A high-resolution Typical Meteorological Year dataset for solar radiation evaluation in Australia@en

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 However, conventional TMY datasets, limited by sparse ground-based station coverage and outdated updates, fail to meet the demands of detailed urban-scale simulations.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.