Extrapolation based on radar data is one of the important methods for weather nowcasting. With the
increasing scale of the national weather radar network construction, and the enhancing about the refinement of
meteorological observational data, the extrapolation forecast based on regional and even national radar puzzle,
the computation time is very long. The time waiting for extrapolation computation usually lags behind the data’s
observation frequency which is once every six minutes. To solve the problem that the traditional extrapolation
algorithm is of high computational complexity and poor real-time, this paper discusses the heterogeneous computing
model based on GPU, and presents a parallel algorithm with OpenCL to achieve high performance, then analyzes the
bottlenecks of this application, and discusses how to bring up the computation speed by algorithm process
improvement and test data comparison. Some methods such as optimizing the mapping relationship of memory and
threads, utilizing local memory as high speed cache, and hiding CPU execution time, not only bring the efficiency
of the algorithm significantly improved, but also provide a reference for other optimization based on OpenCL
heterogeneous computing. Using AMD Graphic Core Next and Northern Islands which are two generation GPU
architectures as test platforms, and using Intel CPU parallel computing as a test reference, the test results show
that the improved algorithm consuming the same power dissipation under different hardware, the computing
performance is improved 15-22 times.
