Atmospheric Delay Correction Research Articles

Study of high-precision time transfer method enhanced by PPP-AR/PPP-RTK

Abstract With the continuous development and improvement of Global Navigation Satellite Systems (GNSS), the use of GNSS for high-precision time transfer has become increasingly mature. To delve into the contribution of information-enhanced GNSS PPP to time transfer performance, this paper focuses on the comprehensive evaluation and analysis of the time transfer performance of PPP-AR and PPP-RTK. Using GPS as a case study, experimental results demonstrate that the average success fixing rate of PPP integer ambiguity resolution across five stations is 93.57%, with initial fixing times consistently below 20 minutes. The stability of the station clock offset sequence of PPP-AR is better than that of PPP floating solution. Compared with the PPP floating solution, the average improvement of PPP-AR stability is 16.54%. Furthermore, using PPP-AR for time transfer not only enhances the stability of the time transfer link clock offset sequence, but also improves its noise level. The stability is increased by 14.41% on average, and the noise level is improved by 9.11% on average. PPP-RTK also improves the stability and noise level of time transfer even better than PPP-AR. However, as the reference network scale increases, the accuracy of the interpolated atmospheric delay correction decreases, impacting the performance of PPP-RTK.

Assessing Slip Rates on the Xianshuihe Fault Using InSAR with Emphasis on Phase Unwrapping Error and Atmospheric Delay Corrections

Located on the southeastern periphery of the Tibetan Plateau, the Xianshuihe fault (XSHF) is an active left-lateral strike-slip fault renowned for its frequent and intensive seismic activities. This highlights the necessity of employing advanced geodetic methodologies to precisely evaluate the fault kinematics and seismic hazard potential along this fault. Among these techniques, interferometric synthetic aperture radar (InSAR) stands out for its high spatial resolution and regular revisit intervals, enabling accurate mapping of interseismic deformation associated with fault motion. However, the precision of InSAR in measuring deformation encounters several challenges, particularly artifacts stemming from phase unwrapping errors and atmospheric phase delays. In this study, we utilize ascending and descending Sentinel-1 InSAR images spanning from January 2017 to January 2023 to drive the line-of-sight (LOS) mean crustal velocities associated with the XSHF with emphasis on phase unwrapping errors and atmospheric delay corrections. Then, the reliability of the derived LOS velocities is assessed using independent observations from the Global Navigation Satellite System (GNSS). The inferred fault slip rate along the XSHF shows significant along-strike variations, gradually decreasing from ~11.1 mm/yr at the Luhuo section to ~6.6 mm/yr at the Kangding section and then sharply increasing to ~13.0 mm/yr towards its eastern terminus at the Moxi section. The fault locking depth shows similar along-strike variations, decreasing from ~19.5 km in the northwestern part to ~4.8 km at the Kangding section, before increasing to 19.6 km at the Moxi segment. Notably, apparent surface fault creeping, characterized by a slip rate of ~2.7 mm/yr, is observed at the Kangding segment, likely resulting from postseismic slip following the 2014 Mw 6.3 Kangding earthquake.

Crowdsourcing RTK: a new GNSS positioning framework for building spatial high-resolution atmospheric maps based on massive vehicle GNSS data

High-quality spatial atmospheric delay correction information is essential for achieving fast integer ambiguity resolution (AR) in precise positioning. However, traditional real-time precise positioning frameworks (i.e., NRTK and PPP-RTK) depend on spatial low-resolution atmospheric delay correction through the expensive and sparsely distributed CORS network. This results in limited public appeal. With the mass production of autonomous driving vehicles, more cost-effective and widespread data sources can be explored to create spatial high-resolution atmospheric maps. In this study, we propose a new GNSS positioning framework that relies on dual base stations, massive vehicle GNSS data, and crowdsourced atmospheric delay correction maps (CAM). The map is easily produced and updated by vehicles equipped with GNSS receivers in a crowd-sourced way. Specifically, the map consists of between-station single-differenced ionospheric and tropospheric delays. We introduce the whole framework of CAM initialization for individual vehicles, on-cloud CAM maintenance, and CAM-augmented user-end positioning. The map data are collected and preprocessed in vehicles. Then, the crowdsourced data are uploaded to a cloud server. The massive data from multiple vehicles are merged in the cloud to update the CAM in time. Finally, the CAM will augment the user positioning performance. This framework forms a beneficial cycle where the CAM’s spatial resolution and the user positioning performance mutually improve each other. We validate the performance of the proposed framework in real-world experiments and the applied potency at different spatial scales. We highlight that this framework is a reliable and practical positioning solution that meets the requirements of ubiquitous high-precision positioning.

