Detecting rain-on-snow events in the Arctic using passive microwave satellite data

Rain-on-snow (ROS) events occur when liquid precipitation falls onto snowpack, leading to accelerated snowmelt and the formation of ice layers. These events decrease albedo, intensifying the snow-albedo feedback, and can trigger avalanches and increase flood risk due to the combined effects of rainfall and snowmelt. As climate change shifts precipitation patterns from snow to rain, ROS events are becoming more frequent and intense, making their accurate detection increasingly important. Passive microwave satellite data offer promising potential for detecting ROS events. However, most previous studies have focused on a limited number of frequency channels, typically 19 and 37 GHz. Recently, L-band (1.4 GHz) has gained attention, but a comprehensive evaluation across the full microwave spectrum is still lacking. This study aims to address this gap by assessing the suitability of multiple microwave channels for ROS detection. We analyze the feasibility of identifying ROS events using brightness temperature (Tb) data from the Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR-2) satellites (1.4-89.0 GHz), evaluated against in situ observations. Additionally, we use the Snow Microwave Radiative Transfer (SMRT) model to simulate the impact of ROS events on Tb across a wide range of microwave frequencies. The results reveal distinct Tb changes during ROS, confirming microwave sensitivity to liquid water in snow. Using the Normalized Polarization Ratio (NPR), we find that L‑band NPR is particularly effective for identifying ROS events, consistent with SMRT simulations. Our analysis shows that a high NPR indicates a ROS event. We further apply this method across the Arctic and compare the results with ERA5 reanalysis, showing promising agreement. Overall, our findings advance the development of microwave-based approaches for detecting and monitoring ROS events under changing Arctic climate conditions.