1 citations · 1 across the 2 of their papers we have counts for
4 papers
Generative and deterministic deep learning models comparison for fine-scale precipitation retrievals from infrared brightness temperature
Matthieu Meignin, Cécile Mallet, Nicolas Viltard
Accurate precipitation estimation at fine spatial scales is critical for hydrology, agriculture, and climate studies. Infrared brightness temperatures from geostationary satellites…
Unsupervised Domain Adaptation for Enhanced Radiometer Image Precipitation Estimation using Conditional Flow Matching
Victor Enescu, Assaad Zeghina, Matthieu Meignin +2
Deep generative networks have recently achieved unprecedented performance in precise image and video editing using sophisticated textual prompts. However, the effectiveness of such…
Evaluation of drain, a deep-learning approach to rain retrieval from gpm passive microwave radiometer
Nicolas Viltard, Vibolroth Sambath, Pierre Lepetit +3
Retrieval of rain from Passive Microwave radiometers data has been a challenge ever since the launch of the first Defense Meteorological Satellite Program in the late 70s. Enormous…
Study of the impact of climate change on precipitation in Paris area using method based on iterative multiscale dynamic time warping (IMS-DTW)
Mohamed Djallel Dilmi, Laurent Barthès, Cécile Mallet +1
Studying the impact of climate change on precipitation is constrained by finding a way to evaluate the evolution of precipitation variability over time. Classical approaches (featu…