Refining Infrared-Only Rainfall Estimation with Deep Learning

Matthieu Meignin<sup>a</sup>, Nicolas Viltard<sup>a</sup>, Laurent Barthes<sup>a</sup>, Cécile Mallet<sup>a</sup>

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

Accurate precipitation estimation is crucial for applications such as hydrological modeling, climate research, and disaster management. In areas with sparse ground-based measurements—like oceans and remote regions—satellite-derived precipitation estimates play a key role. However, improving the accuracy of these estimates remains a challenge, especially when relying solely on infrared (IR) satellite data. While microwave (MW) data is often preferred due to its strong correlation with precipitation[1], IR data offers superior spatial and temporal coverage, making it essential for global observations.

This study explores the application of machine learning techniques to enhance IR-based precipitation estimates. Specifically, We use a U-Net, a convolutional neural network well-suited for capturing spatial patterns, to refine precipitation estimates based solely on IR imagery[2]. Using data from the Meteosat Second Generation (MSG) satellite, we develop a model that enhances precipitation retrieval from IR imagery alone.

Our approach leverages IR brightness temperature data from three IR channels (8.7, 10.8, and 12.0 µm). These channels capture a range of thermal emissions primarily from cloud tops, helping the model improve precipitation rate estimation[3]. To train and validate the model, we use a 13-year dataset, co-locating satellite IR data with a high-quality radar mosaic from Météo-France, corrected with gauge data for improved accuracy. This long-term dataset captures a wide range of weather conditions, making it ideal for robust model training.

Initial results suggest that the U-Net model enhances precipitation estimation by capturing spatial patterns more effectively, despite the known limitations of IR data. To evaluate performance, we use specialized metrics that consider precipitation intensity and spatial distribution, ensuring a thorough assessment of the model's accuracy.

These findings highlight the potential of deep learning to enhance satellite-derived precipitation estimates from IR data. Future work will focus on integrating microwave and IR data to further improve accuracy and consistency. Additionally, we aim to explore advanced deep learning architectures designed specifically for precipitation estimation, with the goal of optimizing satellite-based rainfall retrieval models.

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