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Qin Y.,National Marine Environment Forecast Center | Qin Y.,Key Laboratory of Research on Marine Hazards Forecasting | Gong J.,Center for Numerical Weather Prediction | Li Z.,National Meteorological Center | Sheng R.,Weather Modification Office of Peoples Government of Shandong
Journal of Meteorological Research | Year: 2014

The effectiveness of using an Ensemble Square Root Filter (EnSRF) to assimilate real Doppler radar observations on convective scale is investigated by applying the technique to a case of squall line on 12 July 2005 in midwest Shandong Province using the Weather Research and Forecasting (WRF) model. The experimental results show that: (1) The EnSRF system has the potential to initiate a squall line accurately by assimilation of real Doppler radar data. The convective-scale information has been added into the WRF model through radar data assimilation and thus the analyzed fields are improved noticeably. The model spin-up time has been shortened, and the precipitation forecast is improved accordingly. (2) Compared with the control run, the deterministic forecast initiated with the ensemble mean analysis of EnSRF produces more accurate prediction of microphysical fields. The predicted wind and thermal fields are reasonable and in accordance with the characteristics of convective storms. (3) The propagation direction of the squall line from the ensemble mean analysis is consistent with that of the observation, but the propagation speed is larger than the observed. The effective forecast period for this squall line is about 5–6 h, probably because of the nonlinear development of the convective storm. © The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2014. Source

Wu X.,National University of Defense Technology | Wu X.,Center for Numerical Weather Prediction | Chen D.,Center for Numerical Weather Prediction | Song J.,National University of Defense Technology | And 3 more authors.
Acta Meteorologica Sinica | Year: 2010

The Global/Regional Assimilation and PrEdiction System (GRAPES) is a new-generation operational numerical weather prediction (NWP) model developed by the China Meteorological Administration (CMA).It is a grid-point model with a code structure different from that of spectral models used in other operationalNWP centers such as the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), and Japan Meteorological Agency (JMA), especially in thecontext of parallel computing. In the GRAPES global model, a semi-implicit semi-Lagrangian scheme is used for the discretization over a sphere, which requires careful planning for the busy communicationsbetween the arrays of processors, because the Lagrangian differential scheme results in shortened trajectories interpolated between the grid points at the poles and in the associated adjacent areas. This means that the latitude-longitude partitioning is more complex for the polar processors. Therefore, a parallel strategy with efficient computation, balanced load, and synchronous communication shall be developed. In this paper, a message passing approach based on MPI (Message Passing Interface) group communication is proposed. Its key-point is to group the polar processors in row with matrix-topology during the processor partitioning.A load balance task distribution algorithm is also discussed. Test runs on the IBM-cluster 1600 at CMA show that the new algorithm is of desired scalability, and the readjusted load balance scheme can reduce the absolute wall clock time by 10% or more. The quasi-operational runs of the model demonstrate that the wall clock time secured by the strategy meets the real-time needs of NWP operations. Source

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