPFBC预报质量提高。展开更多
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...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,展开更多
文摘针对贝叶斯概率预报模型(Bayesian processor of forecasts,BPF)中输入数据的正态转换问题,探讨了Meta-Gaussian模型(MG)和Box-Cox变换(BC)对BPF模型性能的影响。首先利用MG和BC分别对BPF模型输入数据进行正态转换,然后分别建立BPF-MG和BPF-BC模型进行概率预报,最后对BPF-MG和BPF-BC在不同预见期和不同数据样本条件下的预报能力进行了分析。结果表明,当数据样本较少时,BPF-MG具有较高的稳定性,但BC转换比MG更简单,BC变换系数非常敏感;当数据样本增多后,BC变换的转换系数稳定,BPFBC预报质量提高。
基金Supported by the National Natural Science Foundation of China (41075035)National Science and Technology Support Program of China (2009BAC51B00)+1 种基金National (Key) Basic Research and Development (973) Program of China (2012CB417204)China Meteorological Administration Special Public Welfare Research Fund (GYHY200906007)
文摘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,
