Machine Learning Global Simulation of Nonlocal Gravity Wave Propagation
Authors:
Aman Gupta, Aditi Sheshadri, Sujit Roy, Vishal Gaur, Manil Maskey, and Rahul Ramachandran
Abstract:
Global climate models typically operate at a grid resolution of hundreds of kilometers and fail to resolve atmospheric mesoscale processes, e.g., clouds, precipitation, and gravity waves (GWs). Model representation of these processes and their sources is essential to the global circulation and planetary energy budget, but subgrid scale contributions from these processes are often only approximately represented in models using parameterizations. These parameterizations are subject to approximations and idealizations, which limit their capability and accuracy.
The most drastic of these approximations is the “single-column approximation” which completely neglects the horizontal evolution of these processes, resulting in key biases in current climate models. With a focus on atmospheric GWs, we present the first-ever global simulation of atmospheric GW fluxes using machine learning (ML) models trained on the WINDSET dataset to emulate global GW emulation in the atmosphere, as an alternative to traditional single-column parameterizations. Using an Attention U-Net-based architecture trained on globally resolved GW momentum fluxes, we illustrate the importance and effectiveness of global nonlocality, when simulating GWs using data-driven schemes.
Plain Language Summary:
This study presents data on atmospheric gravity waves (GWs) obtained from a high-resolution global simulation conducted by the European Centre for Medium-Range Weather Forecasts (ECMWF). The simulation, referred to as a “nature run,” was performed at a 1 km horizontal resolution over a four-month period. The dataset includes momentum fluxes associated with GWs, which are crucial for understanding their impact on atmospheric dynamics. By providing detailed information on GW momentum fluxes, this dataset aims to enhance the representation of GWs in weather and climate models, leading to improved predictions and a better grasp of atmospheric processes.