International Journal of Biometeorology

, Volume 56, Issue 3, pp 421–428

UTCI—Why another thermal index?

  • Gerd Jendritzky
  • Richard de Dear
  • George Havenith
Special Issue (UTCI)


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.


Outdoor climate Thermal assessment Index Thermal stress Thermo-physiology Model 


The close relationship of humans to the thermal component of the atmospheric environment is self-evident and belongs to everybody’s daily experience. Thus, issues related to thermal comfort, discomfort, and health impacts are the reason that the assessment and forecast of the thermal environment form one of the fundamental and enduring themes within human biometeorology. In this context, the term “thermal environment” encompasses both atmospheric heat exchanges with the body (stress) and the body’s physiological response (strain).

The following fields of applications are considered as particularly significant for users:
  1. (1)

    Public weather service (PWS). The issue is how to inform and advise the public on thermal conditions on short time scales (weather forecast) for outdoor activities, appropriate behaviour, and climate-therapy. Currently, various national meteorological services around the world use a plethora of indices in their public weather advice. But in an increasingly internationalised weather information sphere, the use of local weather dialects seems no longer to be appropriate.

  2. (2)

    Public health system (PHS). In order to mitigate the adverse health effects of extreme weather events (heat waves and cold spells) it is necessary to implement appropriate disaster preparedness plans. This requires warnings about extreme thermal stress so that interventions can be released in order to save lives and reduce health impacts.

  3. (3)

    Precautionary planning. This refers to a wide range of applications in public and individual spheres, such as urban and regional planning, and in the tourism industry. The increasing reliability of monthly or seasonal forecasts should be considered to help develop appropriate operational products.

  4. (4)

    Climate impact research in the health sector. The increasing awareness of climate change and the related health impacts requires epidemiological studies based on cause-effect related approaches.


Balancing the human heat budget, i.e. equilibration of the organism to variable environmental (atmospheric) and metabolic heat loads is controlled by a very efficient (for healthy people) autonomous thermoregulatory system. This is supported additionally by behavioural adaptation (e.g. eating and drinking, activity and resting, clothing, exposure, housing, migration) driven by conscious sensations of thermal discomfort. These capabilities enable the (healthy) human being to live and to work in virtually any climate zone on Earth, albeit with varying degrees of discomfort.

The heat exchange between the human body and its environment takes place by sensible and latent heat fluxes, radiation and (generally negligible) conduction. Comprehensively characterising the thermal environment in thermo-physiologically significant terms requires application of a complete heat budget model that takes all mechanisms of heat exchange into account (Buettner 1938; Parsons 2003). Atmospheric environmental parameters governing all of the abovementioned heat exchanges include air temperature, water vapour pressure, wind velocity, mean radiant temperature, and including the short- and long-wave radiation fluxes of the atmosphere (see Weihs et al. 2011 elsewhere in this issue), in addition to metabolic rate and clothing insulation worn by the subject. Only thermal climate models that incorporate all parameters of the human heat budget can be utilised universally across the full gamut of biometeorological applications, and across all climate zones, regions and seasons (e.g. Jendritzky and de Dear 2009).

Recognising that the human thermal environment cannot be represented adequately with just a single parameter, air temperature (Ta), over the last 150 years or so more than 100 simple thermal indices have been developed, most of them two-parameter indices. For warm conditions, such indices consist usually of combinations of Ta and one of a variety of expressions for humidity, while for cold conditions the combination consists typically of Ta combined in some way with air speed (v). Simple indices are easy to calculate and, therefore, easy to forecast. In addition they are communicated readily to the general public and stakeholders such as health service providers (Koppe et al. 2004). However, due to their simple formulation, i.e. neglecting significant fluxes or variables, these indices can never fulfil the essential requirement that for each index value there must always be a corresponding and unique thermo-physiological state (strain), regardless of the combination of the meteorological input values (stress). Simple indices are of only limited value, the results are often not comparable and often lead to misrepresentations of the thermal environment, and additional features such as safety thresholds, etc., have to be defined arbitrarily and cannot be transferred to other locations.

