Applying the WRF Double-Moment Six-Class Microphysics Scheme in the GRAPES_Meso Model: A Case Study

Meng Zhang,Xiaoye Zhang,Yue Peng,Hong Wang,Huizheng Che

doi:10.1007/s13351-018-7066-1

Abstract

This study incorporated the Weather Research and Forecasting (WRF) model double-moment 6-class (WDM6) microphysics scheme into the mesoscale version of the Global/Regional Assimilation and PrEdiction System (GRAPES_Meso). A rainfall event that occurred during 3–5 June 2015 around Beijing was simulated by using the WDM6, the WRF single-moment 6-class scheme (WSM6), and the NCEP 5-class scheme, respectively. The results show that both the distribution and magnitude of the rainfall simulated with WDM6 were more consistent with the observation. Compared with WDM6, WSM6 simulated larger cloud liquid water content, which provided more water vapor for graupel growth, leading to increased precipitation in the cold-rain processes. For areas with the warm-rain processes, the sensitivity experiments using WDM6 showed that an increase in cloud condensation nuclei (CCN) number concentration led to enhanced CCN activation ratio and larger cloud droplet number concentration (Nc) but decreased cloud droplet effective diameter. The formation of more small-size cloud droplets resulted in a decrease in raindrop number concentration (Nr), inhibiting the warm-rain processes, thus gradually decreasing the amount of precipitation. For areas mainly with the cold-rain processes, the overall amount of precipitation increased; however, it gradually decreased when the CCN number concentration reached a certain magnitude. Hence, the effect of CCN number concentration on precipitation exhibits significant differences in different rainfall areas of the same precipitation event.

Highlights

Clouds are a key link between the water cycle and the radiative balance in the earth–atmosphere system (Hartmann et al, 1992; Baker, 1997; Ramanathan et al, 2001; Stephens, 2005)
The GRAPES_Meso model (Chen and Shen, 2006; Chen et al, 2008) is a new generation of regional numerical forecast system developed by the Chinese Academy of Meteorological Sciences (CAMS), the China Meteorological Administration (CMA)
This study employed level-3 products of the Terra and Aqua satellites equipped with Moderate Resolution Imaging Spectrometer (MODIS); times of passing territory were 0230 and 0530 UTC, respectively, and both satellites were capable of obtaining cloud liquid water path (CLWP) at a 1° × 1° resolution

Summary

Introduction

Clouds are a key link between the water cycle and the radiative balance in the earth–atmosphere system (Hartmann et al, 1992; Baker, 1997; Ramanathan et al, 2001; Stephens, 2005). Morrison and Pinto (2005) incorporated a new double-moment bulk microphysics scheme into the MM5 model to simulate stratiform clouds in the Arctic boundary layer This scheme could forecast the number concentration and mixing ratio of four types of hydrometeors (cloud droplets, small ice crystals, raindrops, and snow) based on the specific aerosol size distri-. Morrison et al (2009) forecasted the mixing ratio and the number concentration of five types of hydrometeors (cloud, ice, snow, rain, and graupel) contained in stratiform precipitation behind a simulated squall line using the Weather Research and Forecasting (WRF) model and compared the results with the single-moment scheme They revealed that the double-moment scheme increased the area of the trailing stratiform precipitation region but greatly weakened convective activities in the convection core.

Model and microphysics scheme

Model parameters

Data used

Results

Analyzing cloud and rain parameters

Sensitivity tests of CCN

Conclusions and discussion