Evaluation of Rainfall-Snowfall Separation Performance in Remote Sensing Datasets

The first step to accurately measure global snowfall is to separate rainfall from snowfall correctly (i.e., precipitation phase discrimination). This study first evaluates the phase discrimination performance in four remote sensing datasets, including observations from ground radar, spaceborne radars, and spaceborne radiometer, relative to ground observations. Results show that the snowfall discrimination accuracy varies greatly among these datasets ranging from 42% to 96%, dependent on whether and how the temperature information are considered. For example, over half of the snowfall from the Global Precipitation Measurement Mission (GPM) spaceborne radar is actually rainfall at the surface since it detects snowfall in the air without considering the temperature information close to the surface. Second, we evaluate the discrimination performance using the temperature information from four reanalysis datasets. It is found that MERRA2 temperature close to the surface is colder than the other three datasets, leading to more rainfall being misclassified as snowfall.