Author

D. Balwada

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Open access Sep 2026

Design and Implementation of a Data‐Driven Parameterization for Mesoscale Eddy‐Induced Transport: Idealized Two‐Layer Configurations

Mesoscale eddies contribute to ocean transport and are a major sink of available potential energy (APE). When unresolved or only partially resolved, their effects must be parameterized to simulate a realistic large‐scale state. Traditionally, this is achieved via the Gent‐McWilliams (GM) parameterization, which represe...

D. Balwada, Pavel Perezhogin, A. Adcroft et al. · Journal of Advances in Model... · 0 citations

#software testing Preprint Oct 2026

legoESM: a modular, differentiable, multiscale, AI-ready Earth system model built with AI agents

Earth system models (ESMs) have grown tremendously in realism, yet key uncertainties persist in the climate response to greenhouse-gas forcing, particularly due to cloud radiative feedbacks. In addition, their software architecture was not designed for accelerator hardware or modern artificial intelligence (AI). Here w...

Pierre Gentine, D. Balwada, Aytaç Paçal et al. · 0 citations

Review Open access Sep 2026

Deep Learning to Infer Ocean Dynamics.

Recent progress in applying deep learning to ocean dynamics is reviewed, including spatiotemporal interpolation of satellite and in situ data, estimation of unobserved ocean variables, neural data assimilation, ocean forecasting, and the development of eddy parameterizations for hybrid models.

G. Manucharyan, Scott A. Martin, D. Balwada et al. · Annual Review of Marine Scie... · 0 citations