Southern California Integrated GPS Network Research Articles

High‐Resolution Water Vapor Maps Obtained by Merging Interferometric Synthetic Aperture Radar and GPS Measurements

AbstractPrevious research has demonstrated several successful meteorological applications of interferometric synthetic aperture radar (InSAR) by mapping high‐resolution precipitable water vapor (PWV) over large areas with satisfactory precision (e.g., 1–2 mm). InSAR, however, can only measure temporal changes in the PWV, that is, the differential PWV (ΔPWV) between two SAR acquisition epochs. The construction of absolute PWV maps using InSAR observations remains an intractable problem. We present here a new method for constructing high‐resolution PWV maps by fusing InSAR and global positioning system (GPS) measurements. Our method involves: (1) extracting temporal differences in the zenith wet delays (ΔZWD) from InSAR observations with the assistance of synchronous GPS measurements, and (2) using a constrained minimum variance estimator (CMVE) to construct the high‐resolution maps of the absolute ZWDs by fusing the InSAR‐ΔZWD and the GPS‐ZWD samples, and finally (3) transforming the high‐resolution ZWDs to PWVs using an elevation‐dependent proportionality. Only a single interferogram was needed in our method, so we did not use any temporal (i.e., time‐series) hypotheses, for example, we did not assume that the average value of the temporal turbulent ZWDs was equal to zero. We validated the new method over the Southern California region using four Sentinel‐1 interferograms related to eight SAR images acquired in different seasons and in different years. We also used synchronous GPS measurements from the Southern California integrated GPS network. Eight high‐resolution maps of PWVs related to the eight SAR acquisitions were generated, covering ∼250 km in area at a ∼160 m spatial resolution. Comparisons between the CMVE method and the other two methods (GPS‐only‐based interpolation and time‐series InSAR stacking) were conducted. Experimental results showed that the CMVE method performed significantly better than conventional methods for constructing high‐resolution PWV maps, which is of great interest to a wide community of geophysicists and meteorologists.

REAL-TIME EARTHQUAKE MONITORING WITH SPATIO-TEMPORAL FIELDS

Abstract. With live streaming sensors and sensor networks, increasingly large numbers of individual sensors are deployed in physical space. Sensor data streams are a fundamentally novel mechanism to deliver observations to information systems. They enable us to represent spatio-temporal continuous phenomena such as radiation accidents, toxic plumes, or earthquakes almost as instantaneously as they happen in the real world. Sensor data streams discretely sample an earthquake, while the earthquake is continuous over space and time. Programmers attempting to integrate many streams to analyze earthquake activity and scope need to write code to integrate potentially very large sets of asynchronously sampled, concurrent streams in tedious application code. In previous work, we proposed the field stream data model (Liang et al., 2016) for data stream engines. Abstracting the stream of an individual sensor as a temporal field, the field represents the Earth’s movement at the sensor position as continuous. This simplifies analysis across many sensors significantly. In this paper, we undertake a feasibility study of using the field stream model and the open source Data Stream Engine (DSE) Apache Spark(Apache Spark, 2017) to implement a real-time earthquake event detection with a subset of the 250 GPS sensor data streams of the Southern California Integrated GPS Network (SCIGN). The field-based real-time stream queries compute maximum displacement values over the latest query window of each stream, and related spatially neighboring streams to identify earthquake events and their extent. Further, we correlated the detected events with an USGS earthquake event feed. The query results are visualized in real-time.

Vertical and horizontal displacements of Los Angeles from InSAR and GPS time series analysis: Resolving tectonic and anthropogenic motions

