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  • Author or Editor: Xiang-Yu Huang x
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Xin Zhang
,
Xiang-Yu Huang
, and
Ning Pan

Abstract

The authors propose a new technique for parallelizations of tangent linear and adjoint codes, which were applied in the redevelopment for the Weather Research and Forecasting (WRF) model with its Advanced Research WRF dynamic core using the automatic differentiation engine. The tangent linear and adjoint codes of the WRF model (WRFPLUS) now have the following improvements: A complete check interface ensures that developers write accurate tangent linear and adjoint codes with ease and efficiency. A new technique based on the nature of duality that existed among message passing interface communication routines was adopted to parallelize the WRFPLUS model. The registry in the WRF model was extended to automatically generate the tangent linear and adjoint codes of the required communication operations. This approach dramatically speeds up the software development cycle of the parallel tangent linear and adjoint codes and leads to improved parallel efficiency. Module interfaces were constructed for coupling tangent linear and adjoint codes of the WRF model with applications such as four-dimensional variational data assimilation, forecast sensitivity to observation, and others.

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Huizhen Yu
,
Hongli Wang
,
Zhiyong Meng
,
Mu Mu
,
Xiang-Yu Huang
, and
Xin Zhang

Abstract

A forecast sensitivity to initial perturbation (FSIP) analysis tool for the WRF Model was developed. The tool includes two modules respectively based on the conditional nonlinear optimal perturbation (CNOP) method and the first singular vector (FSV) method. The FSIP tool can be used to identify regions of sensitivity for targeted observation research and important influential weather systems for a given forecast metric.

This paper compares the performance of the FSIP tool to its MM5 counterpart, and demonstrates how CNOP, local CNOP (a kind of conditional nonlinear suboptimal perturbation), and FSV were detected using their evolutions of cost function. The column-integrated features of the perturbations were generally similar between the two models. More significant differences were apparent in the details of their vertical distribution. With Typhoon Matsa (2005) in the western North Pacific and a winter storm in the United States (2000) as validation cases, this work examined the tool’s capability to identify sensitive regions for targeted observation and to investigate important influential weather systems. The location and pattern of the sensitive areas identified by CNOP, local CNOP, and FSV were quite similar for both the Typhoon Matsa case and the winter storm case. The main differences were mainly in their impact on the growth of forecast difference and the details of their vertical distributions. For both cases, the wind observations might be more important than temperature observations. The results also showed that local CNOP was more capable of capturing the influence of important weather systems on the forecast of total dry energy in the verification area.

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

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 predecessor Multiple 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 lower-resolution 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.

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Xin Zhang
,
Ying-Hwa Kuo
,
Shu-Ya Chen
,
Xiang-Yu Huang
, and
Ling-Feng Hsiao

Abstract

The nonlocal excess phase observation operator for assimilating the global positioning system (GPS) radio occultation (RO) sounding data has been proven by some research papers to produce significantly better analyses for numerical weather prediction (NWP) compared to the local refractivity observation operator. However, the high computational cost and the difficulties in parallelization associated with the nonlocal GPS RO operator deter its application in research and operational NWP practices. In this article, two strategies are designed and implemented in the data assimilation system for the Weather Research and Forecasting Model to demonstrate the capability of parallel assimilation of GPS RO profiles with the nonlocal excess phase observation operator. In particular, to solve the parallel load imbalance problem due to the uneven geographic distribution of the GPS RO observations, round-robin scheduling is adopted to distribute GPS RO observations among the processing cores to balance the workload. The wall clock time required to complete a five-iteration minimization on a demonstration Antarctic case with 106 GPS RO observations is reduced from more than 3.5 h with a single processing core to 2.5 min with 106 processing cores. These strategies present the possibility of application of the nonlocal GPS RO excess phase observation operator in operational data assimilation systems with a cutoff time limit.

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