These inadequacies of two-parameter indices have prompted hundreds of attempts at improvement The Wind-Chill Temperature (ISO 11079 2007) and subsequently the New Wind Chill Index (Osczevski and Bluestein 2005) are illustrative in this regard; the turbulent heat flux is disproportionate and has been critiqued by Shitzer and De Dear (2006). In occupational health, the Wet Bulb Globe Temperature (WBGT, ISO 7243 1989) evolved in the 1950s, but is still popular for consideration of humidity and thermal radiation in warm environments. Comprehensive reviews of simple indices can be found e.g. in Fanger (1970), Landsberg (1972), Driscoll (1992), and Parsons (2003).

During the last 40 years, thermal biomereorology has advanced significantly with the development of heat budget models (see Stollwijk 1971 and related 2-node model associated with Gagge et al. 1986). Subsequent developments, still relatively simple, include models such as MEMI with the output of PET (Höppe 1984, 1999; Matzarakis et al. 2007), and the Outdoor Apparent Temperature1 (Steadman 1984, 1994). More comprehensive models and indices based on the human heat balance equation include the Standard Effective Temperature (SET*) index (Gagge et al. 1986), and OUT_SET* (Pickup and De Dear 2000; De Dear and Pickup 2000), which translates Gagge’s indoor version of the index to an outdoor setting by simplifying the complex outdoor radiative environment down to a mean radiant temperature (Tmrt). Blazejczyk (1994) presented the man-environment heat exchange model MENEX, while the extensive work by Horikoshi et al. (1995, 1997) resulted in a Thermal Environmental Index. While the abovementioned heat budget models are applicable across the full range of heat exchange conditions, the Predicted Heat Strain (PHS, ISO 7933 2004), which is used in occupational medicine, is relevant only to warm environments.

Fanger’s (1970) PMV (Predicted Mean Vote) equation can also be included among the advanced heat budget models if Gagge’s et al. (1986) improvement in the description of latent heat fluxes by the introduction of PMV* is applied. This approach is generally the basis for the operational thermal assessment procedure Klima-Michel-Model KMM (Jendritzky et al. 1979; Jendritzky and Nübler 1981; Jendritzky 1990) of the German national weather service DWD (Deutscher Wetterdienst) with the output parameter “Perceived Temperature, PT” (Staiger et al. 1997; VDI 2008) that considers behavioural adaptation by varying clothing. For more than two decades KMM was the sole assessment procedure to include a complete radiation model to calculate Tmrt on the basis of meteorological data. To date, the DWD is the only national weather service to run a complete heat budget model (KMM-PT) on a routine basis specifically for applications in human biometeorology.

Although each of the heat budget models referred to above is, in principle, appropriate for use in any kind of assessment of the thermal environment, none of them is accepted as a fundamental standard, either by researchers or by end-users. This is probably because of persistent shortcomings in relation to thermo-physiology and heat exchange theory. On the other hand, it is surprising that, after 40 years experience with heat budget modelling and easy access both to computational power and meteorological data, crude and simplistic empirical indices like WBGT continue to be used widely. For comparisons of both selected simple indices and also of more complex heat budget based approaches to the creation of a Universal Thermal Climate Index (UTCI), see Blazejczyk et al. (2011) and the work of Kampmann and colleagues (Kampmann and Bröde 2009; and Kampmann et al. 2011, this issue).

A decade ago, the International Society on Biometeorology (ISB) recognised these shortcomings in thermal indices and established Commission 6: “On the development of a Universal Thermal Climate Index (UTCI)” (Jendritzky et al. 2002). Since 2005, these efforts have been reinforced by the COST Action 730 (Cooperation in Science and Technology, supported by the EU RTD Framework Programme), which has provided the basis for European scientists from 19 countries plus experts from Australia, Canada, Israel and New Zealand to collaborate on the development of such an index (COST UTCI 2004). The aim was an international standard based on the latest scientific progress in human-response-related thermo-physiological modelling over the last four decades. The term “universal” must be understood in terms of being appropriate for all assessments of the outdoor thermal conditions in the major human biometeorological applications such as daily forecasts and warnings of extreme weather, to bioclimatic mapping, urban and regional planning, environmental epidemiology and climate impact research. This covers the fields of public weather service, the public health system, precautionary planning, and climate impact research in the health sector.

