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20242026
most citedPhysically Consistent Global Atmospheric Data Assimilation with Machine Learning in Latent Space

5 citations · 10 across the 18 of their papers we have counts for

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physics.ao-ph2026

Neptune: An AI model for Global Ocean Subseasonal Prediction

Davide Donno, Italo Epicoco, Massimo Cafaro +8

Subseasonal-to-seasonal (S2S) forecasting is societally critical, supporting decision-making in sectors ranging from water and agricultural management to disaster risk reduction, e…

physics.ao-ph2026

Video Diffusion for Satellite-based High-Dynamical-Fidelity Precipitation (HiDFiP) Field Generation

Runze Li, Yan Xia, Yongquan Qu +6

High spatiotemporal fidelity precipitation products that accurately capture storm spatial organization, propagation, and lifecycle evolution, are essential for advancing hydrometeo…

physics.ao-ph2025

LO-SDA: Latent Optimization for Score-based Atmospheric Data Assimilation

Jing-An Sun, Hang Fan, Junchao Gong +8

Data assimilation (DA) plays a pivotal role in numerical weather prediction by systematically integrating sparse observations with model forecasts to estimate optimal atmospheric i…

physics.ao-ph2025

Align-DA: Align Score-based Atmospheric Data Assimilation with Multiple Preferences

Jing-An Sun, Hang Fan, Junchao Gong +8

Data assimilation (DA) aims to estimate the full state of a dynamical system by combining partial and noisy observations with a prior model forecast, commonly referred to as the ba…

physics.ao-ph2025★ 5 cited

Physically Consistent Global Atmospheric Data Assimilation with Machine Learning in Latent Space

Hang Fan, Lei Bai, Ben Fei +6

Data assimilation (DA) integrates observations with model forecasts to produce optimized atmospheric states, whose physical consistency is critical for stable weather forecasting a…