Articles | Volume 9, issue 7
https://doi.org/10.5194/gmd-9-2293-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-9-2293-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
05 Jul 2016
Development and technical paper |
| 05 Jul 2016
Performance evaluation of a throughput-aware framework for ensemble data assimilation: the case of NICAM-LETKF
RIKEN Advanced Institute for Computational Science, Kobe, Japan
Koji Terasaki
RIKEN Advanced Institute for Computational Science, Kobe, Japan
Takemasa Miyoshi
RIKEN Advanced Institute for Computational Science, Kobe, Japan
Application Laboratory, Japan Agency for Marine-Earth Science and
Technology, Yokohama, Japan
University of Maryland, College Park, Maryland, USA
Hirofumi Tomita
RIKEN Advanced Institute for Computational Science, Kobe, Japan
Viewed
Total article views: 3,147 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 12 Feb 2016)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,080 | 939 | 128 | 3,147 | 181 | 158 |
- HTML: 2,080
- PDF: 939
- XML: 128
- Total: 3,147
- BibTeX: 181
- EndNote: 158
Total article views: 2,587 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 05 Jul 2016)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,810 | 654 | 123 | 2,587 | 177 | 150 |
- HTML: 1,810
- PDF: 654
- XML: 123
- Total: 2,587
- BibTeX: 177
- EndNote: 150
Total article views: 560 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 12 Feb 2016)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 270 | 285 | 5 | 560 | 4 | 8 |
- HTML: 270
- PDF: 285
- XML: 5
- Total: 560
- BibTeX: 4
- EndNote: 8
Cited
11 citations as recorded by crossref.
- Global Cloud-Resolving Models M. Satoh et al. 10.1007/s40641-019-00131-0
- The Near-Real-Time SCALE-LETKF System: A Case of the September 2015 Kanto-Tohoku Heavy Rainfall G. Lien et al. 10.2151/sola.2017-001
- Outcomes and challenges of global high-resolution non-hydrostatic atmospheric simulations using the K computer M. Satoh et al. 10.1186/s40645-017-0127-8
- Single Precision in the Dynamical Core of a Nonhydrostatic Global Atmospheric Model: Evaluation Using a Baroclinic Wave Test Case M. Nakano et al. 10.1175/MWR-D-17-0257.1
- Multi-Year Analysis Using the NICAM-LETKF Data Assimilation System K. Terasaki et al. 10.2151/sola.2019-009
- Evaluation and optimisation of the I/O scalability for the next generation of Earth system models: IFS CY43R3 and XIOS 2.0 integration as a case study X. Yepes-Arbós et al. 10.5194/gmd-15-379-2022
- Dynamic load/propagate/store for data assimilation with particle filters on supercomputers S. Friedemann et al. 10.1016/j.jocs.2024.102229
- A 1024-Member NICAM-LETKF Experiment for the July 2020 Heavy Rainfall Event K. Terasaki & T. Miyoshi 10.2151/sola.18A-002
- An offline framework for high-dimensional ensemble Kalman filters to reduce the time to solution Y. Zheng et al. 10.5194/gmd-13-3607-2020
- JAMSTEC Model Intercomparision Project (JMIP) C. Kodama et al. 10.5918/jamstecr.28.5
- Ensemble‐Based Data Assimilation of GPM DPR Reflectivity: Cloud Microphysics Parameter Estimation With the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) S. Kotsuki et al. 10.1029/2022JD037447
11 citations as recorded by crossref.
- Global Cloud-Resolving Models M. Satoh et al. 10.1007/s40641-019-00131-0
- The Near-Real-Time SCALE-LETKF System: A Case of the September 2015 Kanto-Tohoku Heavy Rainfall G. Lien et al. 10.2151/sola.2017-001
- Outcomes and challenges of global high-resolution non-hydrostatic atmospheric simulations using the K computer M. Satoh et al. 10.1186/s40645-017-0127-8
- Single Precision in the Dynamical Core of a Nonhydrostatic Global Atmospheric Model: Evaluation Using a Baroclinic Wave Test Case M. Nakano et al. 10.1175/MWR-D-17-0257.1
- Multi-Year Analysis Using the NICAM-LETKF Data Assimilation System K. Terasaki et al. 10.2151/sola.2019-009
- Evaluation and optimisation of the I/O scalability for the next generation of Earth system models: IFS CY43R3 and XIOS 2.0 integration as a case study X. Yepes-Arbós et al. 10.5194/gmd-15-379-2022
- Dynamic load/propagate/store for data assimilation with particle filters on supercomputers S. Friedemann et al. 10.1016/j.jocs.2024.102229
- A 1024-Member NICAM-LETKF Experiment for the July 2020 Heavy Rainfall Event K. Terasaki & T. Miyoshi 10.2151/sola.18A-002
- An offline framework for high-dimensional ensemble Kalman filters to reduce the time to solution Y. Zheng et al. 10.5194/gmd-13-3607-2020
- JAMSTEC Model Intercomparision Project (JMIP) C. Kodama et al. 10.5918/jamstecr.28.5
- Ensemble‐Based Data Assimilation of GPM DPR Reflectivity: Cloud Microphysics Parameter Estimation With the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) S. Kotsuki et al. 10.1029/2022JD037447
Latest update: 24 Apr 2024
Short summary
We propose the design and implementation of an ensemble data assimilation framework for weather prediction at a high resolution and with a large ensemble size. We consider the deployment of this framework on the data throughput of file I/O and multi-node communication. With regard to high-performance computing systems, where data throughput performance increases at a slower rate than computational performance, our new framework promises drastic reduction of total execution time.
We propose the design and implementation of an ensemble data assimilation framework for weather...