The UTCI must meet the following requirements:
  1. (1)

    Thermo-physiologically responsive to all modes of heat exchange between body and environment

  2. (2)

    Applicable for whole-body calculations but also for local skin cooling (frost bite) (see Shitzer and Tikusis (2011, this issue)

  3. (3)

    Valid in all climates, seasons, and time and spatial scales

  4. (4)

    Appropriate for key applications in human biometeorology (listed above)


Approach and results


For a human being, it is crucial to keep the body’s core temperature within a narrow range around 37°C in order to ensure functioning of the inner organs and of the brain. In contrast, the temperature of the shell, i.e. skin and extremities, can vary significantly depending on the volume of blood it contains, which in turn depends on metabolic and environmental heat loads. Heat is produced by metabolism as a result of activity, sometimes increased by shivering or slightly offset by mechanical work where applicable, e.g. when climbing. The heat must be released to the environment by convection (sensible heat flux), conduction (contact with solids), evaporation (latent heat flux), radiation (long- and short-wave), and respiration (latent and sensible).

From the analytical point of view, the human thermoregulatory system can be separated into two interacting sub-systems: (1) the controlling active system that includes the thermoregulatory responses of shivering [thermo genesis] sweat moisture excretion, and peripheral blood flow regulation; and (2) the controlled, passive system dealing with the physical human body and the heat transfer occurring within it and at its surface (Fig. 1). This accounts for local heat losses from body parts by free and forced convection, long-wave radiation exchange with surrounding surfaces, solar irradiation, and evaporation of moisture from the skin and heat and mass transfer through non-uniform clothing. Under comfort conditions, the active system shows the lowest activity level indicating no strain. Increasing discomfort is associated with increasing strain and related impacts on the cardiovascular and respiratory system. The tolerance to thermal extremes depends on personal characteristics (Havenith 2001, 2005): age, fitness, gender, acclimatisation, morphology, and fat thickness being among the most significant. Of these, age and fitness are the most important predictors and both are closely correlated. High age and/or low fitness level are associated with low cardiovascular reserves, which causes low thermal tolerance.
Fig. 1

a, b Schematic representation of human physiological and behavioural thermoregulation (after Fiala et al. 2001; Havenith 2001)

The heat budget

The heat exchange between the human body and the thermal environment (Fig. 2) can be described in the form of the energy balance equation (Eq. 1); essentially the first theorem of thermodynamics applied to the body’s heat sources (metabolism and environmental), and the various avenues of heat loss to environment (Büttner 1938):
$$ M - W - \left[ {{Q_H}\left( {Ta,v} \right) + Q * \left( {Tmrt} \right)} \right] - \left[ {{Q_L}\left( {e,v} \right) + {Q_{{SW}}}\left( {e,v} \right)} \right] - {Q_{{{\rm Re} }}}\left( {Ta,e} \right)\pm S = 0 $$

Metabolic rate (activity)


Mechanical power


Storage (change in heat content of the body)

Fig. 2

The human heat budget (Havenith 2001)

Peripheral (skin) heat exchanges:

Turbulent flux of sensible heat


Radiation budget


Turbulent flux of latent heat (passive diffusion water vapour through the skin)


Turbulent flux of latent heat (sweat evaporation)

Respiratory heat exchanges:

Respiratory heat flux (sensible and latent)

Thermal environmental parameters:

Air temperature


Mean temperature


Air speed relative to the body


Partial vapour pressure

The meteorological input variables include air temperature Ta , water vapour pressure e, wind velocity v, mean radiant temperature Tmrt including short- and long-wave radiation fluxes, in addition to metabolic rate and clothing insulation. In Eq. 1, the appropriate meteorological variables are attached to the relevant fluxes. However, the internal (physiological) variables (Fig. 1), such as the temperature of the core and the skin, sweat rate, and skin wettedness interacting with the environmental heat exchange conditions are not mentioned explicitly here.

