Developing a High-Resolution Typical Meteorological Year Dataset for Solar Radiation Evaluation in Australia

Abstract. High spatiotemporal resolution typical meteorological year (TMY) data are essential for building energy modelling and urban climate studies. However, conventional TMY datasets, limited by sparse ground-based station coverage and infrequent updates, fail to meet the demands of detailed urban-scale simulations. To overcome these limitations, this study uses Australia as a case study and develops a new high-resolution dataset, the TMY derived from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), hereafter referred to as TMY-MER. A novel weather classification approach was introduced, utilizing 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 the inverse distance weighting (IDW) method. The results reveal several limitations in the previously generated TMY datasets. TMY-MER demonstrates stable accuracy under clear-sky conditions, with annual average errors below 5 %, while under cloudy conditions, influenced by cloud simulation bias, errors can reach up to 50 %. Spatially, annual solar irradiance is overestimated by 30 % in southeastern coastal urban clusters, while errors in inland regions remain below 10 %. Temporally, the peak error during cloudy winter periods reaches 30 %, whereas summer clear-sky errors are under 5 %. Further analysis using the direct-diffuse separation model indicates a systematic overestimation of diffuse horizontal irradiance (DHI) within 6 %, and an approximately 20 % negative bias in direct normal irradiance (DNI). Validation through building cluster simulations shows that the optimized dataset achieves over 90 % consistency with traditional TMY data, with monthly mean errors 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 modelling.