Slow slip event displacement on 2018 offshore Boso Peninsula detected by Sentinel-1 InSAR time-series analysis with numerical weather model assistance

SUMMARY The south-eastern offshore of the Boso Peninsula in Japan periodically experiences short-term slow slip events (SSEs) every few years. On 2018 June, an SSE occurred with the maximum surface horizontal displacement reaching up to 4.7 cm by according to the operational global navigation satellite system (GNSS) network. This study performed a time-series analysis of interferometric synthetic aperture radar (InSAR) with Sentinel-1 SAR images to investigate detailed spatial pattern of surface displacements caused by the SSE. With the assistance of an atmospheric delay correction with a regional numerical weather model output, the InSAR time-series analysis successfully captured displacement signals in three paths, whose maximum amplitudes in line-of-sight directions were 1.46, 1.86 and −0.80 cm. A checkerboard test revealed that the resolution of the slip inversion was higher when InSAR was used than that using GNSS, especially in and around the inland. The slip inversion with the actual displacement data derived from the InSAR time-series analysis was performed with the L-curve optimization, showing that the estimated slip area was concentrated offshore south-eastward from the Boso Peninsula with the maximum slip of 5 cm and the estimated moment magnitude of 6.4. As similar to previous SSEs in the Boso Peninsula, a seismic swarm simultaneously occurred in the downdip area adjacent to the estimated slip with the SSE occurrence, suggesting a different friction characteristics between them. This study demonstrates usefulnesses of the InSAR observation for capturing detailed spatial characteristics of small-displacement events like SSEs and of the hybrid use of the externally derived delay correction with the time-series analysis to improve the displacement detection accuracy.

An Interferometric Synthetic Aperture Radar Tropospheric Delay Correction Method Based on a Global Navigation Satellite System and a Backpropagation Neural Network: More Suitable for Areas with Obvious Terrain Changes.

Atmospheric delay correction remains a major challenge for interferometric synthetic aperture radar (InSAR) technology. In this paper, we first reviewed several commonly used methods for tropospheric delay correction in InSAR. Subsequently, considering the large volume and high temporal resolution of global navigation satellite system (GNSS) station measurement data, we proposed a method for spatial prediction of the InSAR tropospheric delay phase based on the backpropagation (BP) neural network and GNSS zenith total delay (ZTD). Using 42 Sentinel-1 interferograms over the Los Angeles area in 2021 as an example, we validated the accuracy of the BP + GNSS method in spatially predicting ZTD and compared the correction effects of BP + GNSS and five other methods on interferograms using the standard deviation (StaD) and structural similarity (SSIM). The results demonstrated that the BP + GNSS method reduced the root-mean-square error (RMSE) in spatial prediction by approximately 95.50% compared to the conventional interpolation method. After correction using the BP + GNSS method, StaD decreased in 92.86% of interferograms, with an average decrease of 52.03%, indicating significantly better correction effects than other methods. The SSIM of the BP + GNSS method was lower in mountainous and high-altitude areas with obvious terrain changes in the east and north, exhibiting excellent and stable correction performance in different seasons, particularly outperforming the GACOS method in autumn and winter. The BP + GNSS method can be employed to generate InSAR tropospheric delay maps with high temporal and spatial resolution, effectively addressing the challenge of removing InSAR tropospheric delay signals in areas with significant terrain variations.

