Data / Science Data Products / VAPs

XPRECIPRADARHP

Surface Hydrometeor Phase

Evaluation VAP

The Seasonal Surface Hydrometeor Phase Classification value-added product (VAP) provides near-surface precipitation phase classification from scanning X-band weather radar observations. This VAP, known as XPRECIPRADARHP, is designed to support quantitative precipitation estimation and hydrological studies by identifying the phase of surface precipitation and enabling intercomparisons with precipitation products such as the Surface Quantitative Precipitation Estimation (SQUIRE) snow water equivalent data set.

The product uses radar moments processed by the Corrected Moments in Antenna Coordinates (CMAC) VAP as input and applies two complementary classification approaches: a semi-supervised classification (SSC) method and a fuzzy-logic hydrometeor classification (FHC) algorithm. The VAP projects radar measurements onto near-surface grid points derived from SQUIRE to reduce beam blockage effects and better represent surface precipitation.

The output provides gridded hydrometeor phase classifications in netCDF format, including categories for liquid precipitation, low-density frozen precipitation, high-density frozen precipitation, mixed or melting hydrometeors, and unclassified precipitation. Data fields from both classification methods are included to allow users to select the approach most appropriate for their analysis.

The product is particularly useful for studies that require distinguishing between liquid, frozen, and mixed-phase precipitation to improve precipitation accumulation estimates and evaluate snowfall or snow water equivalent retrievals.

The VAP is also used for intercomparison and validation of precipitation products, including the SQUIRE data set and other radar-based precipitation estimates. In addition, the classification fields support investigations of precipitation microphysics, seasonal variability in hydrometeor phase, and evaluation of radar retrieval algorithms.

This evaluation VAP combines two hydrometeor classification approaches (SSC and FHC). Differences between these methods can lead to discrepancies in some classes, particularly mixed or melting hydrometeors, where the semi-supervised approach may classify more pixels as liquid precipitation compared to the fuzzy-logic method. Users should compare both outputs and select the method most appropriate for their application. The classification depends on CMAC-corrected radar variables and environmental temperature profiles, so data quality and environmental inputs can affect results. Additional details on algorithms, variables, and file contents are available in the technical documentation.