Effects of Prognostic Number Concentrations of Snow and Graupel on the Simulated Precipitation over the Korean Peninsula

Juhee Kwon, Kyo Sun Sunny Lim, Sun Young Park, Kwonil Kim, Gyuwon Lee

Research output: Contribution to journalArticlepeer-review

Abstract

A new version of the Weather Research and Forecasting (WRF) double-moment 6-class (WDM6) micro-physics scheme was developed based on the existing WDM6 scheme by predicting snow and graupel number concentrations. The new WDM6 scheme was tested for summer rainfall and winter snowfall cases to evaluate the effects of prognostic number concentration of snow and graupel on the simulated precipitation. The number concentration of snow decreases at the upper layers and the one of graupel also decreases at all layers in the new WDM6 scheme compared to the diagnosed ones in the original WDM6 scheme. Rain number concentration is remarkably reduced in the new WDM6 scheme due to the newly added and modified sink processes. Therefore, the new scheme produces a larger size of raindrops with a reduced number concentration than the original scheme, which hinders raindrop evaporation and produces more surface rain. Even though the enhanced surface rainfall in the new scheme deteriorates the bias score, the new scheme improves the statistical skill of the equitable threat score and probability of detection in most cases. These scores all improved for warm-type summer cases in the new scheme. The new scheme also shows more comparable features to the observation for the probability density functions of simulated liquid equivalent precipitation rates by alleviating the overprediction problem of precipitation frequencies belonging to heavy precipitation categories. Therefore, the new scheme improves the precipitation forecast for warm-type summer cases, which occur most frequently during the summer season over the Korean Peninsula.

Original languageEnglish
Pages (from-to)2591-2612
Number of pages22
JournalWeather and Forecasting
Volume38
Issue number12
DOIs
StatePublished - Dec 2023

Keywords

  • Cloud microphysics
  • Cloud parameterizations
  • Numerical weather prediction/forecasting
  • Parameterization

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