Verification of the Triple-Frequency Retrieval of Snowfall Properties using Coincident Airborne Observations Collected during OLYMPEX

To better quantify the global water cycle, retrievals of both solid and liquid precipitation processes need to be improved. Retrievals of snow particle characteristics (size, number, habit, etc.) and snowfall rates from active and passive remote sensing techniques remain largely unevaluated because of the lack of coincident in-situ and remotely sensed observations. The goal of this research is to evaluate triple-frequency retrieval of snow properties using in-situ data and to illustrate how it can be used to understand snowfall processes within clouds. To accomplish this, coincident observations of triple-frequency (Ku-, Ka-, and W-band) radar reflectivity and Particle Size Distributions (PSDs) collected during the OLYmpic Mountain EXperiment (OLYMPEX) are analyzed. During this field campaign, the University of North Dakota Citation aircraft collected in-situ bulk and size-resolved microphysical characteristics and state parameters while the NASA DC-8 equipped with the Airborne Third Generation Precipitation Radar (APR-3) sampled the same region. Using the Self-Similar Rayleigh Gans (SSRG) scattering technique, a direct evaluation of the triple-frequency retrieval in mixed-phase and ice phase precipitation is conducted. Scattering derived using the SSRG technique on observed PSDs captures most of the variability of the coincident radar observations using a mass-dimension relation constrained from minimizing the chi-squared difference between forward modeled reflectivity and that from coincident S-band radar observations. Particle images and a habit classification algorithm provide direct verification for the use of the triple-frequency radar retrieval and allow for snow property retrieval using the three frequencies. It is shown that these remote retrievals can give more information about particle density and potentially differentiate regions of growth by aggregation and riming within clouds. The increased information about particle density available from these retrievals can lead to better estimates of precipitation rate from space in ice and mixed-phase precipitation.