The Impacts of Rotational Mixing on the Precipitation Simulated by a Convection Permitting Model.
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| Title: | The Impacts of Rotational Mixing on the Precipitation Simulated by a Convection Permitting Model. |
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| Authors: | Hagos, Samson1 (AUTHOR) samson.hagos@pnnl.gov, Feng, Zhe1 (AUTHOR), Varble, Adam C.1 (AUTHOR), Tai, Sheng‐Lun1 (AUTHOR), Chen, Jingyi1,2 (AUTHOR) |
| Source: | Journal of Advances in Modeling Earth Systems. May2025, Vol. 17 Issue 5, p1-17. 17p. |
| Subject Terms: | *Weather forecasting, Mesoscale convective complexes, Meteorological research, Precipitable water, Convective clouds |
| Abstract: | With increased availability of computational resources, regional and global scale convection‐permitting model (CPM, Δx ∼ 1–10 km) simulations are becoming more common. CPMs have improved accuracy in their representation of deep convection and mesoscale convective systems (MCSs) compared to coarser resolution models. However, CPMs still exhibit convective cloud and precipitation biases relative to observations, notably a lesser frequency of light precipitation rates and greater frequency of heavy precipitation rates. In this work we hypothesize that these CPM biases are related to under‐resolved mixing between convective updrafts and their surrounding environment. To test this hypothesis, we introduce a parameterization to the Weather Research and Forecasting model (WRF) that adds a small angular rotation of the grid‐scale flow about the axis perpendicular to the plane of convective drafts. This rotated flow is then allowed to alter advection of moisture and hydrometeors. The effects of such mixing on precipitation characteristics are evaluated in month‐long 4‐km grid spacing simulations over the Amazon. The enhanced mixing transports moisture and condensate from convective cores to other areas including downdrafts. This increases the frequency of low‐precipitable water and light precipitation. It also decreases the frequency of intense precipitation from isolated deep convection and MCSs, increases cloud top temperatures, reduces radar echo‐top heights, and increases overall precipitation by altering the relationship of precipitation with precipitable water, in better agreement with observations. The results suggest when optimized using multiple observations, such an approach may provide a path toward more accurate representation of convection and precipitation statistics in convection‐permitting simulations. Plain Language Summary: Convection permitting models exhibit biases including a lesser frequency of light precipitation rates and greater frequency of heavy precipitation rates from clouds that are too deep and too small. In this work we propose a potential solution to these biases that involves enhancing the mixing between convective updrafts and their surrounding environment. As a demonstration we introduced a parameterization into WRF that adds a small angular rotation of the grid‐scale flow to mimic the strong horizontal vorticity in convection. This rotated flow is then allowed to alter advection of moisture and hydrometeors. The effects of such mixing on precipitation characteristics are evaluated in month‐long 4‐km grid spacing simulations over the Amazon. Examination of the resulting improvement in precipitation statistics and depth of convection suggest when optimized using multiple observations, such an approach may provide a path toward more accurate representation of convection and precipitation statistics in regional and global convection‐permitting simulations. Key Points: We hypothesize that "popcorn" precipitation biases in CPMs are related to under‐resolved mixing between convective updrafts and the environmentWe introduced a parameterization to WRF that adds a small angular rotation to the grid‐scale circulation to represent horizontal vorticity in convectionThe parameterization results in improved precipitation statistics and convection depth as desired [ABSTRACT FROM AUTHOR] |
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| Abstract: | With increased availability of computational resources, regional and global scale convection‐permitting model (CPM, Δx ∼ 1–10 km) simulations are becoming more common. CPMs have improved accuracy in their representation of deep convection and mesoscale convective systems (MCSs) compared to coarser resolution models. However, CPMs still exhibit convective cloud and precipitation biases relative to observations, notably a lesser frequency of light precipitation rates and greater frequency of heavy precipitation rates. In this work we hypothesize that these CPM biases are related to under‐resolved mixing between convective updrafts and their surrounding environment. To test this hypothesis, we introduce a parameterization to the Weather Research and Forecasting model (WRF) that adds a small angular rotation of the grid‐scale flow about the axis perpendicular to the plane of convective drafts. This rotated flow is then allowed to alter advection of moisture and hydrometeors. The effects of such mixing on precipitation characteristics are evaluated in month‐long 4‐km grid spacing simulations over the Amazon. The enhanced mixing transports moisture and condensate from convective cores to other areas including downdrafts. This increases the frequency of low‐precipitable water and light precipitation. It also decreases the frequency of intense precipitation from isolated deep convection and MCSs, increases cloud top temperatures, reduces radar echo‐top heights, and increases overall precipitation by altering the relationship of precipitation with precipitable water, in better agreement with observations. The results suggest when optimized using multiple observations, such an approach may provide a path toward more accurate representation of convection and precipitation statistics in convection‐permitting simulations. Plain Language Summary: Convection permitting models exhibit biases including a lesser frequency of light precipitation rates and greater frequency of heavy precipitation rates from clouds that are too deep and too small. In this work we propose a potential solution to these biases that involves enhancing the mixing between convective updrafts and their surrounding environment. As a demonstration we introduced a parameterization into WRF that adds a small angular rotation of the grid‐scale flow to mimic the strong horizontal vorticity in convection. This rotated flow is then allowed to alter advection of moisture and hydrometeors. The effects of such mixing on precipitation characteristics are evaluated in month‐long 4‐km grid spacing simulations over the Amazon. Examination of the resulting improvement in precipitation statistics and depth of convection suggest when optimized using multiple observations, such an approach may provide a path toward more accurate representation of convection and precipitation statistics in regional and global convection‐permitting simulations. Key Points: We hypothesize that "popcorn" precipitation biases in CPMs are related to under‐resolved mixing between convective updrafts and the environmentWe introduced a parameterization to WRF that adds a small angular rotation to the grid‐scale circulation to represent horizontal vorticity in convectionThe parameterization results in improved precipitation statistics and convection depth as desired [ABSTRACT FROM AUTHOR] |
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| ISSN: | 19422466 |
| DOI: | 10.1029/2024MS004524 |
