A panorama from the GNSS tide gauge at Onsala Space Observatory. When satellites pass over the sky, the GNSS tide gauge uses signals direct from the satellite and signals reflected off the sea surface to measure the sea level. Photo: Johan Löfgren
New Tide Gauge Uses GNSS to Measure Sea-Level Change
A new way of measuring and monitoring sea level — an important facet of researching climate change — has been implemented by scientists at Chalmers University of Technology in Sweden using existing coastal GPS stations.
When satellites pass over the sky, the GNSS tide gauge uses signals direct from the satellite and signals reflected off the sea surface to measure the sea level. Photo: Johan Löfgren
Measuring sea level is an increasingly important part of climate research, and a rising mean sea level is one of the most tangible consequences of climate change. Researchers at Chalmers University of Technology have studied new ways of measuring sea level that could become important tools for testing climate models and for investigating how the sea level along the world’s coasts is affected by climate change.
Johan Löfgren and Rüdiger Haas, scientists at Chalmers Department of Earth and Space Sciences, have developed and tested an instrument that measures the sea level using a GNSS tide gauge.
“The global mean sea level is rising because of climate change, but the change depends on where you are in the world,” said Rüdiger Haas. “We want to be able to make detailed measurements of sea level so that we can understand how coastal societies will be affected in the future.”
The GNSS tide gauge uses GPS and GLONASS signals. BeiDou and Galileo will be added in the future.
“We measure the sea level using the same radio signals that mobile phones and cars use in their satellite navigation systems,” said Johan Löfgren. “As the satellites pass over the sky, the instrument ‘sees’ their signals — both those that come direct and those that are reflected off the sea surface.”
Antenna Setup. Two antennas, covered by small white radomes, measure signals both directly from the satellites and signals reflected off the sea surface. By analyzing these signals together, the sea level and its variation can be measured up to 20 times per second. The sea-level time series is rich in physical phenomena such as tides (caused mostly by the gravitational pull of the Moon and the Sun), meteorological signals (high and low pressure), and signals from climate change. Through advanced signal processing, these signals can be studied further.
Schematic drawing of the GNSS tide gauge for SNR analysis (left) and phase-delay analysis (right). For the SNR analysis, the satellite signal with elevation ε reflects off the sea surface and interferes with the direct satellite signal at the antenna, creating an interference pattern in the recorded SNR observable that can be related to the reflector height, hr. For the phase delay analysis, the phase delays of the direct and the reflected signals are recorded separately, and through geodetic analysis of the phase delay, the baseline between the antennas can be determined and related to the height of the nadir-looking antenna over the sea surface, ha, and the vertical distance between the antenna phase centers, d.
The scientists’ initial study compared sea-level solutions from two analysis methods: signal-to-noise ratio (SNR) analysis and phase-delay analysis. The SNR analysis uses multipath signals observed with an upward-looking antenna, and the phase delay analysis uses the phase delay for both an upward- and a downward-looking antenna (see diagram).
Both GPS and GLONASS L1 and L2 signals were recorded, and the results were compared to independent measurements of sea level from a co-located pressure tide gauge. The GNSS-derived sea level showed a high correlation with the tide-gauge sea level for both analysis methods. Correlation coefficients for the phase-delay analysis and for the SNR analysis using frequency L1 were 0.95 to 0.97, whereas the correlation coefficients for the SNR analysis using frequency L2 were 0.86 to 0.87.
The phase-delay analysis shows a better agreement with the independent tide gauge sea level than the sea level from SNR analysis. Expressed as RMS differences, the phase-delay analysis achieves values of 3.5 cm (GPS) and 3.3 cm (GLONASS), whereas the SNR analysis achieves 4.0 cm (GPS) and 4.7 cm (GLONASS). The scientists concluded that, for the phase-delay analysis, it is possible to use both frequency bands, and for the SNR analysis, frequency band L2 should be avoided if other signals are available.
The GNSS tide gauge at Onsala Space Observatory uses signals from satellite navigation systems like GPS to measure the sea level. Photo: Johan Löfgren
Land and Sea. Unlike traditional tide gauges, the new GNSS tide gauge can measure changes in both land and sea at the same time, in the same location. That means both long-term and short-term land movements (post-glacial rebound and earthquakes) can be taken into consideration.
“Now we can measure the sea level both relative to the coast and relative to the center of the Earth, which means we can clearly tell the difference between changes in the water level and changes in the land,” said Johan Löfgren.
This summer, other high-precision instruments are being installed to work with the Onsala GNSS tide gauge, in collaboration with SMHI, the Swedish Meteorological and Hydrological Institute.
“Our tide gauge station will become part of a network of stations along the coast of Sweden that will be able to monitor changes in the water level to millimeter precision well into the future,” said Gunnar Elgered, professor at Chalmers Department of Earth and Space Sciences.
The scientists have also shown that existing coastal GNSS stations, installed primarily for the purpose of measuring land movements, can be used to make sea-level measurements.
“We’ve successfully tested a method where only one of the antennas is used to receive the radio signals. That means that existing coastal GNSS stations — there are hundreds of them all over the world — can also be used to measure the sea level,” said Johan Löfgren.
This work was previously reported in these publications: Larson, K.M., J. Lofgren, and R. Haas, “Coastal Sea Level Measurements Using A Single Geodetic GPS Receiver,” Adv. Space Res., Vol. 51(8), 1301-1310, 2013, doi:10.1016/j.asr.2012.04.017, 2013; and Larson, K.M., R. Ray, F. Nievinski, and J. Freymueller, “The Accidental Tide Gauge: A Case Study of GPS Reflections from Kachemak Bay, Alaska,” IEEE GRSL, Vol 10(5), 1200-1205, doi:10.1109/LGRS.2012.2236075, 2013.
An alternative tool for detecting underground nuclear explosions?
By Dorota A. Grejner-Brzezinska, Jihye Park, Joseph Helmboldt, Ralph R. B. von Frese, Thomas Wilson, and Jade Morton
Well-concealed underground nuclear explosions may go undetected by International Monitoring System sensors. An independent technique of detection and verification may be offered by GPS-based analysis of local traveling ionospheric disturbances excited by an explosion. Most of the work to date has been at the research demonstration stage; however, operational capability is possible, based on the worldwide GPS network of permanently tracking receivers. This article discusses a case study of detecting underground nuclear explosions using observations from GPS tracking stations and the Very Large Array radio telescope in New Mexico.
More than 2,000 nuclear tests were carried out between 1945 and 1996, when the Comprehensive Nuclear Test Ban Treaty was adopted by the United Nations General Assembly. Signatory countries and the number of tests conducted by each country are the United States (1000+), the Soviet Union (700+), France (200+), the United Kingdom, and China (45 each). Three countries have broken the de facto moratorium and tested nuclear weapons since 1996: India and Pakistan in 1998 (two tests each), and the Democratic People’s Republic of Korea (DPRK) in 2006 and 2009, and most recently, in 2013.
To date, 183 countries have signed the treaty. Of those, 159 countries have also ratified the treaty, including three nuclear weapon states: France, the Russian Federation, and the United Kingdom. However, before the treaty can enter into force, 44 specific nuclear-technology-holder countries must sign and ratify. Of these, India, North Korea and Pakistan have yet to sign the CTBT, and China, Egypt, Iran, Israel, and the United States have not ratified it.
The treaty has a unique and comprehensive verification regime to make sure that no nuclear explosion goes undetected. The primary components of the regime are:
The International Monitoring System: The IMS includes 337 facilities (85 percent completed to date) worldwide to monitor for signs of any nuclear explosions.
International Data Center: The IDC processes and analyzes data registered at IMS stations and produces data bulletins.
Global Communications Infrastructure: This transmits IMS data to the IDC, and transmits data bulletins and raw IMS data from IDC to member states.
Consultation and Clarification: If a member state feels that data collected imply a nuclear explosion, this process can be undertaken to resolve and clarify the matter.
On-Site Inspection: OSI is regarded as the final verification measure under the treaty.
Confidence-Building Measures: These are voluntary actions. For example, a member state will notifying CTBTO when there will be large detonations, such as a chemical explosion or a mining blast.
