Stochastic estimation of tropospheric path delays in global positioning system geodetic measurements

HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model.

Precise Point Positioning on the Reliable Detection of Tropospheric Model Errors

Precise point positioning (PPP) is one of the well-known applications of Global Navigation Satellite System (GNSS) and provides precise positioning solutions using accurate satellite orbit and clock products. The tropospheric delay due to the neutral atmosphere for microwave signals is one of the main sources of measurement error in PPP. As one component of this delay, the hydrostatic delay is usually compensated by using an empirical correction model. However, how to eliminate the effects of the wet delay during a weather event is a challenge because current troposphere models are not capable of considering the complex atmosphere around the receiver during situations such as typhoons, storms, heavy rainfall, et cetera. Thus, how positioning results can be improved if the residual wet delays are taken into account needs to be investigated . In this contribution, a real-time procedure of recursive detection, identification and adaptation (DIA) is applied to detect the model errors which have the same effects on both phase and code observables; e.g., the model error caused by the tropospheric delay. Once the model errors are identified, additional parameters are added to the functional model to account for the measurement residuals. This approach is evaluated with Global Positioning System (GPS) data during two rainfall events in Darwin, Australia, proving the usefulness of compensated residual slant wet delay for positioning results. Comparisons with the standard approach show that the precision of the up component is improved significantly during the periods of the weather events; for the two case studies, 72.46% and 64.41% improvements of root mean squared error (RMS) resulted, and the precision of the horizontal component obtained by the proposed approach is also improved more than 30% compared to the standard approach. The results also show that the identified model errors are concentrated at the beginning of both heavy rainfall processes when the front causes significant spatial and temporal gradients of the integrated water vapor above the receiver.

Comparison of total water vapor column from GOME-2 on MetOp-A against ground-based GPS measurements at the Iberian Peninsula

Water vapor column (WVC) obtained by GOME-2 instrument (GDP-4.6 version) onboard MetOp-A satellite platform is compared against reference WVC values derived from GPS (Global Positioning System) instruments from 2007 to 2012 at 21 places located at Iberian Peninsula. The accuracy and precision of GOME-2 to estimate the WVC is studied for different Iberian Peninsula zones using the mean (MBE) and the standard deviation (SD) of the GOME-2 and GPS differences. A direct comparison of all available data shows an overestimation of GOME-2 compared to GPS with a MBE of 0.7 mm (10%) and a precision quantified by a SD equals to 4.4mm (31%). South-Western zone presents the highest overestimation with a MBE of 1.9 mm (17%), while Continental zone shows the lowest SD absolute value (3.3mm) due mainly to the low WVC values reached at this zone. The influence of solar zenith angle (SZA), cloud fraction (CF), and the type of surface and its albedo on the differences between GOME-2 and GPS is analyzed in detail. MBE and SD increase when SZA increases, but MBE decreases (taking negative values) when CF increases and SD shows no significant dependence on CF. Under cloud-free conditions, the differences between WVC from GOME-2 and GPS are within the WVC error given by GOME-2. The changes of MBE and SD on Surface Albedo are not so evident, but MBE slightly decreases when the Surface Albedo increases. WVC from GOME-2 is, in general, more precise for land than for sea pixels.

Surface Displacement of the 17 May 1993 Eureka Valley, California, Earthquake Observed by SAR Interferometry

Satellite synthetic aperture radar (SAR) interferometry shows that the magnitude 6.1 Eureka Valley earthquake of 17 May 1993 produced an elongated subsidence basin oriented north-northwest, parallel to the trend defined by the aftershock distribution, whereas the source mechanism of the earthquake implies a north-northeast-striking normal fault. The +/-3-millimeter accuracy of the radar-observed displacement map over short spatial scales allowed identification of the main surface rupture associated with the event. These observations suggest that the rupture began at depth and propagated diagonally upward and southward on a west-dipping, north-northeast fault plane, reactivating the largest escarpment in the Saline Range.