Pre-analysis of GNSS tomography solution using the concept of spread of model resolution matrix

Abstract

GNSS tropospheric tomography is one of the applications of the Global Navigation Satellite Systems (GNSS) signals which attracts more and more interest in the field of meteorology. This method can reconstruct the water vapour of the atmosphere, which has a considerable effect on weather forecasting and early warning systems of severe weather. In GNSS tomography, traditionally, a regular spaced 3D grid stretches from the GNSS network to the effective height of the troposphere in the area of interest. Therefore, this method can offer a permanent monitoring service for water vapour and wet refractivity fields at a low cost and a reasonable spatial resolution compared to conventional observations, like radiosonde and radio occultation profiles. Nevertheless, the quality of the reconstructed field is still one of the challenges in the GNSS tomography. In this research, we propose the concept of spread as a mathematical tool to provide a quality measure without using the reference field and calculating statistical measures like RMSE and Bias. Thereby, two synthetic and one real datasets (part of Germany and Czechia) covering overlapping periods between 29 May and 14 Jun of the year 2013 (DoY 149-165; DoY 160-165; DoY 160-165, 2013) have been tested to investigate the proposed method. According to the obtained results, the proposed tool shows a strong correlation (up to 0.81 for synthetic and 0.72 for real observations) with the standard deviation of the reconstructed wet refractivity with respect to the radiosonde profile reference. The correlation between spread and the Bias of the retrieved wet refractivity field is also significant. However, there is no clear picture depending on the applied spread computation models. Therefore, the spread of the resolution matrix can be used as a proxy for the accuracy of the tomography reconstruction field based on the quality of the observations, the initial field, and the design matrix.