当前位置: X-MOL 学术Q. J. R. Meteorol. Soc. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Statistical post‐processing of heat index ensemble forecasts: Is there a royal road?
Quarterly Journal of the Royal Meteorological Society ( IF 3.0 ) Pub Date : 2020-06-17 , DOI: 10.1002/qj.3853
Sándor Baran 1, 2 , Ágnes Baran 1 , Florian Pappenberger 2 , Zied Ben Bouallègue 2
Affiliation  

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.

中文翻译:

热指数集合预报的统计后处理:是否有皇家之路?

我们调查统计后处理对不适指数(DI)和室内湿球温度(WBGTid)总体预报概率技能的影响,这两者都是根据相应的温度和露点温度预报计算得出的。比较了两种不同的校准方法。在第一种情况下,我们首先对温度和露点进行联合后处理,然后使用从获得的双变量预测分布中获得的样本创建DI和WBGTid的校准样本。将该方法与热指数集合预报的直接后处理进行了比较。为此,提出了一种基于广义极值分布的新型集成模型输出统计模型。两种方法的预测性能都在欧洲中距离天气预报中心的工作温度和露点集合预报以及相应的DI和WBGTid预报中进行了测试。对于较短的交货时间(直到第6天),两种方法都可以显着提高预测技能。在竞争性的后处理方法中,热指数的直接校准表现出最佳的预测性能,紧随其后的是基于温度和露点温度联合校准的更通用方法。此外,还对一种机器学习方法进行了测试,并且该方法在仅对预测热量指数警告级别类别感兴趣的情况下具有可比的性能。
更新日期:2020-06-17
down
wechat
bug