works on

From the 1 of 8 linked papers with an AI index.

most citedAIMIP Phase 1: systematic evaluations of AI weather and climate models

1 citations · 1 across the 2 of their papers we have counts for

collaborators

8 papers

physics.ao-ph2026

FESOM2-JAX v1.0: a differentiable shadow of the ocean-sea-ice model FESOM2, cast onto GPUs

Nikolay V. Koldunov, Sergey Danilov, Suvarchal Cheedela +9

We present FESOM2-JAX, a Python re-implementation of the Finite-volumE Sea ice-Ocean Model (FESOM2) in JAX. The model retains the unstructured-mesh, cell-vertex finite-volume formu…

physics.ao-ph20261 cited

AIMIP Phase 1: systematic evaluations of AI weather and climate models

Brian Henn, Christopher S. Bretherton, Nikolay Koldunov +18

The paper introduces AIMIP Phase 1, an intercomparison framework for AI‑based weather and climate models that evaluates their ability to simulate historical atmospheric conditions…

physics.ao-ph2026

Machine learning is revolutionizing weather forecasting -- the next step is a change in how we work

Peter Dueben, Peter Bauer, Oliver Fuhrer +2

Following the success of machine learning in producing weather predictions with competitive skill compared to complex traditional systems, this article shifts attention from foreca…

physics.ao-ph2026

CMIP-Forge: An Agentic System that Retrieves, Computes, and Self-Reviews Climate Science

Dmitrii Pantiukhin, Boris Shapkin, Ivan Kuznetsov +2

The Coupled Model Intercomparison Project Phase 6 (CMIP6) has generated thousands of peer-reviewed publications documenting model configurations, evaluation procedures, emergent co…

physics.ao-ph2026

An Ocean Model Ported by a Large Language Model: Experience and Lessons from FESOM2 (Fortran to C to C++/Kokkos)

Nikolay V. Koldunov, Suvarchal K. Cheedela, Sergey Danilov +3

Large language models (LLMs) can translate and modify source code, and have been shown to do so for codes of different complexity. Whether they can port a complete, production geop…

cs.AI2026

A Hierarchical Multi-Agent System for Autonomous Discovery in Geoscientific Data Archives

Dmitrii Pantiukhin, Ivan Kuznetsov, Boris Shapkin +3

The rapid accumulation of Earth science data has created a significant scalability challenge; while repositories like PANGAEA host vast collections of datasets, citation metrics in…