We resolve the complex ground deformation associated with tectonic and anthropogenic activities in Los Angeles, California by combining Interferometric Synthetic Aperture Radar (InSAR) and Global Positioning System (GPS) measurements acquired from 2003 to 2007. The three-dimensional (3-D) cumulative displacement velocity field is first derived based on Weighted Least Squares (WLS) InSAR analysis of both ascending and descending ENVISAT ASAR data aided by GPS measurements at 54 Southern California Integrated GPS Network (SCIGN) stations. Clear subsidence can be observed from the results in the Pomona and San Gabriel areas, respectively, while uplift in the Santa Fe Springs. The displacements are mainly due to the extraction and injection of groundwater or oil. The northwestern horizontal displacement detected also suggests that the area had experienced interseismic strain accumulation related to the San Andreas Fault. The varying northeastern displacement magnitudes indicate increasing displacement accumulation away from the San Andreas Fault. The results are validated with GPS measurements at 15 independent stations, with the root mean squares (RMS) residuals of the discrepancies being 2.4, 1.5 and 0.9mm/yr in vertical, northern and eastern directions, respectively. The seasonal displacement in Santa Ana Basin due to anthropogenic activities is examined with eight pairs of ascending and descending InSAR LOS displacement estimations selected from the WLS InSAR solution. It is clear that almost the entire Santa Ana Basin experiences significant seasonal vertical displacement with the maximal displacement reaching 43mm. The Newport-Inglewood Fault forms a sharp boundary of the seasonal vertical displacement field. In addition, seasonal oscillations of 10–20mm in east direction are detected near the margin of the Santa Ana Basin, across the Newport-Inglewood Fault. Strong correlations are found between the groundwater levels and the seasonal displacements at three wells, yielding an average elastic skeletal storage coefficients of 5.9×10−4. This indicates that the Santa Ana basin is dominated by the elastic deformation.

Wavelet packets based denoising method for measurement domain repeat-time multipath filtering in GPS static high-precision positioning

Repeatable satellite orbits can be used for multipath mitigation in GPS-based deformation monitoring and other high-precision GPS applications that involve continuous observation with static antennas. Multipath signals at a static station repeat when the GPS constellation repeats given the same site environment. Repeat-time multipath filtering techniques need noise reduction methods to remove the white noise in carrier phase measurement residuals in order to retrieve the carrier phase multipath corrections for the next day. We propose a generic and robust three-level wavelet packets based denoising method for repeat-time-based carrier phase multipath filtering in relative positioning; the method does not need tuning to work with different data sets. The proposed denoising method is tested rigorously and compared with two other denoising methods. Three rooftop data sets collected at the University of Nottingham Ningbo China and two data sets collected at three Southern California Integrated GPS Network high-rate stations are used in the performance assessment. Test results of the wavelet packets denoising method are compared with the results of the resistor–capacitor (RC) low-pass filter and the single-level discrete wavelet transform (DWT) denoising method. Multipath mitigation efficiency in carrier phase measurement domain is shown by spectrum analysis of two selected satellites in two data sets. The positioning performance of the repeat-time-based multipath filtering techniques is assessed. The results show that the performance of the three noise reduction techniques is about 1–46 % improvement on positioning accuracy when compared with no multipath filtering. The statistical results show that the wavelet packets based denoising method is always better than the RC filter by 2–4 %, and better than the DWT method by 6–15 %. These results suggest that the proposed wavelet packets based denoising method is better than both the DWT method and the relatively simple RC low-pass filter for noise reduction in multipath filtering. However, the wavelet packets based denoising method is not significantly better than the RC filter.

Determination of Plate Tectonics Using Least Squares Spectral Analysis of Daily GPS Time Series: Case Study of Southern California Integrated GPS Network

The Global Positioning System represents one of the measurement systems in understanding the movement of the Earth's surface. The accurate determination of station velocities – rates and directions as geophysical parameters of plate tectonics, requires that positional offsets and systematic effects in coordinate time series are correctly found and compensated. In our study we use a least-squares spectral analysis to compute and extract significant periodicities in GPS coordinate time series. The method was implemented on 125 continuously operating GPS stations of the Southern California Integrated GPS Network.

