Literature DB >> 22187087

UTCI--why another thermal index?

Gerd Jendritzky1, Richard de Dear, George Havenith.   

Abstract

Existing procedures for the assessment of the thermal environment in the fields of public weather services, public health systems, precautionary planning, urban design, tourism and recreation and climate impact research exhibit significant shortcomings. This is most evident for simple (mostly two-parameter) indices, when comparing them to complete heat budget models developed since the 1960s. ISB Commission 6 took up the idea of developing a Universal Thermal Climate Index (UTCI) based on the most advanced multi-node model of thermoregulation representing progress in science within the last three to four decades, both in thermo-physiological and heat exchange theory. Creating the essential research synergies for the development of UTCI required pooling the resources of multidisciplinary experts in the fields of thermal physiology, mathematical modelling, occupational medicine, meteorological data handling (in particular radiation modelling) and application development in a network. It was possible to extend the expertise of ISB Commission 6 substantially by COST (a European programme promoting Cooperation in Science and Technology) Action 730 so that finally over 45 scientists from 23 countries (Australia, Canada, Israel, several Europe countries, New Zealand, and the United States) worked together. The work was performed under the umbrella of the WMO Commission on Climatology (CCl). After extensive evaluations, Fiala's multi-node human physiology and thermal comfort model (FPC) was adopted for this study. The model was validated extensively, applying as yet unused data from other research groups, and extended for the purposes of the project. This model was coupled with a state-of-the-art clothing model taking into consideration behavioural adaptation of clothing insulation by the general urban population in response to actual environmental temperature. UTCI was then derived conceptually as an equivalent temperature (ET). Thus, for any combination of air temperature, wind, radiation, and humidity (stress), UTCI is defined as the isothermal air temperature of the reference condition that would elicit the same dynamic response (strain) of the physiological model. As UTCI is based on contemporary science its use will standardise applications in the major fields of human biometeorology, thus making research results comparable and physiologically relevant.

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Year:  2011        PMID: 22187087     DOI: 10.1007/s00484-011-0513-7

Source DB:  PubMed          Journal:  Int J Biometeorol        ISSN: 0020-7128            Impact factor:   3.787


  14 in total

1.  A computer model of human thermoregulation for a wide range of environmental conditions: the passive system.

Authors:  D Fiala; K J Lomas; M Stohrer
Journal:  J Appl Physiol (1985)       Date:  1999-11

2.  The physiological equivalent temperature - a universal index for the biometeorological assessment of the thermal environment.

Authors:  P Höppe
Journal:  Int J Biometeorol       Date:  1999-10       Impact factor: 3.787

3.  Advances, shortcomings, and recommendations for wind chill estimation.

Authors:  Avraham Shitzer; Peter Tikuisis
Journal:  Int J Biometeorol       Date:  2010-09-18       Impact factor: 3.787

4.  Physiological responses to temperature and humidity compared to the assessment by UTCI, WGBT and PHS.

Authors:  Bernhard Kampmann; Peter Bröde; Dusan Fiala
Journal:  Int J Biometeorol       Date:  2011-02-20       Impact factor: 3.787

5.  UTCI-Fiala multi-node model of human heat transfer and temperature regulation.

Authors:  Dusan Fiala; George Havenith; Peter Bröde; Bernhard Kampmann; Gerd Jendritzky
Journal:  Int J Biometeorol       Date:  2011-04-19       Impact factor: 3.787

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

Authors:  Philipp Weihs; Henning Staiger; Birger Tinz; Ekaterina Batchvarova; Harald Rieder; Laurent Vuilleumier; Marion Maturilli; Gerd Jendritzky
Journal:  Int J Biometeorol       Date:  2011-02-23       Impact factor: 3.787

7.  Comparison of UTCI to selected thermal indices.

Authors:  Krzysztof Blazejczyk; Yoram Epstein; Gerd Jendritzky; Henning Staiger; Birger Tinz
Journal:  Int J Biometeorol       Date:  2011-05-26       Impact factor: 3.787

