Statistical post-processing of heat index ensemble forecasts: is there a royal road?

Abstract

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.

arXiv comment29 pages, 12 figures

References in corpus (11)

• Uncertainty Quantification in Complex Simulation Models Using Ensemble Copula Coupling
• Probabilistic Quantitative Precipitation Forecasting Using Ensemble Model Output Statistics
• Comparison of nonhomogeneous regression models for probabilistic wind speed forecasting
• Forecast verification for extreme value distributions with an application to probabilistic peak wind prediction
• Ensemble model output statistics for wind vectors
• Censored and shifted gamma distribution based EMOS model for probabilistic quantitative precipitation forecasting
• Similarity-based semi-local estimation of EMOS models
• Generation of scenarios from calibrated ensemble forecasts with a dual ensemble copula coupling approach
• Bivariate ensemble model output statistics approach for joint forecasting of wind speed and temperature
• Combining low-dimensional ensemble postprocessing with reordering methods
• Statistical post-processing of dual-resolution ensemble forecasts

Cited by in corpus (4)

• Truncated generalized extreme value distribution based EMOS model for calibration of wind speed ensemble forecasts
• Machine learning-based probabilistic forecasting of solar irradiance in Chile
• Statistical post-processing of visibility ensemble forecasts
• Parametric model for post-processing visibility ensemble forecasts