Int J Biometeorol DOI 10.1007/s00484-015-0990-1
ORIGINAL PAPER
Customized rating assessment of climate suitability (CRACS): climate satisfaction evaluation based on subjective perception Tzu-Ping Lin 1 & Shing-Ru Yang 1 & Andreas Matzarakis 2
Received: 24 November 2014 / Revised: 28 March 2015 / Accepted: 29 March 2015 # ISB 2015
Abstract Climate not only influences the behavior of people in urban environments but also affects people’s schedules and travel plans. Therefore, providing people with appropriate long-term climate evaluation information is crucial. Therefore, we developed an innovative climate assessment system based on field investigations conducted in three cities located in Northern, Central, and Southern Taiwan. The field investigations included the questionnaire surveys and climate data collection. We first analyzed the relationship between the participants and climate parameters comprising physiologically equivalent temperature, air temperature, humidity, wind speed, solar radiation, cloud cover, and precipitation. Second, we established the neutral value, comfort range, and dissatisfied range of each parameter. Third, after verifying that the subjects’ perception toward the climate parameters vary based on individual preferences, we developed the customized rating assessment of climate suitability (CRACS) approach, which featured functions such as personalized and default climate suitability information to be used by users exhibiting varying demands. Finally, we performed calculations using the climate conditions of two cities during the past 10 years to demonstrate the performance of the CRACS approach. The results can be used as a reference when planning activities in the city or when organizing future travel plans. The flexibility of the assessment system enables it to be adjusted for varying regions and usage characteristics.
* Tzu-Ping Lin
[email protected];
[email protected] 1
Department of Architecture, National Cheng Kung University, 1 University Road, Tainan 701, Taiwan
2
Faculty of Environment and Natural Resources, Albert-LudwigsUniversity Freiburg, D-79085 Freiburg, Germany
Keywords Climate information . Thermal perception . Dissatisfied range . Rating assessment . Taiwan
Introduction Climate exhibits a substantial influence on the activities and behavior of people. Previous studies have demonstrated that people’s behavior in urban environments is closely related to the thermal environment (Nikolopoulou et al. 2001; Thorsson et al. 2004; Eliasson et al. 2007; Nikolopoulou and Lykoudis 2007; Lin 2009; Lin et al. 2012, 2013a, b). In addition, climate factors are a key consideration for tourists when planning trips (Hamilton and Lau 2005; Lin et al. 2006). Numerous studies have confirmed the close relationship between weather conditions and number of tourists (Hamilton 2004; Gomez-Martin and Martinez-Ibarra 2012). Therefore, the provision and application of appropriate long-term climate evaluation information can help evaluate the climate suitability of a particular location during various seasons and time periods, which is helpful for space utilization and travel planning (Matzarakis 2006; Lin and Matzarakis 2008, 2011). Because the use of basic data such as air temperature (Ta), relative humidity (RH), and precipitation as weather information is an oversimplified approach, numerous previous studies have used integrated climate indices to assess the long-term climate suitability of a particular location for outdoor activities and tourism. Other studies have integrated various climate parameters and used empirical formulas to conduct assessments, producing indices such as the wind-chill index (Steadman 1971), the discomfort index (Thom 1959), apparent temperature (Steadman 1979), and the tourism climate index (Mieczkowski 1985). In other studies, indices based on energy balance of human body were produced, including standard effective temperature (Gagge et al. 1986),
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physiologically equivalent temperature (PET) (Mayer and Höppe 1987; Höppe 1999), universal thermal climate index (UTCI; Fiala et al. 2012; Jendritzky et al. 2012), and outdoor standard effective temperature (de Dear and Pickup 2000; Pickup and de Dear 2000). Composite indices that combined various climate parameters with the users’ subjective parameters have also been used in previous studies. The climate index for tourism (CIT; de Freitas et al. 2008), which includes the energy balance of the human body, integrates thermal, aesthetic, and physical facets of weather. The climate–tourism information scheme (CTIS) addresses most of meteorological parameters and also includes heat balance of the human body (Lin and Matzarakis 2008; Matzarakis 2014). Concerning the display or visualization of climate evaluation information, numerous studies have established comfort/accepted ranges as criteria and applied them on long-term climate data to determine climate suitability, which was subsequently used to assess climate suitability for outdoor activities and tourism as well as to analyze thermal stress (Zaninovic and Matzarakis 2009; Lin and Matzarakis 2011). Further improvements and in-depth studies on the aforementioned climate indices and the display of these indices should be conducted. First, appropriate climate parameters should be considered in the assessment system. People’s perception to