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ハドレー循環と熱帯低気圧の数日規模の相互作用

Research Project

Project/Area Number 20J21462
Research Category

Grant-in-Aid for JSPS Fellows

Allocation TypeSingle-year Grants
Section国内
Review Section Basic Section 17020:Atmospheric and hydrospheric sciences-related
Research InstitutionTohoku University

Principal Investigator

王 心月 (2020-2021)  東北大学, 理学研究科, 特別研究員(DC1)

Research Fellow 王 心月 (2022)  東北大学, 理学研究科, 特別研究員(DC1)
Project Period (FY) 2020-04-24 – 2023-03-31
Project Status Discontinued (Fiscal Year 2022)
Budget Amount *help
¥2,500,000 (Direct Cost: ¥2,500,000)
Fiscal Year 2022: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2021: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2020: ¥900,000 (Direct Cost: ¥900,000)
KeywordsCloud properties / deep neural network / Deep neural network / Cloud retrieval / Himawari-8
Outline of Research at the Start

In this study, the synoptic-scale variations of the regional Hadley cell will be first depicted and analyzed over the tropical area by utilizing observation and reanalysis data, especially from a meridional perspective. Then its modulation effect on the initialization and intensification of the tropical cyclones will be explored. During the conduction of this project, a dataset of the deep convective cloud will be generated with machine learning method. The developed algorithm can be expected to pioneer the satellite-based nighttime retrieval of deep cloud.

Outline of Annual Research Achievements

In this year, we applied the cloud properties, that retrieved by our newly developed deep neural network model, to a case study of convections over the south China coastal area (SCCA). The cloud properties are analysed for SCCA offshore and inland regions respectively and different diurnal features are found for these two regions. The obtained results also suggest the inner-structures and type of convection may differ over the offshore and inland regions of SCCA due to the orthography and the background meteorological fields of this coastal area. A convection tracking algorithm is utilized to obtain parameters such as size, location, and duration time of convections. In this study, we provide a new workflow for convection / cloud system investigation by combining new techniques, and the findings can be helpful to future studies, especially these on model validation.

Research Progress Status

令和4年度が最終年度であるため、記入しない。

Strategy for Future Research Activity

令和4年度が最終年度であるため、記入しない。

Report

(3 results)
  • 2022 Annual Research Report
  • 2021 Annual Research Report
  • 2020 Annual Research Report
  • Research Products

    (3 results)

All 2022 2021

All Journal Article (2 results) (of which Peer Reviewed: 2 results,  Open Access: 1 results) Presentation (1 results) (of which Int'l Joint Research: 1 results)

  • [Journal Article] Analysis of Diurnal Evolution of Cloud Properties and Convection Tracking over the South China Coastal Area2022

    • Author(s)
      Wang Xinyue、Iwabuchi Hironobu、Courbot Jean-Baptiste
    • Journal Title

      Remote Sensing

      Volume: 14 Issue: 19 Pages: 5039-5039

    • DOI

      10.3390/rs14195039

    • Related Report
      2022 Annual Research Report
    • Peer Reviewed
  • [Journal Article] Cloud identification and property retrieval from Himawari-8 infrared measurements via a deep neural network2022

    • Author(s)
      Wang Xinyue、Iwabuchi Hironobu、Yamashita Takaya
    • Journal Title

      Remote Sensing of Environment

      Volume: 275 Pages: 113026-113026

    • DOI

      10.1016/j.rse.2022.113026

    • Related Report
      2021 Annual Research Report 2020 Annual Research Report
    • Peer Reviewed / Open Access
  • [Presentation] Retrieval of cloud properties from Himawari-8 measurement with a deep neural network method2021

    • Author(s)
      Xinyue Wang
    • Organizer
      JpGU-2021
    • Related Report
      2020 Annual Research Report
    • Int'l Joint Research

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Published: 2020-07-07   Modified: 2024-12-25  

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