[E] Oral

M (Multidisciplinary and Interdisciplinary) » M-GI General Geosciences, Information Geosciences & Simulations

[M-GI28] Data assimilation: A fundamental approach in geosciences

Mon. May 25, 2026 1:45 PM - 3:15 PM Exhibition Hall Special Setting (2) (Exhibition Hall 7&8, Makuhari Messe)

Chairperson:Ohishi Shun(RIKEN Center for Computational Science), Kano Masayuki(Disaster Prevention Research Institute)

Data assimilation is an approach to combine dynamical models and observations using principles of dynamical systems theory and statistical methods. Data assimilation is now widely used not only in meteorology and oceanography but also other geoscience fields 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.

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1:45 PM - 2:00 PM

[MGI28-01] Exploring the use of the Gamma-Inverse-Gamma ensemble Kalman filtering to estimating global precipitation fields

★Invited Papers

*Yuka Muto¹, Craig Huntly Bishop², Shunji Kotsuki¹ (1. Chiba University, 2. University of Melbourne)

2:00 PM - 2:15 PM

[MGI28-02] Merging Particle Filter with Systematic Coefficient Generation and Pre-Merging Estimation

*Naoki Hiramoto¹, Genta Ueno^2,1, Daisuke Murakami^2,1 (1. The Graduate University for Advanced Studies, 2. The Institute of Statistical Mathematics)

2:15 PM - 2:30 PM

[MGI28-03] Approximate Bayesian Data Assimilation with Deep Learning Surrogate Models

*Alexandre M Tartakovsky¹ (1. University of Illinois at Urbana-Champaign)

2:30 PM - 2:45 PM

[MGI28-04] Evaluation of data assimilation methods suitable for frontal structures

*Saori Nakashita¹, Takeshi Enomoto¹ (1. Disaster Prevention Research Institute, Kyoto University)

2:45 PM - 3:00 PM

[MGI28-05] Estimating Background Error Covariances via Neural Super-Resolution

*Yuki Yasuda¹, Onishi Ryo² (1. Japan Agency for Marine-Earth Science and Technology, 2. Institute of Science Tokyo)

3:00 PM - 3:15 PM

[MGI28-06] DASSH — Diffusion-Accelerated Smoothing Using Score-Based Heuristics

★Invited Papers

*Marios Andreou¹, Nan Chen¹, Daniele Venturi² (1. University of Wisconsin Madison, 2. UC Santa Cruz)

Session information

[M-GI28] Data assimilation: A fundamental approach in geosciences

[MGI28-01] Exploring the use of the Gamma-Inverse-Gamma ensemble Kalman filtering to estimating global precipitation fields

★Invited Papers

[MGI28-02] Merging Particle Filter with Systematic Coefficient Generation and Pre-Merging Estimation

[MGI28-03] Approximate Bayesian Data Assimilation with Deep Learning Surrogate Models

[MGI28-04] Evaluation of data assimilation methods suitable for frontal structures

[MGI28-05] Estimating Background Error Covariances via Neural Super-Resolution

[MGI28-06] DASSH — Diffusion-Accelerated Smoothing Using Score-Based Heuristics

★Invited Papers

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