4.3. The impact of the choice for the temporal resolution of the inter-technique cable delay model
In order to evaluate whether the combination of VLBI and GPS leads to an improvement of the frequency transfer stability w.r.t. the GPS-only solution, the ratio
Equation (3)
is defined, where N is the number of baselines that are averaged. The ratio κ(τ) describes the average improvement/degradation when combining VLBI with GPS on the observation level for frequency transfer, compared to a GPS-only solution. Since the intervals for the PLO clock model were set to 5 min (see table 1) in both solutions one can compute κ(τ) in a straightforward way. Improvements in the overall frequency transfer performance will then be reflected as κ(τ) > 1, whereas degradations can be recognized when κ(τ) < 1. Since the data were processed in batches of 3 consecutive days one would expect to have N = 5centerdot6centerdot5/2 = 75 baselines when analysing the 15 d long CONT11 campaign with the reduced 6 station network depicted in figure 2. However, since the GPS receiver at TSKB lost lock on the third day of CONT11 and the GPS receiver at HRAO had a similar problem on the 5th day, these two stations were excluded from the first and second 3 d batch solutions, respectively. Thus, the total number of baselines over which κ(τ) can be computed reduces to N = 65. Figure 4 shows how well the clock differences match those obtained from the single-technique GPS PPP solution. VCE helps to determine technique specific weights that integrate VLBI observations in a way which does not degrade the frequency transfer performance, mainly determined by GPS. In order to study this in more detail one needs to evaluate κ(τ) for the different processing options. Figure 6 depicts κ(τ) for solutions with different choices for the temporal resolution of the inter-technique cable delay model Δclk(t). Overall, it can be seen that the combination of VLBI with GPS tends to improve the average frequency transfer stability w.r.t. the GPS-only solution. However, as anticipated in the previous section, the choice of the temporal resolution of Δclk(t) is crucial. The use of an interval length of 1 h for the PLO of Δclk(t) absorbs almost all benefit gained from adding VLBI. On the other hand, it is clearly visible that daily estimates or parametrization as a constant lead to a degradation of the short term stability while improving the long-term stability more than any of the other choices for the temporal resolution of Δclk(t). In general, one can see that VLBI improves the frequency transfer stability for averaging periods between 3000 and 20 000 seconds as well as for periods close to one day. The latter improvement can be explained by the fact that VLBI helps to smooth the jumps introduced by day boundary discontinuities of the used IGS orbit and clock products. However, one needs to consider also the lower significance (higher uncertainty) of the MDEV at the far end of the long averaging period domain. The improvement between 3000 and 30 000 seconds is thought to have its origin in the parametrization of tropospheric estimates, which become more robust against data artifacts, when combining VLBI and GPS. In addition, a temporal resolution of 12 h or longer for Δclk(t) leads to an improvement for averaging periods of 12 h, which might relate to the orbital period of GPS satellites.
4.3.ผลกระทบของทางเลือกสำหรับการแก้ปัญหาชั่วคราวรุ่นเลื่อนสายเทคนิคระหว่างเพื่อประเมินว่า VLBI และ GPS ที่นำไปสู่การปรับปรุงความถี่โอนเสถียรภาพ w.r.t. แก้ปัญหา GPS เดียว อัตราส่วนสมการ (3)is defined, where N is the number of baselines that are averaged. The ratio κ(τ) describes the average improvement/degradation when combining VLBI with GPS on the observation level for frequency transfer, compared to a GPS-only solution. Since the intervals for the PLO clock model were set to 5 min (see table 1) in both solutions one can compute κ(τ) in a straightforward way. Improvements in the overall frequency transfer performance will then be reflected as κ(τ) > 1, whereas degradations can be recognized when κ(τ) < 1. Since the data were processed in batches of 3 consecutive days one would expect to have N = 5centerdot6centerdot5/2 = 75 baselines when analysing the 15 d long CONT11 campaign with the reduced 6 station network depicted in figure 2. However, since the GPS receiver at TSKB lost lock on the third day of CONT11 and the GPS receiver at HRAO had a similar problem on the 5th day, these two stations were excluded from the first and second 3 d batch solutions, respectively. Thus, the total number of baselines over which κ(τ) can be computed reduces to N = 65. Figure 4 shows how well the clock differences match those obtained from the single-technique GPS PPP solution. VCE helps to determine technique specific weights that integrate VLBI observations in a way which does not degrade the frequency transfer performance, mainly determined by GPS. In order to study this in more detail one needs to evaluate κ(τ) for the different processing options. Figure 6 depicts κ(τ) for solutions with different choices for the temporal resolution of the inter-technique cable delay model Δclk(t). Overall, it can be seen that the combination of VLBI with GPS tends to improve the average frequency transfer stability w.r.t. the GPS-only solution. However, as anticipated in the previous section, the choice of the temporal resolution of Δclk(t) is crucial. The use of an interval length of 1 h for the PLO of Δclk(t) absorbs almost all benefit gained from adding VLBI. On the other hand, it is clearly visible that daily estimates or parametrization as a constant lead to a degradation of the short term stability while improving the long-term stability more than any of the other choices for the temporal resolution of Δclk(t). In general, one can see that VLBI improves the frequency transfer stability for averaging periods between 3000 and 20 000 seconds as well as for periods close to one day. The latter improvement can be explained by the fact that VLBI helps to smooth the jumps introduced by day boundary discontinuities of the used IGS orbit and clock products. However, one needs to consider also the lower significance (higher uncertainty) of the MDEV at the far end of the long averaging period domain. The improvement between 3000 and 30 000 seconds is thought to have its origin in the parametrization of tropospheric estimates, which become more robust against data artifacts, when combining VLBI and GPS. In addition, a temporal resolution of 12 h or longer for Δclk(t) leads to an improvement for averaging periods of 12 h, which might relate to the orbital period of GPS satellites.
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