A GPS water vapour tomography method based on a genetic algorithm
Abstract. Water vapour is an important substituent of the atmosphere
but its spatial and temporal distribution is difficult to detect. Global
Positioning System (GPS) water vapour tomography, which can sense
three-dimensional water vapour distribution, has been developed as a research
area in the field of GPS meteorology. In this paper, a new water vapour
tomography method based on a genetic algorithm (GA) is proposed to overcome
the ill-conditioned problem. The proposed approach does not need to perform
matrix inversion, and it does not rely on excessive constraints, a priori
information or external data. Experiments in Hong Kong under rainy and
rainless conditions using this approach show that there is a serious ill-conditioned
problem in the tomographic matrix by grayscale and condition numbers.
Numerical results show that the average root mean square error (RMSE) and
mean absolute error (MAE) for internal and external accuracy are 1.52∕0.94 and 10.07∕8.44  mm, respectively, with the GAMIT-estimated slant water
vapour (SWV) as a reference. Comparative results of water vapour density (WVD)
derived from radiosonde data reveal that the tomographic results based on GA
with a total RMSE  ∕  MAE of 1.43∕1.19  mm are in good agreement with that of
radiosonde measurements. In comparison to the traditional least squares
method, the GA can achieve a reliable tomographic result with high accuracy
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 in this paper.