climate parameters is comprehensive, and thus more climate parameters should be included in the indices, particularly those used by standard weather stations. For example, PET integrates parameters related to the thermal environment, i.e., air temperature, vapor pressure, wind speed, and mean radiant temperature, and uses energy balance of the human body to perform assessments (Mayer and Höppe 1987; Höppe 1999). However, this causes the individual characteristics of each climate parameter and nonphysiological perceptions to be frequently overlooked, particularly for outdoor activities (e.g., travel, business trips, and exhibitions), which have also been indicated before (Mateeva et al. 2009). The PET is unable to completely determine people’s perceptions of climate under rainy weather conditions. In addition, even a low air temperature condition, which contributes the low PET, it maybe still suitable/comfortable for people in the beach with a clear sky and strong solar radiation. Therefore, all possible climate parameters that influence people’s climate assessments should be considered when determining people’s perceptions of climate. Second, assessment system must feature a reasonable assessment threshold for various climate parameters. When evaluating the long-term climate conditions by the aforementioned climate indices, a critical question is how to define the climate data as comfortable/satisfiable to users. In other words, when selecting a climate index, the subjective assessment thresholds of the users must be considered. The PET has been used in studies of
various climate zones, in which questionnaires were used to measure the users’ subjective perception of thermal comfort to define the thermal comfort threshold (Matzarakis and Mayer 1996; Oliveira and Andrade 2007; Lin and Matzarakis 2008). However, information regarding climate parameters not related to the thermal environment, such as cloud cover and amount of precipitation, remained insufficient. The users were thus only able to discuss and provide a qualitative presentation of prevalent parameters, resulting in poor inclusive usage. This indicated that the users’ subjective perceptions must be included in climate assessment system to reflect the actual perceptions of the people. Third, climate indices and climate evaluation information require user personality differences. Previous studies have demonstrated that thermal comfort is affected by psychological state, experiences, and expectations (McIntyre 1980; Paciuk 1990; Malama et al. 1998; Karyono 2000; Brager et al. 2004; Feriadi and Wong 2004; Nikolopoulou and Lykoudis 2006; Hwang and Lin 2007; Lin and Matzarakis 2008; Mateeva 2011). Not only do the participants demonstrate differences in thermal perception based on their longterm exposure to varying climate conditions, but differences in physique and subjective awareness also result in varying preferences for climate conditions. For example, people living in the same city may prefer warm or cool conditions based on differences in their physique and living habits. Subjective consciousness that results in climate preference must be considered when assessing climate conditions. This evaluation information should also be displayed in the results and be accessible to users based on their varying needs. Furthermore, because demands for climate parameters differ based on location and desired activity (e.g., swimming at the beach and exercising in the city), a single index/threshold may be insufficient to fulfill the demands of all users with various activities. Therefore, multiple indices/thresholds designs should be created to provide users with diverse selections. Finally, in addition to providing multiple selections, a default set of conditions and respective results should be provided. Providing multiple selections implies that users’ demands are clear and that they possess a basic understanding of climate evaluation information, but this may not necessarily be true. If an integrated assessment can be used to provide a general description of each period of the year using a simple way to display the climate information, the users can understand and use this climate evaluation information easily. Simple default assessment systems or approaches are therefore necessary for nonprofessional users and for nonspecific application. In this study, a survey was conducted to identify the weather perceptions and preferences of people in Taiwan. A climate satisfaction evaluation based on human’s subjective
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perception was used to develop the customized rating assessment of climate suitability (CRACS) approach. The study objectives were as follows: (1) Develop a reasonable climate evaluation system that accounts for climate parameters that may influence people’s climate assessments; (2) Include users’ subjective perceptions to reflect actual experiences; (3) Provide appropriate climate evaluation approach that can be assembled in various manners by users to reflect diversity and demand complexity; and (4) Integrate appropriate weighted climate evaluation information that can be easily applied. (5) The assessment system produced in this study accounted for the background characteristics of users as well as climate parameters. This system can be used by the general public or by professionals, for specific or general purposes.
