热指数集合预报的统计后处理：有一条康庄大道吗？
Statistical post-processing of heat index ensemble forecasts: is there a royal road?
摘要：
我们调查的影响统计后处理的概率技能的不适指数（DI）和室内湿球全球温度（WBGTid）集合预报，都从相应的温度和露点温度的预测计算。比较了两种不同的校准方法。在第一种情况下，我们从温度和露点预报的联合后处理开始，然后使用来自所获得的二元预测分布的样本创建DI和WBGTid的校准样本。这种方法相比，直接后处理的热量指数集合预报。为此，提出了一种基于广义极值分布的集成模型输出统计模型。这两种方法的预测性能进行测试的业务温度和露点集合预报的欧洲中期天气预报中心和相应的预测DI和WBGTid。对于较短的提前期（最多6天），这两种方法都显着提高了预测技能。在相互竞争的后处理方法中，热指数的直接校准表现出最好的预测性能，紧随其后的是基于温度和露点温度联合校准的更一般的方法。此外，一个机器学习的方法进行了测试，并显示出相当的性能的情况下，只有在预测热指数预警级别类别感兴趣。
英文摘要：
We investigate the effect of statistical post-processing on the probabilistic skill of discomfort index (DI) and indoor wet-bulb globe temperature (WBGTid) ensemble forecasts, both calculated from the corresponding forecasts of temperature and dew point temperature. Two different methodological approaches to calibration are compared. In the first case, we start with joint post-processing of the temperature and dew point forecasts and then create calibrated samples of DI and WBGTid using samples from the obtained bivariate predictive distributions. This approach is compared with direct post-processing of the heat index ensemble forecasts. For this purpose, a novel ensemble model output statistics model based on a generalized extreme value distribution is proposed. The predictive performance of both methods is tested on the operational temperature and dew point ensemble forecasts of the European Centre for Medium-Range Weather Forecasts and the corresponding forecasts of DI and WBGTid. For short lead times (up to day 6), both approaches significantly improve the forecast skill. Among the competing post-processing methods, direct calibration of heat indices exhibits the best predictive performance, very closely followed by the more general approach based on joint calibration of temperature and dew point temperature. Additionally, a machine learning approach is tested and shows comparable performance for the case when one is interested only in forecasting heat index warning level categories.
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