Articles | Volume 15, issue 8
https://doi.org/10.5194/gmd-15-3433-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-3433-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
02 May 2022
Development and technical paper |
| 02 May 2022
| 02 May 2022
Efficient high-dimensional variational data assimilation with machine-learned reduced-order models
Romit Maulik
CORRESPONDING AUTHOR
240, Argonne National Laboratory, Lemont, IL 60439, USA
Vishwas Rao
240, Argonne National Laboratory, Lemont, IL 60439, USA
Jiali Wang
240, Argonne National Laboratory, Lemont, IL 60439, USA
Gianmarco Mengaldo
Department of Mechanical Engineering, National University of Singapore, Block EA, #07-08, 9 Engineering Drive 1, Singapore
Emil Constantinescu
240, Argonne National Laboratory, Lemont, IL 60439, USA
Bethany Lusch
240, Argonne National Laboratory, Lemont, IL 60439, USA
Prasanna Balaprakash
240, Argonne National Laboratory, Lemont, IL 60439, USA
Ian Foster
240, Argonne National Laboratory, Lemont, IL 60439, USA
Rao Kotamarthi
240, Argonne National Laboratory, Lemont, IL 60439, USA
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Short summary
In numerical weather prediction, data assimilation is frequently utilized to enhance the accuracy of forecasts from equation-based models. In this work we use a machine learning framework that approximates a complex dynamical system given by the geopotential height. Instead of using an equation-based model, we utilize this machine-learned alternative to dramatically accelerate both the forecast and the assimilation of data, thereby reducing need for large computational resources.
In numerical weather prediction, data assimilation is frequently utilized to enhance the...