Assessment of Pseudorange Multipath at Continuous GPS Stations in Mexico

We conducted a study to quantify the amount of pseudorange multipath at continuous Global Positioning System (CGPS) stations in the Mexican territory. These CGPS stations serve as reference stations enabling rapid high-precision three-dimensional positioning capabilities, supporting a number of commercial and public safety applications. We studied CGPS data from a large number of publicly available networks spanning Mexico. These include the RGNA (National Active Geodetic Network) administered by INEGI (National Institute of Statistics and Geography), the PBO network (Plate Boundary Observatory) funded by the National Science Foundation (NSF) and operated by UNAVCO (University NAVstar Consortium), the Southern California Integrated GPS Network (SCIGN), which is a collaboration effort of the United States Geological Survey (USGS), Scripps Institution of Oceanography and the Jet Propulsion Laboratory (JPL), the UNAM network, operated by the National Seismological System (SSN) and the Institute of Geophysics of the National Autonomous University of Mexico (UNAM), the Suominet Geodetic Network (SNG) and the CORS (Continuously Operating Reference Station) network, operated by the Federal Aviation Administration (FAA). We evaluated a total of 53 CGPS stations, where dual-frequency geodetic-grade receivers collected GPS data continuously during the period from 1994 to 2012. Despite carefully selected locations, all GPS stations are, to some extent, affected by the presence of signal multipath. For GPS network users that rely on pseudorange observables, the existence of pseudorange multipath could be a critical source of error depending on the time scale of the application. Thus, to identify the most and the least affected GPS stations, we analyzed the averaged daily root mean square pseudorange multipath variations (MP1-RMS and MP2-RMS) for all feasible satellites tracked by the CGPS networks. We investigated the sources of multipath, including changes associated with hardware replacement (i.e., receiver and antenna type) and receiver firmware upgrades.

Spatial–temporal surface deformation of Los Angeles over 2003–2007 from weighted least squares DInSAR

The spatial–temporal evolution of surface displacement of Los Angeles area over 2003–2007 is measured with weighted least squares (WLS) small baseline (SB) DInSAR technique. 32 small baseline interferograms are generated from 18 SAR images acquired by the ENVISAT satellite and separated into two independent subsets. An additional interferogram with a longer baseline but good interferometric quality is used to link the two subsets. A time series of displacements with their corresponding standard deviations (STD) are derived from the WLS DInSAR solution by considering the interferometric displacement variances when determining the weighting scheme.Both the long-term trends and the seasonal variations of the displacements in the area are determined in the study and validated with GPS measurements from a number of stations of the Southern California Integrated GPS Network (SCIGN). The mean line-of-sight (LOS) displacement velocity map shows up to 3cm/year of ground motion and up to 10cm of accumulated displacements in Santa Fe Springs and −8cm in Pomona over 2003–2007. Seasonal variations are identified in Santa Ana basin, the San Gabriel valley and the Lytle Creek basin, respectively.

Mapping ground surface deformation using temporarily coherent point SAR interferometry: Application to Los Angeles Basin

Multi-temporal interferometric synthetic aperture radar (InSAR) is an effective tool to detect long-term seismotectonic motions by reducing the atmospheric artifacts, thereby providing more precise deformation signal. The commonly used approaches such as persistent scatterer InSAR (PSInSAR) and small baseline subset (SBAS) algorithms need to resolve the phase ambiguities in interferogram stacks either by searching a predefined solution space or by sparse phase unwrapping methods; however the efficiency and the success of phase unwrapping cannot be guaranteed. We present here an alternative approach – temporarily coherent point (TCP) InSAR (TCPInSAR) – to estimate the long term deformation rate without the need of phase unwrapping. The proposed approach has a series of innovations including TCP identification, TCP network and TCP least squares estimator. We apply the proposed method to the Los Angeles Basin in southern California where structurally active faults are believed capable of generating damaging earthquakes. The analysis is based on 55 interferograms from 32 ERS-1/2 images acquired during Oct. 1995 and Dec. 2000. To evaluate the performance of TCPInSAR on a small set of observations, a test with half of interferometric pairs is also performed. The retrieved TCPInSAR measurements have been validated by a comparison with GPS observations from Southern California Integrated GPS Network. Our result presents a similar deformation pattern as shown in past InSAR studies but with a smaller average standard deviation (4.6 mm) compared with GPS observations, indicating that TCPInSAR is a promising alternative for efficiently mapping ground deformation even from a relatively smaller set of interferograms.

