Work

Building AI–Physics Digital Twins for Hurricanes With FourCastNet v2 and ERF

Year: 2026

Type: article

Abstract: While purely physical numerical weather models are computationally expensive, pure artificial intelligence (AI) models often struggle to respect physical conservation laws and accurately capture localized extreme events, such as rapid hurricane intensification. To address this challenge, we present a research software workflow that enables a hybrid AI–physics digital twin architecture, combining the execution speed of deep learning with the physical rigor of high-resolution numerical solvers. Our workflow utilizes global weather reanalysis data (GDAS) as initial conditions to drive the ECMWF-FourCastNet v2 machine learning framework. FourCastNet v2 rapidly generates multi-day forecast trajectories, providing time-dependent boundary conditions to force the Energy Research and Forecasting (ERF) model–an open-source atmospheric solver built on the exascale AMReX framework. Using an 8 km horizontal resolution mesh with 42 million cells, ERF computes internal convective fluid dynamics, employing Smagorinsky turbulence, Kessler microphysics, and bulk surface parameterizations. We evaluate this framework on a 3-day tracking case study of Hurricane Laura (August 2020) executed across 16 NVIDIA A100 GPUs on NERSC’s Perlmutter supercomputer, completing in 1.5 hours. While both AI-only and hybrid approaches accurately constrain storm trajectory within the NOAA track forecast cone, AI-only predictions severely underestimate storm intensification. Conversely, the hybrid AI–physics framework reasonably reproduces the rapid intensification phase, reaching maximum wind speeds of ~120 knots, broadly consistent with IBTrACS observations. This work highlights the power of coupling AI boundary forcing with exascale physics solvers for high-fidelity extreme weather forecasting. Keywords Digital Twins, AI–Physics, Hurricanes, GDAS, IBTrACS, FourCastNet v2, ERF, AMReX

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.23327490

Authors: Natarajan, Mahesh, Lattanzi, Aaron, Almgren, Ann, Sexton, Jean, Zhang, Weiqun, O'Brien, Travis, Collins, William

Institutions: Lawrence Berkeley National Laboratory, Indiana University, University of California, Berkeley

Topic: Meteorological Phenomena and Simulations

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Climate action

Open Access status: green

OpenAlex ID: W7221765136

FWCI:

Cites:

Cited by:

Related to: