A high-resolution Typical Meteorological Year dataset for solar radiation evaluation in Australia
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| Main Authors: | , , , , , , |
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| Format: | Dataset Open Access |
| Language: | en |
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PANGAEA
2025
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| _version_ | 1867170135938170880 |
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| author | Yan, Zengfeng Prasad, Deo Qin, Guojin Lei, Fuming Yue, Yingjun Yan, Jiaqing He, Bao-Jie |
| author_facet | Yan, Zengfeng Prasad, Deo Qin, Guojin Lei, Fuming Yue, Yingjun Yan, Jiaqing He, Bao-Jie |
| collection | Datos científicos de ciencias marinas y ambientales |
| contents | High spatiotemporal resolution Typical Meteorological Year (TMY) data are essential for building energy modeling and urban climate studies. However, conventional TMY datasets, limited by sparse ground-based station coverage and outdated updates, fail to meet the demands of detailed urban-scale simulations. To overcome these limitations, we developed a high-resolution TMY-MER dataset for Australia based on MERRA2 reanalysis data. A novel weather classification approach was introduced, leveraging a mean relative error index derived from the ratio of daily to monthly maximum solar radiation to identify clear-sky conditions. Uncertainty errors were spatially interpolated using inverse distance weighting (IDW). Results indicate that traditional methods for generating TMY data suffer from several limitations: under clear-sky conditions, TMY-MER maintains stable accuracy (annual mean error below 5%), whereas cloud modeling inaccuracies can cause errors up to 50% on overcast days. Spatially, solar radiation is overestimated by approximately 30% in southeastern coastal areas, with inland errors remaining within 10%. Temporally, winter cloud periods show peak deviations around 30%, whereas summer clear-sky conditions yield errors below 5%. Radiative component analysis reveals a diffuse horizontal irradiance (DHI) overestimation under 6% and a direct normal irradiance (DNI) underestimation of about 20%. Validation through building cluster simulations confirms that the TMY-MER dataset generated using the method proposed in this study achieves over 90% agreement with conventional TMY data, with monthly mean deviations below 5%. The multidimensional error assessment framework significantly enhances the reliability of reanalysis data for use in complex climate zones, supporting dynamic energy system planning and urban thermal environment modeling. |
| format | Dataset Open Access |
| id | pangaea_https___doi_org_10_1594_PANGAEA_983159 |
| institution | PANGAEA |
| language | en |
| publishDate | 2025 |
| publisher | PANGAEA |
| record_format | pangaea |
| spellingShingle | A high-resolution Typical Meteorological Year dataset for solar radiation evaluation in Australia Yan, Zengfeng Prasad, Deo Qin, Guojin Lei, Fuming Yue, Yingjun Yan, Jiaqing He, Bao-Jie Atmospheric reanalysis datasets; Australia; Binary Object; Binary Object (File Size); File content; File format; solar radiation; typical meteorological year; weather type segmentation High spatiotemporal resolution Typical Meteorological Year (TMY) data are essential for building energy modeling and urban climate studies. However, conventional TMY datasets, limited by sparse ground-based station coverage and outdated updates, fail to meet the demands of detailed urban-scale simulations. To overcome these limitations, we developed a high-resolution TMY-MER dataset for Australia based on MERRA2 reanalysis data. A novel weather classification approach was introduced, leveraging a mean relative error index derived from the ratio of daily to monthly maximum solar radiation to identify clear-sky conditions. Uncertainty errors were spatially interpolated using inverse distance weighting (IDW). Results indicate that traditional methods for generating TMY data suffer from several limitations: under clear-sky conditions, TMY-MER maintains stable accuracy (annual mean error below 5%), whereas cloud modeling inaccuracies can cause errors up to 50% on overcast days. Spatially, solar radiation is overestimated by approximately 30% in southeastern coastal areas, with inland errors remaining within 10%. Temporally, winter cloud periods show peak deviations around 30%, whereas summer clear-sky conditions yield errors below 5%. Radiative component analysis reveals a diffuse horizontal irradiance (DHI) overestimation under 6% and a direct normal irradiance (DNI) underestimation of about 20%. Validation through building cluster simulations confirms that the TMY-MER dataset generated using the method proposed in this study achieves over 90% agreement with conventional TMY data, with monthly mean deviations below 5%. The multidimensional error assessment framework significantly enhances the reliability of reanalysis data for use in complex climate zones, supporting dynamic energy system planning and urban thermal environment modeling. |
| title | A high-resolution Typical Meteorological Year dataset for solar radiation evaluation in Australia |
| topic | Atmospheric reanalysis datasets; Australia; Binary Object; Binary Object (File Size); File content; File format; solar radiation; typical meteorological year; weather type segmentation |
| url | https://doi.org/10.1594/PANGAEA.983159 |