Many challenges in scientific imaging involve solving ill-posed inverse problems, where the goal is to recover spatio-temporal fields from indirect, noisy, and highly sparse measurements - often without access to ground truth data or reliable simulators. To address this challenging scenario, we present NeuralDMD, an in...
Ali SaraerToosi, Renbo Tu, Esther Y. H. Lin et al.· 0 citations
Weather prediction is undergoing an important shift, with data-driven AI models outperforming leading numerical weather prediction systems on average at a fraction of the computational cost. Yet, whether this success extends to extreme events remains contested. Earlier evaluations have exposed important shortcomings, b...
N. Chalumeau, X. Renard, R. Meynadier et al.· 0 citations
Global streamflow forecasting supports flood preparedness, yet data-driven models trained on reanalysis lose skill when driven by operational forecasts, increasingly so with lead time. AIFL addresses this shift with a two-stage transfer-learning recipe and a deterministic LSTM, but it feeds one forecast lead time into...
Utilities request mutual aid crews to restore power after severe storms, and requesting early secures more crews but relies on less accurate forecasts. We examined how forecast lead time affects the value of outage predictions for crew deployment, using operational forecasts from two AI weather models, GraphCast and Fo...
Mohammad Kian Golkar, Luciano Alves de Oliveira, Mohamad Alipour· 0 citations
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Njord, a probabilistic data-driven model for ocean forecasting, applicable to both global and regional domains, is introduced, combining a deep latent variable framework with a graph neural network architecture, enabling sampling each forecast step in a single forward pass.
Daniel Holmberg, Joel Oskarsson, Erik Wikingsson et al.· arXiv.org· 2 citations
Accurate long-range weather forecasting remains a major challenge for AI models, both because errors accumulate over autoregressive rollouts and because reanalysis training datasets provide limited sampling of the slow modes of variability underpinning predictability. Most AI and all numerical weather prediction models...
Scott A. Martin, Noah Brenowitz, Dale Durran et al.· 0 citations
ML climate model emulators are useful for scenario planning and adaptation, allowing for cost-efficient experimentation. Recently, the diffusion model Climate in a Bottle (cBottle) has been proposed for generation of atmospheric states compatible with boundary conditions of solar position and sea surface temperatures....
Peter Manshausen, Noah Brenowitz, Julius Berner et al.· 0 citations
Many approaches have recently been proposed to combine data assimilation with machine learning for improved forecasts with geophysical models. Here, we focus on combining advanced sequential data assimilation schemes with a generative forecasting model which accounts for complex model errors. This generative model prov...
Global reanalyses are too coarse to resolve the convective systems that produce most West African monsoon rainfall. In this work, we use U-Net based architectures to downscale daily ERA5 fields (0.25{\deg}) to IMERG V07 satellite precipitation (0.1{\deg}) over West Africa for the June to October seasons of 2000 to 2024...
David Sagna, Redouane Lguensat, Samo Diatta· 0 citations
Deep-learning models increasingly rival numerical simulation in Earth-system forecasting, yet forecast error alone cannot determine whether their learned input-output dependencies are physically consistent. Here we introduce sensitivity fidelity as an additional criterion: alongside prediction error, model responses to...
Qi Chen, Peng Zhan, Hongxing Cui et al.· 0 citations
Skillful prediction of biomass-burning activity several days in advance is important for air-quality forecasting and aerosol prediction. Two operational constraints motivate this work. First, the GBBEPx satellite fire radiative power (FRP) product used to initialize NOAA's GEFS-Aerosols is available with about a 1.5-da...
Li Zhang, Jun Wang, Isidora Jankov et al.· 0 citations
The intermittent and variable nature of precipitation makes its accurate estimation over extended domains difficult, yet its spatiotemporal structure suggests that a low-rank representation may be possible. This work represents daily precipitation over the contiguous United States (CONUS) as spatiotemporal tensors and...
Ryan Solgi, Rohan Shankar, Hugo A. Loaiciga· 0 citations
