Diagnosis of winter precipitation types using the spectral bin model (version 1DSBM-19M): comparison of five methods using ICE-POP 2018 field experiment data

Abstract

Winter precipitation types (WPTs) are controlled by many factors, including thermodynamic and microphysical processes. Therefore, realistically simulating interactions between precipitation particles and the atmosphere is important when diagnosing the WPT. In the present study, we analyze the performance of a modified version of the one-dimensional spectral bin model (SBM; version 1DSBM-19M) of Carlin and Ryzhkov (2019), which simulates the change in the physical characteristics of precipitation particles of various sizes as they fall from the cloud top to the ground and diagnoses surface WPTs. We compare the performance of the SBM and four other diagnostic methods that use the following variables: (1) atmospheric thickness, (2) wet-bulb temperature, (3) temperature and relative humidity, and (4) wet-bulb temperature and low-level lapse rate. Three reference WPTs (snow (SN), rain (RA), and RASN) are obtained from particle size velocity (PARSIVEL) disdrometer data using a newly proposed decision tree algorithm. The results show that the SBM has the highest overall hit rate for all cases among five diagnostic methods. In contrast, the hit rate of the SBM for each WPT shows lower performance for RA than for the other methods. These results indicate that the SBM simulations tend to underestimate melting compared to observations. We thus explore the effects of the SBM's microphysics scheme on the extent of melting in cases of misdiagnosed RA. An optimized SBM that uses the climatological snow density-diameter relationship for the Pyeongchang region produces an increased amount of melting and achieves improved skill scores compared to the current SBM, which uses a snow density-diameter relationship for the Colorado region.