A REGRESSION STUDY ON RELATIVE GPS ACCURACY FOR DIFFERENT VARIABLES

We know that position accuracies from relative and static Global Positioning System (GPS) measurements are degraded by a variety of physical, hardware and software effects. As a result, users often take special precautions to minimise these effects and, hopefully, improve their accuracies when planning and making measurements. In this study, the effects of the baseline length (BL) and observation duration (OD) on vertical and horizontal positioning errors for relative, static GPS measurements were experimentally evaluated. In making this evaluation, 5 min to 24 hours GPS data, collected over a 15 day period, from the Southern California Integrated GPS Network (SCIGN), were analyzed. The root mean square (RMS) of the differences between the true and measured vertical (up/ellipsoidal height) and horizontal (north and east/latitude and longitude) positions of the included stations were computed and then used to create empirical error equations dependent upon the BL and OD. Considerations for the number of satellites (SVs) and the positional dilution of precision (PDOP) were included too by necessity. With these equations, GPS users can better estimate their vertical and horizontal position accuracies based on the known BLs, desired ODs, available SVs, and predicted PDOPs when planning a project, or have an external standard of comparison when evaluating their results.

Interpolating atmospheric water vapor delay by incorporating terrain elevation information

In radio signal-based observing systems, such as Global Positioning System (GPS) and Interferometric Synthetic Aperture Radar (InSAR), the water vapor in the atmosphere will cause delays during the signal transmission. Such delays vary significantly with terrain elevation. In the case when atmospheric delays are to be eliminated from the measured raw signals, spatial interpolators may be needed. By taking advantage of available terrain elevation information during spatial interpolation process, the accuracy of the atmospheric delay mapping can be considerably improved. This paper first reviews three elevation-dependent water vapor interpolation models, i.e., the Best Linear Unbiased Estimator in combination with the water vapor Height Scaling Model (BLUE + HSM), the Best Linear Unbiased Estimator coupled with the Elevation-dependent Covariance Model (BLUE + ECM), and the Simple Kriging with varying local means based on the Baby semi-empirical model (SKlm + Baby for short). A revision to the SKlm + Baby model is then presented, where the Onn water vapor delay model is adopted to substitute the inaccurate Baby semi-empirical model (SKlm + Onn for short). Experiments with the zenith wet delays obtained through the GPS observations from the Southern California Integrated GPS Network (SCIGN) demonstrate that the SKlm + Onn model outperforms the other three. The RMS of SKlm + Onn is only 0.55 cm, while those of BLUE + HSM, BLUE + ECM and SKlm + Baby amount to 1.11, 1.49 and 0.77 cm, respectively. The proposed SKlm + Onn model therefore represents an improvement of 29–63% over the other known models.

Network strain filter: A new tool for monitoring and detecting transient deformation signals in GPS arrays

We have developed a tool to detect transient deformation signals from large‐scale (principally GPS) geodetic arrays, referred to as a Network Strain Filter (NSF). The strategy is to extract spatially and temporally coherent signals by analyzing data from entire geodetic networks simultaneously. The NSF models GPS displacement time series as a sum of contributions from secular motion, transient displacements, site‐specific local benchmark motion, reference frame errors, and white noise. Transient displacements are represented by a spatial wavelet basis with temporally varying coefficients that are estimated with a Kalman filter. A temporal smoothing parameter is also estimated online by the filter. The problem is regularized in the spatial domain by minimizing a smoothing (Laplacian) norm of the transient strain rate field. To test the performance of the NSF, we carried out numerical tests using the Southern California Integrated GPS Network station distribution and a 3 year long synthetic transient in a 6 year time series. We demonstrate that the NSF can identify the transient signal, even when the colored noise amplitude is comparable to that of transient signal. Application of the method to actual GPS data from the Japanese GPS network (GEONET) on the Boso Peninsula also shows that the NSF can detect transient motions resulting from aseismic fault slip.

Advanced InSAR atmospheric correction: MERIS/MODIS combination and stacked water vapour models

