Articles | Volume 15, issue 8
Geosci. Model Dev., 15, 3433–3445, 2022
https://doi.org/10.5194/gmd-15-3433-2022
Geosci. Model Dev., 15, 3433–3445, 2022
https://doi.org/10.5194/gmd-15-3433-2022
Development and technical paper
02 May 2022
Development and technical paper | 02 May 2022

Efficient high-dimensional variational data assimilation with machine-learned reduced-order models

Romit Maulik et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-415', Anonymous Referee #1, 18 Feb 2022
    • AC2: 'Reply on RC1', Romit Maulik, 14 Mar 2022
  • CEC1: 'Comment on gmd-2021-415', Juan Antonio Añel, 22 Feb 2022
    • AC1: 'Reply on CEC1', Romit Maulik, 28 Feb 2022
  • RC2: 'Comment on gmd-2021-415', Anonymous Referee #2, 09 Mar 2022
    • AC3: 'Reply on RC2', Romit Maulik, 14 Mar 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Romit Maulik on behalf of the Authors (14 Mar 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to minor revisions (review by editor) (23 Mar 2022) by Xiaomeng Huang
AR by Romit Maulik on behalf of the Authors (24 Mar 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (26 Mar 2022) by Xiaomeng Huang
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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.