Diagnosis of winter precipitation types using Spectral Bin Model (SBM): 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 the one-dimensional spectral bin model (SBM) developed by 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 WPT. 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 algorithm. The results show that the SBM has the highest overall skill score for winter precipitation, especially at the mountain sites. In contrast, the skill score of the SBM is lower than the other methods for RA. 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 an improved skill score compared to the original SBM, which uses climatological relationships for Colorado region.