Crowdsourced Data Highlight Precipitation Phase Partitioning Variability in Rain‐Snow Transition Zone

Abstract

Abstract To increase the number of direct observations of rain and snow, we started a citizen science project that crowdsources precipitation phase reports from volunteers using a smartphone app. We focused on the Lake Tahoe region of California and Nevada, USA which forms part of the rain‐snow transition zone, an area where both solid and liquid precipitation occur in winter months. In two study years, we received 2,495 reports, of which 2,248 (90.1%) passed our quality control checks. Snow was the most frequent phase (64.0%), followed by rain (21.0%) and mixed precipitation (15.0%). We compared these values to estimates from 14 common precipitation phase partitioning methods that use near‐surface meteorology as well as to two remote sensing products from the Global Precipitation Measurement mission (GPM). We found the meteorology‐based methods tended to underestimate snowfall on average (60.9%) with a sizable standard deviation of 18%. The Integrated Multi‐satellitE Retrievals for GPM level 3 probabilityLiquidPrecipitation product also underestimated snowfall (57.5%) relative to the crowdsourced data, while the Dual‐frequency Precipitation Radar level 2A phaseNearSurface product had little spatiotemporal overlap with the observations. We also found slight differences in the rain‐snow line elevations measured by a freezing‐level radar versus those estimated from the crowdsourced data, with the former being 165 m lower than the latter on average. These findings underscore the importance of collecting ground‐truth observations of precipitation phase in the rain‐snow transition zone. We hope future studies will consider the use of crowdsourced data for improved insights into and better representation of hydrometeorological processes.

Keywords

• rain
• snow
• precipitation phase
• citizen science
• crowdsourced data
• remote sensing