Preprints
https://doi.org/10.5194/gmd-2018-310
© Author(s) 2018. 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-2018-310
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Submitted as: development and technical paper
| 
10 Dec 2018
Submitted as: development and technical paper |
| 10 Dec 2018
| 10 Dec 2018
Status: this preprint was under review for the journal GMD but the revision was not accepted.
Optimization of the WRFV3.7 adjoint model
Abstract. This work focused on a new strategy for productively improving the performance of adjoint models. By using several techniques including the push/pop-free method, careful Input/Output (IO) analysis and the use of the conception of adjoint locality, we reduced the adjoint cost of the Weather Research and Forecasting plus (WRFPLUS) by almost half on different numbers of processors especially with a slight decrease in total memory. Several experiments are conducted using the four-dimensional variational data assimilation (4DVar) method. The results show that the total time cost of running a 4DVar application is decreased by approximately 1/3.
How to cite. Cheng, Q., Liu, J., and Wang, B.: Optimization of the WRFV3.7 adjoint model, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2018-310, 2018.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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RC1: 'Review of "Optimization of the WRFV3.7 adjoint model" (gmd-2018-310) by Qiang Cheng et al.', Anonymous Referee #1, 01 Jan 2019
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AC1: 'Response to Anonymous Reviewer 1', Juanjuan Liu, 23 Apr 2019
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AC1: 'Response to Anonymous Reviewer 1', Juanjuan Liu, 23 Apr 2019
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RC2: 'Interactive comment on “Optimization of the WRFV3.7 adjoint model”', Anonymous Referee #2, 07 Mar 2019
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AC1: 'Response to Anonymous Reviewer 1', Juanjuan Liu, 23 Apr 2019
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AC2: 'Response to Anonymous Reviewer 2', Juanjuan Liu, 23 Apr 2019
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AC1: 'Response to Anonymous Reviewer 1', Juanjuan Liu, 23 Apr 2019
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
- Printer-friendly version
- Supplement
-
RC1: 'Review of "Optimization of the WRFV3.7 adjoint model" (gmd-2018-310) by Qiang Cheng et al.', Anonymous Referee #1, 01 Jan 2019
-
AC1: 'Response to Anonymous Reviewer 1', Juanjuan Liu, 23 Apr 2019
-
AC1: 'Response to Anonymous Reviewer 1', Juanjuan Liu, 23 Apr 2019
-
RC2: 'Interactive comment on “Optimization of the WRFV3.7 adjoint model”', Anonymous Referee #2, 07 Mar 2019
-
AC1: 'Response to Anonymous Reviewer 1', Juanjuan Liu, 23 Apr 2019
-
AC2: 'Response to Anonymous Reviewer 2', Juanjuan Liu, 23 Apr 2019
-
AC1: 'Response to Anonymous Reviewer 1', Juanjuan Liu, 23 Apr 2019
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Latest update: 20 Nov 2024
Qiang Cheng
School of Computer & Information Sciences, Southwest University, Chongqing, 400715
Juanjuan Liu
LASG, Institute of Atmospheric Physics, Beijing 100029, China
University of Chinese Academy of Sciences, Beijing 100049, China
Bin Wang
LASG, Institute of Atmospheric Physics, Beijing 100029, China
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
University of Chinese Academy of Sciences, Beijing 100049, China
Short summary
Adjoint models are usually used to improve the weather forecast, but It's very time consuming. What we would like to do is determining how to significantly reduce the running cost of the adjoint model.The manuscript presented several methods. With them, we reduced the adjoint cost of the Weather Research and Forecasting plus (WRFPLUSV3.7) by almost half. Apparently, these are also productive in other applications in terms of adjoint model such as parameter estimation, singular vector etc.
Adjoint models are usually used to improve the weather forecast, but It's very time consuming....