Assimilating GNSS TEC with an LETKF over Yunnan, China

A robust ionospheric model is indispensable for providing the atmospheric delay corrections for global navigation satellite system (GNSS) navigation and positioning and forecasting the space environment. The accuracy of ionospheric models is limited due to the simplified model structures. Complicated spatiotemporal variations in total electron content (TEC) biases between GNSS and international reference ionosphere (IRI) suggest a robust strategy to optimally combine GNSS and IRI TEC for high-precision modeling. In this paper, we propose a novel ionospheric data assimilation method, which is a local ensemble transform Kalman filter (LETKF), to construct an ionospheric model over Yunnan in southwestern China. We used the LETKF method to assimilate the ionospheric TEC extracted from GNSS observations in Yunnan into the IRI-2016 model. The experimental results indicate that the ionospheric data assimilation has a more pronounced improvement effect on the IRI empirical model during periods of geomagnetic quiet than during periods of geomagnetic disturbance. On quiet magnetic days, the skill score (SKS) of the assimilation is 0.60 and the root mean square error (RMSE) values before and after assimilation are 5.08 TECU and 2.02 TECU, respectively. The correlation coefficient after assimilation increases from 0.94 to 0.99. On magnetic storm days, the SKS of the assimilation is 0.42 and the RMSE values before and after assimilation are 5.99 TECU and 3.46 TECU, respectively. The correlation coefficient after assimilation increases from 0.98 to 0.99. The results suggest that the LETKF algorithm can be considered an effective method for ionospheric data assimilation.

Study on the Variations in Water Storage in Lake Qinghai Based on Multi-Source Satellite Data

Performing research on the variation in lake water on the Qinghai–Tibet Plateau (QTP) can give the area’s ecological environmental preservation a scientific foundation. In this paper, we first created a high-precision dataset of lake water level variation every 10 days, from July 2002 to December 2022, using multi-source altimetry satellite SGDR data (Envisat RA-2, SARAL, Jason-1/2, and Sentinel-3A/3B SRAL), which integrated the methods of atmospheric path delay correction, waveform re-tracking, outlier detection, position reduction using a height difference model, and inter-satellite deviation adjustment. Then, using Landsat-5 Thematic Mapper, Landsat-7 Enhanced Thematic Mapper, and Landsat 8 Operational Land Imager data, an averaged area series of Lake Qinghai (LQ) from September to November, each year from 2002 to 2019, was produced. The functional connection between the water level and the area was determined by fitting the water level–area series data, and the lake area time series, of LQ. Using the high-precision lake water level series, the fitted lake surface area time series, and the water storage variation equation, the water storage variation time series of LQ was thus calculated every 10 days, from July 2002 to December 2022. When the hydrological gauge data from the Xiashe station and data from the worldwide inland lake water level database are used as references, the standard deviations of the LQ water level time series are 0.0676 m and 0.1201 m, respectively. The results show that the water storage of LQ increases by 11.022 × 109 m3 from July 2002 to December 2022, with a growth rate of 5.3766 × 108 m3/a. The growth rate from January 2005 to January 2015 is 4.4850 × 108 m3/a, and from January 2015 to December 2022, the growth rate is 8.9206 × 108 m3/a. Therefore, the increased rate of water storage in LQ over the last 8 years has been substantially higher than in the previous 10 years.

Improved PPP-RTK positioning performance by using the elevation-dependent weighting model for the atmospheric delay corrections

With satellite phase bias products, integer ambiguity resolution (AR) enabled precise point positioning (PPP) can be achieved, that is, PPP-real-time kinematic (RTK). However, PPP-AR still requires minutes to converge to centimeter-level accuracy, as atmospheric delay parameters need to be estimated in the user un-combined (UC) model. To shorten the convergence time, reference-network-derived atmospheric delay corrections are often directly corrected for raw code/phase observables, without considering their uncertainties, thereby causing model errors. In this study, we propose a new ionosphere-weighted model, in which an elevation-dependent weight is adopted. To evaluate the proposed model, on-board kinematic experiments were conducted based on GPS/Galileo/BDS2/ BDS3 dual-frequency measurements. Compared with the positioning results of the traditional determined variance weight, the proportion of horizontal errors less than 5 cm is increased from 70% to 80%, and horizontal accuracy is improved by 22% and 9.8% at the 68th and 95th percentile, respectively, indicating that the proposed model can improve the PPP-RTK performance.

