ML4WX

Climate analysis & attribution

Regional Variability in the Environmental Controls of Precipitation Regimes in the Tropics

Samson Hagos, Zhe Feng, Sheng‐Lun Tai, Jingyi Chen

Journal of Geophysical Research Atmospheres ·

PaperPDF

In one sentence. A simple ML model links PW, CIN and wind shear to tropical rain-regime transitions, with a PW above 60 mm threshold, proposed as a convection trigger.

Abstract

Abstract The environmental factors that control precipitation regimes in Boreal winter rainfall in the tropics and their regional variabilities are examined using a simple statistical analysis and a machine learning (ML) model. Radar‐derived precipitation from field campaigns at Darwin Australia, Manaus Brazil, and the Equatorial Indian Ocean, along with the corresponding large‐scale environmental variables from ERA5 reanalysis, are used. The dependence of marginal distributions of frequencies of these regimes on five environmental variables are calculated. The variables are hourly column integrated precipitable water (PW), convective available potential energy, convective inhibition (CIN), and lower and upper tropospheric wind shear. The simple ML model that predicts the probability of transition from suppressed to an active regime as a function of the environmental variables is designed and optimized for a potential application as a trigger function for convection parameterizations. To the first order the analysis shows an abrupt increase in the probability of an active regime near PW > 60 mm and CIN of <100 J kg−1. The key differences in the frequencies of active regimes among the regions are found to be related to the fact that over Darwin there is strong variability in PW while CIN is generally low. Over Amazon, on the other hand, both PW and CIN are quite variable. Over the Dynamics of the Madden‐Julian Oscillation domain the comparatively frequent low PW is compensated for by consistently low CIN resulting in a moderate frequency of an active regime.

Labels

Topic
Climate analysis & attribution, Hybrid physics-ML & parameterization
Type
New model or method
Method
Classical ML (trees, GP, linear, kernels)
Domain
Regional
Data
ERA5 / reanalysis, Station / in-situ
Status
Research
Identifiers
DOI:10.1029/2023jd038927 OpenAlex:W4386525961
Added
2026-10-22

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