The IMS (see Figure 1) uses the following state-of-the-art technologies. Numbers given reflect the target configuration:
Seismic: Fifty primary and 120 auxiliary seismic stations monitor shockwaves in the Earth. The vast majority of these shockwaves — many thousands every year — are caused by earthquakes. But man-made explosions such as mine explosions or the North Korean nuclear tests in 2006, 2009, and 2013 are also detected.
Hydroacoustic: As sound waves from explosions can travel extremely far underwater, 11 hydroacoustic stations “listen” for sound waves in the Earth oceans.
Infrasound: Sixty stations on the surface of the Earth can detect ultra-low-frequency sound waves that are inaudible to the human ear, which are released by large explosions.
Radionuclide: Eighty stations measure the atmosphere for radioactive particles; 40 of them can also detect the presence of noble gas.
Figure 1. The International Monitoring System (IMS): worldwide facilities grouped by detection technologies used.
Only the radionuclide measurements can give an unquestionable indication as to whether an explosion detected by the other methods was actually nuclear or not. The observing stations are supported by 16 radionuclide laboratories.
Since radionuclide detection method provides the ultimate verification as far as the type of blast goes, it should be mentioned that while the 2006 North Korean event (yield of less than a kiloton) was detected by the IMS stations in more than 20 different sites within two hours of detonation, and both seismic signal and radioactive material were detected, the 2009 event (yield of a few kilotons) was detected by 61 IMS stations; seismic and infrasound signals were detected, but no radioactive material was picked up by the radionuclide stations. Seismic signal was consistent with a nuclear test, but there was no “ultimate” proof by the radionuclide method.
Thus, well-concealed underground nuclear explosions (UNEs) may be undetected by some of the IMS sensors (such as the radionuclide network). This raises a question: Is there any other technology that is readily available that can detect and discriminate various types of blasts, particularly those of nuclear type? Recent experiments have shown that an independent technique of detection and verification may be offered by GPS-based analysis of local traveling ionospheric disturbances (TIDs) excited by an explosion.
GNSS-Based Detection
Atmospheric effects from mostly atmospheric nuclear explosions have been studied since the 1960s.The ionospheric delay in GNSS signals observed by the ground stations can be processed into total electron content (TEC), which is the total number of electrons along the GNSS signal’s path between the satellite and the receiver on the ground. The TEC derived from the slant signal path, referred to as the slant TEC (STEC), can be observed and analyzed to identify disturbances associated with the underground nuclear explosion.
STEC signature (in spectral and/or spatial-temporal domains) can be analyzed to detect local traveling ionospheric disturbances (TID).
TID can be excited by acoustic gravity waves from a point source, such as surface or underground explosions, geomagnetic storms, tsunamis, and tropical storms. TIDs can be classified as Large-Scale TID (LSTID) and Medium-Scale TID (MSTID) based on their periods regardless of the generation mechanism. The periods of LSTIDs generally range between 30–60 minutes to several hours, and those of MSTIDs range from 10 to 40 or even 60 minutes. LSTIDs mostly occur from geophysical events, such as geomagnetic storms, which can be indicated by global Kp indices, while MSTIDs are genrally not related to any high score Kp indices. An underground nuclear explosion can result in an MSTID.
TIDs are generated either by internal gravity wave (IGW) or by acoustic gravity wave (AGW). The collisional interaction between the neutral and charged components cause ionospheric responses. The experimental results indicate IGWs can change the ozone concentration in the atmosphere. In the ionosphere, the motion of the neutral gas in the AGW sets the ionospheric plasma into motion.
The AGW changes the iso-ionic contours, resulting in a traveling ionospheric disturbance.
The past 10–15 years has resulted in a significant body of research, and eventually a practical application, with worldwide coverage, of GPS-based ionosphere monitoring. A significant number of International GNSS Service (IGS) permanent GNSS tracking stations (see Figure 2) form a powerful scientific tool capable of near real-time monitoring and detection of various ionospheric anomalies, such as those originating from the underground nuclear explosions (UNEs).
Figure 2. The IGS global tracking network of 439 stations.
