Solutions Of Meteorological Parameters Based On Radio Occultation Data Assimilation

Numerical weather prediction is the understructure of the weather forecast. And the results from numerical weather prediction models are closely related to their initial values. GPS Radio Occultation (RO) technology offers a new type of observation data with the advantages of all-weather, high vertical resolution and global averaged distribution. The assimilation of the radio occultation data has a significant positive impact on numerical weather prediction (global and regional weather prediction) through improved definition of temperature, water vapor and wind fields. However, the existing optimization methods of the cost function of the radio occultation technology are not easy to implement. In this thesis, the SPSA (Simultaneous perturbation stochastic approximation) algorithm is used to optimize the cost function and solve atmospheric parameters.The main research contents and innovations are as follows:(1) This thesis focuses on the occultation data assimilation technique. And it analyzes the cost function, the observation operator and optimization methods in detail. It is concluded that the solution method of atmospheric parameters is essentially a multidimensional nonlinear function optimization strategy.(2) This thesis proposes a new solution method of atmospheric parameters. The SPSA algorithm is widely used in multidimensional nonlinear optimization problems. The results of observing system simulation experiments demonstrate that the SPSA algorithm is an effective method of solving atmospheric parameters.The radio occultation technology, of which the core is the optimization algorithm, is a hotspot of numerical weather prediction. The theory and method proposed in this thesis has a role in the application of the occultation data.