AMERICAN METEOROLOGICAL SOCIETY Bulletin of the American Meteorological Society
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Smartphone App brings human thermal comfort forecast in your hands
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B.G. Heusinkveld*, G. Sterenborg*, G.J. Steeneveld*, J.J. Attema+, R.J. Ronda*, A.A.M.
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Holtslag*
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*Meteorology and Air Quality Section, Wageningen University, Wageningen, The
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Netherlands
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Netherlands eScience Center, Amsterdam, The Netherlands
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Corresponding author:
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Bert Heusinkveld
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P.O. Box 47
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6700 AA Wageningen
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The Netherlands
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Tel.:+31317482936
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Email:
[email protected]
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What activity would you consider doing tomorrow if the weather forecast predicts a
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temperature of 270 C? Taking your kids or grandparents for a walk, or maybe a good
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opportunity to visit that new art museum? That walk may become stressful if it’s sunny and
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the path offers little shade along the way. However, on a windy day, or a route shaded from
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direct sunlight, e.g. a narrow street canyon, thermal conditions may be just fine. By utilising
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expert knowledge of the combined effects of temperature, humidity, wind speed and radiation
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on the human thermal energy balance, it is possible to refine the general forecast to be more
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site-specific about human comfort while outdoors. Climate change (McCarthy et al., 2010,
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Coumou, et al., 2012&2013), urbanization related heating (the urban heat island effect (UHI))
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and recorded excess mortality during recent European heat waves generated an acute
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awareness for heat related health risks. The elderly, people with cardiovascular diseases, as
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well as outdoor workers are particularly vulnerable (Zander et al, 2015). Accordingly, the
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need for easily accessible and local forecasting of human thermal comfort is growing. This
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paper presents a new smartphone App that communicates a location-specific human thermal
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comfort forecast based on the latest innovations in urban climate and biometeorology,
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computer science, communication technology and land-use mapping. The forecasts were
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evaluated against four years of observations in The Netherlands.
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Humans are relatively limited in physiological thermoregulation strategies to cope
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with heat or cold and must rely on choice of clothing, shelter or behavioural thermoregulation.
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Such strategies may also affect the choice of outdoor activities. A person’s interpretation of a
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standard weather forecast is subjective and various empirical thermal indices to enhance
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weather forecasts have been introduced. These include the wind chill factor (Osczevski et al.,
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2005) to quantify wind speed effects on the severity of cold weather conditions, or the heat
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index (Steadman, 1979) for hot conditions that couples the effect of humidity on a perceived
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temperature scale. Unfortunately, none of these empirical indices build upon a physically 2
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sound human thermal energy balance model, and site-specific information, as introduced by
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urban morphology, has been neglected.
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Human thermal energy balance modelling has resulted in an “apparent” temperature
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that combines weather conditions such as temperature, humidity, wind speed and radiation.
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The latest insights are represented in the Universal Thermal Climate Index (UTCI) (Blzejczyk
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et al., 2012, Bröde, et al., 2012). The UTCI is favoured because it marries a dynamic clothing
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model and a state-of-the-art thermo-physiological model of human heat transfer and
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temperature regulation (Fiala et al., 2012). The dynamic clothing model is based on what a
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person would wear according to the outdoor temperature (Havenith, et al., 2012). The total
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human radiation load as part of the human thermal energy balance is expressed by a single
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temperature and is called the mean radiant temperature (Tmrt) (Fanger, 1970, and Text Box 1).
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The UTCI covers the full thermal exposure range (covering cold and heat exposure)
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and is categorized into a basic ten-class human thermal stress scale. The forecasted level of
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thermal exposure is presented by a color code and forms the basis for our smartphone App
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(Fig. 1). This allows for communicating thermal conditions using simple descriptive words
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such as “extreme heat stress” or “moderate cold stress” (see Text Box 1). The UTCI was
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further tested across a range of climates from arctic to desert conditions and, among a range of
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thermal indices, the UTCI was best able to represent thermal stress levels (Blazejczyk et al.,
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2012).
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Human thermoregulation is capable of handling a certain thermal range very well, but
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outside the “no thermal stress” range and especially on the hot side, the scale can shift easily
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to the next stress level (see widths of thermal classes in Fig. 1). The small stress steps on the
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hot side are related to the limited adaptation that a clothing change can offer, since clothing is
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most effective in the cold range. Because UTCI standardizes the human being (body, dynamic
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clothing, metabolic rate, etc.) there is still some translation required to make a personalized
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judgement (fitness level, age, exercise level, etc.) and which is more straightforward than
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using standard weather forecast data. Note that precipitation is not included in the human
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thermal energy balance and clothing model for UTCI. The impact of wind and radiation on
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UTCI is large and therefore we consider it as a great step forward to use a combination of
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high resolution NWP and geo-information to improve a standardized human thermal stress
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forecast.
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Figure 2 illustrates the range of UTCI classes (-29 to 40°C) on basis of 4 years of
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observations (September 2011-2015) covering typical winter and summer weather conditions
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at the ‘Veenkampen’ weather station in Wageningen, The Netherlands (www.maq.wur.nl)
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(Lat 51.9809, Lon 5.6197), which is a centrally located and representative weather station for
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the Netherlands. The station is equipped with solar and thermal radiometers (first class)
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mounted on a sun tracker platform for direct, diffuse and reflected solar radiation, and
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incoming thermal and shortwave radiation is measured separately. This allows for calculating
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Tmrt directly. In this maritime temperate rural climate (Köppen class Cfb), the UTCI is skewed
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towards cold stress conditions and extreme UTCI classes do not occur. It is also seen that
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wind protected or sunny locations increase heat stress classes.
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Considering numerical weather forecasts, a tremendous increase in diversity,
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availability and frequency of weather forecast communications has emerged. These
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communications now include newspapers, radio, television, text messaging, internet,
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smartphones, etc. The number of products as derived from NWP models has increased
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accordingly. The demand ranges from now-casting, to short and medium range seasonal and
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climatological forecasting. This has generated new services that include drought, fire risk,
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flooding, etc. Currently, where smartphones have become a major tool for communication,
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planning and weather updates, smartphones are also excellent tools to access a location-
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specific weather forecast.
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Advances in NWP represent a quiet revolution because the improvements were
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gradual, based on scientific and technological advances (Bauer et al., 2015). Typical NWP
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models are already performing very well when benchmarked against WMO stations. The
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models are usually biased towards rural regions because NWP models have been optimized
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through data assimilation and model verification using WMO weather stations in rural
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locations. Current NWP models lack the resolution to resolve complex environments such as
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cities. However, urbanized areas experience microclimates that differ greatly from rural
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settings (Oke, 1992). A smartphone, however, knows its location and therefore can request
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very location-specific forecasts that may better suit user needs.
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A similar increase in forecast skill can be expected by exploiting recent advances in
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geo- information mapping. In the Netherlands geo-information (open access) databases are
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regularly updated: Lidar altimetry measurements with a density of >4 points per m2 and an
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accuracy
