Development of an Evaluation Method for Precipitation Particle Types by Using Disdrometer Data

Abstract

Abstract A method for evaluating precipitation particle types from the size–fall velocity data observed by disdrometers by combining an expectation–maximization (EM) algorithm and a self-organizing map (SOM) was developed. An EM algorithm was used to estimate the particle size–fall velocity relationships according to previous work, and the SOM map was used to classify the relationships into graupel or snow categories. The method was applied for snowfall data observed by the volume scanning video disdrometer at a site in Sapporo, Japan, and the daily graupel-to-solid precipitation ratio ( G / S ratio) from 1 December 2017 to 28 February 2018 was obtained. The sensitivity of the results to different SOM configurations, SOM node groupings, and training datasets was also tested. The G / S ratio obtained from the simulation data of a meteorological model was compared as an example of the application of the product created by the new method. Significance Statement A method using machine learning for evaluating the precipitation particle types from size and fall velocity data observed by a disdrometer was developed. This method was applied to classify data observed in Sapporo, Japan, in a single winter into graupel and snow, and the daily graupel-to-solid precipitation ratio was obtained. The daily graupel-to-solid precipitation ratio was compared with that simulated by a meteorological model as an example of the application of disdrometer data.