Received: 05 Dec 2018 – Accepted for review: 07 Dec 2018 – Discussion started: 10 Dec 2018
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.
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....