Mathematical modeling of the human thermal system goes back 70 years. In the past four decades more detailed, multi-node models of human thermoregulation have been developed, e.g. Stolwijk (1971), Konz et al. (1977), Wissler (1985), Fiala et al. (1999, 2001), Huizenga et al. (2001) and Tanabe et al. (2002). These models simulate phenomena of the human heat transfers within the body and at its surface, taking into account the anatomical, thermal and physiological properties of the human body (see Fig. 1). Environmental heat losses from body parts are modelled considering the inhomogeneous distribution of temperature and thermoregulatory responses over the body surface. Besides overall thermo-physiological variables, multi-segmental models are capable of predicting “local” characteristics such as skin temperatures of individual body parts. Validation studies have shown that recent multi-node models accurately reproduce the human dynamic thermal responses over a wide range of thermal circumstances (Fiala et al. 2001, 2003; Havenith 2001; Huizenga et al. 2001). These models have become valuable research tools contributing to a deeper understanding of the principles of human thermoregulation.


As the assessment of thermal stress should ultimately be based on the physiological response of the human body (thermal strain), ISB Commission 6 decided from the outset that this was to be simulated by one of the most advanced (multi-node) thermo-physiological models. After accessible models of human thermoregulation had been evaluated (Fiala et al. 1999; Tanabe et al. 2002), Fiala’s multi-node human physiology and thermal comfort (FPC) model (Fiala et al. 1999, 2001, 2003, 2010) was adopted for this study, extensively validated (Psikuta 2009; Psikuta et al. 2007; see also Psikuta et al. 2011, this issue), and extended for purposes of the project (Fiala et al. 2007; see also Fiala et al. 2011, this issue).

The passive system of the Fiala model (Fiala et al. 1999, 2001) consists of a multi-segmental, multi-layered representation of the human body with spatial subdivisions. Each tissue node is assigned appropriate thermo-physical and thermo-physiological properties. The overall data replicates an average person with respect to body weight, body fat content, and Dubois surface area. The physiological data aggregates to a basal [whole body] heat output and basal cardiac output, which are appropriate for a nude, reclining adult in a thermo-neutral environment of 30°C. In these conditions, where thermoregulatory activity is minimal, the model predicts a basal skin wettedness of 6%; a mean skin temperature of 34.4°C; and body core temperatures of 37.0°C in the head core (hypothalamus) and 36.9°C in the abdomen core (rectum) (Fiala et al. 1999). Verification and validation work using independent experiments from air exposures to cold stress, cold, moderate, warm and hot stress conditions, and a wide range of exercise intensities revealed good agreement with measured data for regulatory responses, mean and local skin temperatures, and internal temperatures across the whole spectrum of boundary conditions considered (Richards and Havenith 2007). By including as yet unused data from other research groups, the FPC model (Fiala et al. 2010) could be substantially advanced. FPC was adopted by the ISB Commission 6 as benchmark (“most advanced”) in terms of thermo-physiology and heat exchange theory.

In the next step, a state-of-the-art adaptive clothing model was developed and integrated (Richards and Havenith 2007; Havenith et al. 2011, this issue). This model considers
  1. (1)

    the behavioural adaptation of clothing insulation observed for the general urban population in relation to the actual environmental temperature,

  2. (2)

    the distribution of the clothing over different body parts providing local insulation values for the different anatomical segments, and

  3. (3)

    the reduction of thermal and evaporative clothing resistances caused by wind and limb movements of the wearer, who is assumed to be walking at a speed of 4 km/h on level ground (2.3 MET = 135 W/m2).


UTCI was then developed following the concept of an equivalent temperature (ET). This involved the definition of a reference environment with 50% relative humidity (but vapour pressure not exceeding 20 hPa), with calm air and radiant temperature equalling air temperature, to which all other climatic conditions are compared. Equal physiological conditions are based on the equivalence of the dynamic physiological response predicted by the model for the actual and the reference environment. As this dynamic response is multidimensional (body core temperature, sweat rate, and skin wettedness, etc., at different exposure times), a strain index was calculated by principal component analysis as single dimensional representation of the model response (Bröde et al. 2009a, b). The UTCI equivalent temperature for a given combination of wind, radiation, humidity and air temperature is then defined as the air temperature of the reference environment that produces the same strain index value. As calculating the UTCI equivalent temperatures by repeatedly running the thermoregulation model could be too time-consuming for climate simulations and numerical weather forecasts, a fast calculation procedure has been developed and made available (for details see Bröde et al. 2008, 2009a; Bröde et al. 2011, this issue).


