An object-based method to study the life cycle of mesoscale convective systems and their environment from cloud-resolving AROME-France simulations

The aggregation of storms cells into a single entity generally results in mesoscale convective systems (MCSs) that extend over hundreds of kilometres and frequently produce severe hazards over large areas. The ambient conditions associated with the life cycle of MCSs in the central United States have been studied for more than twenty years, with the most impactful ingredients including potential instability, low-level moist-air advection and deep layer wind shear. However, the way in which the mesoscale environment influences the strengthening or dissipation of MCSs has received little attention in western Europe, although it could provide guidelines for MCS nowcasting. This lack motivates our study based on more than 150 simulated MCSs detected and tracked in mainland France from the outputs of the AROME-France convection-allowing numerical prediction model. MCS objects are detected using a convolutional neural network with simulated reflectivity and infrared brightness temperature images as input. Statistically, the life cycle of simulated MCSs is consistent with that of observed MCSs tracked in radar and satellite images from the same period, showing a classical three-stage pattern with development up to around 30 % of total lifetime, maturity between 30 and 60 % of lifetime and weakening thereafter. Next, we use two complementary methods to quantify the changes that the surrounding 3D environment undergoes during the life cycle of the simulated MCSs. In the first method, ambient variables are defined by averaging the AROME fields in a certain area around the objects. The second method introduces an original ring-shaped composite map approach in which the fields around the objects are projected onto a standard polar grid, enabling the surrounding environment of MCSs of different shapes or sizes to be examined statistically. The main results indicate a monotonic evolution of ambient variables over the life cycle, with the MUCAPE showing the most pronounced and significant decrease linked to drying and cooling at low level, ahead of the MCSs. Changes in upper-level wind are noted but their impact on the MCSs is less clear. At the end, our study highlights some of the ingredients responsible for MCS maintenance in western Europe, providing guidelines for the development of an object-based MCS nowcasting tool.

Authors

Institutions

Publication Details

Published
2025-08-08
DOI
https://doi.org/10.5194/ecss2025-5
Citations
2
Primary Topic
Meteorological Phenomena and Simulations
Type
article
Field-Weighted Citation Impact
2.60
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

An object-based method to study the life cycle of mesoscale convective systems and their environment from cloud-resolving AROME-France simulations

Lucie Rottner, Gabriel Arnould, Jean‐Marc Moisselin, Thibaut Montmerle
2 citations
Meteorological Phenomena and Simulations
2.60
article

An object-based method to study the life cycle of mesoscale convective systems and their environment from cloud-resolving AROME-France simulations

Lucie Rottner, Gabriel Arnould, Jean‐Marc Moisselin, Thibaut Montmerle
article en
2 citations

Abstract

The aggregation of storms cells into a single entity generally results in mesoscale convective systems (MCSs) that extend over hundreds of kilometres and frequently produce severe hazards over large areas. The ambient conditions associated with the life cycle of MCSs in the central United States have been studied for more than twenty years, with the most impactful ingredients including potential instability, low-level moist-air advection and deep layer wind shear. However, the way in which the mesoscale environment influences the strengthening or dissipation of MCSs has received little attention in western Europe, although it could provide guidelines for MCS nowcasting. This lack motivates our study based on more than 150 simulated MCSs detected and tracked in mainland France from the outputs of the AROME-France convection-allowing numerical prediction model. MCS objects are detected using a convolutional neural network with simulated reflectivity and infrared brightness temperature images as input. Statistically, the life cycle of simulated MCSs is consistent with that of observed MCSs tracked in radar and satellite images from the same period, showing a classical three-stage pattern with development up to around 30 % of total lifetime, maturity between 30 and 60 % of lifetime and weakening thereafter. Next, we use two complementary methods to quantify the changes that the surrounding 3D environment undergoes during the life cycle of the simulated MCSs. In the first method, ambient variables are defined by averaging the AROME fields in a certain area around the objects. The second method introduces an original ring-shaped composite map approach in which the fields around the objects are projected onto a standard polar grid, enabling the surrounding environment of MCSs of different shapes or sizes to be examined statistically. The main results indicate a monotonic evolution of ambient variables over the life cycle, with the MUCAPE showing the most pronounced and significant decrease linked to drying and cooling at low level, ahead of the MCSs. Changes in upper-level wind are noted but their impact on the MCSs is less clear. At the end, our study highlights some of the ingredients responsible for MCS maintenance in western Europe, providing guidelines for the development of an object-based MCS nowcasting tool.

Centre National de la Recherche Scientifique (FR), Météo-France (FR)
Responsible consumption and production
Openalex Percentile: Top 10%
Meteorological Phenomena and Simulations
2.60
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.