Development and Validation of Comprehensive Closed Formulas for Atmospheric Delay and Altimetry Correction in Ground-Based GNSS-R

Radio wave propagation involved in Global Navigation Satellite System Reflectometry (GNSS-R) is subject to atmospheric refraction. Even for ground-based tracking stations, in applications such as coastal sea-level altimetry, the interferometric or reflection-minus-direct effect might be significant. Although atmospheric propagation delays are best investigated numerically via raytracing, including reflections, such a procedure is not trivial. We have developed simpler closed formulas to account for atmospheric refraction in ground-based GNSS-R, validated against independent raytracing. We provide specific expressions for the two components of the atmospheric interferometric delay and corresponding altimetry correction components, parameterized in terms of refractivity and bending angle. Assessment results showed excellent agreement for both components. We define the interferometric slant factor used to map interferometric zenithal delays to individual satellites.

Time-series InSAR tropospheric atmospheric delay correction based on common scene stacking

Time-series InSAR tropospheric atmospheric delay correction based on common scene stacking

Atmospheric Delay Correction Utilization Method for Out-of-Network PPP-RTK Users

Traditional PPP-RTK algorithm research has focused on users in a reference station network. However, if a proper PPP-RTK user algorithm can be designed for out-of-network users to achieve accurate and rapid positioning, the effective service area of existing reference station networks can be markedly expanded. To address the problem of extrapolating atmospheric corrections for out-of-network users, this study presents a single-reference-station (SRS) PPP-RTK model to address atmospheric delay corrections, by ensuring that only corrections from the closest reference station are used. Several numerical experiments were designed and conducted to compare the performance of the presented model and a widely used interpolation model. The extrapolation error distribution, mean and the maximum of absolute values of errors, and standard deviation of errors were selected as indicators. The results show that the SRS PPP-RTK model is significantly more accurate than the interpolation model. Thus, for out-of-network users, we suggest that the presented SRS PPP-RTK model should be used, and the residual atmospheric delays are needed to be estimated.

Ground Deformation in Yuxi Basin Based on Atmosphere-Corrected Time-Series InSAR Integrated with the Latest Meteorological Reanalysis Data

Time-series interferometric synthetic aperture radar (TS-InSAR) is often affected by tropospheric artifacts caused by temporal and spatial variability in the atmospheric refractive index. Conventional temporal and spatial filtering cannot effectively distinguish topography-related stratified delays, leading to biased estimates of the deformation phases. Here, we propose a TS-InSAR atmospheric delay correction method based on ERA-5; the robustness and accuracy of ERA-5 data under the influence of different atmospheric delays were explored. Notably, (1) wet delay was the main factor affecting tropospheric delay within the interferogram; the higher spatial and temporal resolution of ERA-5 can capture the wet delay signal better than MERRA-2. (2) The proposed method can mitigate the atmospheric delay component in the interferogram; the average standard deviation (STD) reduction for the Radarsat-2 and Sentinel-1A interferograms were 19.68 and 14.75%, respectively. (3) Compared to the empirical linear model, the correlation between the stratified delays estimated by the two methods reached 0.73. We applied this method for the first time to a ground subsidence study in the Yuxi Basin and successfully detected three subsidence centers. We analyzed and discussed ground deformation causes based on rainfall and fault zones. Finally, we verified the accuracy of the proposed method by using leveling monitoring data.

