Meteorological satellite ground application system resources are limited. Abnormal satellite missions often lead to hopple of system resources. In order to analyze the problem, this paper presents an anomaly analysis method for meteorological satellite ground system based on resource bottleneck. Through the CPU, memory and I/O, several types of resources in-depth were analyzed to find the bottleneck caused by the problem, thus providing recommendations for application optimization. Experimental analysis shows that the proposed method can reasonably analyze the resource bottleneck of CPU, memory and I/ O, and draw a good conclusion. To solve the meteorological satellite application system application anomaly caused by the bottleneck of the problem, the application of optimization to a certain extent plays a positive role.

With the development of satellite remote sensing technology, more and more requirements are put forward on the timeliness and stability of the satellite weather service system. The FY satellite rainfall estimate day knock off product algorithm runs longer, about 20 minutes, which affects the estimated rainfall product generated timeliness. Research and development of parallel optimization algorithms based on the needs of satellite meteorological services and their effectiveness in practical applications are necessary ways to enhance the high-performance and high-availability capabilities of satellite meteorological services. So aiming at this problem, we started the parallel algorithm research based on the analysis of precipitation estimation algorithm. Firstly, we explained the steps of precipitation estimated date knock off product algorithm; secondly, we analyzed the four main calculation module calculating the amount of algorithms; thirdly, multithreaded parallel algorithm and MPI parallelization was designed. Finally, the multithreaded parallel and MPI parallelization were realized. Experimental results show that the multithreaded parallel and MPI parallelization algorithm could greatly improve the overall degree of computational efficiency. And, MPI parallelization mode has a higher operating efficiency. The performance of parallel processing is closely related to the architecture of the computer. From the perspective of service scheduling and product algorithms, the MPI parallelization approach is adopted to achieve the purpose of improving service quality.