Weather perception and preference survey Project background A related project, called the Field Investigation of Outdoor Thermal Comfort (FIOT) project (Hwang and Lin 2007; Lin et al. 2011), was conducted in Taiwan between 2005 and 2006. The database used in this project contained the data from an outdoor and semioutdoor thermal comfort survey, which included data from 8,077 indoor and outdoor surveys. A total of 1,644 outdoor surveys were selected from the thermal comfort database to conduct the analysis for their study. The FIOT project only explored the thermal comfort of the participants; no complete climate parameter analysis was conducted. In addition, the project only studied the central region of Taiwan. To continue this project and achieve the aforementioned study objectives, a weather perception and preference survey (WEPPS) was conducted. To successfully achieve objectives 1 and 2, the WEPPS project used a more complete and diverse set of questionnaire items than the aforementioned project to collect the physical quantities of the climate parameters as well as expand the area of investigation. Detailed explanations are provided in the following sections. Field investigation The WEPPS project contained a questionnaire surveys and climate data collections. Questions in the questionnaires included participant’s personal demographic information, such as gender, age, level of activity, and amount of clothing. The questionnaires also covered the participants’ climatic
background and experience (i.e., birthplace, current location of residence, current education level, current occupation, and the city or county in which the participant has lived the longest), source of climate information and assessment, current activity and reason for engaging in the said activity, and symptoms of discomfort resulting from the effects of climate. To measure the participants’ subjective perception toward the climate, the perception, preference, long-term preference, and expectation for each climate parameters are investigated. Because of the limitation of article length, only the questionnaire items related to the current research purpose were described in detail. Analysis of the remaining items will be discussed in future works. The participants were first asked about their perceptions toward the weather Bat the present moment.^ The purpose of this question was to elicit the participants’ direct response. Such a method has been used previously as the base information in thermal environment investigations. The questions used in this study were as follows: Q1 Please describe your feelings toward the various climate factors at the present moment. The questionnaire contained questions regarding the four parameters that influences thermal comfort (i.e., air temperature, air humidity, sun, and wind), as well as questions regarding two additional physical climate parameters included in this study (i.e., cloud cover and amount of precipitation) to comprehensively explore people’s perceptions toward the various climate parameters. These parameters were measured using the American Society of Heating, Refrigerating, and Air Conditioning Engineers (ASHRAE) seven-point rating method (ASHRAE 2010); for example, regarding people’s perceptions toward the overall thermal environment (TSV), the possible values were −3 (cold), −2 (cool), −1 (slightly cool), 0 (neutral), 1 (slightly warm), 2 (warm), and 3 (hot). Similarly, the numerical values representing the perceptions of the participants concerning the other parameters were described as follows: air temperature (ASV), from −3 (very low) to +3 (very high); air humidity (HSV), from −3 (very dry) to +3 (very wet); wind (WSV), from −3 (very weak wind) to +3 (very strong wind); sun (SSV), from −3 (very weak solar radiation) to +3 (very strong solar radiation); cloud cover (CSV), from −3 (minimal cloud cover) to +3 (heavy cloud cover); and amount of precipitation, from −3 (minimal amount of precipitation) to +3 (large amount of precipitation). Subsequently, to analyze the participants’ general preference for climate parameters, the following question was asked: Q2 Please describe your usual preference for the various climate factors.
