Remote sensing snowfall retrievals are powerful tools for advancing our understanding of global snow accumulation patterns. However, current satellite-based snowfall retrievals rely on assumptions about snowfall particle shape, size, and distribution that contribute to uncertainty and biases in their estimates. Vertical radar reflectivity profiles provided by the vertically pointing X-band radar (VertiX) instrument in Egbert, Ontario, Canada, are compared with in situ surface snow accumulation measurements from January to March 2012 as a part of the Global Precipitation Measurement (GPM) Cold Season Precipitation Experiment (GCPEx). In this work, we train a random forest (RF) machine learning model on VertiX radar profiles and ERA5 atmospheric temperature estimates to derive a surface snow accumulation regression model. Using event-based training-testing sets, the RF model demonstrates high predictive skill in estimating surface snow accumulation at 5-min intervals with a low mean-square error of approximately 1.8 x 10(-3) mm(2) when compared with collocated in situ measurements. The machine learning model outperformed other common radar-based snowfall retrievals (Z(e)-S relationships) that were unable to accurately capture the magnitudes of peaks and troughs in observed snow accumulation. The RF model also displayed consistent skill when applied to unseen data at a separate experimental site in South Korea. An estimate of predictor importance from the RF model reveals that combinations of multiple reflectivity measurement bins in the boundary layer below 2 km were the most significant features in predicting snow accumulation. Nonlinear machine learning-based retrievals like those explored in this work can offer new, important insights into global snow accumulation patterns and overcome traditional challenges resulting from sparse in situ observational networks.
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