5:48 PM - 5:51 PM
[SGD21-P02] Verification of the separation precision between tropospheric and coordinate parameters in kinematic PPP analysis
3-min talk in an oral session
Keywords:GPS, kinematic PPP analysis, tropospheric delay
Recently, kinematic GNSS analysis is generally used for crustal deformation phenomena within the day such as postseismic deformation after the large earthquake. The kinematic GNSS analysis, however, have a fundamental problem for the separation precision between unknown parameters such as the coordinate and tropospheric parameters, because of the both parameters have strong correlation between each others. In this study, we focused on the improvement of the separation precision between coordinate time series of kinematic GNSS and wet zenith tropospheric delay (WZTD).
We used GIPSY-OASIS II Ver. 6.3 software for the processing of whole sites of the GEONET in 10th March 2011. We applied the kinematic PPP strategy for the coordinate estimation. In the processing, we applied the every 6 hours nominal WZTD value as a priori information based on the ECMWF global numerical climate model. We also processed the data without a priori information for the comparison. In the processing, we assumed the white noise and random walk stochastic process for the coordinate time series and tropospheric parameters, respectively. These unknown parameters are very sensitive to assumed process noise parameters for each stochastic process. Thus, we also evaluated the effect of process noise value for WZTD parameter. We changed the value for the WZTD as (1) 1×10-8, (2) 1×10-7 and (3) 1×10-6 (unit: km/sqrt(sec)).We named the model applied a priori information of WZTD as “A”, and named the A1, A2, and A3 model for the each different process noise parameter result. In the same way, we named the result without a priori value as “N” and named N1, N2, and N3 model represented the each process noise result.
Based on these results, we found that clear offset in estimated WZTD value appeared between result with or without a priori information. It suggests that the a priori information of WZTD may give the impact to the accuracy of the vertical coordinate time series. Furthermore, the standard deviations of estimated coordinate time series did not depend on the with/without a priori value of the WZTD. It strongly depends on the assumed process noise of the WZTD. For example, the standard deviations of UD component at 0430 (Imabari) site in each model of A1, A2, A3, N1, N2, N3 are 20.9, 26.0, 44.2, 20.8, 26.0, 44.2 (unit: mm), respectively. This results suggest that the assumption of optimal process noise may be important for the precision. In the presentation, we will propose the optimal value of process noise in order to obtain time series of kinematic GNSS analysis with high accuracy and precision from more data sets.
We used GIPSY-OASIS II Ver. 6.3 software for the processing of whole sites of the GEONET in 10th March 2011. We applied the kinematic PPP strategy for the coordinate estimation. In the processing, we applied the every 6 hours nominal WZTD value as a priori information based on the ECMWF global numerical climate model. We also processed the data without a priori information for the comparison. In the processing, we assumed the white noise and random walk stochastic process for the coordinate time series and tropospheric parameters, respectively. These unknown parameters are very sensitive to assumed process noise parameters for each stochastic process. Thus, we also evaluated the effect of process noise value for WZTD parameter. We changed the value for the WZTD as (1) 1×10-8, (2) 1×10-7 and (3) 1×10-6 (unit: km/sqrt(sec)).We named the model applied a priori information of WZTD as “A”, and named the A1, A2, and A3 model for the each different process noise parameter result. In the same way, we named the result without a priori value as “N” and named N1, N2, and N3 model represented the each process noise result.
Based on these results, we found that clear offset in estimated WZTD value appeared between result with or without a priori information. It suggests that the a priori information of WZTD may give the impact to the accuracy of the vertical coordinate time series. Furthermore, the standard deviations of estimated coordinate time series did not depend on the with/without a priori value of the WZTD. It strongly depends on the assumed process noise of the WZTD. For example, the standard deviations of UD component at 0430 (Imabari) site in each model of A1, A2, A3, N1, N2, N3 are 20.9, 26.0, 44.2, 20.8, 26.0, 44.2 (unit: mm), respectively. This results suggest that the assumption of optimal process noise may be important for the precision. In the presentation, we will propose the optimal value of process noise in order to obtain time series of kinematic GNSS analysis with high accuracy and precision from more data sets.