| 研究課題/領域番号 |
21K14386
|
|---|
| 研究種目 |
若手研究
|
| 配分区分 | 基金 |
|---|
| 審査区分 |
小区分25030:防災工学関連
|
|---|
| 研究機関 | 東京大学 |
|---|
研究代表者 |
馬 文超 東京大学, 生産技術研究所, 特任研究員 (60743101)
|
|---|
| 研究期間 (年度) |
2021-04-01 – 2024-03-31
|
| 研究課題ステータス |
交付 (2021年度)
|
| 配分額 *注記 |
4,680千円 (直接経費: 3,600千円、間接経費: 1,080千円)
2023年度: 1,040千円 (直接経費: 800千円、間接経費: 240千円)
2022年度: 1,170千円 (直接経費: 900千円、間接経費: 270千円)
2021年度: 2,470千円 (直接経費: 1,900千円、間接経費: 570千円)
|
|---|
| キーワード | flood forecasting / ensemble system |
|---|
| 研究開始時の研究の概要 |
This project will develop a world-leading and computing cost-effective ensemble forecasting system for issuing quick and reliable flood warnings. We will estimate the return period of river discharge for each forecasted ensemble member, and to compare multiple sourced ensemble forecasting results.
|
| 研究実績の概要 |
To achieve the 1st year’s target, I have finished downloading historical data from the ECMWF center. After that, decoding the original data for running the land surface model, MATSIRO, and CaMa-flood is finished from 2001 to 2020. Decoding ECMWF forcing data involves spatial and temporal downscaling. Simulation of the land surface model and CaMa-flood have been smoothly carried out. Using this forcing dataset, we successfully get more than eight days of forecasting, which is more than five times longer than our previous flood forecasting achievement (39 hours by MSM, in Japan only).These historical results will contribute to analyzing historical river water depth worldwide. The automatic system is partly built and will finish construction in the 2nd year.
|
| 現在までの達成度 (区分) |
現在までの達成度 (区分)
4: 遅れている
理由
Due to the impact of the slow delivery of electronic chips and other products in the spring of 2021, the scheduled server received it at the end of July, four months later than the project expected. However, after receiving the device, the forcing data preparation and analysis proceed as scheduled.
|
| 今後の研究の推進方策 |
This year, I will finish constructing the automatic data decoding in real time to provide near-real-time flood forecasting results. Also, based on the historical flood results analysis, the return period on a global scale will be prepared. After that, the first version of high accuracy flood forecasting system is expected to be available.
|
