Výsledky vyhledávání - "データ同化"

Autor: ONO, Akiyuki, YAMAGUCHI, Kosei, NAKAKITA, Eiichi

Publikováno v: 京都大学防災研究所年報. B. 64:277-293

In this research, potential temperature gradient based vorticity which is the expression of baroclinic is obtained to analyze the energy structure of the vorticity field in developing clouds. First, applying the variational method enables us to obtai

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=jairo_______::f294792fb1b1602d676a58b07fd4d79f
http://hdl.handle.net/2433/268160

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Assimilation of Nonlinear Observations Using the Maximum Likelihood Ensemble Filter

Autor: NAKASHITA, Saori, ENOMOTO, Takeshi

Publikováno v: 京都大学防災研究所年報. B. 64:294-304

We investigate the performance of the Maximum Likelihood Ensemble Filter (MLEF) in assimilation of nonlinear observations. MLEF is a variational-ensemble data assimilation method, and can treat differentiable or non-differentiable nonlinear observati

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=jairo_______::33bfcf7efc157c7d2497267ca0a2561d
http://hdl.handle.net/2433/268161

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惑星気象研究センター活動報告

Autor: TAKAGI, Masahiro, TAKAYA, Koutarou, SAGAWA, Hideo, ANDO, Hiroki, SUZUKI, Anna

Publikováno v: 京都産業大学総合学術研究所所報. 15:233-246

金星や火星・木星など，太陽系惑星のほとんど惑星には大気が存在し，そこでは地球の気象学では説明できない多彩な大気現象が生じている。地球や惑星の大気現象とその表層環境を統�

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::3d612ec740186b8342f1e7c2f34583c3
https://ksu.repo.nii.ac.jp/records/10549

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Center for Planetary Atmospheric Science: Research Activity Annual Report 2017

Autor: TAKAGI, Masahiro, TAKAYA, Koutarou, SAGAWA, Hideo, ANDO, Hiroki, SUZUKI, Anna

Publikováno v: 京都産業大学総合学術研究所所報. 14:205-215

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=jairo_______::c2287c5b1228f66e13b9a3c5e4bbee1f
https://ksu.repo.nii.ac.jp/records/10376

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データ同化のための統合型衛星シミュレータの高度化

Autor: IKUTA, Yasutaka

会議情報：日本地球惑星科学連合2021年大会（2021年5月30日～6月6日. オンライン会議）
Meeting Information: Japan Geoscience Union Meeting 2021(May 30-June 6, 2021. Online meeting)
資料番号: AC2200002000

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=jairo_______::60cb589b71495918e5cbb86f043b4def
http://id.nii.ac.jp/1696/00049562/

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Development of an ensemble Kalman filter-based regional ocean data assimilation system

Autor: OHISHI, Shun, HIHARA, Tsutomu, AIKI, Hidenori, ISHIZAKA, Joji, MIYAZAWA, Yasumasa, KACHI, Misako, MIYOSHI, Takemasa

Publikováno v: JpGU - AGU Joint Meeting 2020: Virtual.

JpGU - AGU Joint Meeting 2020: Virtual(2020年7月12日-16日. 日本地球惑星科学連合(JpGU) : 米国地球物理学連合(AGU)オンライン会議)
JpGU - AGU Joint Meeting 2020: Virtual (July 12-16, 2020. Japan Geoscience Union (JpGU)

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=jairo_______::4e502999bc3c176e4b918164bf9134f9
http://id.nii.ac.jp/1696/00048412/

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GCOM-C SGLIエーロゾルプロダクトによるエーロゾルデータ同化実験

Autor: TANAKA, Taichu Y., YUMIMOTO, Keiya, YOSHIDA, Mayumi, MURAKAMI, Hiroshi, NAGAO, Takashi M., OKATA, Megumi

Publikováno v: JpGU - AGU Joint Meeting 2020: Virtual.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=jairo_______::56122c405835db4b46b8aac22d9897bc
http://id.nii.ac.jp/1696/00048413/

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Forecasting a Line-shaped Mesoscale Convective System: the Dominant Scale and a First Attempt to Assimilate Hydrometeors in the Melting Layer

Autor: YAMAGUCHI, Kosei, HORIIKE, Yosuke, NAKAKITA, Eiichi

Publikováno v: 京都大学防災研究所年報. B. 61:533-554

Mesoscale convective systems (MCSs) often lead to events such as floods, and debris flow. Initial condition is difficult to determine for Numerical Weather Prediction (NWP) of rainfall prediction. Data assimilation (DA) using radar data is expected t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=jairo_______::b787f3e3b27cbb003e64a9a35182bcbf
http://hdl.handle.net/2433/235868

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Guerrilla-Heavy Rainfall Prediction by Assimilation of Observation Data from Multi-Sensors Including Hamawari-8

Autor: YAMAGUCHI, Kosei, UESHIMA, Kazuki, HORIIKE, Yosuke, NAKAKITA, Eiichi

Publikováno v: 京都大学防災研究所年報. B. 60:599-614

As the growing process of a cumulonimbus cloud is very short, guerrilla-heavy rainfall is difficult to accurately predict in advance. In this study, we aim to improve the accuracy of early prediction by using assimilation of observation data from mul

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=jairo_______::7d22632ef89c3fb5c76891204d476d40
http://hdl.handle.net/2433/229368

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