Global 0.25-degree gridded Snow water equivalent data derived from machine learning using in-situ measurements.

In this study, we developed a machine learning-based global daily SWE product (SWEML) with 0.25° (~25 km) resolution for 1980-2020. Using k-means clustering, in-situ SWE measurements were grouped into 14 clusters, and a random forest model was trained on 11,687 grid points with meteorological forcing and terrain attributes. SWEML was compared with ten reference datasets representing diverse approaches, including land-atmosphere reanalysis without data assimilation (DA), systems incorporating DA, snow model simulations of varying complexity driven by reanalysis forcing with or without DA, and remote sensing products. The overall root mean square error (RMSE) and bias were 10.33 mm and -7.13 mm, respectively. Notably, SWEML achieved high accuracy in high-elevation regions such as the Rocky Mountains, with an RMSE of 7.30 mm and correlation coefficient of 0.98. It also agreed with the Gamma airborne SWE over North America and showed similar spatial patterns and peak SWE time series of the Andes Snow Reanalysis. These results highlight the robustness of SWEML in regions with and without training data.