Land-Atmosphere-Coupled Data Assimilation: Improving Atmospheric and Hydrological Predictions by Hydrological Big Data Assimilation
| Project/Area Number |
18H01549
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Review Section |
Basic Section 22040:Hydroengineering-related
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| Research Institution | Chiba University (2019-2020)
Institute of Physical and Chemical Research (2018) |
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Principal Investigator |
Kotsuki Shunji 千葉大学, 環境リモートセンシング研究センター, 准教授 (90729229)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥17,420,000 (Direct Cost: ¥13,400,000、Indirect Cost: ¥4,020,000)
Fiscal Year 2020: ¥5,070,000 (Direct Cost: ¥3,900,000、Indirect Cost: ¥1,170,000)
Fiscal Year 2019: ¥5,070,000 (Direct Cost: ¥3,900,000、Indirect Cost: ¥1,170,000)
Fiscal Year 2018: ¥7,280,000 (Direct Cost: ¥5,600,000、Indirect Cost: ¥1,680,000)
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| Keywords | データ同化 / 気象予測 / 土壌水分 / 結合システム / 降水予測 / 水文 / 降水 / 結合同化 / 数値天気予報 / 大気陸面相互作用 / ビッグデータ / 予測 / 気象 / 衛星観測 / 全球陸面再解析 / NICAM-LETKF / 大気 / 天気予報 |
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| Outline of Final Research Achievements |
This study aims at exploring advanced methods to assimilate hydrological observations into the global data assimilation system for improving medium-range weather/hydrological forecasts. With operational global atmospheric data assimilation system NICAM-LETKF, we have reached to following achievements:
(1) We have pioneered a method for effectively assimilating soil moisture observations from GLDAS to NICAM by strongly coupled data assimilation. It was shown that updating atmospheric variables by soil moisture assimilation was beneficial, while the updating of land surface variables by atmospheric variable assimilation has was unbeneficial. (2) We explored to NICAM model parameter estimations by assimilation of satellite-sensed hydrological observations. GSMaP-based parameter estimation improved precipitation forecasts, and GCOM2_AMSR2-based parameter estimation improved radiation budgets successfully.
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| Academic Significance and Societal Importance of the Research Achievements |
諸国の経済が国境を越えて密接に関わる現代では、自国のみならず、「場所を問わず」他国の災害可能性をモニタリングする必要がある。自然災害の多くは気象現象、特に降水により引き起こされるため、2週間程度の中期天気予報を改善し、災害の予報スキルを向上させることは重要な社会的使命である。本研究では、現業機関でほとんど活用されていない陸域の水文観測ビッグデータを用いた中期天気予報改善へ取り組んだ。全球の土壌水分・降水量・可降水量などのデータを有効に天気予報に取り込む手法を探求し、データ同化手法の高度化やモデルパラメータ推定により、降水予報を始めとする天気予報改善を達成した。
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Report
(4 results)
Research Products
(55 results)
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[Journal Article] Precipitation Ensemble Data Assimilation in NWP Models2020
Author(s)
Miyoshi Takemasa、Kotsuki Shunji、Terasaki Koji、Otsuka Shigenori、Lien Guo-Yuan、Yashiro Hisashi、Tomita Hirofumi、Satoh Masaki、Kalnay Eugenia
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Journal Title
Advances in Global Change Research
Volume: 69
Pages: 983-991
DOI
ISBN
9783030357979, 9783030357986
Related Report
Peer Reviewed / Int'l Joint Research
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