activity
20242026
collaborators

7 papers

cs.LG2026

High-Resolution Climate Projections Using Diffusion-Based Downscaling of a Lightweight Climate Emulator

Haiwen Guan, Dibyajyoti Chakraborty, Moein Darman +3

The proliferation of data-driven models in weather and climate sciences has marked a significant paradigm shift, with advanced models demonstrating exceptional skill in medium-rang…

physics.ao-ph2026

High-resolution probabilistic estimation of three-dimensional regional ocean dynamics from sparse surface observations

Niloofar Asefi, Tianning Wu, Ruoying He +1

The ocean interior regulates Earth's climate but remains sparsely observed due to limited in situ measurements, while satellite observations are restricted to the surface. We prese…

cs.LG2025

Generative forecasting with joint probability models

Patrick Wyrod, Ashesh Chattopadhyay, Daniele Venturi

Chaotic dynamical systems exhibit strong sensitivity to initial conditions and often contain unresolved multiscale processes, making deterministic forecasting fundamentally limited…

physics.flu-dyn2025

Lazy Diffusion: Mitigating spectral collapse in generative diffusion-based stable autoregressive emulation of turbulent flows

Anish Sambamurthy, Ashesh Chattopadhyay

Turbulent flows posses broadband, power-law spectra in which multiscale interactions couple high-wavenumber fluctuations to large-scale dynamics. Although diffusion-based generativ…

cs.LG2025

LUCIE-3D: A three-dimensional climate emulator for forced responses

Haiwen Guan, Troy Arcomano, Ashesh Chattopadhyay +1

We introduce LUCIE-3D, a lightweight three-dimensional climate emulator designed to capture the vertical structure of the atmosphere, respond to climate change forcings, and mainta…

cs.LG2025

LUCIE: A Lightweight Uncoupled ClImate Emulator with long-term stability and physical consistency for O(1000)-member ensembles

Haiwen Guan, Troy Arcomano, Ashesh Chattopadhyay +1

We present a lightweight, easy-to-train, low-resolution, fully data-driven climate emulator, LUCIE, that can be trained on as low as years of -hourly ERA5 data. Unlike most…