A data-driven method for the stochastic parametrisation of subgrid-scale tropical convective area fraction

TL;DR

This paper develops data-driven stochastic models to parameterise subgrid-scale tropical convection, capturing its statistical behavior conditioned on large-scale atmospheric states, and demonstrates their transferability across different geographic locations.

Contribution

Introduces two novel stochastic approaches for tropical convection parametrisation based on observational data, enabling transferability between different locations and regimes.

Findings

Models accurately reproduce observed convective statistics.

Models trained at one location can generate realistic activity at another.

Approaches work across diverse atmospheric regimes.

Abstract

Observations of tropical convection from precipitation radar and the concurring large-scale atmospheric state at two locations (Darwin and Kwajalein) are used to establish effective stochastic models to parameterise subgrid-scale tropical convective activity. Two approaches are presented which rely on the assumption that tropical convection induces a stationary equilibrium distribution. In the first approach we parameterise convection variables such as convective area fraction as an instantaneous random realisation conditioned on the large-scale vertical velocities according to a probability density function estimated from the observations. In the second approach convection variables are generated in a Markov process conditioned on the large-scale vertical velocity, allowing for non-trivial temporal correlations. Despite the different prevalent atmospheric and oceanic regimes at the two locations, with Kwajalein being exposed to a purely oceanic weather regime and Darwin exhibiting land-sea interaction, we establish that the empirical measure for the convective variables conditioned on large-scale mid-level vertical velocities for the two locations are close. This allows us to train the stochastic models at one location and then generate time series of convective activity at the other location. The proposed stochastic subgrid-scale models adequately reproduce the statistics of the observed convective variables and we discuss how they may be used in future scale-independent mass-flux convection parameterisations.