The main objective of this collaboration between 45 scientists from 23 countries was to develop a readily accessible thermal index based on a state-of-the-art thermo-physiological model. The UTCI resulting from this research is intended to significantly enhance applications related to human health and well-being in the fields of public weather services, public health systems, precautionary planning, and climate impact research. The development of UTCI required co-operation of experts from diverse disciplines including thermo-physiology, occupational medicine, physics, meteorology, biometeorological and environmental sciences. After many decades of frustrating attempts by individual researchers working on thermal indices in isolation from cognate disciplines, the UTCI team’s multidisciplinary approach facilitated the research synergies necessary for a universal solution to the problem of characterising the human thermal environment. Embedding the UTCI project within a Commission of the International Society of Biometeorology and also a European COST Action provides an international framework for this new climatic index to evolve into a methodological standard.

The UTCI assesses the outdoor thermal environment for biometeorological applications by simulating the dynamic physiological response with a model of human thermoregulation coupled with a state-of-the-art clothing model. The operational procedure (available as software from the UTCI website shows plausible responses to humidity and radiative loads in hot environments, as well as to wind in the cold. UTCI was in good agreement with the assessment of other standards concerned with the thermal environment (Psikuta et al. 2011, this issue). Local cooling of exposed skin, including frostbite risk (wind chill effects), should best be regarded as a transient, rather than a steady-state phenomenon (Shitzer 2006; Tikuisis and Osczevski 2002, 2003). The consensus final procedure for cold exposure using UTCI, however, still remains to be determined (Shitzer and Tikuisis 2011, this issue).


  1. 1.

    The Indoor AT, which forms the basis of the US Heat Index, often used in outdoor applications by neglecting the prefix “Indoor” belongs to the simple two-parameter indices.



The UTCI project was performed within COST Action 730, funded by the European Union TRD Framework Programme and the International Society of Biometeorology Commission 6, under the umbrella of the World Meteorological Organization’s Commission on Climatology CCl.