A major source of error for repeat‐pass Interferometric Synthetic Aperture Radar (InSAR) is the phase delay in radio signal propagation through the atmosphere (especially the part due to tropospheric water vapour). Based on experience with the Global Positioning System (GPS)/Moderate Resolution Imaging Spectroradiometer (MODIS) integrated model and the Medium Resolution Imaging Spectrometer (MERIS) correction model, two new advanced InSAR water vapour correction models are demonstrated using both MERIS and MODIS data: (1) the MERIS/MODIS combination correction model (MMCC); and (2) the MERIS/MODIS stacked correction model (MMSC). The applications of both the MMCC and MMSC models to ENVISAT Advanced Synthetic Aperture Radar (ASAR) data over the Southern California Integrated GPS Network (SCIGN) region showed a significant reduction in water vapour effects on ASAR interferograms, with the root mean square (RMS) differences between GPS‐ and InSAR‐derived range changes in the line‐of‐sight (LOS) direction decreasing from ∼10 mm before correction to ∼5 mm after correction, which is similar to the GPS/MODIS integrated and MERIS correction models. It is expected that these two advanced water vapour correction models can expand the application of MERIS and MODIS data for InSAR atmospheric correction. A simple but effective approach has been developed to destripe Terra MODIS images contaminated by radiometric calibration errors. Another two limiting factors on the MMCC and MMSC models have also been investigated in this paper: (1) the impact of the time difference between MODIS and SAR data; and (2) the frequency of cloud‐free conditions at the global scale.

A method for detecting ionospheric disturbances and estimating their propagation speed and direction using a large GPS network

A technique is developed for detecting short period (3–10 min) ionospheric disturbances and estimating their propagation speed and direction using data from a large GPS network (a hundred or more receivers). This method increases the signal‐to‐noise ratio of small signals and could be applied, autonomously, to process a large set of data for the study of the potential signal sources and statistical distributions of these disturbances. The integral electron content (IEC) for every satellite‐station pair in the network is extracted from dual frequency phase data. These IEC time series are then band‐pass‐filtered and cross‐correlated with each other. The resulting correlation power is an indication of the presence of a common disturbance recorded at the two stations, and the delay to the maximum correlation is a measurement of the propagation time between the ionospheric pierce points of the respective stations. A threshold on correlation power is used to select a subset of these delay measurements. The velocity of the detected perturbation is then estimated by fitting a two‐dimensional plane wave model to this subset of measurements. A technique is developed to remove the effects of time‐varying satellite motion and to reconstruct the waveform that would have been observed at a fixed point within the ionosphere. Consistency of the resulting velocity estimates is checked using a stacking‐alignment method and a time‐distance mapping that accounts for the motion of the GPS satellites. The sensitivity of the velocity estimate to both the assumed height of a thin‐layer ionosphere and the detection threshold value is studied. A simulation is used to demonstrate the IEC waveform distortion due to satellite motion, and an example is shown in which this distortion is able to shift the dominant frequencies of an actual disturbance outside of the passband of the filter, thereby preventing detection. Four weeks of data, in different seasons, collected using the Southern California Integrated GPS Network (SCIGN), were processed. Over the total of 28 days, 127 significant disturbances were detected, most with horizontal propagation speeds between 50–1000 m/s and westward directions of propagation. A few cases with exceptionally high speed (>2000 m/s) were observed. It is hypothesized that these are manifestations of disturbances that occur simultaneously throughout the ionosphere, rather than traveling waves. The rate of occurrence of disturbances in the 3–10 min band was found to be larger than expected. Observational biases of this method are discussed.

Influence of GPS Precipitable Water Vapor Retrievals on Quantitative Precipitation Forecasting in Southern California

Abstract The effects of precipitable water vapor (PWV) retrievals from the Southern California Integrated GPS Network (SCIGN) on quantitative precipitation forecast (QPF) skill are examined over two flood-prone regions of Southern California: Santa Barbara (SB) and Ventura County (VC). Two sets of QPFs are made, one using the initial water vapor field from the NCEP 40-km Eta initial analysis, and another in which the initial Eta water vapor field is modified by incorporating the PWV data from the SCIGN receivers. Lateral boundary data for the QPFs, as well as the hydrostatic component of the GPS zenith delay data, are estimated from the Eta analysis. Case studies of a winter storm on 2 February during the 1997/98 El Niño, and storms leading up to the La Conchita, California, landslide on 10 January 2005, show notably improved QPFs for the first 3–6 h with the addition of GPS PWV data. For a total of 47 winter storm forecasts between February 1998 and January 2005 the average absolute QPF improvement is small; however, QPF improvements exceed 5 mm in several underpredicted rainfall events, with GPS data also improving most cases with overpredicted rainfall. The GPS improvements are most significant (above or near the 2σ level) when the low-level winds off the coast of Southern California are from the southern (SW to SE) quadrant. To extend the useful forecast skill enhancement beyond six hours, however, additional sources of water vapor data over broader areas of the adjacent Pacific Ocean are needed.

