Refined Tropospheric Delay Models for CONT11

2015 ◽  
pp. 65-69 ◽  
Author(s):  
D. Landskron ◽  
A. Hofmeister ◽  
J. Böhm
Keyword(s):  
Tropospheric Delay ◽  
Delay Models

Evaluation of Spatio‐Temporal Characteristics of Different Zenith Tropospheric Delay Models in Antarctica

Radio Science ◽  
2020 ◽  
Vol 55 (5) ◽  
Author(s):  
Fei Li ◽  
Qingchuan Zhang ◽  
Shengkai Zhang ◽  
Jintao Lei ◽  
Wenhao Li
Keyword(s):  
Tropospheric Delay ◽  
Temporal Characteristics ◽  
Zenith Tropospheric Delay ◽  
Delay Models ◽  
Spatio Temporal

Assessment of ECMWF-derived tropospheric delay models within the EUREF Permanent Network

GPS Solutions ◽  
2010 ◽  
Vol 15 (1) ◽  
pp. 39-48 ◽  
Author(s):  
F. Fund ◽  
L. Morel ◽  
A. Mocquet ◽  
J. Boehm
Keyword(s):  
Tropospheric Delay ◽  
Delay Models

scholarly journals Assessment of Three Tropospheric Delay Models (IGGtrop, EGNOS and UNB3m) Based on Precise Point Positioning in the Chinese Region

Sensors ◽  
2016 ◽  
Vol 16 (1) ◽  
pp. 122 ◽  
Author(s):  
Hongxing Zhang ◽  
Yunbin Yuan ◽  
Wei Li ◽  
Ying Li ◽  
Yanju Chai
Keyword(s):  
Precise Point Positioning ◽  
Tropospheric Delay ◽  
Delay Models ◽  
Point Positioning

scholarly journals Regional Zenith Tropospheric Delay Modeling Based on Least Squares Support Vector Machine Using GNSS and ERA5 Data

2021 ◽  
Vol 13 (5) ◽  
pp. 1004
Author(s):  
Song Li ◽  
Tianhe Xu ◽  
Nan Jiang ◽  
Honglei Yang ◽  
Shuaimin Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
High Efficiency ◽  
Rapid Development ◽  
Support Vector ◽  
Tropospheric Delay ◽  
Improvement Rate ◽  
Zenith Tropospheric Delay ◽  
Background Data ◽  
Mean Square Errors

The meteorological reanalysis data has been widely applied to derive zenith tropospheric delay (ZTD) with a high spatial and temporal resolution. With the rapid development of artificial intelligence, machine learning also begins as a high-efficiency tool to be employed in modeling and predicting ZTD. In this paper, we develop three new regional ZTD models based on the least squares support vector machine (LSSVM), using both the International GNSS Service (IGS)-ZTD products and European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) data over Europe throughout 2018. Among them, the ERA5 data is extended to ERA5S-ZTD and ERA5P-ZTD as the background data by the model method and integral method, respectively. Depending on different background data, three schemes are designed to construct ZTD models based on the LSSVM algorithm, including the without background data, with the ERA5S-ZTD, and with the ERA5P-ZTD. To investigate the advantage and feasibility of the proposed ZTD models, we evaluate the accuracy of two background data and three schemes by segmental comparison with the IGS-ZTD of 85 IGS stations in Europe. The results show that the overall average Root Mean Square Errors (RMSE) value of all sites is 30.1 mm for the ERA5S-ZTD, and 10.7 mm for the ERA5P-ZTD. The overall average RMSE is 25.8 mm, 22.9 mm, and 9 mm for the three schemes, respectively. Moreover, the overall improvement rate is 19.1% and 1.6% for the ZTD model with ERA5S-ZTD and ERA5P-ZTD, respectively. In order to explore the reason of the lower improvement for the ZTD model with ERA5P-ZTD, the loop verification is performed by estimating the ZTD values of each available IGS station. In actuality, the monthly improvement rate of estimated ZTD is positive for most stations, and the biggest improvement rate can even reach about 40%. The negative rate mainly comes from specific stations, these stations are located on the edge of the region, near the coast, as well as the lower similarity between the individual verified station and training stations.

Estimation and Compensation of Turbulent Tropospheric Delay in High-Resolution SAR Image

2021 ◽  
pp. 1-15 ◽  
Author(s):  
Dexin Li ◽  
Zhen Dong ◽  
Yu Anxi ◽  
Yongsheng Zhang ◽  
Qilei Zhang ◽  
...  
Keyword(s):  
High Resolution ◽  
Tropospheric Delay ◽  
Sar Image
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