Diagnosing wet bulb globe temperature from numerical weather prediction model output using empirical, physics based, and machine learning methods

The wet bulb globe temperature (WBGT) is a linear combination of the natural wet bulb temperature (NWBT), the black globe temperature (BGT), and the dry bulb temperature (DBT). WBGT is used as a criterion for heat stress advisories by the United States Department of Defense and in the sports community. Strenuous outdoor activities under the two most severe (red and black) WBGT flag categories can potentially lead to heat exhaustion and heat stroke. Even less severe WBGT categories can lead to heat strokes as well, especially in cases of prolonged exposure and for unacclimatized individuals. However, given the complexity of the variables, NWBT and BGT are not directly available from numerical weather prediction (NWP) output. In this study, we diagnose the WBGT from NWP output at the Phillips Army Airfield managed by Aberdeen Proving Ground in Maryland. Multiple diagnostic methods are used, including empirical formulas from the literature, a physics-based formulation, multiple linear regression (MLR), and machine learning methods such as extreme gradient boosting (XGB) and neural network (NN) models. From white to red WBGT flag categories, the top three methods were from MLR, XGB and NN models which scored similarly in a derived composite index, followed by an empirical formula from the literature. The physics-based formulation scored the lowest in the combined composite index. Numerical weather prediction output was used to diagnose a heat stress index – wet bulb globe temperature. Diagnostic methods for wet bulb globe temperature include physics-based, empirical, and machine learning methods. The best diagnostic methods were from multiple linear regression, extreme gradient boosting, and neural network model.