Tahoe rain or snow precipitation phase observations
Data files
Mar 24, 2023 version files 718.51 KB
Abstract
These data include observations of rain, snow, and mixed precipitation from the Tahoe Rain or Snow citizen science project. Included with each observation is a set of ancillary variables, including latitude and longitude, elevation, modeled meteorological data, and additional info. Please see the metadata file for the description and units of each data column.
For more info, please see Jennings et al. (2023) and Arienzo et al. (2021):
- Jennings, Keith S., Monica M. Arienzo, Meghan Collins, Benjamin Hatchett, Anne W. Nolin, and Graeme Aggett. "Crowdsourced Data Highlight Precipitation Phase Partitioning Variability in Rain-Snow Transition Zone." Earth and Space Science (2023). https://doi.org/10.1029/2022EA002714
- Arienzo, Monica M., Meghan Collins, and Keith S. Jennings. "Enhancing engagement of citizen scientists to monitor precipitation phase." Frontiers in Earth Science 9 (2021): 617594. https://doi.org/10.3389/feart.2021.617594
For the code used to process these data: https://github.com/SnowHydrology/MountainRainOrSnow/tree/tahoe_ros
Methods
Please see Jennings et al. (2023) and Arienzo et al. (2021) for full methodological details. In brief:
- We collected observations of rain, snow, and mixed precipitation from community observers using the Citizen Science Tahoe mobile app
- The app automatically timestamped and geolocated each observation
- We associated each data point with elevation and modeled meteorological data, along with the percentage probability of liquid precipitation from the Integrated Multi-satellitE Retrievals for GPM (IMERG) Level 3 v. 6 product
- We quality-controlled observations based on modeled air temperature, nearby precipitation, nearby relative humidity, distance from meteorological stations, and duplicate timestamps
For the code used to process these data: https://github.com/SnowHydrology/MountainRainOrSnow/tree/tahoe_ros
Usage notes
These data can be opened and examined using a text reader. The user may also utilize open-source software such as R or Python or commercial software such as Microsoft Excel to evaluate the data or run additional analyses.