QUANTITATIVE ASSESMENT AND REFINEMENT OF TROPOSPHERIC EFFECT ON EQUATOR AREA
Tropospheric delays have always been an important issue that affect both optical and microwave frequencies such as satellite laser ranging (SLR), global navigation satellite system (GNSS) and also very long baseline interferometry (VLBI). One of the determining factors in tropospheric delays is the...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/55519 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Tropospheric delays have always been an important issue that affect both optical and microwave frequencies such as satellite laser ranging (SLR), global navigation satellite system (GNSS) and also very long baseline interferometry (VLBI). One of the determining factors in tropospheric delays is the particles in the atmosphere, which causes atmospheric refraction in the troposphere. This is where the zenith hydrostatics delays (ZHD) and zenith non-hydrostatics delays come in to predict the delay at optical and microwave frequencies. ZHD can be modeled using two methods: Saastamoinen and Vertical Integration of density. ZHD model from Saastamoinen formula will be using three separate yearly data of surface pressure from in-situ meteorological measurement, ERA5 ECMWF and GPT2 model. ZHD model from vertical integral will be using density data from ERA5. These models then will be statistically analyzed using correlation, standard deviation, root mean square and accuracy of each ZHD model to determine the accuracy of input data and errors in GNSS positioning. By referencing to the in-situ meteorological measurement, ERA5 and GPT2 model on one year period of 2019, it is found that there is strong correlation between the three-input data. The accuracy of ZHD modelling in equator area varies 1-3mm differences which resulted erros in GNSS positioning up to 17.8 mm. This means that ZHD modelling in equatorial modelling using in situ pressure data have a significance difference up to 2.32mm compared to the one using data pressure model. Limitations of the present models are discussed and suggestions for further improvements are given.
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