Preprints
https://doi.org/10.5194/gmd-2021-417
https://doi.org/10.5194/gmd-2021-417
Submitted as: model evaluation paper
24 Jan 2022
Submitted as: model evaluation paper | 24 Jan 2022
Status: a revised version of this preprint was accepted for the journal GMD.

Simulated microphysical properties of winter storms from bulk-type microphysics schemes and their evaluation in the WRF (v4.1.3) model during the ICE-POP 2018 field campaign

Jeong-Su Ko1, Kyo-Sun Sunny Lim1, Kwonil Kim1, Gyuwon Lee1, Gregory Thompson2, and Alexis Berne3 Jeong-Su Ko et al.
  • 1School of Earth System Sciences, Center for Atmospheric Remote sensing (CARE), Kyungpook National University, Daegu, Republic of Korea
  • 2National Center for Atmospheric Research, Boulder, CO, United States
  • 3Environmental Remote Sensing Laboratory (LTE), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

Abstract. This study evaluates the performance of four bulk-type microphysics schemes, Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6), WRF Double-Moment 7-class (WDM7), Thompson, and Morrison, focusing on hydrometeors and microphysics budgets in the WRF model version 4.1.3. Eight snowstorm cases, which can be subcategorized as cold-low, warm-low, and air-sea interaction cases, depending on the synoptic environment during the International Collaborative Experiment held at the Pyeongchang 2018 Olympics and Winter Paralympic Games (ICE-POP 2018) field campaign, are selected. All simulations present a positive bias in the simulated surface precipitation for cold-low and warm-low cases. Furthermore, the simulations for the warm-low cases show a higher probability of detection score than simulations for the cold-low and air-sea interaction cases even though the simulations fail to capture the accurate transition layer for wind direction. WDM6 and WDM7 simulate abundant cloud ice for the cold-low and warm-low cases, so snow is mainly generated by aggregation. Meanwhile, Thompson and Morrison simulate insignificant cloud ice amounts, especially over the lower atmosphere, where cloud water is simulated instead. Snow in Thompson and Morrison is mainly formed by the accretion between snow and cloud water and deposition. The melting process is analyzed as a key process to generate rain in all schemes. The discovered positive precipitation bias for the warm-low and cold-low cases can be mitigated by inefficient melting using all schemes. The contribution of melting to rain production is reduced for the air-sea interaction case with decreased solid-phase hydrometeors and increased cloud water in all simulations.

Jeong-Su Ko et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-417', Anonymous Referee #1, 01 Mar 2022
    • AC1: 'Reply on RC1', Kyo-Sun Lim, 17 Mar 2022
  • RC2: 'Comment on gmd-2021-417', Anonymous Referee #2, 09 Mar 2022

Jeong-Su Ko et al.

Jeong-Su Ko et al.

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Short summary
This study evaluates the performance of the four microphysics parameterizations, WDM6, WDM7, Thompson, and Morrison, in simulating snowfall events during the ICE-POP 2018 field campaign. Eight snowfall events, classified into three categories (cold-low, warm-low, and air-sea interaction), depending on the synoptic characteristics, are selected. The evaluation is conducted focusing on the simulated hydrometeors, microphysics budgets, wind fields, and precipitation using the measurement data.