An MT-InSAR Data Partition Strategy for Sentinel-1A/B TOPS Data

The Sentinel-1A/B satellite launched by European Space Agency (ESA) in 2014 provides a huge amount of free Terrain Observation by Progressive Scans (TOPS) data with global coverage to the public. The TOPS data have a frame width of 250 km and have been widely used in surface deformation monitoring. However, traditional Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) methods require large computer memory and time when processing full resolution data with large width and long strips. In addition, they hardly correct atmospheric delays and orbital errors accurately over a large area. In order to solve these problems, this study proposes a data partition strategy based on MT-InSAR methods. We first process the partitioned images over a large area by traditional MT-InSAR method, then stitch the deformation results into a complete deformation result by correcting the offsets of adjacent partitioned images. This strategy is validated in a flat urban area (Changzhou City in Jiangsu province, China), and a mountainous region (Qijiang in Chongqing City, China). Compared with traditional MT-InSAR methods, the precision of the results obtained by the new strategy is improved by about 5% for Changzhou city and about 15% for Qijiang because of its advantage in atmospheric delay correction. Furthermore, the proposed strategy needs much less memory and time than traditional methods. The total time needed by the traditional method is about 20 h, and by the proposed method, is about 8.7 h, when the number of parallel processing is 5 in the Changzhou city case. The time will be further reduced when the number of parallel processes increases.

InSAR Atmospheric Delay Correction Model Integrated from Multi-Source Data Based on VCE

With the rapid development of interferometric synthetic aperture radar (InSAR) measurement technology, its measurement accuracy requirements are increasing. Atmospheric delay errors must be corrected, especially in the case of crustal deformation monitoring, the 20% variation of tropospheric water vapor among InSAR pairs generally produces range from 10 cm to 14 cm deformation errors. Such errors can be of the same magnitude as the annual changes in crustal deformation, or even greater, masking crustal deformation information and seriously affecting the results of crustal deformation monitoring. Therefore, in order to obtain a more accurate InSAR atmospheric delay correction model, this paper calculated and integrated atmospheric delays that were estimated by different sources, including the 37 pressure levels of the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF)) numerical weather prediction model, ECMWF Reanalysis v5 (ERA5), and Global Navigation Satellite System (GNSS) measurement data from the crustal movement observation network of China, based on the variance component estimation (VCE) weighting method. The results showed that the integrated model, based on the VCE method, is better than the generic atmospheric correction online service (GACOS) model for InSAR measuring of crustal deformation. The precision in monitoring crustal deformations was improved by approximately 5 mm, the correlation coefficient of atmospheric delay errors and crustal deformations improved from 0.287 to 0.347, and accuracy improved by approximately 25%. However, the improvement in accuracy was limited because of system error decoherence that was induced by atmospheric noise caused by abundant vegetation or snow cover. Therefore, in order to achieve more accurate results, we recommend the adoption of the multi-source integrated atmospheric delay correction model, based on the VCE method, for InSAR high-precision measuring of crustal deformation and seismic activities.

Correction of Atmospheric Delay Error of Airborne and Spaceborne GNSS-R Sea Surface Altimetry

Improving the measurement accuracy is a necessary condition for sea surface altimetry using the Global Navigation Satellite System Reflectometry (GNSS-R). The ionosphere and troposphere delay the transmission of satellite signals, which directly affect the measuring accuracy. The influence of the atmospheric environment on GNSS-R altimetry differs from different platforms. By analyzing and sorting out the altimetry data of airborne and spaceborne platforms, this paper studies the variation law of signal delay in the altimetry process from the point of view of mathematical geometry, which provides an example for improving the precision of GNSS-R altimetry measurements. Firstly, in order to facilitate data analysis, this paper constructed an altimetry model with the GNSS satellite position, specular reflection point position, receiver position as nodes, classified direct signals, and reflected signals. Secondly, calculate ionospheric puncture point coordinates , and interpolate GIM products provided by IGS using time and puncture point coordinates to obtain the VTEC value in the vertical direction of the puncture point, which was converted into the path direction STEC by projection function, the ionospheric delay of each part was obtained in this way. The tropospheric delay of each part is considered for the along-path component and the geometric component, the delay of along-path component was calculated by the UNB3m model, and the delay of geometric component was calculated by the equation provided by Nikolaidou (Nikolaidou et al., 2021). Thirdly, by comparing the sea surface height inversion results with or without atmospheric delay correction with the mean sea surface height provided by DTU15, the measurement accuracy with atmospheric delay correction is obviously improved. The study results of the influence of atmospheric delay on the altimetry experiments precision error of airborne and spaceborne platforms show that the error magnitude is consistent with the existing literature. In the airborne experiment, the influence of the ionosphere is negligible and the troposphere has sub-meter influence on altimetry results, among which the tropospheric along-path delay component occupies a high proportion. The geometric delay component has a high correlation with the satellite elevation angle and its influence on the measurement accuracy decreases with the elevation angle increase. The effect of this factor can be effectively weakened by setting a high satellite cutoff angle. In the spaceborne experiment, the effect of atmospheric delay on altimetry results fluctuates in the range of 3∼5 m when the satellite elevation angle is greater than 60°. In this paper, the method of calculating signal atmospheric delay through geometric relation to improving the measurement accuracy can provide an example for the atmospheric delay correction of GNSS-R ocean altimetry with high precision and spatial resolution in future research.

