Evaluation of Rainfall‐Snowfall Separation Performance in Remote Sensing Datasets. Issue 21 (29th October 2021)

• Record Type: Journal Article
• Authors: You, Yalei; Peters‐Lidard, Christa; Ringerud, Sarah; Haynes, John M.
• Is Part Of: Geophysical research letters. Volume 48:Issue 21(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-10-29
• Subjects: Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1029/2021GL094180
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: 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. Plain Language Summary: Satellite remote sensing provides the only means of measuring rainfall/snowfall on the global scale. Misclassifying the precipitation phase (i.e., rainfall as snowfall, or vice versa) could lead to the estimated precipitation rate being one order of magnitude smaller or larger. Our results reveal that the snowfall discrimination accuracy varies greatly among four remote sensing datasets ranging from 42% to 96%. For example, over half of the snowfall from the state‐of‐the‐art precipitation product based on the Global Precipitation Measurement radar is rainfall at the surface without considering the temperature information close to surface. Additionally, the temperature discrepancy among different reanalysis datasets also greatly affects precipitation phase discrimination. Our results show that MERRA2 temperature close to the surface is colder than the other three major datasets, leading to more rainfall pixels being misclassified as snowfall pixels. Key Points: Snowfall determination accuracy varies greatly among four remote sensing datasets ranging from 42% to 96% More than half of the snowfall indicated by Global Precipitation Measurement Mission (GPM) dual frequency precipitation radar (DPR) is rainfall on the ground MERRA2 temperature close to the surface is noticeably colder than observed, leading to more rainfall being classified as snowfall