| 丁茂华,李振,丁家庭,彭卓越,姚晔.高精度天顶静力学延迟与天顶湿延迟分离算法研究[J].电子测量与仪器学报,2026,40(2):281-290 |
| 高精度天顶静力学延迟与天顶湿延迟分离算法研究 |
| Highly accurate algorithm for separation between zenithhydrostatic delay and zenith wet delay |
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| DOI： |
| 中文关键词: 全球卫星导航系统 对流层天顶延迟产品 无实测气象参数的ZHD模型 无实测气象参数的ZWD模型 多层前馈神经网络 |
| 英文关键词:global navigation satellite system zenith tropospheric delay products ZHD model without measured meteorological parameters ZWD model without measured meteorological parameters multilayer feedforward neural network |
| 基金项目:江苏省自然科学基金（BK20230603）、江苏省社会科学基金（24ZHC010）项目资助 |
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| Author | Institution |
| Ding Maohua | College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225009，China |
| Li Zhen | College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225009，China |
| Ding Jiating | College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225009，China |
| Peng Zhuoyue | College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225009，China |
| Yao Ye | College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225009，China |
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| 全文下载次数: 166 |
| 中文摘要: |
| 基于高精度的全球卫星导航系统（GNSS）测量技术，GNSS用户能够获取精确的对流层天顶延迟（ZTD）产品。为了应用GNSS测量的ZTD产品，分离天顶静力学延迟（ZHD）与天顶湿延迟（ZWD）是重要步骤。在ZHD/ZWD分离过程中，需依托于高精度的ZHD模型。提出了一个新的ZHD模型——NNSZHD模型，该模型专用于ZHD/ZWD分离。NNSZHD模型的建立基于人工智能算法，将直接计算法获取的ZHD和间接计算法获取的ZHD进行组合建模。直接计算法是基于无实测气象参数的ZHD模型计算的ZHD值，间接计算法是采用ZTD测量值减去无实测气象参数的ZWD模型计算的ZWD。基于此建模思路，并依托当前较高精度的无实测气象参数的ZTD模型——GTrop模型，选取3个关键参数（由GTrop模型计算的ZHDGTrop和ZWDGTrop以及ZTD测量值）作为建模参数，并采用多层前馈神经网络作为NNSZHD模型的建模框架。通过全球389个探空站的数据对NNSZHD模型进行训练，并使用另外的375个站点的数据对该模型进行精度评估。结果表明，NNSZHD模型在地表附近的模型精度为12.35 mm，较GTrop模型和GPT2w模型精度分别提升3.78和3.40 mm。在10 km高度以下对流层区域，NNSZHD模型的精度为7.52 mm；与GTrop模型和GPT2w模型相比，精度分别提升了7.08和35.13 mm。在仅能使用无实测气象参数的ZTD模型的条件下，NNSZHD模型在GNSS对流层延迟误差改正和GNSS气象学等领域具有广泛的应用前景。 |
| 英文摘要: |
| Because the measurement techniques using global navigation satellite system (GNSS) have high accuracies, GNSS users can obtain accurate zenith tropospheric delay (ZTD) products. To use GNSS-measured ZTD products, the separation between zenith hydrostatic delay (ZHD) and zenith wet delay (ZWD) is an important step. In this separation step, high accurate ZHD models are required. This study proposes a new ZHD model—the NNSZHD model, specifically designed for the separation between ZHD and ZWD. The development of the NNSZHD model is based on a novel modeling approach, which combines the ZHD values obtained from the direct calculation method and those from the indirect calculation method and is carried out by using an artificial intelligence algorithm. The direct calculation method refers to ZHD values calculated from ZHD model without measured meteorological parameters; while the indirect calculation method refers to ZHD values obtained by subtracting ZWD values (from ZWD model without measured meteorological parameters) from ZTD measurements. Then this study is based on this modeling framework and the GTrop model, which is a state-of-the-art ZTD model without measured meteorological parameters. Three key parameters (ZHDGTrop and ZWDGTrop obtained from the GTrop model, as well as ZTD measurements) are set as the modeling parameters. A multilayer feedforward neural network is used to develop the modeling framework of the NNSZHD model. The NNSZHD model is trained using data from 389 global sounding stations and the evaluation of its accuracy is carried out by using data from an additional 375 stations. The results show that for the region near the surface, the NNSZHD model has an accuracy of 12.35 mm, and its accuracy improves by 3.78 and 3.40 mm respectively compared with the GTrop and GPT2w models. For the tropospheric region with heights below 10 km, the model’s accuracy reaches 7.52 mm, and it shows improvements of respective 7.08 and 35.13 mm compared with the GTrop and GPT2w models. When we can only rely on ZTD models without measured meteorological parameters, the NNSZHD model has significant potential applications in GNSS tropospheric delay corrections and GNSS meteorology. |
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