Adaptive Fusion of Multi-Source Tropospheric Delay Estimates for InSAR Deformation Measurements

Atmospheric propagation delay correction is the key to improving the accuracy of deformation measurement of satellite interferometric synthetic aperture radar (InSAR). The empirical phase-elevation models and external data-based models present uneven performances of atmospheric delay correction for InSAR deformation monitoring. In this study, based on our previous fusion of delays predicted by multiple weather models (FDWM), we propose a new approach of adaptive fusion of multi-source tropospheric delay (AFMTD) estimates derived from multiple models over wide areas, i.e., ERA5, GACOS, WRF, MERRA2, NARR, MODIS, Linear model, and Powerlaw model. The spatially varying scaling algorithm is employed to refine the tropospheric delays predicted by the weather models. Meanwhile, we adopt a multiple-window strategy to cope with the spatially lateral variation of tropospheric delays. The AFMTD not only improves the spatial heterogeneity of tropospheric delay, but also adaptively combines multiple models to achieve a more reliable delay estimation. This AFMTD method is incorporated into the StaMPS-SBAS procedure. We compared the AFMTD with other single models using ENVISAT ASAR and Sentinel-1 datasets over Los Angeles of Southern California. The result of ASAR first demonstrates the effectiveness and reliability of the AFMTD method by referring to the assumed ground truth of simultaneous MERIS observations. The results of Sentinel-1 data show that over 95% of unwrapped interferograms have the minimum root-mean-square values after AFMTD correction for both descending and ascending tracks. The validation against GPS observation presents that the RMSEs of InSAR displacement time series after AFMTD correction decreases at more than 90% of 125 GPS stations. The average reductions of RMSE are 35.79% and 36.28% for descending and ascending data, respectively, and the maximum improvement is more than 70%. Overall, the proposed AFMTD method outperforms any single model for InSAR tropospheric delay correction and provides an open framework to fuse multi-source tropospheric delay estimates.

Development of InSAR Neutral Atmospheric Delay Correction Model by Use of GNSS ZTD and Its Horizontal Gradient

Interferometric Synthetic Aperture Radar (InSAR) often suffers from atmospheric disturbances due to the microwave propagation delay effect, which limits the surface displacement detection accuracy to an order of centimeters or more. Here I developed a new neutral atmospheric delay correction model for InSAR by using the global navigation satellite system (GNSS) zenith total delay (ZTD) and its horizontal gradient data. The proposed model at first retrieves the regularly gridded ZTD distribution at sea level and the linear height dependence from GNSS ZTD and gradient observations by the least squares method. Then, the gridded ZTD is projected onto the InSAR coordinate to correct the neutral atmospheric delay. I evaluated the correction model performance by applying it to L-band ALOS-2/PALSAR-2 ScanSAR interferograms over the Kanto plain in Japan. The correction result showed that by applying the proposed delay correction the phase standard deviation decreased by 33.87 % on average. By comparing it with the generic atmospheric correction online service for InSAR (GACOS) model and the correction by the Japanese regional mesoscale weather model (MSM), the proposed GNSS-based model outperformed others in my test case. The sensitivity test indicated that including the delay gradient could improve delay reproducibility under situations with fewer available GNSS stations. Although the proposed correction model’s applicability depends on the number of available GNSS stations at the area of interest, the proposed model has a potential to effectively mitigate the neutral atmospheric delay and to improve the detection ability for smallamplitude surface displacements.

