An All-Seasons Surface Hydrometeor Classification Algorithm (sfcHCA) for the WSR-88D Radar Network

Carlin, Jacob T.; Dunnavan, Edwin L.; Johnson, Marcus; Krause, John

doi:10.1175/JTECH-D-25-0054.1

i

An All-Seasons Surface Hydrometeor Classification Algorithm (sfcHCA) for the WSR-88D Radar Network

2026
By Carlin, Jacob T. ; Dunnavan, Edwin L. ; Johnson, Marcus ; ...

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Journal Title:

Journal of Atmospheric and Oceanic Technology
Personal Author:

Carlin, Jacob T. ; Dunnavan, Edwin L. ; Johnson, Marcus ; Krause, John
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)
Description:

Determining surface precipitation type is an operational forecasting challenge, particularly in complex cold-season precipitation events and in areas with a dearth of observations. This paper describes a new radar algorithm proposed for the NEXRAD network called the surface hydrometeor classification algorithm (sfcHCA). The sfcHCA produces estimates of surface precipitation types within the radar surveillance area by merging output from the existing NEXRAD HCA level-III product with surface precipitation type diagnosed from a 1D steady-state discrete particle model initialized from numerical weather prediction model data. The sfcHCA introduces three new HCA classes for freezing rain, ice pellets, and freezing rain/ice pellet mixtures, as well as several novel features such as the utilization of range-defined quasi-vertical profiles, polarimetric radar particle size distribution retrievals for model initialization, merging logic for combining model-diagnosed surface precipitation-type classifications with existing HCA output, and a radar-melting-layer-based adjustment algorithm to correct for erroneous background model data. The sfcHCA was validated against more than 15 000 manned ASOS station reports and over 30 000 Mobile Precipitation Identification Near the Ground (mPING) reports across 225 cases spanning all CONUS NEXRAD sites. Probability of detection rates within 20 km of manned ASOS observations were 97.1%, 78.3%, 72.7%, 68.1%, and 56.0% for rain, snow, rain/snow mix, freezing rain, and ice pellets, respectively. This algorithm should provide useful information for nowcasting surface-precipitation-type evolution and offering impact-based decision support.
Source:

Journal of Atmospheric and Oceanic Technology, 43(1), 21-44
DOI:

https://doi.org/10.1175/JTECH-D-25-0054.1
ISSN:

0739-0572 ; 1520-0426
Format:

pdf
Publisher:

American Meteorological Society
Document Type:

Journal Article
Funding:

Grant no. NA21OAR4320204
Rights Information:

Other
Compliance:

Submitted
Main Document Checksum:

urn:sha-512:4312779135c3296a8e06ead389291f5bc9316d5d9259c20855ed02537f671d0e167d71dd06b4e71f9970ae7a894c34f0026745e5258f2dc0ffe857b3632bb5a7
Download URL:

https://repository.library.noaa.gov/view/noaa/72340/noaa_72340_DS1.pdf
File Type:

[PDF - 20.81 MB ]

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