| 研究課題/領域番号 |
21K20443
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| 研究機関 | 東京大学 |
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研究代表者 |
尹 高虹 東京大学, 生産技術研究所, 特任研究員 (00906282)
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| 研究期間 (年度) |
2021-08-30 – 2023-03-31
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| キーワード | GRACE / TWS / Downscaling / Deep Learning / Flood / Drought |
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| 研究実績の概要 |
(1) All required satellite-based and model-based data has been prepared. (2) A synthetic experiment has been set up. Synthetic TWS was generated by upscaling model-based TWS to GRACE mascon scale, afterward, a observational error was added to the model-based TWS. (3) Synthetic experiment has been tested using Long short-term memory (LSTM) deep learning method. (4) Synthetic results has been evaluated against synthetic truth, and results demonstrated the feasibility of the proposed method across the globe.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
1: 当初の計画以上に進展している
理由
The progress of the project is more smoothly than initially planned. As I am familiar with the used satellite-based and model-based data sets, data preparation process was faster than expected. An synthetic experiment was conducted based on plan. The learning process of deep learning algorithms goes well, and the computational efficiency of the selected method, i.e., LSTM, was higher than expected. Therefore, the synthetic experiment was almost finished at a global scale.
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| 今後の研究の推進方策 |
For the future work, following work is expected to be accomplished:
(1) More investigation on the synthetic models in order to select the best combination of predictors, the best deep learning model, as well as the modeling strategy.
(2) Conduct downscaling experiment using real GRACE and GRACE-FO data based on the selected method from step 1.
(3) Assimilating the downscaled TWS into land surface models for flood and drought monitoring.
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| 次年度使用額が生じた理由 |
For the next year, article costs including official goods, computational spent will be needed. Most important results will come out in the next fiscal year. Therefore, publication fee, conference attendee and traveling fee will be required in the next year.
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