Development of an efficient regional four-dimensional variational data assimilation system for WRF

Xin Zhang, Xiang Yu Huang, Jianyu Liu, Jonathan Poterjoy, Yonghui Weng, Fuqing Zhang, Hongli Wang

Research output: Contribution to journalArticle

15 Scopus citations

Abstract

This paper presents the development of a single executable four-dimensional variational data assimilation (4D-Var) system based on the Weather Research and Forecasting (WRF) Model through coupling the variational data assimilation algorithm (WRF-VAR) with the newly developed WRF tangent linear and adjoint model (WRFPLUS). Compared to the predecessorMultiple Program Multiple Data version, the new WRF 4D-Var system achieves major improvements in that all processing cores are able to participate in the computation and all information exchanges between WRF-VAR and WRFPLUS are moved directly from disk to memory. The single executable 4D-Var system demonstrates desirable acceleration and scalability in terms of the computational performance, as demonstrated through a series of benchmarking data assimilation experiments carried out over a continental U.S. domain. To take into account the nonlinear processes with the linearized minimization algorithm and to further decrease the computational cost of the 4D-Var minimization, a multi-incremental minimization that uses multiple horizontal resolutions for the inner loop has been developed. The method calculates the innovations with a high-resolution grid and minimizes the cost function with a lowerresolution grid. The details regarding the transition between the high-resolution outer loop and the low-resolution inner loop are introduced. Performance of the multi-incremental configuration is found to be comparable to that with the full-resolution 4D-Var in terms of 24-h forecast accuracy in the week-long analysis and forecast experiment over the continental U.S. domain. Moreover, the capability of the newly developed multi-incremental 4D-Var system is further demonstrated in the convection-permitting analysis and forecast experiment for Hurricane Sandy (2012), which was hardly computationally feasible with the predecessor WRF 4D-Var system.

Original languageEnglish (US)
Pages (from-to)2777-2794
Number of pages18
JournalJournal of Atmospheric and Oceanic Technology
Volume31
Issue number12
DOIs
StatePublished - Jan 1 2014

All Science Journal Classification (ASJC) codes

  • Ocean Engineering
  • Atmospheric Science

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