How to Make Downscaling Model Inputs Closer to Real Distribution Patterns?

Due to the excellent temporal continuity, reanalysis datasets are often used as input data for downscaling models. However, because of the relatively coarse spatial resolution, reanalysis datasets often exhibit significant value differences between adjacent pixels, making it challenging to accurately capture the distribution of meteorological parameters in heterogeneous urban areas. Although many downscaling studies have utilized reanalysis data, none have explored how to preprocess these datasets to achieve smoother patterns in the distribution of meteorological parameters at the urban level, making them closer to real distribution patterns. To address this limitation, this study proposes a novel iterative Gaussian filtering method. This method applies iterative Gaussian filtering while keeping the mean values unchanged within the coarse-resolution pixels to generate fine-resolution data with smoother distribution patterns. In this study, the 1-km land surface temperatures obtained from MODIS and its reprojected 0.1˚ resolution data are assumed to represent the true fine-resolution values and coarse-resolution values, respectively, to validate the effectiveness of the proposed method. The results indicate that, compared to the coarse-resolution data, the fine-resolution data processed through iterative Gaussian filtering achieves higher accuracy, with RMSE and MAE improvements of 11.06% and 11.89%, respectively. The distribution patterns of the fine-resolution data are also closer to real distribution patterns than those of the coarse-resolution data. These findings suggest that our proposed method could serve as a valuable tool for enhancing the accuracy of downscaling models in future studies.