Session outline
| Multidisciplinary and Interdisciplinary (M) | ||
|---|---|---|
| Session Sub-category | General Geosciences, Information Geosciences & Simulations(GI) | |
| Session ID | M-GI28 | |
| 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 | Masayuki Kano |
| Affiliation | Disaster Prevention Research Institute | |
| Co-Convener 2 | Name | Shun Ohishi |
| Affiliation | RIKEN Center for Computational Science | |
| Co-Convener 3 | Name | Keiichi Kondo |
| Affiliation | Meteorological Research Institute | |
| Session Language |
E |
|
| Scope |
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. |
|
| Presentation Format | Oral and Poster presentation | |
| Time | Presentation No | Title | Presenter |
|---|---|---|---|
| Oral Presentation May 25 PM1 | |||
| 13:45 - 14:00 | MGI28-01 | Exploring the use of the Gamma-Inverse-Gamma ensemble Kalman filtering to estimating global precipitation fields | Yuka Muto |
| 14:00 - 14:15 | MGI28-02 | Merging Particle Filter with Systematic Coefficient Generation and Pre-Merging Estimation | Naoki Hiramoto |
| 14:15 - 14:30 | MGI28-03 | Approximate Bayesian Data Assimilation with Deep Learning Surrogate Models | Alexandre M Tartakovsky |
| 14:30 - 14:45 | MGI28-04 | Evaluation of data assimilation methods suitable for frontal structures | Saori Nakashita |
| 14:45 - 15:00 | MGI28-05 | Estimating Background Error Covariances via Neural Super-Resolution | Yuki Yasuda |
| 15:00 - 15:15 | MGI28-06 | DASSH — Diffusion-Accelerated Smoothing Using Score-Based Heuristics | Marios Andreou |
| Presentation No | Title | Presenter |
|---|---|---|
| Poster Presentation May 25 PM3 | ||
| MGI28-P01 | LETKF-based Ocean Research Analysis (LORA) for a quasi-global domain | Shun Ohishi |
| MGI28-P02 | On-the-fly Observation Operators in ASUCA-RDA-LETKF for Scalable Ensemble Data Assimilation | Koji Terasaki |
| MGI28-P03 | Online model error estimation using a two-scale Lorenz-96 model | Hiroki Tsuribe |
| MGI28-P04 | Dynamical estimation of land surface emissivity and temperature for microwave radiance assimilation in the global NWP system of JMA | Keiichi Kondo |
| MGI28-P05 | Initial Sensitivity Analysis for Nonlinear Models using Sliding Window Method | Minori Fukushima |
| MGI28-P06 | Data Assimilation for Data-Driven Model Generation with Varying Imperfect Initial Models | Michael Goodliff |
| MGI28-P07 | Data assimilation into an emulator of a magnetosphere-ionosphere model for reproducing the electric current system in the polar ionosphere | Shin ya Nakano |