智利圣地亚哥温度集合预报的统计后处理
Statistical post-processing of ensemble forecasts of temperature in Santiago de Chile
摘要：
目前，所有主要的气象中心都使用其业务集合预报系统生成集合预报;然而，一个普遍的问题是，集合的传播范围太小，导致预报分散不足，导致缺乏校准。为了纠正这个问题，在过去的二十年中，已经开发了不同的统计校准技术。在目前的工作中，不同的后处理技术进行了测试，校准9成员集合预报的温度智利圣地亚哥，天气研究和预报（WRF）模式，使用不同的行星边界层和陆面模式参数化。特别是，集合模型输出统计（EMOS）和贝叶斯模型平均技术的实施，由于观测的特点是大的高度差，模型参数的估计是适应手头的实际条件。与原始集合相比，所有测试的后处理方法显着提高了概率的校准和点预报的准确性。EMOS方法使用基于台站专家聚类（根据它们的高度）的参数估计，显示出最好的预报技巧。
英文摘要：
Currently all major meteorological centres generate ensemble forecasts using their operational ensemble prediction systems; however, it is a general problem that the spread of the ensemble is too small, resulting in underdispersive forecasts, leading to a lack of calibration. In order to correct this problem, different statistical calibration techniques have been developed in the last two decades. In the present work different post-processing techniques are tested for calibrating 9 member ensemble forecast of temperature for Santiago de Chile, obtained by the Weather Research and Forecasting (WRF) model using different planetary boundary layer and land surface model parametrization. In particular, the ensemble model output statistics (EMOS) and Bayesian model averaging techniques are implemented and since the observations are characterized by large altitude differences, the estimation of model parameters is adapted to the actual conditions at hand. Compared to the raw ensemble all tested post-processing approaches significantly improve the calibration of probabilistic and the accuracy of point forecasts. The EMOS method using parameter estimation based on expert clustering of stations (according to their altitudes) shows the best forecast skill.
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