Global Extreme Heat Forecasting Using Neural Weather Models in: Artificial Intelligence for the
Earth Systems Volume 2 Issue 1 (2023) Jump to Content Logo Logo Logo Logo Logo Logo …

In climate models, subgrid parameterizations of convection and clouds are one of the main
causes of the biases in precipitation and atmospheric circulation simulations. In recent …

Most machine learning applications in Earth system modeling currently rely on gradient‐
based supervised learning. This imposes stringent constraints on the nature of the data used …

Accelerated progress in climate modeling is urgently needed for proactive and effective
climate change adaptation. The central challenge lies in accurately representing processes …

Accelerating progress in climate modeling is urgent for proactive and effective climate
change adaptation. The central challenge lies in accurately representing processes that are …

A modification to the mixing length formulation in a planetary boundary layer (PBL) scheme
is introduced to improve the intensity forecast of tropical cyclones (TCs) in a basin-scale …

A minimalist model of microphysical properties in cloudy Rayleigh-Bénard convection is
developed based on mass and number balances for cloud droplets growing by vapor …

Two‐fluid modelling has recently emerged as a promising approach to representing
cumulus convection in weather and climate models. This study applies the two‐fluid model …

We demonstrate that an extended eddy‐diffusivity mass‐flux (EDMF) scheme can be used
as a unified parameterization of subgrid‐scale turbulence and convection across a range of …

Chaotic systems, such as turbulent flows, are ubiquitous in science and engineering.
However, their study remains a challenge due to the large range scales, and the strong …