<?xml version="1.0"?>
<metadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/"><dc:title>Verification of operational Universal Thermal Climate Index forecasts for Slovenia</dc:title><dc:creator>Kuzmanović,	Danijela	(Avtor)
	</dc:creator><dc:creator>Skok,	Gregor	(Mentor)
	</dc:creator><dc:creator>Banko,	Jana	(Komentor)
	</dc:creator><dc:subject>UTCI</dc:subject><dc:subject>ALADIN</dc:subject><dc:subject>BioKlima</dc:subject><dc:subject>verification</dc:subject><dc:subject>thermal indices</dc:subject><dc:subject>heat stress</dc:subject><dc:subject>cold stress</dc:subject><dc:description>The Universal Thermal Climate Index (UTCI ) can be described as the air temperature of the reference environment which causes the same human body response as an actual state of environment with a given combination of meteorological parameters (air temperature, wind, relative humidity and radiation). It is one of the thermal indices based on a heat balance model, taking into account the meteorological factors and the influence of the clothing. The UTCI has been widely applied in research and recently it was also implemented in ALADIN model and used in Slovenia. The main goal of this master thesis was a verification of operational UTCI forecasts for Slovenia. The Slovenian Environment Agency (ARSO) has provided us with archived operational outputs of ALADIN’s UTCI forecasts for the period 2013-2018 and for the year 2020. We verified it based on the measured meteorological data, where the UTCI values were calculated with BioKlima model. The measured meteorological data came from 42 stations in Slovenia, where the radiation measurements were preformed. The verification was done for the dataset from all stations together and separately for 9 selected stations. The results show that the model on average overestimates UTCI values. The overestimation is more pronounced in the morning, when the hourly average mean error (ME) reaches 8 °C. The daily average ME is 2.57 °C and daily average MAE is 5.02 °C. There is a small percentage of cases with the very large error (up to 40 °C). The secondary goal of the master thesis was to improve the operational ALADIN forecasts of UTCI. We used two methods, linear regression (LR) and neural network (NN). Both methods have successfully managed to reduce the ME and MAE. Also, according to MAE, NN performed better than LR, but that difference is rather small.</dc:description><dc:date>2022</dc:date><dc:date>2023-01-13 08:15:13</dc:date><dc:type>Magistrsko delo/naloga</dc:type><dc:identifier>143822</dc:identifier><dc:identifier>VisID: 130094</dc:identifier><dc:identifier>COBISS_ID: 137599747</dc:identifier><dc:language>sl</dc:language></metadata>
