Snow-to-Liquid Ratio Prediction over the Contiguous United States Using Machine Learning

Pletcher, Michael D.; Veals, Peter G.; Chase, Randy J.; Hilberg, Steven D.; Newman, Noah; Rosenow, Andrew A.; Steenburgh, W. James

doi:10.1175/WAF-D-25-0124.1

i

Snow-to-Liquid Ratio Prediction over the Contiguous United States Using Machine Learning

2026
By Pletcher, Michael D. ; Veals, Peter G. ; Chase, Randy J. ; ...

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Journal Title:

Weather and Forecasting
Personal Author:

Pletcher, Michael D. ; Veals, Peter G. ; Chase, Randy J. ; Hilberg, Steven D. ; Newman, Noah ; Rosenow, Andrew A. ; Steenburgh, W. James
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; CIWRO (Cooperative Institute for Severe and High-Impact Weather Research and Operations) ; NSSL (National Severe Storms Laboratory) ; WPO (Weather Program Office)
Description:

Winter storms have widespread impacts across the contiguous United States (CONUS), resulting in travel and economic disruptions, loss of life, and damage to property and infrastructure. Snow-to-liquid ratio (SLR), the ratio of new (i.e., freshly fallen) snow to liquid precipitation equivalent, is used operationally to forecast snowfall amount during winter storms. However, no clear best prediction method for SLR has been established, and some operational SLR prediction methods were developed for specific regions, potentially limiting their utility for CONUS-wide applications. Using Community Collaborative Rain, Hail and Snow Network (CoCoRaHS) snowfall observations, station geographic information, and fifth generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5) vertical profiles of atmospheric variables, we developed a random forest (RF)-based algorithm for predicting SLR across the CONUS. SLR forecasts and quantitative snowfall forecasts (QSFs) based on this algorithm were then derived from the NOAA High-Resolution Rapid Refresh (HRRR). The resulting RF SLR mean absolute error (MAE) was 2.92 across the CONUS compared to MAEs for current operational methods of 3.51–4.76. Improvements were also seen in QSF. Additionally, the RF outperformed operational SLR methods within individual regional snow climates. Despite these improvements, there were situations when the RF struggled, such as high (>20) and low (<5) SLR events that featured relatively cold and calm and warm and windy environments, respectively, in the eastern CONUS. The use of CoCoRaHS observations for training provides a template for improving snowfall forecasts at a CONUS-wide scale with the RF providing an algorithm for improving SLR prediction for operational applications.
Source:

Weather and Forecasting, 41(2), 317-338
DOI:

https://doi.org/10.1175/WAF-D-25-0124.1
ISSN:

0882-8156 ; 1520-0434
Format:

pdf
Publisher:

American Meteorological Society
Document Type:

Journal Article
Funding:

Grant no. NA19OAR4590137 ; Grant no. NA22OAR4590531 ; Grant no. NA23OAR4310279-T1-01 ; Grant no. NA21OAR4320204
Rights Information:

Other
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Submitted
Main Document Checksum:

urn:sha-512:5f24dcb47003f4e44319993675901140ed20dd302f891c8a991561815e942c429f7ed1543b7e75f5bcb6e7ab623b92fa338020d82ef1bed15b706d3aa7e83a0e
Download URL:

https://repository.library.noaa.gov/view/noaa/73136/noaa_73136_DS1.pdf
File Type:

[PDF - 34.21 MB ]

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