The uncertainty of UTCI due to uncertainties in the determination of radiation fluxes derived from numerical weather prediction and regional climate model simulations.

In this study we examine the determination accuracy of both the mean radiant temperature (Tmrt) and the Universal Thermal Climate Index (UTCI) within the scope of numerical weather prediction (NWP), and global (GCM) and regional (RCM) climate model simulations. First, Tmrt is determined and the so-called UTCI-Fiala model is then used for the calculation of UTCI. Taking into account the uncertainties of NWP model (among others the HIgh Resolution Limited Area Model HIRLAM) output (temperature, downwelling short-wave and long-wave radiation) stated in the literature, we simulate and discuss the uncertainties of Tmrt and UTCI at three stations in different climatic regions of Europe. The results show that highest negative (positive) differences to reference cases (under assumed clear-sky conditions) of up to -21°C (9°C) for Tmrt and up to -6°C (3.5°C) for UTCI occur in summer (winter) due to cloudiness. In a second step, the uncertainties of RCM simulations are analyzed: three RCMs, namely ALADIN (Aire Limitée Adaptation dynamique Développement InterNational), RegCM (REGional Climate Model) and REMO (REgional MOdel) are nested into GCMs and used for the prediction of temperature and radiation fluxes in order to estimate Tmrt and UTCI. The inter-comparison of RCM output for the three selected locations shows that biases between 0.0 and ±17.7°C (between 0.0 and ±13.3°C) for Tmrt (UTCI), and RMSE between ±0.5 and ±17.8°C (between ±0.8 and ±13.4°C) for Tmrt (UTCI) may be expected. In general the study shows that uncertainties of UTCI, due to uncertainties arising from calculations of radiation fluxes (based on NWP models) required for the prediction of Tmrt, are well below ±2°C for clear-sky cases. However, significant higher uncertainties in UTCI of up to ±6°C are found, especially when prediction of cloudiness is wrong.

The uncertainty of UTCI due to uncertainties in the determination of radiation fluxes derived from measured and observed meteorological data.

In the present study, we investigate the determination accuracy of the Universal Thermal Climate Index (UTCI). We study especially the UTCI uncertainties due to uncertainties in radiation fluxes, whose impacts on UTCI are evaluated via the mean radiant temperature (Tmrt). We assume "normal conditions", which means that usual meteorological information and data are available but no special additional measurements. First, the uncertainty arising only from the measurement uncertainties of the meteorological data is determined. Here, simulations show that uncertainties between 0.4 and 2 K due to the uncertainty of just one of the meteorological input parameters may be expected. We then analyse the determination accuracy when not all radiation data are available and modelling of the missing data is required. Since radiative transfer models require a lot of information that is usually not available, we concentrate only on the determination accuracy achievable with empirical models. The simulations show that uncertainties in the calculation of the diffuse irradiance may lead to Tmrt uncertainties of up to ±2.9 K. If long-wave radiation is missing, we may expect an uncertainty of ±2 K. If modelling of diffuse radiation and of longwave radiation is used for the calculation of Tmrt, we may then expect a determination uncertainty of ±3 K. If all radiative fluxes are modelled based on synoptic observation, the uncertainty in Tmrt is ±5.9 K. Because Tmrt is only one of the four input data required in the calculation of UTCI, the uncertainty in UTCI due to the uncertainty in radiation fluxes is less than ±2 K. The UTCI uncertainties due to uncertainties of the four meteorological input values are not larger than the 6 K reference intervals of the UTCI scale, which means that UTCI may only be wrong by one UTCI scale. This uncertainty may, however, be critical at the two temperature extremes, i.e. under extreme hot or extreme cold conditions.