Exploring Specific Differential Phase Estimators Accuracy for Snow Events Observed during the NASA IMPACTS Field Campaign

Cloud and precipitation microphysics play a fundamental role in governing the Earth's climate system, from affecting the radiative energy budget and the scaling of precipitation extremes with climate change to the distribution and characteristics of snowfall at the surface and its downstream hydrological and societal impacts. In particular, winter snowstorms are frequent on the Eastern Seaboard and cause major disruptions to transportation, commerce, and public safety. Snowfall within these storms is frequently organized in banded structures that are vaguely understood by scientists and moderately predicted by current numerical models. This is especially the case for snow and ice events because of the diversity and complexity of their attendant microphysical processes. Specific differential phase (KDP) is a useful variable for quantitatively studying ice microphysics, but there are several methods for estimating this variable. Nevertheless, there are no studies comparing the performance of these estimators in snow/ice, and there are only a few studies individually quantifying their accuracy. Investigating which method is more efficient in accurately estimating KDP during snow and ice events is crucial for meteorological interpretation. In the case of an extreme event, being able to better forecast how much snow will occur would aid the public in preparing for the impacts.

This study uses dual-polarization radar data collected during NASA’s Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign (McMurdie et al. 2022) and data from the National Weather Service’s WSR-88D radars to evaluate the performance of different KDP estimators and their associated hydrometeor classification outputs. Specifically, we evaluate the performance of three well-known methods: the operational National Weather Service estimator, the Maesaka estimator (Maesaka et al. 2012), and the Vulpiani estimator (Vulpiani et al. 2012, 2015). In addition to the NEXRAD data, data from the University of Oklahoma’s (OU) Advanced Radar Research Center (ARRC) Rapid-scanning X-band Polarimetric (RaXPol) radar from two events is used, namely, the 29 January 2022 Plymouth, MA Nor’easter blizzard and the 25 February 2022 Albany, NY snowstorm. We envision creating a new hybrid KDP estimator that combines previous algorithms and selects the best estimation method based on HCA classification outputs with the goal of maximizing the accuracy of data-derived products. This hybrid method will aid in the quantification and prediction of snow and ice.

References:

• McMurdie, L. A., and Coauthors, 2022: Chasing Snowstorms: The Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) Campaign. Bull. Amer. Meteor. Soc., 103, E1243–E1269. DOI: https://doi.org/10.1175/BAMS-D-20-0246.1
• Maesaka, T., K. Iwanami, and M. Maki, 2012: Non-negative KDP estimation by monotone increasing ΦDP assumption below melting layer. Seventh European Conf. on Radar in Meteorology and Hydrology, Toulouse, France, ERAD, http://www.meteo.fr/cic/meetings/2012/ERAD/extended_abs/QPE_233_ext_abs.pdf.
• Vulpiani, G., L. Baldini, and N. Roberto, 2015: Characterization of Mediterranean hail-bearing storms using an operational polarimetric X-band radar. Atmos. Meas. Tech., 8, 4681–4698, https://doi.org/10.5194/amt-8-4681-2015.
• Vulpiani, G., M. Montopoli, L. D. Passeri, A. G. Gioia, P. Giordano, and F. S. Marzano, 2012: On the use of dual-polarized C-band radar for operational rainfall retrieval in mountainous areas. J. Appl. Meteor. Climatol., 51, 405–425, https://doi.org/10.1175/JAMC-D-10-05024.1.