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For this question, the same six weather parameters were used. Concerning air temperature, the possible answers consisted of BI prefer a warm condition,^ BI prefer a cool condition,^ and BI have no preference.^ Similarly, the other parameters and the corresponding answers were as follows: air humidity (prefer humid, prefer dryness, and no preference), wind speed (prefer less wind, prefer more wind, and no preference), sun (prefer more sun, prefer less sun, and no preference), cloud cover (prefer more cloud, prefer less cloud, and no preference), and amount of precipitation (prefer more precipitation, prefer less precipitation, and no preference). Unlike previous studies, which asked the participants’ current thermal environment preference, this study examined the participants’ long-term thermal environment preference, which is a characteristic determined by physique and habits. To obtain the microclimatic conditions of the actual locations while the participants completed the questionnaires, related climate parameters were simultaneously collected. Climate parameters, such as air temperature, relative humidity, wind speed, and mean radiant temperature, were measured onsite, and cloud cover and solar radiation data were obtained from the closest weather station. In addition, fisheye cameras were used to record the shelter conditions and the surrounding environment of the sites. Survey areas Previous studies on thermal comfort typically surveyed the perceptions or preferences of users in one city. However, characteristic differences in climate of various regions may cause people to thermally adjust to a region where they live, work, or stay for an extended period of time, changing their demands for thermal comfort. Because the objective of this study was to create a database that can be used for general users in Taiwan, thermal comfort surveys were conducted in multiple regions with varying typical climates. This not only enabled following integrated analyses that represented the overall thermal comfort situation in Taiwan but also enabled future analysis of the existence of differences in thermal comfort in people living in varying climate regions. Because Taiwan’s western plains region features the highest population density among all regions in Taiwan, this region was included in the survey. Generally, the air temperature increases from Northern to Southern Taiwan. To enable the target regions to represent the differing climatic characteristics, geography, and cultures of Taiwan, three cities from Northern, Central, and Southern Taiwan were investigated, i.e., Keelung (25° 08 N 121° 44 E), Taichung (24° 09 N 120° 40 E), and Tainan (22° 59 N 120° 11 E), respectively. Keelung City is the northernmost city of Taiwan, 95 % of its territory is hilly land. There are mountains in the east, west, and south of the city, and the sea is in the north. Facing the northeast monsoon, the rainy climate all year round is formed, and it is known as the Bcity of rain.^ According to
the statistics of Taiwan Central Weather Bureau in 1971– 2000, the mean annual temperature of Keelung City is 25.1 °C, the mean maximum is 32.6 °C (July), the mean minimum temperature is 18 °C (January), the average annual rainfall is 3,755 mm, the mean annual number of rainy days is 205.3, the total number of sunshine hours is 1,217 h. Taichung City is located in the middle part of Taiwan, where the climate is comfortable for Taiwanese. The mean temperature of Taichung City is 23.3 °C, the mean maximum temperature is 28.1 °C, the mean minimum temperature is 19.2 °C, the number of sunshine hours is 2,043 h, and the rainfall is 1,773 mm. Tainan City is in the south of Taiwan, on the south of Tropic of Cancer, and it is a tropical region. The mean temperature of Tainan City is 24.1 °C, the mean maximum temperature is 28.9 °C, the mean minimum temperature is 20.7 °C, the number of sunshine hours is 2,263 h, and the rainfall is 1,672 mm. Keelung, in the North, represented a city with heavy precipitation and cloud cover, where it is cold in the winter; Taichung represented a city with an intermediate climate, and Tainan, in the South, represented a city with a hot condition throughout the year. Because the three cities represented three typical yet varying climates, they were selected as the study areas. The actual survey period of this study lasted from 2011 to 2014, and the seasonal data of the three cities were recorded. A total of 2,071 questionnaires were collected in three cities in hot and cold seasons. The PET values are calculated by the RayMan model (Matzarakis et al. 2007, 2010) from the field survey data.
