# Enhancing IR-Based Satellite Precipitation Estimates Using Machine Learning.

Matthieu Meignin, Nicolas Viltard, Laurent Barthès, Cécile Mallet

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## To cite this version:

Matthieu Meignin, Nicolas Viltard, Laurent Barthès, Cécile Mallet. Enhancing IR-Based Satellite Precipitation Estimates Using Machine Learning. EGU General Assembly 2025, Apr 2025, Vienne, Austria. 2025, 10.5194/egusphere-egu25-6682. hal-05280694

HAL Id: hal-05280694

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Submitted on 24 Sep 2025

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EGU25-6682, updated on 24 Sep 2025
https://doi.org/10.5194/egusphere-egu25-6682
EGU General Assembly 2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

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# Enhancing IR-Based Satellite Precipitation Estimates Using Machine Learning.

Matthieu Meignin, Nicolas Viltard, Laurent Barthès, and Cécile Mallet

LATMOS-CNRS/UVSQ/UPSaclay, 11 boulevard d'Alembert, 78280 Guyancourt, France.

**Abstract:** Accurate precipitation estimation is essential for various applications, including hydrological modelling, climate studies, and disaster management. Satellite-derived precipitation estimates are particularly valuable in regions with limited ground-based measurements, such as oceans and remote areas. However, challenges persist in improving the accuracy of these estimates, especially when relying solely on infrared (IR) satellite data. While microwave (MW) data has traditionally been favoured for precipitation estimation due to its strong correlation with precipitation[1], infrared (IR) data has become increasingly important, offering superior spatio-temporal coverage and resolution, essential for global observations.

This study explores the application of machine learning techniques to enhance IR-based precipitation estimates. Specifically, we employ U-Net, a convolutional neural network, known for its ability to capture spatial dependencies and local patterns in data, making it ideal for improving the spatial resolution and accuracy of precipitation estimates using only IR channels[2]. We leverage IR data from the MSG satellite to develop a model that enhances precipitation extraction from IR imagery alone

To achieve this, we utilise a database of IR brightness temperatures from three distinct IR channels (87, 108, and 120 µm). These channels capture a broad spectrum of thermal emissions, from cloud tops to deeper atmospheric layers, enabling the model to estimate precipitation rates more effectively[3]. These data are co-located with a radar mosaic from Météo-France, gauge-corrected for improved accuracy, which serves as a reference to evaluate the performance of the U-Net model and ensure alignment with actual measurements.

Our dataset spans 13 years, providing a diverse range of scenarios, including varying weather patterns and seasonal fluctuations. Initial results indicate that the U-Net model enhances precipitation estimation by accurately capturing spatial patterns, even with the inherent limitations of IR channels. In evaluating this approach, we consider a range of metrics specifically designed to address the unique characteristics of precipitation, such as intensity and spatial distribution. This targeted evaluation ensures a comprehensive assessment of the model's ability to account for the variability and intensity of precipitation, key challenges in accurate satellite-based Precipitation estimation.

These promising results highlight the potential of deep learning techniques to improve satellite-
derived precipitation estimates from IR data. Looking ahead, we will explore the integration of
microwave and IR satellite data to further refine the consistency and accuracy of these estimates.
Additionally, we plan to investigate cutting-edge deep learning architectures tailored to this
specific use case, aiming to optimise model performance and address the complexities of satellite-
based precipitation retrieval.

References:

[1] Viltard, N., Sambath, V., Lepetit, P., Martini, A., Barthes, L., & Mallet, C. (2023). Evaluation of DRAIN, a deep-learning approach to rain retrieval from GPM passive microwave radiometer. *IEEE Transactions on Geoscience and Remote Sensing*. https://doi.org/10.1109/TGRS.2023.3293932

[2] Wang, C., Tang, G., Xiong, W., Ma, Z., & Zhu, S. (2021). Infrared precipitation estimation using convolutional neural network for FengYun satellites. *Journal of Hydrology*, 603(C), 127113.
https://doi.org/10.1016/j.jhydrol.2021.127113

[3] Sadeghi, Mojtaba, Nguyen, Phu, Hsu, Kuolin, & Sorooshian, Soroosh (2020). Improving near real-time precipitation estimation using a U-Net convolutional neural network and geographical information. *Environmental Modelling and Software*, 134(C).
https://doi.org/10.1016/j.envsoft.2020.104856.