Preprints
https://doi.org/10.5194/gmd-2018-310
https://doi.org/10.5194/gmd-2018-310

Submitted as: development and technical paper 10 Dec 2018

Submitted as: development and technical paper | 10 Dec 2018

Review status: this preprint was under review for the journal GMD but the revision was not accepted.

Optimization of the WRFV3.7 adjoint model

Qiang Cheng1, Juanjuan Liu2,4, and Bin Wang2,3,4 Qiang Cheng et al.
  • 1School of Computer & Information Sciences, Southwest University, Chongqing, 400715
  • 2LASG, Institute of Atmospheric Physics, Beijing 100029, China
  • 3Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
  • 4University of Chinese Academy of Sciences, Beijing 100049, China

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.

Qiang Cheng et al.

 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Qiang Cheng et al.

Qiang Cheng et al.

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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.