arXiv AI
Patch-Based Deep Kernel Learning for Data Assimilation and Bias Correction with Extremely Sparse Observations
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arXiv AI
Oct 2
Benchmarking Generative Models for Weather Data Assimilation on Real Station Observations
This study introduces the first controlled benchmark of generative models for weather data assimilation using real station observations from 11,849 NOAA MADIS stations across the U.S. It evaluates key design choices—diffusion vs. flow matching, pixel vs. latent-space formulations, and inference-time conditioning strategies—against a classical 3D-Var baseline. The benchmark finds that learned generative priors and full-gradient guidance improve RMSE over ERA5, while other design variations offer minimal benefit, especially under sparse observation conditions.
arXiv Machine Learning
Oct 8
Benchmarking Generative Models for Near-Surface Data Assimilation on Real Station Observations
arXiv:2610.00728v2 Announce Type: replace Abstract: Weather reanalysis products rely on computationally intensive numerical weather predictions followed by data assimilation that corrects the forecas...
arXiv AI
Jul 22
Incomplete Observations Boost Evolutionary Performance in Ocean Modeling
arXiv:2607. 19147v1 Announce Type: cross Abstract: Data-driven methods have revolutionized ocean modeling, yet current approaches rely heavily on complete reanalysis datasets, imposing computational constraints and limiting model performance to that of the training data.
arXiv Machine Learning
Jun 9
Observation-driven correction of numerical weather prediction for marine winds
arXiv:2512. 03606v2 Announce Type: replace Abstract: Accurate marine wind forecasts are essential for safe navigation, ship routing, and energy operations, yet they remain challenging because observations over the ocean are sparse, heterogeneous, and temporally variable.
arXiv AI
Oct 23
WindINR: Latent-State INR for Fast Local Wind Query and Correction in Complex Terrain
WindINR is a latent‑state implicit neural representation that enables fast, continuous high‑resolution local wind queries and corrections in complex terrain using sparse observations. It decouples reusable representation learning from sample‑specific latent correction, training a privileged encoder to infer latent states from high‑resolution data and a deployable predictor to estimate initial states from inference‑time inputs. During inference, only the low‑dimensional latent state is updated via a regularized objective, yielding a 14‑fold speedup over full‑network fine‑tuning while maintaining continuous spatial queries.
arXiv Machine Learning
Jun 17
Amortized Probabilistic Retrieval of Atmospheric CO2 from OCO-2 Spectra Using Deep Learning with Laplace Approximations and Normalizing Flows
arXiv:2606. 17413v1 Announce Type: new Abstract: Space-based monitoring of atmospheric carbon dioxide (CO2) is essential for constraining the global carbon budget.