Atmospheric phase delay correction of PS-InSAR to Monitor Land Subsidence in Surabaya

Atmospheric phase delay is one of the most significant errors limiting the accuracy of Interferometric Synthetic Aperture Radar (InSAR) results. In this research, we used the Generic Atmospheric Correction Online Service for InSAR (GACOS) data to correct the tropospheric delay modeling from the persistent scatterers’ InSAR monitoring. Eighty-one (81) Sentinel-1A images and tropospheric delay maps from GACOS monitored land subsidence in Surabaya city between 2017 and 2019. InSAR processing was carried out using the GMTSAR software, continued with StaMPS and TRAIN, which were used to correct the tropospheric delay of PSInSAR-derived deformation measurements. The results before and after the atmospheric phase delay correction using GACOS were confirmed and analyzed in the main subsidence area. The findings of the experiments reveal that the atmospheric phase affects the mean LOS velocity results to some extent. The average difference between PS-InSAR before and after tropospheric correction is 1.734 mm/year with a standard deviation of 0.550 mm/year. The significance test of the two variables, 95%, showed that the tropospheric correction with GACOS data could affect the PS-InSAR results. Furthermore, GACOS correction may increase the error at some points, which could be due to its turbulence data’s low accuracy.

Performance of Earth Troposphere Calibration Measurements With the Advanced Water Vapor Radiometer for the Juno Gravity Science Investigation

AbstractThe performance of an atmospheric water vapor radio path delay measurement and correction is reported using observations of the Juno Spacecraft during gravity field mapping of Jupiter. The Juno Gravity Science instrument measures the Doppler shift on the radio signals between the Juno spacecraft and the Earth‐based observing stations of NASA's Deep Space Network (DSN) during times of closest approach to Jupiter in order to map Jupiter's gravity field. These Doppler measurements are affected by noise sources between the spacecraft and DSN which include charged particles plasma and Earth's troposphere. A pair of redundant Advanced Water Vapor Radiometers (AWVR) co‐located at the DSN's DSS‐25 antenna in Goldstone, CA measure the brightness temperatures at the 22.2, 23.8 and 31.4 GHz spectral lines for water vapor content in Earth's atmosphere. The measurements of water vapor content are reduced into a measurement of radio path delay, which provides high‐precision calibrations of tropospheric noise on the radio links. Analyses of AWVR measurements show that spacecraft tracking errors, after removing other measurable errors from charged particles, are reduced by 46% on average and by as much as 70%, depending on local weather conditions near the Goldstone site.

Modified Interpolation Method of Multi-Reference Station Tropospheric Delay Considering the Influence of Height Difference

In network real-time kinematic (NRTK) positioning, atmospheric delay information is critical for generating virtual observations at a virtual reference station (VRS). The traditional linear interpolation method (LIM) is widely used to obtain the atmospheric delay correction. However, even though the conventional LIM is robust in the horizontal direction of the atmospheric error, it ignores the influence of the vertical direction, especially for the tropospheric error. If the height difference between the reference stations and the rover is large and, subsequently, tropospheric error and height are strongly correlated, the performance of the traditional method is degraded for tropospheric delay interpolation at the VRS. Therefore, considering the height difference between the reference stations and the rover, a modified linear interpolation method (MLIM) is proposed to be applied to a conventional single Delaunay triangulated network (DTN). The systematic error of the double-differenced (DD) tropospheric delay in the vertical direction is corrected first. The LIM method is then applied to interpolate the DD tropospheric delay at the VRS. In order to verify the performance of the proposed method, we used two datasets from the American NOAA continuously operating reference stations (CORS) network with significant height differences for experiments and analysis. Results show that the DD tropospheric delay interpolation accuracy obtained by the modified method is improved by 84.1% and 69.6% on average in the two experiments compared to the conventional method. This improvement is significant, especially for low elevation satellites. In rover positioning analysis, the traditional LIM has a noticeable systematic deviation in the up component. Compared to the conventional method, the positioning accuracy of the MLIM method is improved in the horizontal and vertical directions, especially in the up component. The accuracy of the up component is reduced from tens of centimeters to a few centimeters and demonstrates better positioning stability.