A Dynamical Model for the Shallow‐to‐Deep Transition of Amazonian Moist Atmospheric Convection

Abstract The development of deep convection and the timing of storm convection initiation over land are generally poorly represented by weather and climate models, probably due to the poor representation of the interaction between shallow and deep convection. The present work aims to present a prognostic model for the shallow‐to‐deep transition of Amazonian convection in which the shallow and deep clouds are described under a unified framework. Three types of clouds are considered in the present model: shallow cumuli, cumulus congestus, and deep cumulonimbus clouds. The model is based on the idea that the shallow‐to‐deep transition of atmospheric convection is primarily controlled by the interaction between updrafts and passive cloud volumes (non‐convective cumulus cloud volumes in the decaying stage) from which moist air can be entrained in the updrafts. In this framework, the cold pools accelerate the shallow‐to‐deep transition due to a larger cloud‐updraft interaction, allowing the updrafts to be less susceptible to entrainment of dry environmental air. A dynamical model for the diurnal cycle of shallow and deep is obtained and tested against idealized large‐eddy simulations. For idealized cases of Amazonian atmospheric convection, it is shown that the model represents the transition from shallow to deep convection reasonably well, showing that the model could lead to improvements in the forecast for storm convection initiation.

Authors

Institutions

Publication Details

Journal
Journal of Geophysical Research Atmospheres
Published
2026-03-04
DOI
https://doi.org/10.1029/2025jd045128
Primary Topic
Meteorological Phenomena and Simulations
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Dynamical Model for the Shallow‐to‐Deep Transition of Amazonian Moist Atmospheric Convection

Cristian V. Vraciu
Journal of Geophysical Research Atmospheres
Meteorological Phenomena and Simulations
article

A Dynamical Model for the Shallow‐to‐Deep Transition of Amazonian Moist Atmospheric Convection

Cristian V. Vraciu
article en

Abstract

Abstract The development of deep convection and the timing of storm convection initiation over land are generally poorly represented by weather and climate models, probably due to the poor representation of the interaction between shallow and deep convection. The present work aims to present a prognostic model for the shallow‐to‐deep transition of Amazonian convection in which the shallow and deep clouds are described under a unified framework. Three types of clouds are considered in the present model: shallow cumuli, cumulus congestus, and deep cumulonimbus clouds. The model is based on the idea that the shallow‐to‐deep transition of atmospheric convection is primarily controlled by the interaction between updrafts and passive cloud volumes (non‐convective cumulus cloud volumes in the decaying stage) from which moist air can be entrained in the updrafts. In this framework, the cold pools accelerate the shallow‐to‐deep transition due to a larger cloud‐updraft interaction, allowing the updrafts to be less susceptible to entrainment of dry environmental air. A dynamical model for the diurnal cycle of shallow and deep is obtained and tested against idealized large‐eddy simulations. For idealized cases of Amazonian atmospheric convection, it is shown that the model represents the transition from shallow to deep convection reasonably well, showing that the model could lead to improvements in the forecast for storm convection initiation.

Journal of Geophysical Research AtmospheresVol. 131(5)
University of Bucharest (RO), University of Reading (GB)
Climate action
Openalex Percentile: Top 48%
Meteorological Phenomena and Simulations
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.