LATMOS-IPSL, Université Paris-Saclay, UVSQ, CNRS, 78280, Guyancourt, France *Météo-France, Avenue Coriolis, Toulouse Abstract-- Retrieval of rain from Passive Microwave from about 52,000 images to about 103,000 allowing us radiometers data has been a challenge ever since the to build a training database of 70,000 images for training launch of the first Defense Meteorological Satellite and 33,000 images for validation. Enormous progress has been years 2014 to 2018 and a few months from 2020 and made since the launch of the Tropical Rainfall 2021 are used but the whole year 2019 was kept separate Measuring Mission (TRMM) in 1997 but until for the performance assessment (test) and most results recently the data were processed pixel-by-pixel or presented hereafter are computed for that year. Deep large database is meant to dampen the effects of learning has obtained remarkable improvement in seasonal and interannual variability of rain. the computer vision field, and offers a whole new Second, DRAIN retrieves now a set of 99 quantiles way to tackle the rain retrieval problem. The Global instead of a simple averaged rain rate as in [1]. These Precipitation Measurement (GPM) Core satellite quantiles represent the probability that the rain rate is carries similarly to TRMM, a passive microwave below a certain threshold.

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 (feature-based) have shown their limitations for this issue due to the intermittent and irregular nature of precipitation. In this study, we present a novel variant of the Dynamic time warping method quantifying the dissimilarity between two rainfall time series based on shapes comparisons, for clustering annual time series recorded at daily scale. This shape based approach considers the whole information (variability, trends and intermittency). We further labeled each cluster using a feature-based approach. While testing the proposed approach on the time series of Paris Montsouris, we found that the precipitation variability increased over the years in Paris area.