A GPS water vapor tomography method based on a geneticalgorithm

Abstract. Water vapor is an important part in the atmosphere, but its spatial and temporal distribution is difficult to detect. Global Positioning System (GPS) water vapor tomography, which can sense three-dimensional water vapor distribution, has been developed as a research point in the fields of GPS meteorology. In this paper, a new water vapor tomography method based on a genetic algorithm (GA) is proposed to overcome the ill-conditioned problem. By using the proposed approach, it is not necessary to perform the matrix inversion process, and the water vapor tomography is no longer dependent on excessive constraints, priori information and external data, which give rise to many limitations and difficulties. Experiments in Hong Kong under rainy and rainless conditions show a serious ill-conditioned problem in the tomographic matrix by grayscale and condition numbers. Numerical results indicate that the proposed method achieves high levels of agreement and internal/external accuracy with the GAMIT-estimated slant water vapor (SWV) as a reference. Comparative results of water vapor density (WVD) derived from radiosonde data reveal that the tomographic results based on the GA are in good agreement with that of radiosonde measurements. In comparison to the traditional Least squares method, a reliable tomographic result with high accuracy can be achieved by the GA without the restrictions mentioned-above. Furthermore, the tomographic results in a rainless scenario are better than those of a rainy scenario, and the reasons are discussed in detail.