  1. Blazejcyk K, Epstein Y, Jendritzky G, Staiger H, Tinz B (2011) Comparison of UTCI to selected thermal indices. Int J Biometeorol, this issueGoogle Scholar
  2. Blazejczyk K (1994) New climatological- and -physiological model of the human heat balance outdoor (MENEX) and its applications in bioclimatological studies in different scales. Zeszyty IgiPZ PAN 28:27–58Google Scholar
  3. Bröde P, Kampmann B, Havenith G, Jendritzky G (2008) Effiziente Berechnung des klimatischen Belastungs-Index UTCI. In: Gesellschaft für Arbeitswissenschaft (ed.): Produkt- und Produktions-Ergonomie - Aufgabe für Entwickler und Planer, GfA-Press, Dortmund, pp 271–274Google Scholar
  4. Bröde P, Fiala D, Blazejczyk K, Epstein Y, Holmér I, Jendritzky G, Kampmann B, Richards M, Rintamäki H, Shitzer A, Havenith G (2009a) Calculating UTCI Equivalent Temperature. In: Castellani JW, Endrusick TL (eds) Environmental Ergonomics XIII. University of Wollongong, Wollongong, pp 49–53Google Scholar
  5. Bröde P, Fiala D, Kampmann B, Havenith G, Jendritzky G (2009b) Der Klimaindex UTCI—Multivariate Analyse der Reaktion eines thermophysiologischen Simulationsmodells. In: Gesellschaft für Arbeitswissenschaft (ed.): Arbeit, Beschäftigungsfähigkeit und Produktivität im 21. Jahrhundert, GfA-Press, Dortmund, pp 705–708Google Scholar
  6. Bröde P, Fiala D, Blazejcyk K, Holmér I, Jendritzky G, Kampmann B, Tinz B, Havenith G (2011) Deriving the operational procedure for the Universal Thermal Climate Index UTCI. Int J Biometeorol, this issueGoogle Scholar
  7. Büttner K (1938) Physikalische Bioklimatologie. Probleme und Methoden. Akad. Verl. Ges, LeipzigGoogle Scholar
  8. COST UTCI (2004) Towards a Universal Thermal Climate Index UTCI for Assessing the Thermal Environment of the Human Being. MoU of COST Action 730. 17 pp
  9. De Dear R, Pickup J (2000) An Outdoor Thermal Environment Index (OUT_SET*) - Part II - Applications. In: De Dear R, Kalma J, Oke T, Auliciems A (eds.), Biometeorology and Urban Climatology at the Turn of the Millennium. Selected Papers from the Conference ICB-ICUC'99 (Sydney, 8–12 November 1999). WMO, Geneva, WCASP-50, pp 258–290Google Scholar
  10. Driscoll DM (1992) Thermal comfort indexes. Current uses and abuses. Nat Weather Digest 17(4):33–38Google Scholar
  11. Fanger PO (1970) Thermal comfort. Analysis and application in environment engineering. Danish Technical Press, CopenhagenGoogle Scholar
  12. Fiala D, Lomas KJ, Stohrer M (1999) A computer model of human thermoregulation for a wide range of environmental conditions: the passive system. J Appl Physiol 87(5):1957–1972Google Scholar
  13. Fiala D, Lomas KJ, Stohrer M (2001) Computer prediction of human thermoregulatory and temperature responses to a wide range of environmental conditions. Int J Biometeorol 45:143–159CrossRefGoogle Scholar
  14. Fiala D, Lomas KJ, Stohrer M (2003) First principles modeling of thermal sensation responses in steady-state and transient conditions. ASHRAE Transactions: Research Vol. 109, Part I, pp 179–186Google Scholar
  15. Fiala D, Lomas KJ, Stohrer M (2007) Dynamic simulation of human heat transfer and thermal comfort. In: Mekjavic IB, Kounalakis SN, Taylor NAS (eds) Environmental ergonomics XII. Biomed, Ljubljana, pp 513–515Google Scholar
  16. Fiala D, Psikuta A, Jendritzky G, Paulke S, Nelson DA, v Marken Lichtenbelt WD, Frijns AJH (2010) Physiological modelling for technical, clinical and research applications. Front Biosci S2:939–968CrossRefGoogle Scholar
  17. Fiala D, Havenith G, Bröde P, Kampmann B, Jendritzky G (2011) UTCI-Fiala multi-node model human heat transfer and thermal comfort. Int J Biometeorol, this issueGoogle Scholar
  18. Gagge AP, Fobelets AP, Berglund PE (1986) A standard predictive index of human response to the thermal environment. ASHRAE Trans 92:709–731Google Scholar
  19. Havenith G (2001) An individual model of human thermoregulation for the simulation of heat stress response. J Appl Physiol 90:1943–1954Google Scholar
  20. Havenith G (2005) Temperature regulation, heat balance and climatic stress. In: Kirch W, Menne B, Bertollini R (eds) Extreme weather events and public health responses. Springer, Heidelberg, pp 69–80Google Scholar
  21. Havenith G, Fiala D, Blazejcyk K, Richards M, Bröde P, Holmér I, Rintamäki H, Benshabat Y, Jendritzky G (2011) The UTCI clothing model. Int J Biometeorol, this issueGoogle Scholar
