Abstract
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.
Highlights
With the rapid development of Global Navigation Satellite System (GNSS) technique, it has become an indispensable tool for monitoring water vapor variation [1,2]
At the positions at of the stations are available per month, and all of them are employed to than 50 International GNSS Service (IGS) stations are available per month, and all of them are employed to investigate investigate the consistency of the extended the consistency of the extended ERA5S-zenith tropospheric delay (ZTD) and ERA5P-ZTD with IGS-ZTD
We extend the ERA5 than the IGS-ZTD
Summary
The troposphere constitutes most of the mass and water vapor of the entire atmosphere. The water vapor mainly concentrated in the troposphere below 10–12 km, which is an important meteorological factor causing climate change. 2 shows theare monthly bias and RMSE and ERA5P-ZTD by RMSE values in the range of −. 2.2–15.7 mm, with the corresponding average comparison with the IGS-ZTD at all available stations. 2021, 13, x FOR PEER REVIEW bias values are in the range of -2.2-15.7 mm, with the corresponding average RMSE values of 17.4-42.3. Higher the value of thevalue monthly bias has, thebias big-has, values ofmm. The largest average monthly bias is 15.7 mm maximum and minimum valuesvalues of 68.4ofmm mm August
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
