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

Optimization of the WRFV3.7 adjoint model

Qiang Cheng, Juanjuan Liu, and Bin Wang

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.

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Qiang Cheng, Juanjuan Liu, and Bin Wang
 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
 
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, Juanjuan Liu, and Bin Wang
Qiang Cheng, Juanjuan Liu, and Bin Wang

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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.