  22. Höppe P (1984) Die Energiebilanz des Menschen. München Universitäts Schriften, Fachbereich Physic, Wissenschaftliche Mitteilungen 49Google Scholar
  23. Höppe P (1999) The physiological equivalent temperature—a universal index for the biometeorological assessment of the thermal environment. Int J Biometeorol 43:71–75CrossRefGoogle Scholar
  24. Horikoshi T, Tsuchikawa T, Kurazumi Y, Matsubara N (1995) Mathematical expression of combined and separate effect of air temperature, humidity, air velocity and thermal radiation on thermal comfort. Arch Complex Environ Stud 7(3–4):9–12Google Scholar
  25. Horikoshi T, Einishi M, Tsuchikawa T, Imai H (1997) Geographical distribution and annual fluctuation of thermal environmental indices in Japan. Development of a new thermal environmental index for outdoors and its application. J Human–Environment System 1(1):87–92Google Scholar
  26. Huizenga C, Zhang H, Arens E (2001) A model of human physiology and comfort for assessing complex thermal environments. Build Environ 36:691–699CrossRefGoogle Scholar
  27. ISO 11079 (2007) Ergonomics of the thermal environment—determination and interpretation of cold stress when using required clothing insulation (IREQ) and local cooling effects. International Organisation for Standardisation, GenevaGoogle Scholar
  28. ISO 7243 (1989) Hot Environments; Estimation of the Heat Stress on Working Man, Based on the WBGT-Index (Wet Bulb Globe Temperature). International Organisation for Standardisation, GenevaGoogle Scholar
  29. ISO 7933 (2004) Ergonomics of the Thermal Environment - Analytical Determination and Interpretation of Heat Stress Using Calculation of the Predicted Heat Strain. International Organisation for Standardisation, GenevaGoogle Scholar
  30. Jendritzky G (1990) Bioklimatische Bewertungsgrundlage der Räume am Beispiel von mesoskaligen Bioklimakarten. In: Jendritzky G, Schirmer H, Menz G, Schmidt-Kessen W: Methode zur raumbezogenen Bewertung der thermischen Komponente im Bioklima des Menschen (Fortgeschriebenes Klima-Michel-Modell). Akad Raumforschung Landesplanung, Hannover. Beiträge 114:7–69Google Scholar
  31. Jendritzky G, de Dear R (2009) Adaptation and thermal environment. In: Ebi KL, Burton I, McGregor GR (eds.) Biometeorology for adaptation to climate variability and change. Biometeorology 1, Springer, Berlin, pp 9–32Google Scholar
  32. Jendritzky G, Nübler W (1981) A model analysing the urban thermal environment in physiologically significant terms. Arch Met Geoph Biokl B 29(4):313–326CrossRefGoogle Scholar
  33. Jendritzky G, Sönning W, Swantes HJ (1979) Ein objektives Bewertungsverfahren zur Beschreibung des thermischen Milieus in der Stadt- und Landschaftsplanung (“Klima-Michel-Modell”). Beiträge Akad. Raumforschung Landesplanung, 28, HannoverGoogle Scholar
  34. Jendritzky G, Maarouf A, Fiala D, Staiger H (2002) An update on the development of a Universal Thermal Climate Index. 15th Conf. Biomet. Aerobiol. and 16th ICB02, 27 Oct – 1 Nov 2002, Kansas City, AMS, pp 129–133Google Scholar
  35. Kampmann B, Bröde P (2009) Physiological responses to temperature and humidity compared with predictions of PHS and WBGT. In: Castellani JW, Endrusick TL (eds) Environmental ergonomics XIII. University of Wollongong, Wollongong, pp 54–58Google Scholar
  36. Kampmann B, Broede P, Fiala D (2011) Physiological responses to temperature and humidity compared to the assessment by UTCI, WGBT and PHS. Int J Biometeorol, this issueGoogle Scholar
  37. Konz S, Hwang C, Dhiman B, Duncan J, Masud A (1977) An experimental validation of mathematical simulation of human thermoregulation. Comput Biol Med 7:71–82CrossRefGoogle Scholar
  38. Koppe C, Kovats S, Jendritzky G, Menne B (2004) Heat-waves: risks and responses. World Health Organization. Health and Global Environmental Change, Series, No. 2, Copenhagen, DenmarkGoogle Scholar
  39. Landsberg HE (1972) The assessment of human bioclimate, a limited review of physical parameters. World Meteorological Organization, Technical Note No. 123, WMO-No. 331, GenevaGoogle Scholar
  40. Matzarakis A, Rutz F, Mayer H (2007) Modelling radiation fluxes in simple and complex environments—application of the RayMan model. Int J Biometeorol 51:323–334CrossRefGoogle Scholar
  41. Osczevski R, Bluestein M (2005) The new wind chill equivalent temperature chart. Bull Am Meteorol Soc 86(10):1453–1458CrossRefGoogle Scholar
