Bioclimatic conditions of the Lower Silesia region (South-West Poland) from 1966 to 2017
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SPECIAL ISSUE: UTCI - 10 YEARS OF APPLICATIONS
Bioclimatic conditions of the Lower Silesia region (South-West Poland) from 1966 to 2017 Arkadiusz Głogowski1
· Krystyna Bry´s1 · Paolo Perona2
Received: 31 October 2019 / Revised: 16 June 2020 / Accepted: 15 July 2020 © The Author(s) 2020
Abstract This work analyses the temporal and spatial characteristics of bioclimatic conditions in the Lower Silesia region. The daily time values (12UTC) of meteorological variables in the period 1966–2017 from seven synoptic stations of the Institute of ´ zka) were Meteorology and Water Management (IMGW) (Jelenia G´ora, Kłodzko, Legnica, Leszno, Wrocław, Opole, Snie˙ used as the basic data to assess the thermal stress index UTCI (Universal Thermal Climate Index). The UTCI can be interpreted by ten different thermal classes, representing the bulk of these bioclimatic conditions. Stochastic autoregressive moving-average modelling (ARMA) was used for the statistical analysis and modelling of the UTCI as well as separately for all meteorological components. This made it possible to test differences in predicting UTCI as a full index or reconstructing it from single meteorological variables. The results show an annual and seasonal variability of UTCI for the Lower Silesia region. Strong significant spatial correlations in UTCI were also found in all stations of the region. “No thermal stress” is the most commonly occurring thermal class in this region (about 38%). Thermal conditions related to cold stress classes occurred more frequently (all cold classes at about 47%) than those of heat stress classes (all heat classes at about 15%). Over the available 52-year period, the occurrence of “extreme heat stress” conditions was not detected. Autoregressive analysis, although successful in predicting UTCI, was nonetheless unsuccessful in reconstructing the wind speed, which showed a persistent temporal correlation possibly due to its vectorial origin. We conclude thereby that reconstructing UTCI using linear autoregressive methods is more suitable when working directly on the UTCI as a whole rather than reconstructing it from single variables. Keywords UTCI · Biometeorology · Forecasting · ARMA model
Introduction Bioclimatology finds applications in many fields such as climate change (Wu et al. 2019), health research (Br¨ode et al. 2018), epidemiology (Di Napoli et al. 2018), military (Galan and Guedes 2019), and urban planning or even to determine the attractiveness of tourist places such as coastal towns or health resorts (Bła˙zejczyk and Kunert 2011b; Ge et al. 2017). However, at a time when meteorological weather forecasts can be modelled anywhere on the planet,
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00484-020-01970-5) contains supplementary material, which is available to authorized users. Arkadiusz Głogowski
[email protected]
Extended author information available on the last page of the article.
there are still many locations that do not possess historical r
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