8.  Computer prediction of human thermoregulatory and temperature responses to a wide range of environmental conditions.

Authors:  D Fiala; K J Lomas; M Stohrer
Journal:  Int J Biometeorol       Date:  2001-09       Impact factor: 3.787

9.  An experimental validation of mathematical simulation of human thermoregulation.

Authors:  S Konz; C Hwang; B Dhiman; J Duncan; A Masud
Journal:  Comput Biol Med       Date:  1977-01       Impact factor: 4.589

10.  Individualized model of human thermoregulation for the simulation of heat stress response.

Authors:  G Havenith
Journal:  J Appl Physiol (1985)       Date:  2001-05
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  79 in total

1.  Thermal comfort in Quebec City, Canada: sensitivity analysis of the UTCI and other popular thermal comfort indices in a mid-latitude continental city.

Authors:  Simon Provençal; Onil Bergeron; Richard Leduc; Nathalie Barrette
Journal:  Int J Biometeorol       Date:  2015-09-08       Impact factor: 3.787

2.  Assessment of indoor heat stress variability in summer and during heat warnings: a case study using the UTCI in Berlin, Germany.

Authors:  Nadine Walikewitz; Britta Jänicke; Marcel Langner; Wilfried Endlicher
Journal:  Int J Biometeorol       Date:  2015-09-30       Impact factor: 3.787

3.  Application of spatial synoptic classification in evaluating links between heat stress and cardiovascular mortality and morbidity in Prague, Czech Republic.

Authors:  Aleš Urban; Jan Kyselý
Journal:  Int J Biometeorol       Date:  2015-09-04       Impact factor: 3.787

4.  Mortality risks during extreme temperature events (ETEs) using a distributed lag non-linear model.

Authors:  Michael J Allen; Scott C Sheridan
Journal:  Int J Biometeorol       Date:  2015-12-08       Impact factor: 3.787

5.  A comparison of THI indices leads to a sensible heat-based heat stress index for shaded cattle that aligns temperature and humidity stress.

Authors:  A Berman; Talia Horovitz; M Kaim; H Gacitua
Journal:  Int J Biometeorol       Date:  2016-01-27       Impact factor: 3.787

6.  Comparison of different methods of estimating the mean radiant temperature in outdoor thermal comfort studies.

Authors:  E L Krüger; F O Minella; A Matzarakis
Journal:  Int J Biometeorol       Date:  2013-12-28       Impact factor: 3.787

7.  Customized rating assessment of climate suitability (CRACS): climate satisfaction evaluation based on subjective perception.

Authors:  Tzu-Ping Lin; Shing-Ru Yang; Andreas Matzarakis
Journal:  Int J Biometeorol       Date:  2015-04-22       Impact factor: 3.787

Review 8.  Past, present and future of the climate and human health commission.

Authors:  Pablo Fdez-Arroyabe; Daysarih Tápanes Robau
Journal:  Int J Biometeorol       Date:  2017-07-22       Impact factor: 3.787

9.  Effects of ventilation behaviour on indoor heat load based on test reference years.

Authors:  Madeleine Rosenfelder; Christina Koppe; Jens Pfafferott; Andreas Matzarakis
Journal:  Int J Biometeorol       Date:  2015-06-07       Impact factor: 3.787

10.  A glossary for biometeorology.

Authors:  Simon N Gosling; Erin K Bryce; P Grady Dixon; Katharina M A Gabriel; Elaine Y Gosling; Jonathan M Hanes; David M Hondula; Liang Liang; Priscilla Ayleen Bustos Mac Lean; Stefan Muthers; Sheila Tavares Nascimento; Martina Petralli; Jennifer K Vanos; Eva R Wanka
Journal:  Int J Biometeorol       Date:  2014-02-19       Impact factor: 3.787

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