  42. Parsons KC (2003) Human thermal environments: the effects of hot, moderate, and cold environments on human health, comfort and performance. Taylor & Francis, LondonGoogle Scholar
  43. Pickup J, de Dear R (2000) An Outdoor Thermal Comfort Index (OUT_SET*). Part I–The model and its assumptions. In: de Dear R, Kalma J, Oke T, Auliciems A (eds) Biometeorology and urban climatology at the turn of the millenium. Selected Papers from the Conference ICB-ICUC'99 (Sydney, 8–12 November 1999). WMO, Geneva, WCASP-50, pp 279–283Google Scholar
  44. Psikuta A (2009) Development of an ‘artificial human’ for clothing research. PhD Thesis, IESD, De Montfort University, Leicester, UKGoogle Scholar
  45. Psikuta A, Fiala D, Richards M (2007) Validation of the Fiala Model of Human Physiology and Comfort for COST 730. In: Mekjavic IB, Kounalakis SN, Taylor NAS (eds) Proceedings of the 12th International Conference on Environmental Ergonomics XII. Biomed, Ljubljana, p 516Google Scholar
  46. Psikuta A, Fiala D, Laschewski D, Jendritzky G, Richards M, Blazejcyk K, Mekjavic I, Rintamäki H, de Dear R, Havenith G (2011) Evaluation of Fiala multi-node thermo-physiological model for UTCI application. Int J Biometeorol, this issueGoogle Scholar
  47. Richards M, Havenith G (2007) Progress towards the final UTCI model. In: Mekjavic IB, Kounalakis SN, Taylor NAS (eds) Proceedings of the 12th International Conference on Environmental Ergonomics. 19–24 August 2007. Biomed, Ljubljana, Piran Slovenia, pp 521–524Google Scholar
  48. Shitzer A (2006) Wind-chill-equivalent temperatures: regarding the impact due to the variability of the environmental convective heat transfer coefficient. Int J Biometeorol 50(4):224–232CrossRefGoogle Scholar
  49. Shitzer A, De Dear R (2006) Inconsistencies in the “new” wind chill chart at low wind speeds. J Appl Meteorol Climatol 45:787–790Google Scholar
  50. Shitzer A, Tikuisis P (2011) Advances, shortcomings, and recommendations for wind chill estimation. Int J Biometeorol, this issueGoogle Scholar
  51. Staiger H, Bucher K, Jendritzky G (1997) Gefühlte Temperatur. Die physiologisch gerechte Bewertung von Wärmebelastung und Kältestress beim Aufenthalt im Freien in der Maßzahl Grad Celsius. Ann Meteorol Deutscher Wetterdienst, Offenbach 33:100–107Google Scholar
  52. Steadman RG (1984) A universal scale of apparent temperature. J Climate Appl Meteorol 23:1674–1687CrossRefGoogle Scholar
  53. Steadman RG (1994) Norms of apparent temperature in Australia. Aust Met Mag 43:1–16Google Scholar
  54. Stolwijk JAJ (1971) A mathematical model of physiological temperature regulation in man. NASA contractor report, NASA CR-1855, Washington DCGoogle Scholar
  55. Tanabe SI, Kobayashi K, Nakano J, Ozeki Y, Konishi M (2002) Evaluation of thermal comfort using combined multi-node thermoregulation (65MN) and radiation models and computational fluid dynamics (CFD). Energ Buildings 34:637–646CrossRefGoogle Scholar
  56. Tikuisis P, Osczevski RJ (2002) Dynamic model of facial cooling. J Appl Meteor 41:1241–1246CrossRefGoogle Scholar
  57. Tikuisis P, Osczevski RJ (2003) Facial cooling during cold air exposure. BAMS July 2003:927–934CrossRefGoogle Scholar
  58. VDI (2008) Environmental meteorology. Methods for the human biometeorological evaluation of climate and air quality for urban and regional planning. Verein Deutscher Ingenieure VDI. Part I: Climate. Beuth, BerlinGoogle Scholar
  59. Weihs P, Staiger H, Tinz B, Batchvarova E, Rieder H, Bröde P, Vuillemier L, Jendritzky G (2011) The uncertainty of UTCI due to uncertainties in the determination of radiation fluxes derived from measured and observed meteorological data. Int J Biometeorol, this issueGoogle Scholar
  60. Wissler EH (1985) Mathematical simulation of human thermal behavior using whole body models. In: Shitzer A, Eberhart RC (eds) Heat transfer in medicine and biology—analysis and applications. Plenum, New York, pp 325–373Google Scholar

Copyright information

© ISB 2011

Authors and Affiliations

  • Gerd Jendritzky
    • 1
  • Richard de Dear
    • 2
  • George Havenith
    • 3
  1. 1.Meteorological InstituteUniversity of FreiburgFreiburgGermany
  2. 2.Faculty of Architecture, Design & PlanningThe University of SydneySydneyAustralia
  3. 3.Loughborough Design School, Environmental Ergonomics Research CentreLoughborough UniversityLoughboroughUK

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