GNSS水汽层析的自适应非均匀指数分层方法

GNSS水汽层析技术在中小尺度灾害性天气的监测和预警中发挥着重要作用。常见的GNSS水汽层析技术在垂直分层时采用均匀分层，不符合大气水汽在垂直方向上的实际分布情况。本文以大气水汽密度为依据，提出一种自适应非均匀指数分层方法。该方法大大降低了各层之间的水汽密度差异，提高了水汽层析模型分层精度，且能够实现对任意给定层析区域的自适应分层建模。利用2019年8月香港CORS实测数据和探空数据对该方法进行试验与分析，与传统均匀分层相比，自适应非均匀指数分层的均方根误差和平均绝对误差分别降低了0.401 g/m³和0.223 g/m³，在低海拔处和恶劣天气下层析结果的精度和质量显著提高。

An adaptive non-uniform vertical stratification for GNSS water vapor tomography

GNSS tomography technique plays an important role in the monitoring and early warning of meso- and small-scale severe weather. Common GNSS tomography technique uses uniform stratification during vertical stratification, which does not consistent with the actual distribution of water vapor in the vertical direction. To resolve this problem, this paper proposes an adaptive non-uniform exponential stratification method that follows the dynamic exponential distribution of atmospheric water vapor. The proposed method greatly improves the accuracy of stratification of the tomographic model by reducing the difference in water vapor density of each layer. Moreover, it could adaptively calculate the optimal non-uniform vertical resolutions for any given tomographic region. This paper utilizes the Hong Kong Continuously Operating Reference Stations (CORS) measured data and radiosonde data in August 2019 to experiment and analyze the method. Compared with the traditional uniform stratification, the root mean squared error and the mean absolute error of the adaptive non-uniform exponential stratification are reduced by 0.40 g/m³ and 0.223 g/m³ respectively. In addition, the accuracy and quality of tomographic results are significantly improved at the lower height or in severe weather.