Berthomier, L.; Perier, L. Espresso: A Global Deep Learning Model to Estimate Precipitation from Satellite Observations. Meteorology2023, 2, 421-444.
Berthomier, L.; Perier, L. Espresso: A Global Deep Learning Model to Estimate Precipitation from Satellite Observations. Meteorology 2023, 2, 421-444.
Berthomier, L.; Perier, L. Espresso: A Global Deep Learning Model to Estimate Precipitation from Satellite Observations. Meteorology2023, 2, 421-444.
Berthomier, L.; Perier, L. Espresso: A Global Deep Learning Model to Estimate Precipitation from Satellite Observations. Meteorology 2023, 2, 421-444.
Abstract
Estimating precipitation is of critical importance to climate systems and decision-making processes. This paper presents Espresso, a deep learning model designed for estimating precipitation from satellite observations on a global scale. Conventional methods, like ground-based radars, are limited in terms of spatial coverage. Satellite observations, on the other hand, allow global coverage. Combined with deep learning methods these observations offer the opportunity to address the challenge of estimating precicpation on a global scale. This research paper presents the development of a deep learning model using geostationary satellite data as input and generating instantaneous rainfall rates, calibrated using data from the Global Precipitation Measurement Core Observatory (GPMCO). The performance impact of various input data configurations on Espresso was investigated. These configurations include a sequence of four images from geostationary satellites and the optimal selection of channels. Additional descriptive features were explored to enhance the model’s robustness for global aplications. When evaluated against the GPMCO test set, Espresso demonstrated highly accurate precipitation estimation, especially within equatorial regions. A comparison against six other operational products using multiple metrics indicated its competitive performance. The model’s superior storm localization and intensity estimation were further confirmed through visual comparisons in case studies. Espresso has been incorporated as an operational product at Météo-France, delivering high-quality, real-time global precipitation estimates every 30 minutes.
Keywords
Meteorology; Precipitations; Remote-sensing; Deep Learning
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
