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Evaluation of Simulated Winter Precipitation Using WRF-ARW during the ICE-POP 2018 Field Campaign

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  • 1 a School of Earth System Sciences, Kyungpook National University, Daegu, South Korea
  • | 2 b Department of Atmospheric Science, Kongju National University, Gongju, South Korea
  • | 3 c I.M. Systems Group, Inc., Rockville, and Environmental Modeling Center, National Oceanic and Atmospheric Administration/National Centers for Environmental Prediction, College Park, Maryland
  • | 4 d Earth and Space Sciences Research Group, Institute of Environmental Sciences, University of Castilla-La Mancha, Toledo, Spain
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Abstract

This study evaluates the performance of several cloud microphysics parameterizations in simulating surface precipitation for two snowstorm cases during the International Collaborative Experiment held at the PyeongChang 2018 Olympics and Winter Paralympic Games (ICE-POP 2018) field campaign. We compared four different schemes in the Weather Research and Forecasting (WRF) Model, namely the double-moment 6-class (WDM6), the WRF single-moment 6-class (WSM6), and Thompson and Morrison parameterizations. Both WSM6 and WDM6 overestimated the precipitation amount for the shallow precipitation system because of the substantial amount of cloud ice, mostly generated by the deposition process. The simulated precipitation amount and distribution for the deep precipitation system showed no noticeable differences in the different cloud microphysics parameterizations. However, the simulated hydrometeor type at the surface using WSM6 and WDM6 showed good agreement with observations for all cases. The accuracy of the mean mass-weighted terminal velocity of cloud ice VI¯ applied in WSM6 and WDM6 is ±20%. The number concentration of cloud ice and the ice microphysics processes are newly retrieved with 1.2 times increased VI¯. For the shallow snowstorm, the precipitation amount was reduced by approximately 8% because of the inefficient deposition and its effects on the subsequent ice microphysical processes, such as the accretion of cloud ice by snow and the conversion from cloud ice to snow.

Corresponding author: Eun-Chul Chang, echang@kongju.ac.kr

Abstract

This study evaluates the performance of several cloud microphysics parameterizations in simulating surface precipitation for two snowstorm cases during the International Collaborative Experiment held at the PyeongChang 2018 Olympics and Winter Paralympic Games (ICE-POP 2018) field campaign. We compared four different schemes in the Weather Research and Forecasting (WRF) Model, namely the double-moment 6-class (WDM6), the WRF single-moment 6-class (WSM6), and Thompson and Morrison parameterizations. Both WSM6 and WDM6 overestimated the precipitation amount for the shallow precipitation system because of the substantial amount of cloud ice, mostly generated by the deposition process. The simulated precipitation amount and distribution for the deep precipitation system showed no noticeable differences in the different cloud microphysics parameterizations. However, the simulated hydrometeor type at the surface using WSM6 and WDM6 showed good agreement with observations for all cases. The accuracy of the mean mass-weighted terminal velocity of cloud ice VI¯ applied in WSM6 and WDM6 is ±20%. The number concentration of cloud ice and the ice microphysics processes are newly retrieved with 1.2 times increased VI¯. For the shallow snowstorm, the precipitation amount was reduced by approximately 8% because of the inefficient deposition and its effects on the subsequent ice microphysical processes, such as the accretion of cloud ice by snow and the conversion from cloud ice to snow.

Corresponding author: Eun-Chul Chang, echang@kongju.ac.kr
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