Development of future typical meteorological year (TMY) for major cities in Indonesia: Identification of suitable GCM
Today, with the rapid process of urbanization, the proportion of building energy consumption will continue to increase and speed up the emission of greenhouse gases which can intensify the process of global warming. Thus, building energy conservation has become one of the essential aspects of a sust...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/33/e3sconf_iaqvec2023_05001.pdf |
| _version_ | 1827921241975029760 |
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| author | Bhanage Vinayak Lee Han Soo Pradana Radyan Putra Kubota Tetsu Nimiya Hideyo Putra I. Dewa Gede Arya Sopaheluwakan Ardhasena Alfata Muhammad Nur Fajri |
| author_facet | Bhanage Vinayak Lee Han Soo Pradana Radyan Putra Kubota Tetsu Nimiya Hideyo Putra I. Dewa Gede Arya Sopaheluwakan Ardhasena Alfata Muhammad Nur Fajri |
| author_sort | Bhanage Vinayak |
| collection | DOAJ |
| description | Today, with the rapid process of urbanization, the proportion of building energy consumption will continue to increase and speed up the emission of greenhouse gases which can intensify the process of global warming. Thus, building energy conservation has become one of the essential aspects of a sustainable development strategy. A typical meteorological year (TMY) is frequently used in building energy simulation to assess the expected heating and cooling costs in the design of the building. Therefore, by considering the future alternations in climate, it is important to develop future TMY data. To generate the TMY for future climate, the projected weather dataset obtained from GCMs from the IPCC coupled inter comparison project phase 6 (CMIP6) can be helpful. However, a key issue with the use of GCM data is the low resolution and bias of the data. Thus, it is important to identify best suitable GCM for a particular region. Therefore, present study aims to evaluate the performance of 6 global GCMs from the CMIP6 for simulating the surface air temperature over the 29 major cities in Indonesia during 1980-2014. Here, dataset (MERRA-2) was utilized to compare the simulations of GCMs. Further three statistical metrics viz. correlation coefficient, standard deviation and centered root mean square error were computed to check the performance of each GCM against the reanalysis data. For most cities, the correlation coefficient values between the results of GCMs, and the reanalysis dataset ranges from 0.3 to 0.7 whereas the value of standard deviation varies from 0.3 to 1. The result revelled that among all the GCMs MPI-HR is one of the most appropriate choices to simulate the surface air temperature over 8 different cities. However, Nor-MM shows the worse performance over the cities located in Indonesia. For the future period, the input dataset from the best identified GCMs will be downscaled for the generation of TMY for future climate. |
| first_indexed | 2024-03-13T04:23:18Z |
| format | Article |
| id | doaj.art-dd12a55fec164330bc56291bd9d17c6c |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-13T04:23:18Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-dd12a55fec164330bc56291bd9d17c6c2023-06-20T09:04:07ZengEDP SciencesE3S Web of Conferences2267-12422023-01-013960500110.1051/e3sconf/202339605001e3sconf_iaqvec2023_05001Development of future typical meteorological year (TMY) for major cities in Indonesia: Identification of suitable GCMBhanage Vinayak0Lee Han Soo1Pradana Radyan Putra2Kubota Tetsu3Nimiya Hideyo4Putra I. Dewa Gede Arya5Sopaheluwakan Ardhasena6Alfata Muhammad Nur Fajri7Transdisciplinary Science and Engineering Program, Graduate School of Advanced Science and Engineering, Hiroshima UniversityTransdisciplinary Science and Engineering Program, Graduate School of Advanced Science and Engineering, Hiroshima UniversityTransdisciplinary Science and Engineering Program, Graduate School of Advanced Science and Engineering, Hiroshima UniversityTransdisciplinary Science and Engineering Program, Graduate School of Advanced Science and Engineering, Hiroshima UniversityGraduate School of Science and Engineering, Kagoshima UniversityGraduate School of Science and Engineering, Kagoshima UniversityCenter for Applied Climate Information Services, Indonesian Agency for Meteorology Climatology and Geophysics (BMKG)Directorate Engineering Affairs for Human Settlements, Ministry of Public Works, and HousingToday, with the rapid process of urbanization, the proportion of building energy consumption will continue to increase and speed up the emission of greenhouse gases which can intensify the process of global warming. Thus, building energy conservation has become one of the essential aspects of a sustainable development strategy. A typical meteorological year (TMY) is frequently used in building energy simulation to assess the expected heating and cooling costs in the design of the building. Therefore, by considering the future alternations in climate, it is important to develop future TMY data. To generate the TMY for future climate, the projected weather dataset obtained from GCMs from the IPCC coupled inter comparison project phase 6 (CMIP6) can be helpful. However, a key issue with the use of GCM data is the low resolution and bias of the data. Thus, it is important to identify best suitable GCM for a particular region. Therefore, present study aims to evaluate the performance of 6 global GCMs from the CMIP6 for simulating the surface air temperature over the 29 major cities in Indonesia during 1980-2014. Here, dataset (MERRA-2) was utilized to compare the simulations of GCMs. Further three statistical metrics viz. correlation coefficient, standard deviation and centered root mean square error were computed to check the performance of each GCM against the reanalysis data. For most cities, the correlation coefficient values between the results of GCMs, and the reanalysis dataset ranges from 0.3 to 0.7 whereas the value of standard deviation varies from 0.3 to 1. The result revelled that among all the GCMs MPI-HR is one of the most appropriate choices to simulate the surface air temperature over 8 different cities. However, Nor-MM shows the worse performance over the cities located in Indonesia. For the future period, the input dataset from the best identified GCMs will be downscaled for the generation of TMY for future climate.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/33/e3sconf_iaqvec2023_05001.pdftypical meteorological yearbias correctionbuilding energy simulationfuture tmysandia national laboratory methodstatistical downscaling. |
| spellingShingle | Bhanage Vinayak Lee Han Soo Pradana Radyan Putra Kubota Tetsu Nimiya Hideyo Putra I. Dewa Gede Arya Sopaheluwakan Ardhasena Alfata Muhammad Nur Fajri Development of future typical meteorological year (TMY) for major cities in Indonesia: Identification of suitable GCM E3S Web of Conferences typical meteorological year bias correction building energy simulation future tmy sandia national laboratory method statistical downscaling. |
| title | Development of future typical meteorological year (TMY) for major cities in Indonesia: Identification of suitable GCM |
| title_full | Development of future typical meteorological year (TMY) for major cities in Indonesia: Identification of suitable GCM |
| title_fullStr | Development of future typical meteorological year (TMY) for major cities in Indonesia: Identification of suitable GCM |
| title_full_unstemmed | Development of future typical meteorological year (TMY) for major cities in Indonesia: Identification of suitable GCM |
| title_short | Development of future typical meteorological year (TMY) for major cities in Indonesia: Identification of suitable GCM |
| title_sort | development of future typical meteorological year tmy for major cities in indonesia identification of suitable gcm |
| topic | typical meteorological year bias correction building energy simulation future tmy sandia national laboratory method statistical downscaling. |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/33/e3sconf_iaqvec2023_05001.pdf |
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