Spatial prediction of outdoor thermal comfort using an operational NWP model for city and regional scales: A case study in Korea

Abstract

The outdoor thermal environment has key implications for human health, and numerical simulation models have been used to predict thermal comfort. The potential of a numerical weather prediction (NWP)-based thermal environment prediction model has not yet been comprehensively explored. Using a predicted mean vote-based thermal comfort index (i.e., perceived temperature [PT]) in South Korea, this study developed and evaluated a thermal environment prediction model using an operational NWP model of the Korea Meteorological Administration. The suitability of four bias-adjustment methods to reduce bias in PT prediction was examined, applying the outputs of the NWP model using in-situ data for 2018–2019. Bias-adjusted PT prediction performance was assessed for 2020 based on PT and thermo-physiological stress categories for South Korea. The predicted PT successfully represented PT spatial information. Mean absolute errors (MAEs) were 2.17 and 1.15 °C for hourly and daily PT, and the bias-adjustment method improved prediction accuracy by 18 and 36%, respectively, based on the MAE. PT prediction via NWP modeling can be further improved by reducing the statistical error in surface solar radiation modeling. The proposed scheme can be applied in urban heat mitigation, developing early warning systems for thermal health risks, energy consumption prediction, and tourism planning.