CHDNI: Daily Surface Direct Normal Irradiance Dataset in China (1980-2022, 10 km) based on REST2_v9.1 Model and Stacking Machine Learning Techniques

Evaluating, mapping, and monitoring the high-quality direct normal irradiance (DNI) is of vital importance for the proper design, financing, and operation of solar power plants using concentrating technologies. We constructed a 43-year (1980–2022) daily gridded DNI dataset (CHDNI), using ERA5 and MERRA_2 reanalysis data, with a spatial resolution of 10 km through a developed hybrid model, which integrated REST2_v9.1 radiation transfer model and stacking machine learning model. The hybrid model effectively enhances the accuracy of DNI estimation by introducing physical models, improving machine learning models, and optimizing model inputs. The validation results, with ground-based measurements, showed that hybrid model had a high and stable performance with the correlation coefficient (R), root-mean-square error (RMSE), and mean absolute error (MAE) for the sample-based cross-validations of 0.92, 32.04Wm⁻², and 23.68Wm⁻², respectively. Considering the large volume of this dataset, we only uploaded the CHDNI data during 2008/1/1-2008/12/31. CHDNI had good consistency with observations in 2008, where R, RMSE and MAE were 0.92, 29.13Wm⁻², and 21.62Wm⁻², respectively. For complete data series during 1980-2022, please contact us via email ([e-mail]).