Generative REconstruction (GRE) is introduced, a 5 sequential assimilation framework that replaces prescribed covariances with a generative prior learned from satellite-era grid-ded fields that encodes multivariate spatial structure including heavy-tailed extremes.
Satellite sea surface temperature (SST) observations are frequently obscured by cloud cover, creating large gaps that must be reconstructed for many oceanographic and climate applications. Because multiple high-resolution SST fields may be consistent with the same sparse observations, this reconstruction problem is inh...
Grega Rovšček, M. Ličer, A. Barth et al.· Geoscientific Model Developm...· 0 citations
Local heatwave hazard depends on fine-scale air temperature, but ground stations are sparse and reanalysis products such as ERA5 cannot resolve the terrain and land-surface contrasts that shape real heat exposure. We present a conditional diffusion emulator for high-resolution 2-m temperature downscaling, conditioned o...
Anirudh Avireddy, Manmeet Singh, Shivanshi Singh et al.· 0 citations
This work forms this reconstruction problem as generative atmospheric super-resolution and introduces composable observation interfaces for conditioning a single pretrained 13-variable atmospheric diffusion model without retraining the underlying model.
Yang Xu, Dibyajyoti Chakraborty, Hai-Wen Guan et al.· 0 citations
Results indicate that cross-attention conditioning offers advantages over simple concatenation for probabilistic precipitation downscaling, and that pre-trained foundation model representations may offer benefits in data-limited settings.
Victor Nascimento Ribeiro, Jorge Guevara, J. Moraga et al.· 0 citations
Localized extreme precipitation is a major trigger of urban flash floods and landslides, yet producing nowcasts that combine fine spatial detail with probabilistic uncertainty remains challenging. Here we introduce exPreCast-ENS, a conditional residual diffusion framework that transforms the deterministic 4 km radar no...
Dohyun Park, Changhoon Song, Teng-Yuan Chang et al.· 0 citations
This work presents a single-pass model that jointly forecasts GRIDSAT-B1 infrared imagery and four ERA5 atmospheric fields out to nine hours and reward-fine-tuned against a differentiable track error derived from the predicted winds through a steering-flow calculation.
M. Zannat, Sk. Md. Masudul Ahsan· 0 citations
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