Crowdsourced Data Highlight Precipitation Phase Partitioning Variability in Rain‐Snow Transition Zone. Issue 3 (21st March 2023)

• Record Type: Journal Article
• Authors: Jennings, Keith S.; Arienzo, Monica M.; Collins, Meghan; Hatchett, Benjamin J.; Nolin, Anne W.; Aggett, Graeme
• Is Part Of: Earth and space science. Volume 10:Issue 3(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-03-21
• Subjects: rain -- snow -- precipitation phase -- citizen science -- crowdsourced data -- remote sensing; Space sciences -- Periodicals; Geophysics -- Periodicals; 500.5
• DOI: 10.1029/2022EA002714
• Languages: English
• ISSNs: 2333-5084

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. Plain Language Summary: In the face of scarce observations, researchers, and practitioners often rely on computer models or remote sensing data to determine whether it is raining or snowing. While these methods are effective at cold and warm temperatures, they tend to struggle at air temperatures near and slightly above freezing. We started a citizen science project with the goal of better monitoring precipitation phase (rain, snow, and mixed) by crowdsourcing visual reports through a smartphone app. We found that methods based on weather and remote sensing data tended to underestimate the amount of snow falling in our study area relative to the crowdsourced reports. Concerningly, the frequency of snow and rain varied markedly from method to method, highlighting how important it is to have observations of precipitation phase. We found the crowdsourced data provided a new perspective on weather and water processes, a finding that could benefit future research in other fields. Key Points: Community observers submitted visual reports of precipitation phase over 2 years in the rain‐snow transition zone near Lake Tahoe Meteorology‐based methods and remote sensing products provided variable estimates of precipitation phase relative to observations Crowdsourced data offer an additional way of monitoring and understanding hydrometeorological processes