arXiv subjects

Clement Guilloteau

Publications and source records attributed to Clement Guilloteau.

4 recordsLinked to original sources

arXiv2026

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

High spatiotemporal fidelity precipitation products that accurately capture storm spatial organization, propagation, and lifecycle evolution, are essential for advancing hydrometeorological research and operations at regional and global scales. Satellite products offer the only near-global precipitation observations, but they still fall short of reproducing the spatiotemporal structure of ground-based references, due largely to dynamic distortions from the inhomogeneity, intermittency, and indirectness of satellite retrievals. Here we propose a video-diffusion framework for satellite-based High-Dynamical-Fidelity Precipitation (HiDFiP) field generation beyond the space-time coverage of ground-radar, using radar-rich CONUS as a testbed. The framework performs explicit spatiotemporal modeling with IMERG as the primary source and leverages four-dimensional storm-environment information from about 40 ERA5/ERA5-Land atmospheric/land fields to compensate for the temporal information deficit inherent to satellite retrievals. We introduce an extensive metric suite to assess HiDFiP dynamical fidelity in temporal-reconstruction and spatial-transfer settings against MRMS ground-radar precipitation over CONUS. Relative to IMERG and image-wise diffusion baselines, HiDFiP accurately reproduces the storm space-time spectral characteristics; storm timing, location, and directional propagation; precipitation-event episodicity and temporal structure; precipitation-system morphology and spatial organization; and storm-track kinematics and lifecycle evolution. Transferability experiments indicate that HiDFiP generalizes reasonably well to an unseen region. This work advances a video-diffusion paradigm for satellite-based high-spatiotemporal-fidelity precipitation generation and provides an algorithmic and diagnostic foundation for long-term global radar-grade precipitation records.

physics.ao-ph↗

arXiv2025

A Diffusion-Based Framework for High-Resolution Precipitation Forecasting over CONUS

Accurate precipitation forecasting is essential for hydrometeorological risk management, especially for anticipating extreme rainfall that can lead to flash flooding and infrastructure damage. This study introduces a diffusion-based deep learning (DL) framework that systematically compares three residual prediction strategies differing only in their input sources: (1) a fully data-driven model using only past observations from the Multi-Radar Multi-Sensor (MRMS) system, (2) a corrective model using only forecasts from the High-Resolution Rapid Refresh (HRRR) numerical weather prediction system, and (3) a hybrid model integrating both MRMS and selected HRRR forecast variables. By evaluating these approaches under a unified setup, we provide a clearer understanding of how each data source contributes to predictive skill over the Continental United States (CONUS). Forecasts are produced at 1-km spatial resolution, beginning with direct 1-hour predictions and extending to 12 hours using autoregressive rollouts. Performance is evaluated using both CONUS-wide and region-specific metrics that assess overall performance and skill at extreme rainfall thresholds. Across all lead times, our DL framework consistently outperforms the HRRR baseline in pixel-wise and spatiostatistical metrics. The hybrid model performs best at the shortest lead time, while the HRRR-corrective model outperforms others at longer lead times, maintaining high skill through 12 hours. To assess reliability, we incorporate calibrated uncertainty quantification tailored to the residual learning setup. These gains, particularly at longer lead times, are critical for emergency preparedness, where modest increases in forecast horizon can improve decision-making. This work advances DL-based precipitation forecasting by enhancing predictive skill, reliability, and applicability across regions.

cs.LG↗

arXiv2025

A Benchmark Dataset for Satellite-Based Estimation and Detection of Rain

Accurately tracking the global distribution and evolution of precipitation is essential for both research and operational meteorology. Satellite observations remain the only means of achieving consistent, global-scale precipitation monitoring. While machine learning has long been applied to satellite-based precipitation retrieval, the absence of a standardized benchmark dataset has hindered fair comparisons between methods and limited progress in algorithm development. To address this gap, the International Precipitation Working Group has developed SatRain, the first AI-ready benchmark dataset for satellite-based detection and estimation of rain, snow, graupel, and hail. SatRain includes multi-sensor satellite observations representative of the major platforms currently used in precipitation remote sensing, paired with high-quality reference estimates from ground-based radars corrected using rain gauge measurements. It offers a standardized evaluation protocol to enable robust and reproducible comparisons across machine learning approaches. In addition to supporting algorithm evaluation, the diversity of sensors and inclusion of time-resolved geostationary observations make SatRain a valuable foundation for developing next-generation AI models to deliver more accurate, detailed, and globally consistent precipitation estimates.

physics.ao-ph↗

arXiv2024

A Generative Diffusion Model for Probabilistic Ensembles of Precipitation Maps Conditioned on Multisensor Satellite Observations

A generative diffusion model is used to produce probabilistic ensembles of precipitation intensity maps at the 1-hour 5-km resolution. The generation is conditioned on infrared and microwave radiometric measurements from the GOES and DMSP satellites and is trained with merged ground radar and gauge data over southeastern United States. The generated precipitation maps reproduce the spatial autocovariance and other multiscale statistical properties of the gauge-radar reference fields on average. Conditioning the generation on the satellite measurements allows us to constrain the magnitude and location of each generated precipitation feature. The mean of the 128- member ensemble shows high spatial coherence with the reference fields with 0.82 linear correlation between the two. On average, the coherence between any two ensemble members is approximately the same as the coherence between any ensemble member and the ground reference, attesting that the ensemble dispersion is a proper measure of the estimation uncertainty. From the generated ensembles we can easily derive the probability of the precipitation intensity exceeding any given intensity threshold, at the 5-km resolution of the generation or at any desired aggregated resolution.

physics.ao-ph↗