Normally, we think of rain falling at air temperatures above 32°F – but in the world of weather forecasting and hydrologic prediction, that isn’t always the case. In some mountain regions, the shift from snow to rain at ground level may actually occur at warmer temperatures approaching 39.5°F.
We launched Mountain Rain or Snow to take on the challenge of improving the prediction of snow accumulation and rainfall near freezing. Our team mobilizes a large, coast-to-coast network of community observers to gather real-time observations of winter weather. Our scientists use these crowdsourced observations to help to update the technologies, techniques, and models that drive our global weather observation systems and local forecasts.
The Mountain Rain or Snow team is made up of many scientists from different backgrounds, including hydrology, meteorology, remote sensing, data science, and social science. Our combined perspectives help to answer key research questions faced by both research and operational science communities.
Citizen science data are necessary because many existing weather monitoring technologies have little or no coverage in mountain regions. Remote sensing platforms have poor spatiotemporal coverage, and the reanalysis products that rely on remote sensing data have spatially variable performance. Our ability to infer precipitation phase from models is limited. These model-based methods struggle at air temperatures near freezing and often have trouble identifying freezing rain from warm snow. Even complex machine learning techniques offer only marginal improvements over traditional phase partitioning methods.
All of these challenges point to the valuable role that observations made by people have in tackling these large challenges in meteorology and hydrology.
A primary goal of collecting rain, snow, and mixed precipitation observations is to improve satellite-derived estimates of precipitation phase from the Global Precipitation Monitoring (GPM) mission. We have compared Mountain Rain or Snow data to numerous meteorologic and geospatial rain-snow partitioning methods, helping to identify where these methods struggle and pointing to potential routes to improvement.
Our team is also examining ways that crowdsourced data can improve the forecasting of winter weather and hazards, such as rain-on-snow events and avalanches, using the National Blend of Models. We have also explored preliminary work that compares distrometer and micro rain radar estimations of snow levels with our crowdsourced observations.
Finally, our work has contributed to effective practices for citizen science project design. We have innovated methods for training, activating, and communicating with observers through text-based communication. We also developed a science communication method for reporting results back to community observers (known as a “report back”). These methods are designed to be scalable, easily customized for other projects.
Mountain Rain or Snow will advance our knowledge of the water cycle in mountain regions, knowledge of weather and winter weather hazards, and improve NASA’s Earth observational capabilities for winter storms, including rain-on-snow events.
Mountain Rain or Snow is supported by NASA.
The full title of this project is “Using Citizen Science Observations to Monitor the Rain-Snow Transition of the Western US and Improve Satellite Estimates of Precipitation Phase”. It is funded by a grant from the National Aeronautics and Space Administration (NASA) ROSES Earth Science Research Program. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of NASA. For more information about NASA citizen science, visit science.nasa.gov/citizenscience or nasa.gov/get-involved.
Rain or Snow? Citizen scientists help study Sierra storms - Tahoe Daily Tribune
Rain or snow? Answering the question with citizen scientists - EGU Climate Change and Cryosphere
All of our quality-controlled data are available on our GitHub page. More recently processed observations are available on our dashboard.
Meet the team
Keith Jennings, University of Vermont
Data analysis
Meghan Collins, DRI
Engagement strategy
Monica Arienzo, DRI
Engagement analysis
Anne Nolin, UNR
Hydroclimatology
Nayoung Hur, Lynker
Data & engagement
Katherine Moore Powell, Lynker
Engagement
Anne Heggli, DRI
Operational & observer engagement
Guo Yu, DRI
Flood hydrology
Emma Golub , Lynker
Data & engagement
Sonia Tonino, DRI
Engagement
Past team members:
Brian Jenkins, UNR
Hailey Bogle, UNR
Jessica Garrett, Lynker
Sonia Tonino, DRI
Angus Watters, Lynker
Griffin Shelor, UNR