Multidisciplinary and Interdisciplinary (M)
Session Sub-category General Geosciences, Information Geosciences & Simulations(GI)
Session ID M-GI24
Title Data assimilation: A fundamental approach in geosciences
Short Title Data assimilation
Main Convener Name Shin ya Nakano
Affiliation The Institute of Statistical Mathematics
Co-Convener 1 Name Yosuke Fujii
Affiliation Meteorological Research Institute, Japan Meteorological Agency
Co-Convener 2 Name Takemasa Miyoshi
Affiliation RIKEN
Co-Convener 3 Name Masayuki Kano
Affiliation Graduate school of science, Tohoku University
Session Language 
E
Scope 
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.
Presentation Format Oral and Poster presentation
Invited Authors Arata Amemiya (RIKEN Center for Computational Science)
Michael Goodliff (RIKEN Center for Computational Science)
Yuchen Wang (Japan Agency for Marine-Earth Science and Technology)
Izumi Okabe (Meteorological Research Institute of Japan Meteorological Agency)
Time Presentation No Title Presenter
Oral Presentation May 30 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 Arata Amemiya
09:15 - 09:30 MGI24-02 Advancing Forecast Precision: Data-Driven Model Generation via Data Assimilation Michael Goodliff
09:30 - 09:45 MGI24-03 Introduction of global error covariance to nested ensemble variational assimilation Saori Nakashita
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 Kaman Kong
10:00 - 10:15 MGI24-05 Strongly vs. Weakly Coupled Data Assimilation in Coupled Systems with Various Inter-Compartment Interactions Yohei Sawada
Oral Presentation May 30 AM2
10:45 - 11:00 MGI24-06 Comparison of the impact of all-sky and clear-sky infrared radiance assimilation for the global geostationary satellites in the JMA’s global NWP system Izumi Okabe
11:00 - 11:15 MGI24-07 Tsunami data assimilation using high-frequency ocean radar system in the Kii Channel, Japan Yuchen Wang
11:15 - 11:30 MGI24-08 Short-term forecast of the geomagnetic secular variation using recurrent neural networks trained by Kalman filter Sho Sato
11:30 - 11:45 MGI24-09 Ionospheric data assimilation using a whole atmosphere-ionosphere model GAIA Hidekatsu Jin
11:45 - 12:00 MGI24-10 Deterministic and ensemble forecasts of Kuroshio south of Japan Shun Ohishi
Presentation No Title Presenter
Poster Presentation May 30 PM3
MGI24-P01 Second Year Progress of PREVENIR: Japan-Argentina Cooperation Project for Heavy Rain and Urban Flood Disaster Prevention Takemasa Miyoshi
MGI24-P02 Adjoint models using automatic differentiation Takeshi Enomoto
MGI24-P03 Online state and time-varying parameter estimation using the implicit equal-weights particle filter Mineto Satoh
MGI24-P04 Preliminary result on implementing flow-dependent background error covariances in JMA Meso-scale analysis Akane Saya
MGI24-P05 Ocean Data Assimilation Focusing on Integral Quantities Characterizing Observation Profiles Nozomi Sugiura
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 Naoki Hirose
MGI24-P08 Observing System Experiments (OSEs) with JMA’s operational seasonal prediction system JMA/MRI-CPS3 for participating the SynObs Flagship OSEs Ichiro Ishikawa
MGI24-P09 Assimilation of polar ionospheric data into a newly-developed emulator of global MHD simulation Shin ya Nakano