Publication Date:
2023-11-17
Description:
One important component of precipitating convection is the formation of convective downdrafts. They can terminate the initial updraft, affect the mean properties of the boundary layer, and cause strong winds at the surface. While the basic forcing mechanisms for downdrafts are well understood, it is difficult to formulate general relationships between updrafts, environmental conditions, and downdrafts. To better understand what controls different downdraft properties, we analyze downdrafts over tropical oceans in a global storm resolving simulation. Using a global model allows us to examine a large number of downdrafts under naturally varying environmental conditions. We analyze the various factors affecting downdrafts using three alternative methods. First, hierarchical clustering is used to examine the correlation between different downdraft, updraft, and environmental variables. Then, either random forests or multiple linear regression are used to estimate the relationships between downdraft properties and the updraft and environmental predictors. We find that these approaches yield similar results. Around 75% of the variability in downdraft mass flux and 37% of the variability in downdraft velocity are predictable. Analyzing the relative importance of our various predictors, we find that downdrafts are coupled to updrafts via the precipitation generation argument. In particular, updraft properties determine rain amount and rate, which then largely control the downdraft mass flux and, albeit to a lesser extent, the downdraft velocity. Among the environmental variables considered, only lapse rate is a valuable predictor: a more unstable environment favors a higher downdraft mass flux and a higher downdraft velocity.
Description:
Plain Language Summary:
Once a cloud begins to rain, the air inside or below the cloud can gain negative buoyancy and sink to the ground. This downward movement of air is called a downdraft. Downdrafts can end the life cycle of a cloud and also result in strong, sometimes destructive, wind gusts at the surface. The basic driving forces for downdrafts are well understood. For example, we know that evaporation of rain and the associated latent cooling of air is usually critical in causing the air to become negatively buoyant. Even though the basic driving forces are known, many interrelated processes contribute simultaneously to the strength of the downdraft, making it difficult to predict the strength of a downdraft under specific conditions. In this study, we use an atmospheric simulation whose model domain spans the globe and can explicitly resolve rain clouds. Compared to previous studies, the use of a global domain allows us to study a very large number of rain clouds, and their associated downdrafts, which form under very different, naturally varying environmental conditions. Machine learning techniques and traditional statistical methods agree on the result that the strength of the downdraft can be well predicted if we know the strength of the updraft that caused the downdraft or, even better, if we know the amount of rain that an updraft produced. Surprisingly, we have found that downdrafts can be predicted only slightly better if we also know other environmental conditions of the air surrounding the downdraft, such as the temperature and/or humidity profiles.
Description:
Key Points:
The best predictors of downdraft mass flux and velocity are rain amount and rate, respectively.
Updraft properties impact downdraft properties through their control on rain formation.
For a given rain amount and rate, environmental conditions add little skill to downdraft prediction.
Description:
Max Planck Institute for Meteorology
Description:
ARC Centre of Excellence for Climate Extremes
Description:
https://mpimet.mpg.de/en/science/modeling-with-icon/code-availability
Description:
http://hdl.handle.net/21.11116/0000-0009-A854-B
Keywords:
ddc:551.6
;
convective downdrafts
;
global storm resolving simulation
;
machine learning
;
random forest
;
multiple linear regression
Language:
English
Type:
doc-type:article
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