Development of Daily and Extreme Temperature Estimation Model for Building Structures Based on Raw Meteorological Data
For building environments, meteorological factors such as daily mean temperature, extreme temperature and seasonal temperature changes, are essential, as they impact building structures significantly. Due to the importance of detailed and accurate temperature data, and taking Beijing, China, as an e...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/22/11582 |
| _version_ | 1827645085695606784 |
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| author | Jianyu Yang Yongda Yang Jiaming Zou Weijun Yang |
| author_facet | Jianyu Yang Yongda Yang Jiaming Zou Weijun Yang |
| author_sort | Jianyu Yang |
| collection | DOAJ |
| description | For building environments, meteorological factors such as daily mean temperature, extreme temperature and seasonal temperature changes, are essential, as they impact building structures significantly. Due to the importance of detailed and accurate temperature data, and taking Beijing, China, as an example, this paper developed a fast and effective interpolation method to extract hourly meteorological data, based on 30 years’ raw meteorological data. With the interpolated data, this paper defined the extreme weather for buildings. Moreover, a temperature model based on probability and statistical analysis was constructed, and the general climate standard for days and extreme climate for typical days with different return periods were obtained. Furthermore, meteorological models for standard annual temperature were also achieved, reflecting the daily variation and annual variation of temperature, and can provide continuous-numerical-simulation parameters for analyzing daily and annual temperature. According to the daily temperature difference obeys the Gumble Distribution, the daily temperature difference in different return periods and extreme climates is obtained by analysis. Therefore, annual temperature ranges of different recurrence intervals and extreme climate are also achieved, and the annual temperature ranges can be used to analyze the effect of different recurrence intervals and extreme weather on building structures. |
| first_indexed | 2024-03-09T18:29:32Z |
| format | Article |
| id | doaj.art-a03324a130c64ffe905a9182fd4607b8 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T18:29:32Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a03324a130c64ffe905a9182fd4607b82023-11-24T07:37:36ZengMDPI AGApplied Sciences2076-34172022-11-0112221158210.3390/app122211582Development of Daily and Extreme Temperature Estimation Model for Building Structures Based on Raw Meteorological DataJianyu Yang0Yongda Yang1Jiaming Zou2Weijun Yang3School of Civil Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaHunan Construction Investment Group Co., Ltd., Changsha 410018, ChinaSchool of Civil Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaSchool of Civil Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaFor building environments, meteorological factors such as daily mean temperature, extreme temperature and seasonal temperature changes, are essential, as they impact building structures significantly. Due to the importance of detailed and accurate temperature data, and taking Beijing, China, as an example, this paper developed a fast and effective interpolation method to extract hourly meteorological data, based on 30 years’ raw meteorological data. With the interpolated data, this paper defined the extreme weather for buildings. Moreover, a temperature model based on probability and statistical analysis was constructed, and the general climate standard for days and extreme climate for typical days with different return periods were obtained. Furthermore, meteorological models for standard annual temperature were also achieved, reflecting the daily variation and annual variation of temperature, and can provide continuous-numerical-simulation parameters for analyzing daily and annual temperature. According to the daily temperature difference obeys the Gumble Distribution, the daily temperature difference in different return periods and extreme climates is obtained by analysis. Therefore, annual temperature ranges of different recurrence intervals and extreme climate are also achieved, and the annual temperature ranges can be used to analyze the effect of different recurrence intervals and extreme weather on building structures.https://www.mdpi.com/2076-3417/12/22/11582cubic spline functionmeteorological modelextreme weatherdaily temperature deferencerecurrence interval |
| spellingShingle | Jianyu Yang Yongda Yang Jiaming Zou Weijun Yang Development of Daily and Extreme Temperature Estimation Model for Building Structures Based on Raw Meteorological Data Applied Sciences cubic spline function meteorological model extreme weather daily temperature deference recurrence interval |
| title | Development of Daily and Extreme Temperature Estimation Model for Building Structures Based on Raw Meteorological Data |
| title_full | Development of Daily and Extreme Temperature Estimation Model for Building Structures Based on Raw Meteorological Data |
| title_fullStr | Development of Daily and Extreme Temperature Estimation Model for Building Structures Based on Raw Meteorological Data |
| title_full_unstemmed | Development of Daily and Extreme Temperature Estimation Model for Building Structures Based on Raw Meteorological Data |
| title_short | Development of Daily and Extreme Temperature Estimation Model for Building Structures Based on Raw Meteorological Data |
| title_sort | development of daily and extreme temperature estimation model for building structures based on raw meteorological data |
| topic | cubic spline function meteorological model extreme weather daily temperature deference recurrence interval |
| url | https://www.mdpi.com/2076-3417/12/22/11582 |
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