Li Lei, Xu Ying, Yan Lizi, Wang Shengli, Liu Guolin, Liu Fan
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China.
Key Laboratory of Geomatics and Digital Technology of Shandong Province, Shandong University of Science and Technology, Qingdao 266590, China.
Sensors (Basel). 2020 Jun 3;20(11):3167. doi: 10.3390/s20113167.
Tropospheric delay is a major error source that affects the initialization and re-initialization speed of the Global Navigation Satellite System's (GNSS) medium-/long-range baseline in Network Real-Time Kinematic (NRTK) positioning. Fusing the meteorological data from the Numerical Weather Prediction (NWP) model to estimate the zenith tropospheric delay (ZTD) is one of the current research hotspots. However, research has shown that the ZTD derived from NWP models is still not accurate enough for high-precision GNSS positioning applications without the estimation of the residual tropospheric delay. To date, General Regression Neural Network (GRNN) has been applied in many fields. It has a high learning speed and simple structure, and can approximate any function with arbitrary precision. In this study, we developed a regional NWP tropospheric delay inversion method based on a GRNN model to improve the accuracy of the tropospheric delay derived from the NWP model. The accuracy of the tropospheric delays derived from reanalysis data of the European Center for Medium-Range Weather Forecasts (ECMWF) and the US National Centers for Environmental Prediction (NCEP) was assessed through comparisons with the results of the International GPS Service (IGS). The variation characteristics of the residual of the ZTD inverted by NWP data were analyzed considering the factors of temperature, humidity, latitude, and season. To evaluate the performance of this new method, the National Center Atmospheric Research (NCAR) troposphere data of 650 stations in Japan in 2005 were collected as a reference to compare the accuracy of the ZTD before and after using the new method. The experimental results showed that the GRNN model has obvious advantages in fitting the NWP ZTD residual. The mean residual and the root mean square deviation (RMSD) of the ZTD inverted using the method of this study were 9.5 mm and 12.7 mm, respectively, showing reductions of 20.8% and 19.1%, respectively, as compared to the standard NWP model. For long-range baseline (155 km and 207 km), the corrected NWP-constrained RTK showed a reduction of over 43% in the initialization time compared with the standard RTK, and showed a reduction of over 24% in the initialization time compared with the standard NWP-constrained RTK.
对流层延迟是影响全球导航卫星系统（GNSS）网络实时动态（NRTK）定位中/长基线初始化和重新初始化速度的主要误差源。融合数值天气预报（NWP）模型的气象数据来估计天顶对流层延迟（ZTD）是当前的研究热点之一。然而，研究表明，对于高精度GNSS定位应用而言，在不估计对流层延迟残差的情况下，由NWP模型得出的ZTD仍不够精确。迄今为止，广义回归神经网络（GRNN）已在许多领域得到应用。它具有学习速度快、结构简单的特点，并且能够以任意精度逼近任何函数。在本研究中，我们开发了一种基于GRNN模型的区域NWP对流层延迟反演方法，以提高由NWP模型得出的对流层延迟的精度。通过与国际GPS服务（IGS）的结果进行比较，评估了欧洲中期天气预报中心（ECMWF）和美国国家环境预测中心（NCEP）再分析数据得出的对流层延迟的精度。考虑温度、湿度、纬度和季节等因素，分析了由NWP数据反演的ZTD残差的变化特征。为了评估这种新方法的性能，收集了2005年日本650个站点的美国国家大气研究中心（NCAR）对流层数据作为参考，以比较使用新方法前后ZTD的精度。实验结果表明，GRNN模型在拟合NWP ZTD残差方面具有明显优势。使用本研究方法反演的ZTD的平均残差和均方根偏差（RMSD）分别为9.5毫米和12.7毫米，与标准NWP模型相比，分别降低了20.8%和19.1%。对于长基线（155公里和207公里），校正后的NWP约束RTK与标准RTK相比，初始化时间减少了43%以上，与标准NWP约束RTK相比，初始化时间减少了24%以上。