Results General climate perception and dissatisfied threshold In order to include users’ subjective climate perceptions to reflect actual experiences in the climate evaluation system as described in objective 2, the neutral value, comfortable and dissatisfied ranges, was established using the results of the questionnaires. The neutral value of each climate parameter was obtained using the WEPPS data, and the comfortable and dissatisfied range for each climate parameter was identified. The related methods are described as follows. Neutral values Neutral temperature (Tn) represents the optimal comfortable temperature, a temperature at which the participants neither feel hot nor cold (Fanger 1972). This information is crucial to the study of thermal comfort. The typical method for preventing individual perceptual differences from influencing the results is to identify the TSV of each participant for every small temperature interval and calculate the mean TSV (MTSV) of each temperature interval. The MTSV represents the vote submitted by the participants regarding the thermal
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perception that they rated for each temperature interval. Next, a regression analysis is used to establish a linear relationship between the MTSV of each temperature interval and the neutral temperature of the interval. This study used PET as the thermal index, with a 1 °C PET interval for analysis. The MTSV of the participants and the corresponding PET scatter plot is shown in Fig. 1. The equation for the optimal MTSV linear regression line is as follows: MTSV ¼ 0:1711 PET−4:8123
ð1Þ
The PET value for the point at which MTSV=0 intersected the regression line was obtained. This value represented the Tn and was recorded at 28.1 °C PET in this study. By applying this concept to all of the other climate parameters, the neutral value of each climate parameter was identified. Regarding the participants’ mean perception of the climate parameters, namely, air temperature (MASV), relative humidity (MHSV), wind speed (MWSV), solar radiation (MSSV), cloud cover (MCSV), and amount of precipitation (MRSV), a value of −3 was used to represent too low or too little, a value of +3 was used to signify too high or too much, and a value of 0 was used to indicate neutral. As the properties of various climatic factors are different, different group intervals are given. The grouping of various parameters is temperature 1 °C, humidity 5 %, wind speed 1 m/s, solar radiation quantity 100 W/m2, and cloud cover 1 okta (0–10 levels), respectively. The rainfall is not grouped, all statistics are used directly to draw the distribution diagram. In terms of cloud cover, the Central Weather Bureau of Taiwan uses 0–10 levels to evaluate the cloud cover. In order to link the local people’s evaluation of cloud cover to Taiwan’s climate data format for displaying subsequent findings, the cloud cover is divided into 0–10 levels in this study. The climate parameter value corresponding to each MxSV are plotted as Fig. 2. Continually, the
3 2 y = 0.1711x - 4.8123 R² = 0.9587
MTSV
1 0 10
15
20
25
30
35
40
45
-1 -2 -3
PET(ƱC) Fig. 1 PET and mean thermal sensation vote (MTSV) distribution diagram
regression equation of each climate parameter was obtained as follows: MASV ¼ 0:2261 Ta−5:9225
ð2Þ
MHSV ¼ 0:0523 RH−3:32
ð3Þ
MWSV ¼ 0:3169 V−0:905
ð4Þ
MSSV ¼ 0:0028 S−1:273
ð5Þ
MCSV ¼ 0:1612 Cloud−1:0
ð6Þ
MRSV ¼ 0:3144 Pr−0:766
ð7Þ
By substituting MxSV=0 into Eqs. (2)–(7), the neutral value for each parameters was obtained, i.e., 26.2 °C for air temperature, 63.6 % for relative humidity, 2.9 m/s for wind speed, 454.8 W/m2 for solar radiation, 6.7 okta for cloud cover, and 2.4 mm/h for precipitation. These neutral values represented the conditions at which the participants felt Bjust right.^
Comfort and dissatisfied ranges Neutral temperature represented the temperature at which the participants generally felt most comfortable. However, because people are normally flexible with the climate that they consider satisfied (i.e., they are only dissatisfied when the climate exceeded or dropped below a specific value), determining the dissatisfaction range for each parameter was necessary. Numerous methods for calculating comfort ranges have been employed in previous studies. One method entailed a comparison between the thermal index obtained using a quadratic regression curve, and the percentage of unacceptance, i.e., a 20 % acceptability criteria, suggested by ASHRAE, was