Investigation of ionospheric electron content variations before earthquakes in southern California, 2003–2004

It has been proposed that earthquakes are preceded by electromagnetic signals detectable from ground‐ and space‐based measurements. Ionospheric anomalies, such as variations in the electron density a few days before earthquakes, are one of the precursory signals proposed. Since Global Positioning System (GPS) data can be used to measure the ionospheric total electron content (TEC), the technique has received attention as a potential tool to detect ionospheric perturbations related to earthquakes. Here, we analyze 2 years (2003–2004) of data from the Southern California Integrated GPS Network (SCIGN), a dense network of 265 continuous GPS stations centered on the Los Angeles basin, for possible precursors. This time period encompasses the December 2003, M6.6, San Simeon and September 2004, M6.0, Parkfield earthquakes. We produce TEC time series at all SCIGN sites and apply three different statistical tests to detect anomalous TEC signals preceding earthquakes. We find anomalous TEC signals but no statistically significant correlation, in time or in space, between these TEC anomalies and the occurrence of earthquakes in southern California for the 2003–2004 period. This result does not disprove the possibility of precursory phenomena but show the signal‐to‐noise ratio of a hypothetical TEC precursor signature is too low to be detected by the analysis techniques employed here. Precursors may still be revealed for future large earthquakes in well instrumented areas such as California and Japan, if the tests can be developed into techniques that can better separate external influences from the actual TEC signal.

Correction for interferometric synthetic aperture radar atmospheric phase artifacts using time series of zenith wet delay observations from a GPS network

Radar interferograms provide precise (millimeter level) measurements of crustal deformation at fine resolution over wide areas. The dominant error source in many interferograms results from spatial heterogeneity of the wet component of atmospheric refractivity, resulting in excess path length of the radar signal propagating through the neutral atmosphere. In this report, we introduce a method to compensate for atmospheric phase artifacts in a radar interferogram using spatially interpolated zenith wet delay (ZWD) data obtained from a network of Global Positioning System (GPS) receivers in the region imaged by the radar. Our method, based on Taylor's “frozen‐flow” hypothesis, uses GPS data recorded prior to and after the individual radar acquisition times to infer denser spatial networks of control points for interpolation than is suggested by the sparse and irregular spatial distribution of GPS receivers. We test this approach by correcting phase fluctuations observed in a radar interferogram over an area covered by the Southern California Integrated GPS Network stations, using maps of atmospheric delay interpolated from the denser network of GPS ZWD measurements generated by our method. We find that the largest improvement results from removing an altitude‐dependent model derived from the GPS data: the uncorrected interferogram in our example has an RMS fluctuation of 16.1 mm, which falls to 8.6 mm after the altitude correction. A map derived from ZWD measurements recorded only at the instances of radar acquisition reduced overall fluctuations in the data from 8.6 to 8.1 mm RMS, while using the inferred denser network reduces the error further to 7.5 mm RMS. Most of this residual variation is due to decorrelation of the signal and does not result from atmospheric propagation, however. After smoothing to remove decorrelation noise, the residual drops from 4.7 to 3.9 mm (GPS stations only) and then to 2.7 mm RMS when the denser network algorithm is used.

Spatiotemporal filtering using principal component analysis and Karhunen‐Loeve expansion approaches for regional GPS network analysis

Spatial filtering is an effective way to improve the precision of coordinate time series for regional GPS networks by reducing so‐called common mode errors, thereby providing better resolution for detecting weak or transient deformation signals. The commonly used approach to regional filtering assumes that the common mode error is spatially uniform, which is a good approximation for networks of hundreds of kilometers extent, but breaks down as the spatial extent increases. A more rigorous approach should remove the assumption of spatially uniform distribution and let the data themselves reveal the spatial distribution of the common mode error. The principal component analysis (PCA) and the Karhunen‐Loeve expansion (KLE) both decompose network time series into a set of temporally varying modes and their spatial responses. Therefore they provide a mathematical framework to perform spatiotemporal filtering. We apply the combination of PCA and KLE to daily station coordinate time series of the Southern California Integrated GPS Network (SCIGN) for the period 2000 to 2004. We demonstrate that spatially and temporally correlated common mode errors are the dominant error source in daily GPS solutions. The spatial characteristics of the common mode errors are close to uniform for all east, north, and vertical components, which implies a very long wavelength source for the common mode errors, compared to the spatial extent of the GPS network in southern California. Furthermore, the common mode errors exhibit temporally nonrandom patterns.

