GNSS观测数据ZTD建模的质量控制方法

针对目前基于GNSS观测数据的对流层天顶总延迟(ZTD)模型缺乏有效质量控制手段的现状,提出了一套综合考虑数据量、网格分辨率以及模型稳定性的ZTD建模质量控制方法,并采用内华达大地测量实验室(NGL)解算的高空间分辨率GNSS对流层数据,选取了近十年德国及周边区域[47°N-55°N,5°E-15°E]183个测站的实测ZTD,对该方法进行了校验。实验结果表明:在该质量控制方法下建立的新模型精度稳定,平均均方根误差(RMS)为3.4 cm,相对于UNB3m、EGNOS、GPT2w+Saas平均改善了42.4%、35.8%、33.3%。本文提出的质量控制方法有效提升了基于GNSS观测数据的ZTD模型的性能,对于ZTD建模研究具有一定的参考价值。     

Exploration and analysis of the factors influencing GNSS PWV for nowcasting applications

Precipitable water vapor (PWV) can be assimilated into a numerical weather model (NWM) to improve the prediction accuracy of numerical weather prediction. In this study, taking GNSS data for the Beijing Fangshan station (BJFS) as an example, based on the method of Pearson correlation coefficient combined with quantitative analysis, GNSS datasets are used to study the relationships between GNSS-derived PWV (GNSS PWV__Met) and its influencing factors, including the internal influencing factors zenith troposphere delay (ZTD), zenith hydrostatic delay (ZHD), zenith wet delay (ZWD), and surface temperature (T_s), and the external influencing factor haze (mainly PM_2.5). Firstly, based on the strong correlation between PWV__Met and ZTD hourly sequences from the International GNSS Service Network’s BJFS station for DOYS 182–212, 2015, the results of experiment prove that the reliability of GNSS ZTD is used to forecast PWV_Met in short-term forecasting. Secondly, based on hourly data of BJFS in 2016, the correlation between PWV__Met and ZTD, ZWD, ZHD, pressure (P) and T_s is analyzed, and then, with the rate of ZTD variation as the main factor, ZTD variation as auxiliary factor, the prediction success rate is 88.24% from hourly data of precipitation event for DOYs 183–213 in Beijing. The experiment indicates that ZTD can help forecast short-term precipitation. Thirdly, based on data from three hazy periods with relatively stable weather conditions, no heavy rainfall, and relatively continuous data in the past three years, the correlation between GNSS PWV_Met/ZTD and PM_2.5 hourly series is analyzed. The results of the experiments suggests that GNSS ZTD should be considered to assist in haze monitoring. So in the absence of radiosonde stations and meteorological elements, ZTDs on retrieval of GNSS stations have more application value in short-term forecast.     

利用探空产品评估GNSS-PPP估计ZTD精度

无线探空测站可以提供高精度和高垂直分辨率的大气产品。为评估GNSS精密单点定位获得的天顶对流层延迟(ZTD)的精度,将无线探空产品获得的ZTD作为真值,选取中国地区4个IGS观测站及其附近的探空测站2014—2018年的数据进行试算和分析。针对两种产品空间分辨率不一致性问题,利用GNSS气象观测值来弥补探空测站与GNSS测站高程的不匹配。实验结果表明:GNSS气象观测能够较好地弥补探空测站与GNSS测站高程的不一致性;GNSS精密单点定位技术获取的ZTD与探空产品估算的ZTD差值的RMS值小于4cm。     

Effects of physical correlations on long-distance GPS positioning and zenith tropospheric delay estimates

The global positioning system (GPS) has become an essential tool for the high precision navigation and positioning. The quality of GPS positioning results mainly depends on the model’s formulations regarding GPS observations, including both a functional model, which describes the mathematical relationships between the GPS measurements and unknown parameters, and a stochastic model, which reflects the physical properties of the measurements. Over the past two decades, the functional models for GPS measurements have been investigated in considerable detail. However, the stochastic models of GPS observation data are simplified, assuming that all the GPS measurements have the same variance and are statistically independent. Such assumptions are unrealistic. Although a few studies of GPS stochastic models were performed, they are restricted to short baselines and short time session lengths. In this paper, the stochastic modeling for GPS long-baseline and zenith tropospheric delay (ZTD) estimates with a 24-h session is investigated using the residual-based and standard stochastic models. Results show that using the different stochastic modelling methods, the total differences can reach as much as 3–6 mm in the baseline component, especially in the height component, and 10 mm in the ZTD estimation. Any misspecification in the stochastic models will result in unreliable GPS baseline and ZTD estimations. Using the residual-based stochastic model, not only the precision of GPS baseline and ZTD estimation is obviously improved, but also the baseline and ZTD estimations are closer to the reference value.