used to determine the results of the comfort range. However, the problem will be arisen while the regression curve does not intersect with the 20 % threshold value, resulting in failure to determine the comfort range. In addition, if results exhibiting a broad range value may become more ineffective as the ability of the value to discriminate between varying comfort levels decreases. Therefore, in this study, a linear regression method was adopted and the perception range (MxSV) was used to determine the results. Concerning the PET, this study set the MTSV±0.5 requirement defined by ASHRAE as the comfort range, and >2 to 39.8 °C PET (too hot) and 2 and 2; low temperature, −2) were set to be identical due to our hypothesis that heat stress and cold stress exhibited the same effect on people. However, the upper and lower perception threshold values for the other climate parameters are not necessarily set to be identical. For example, people tended to express strong perceptions when cloud cover and solar radiation were high and were conservative when the two parameters were low (Fig. 3). Considerations for the upper and lower perception thresholds of these two parameters thus differed from those of temperature. The neutral value, comfort range, and dissatisfied arranges for each climate parameters were obtained based on the aforementioned principles (Table 1). The standard upper and lower perception thresholds for air temperature and relative humidity were +0.5 and −0.5, respectively; the settings for the upper and lower perception thresholds of the other climate parameters differed. The comfort and dissatisfied ranges for the various parameters were listed as Table 1. As mentioned previously, these adjustments were performed after considering local climate characteristics and user preferences. For example, in terms of the PET and Ta indexes, people show obvious differences in high and low values, meaning people have higher expectations for PET and Ta index. Therefore, the comfort range is defined as a large range (−0.5 to +0.5) in this study, and the dissatisfied range is given high thresholds (>2
and 0.5 and 0.1 and 2 (39.8 °C PET) and MTSV2 MTSV2 MASV1 MHSV0.5 MWSV0.7 MSSV0.1 MCSV1
PET>40 PET35 Ta82.7 RH4.4 v700 G7 Cloud0.15
PET [°C]
MTSV=0
28.1
MTSV=−0.5–0.5
25–31
Air temperature [°C]
MASV=0
26.2
MASV=−0.5–0.5
24–28.4
Relative humidity [%]
MHSV=0
63.6
MHSV=−0.5–0.5
54–73
Wind speed [m/s]
MWSV=0
2.9
MWSV=−0.2–0.2
2.2–3.5
Global radiation [W/m2]
MSSV=0
454.8
MSSV=−0.7–0.7
200–700
Cloud cover [–]
MCSV=0
6.7
MCSV=−0.50–0
3–6
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MRSV=0
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MRSV=−0.8–0
0–0.05
a
Mean sensation vote for overall thermal environment (MTSV), air temperature (MASV), relative humidity (MHSV), wind speed (MWSV), solar radiation (MSSV), cloud cover (MCSV), and amount of precipitation (MRSV)
Bpreferred dry conditions^ (73.6 vs. 59.3 %). Similarly, the neutral values for participants who preferred high solar radiation and wind speed (3.4 m/s and 461 W/m2) were higher than those participants who preferred low solar radiation and wind speed (2.1 m/s and 297 W/m2). Customized rating assessment of climate suitability In the discussion of the participants’ subjective consciousness regarding their climate preferences in BDiscussion^ section, the high correlation between the participants’ climate
preferences and their satisfaction with the thermal environment and climate was explained. Therefore, we argued that the universal comfort benchmark shown in Table 1 would not be applicable to every user despite the fact that the system enabled quantifiable assessment. In addition, the assessment system could not be used when activities differed. These disadvantages resulted in the necessity for a new climate assessment index that could provide general reference values for unknown user and unknown usage scenario to identify climate distribution situations, in addition to providing customized reference values through a variety of assessment thresholds
3
Fig. 4 Comparison of the PET and MTSV distribution between participants who preferred warm conditions and those who preferred cool conditions
2
MTSV
1
y = 0.1723x - 4.8085 R² = 0.9496
y = 0.1739x - 5.1123 R² = 0.9119
0 10
20
30
40
50
-1 -2 -3
Subjects who like warmer condion Subjects who like cooler condion Regression: like warmer Regression: like cooler
PET (ƱC)
