This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Global rainfall maps are built from observations collected by multiple satellites, but they have an important limitation. The satellites that provide these measurements cannot see the whole Earth at once.
As a result, some regions of these maps can have no valid observations. Current methods try to fill these gaps by estimating precipitation values from observations made before and after the missing period. However, these methods may not fully capture how precipitation patterns change over time, resulting in inconsistencies in the maps.
To address this challenge, researchers led by assistant professor Daiko Kishikawa from the Institute for Advanced Academic Research/Center for Environmental Remote Sensing, Chiba University, Japan, treated the problem as a type of video reconstruction task. In this approach, the team estimates missing parts of a sequence using information from surrounding frames. The research team, which included Dr.
Yuka Muto from the Center for Environmental Remote Sensing, Chiba University, and Dr. Shunji Kotsuki from the Institute for Advanced Academic Research/Center for Environmental Remote Sensing/Research Institute of Disaster Medicine, Chiba University, proposed a machine learning approach that learns how precipitation patterns change across both space and time and estimates how rainfall would be distributed in the missing areas. This study was published in the journal Earth and Space Science on Sept. 1, 2026.
"Instead of relying on manually designed formulas, our method treats precipitation map sequences as video-like data and repairs missing sections by using information from both nearby time frames and the surrounding regions with additional information," explains Kishikawa. The approach uses a three-dimensional (3D) U-Net, a convolutional neural network architecture commonly used for image segmentation, to capture precipitation patterns across space and time. The U-Net is combined with a 3D condition encoder that processes additional information, including infrared satellite images, geographic coordinates and time, to help reconstruct complete precipitation maps.
Their aim was to reconstruct missing observations in the Global Satellite Mapping of Precipitation (GSMaP), produced by the Japan Aerospace Exploration Agency. To train the model, the researchers used hourly precipitation data from the ERA5 reanalysis data set produced by the European Centre for Medium-Range Weather Forecasts, covering the period from 2020 to 2023. ERA5 provides complete precipitation maps, so the researchers used the locations of missing observations in GSMaP to remove the corresponding areas from the ERA5 maps.
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