{"title":"Improving the operational forecasts of outdoor Universal Thermal Climate Index with post-processing.","authors":"Danijela Kuzmanović, Jana Banko, Gregor Skok","doi":"10.1007/s00484-024-02640-6","DOIUrl":null,"url":null,"abstract":"<p><p>The 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 considers physiological aspects of the human body. 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 as part of 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, at these stations, global solar radiation measurements are performed continuously, which makes estimating the actual value of the UTCI more accurate compared to the situation where no radiation measurements are available. 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. We used two machine-learning methods, linear regression, and neural networks. Both methods have successfully reduced the error in the operational UTCI forecasts. Both methods reduced the daily mean error from about 2.6 <math><msup><mrow></mrow> <mo>∘</mo></msup> </math> C to almost zero, while the daily mean absolute error decreased from 5 <math><msup><mrow></mrow> <mo>∘</mo></msup> </math> C to 3 <math><msup><mrow></mrow> <mo>∘</mo></msup> </math> C for the neural network and 3.5 <math><msup><mrow></mrow> <mo>∘</mo></msup> </math> C for linear regression. Both methods, especially the neural network, also substantially reduced the dependence of the error on the time of the day.</p>","PeriodicalId":588,"journal":{"name":"International Journal of Biometeorology","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Biometeorology","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s00484-024-02640-6","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/3/5 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"BIOPHYSICS","Score":null,"Total":0}
引用次数: 0
Abstract
The 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 considers physiological aspects of the human body. 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 as part of 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, at these stations, global solar radiation measurements are performed continuously, which makes estimating the actual value of the UTCI more accurate compared to the situation where no radiation measurements are available. 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. We used two machine-learning methods, linear regression, and neural networks. Both methods have successfully reduced the error in the operational UTCI forecasts. Both methods 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. Both methods, especially the neural network, also substantially reduced the dependence of the error on the time of the day.
期刊介绍:
The Journal publishes original research papers, review articles and short communications on studies examining the interactions between living organisms and factors of the natural and artificial atmospheric environment.
Living organisms extend from single cell organisms, to plants and animals, including humans. The atmospheric environment includes climate and weather, electromagnetic radiation, and chemical and biological pollutants. The journal embraces basic and applied research and practical aspects such as living conditions, agriculture, forestry, and health.
The journal is published for the International Society of Biometeorology, and most membership categories include a subscription to the Journal.