JpGU Meeting 2024
セッション概要| 領域外・複数領域(M) | |||
|---|---|---|---|
| セッション小記号 | 地球科学一般・情報地球科学(GI) | ||
| セッションID | M-GI24 | ||
| タイトル | 和文 | Data assimilation: A fundamental approach in geosciences | |
| 英文 | Data assimilation: A fundamental approach in geosciences | ||
| タイトル短縮名 | 和文 | Data assimilation | |
| 英文 | Data assimilation | ||
| 代表コンビーナ | 氏名 | 和文 | 中野 慎也 |
| 英文 | Shin ya Nakano | ||
| 所属 | 和文 | 情報・システム研究機構 統計数理研究所 | |
| 英文 | The Institute of Statistical Mathematics | ||
| 共同コンビーナ 1 | 氏名 | 和文 | 藤井 陽介 |
| 英文 | Yosuke Fujii | ||
| 所属 | 和文 | 気象庁気象研究所 | |
| 英文 | Meteorological Research Institute, Japan Meteorological Agency | ||
| 共同コンビーナ 2 | 氏名 | 和文 | 三好 建正 |
| 英文 | Takemasa Miyoshi | ||
| 所属 | 和文 | 理化学研究所 | |
| 英文 | RIKEN | ||
| 共同コンビーナ 3 | 氏名 | 和文 | 加納 将行 |
| 英文 | Masayuki Kano | ||
| 所属 | 和文 | 東北大学理学研究科 | |
| 英文 | Graduate school of science, Tohoku University | ||
| 発表言語 | E | ||
| スコープ | 和文 |
Data assimilation is an inversion approach to estimate the evolution of a system by utilizing a constraint given by a dynamical simulation model. Data assimilation is now widely used not only in meteorology and oceanography but also other fields of geosciences such as hydrology, solid earth science, and space science. This session aims at providing an opportunity for discussion on data assimilation studies among researchers of various field of geosciences. We encourage contributions addressing novel methods and theoretical developments of data assimilation. Contributions dealing with useful applications of data assimilation are also welcome. |
|
| 英文 |
Data assimilation is an inversion approach to estimate the evolution of a system by utilizing a constraint given by a dynamical simulation model. Data assimilation is now widely used not only in meteorology and oceanography but also other fields of geosciences such as hydrology, solid earth science, and space science. This session aims at providing an opportunity for discussion on data assimilation studies among researchers of various field of geosciences. We encourage contributions addressing novel methods and theoretical developments of data assimilation. Contributions dealing with useful applications of data assimilation are also welcome. |
||
| 発表方法 | 口頭および（または）ポスターセッション | ||
| 招待講演 |
雨宮 新 (理化学研究所 計算科学研究センター) Michael Goodliff (RIKEN Center for Computational Science) 王 宇晨 (海洋研究開発機構) 岡部 いづみ (気象庁気象研究所) |
||
| 時間 | 講演番号 | タイトル | 発表者 |
|---|---|---|---|
| 口頭発表 5月30日 AM1 | |||
| 09:00 - 09:15 | MGI24-01 | Reduced non-Gaussianity and improved analysis by assimilating every-30-second radar observation with ensemble Kalman filter: a case of idealized deep convection | 雨宮 新 |
| 09:15 - 09:30 | MGI24-02 | Advancing Forecast Precision: Data-Driven Model Generation via Data Assimilation | Michael Goodliff |
| 09:30 - 09:45 | MGI24-03 | 領域アンサンブル変分法への全球誤差共分散の導入 | 中下 早織 |
| 09:45 - 10:00 | MGI24-04 | Quantifying the relationships between parameter identifiability and parameter ensemble spread in the DA-based parameter estimation: An ideal 2D squall-line experiment | 江 嘉敏 |
| 10:00 - 10:15 | MGI24-05 | Strongly vs. Weakly Coupled Data Assimilation in Coupled Systems with Various Inter-Compartment Interactions | 澤田 洋平 |
| 口頭発表 5月30日 AM2 | |||
| 10:45 - 11:00 | MGI24-06 | 気象庁全球数値予報システムを用いた全球静止衛星の全天候および晴天放射輝度温度同化インパクト比較 | 岡部 いづみ |
| 11:00 - 11:15 | MGI24-07 | Tsunami data assimilation using high-frequency ocean radar system in the Kii Channel, Japan | 王 宇晨 |
| 11:15 - 11:30 | MGI24-08 | 拡張Kalmanフィルタ法で訓練された再帰的ニューラルネットによる地磁気永年変化の短期予測 | 佐藤 匠 |
| 11:30 - 11:45 | MGI24-09 | 大気圏・電離圏モデルGAIAを用いた電離圏データ同化の構築 | 陣 英克 |
| 11:45 - 12:00 | MGI24-10 | Deterministic and ensemble forecasts of Kuroshio south of Japan | 大石 俊 |
| 講演番号 | タイトル | 発表者 |
|---|---|---|
| ポスター発表 5月30日 PM3 | ||
| MGI24-P01 | Second Year Progress of PREVENIR: Japan-Argentina Cooperation Project for Heavy Rain and Urban Flood Disaster Prevention | 三好 建正 |
| MGI24-P02 | 自動微分を用いた随伴モデル | 榎本 剛 |
| MGI24-P03 | Online state and time-varying parameter estimation using the implicit equal-weights particle filter | 佐藤 峰斗 |
| MGI24-P04 | Preliminary result on implementing flow-dependent background error covariances in JMA Meso-scale analysis | 佐谷 茜 |
| MGI24-P05 | Ocean Data Assimilation Focusing on Integral Quantities Characterizing Observation Profiles | 杉浦 望実 |
| MGI24-P06 | Predictability of Kuroshio path using deep learning based uNet model at 30- and 60- days lead time | Kalpesh Ravindra Patil |
| MGI24-P07 | Intercomparison and ensemble project of coastal ocean prediction models in Japan | 広瀬 直毅 |
| MGI24-P08 | SynObs Flagship OSE に対する気象庁季節予測システム(JMA/MRI-CPS3)による取り組み | 石川 一郎 |
| MGI24-P09 | Assimilation of polar ionospheric data into a newly-developed emulator of global MHD simulation | 中野 慎也 |