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Growth of Mesoscale Convective Systems in Observations and a Seasonal Convection-Permitting Simulation over Argentina

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  • 1 a Department of Atmospheric Sciences, University of Utah, Salt Lake City, Utah
  • | 2 b Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington
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Abstract

A 6.5-month, convection-permitting simulation is conducted over Argentina covering the Remote Sensing of Electrification, Lightning, And Mesoscale/Microscale Processes with Adaptive Ground Observations and Clouds, Aerosols, and Complex Terrain Interactions (RELAMPAGO-CACTI) field campaign and is compared with observations to evaluate mesoscale convective system (MCS) growth prediction. Observed and simulated MCSs are consistently identified, tracked, and separated into growth, mature, and decay stages using top-of-the-atmosphere infrared brightness temperature and surface rainfall. Simulated MCS number, lifetime, seasonal and diurnal cycles, and various cloud-shield characteristics including growth rate are similar to those observed. However, the simulation produces smaller rainfall areas, greater proportions of heavy rainfall, and faster system propagations. Rainfall area is significantly underestimated for long-lived MCSs but not for shorter-lived MCSs, and rain rates are always overestimated. These differences result from a combination of model and satellite retrieval biases, in which simulated MCS rain rates are shifted from light to heavy, while satellite-retrieved rainfall is too frequent relative to rain gauge estimates. However, the simulation reproduces satellite-retrieved MCS cloud-shield evolution well, supporting its usage to examine environmental controls on MCS growth. MCS initiation locations are associated with removal of convective inhibition more than maximized low-level moisture convergence or instability. Rapid growth is associated with a stronger upper-level jet (ULJ) and a deeper northwestern Argentinean low that causes a stronger northerly low-level jet (LLJ), increasing heat and moisture fluxes, low-level vertical wind shear, baroclinicity, and instability. Sustained growth corresponds to similar LLJ, baroclinicity, and instability conditions but is less sensitive to the ULJ, large-scale vertical motion, or low-level shear. Growth sustenance controls MCS maximum extent more than growth rate.

Significance Statement

Large, long-lived storms have significant weather and climate impacts, but the ability of storm-resolving models to predict their growth around the world remains uncertain. To evaluate simulated storm growth, we track storms in satellite observations and a seasonal, storm-resolving simulation over Argentina. The simulation reproduces observed storm number, lifetime, diurnal cycle, and cloud-shield properties during storm growth but underestimates raining areas while overestimating heavy rain rates. Growth sustenance controls maximum storm size more than growth rate, with both controlled by low-level heat and moisture transport. These results suggest that storm-resolving models can reproduce many aspects of large storm cloud growth and storm–environment relationships, but rainfall prediction needs improvement.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Zhixiao Zhang, zhixiao.zhang@utah.edu

This article is included in the RELAMPAGO-CACTI special collection.

Abstract

A 6.5-month, convection-permitting simulation is conducted over Argentina covering the Remote Sensing of Electrification, Lightning, And Mesoscale/Microscale Processes with Adaptive Ground Observations and Clouds, Aerosols, and Complex Terrain Interactions (RELAMPAGO-CACTI) field campaign and is compared with observations to evaluate mesoscale convective system (MCS) growth prediction. Observed and simulated MCSs are consistently identified, tracked, and separated into growth, mature, and decay stages using top-of-the-atmosphere infrared brightness temperature and surface rainfall. Simulated MCS number, lifetime, seasonal and diurnal cycles, and various cloud-shield characteristics including growth rate are similar to those observed. However, the simulation produces smaller rainfall areas, greater proportions of heavy rainfall, and faster system propagations. Rainfall area is significantly underestimated for long-lived MCSs but not for shorter-lived MCSs, and rain rates are always overestimated. These differences result from a combination of model and satellite retrieval biases, in which simulated MCS rain rates are shifted from light to heavy, while satellite-retrieved rainfall is too frequent relative to rain gauge estimates. However, the simulation reproduces satellite-retrieved MCS cloud-shield evolution well, supporting its usage to examine environmental controls on MCS growth. MCS initiation locations are associated with removal of convective inhibition more than maximized low-level moisture convergence or instability. Rapid growth is associated with a stronger upper-level jet (ULJ) and a deeper northwestern Argentinean low that causes a stronger northerly low-level jet (LLJ), increasing heat and moisture fluxes, low-level vertical wind shear, baroclinicity, and instability. Sustained growth corresponds to similar LLJ, baroclinicity, and instability conditions but is less sensitive to the ULJ, large-scale vertical motion, or low-level shear. Growth sustenance controls MCS maximum extent more than growth rate.

Significance Statement

Large, long-lived storms have significant weather and climate impacts, but the ability of storm-resolving models to predict their growth around the world remains uncertain. To evaluate simulated storm growth, we track storms in satellite observations and a seasonal, storm-resolving simulation over Argentina. The simulation reproduces observed storm number, lifetime, diurnal cycle, and cloud-shield properties during storm growth but underestimates raining areas while overestimating heavy rain rates. Growth sustenance controls maximum storm size more than growth rate, with both controlled by low-level heat and moisture transport. These results suggest that storm-resolving models can reproduce many aspects of large storm cloud growth and storm–environment relationships, but rainfall prediction needs improvement.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Zhixiao Zhang, zhixiao.zhang@utah.edu

This article is included in the RELAMPAGO-CACTI special collection.

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