Improving the operational forecasts of outdoor UTCI with post-processing

Universal Thermal Climate Index (UTCI ) is a thermal comfort index that describes how the human body experiences ambient conditions. It has units of temperature, and it takes into account the effect of air temperature, humidity, wind, radiation, and clothes. It is increasingly used in many countries as a measure of thermal comfort for outdoor conditions, and its value is calculated together with the operational meteorological forecast. At the same time, forecasts of outdoor UTCI tend to have a relatively large error caused by the error of meteorological forecasts. In Slovenia, there is a relatively dense network of meteorological stations. Crucially, on these stations, global solar radiation measurements are performed, making estimating the true value of UTCI more reliable. We used seven years of measurements in hourly resolution from 42 stations to first verify the operational UTCI forecast for the first forecast day and, secondly, to try to improve the forecast via post-processing. The verification showed that the operational ALADIN model tends to overestimate the UTCI values. The overestimation is most pronounced in the morning when the mean error is about 8 °C. Also, a small percentage of cases have a very large error (up to 40 °C). We used two machine-learning methods to try to improve the forecasts of UTCI: linear regression and neural networks. Both methods have successfully reduced the error in the operational UTCI forecasts. Both reduced the daily mean error from about 2.6 °C to almost zero, while the daily mean absolute error decreased from 5 °C to 3 °C for the neural network and 3.5 °C for linear regression.