标题： 基于右删失和位移伽马分布的EMOS模型用于概率定量降水预报 显示英文标题

标题： Censored and shifted gamma distribution based EMOS model for probabilistic quantitative precipitation forecasting

摘要： 最近，所有主要的天气预测中心都提供了不同天气量的预报集合，这些集合是通过使用不同的初始条件和模型参数化多次运行数值天气预测模型得到的。 然而，集合预报通常表现出发散不足的特性，可能还存在偏差，因此需要进行后处理以弥补这些缺陷。 可能最流行的现代后处理技术是集合模式输出统计（EMOS）和贝叶斯模型平均（BMA），它们提供了可预测天气量密度的估计。 在本研究中提出了一种用于校准降水积累集合预报的EMOS方法，其中预测分布遵循一个截断并平移的伽马（CSG）分布，其参数取决于集合成员。 CSG EMOS模型在八成员华盛顿大学中尺度集合的24小时降水积累集合预报以及匈牙利气象服务操作性有限区域模型集合预测系统产生的11成员集合上进行了测试。 新提出的EMOS方法的预测性能与原始集合的拟合、基于广义极值（GEV）分布的EMOS模型和伽马BMA方法进行了比较。 根据结果，所提出的CSG EMOS模型在概率校准和点预测准确性方面略微优于GEV EMOS方法，并且在预测技能方面显著优于原始集合和BMA模型。 显示英文摘要

摘要： Recently all major weather prediction centres provide forecast ensembles of different weather quantities which are obtained from multiple runs of numerical weather prediction models with various initial conditions and model parametrizations. However, ensemble forecasts often show an underdispersive character and may also be biased, so that some post-processing is needed to account for these deficiencies. Probably the most popular modern post-processing techniques are the ensemble model output statistics (EMOS) and the Bayesian model averaging (BMA) which provide estimates of the density of the predictable weather quantity. In the present work an EMOS method for calibrating ensemble forecasts of precipitation accumulation is proposed, where the predictive distribution follows a censored and shifted gamma (CSG) law with parameters depending on the ensemble members. The CSG EMOS model is tested on ensemble forecasts of 24 h precipitation accumulation of the eight-member University of Washington mesoscale ensemble and on the 11 member ensemble produced by the operational Limited Area Model Ensemble Prediction System of the Hungarian Meteorological Service. The predictive performance of the new EMOS approach is compared with the fit of the raw ensemble, the generalized extreme value (GEV) distribution based EMOS model and the gamma BMA method. According to the results, the proposed CSG EMOS model slightly outperforms the GEV EMOS approach in terms of calibration of probabilistic and accuracy of point forecasts and shows significantly better predictive skill that the raw ensemble and the BMA model.