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when the target users or usage scenarios were clear. This provided users with a variety of options to select and combine when performing assessments. Therefore, we introduced an innovative climate assessment approach, the CRACS, to provide objective climate evaluation information assessments that allow customization based on user needs. The CRACS contained two parts: the first part consisted of personalized climate suitability information, and the second part provided default climate suitability information. For customized suitable climate information, users could select climate assessment items and thresholds based on personal preference and be provided with advanced and complex weather condition information for their individual preferences. For default climate suitability information, climate information have been weighted and integrated, for users to be directly applied. Both parts use standard weather data as the data source to display long-term climate data (e.g., 10 years) created by the hourly climate parameters. Details of the two parts of the CRACS are described, and further explanations are provided in BPersonalized climate suitability information^ and BDefault climate suitability information^. Personalized climate suitability information As shown in Fig. 4, the participants’ level of concern and preference for the climate parameters differed based on their preferences. In this study, the demands of the two groups, i.e., Bprefer warm conditions^ and Bprefer cooler conditions,^ were analyzed, and the climate of Taichung over the past 10 years was provided. Concerning the satisfied threshold value of the climate parameters, because dissatisfied conditions are more concrete for users to understand how their body may suffer, the dissatisfied ranges of the participants listed in Table 1 were used as the reference values for the display of climate data. For example, PET values were >40 and 82.7 % (too wet), wind speed 700 W/m2 (too much solar radiation), and cloud cover 0.15 mm/h) was used for both groups. To assess customized climate suitability information, we applied the two sets of reference values for dissatisfaction to the hourly weather data of Taichung over a period of 10 years (2002–2011). We calculated the frequency of dissatisfaction (FOD) for each 10 days for each climate parameter, which was defined as: FODi j ¼
TDi j Ti
ð8Þ
where i represents a specified assessment period within the year. To facilitate more accurate assessment, the assessment period in this study was set as 10 days (i.e., i=1–36); j represented the climate parameters, which consisted of air temperature, relative humidity, wind speed, solar radiation, cloud cover, and amount of precipitation (j=1–6). FODij signified the frequency of dissatisfaction of climate parameter j in a specified assessment period i; TDij indicated the number of hours of dissatisfaction with climate parameter j in assessment period i, which was calculated by totaling the accumulated dissatisfied hours in which j exceeded the upper or lower threshold values; and Ti indicates the total number of hours in i. Because the assessment interval used in this study was 10 days, Ti =240 h (10 days×24 h). Long-term climate data were displayed based on the frequency of dissatisfaction, and varying shades of red were used to demonstrate the varying frequencies of dissatisfaction; a dark shade of red indicated a high frequency of dissatisfaction. The results are shown in Fig. 5, which revealed remarkable differences between the warm and cool groups. Default climate suitability information To provide a default assessment of climate information for universal usage, we develop an innovative integrated equation, called the weighted frequencies of dissatisfaction (wFOD). The equation integrated multiple climate parameters and displayed the users’ long-term frequency of dissatisfaction with the climate of a particular location after considering their subjective perceptions and the weighted values. The equation was as follows: wFODi j ¼
X6 j¼1
FODi j W j
ð9Þ
where wFODij is the integrated frequency of dissatisfaction for all the six climate parameters for a specified assessment period j. FODij signifies the frequency of dissatisfaction with climate parameter j in assessment period i; the calculation
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Jan +HDWVWUHVV
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PET>40
7RRKRW
Ta>35
7RRZHW
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PET40 °C and PET
