Assessment of the climatic potential for tourism in Iran through biometeorology clustering

Roshan, Gholamreza; Yousefi, Robabe; Błażejczyk, Krzysztof

doi:10.1007/s00484-017-1462-6

Cited by 30 publications

(18 citation statements)

References 52 publications

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“…Yang et al [20] examined the spatial differentiation of China's summer tourist destinations based on the UTCI and tourism resources data and analyzed climatic suitability. Roshan [21] also presented a spatiotemporal analysis of bioclimatic comfort conditions for Iran and demonstrated that there is, at any point in time, a location with climatic conditions suitable for tourism. A new method based on defining comfortable calendar days was proposed to identify regions thermally suitable for sunbird tourism and their comfortable periods in China [22].…”

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Pattern Changes of UTCI in the China-Pakistan Economic Corridor in Recent 40 Years

Zeng

et al. 2020

Atmosphere

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This paper investigated the spatial and temporal variations of the Universal Thermal Climate Index (UTCI) of the China-Pakistan Economic Corridor (CPEC) from 1979 to 2018. The European Centre for Medium-Range Weather Forecasts Re-Analysis-Interim (ERA-Interim) reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) is selected for UTCI calculation in the region and analyzed by a linear trend and correlation analysis. The results showed that (1) the UTCI of CPEC is decreased with the increase of latitude and altitude. There is obvious spatial heterogeneity in the seasonal scale and the spatial distribution of different thermal stress categories. (2) UTCI generally exhibited a positive trend of 0.33 °C/10a over the past 40 years, and the seasonal variation characteristics of UTCI show an upward trend in all four seasons, of which spring is the fastest. On the space scale, the growth trend has significant spatial variations. (3) Temperature has a positive correlation with UTCI. The influence of temperature on UTCI is greater than that of wind speed. The results of this study will be helpful for regional planning and also contribute to comprehending the characteristics of the thermal environment in CPEC.

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Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Pattern Changes of UTCI in the China-Pakistan Economic Corridor in Recent 40 Years

Zeng

et al. 2020

Atmosphere

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“…The clustering analysis implies the Euclidean distance and Ward's D analysis method. 37 The weather data include daily temperatures and relative humidity from 1995 to 2014. The methodology includes the creation of 19…”

Section: Methodsmentioning

confidence: 99%

Climatic clustering analysis for novel atlas mapping and bioclimatic design recommendations

Roshan

Farrokhzad

Attia

2019

Indoor and Built Environment

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Defining and determining climatic zones accurately is crucial to inform the decision making of building designers and planners during early design phases of urban development. Characterizing the climatic zones allows estimation of energy requirements in buildings and develop climate adapted energy polices. Climatic zones can be defined by using the statistical cluster analysis. Data from weather stations can be used after standardization with zero mean and unit variance, to confirm that all variables are weighted equally in the cluster analysis. In this paper, a novel atlas for 19 climatic zones is presented that represent a variety of bioclimatic design strategies and recommendations, for passive design, based on a clustering analysis in Iran. The clustering analysis is based on the statistical analysis of daily temperature and relative humidity from 1995 to 2014. The results visualize 19 different climate zones for Iran and indicate the dominance of passive design strategies. As a result, Iran was divided into eight climatic clusters. The results showed that each of the studied clusters require specific strategies in providing indoor comfort. The outputs of this study shed the light on the importance of up-to-date climate characterization and the effectiveness of climate mapping and recommendations to inform decision makers.

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“…Meteorological conditions are key factors in many areas of human activity such as agriculture, transport, power engineering, insurance and risk assessment [1], industrial and marketing planning [2], tourism, sport, mass events [3,4], national security, and many more where atmospheric conditions may have a direct or indirect impact [5][6][7][8]. Besides the financial and safety relevance of meteorological and hydrological datasets [9], this kind of information is very often crucial to reliably answer a scientific problem [10], which heavily relies on the quality of meteorological dataset used in this kind of research.…”

Section: Introductionmentioning

confidence: 99%

Climate: An R Package to Access Free In-Situ Meteorological and Hydrological Datasets For Environmental Assessment

2020

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Freely available and reliable meteorological datasets are highly demanded in many scientific and business applications. However, the structure of publicly available databases is often difficult to follow, especially for users who only deal with this kind of dataset on occasion. The “climate” R package aims to fill this gap with an easy-to-use interface for downloading global meteorological data in a fast and consistent way. The package provides access to different sources of in-situ meteorological data, including the Ogimet website, atmospheric vertical sounding gathered at the University of Wyoming’s webpage, and hydrological and meteorological measurements collected by the Institute of Meteorology and Water Management—National Research Institute (i.e., Polish Met Office). This article also provides a quick overview of the key functionalities available within the climate R package, and gives examples of an efficient and tidy workflow of meteorological data within the R based environment. The automation procedures included in the packages allow one to download data in a user-defined time resolution (from hourly to annual), for a user-defined time span, and for a specified group of stations or countries. The package also contains metadata, including a list of available stations, their geospatial information, and measurement descriptions with their units. Finally, the obtained datasets can be processed in R or exported to external tools (e.g., spreadsheets or GIS software).

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Assessment of the climatic potential for tourism in Iran through biometeorology clustering

Cited by 30 publications

References 52 publications

Spatial-Temporal Pattern Changes of UTCI in the China-Pakistan Economic Corridor in Recent 40 Years

Spatial-Temporal Pattern Changes of UTCI in the China-Pakistan Economic Corridor in Recent 40 Years

Climatic clustering analysis for novel atlas mapping and bioclimatic design recommendations

Climate: An R Package to Access Free In-Situ Meteorological and Hydrological Datasets For Environmental Assessment

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