2022 Fiscal Year Research-status Report
Data-driven Seasonal Hydrologic Prediction Using Earth Observing Satellites
| Project/Area Number |
18KK0117
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| Research Institution | The University of Tokyo |
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Principal Investigator |
金 炯俊 東京大学, 生産技術研究所, 特任准教授 (70635218)
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| Co-Investigator(Kenkyū-buntansha) |
渡部 哲史 京都大学, 防災研究所, 特定准教授 (20633845)
内海 信幸 京都先端科学大学, ナガモリアクチュエータ研究所, 助教 (60594752)
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| Project Period (FY) |
2018-10-09 – 2024-03-31
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| Keywords | GPM / Multi-task learning / Precipitation |
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| Outline of Annual Research Achievements |
Timely prediction of flood and drought greatly minimize the related losses. It requires precise precipitation estimates as a predictor of terrestrial processes across spatiotemporal scales. In this fiscal year, we proposed a novel precipitation retrieval framework in which regression and classification tasks are simultaneously trained using multi-task learning approach. Satellite-based precipitation estimations provide frequent, large-scale measurements. Recently, deep learning has shown significant potential for improving estimation accuracy. In this project, we designed a novel network architecture and loss function to maximize the benefits of multi-task learning. The proposed multi-task (i.e., two-task) model successfully achieved a better performance than the conventional single-task model possibly due to efficient knowledge transfer between tasks. The product intercomparison showed that our product outperformed existing products in rain rate retrieval and also yielded better skills in the rain/no-rain retrieval task.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
In this fiscal year, we developed a machine learning based satellite precipitation estimation framework and to investigate how different components which consist of the framework, such as preprocessing, network structure, and loss function, contribute to and interplay for the estimation skill. The followings are implementations: 1) to hybridize physics guide concept to the inference framework based on physical system of precipitation and 2) to enhance typical structure of multi-task learning (MTL) to increase the performance. As we planed to exploit various earth observing satellite missions for a data-driven simulation framework, getting a better quality of precipitation data is indispensible. Therefore, the progress in the fiscal year would be a very important part of the entire research in propsed project, and we evaluate the current status is on the plan and it has proceeded smoothly.
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| Strategy for Future Research Activity |
This is the final year which has been extended. Becuase of the pandemic situation, we could not have active exchanges among international counterparts during the project years. In next fiscal year, we will have an international workshop on applications of satellite remote sensing for global water cycle, and actively participate international meetings and conference.
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| Causes of Carryover |
We will continute the research to wrap up the results from this fiscal year into a high-profile scientific journal. Also, we are planning to have an international workshop.
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