Observation and modeling of thermoelastic strain in Southern California Integrated GPS Network daily position time series

We suggest that strain in the elastic part of the Earth's crust induced by surface temperature variations is a significant contributor to the seasonal variations observed in the spatially filtered daily position time series of Southern California Integrated GPS Network (SCIGN) stations. We compute the predicted thermoelastic strain from the observed local atmospheric temperature record assuming an elastically decoupled layer over a uniform elastic half‐space and compare the seasonal variations in thermoelastic strain to the horizontal GPS position time series. We consider three regions (Palmdale, 29 Palms, and Idyllwild), each with one temperature station and three to six GPS stations. The temperature time series is used to compute thermoelastic strain at each station on the basis of its relative location in the temperature field. For each region we assume a wavelength for the temperature field that is related to the local topography. The depth of the decoupled layer is inferred from the phase delay between the temperature record and the GPS time series. The relative amplitude of strain variation at each GPS station, calculated to be on the order of 0.1 μstrain, is related to the relative location of that station in the temperature field. The goodness of fit between model and data is evaluated from the relative amplitudes of the seasonal signals, as well as the appropriateness of the chosen temperature field wavelength and decoupled layer depth. The analysis shows a good fit between the predicted strains and the GPS time series. This suggests that the model captures the key first‐order ingredients that determine the thermoelastic strain in a given area. The results can be used to improve the signal/noise ratio in GPS data.

Interferometric synthetic aperture radar atmospheric correction: GPS topography‐dependent turbulence model

Over the last two decades, repeat pass interferometric synthetic aperture radar (InSAR) has been widely used as a geodetic technique for measuring the Earth's surface, including topography and deformation. Like other astronomical and space geodetic techniques, repeat pass InSAR is limited by the variable spatial and temporal distribution of atmospheric water vapor. In this paper, a topography‐dependent turbulence model (GTTM for short) has been developed using GPS data only to produce zenith path delay difference maps for InSAR atmospheric correction. Application of the GTTM model to ERS Tandem data over the Los Angeles Southern California Integrated GPS Network area has shown that use of the GTTM can reduce water vapor effects on interferograms from ∼10 mm down to ∼5 mm, which is of great interest to a wide community of geophysicists. The principal finding of this paper is that interpolation methods should be applied to zenith total delay (ZTD) differences from different times instead of ZTD values themselves for the purpose of InSAR atmospheric correction. This is crucial to reduce (if not completely remove) topographic effects on ZTD values.

Travelling ionospheric disturbance over California mid 2000

Abstract. In this paper, the GPS data collected by more than 130 permanent GPS stations that belong to the Southern California Integrated GPS Network (SCIGN) around the launch of a Minuteman-II missile on 8 July 2000 (UTC) is processed to reveal traveling ionospheric disturbance (TID) all over the network on average 15 min after the launch. This TID was initially perceived to be excited by the launch itself, but this conclusion is challenged by the propagation direction. This is because this TID seems to travel towards the air force base from where the launch took place, not far away from it. This challenge is based on the assumption that TID is occurring at one single ionospheric altitude. While the nature of ionosphere supports such horizontally-guided propagation, multi-altitude ionospheric pierce points are hypothesized, which would support the suggestion that detected TID is excited by the missile launch itself, despite the apparent reverse direction of propagation. The overall analysis rules out any extra-terrestrial sources like solar flares, or seismic sources like earthquakes, which confirms the conclusion of TID excitation by the launch. There is apparent coherence of the TID for about 45 min and the propagation speed of TID within the layer of ionosphere is calculated to be approximately equal to 1230 m/s. While the usual assumption for TID is that they occur around an altitude of 350 km, such sound speed can only occur at much higher altitudes. Further research is recommended to accurately pinpoint the ionospheric pierce points and develop an algorithm to locate the source of TID in case it is totally unknown.