Abstract
AbstractWe 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.
Highlights
In this century, extreme temperatures have impacted 97 million people causing over 40 billion Euro economic damage (EM-DAT, 2020)
We start with joint post-processing of temperature and dew point ensemble forecasts, either using directly a bivariate normal ensemble model output statistics (EMOS) model or applying first univariate normal EMOS models to both components and ensemble copula coupling (ECC) to restore the dependence structure
The second approach is the direct post-processing of discomfort index (DI) and WBGTid ensemble forecasts with the help of the novel generalized extreme value (GEV) model introduced in Section 3.3 (Figure 1)
Summary
Extreme temperatures have impacted 97 million people causing over 40 billion Euro economic damage (EM-DAT, 2020). In order to mitigate the effect of heat stress, dedicated warning systems have been developed (McGregor et al, 2015; Morabito et al, 2019) These systems require meteorological forecasts from which relevant indicators. The focus is on two indices which can be derived from standard outputs of weather forecast models and are commonly used: the discomfort index (DI) and the indoor version of the wet-bulb globe temperature (WBGTid). Both are functions of temperature and dew point temperature, but have been chosen since they differ in their formulation and complexity which may have an impact on the final findings
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