Influence of cloud microphysics schemes on weather model predictions of heavy precipitation

<p>Cloud microphysics is one of the major sources of uncertainty in numerical weather prediction models. In this work, the ability of a numerical weather prediction model to correctly predict high-impact weather events, i.e., hail and heavy rain, using different cloud microphysics schemes is e...

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Main Authors: G. Köcher, T. Zinner, C. Knote
Format: Article
Language:English
Published: Copernicus Publications 2023-06-01
Series:Atmospheric Chemistry and Physics
Online Access:https://acp.copernicus.org/articles/23/6255/2023/acp-23-6255-2023.pdf
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author G. Köcher
T. Zinner
C. Knote
C. Knote
author_facet G. Köcher
T. Zinner
C. Knote
C. Knote
author_sort G. Köcher
collection DOAJ
description <p>Cloud microphysics is one of the major sources of uncertainty in numerical weather prediction models. In this work, the ability of a numerical weather prediction model to correctly predict high-impact weather events, i.e., hail and heavy rain, using different cloud microphysics schemes is evaluated statistically. Polarimetric C-band radar observations over 30 convection days are used as the observation dataset. Simulations are made using the regional-scale Weather Research and Forecasting (WRF) model with five microphysics schemes of varying complexity (double moment, spectral bin (SBM), and Predicted Particle Properties (P3)). Statistical characteristics of heavy-rain and hail events of varying intensities are compared between simulations and observations. All simulations, regardless of the microphysics scheme, predict heavy-rain events (15, 25, and 40 mm h<span class="inline-formula"><sup>−1</sup></span>) that cover larger average areas than those observed by radar. The frequency of these heavy-rain events is similar to radar-measured heavy-rain events but still scatters by a factor of 2 around the observations, depending on the microphysics scheme. The model is generally unable to simulate extreme hail events with reflectivity thresholds of 55 dBZ and higher, although they have been observed by radar during the evaluation period. For slightly weaker hail/graupel events, only the P3 scheme is able to reproduce the observed statistics. Analysis of the raindrop size distribution in combination with the model mixing ratio shows that the P3, Thompson two-moment (2-mom), and Thompson aerosol-aware schemes produce large raindrops too frequently, and the SBM scheme misses large rain and graupel particles. More complex schemes do not necessarily lead to better results in the prediction of heavy precipitation.</p>
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spelling doaj.art-02230f3ff94247d9acc4060ab07c3b322023-06-07T12:03:16ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242023-06-01236255626910.5194/acp-23-6255-2023Influence of cloud microphysics schemes on weather model predictions of heavy precipitationG. Köcher0T. Zinner1C. Knote2C. Knote3Meteorologisches Institut, Ludwig-Maximilians-Universität, Munich, GermanyMeteorologisches Institut, Ludwig-Maximilians-Universität, Munich, GermanyMeteorologisches Institut, Ludwig-Maximilians-Universität, Munich, GermanyModel-based Environmental Exposure Science, Faculty of Medicine, University of Augsburg, Augsburg, Germany<p>Cloud microphysics is one of the major sources of uncertainty in numerical weather prediction models. In this work, the ability of a numerical weather prediction model to correctly predict high-impact weather events, i.e., hail and heavy rain, using different cloud microphysics schemes is evaluated statistically. Polarimetric C-band radar observations over 30 convection days are used as the observation dataset. Simulations are made using the regional-scale Weather Research and Forecasting (WRF) model with five microphysics schemes of varying complexity (double moment, spectral bin (SBM), and Predicted Particle Properties (P3)). Statistical characteristics of heavy-rain and hail events of varying intensities are compared between simulations and observations. All simulations, regardless of the microphysics scheme, predict heavy-rain events (15, 25, and 40 mm h<span class="inline-formula"><sup>−1</sup></span>) that cover larger average areas than those observed by radar. The frequency of these heavy-rain events is similar to radar-measured heavy-rain events but still scatters by a factor of 2 around the observations, depending on the microphysics scheme. The model is generally unable to simulate extreme hail events with reflectivity thresholds of 55 dBZ and higher, although they have been observed by radar during the evaluation period. For slightly weaker hail/graupel events, only the P3 scheme is able to reproduce the observed statistics. Analysis of the raindrop size distribution in combination with the model mixing ratio shows that the P3, Thompson two-moment (2-mom), and Thompson aerosol-aware schemes produce large raindrops too frequently, and the SBM scheme misses large rain and graupel particles. More complex schemes do not necessarily lead to better results in the prediction of heavy precipitation.</p>https://acp.copernicus.org/articles/23/6255/2023/acp-23-6255-2023.pdf
spellingShingle G. Köcher
T. Zinner
C. Knote
C. Knote
Influence of cloud microphysics schemes on weather model predictions of heavy precipitation
Atmospheric Chemistry and Physics
title Influence of cloud microphysics schemes on weather model predictions of heavy precipitation
title_full Influence of cloud microphysics schemes on weather model predictions of heavy precipitation
title_fullStr Influence of cloud microphysics schemes on weather model predictions of heavy precipitation
title_full_unstemmed Influence of cloud microphysics schemes on weather model predictions of heavy precipitation
title_short Influence of cloud microphysics schemes on weather model predictions of heavy precipitation
title_sort influence of cloud microphysics schemes on weather model predictions of heavy precipitation
url https://acp.copernicus.org/articles/23/6255/2023/acp-23-6255-2023.pdf
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AT cknote influenceofcloudmicrophysicsschemesonweathermodelpredictionsofheavyprecipitation
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