The network is capable of continuously monitoring global ionospheric behavior based on ionospheric delays in the GNSS signals. The GNSS signals are readily accessible anywhere on Earth at a temporal resolution ranging from about 30 seconds up to less than 1 second.
A powerful means to isolate and relate disturbances observed in TEC measurements from different receiver-satellite paths is to analyze the spectral coherence of the disturbances. However, in our algorithms, we emphasize the spatial and temporal relationship among the TEC observations. Spatial and temporal fluctuations in TEC are indicative of the dynamics of the ionosphere, and thus help in mapping TIDs excited by acoustic-gravity waves from point sources, as well as by geomagnetic storms, tropical storms, earthquakes, tsunamis, volcanic explosions, and other effects.
Methodology of UNE Detection
Figure 3 illustrates the concept of the generation of the acoustic gravity wave by a UNE event, and its propagation through the ionosphere that results in a traveling ionospheric disturbance (TID). The primary points of our approach are: (1) STEC is calculated from dual-frequency GPS carrier phase data, (2) after eliminating the main trend in STEC by taking the numerical third order horizontal 3-point derivatives, the TIDs are isolated, (3) we assume an array signature of the TID waves, (4) we assume constant radial propagation velocity, vT, using an apparent velocity, vi, of the TID at the ith observing GNSS station, (5) since the TID’s velocity is strongly affected by the ionospheric wind velocity components, vN and vE, in the north and east directions, respectively, the unknown parameters,vT, vN, and vE, can be estimated relative to the point source epicenter, and (6) if more than six GNSS stations in good geometry observe the TID in GNSS signals, the coordinates of the epicenter can also be estimated.
Figure 3a. Pictorial representation of the scenario describing a GNSS station tracking a satellite and the ionospheric signal (3-point STEC derivative); not to scale.Figure 3b. The scenario describing a GNSS station tracking a satellite and the ionospheric signal and a point source (e.g., UNE) that generates acoustic gravity waves; not to scale.Figure 3c. The scenario describing a GNSS station tracking a satellite and the ionospheric signal, and the propagation of the acoustic gravity waves generated by a point source (e.g., UNE); not to scale.Figure 3d. The scenario describing a GNSS station tracking a satellite and the ionospheric signal, at the epoch when the GNSS signal is affected by the propagation of the acoustic gravity waves generated by a point source (e.g., UNE); not to scale.Figure 3e. Same as 3D, indicating that the geometry between GNSS station, the satellite and the IPP can be recovered and used for locating the point source; multiple GNSS stations are needed to find the point source location and the the velocity components of TID and ionospheric winds; not to scale.Figure 3f. Same as 3D, after the TID wave passed the line of sight between the GNSS stations and the satellite; not to scale.
Figure 4 illustrates the geometry of detection of the point source epicenter. Determination of the epicenter of the point source that induced TIDs can be achieved by trilateration, similarly to GPS positioning concept. The TIDs, generated at the point source, propagate at certain speed, and are detected by multiple GPS stations.
The initial assumption in our work was to use a constant propagation velocity of a TID. By observing the time of TID arrival at the ionospheric pierce point (IPP), the travel distance from the epicenter to the IPP of the GPS station that detected a TID (which is the slant distance from the ith station and the kth satellite) can be derived using a relationship with the propagation velocity. In this study, we defined a thin shell in the ionosphere F layer, 300 kilometers above the surface, and computed the IPP location for each GPS signal at the corresponding time epoch of TID detection.
Figure 4. Geometry of point source detection based on TID signals detected at the IPP of GPS station, i, with GPS satellite k. Unknown: coordinates of the point source, ( ф, λ ); three components of TID velocity vT, vN, and vE ; Observations: coordinates of IPP, (xik, yik, zik) and the corresponding time epoch to TID arrival at IPP, tik; Related terms: slant distance between IPP and UNE, sik; horizontal distance between the point source epicenter and the GPS station coordinates, di; azimuth and the elevation angle of IPP as seen from the UNE, αjk and εjk , respectively.
Very Large Array (VLA)
In addition to GNSS-based method of ionosphere monitoring, there are other more conventional techniques, for example, ground-based ionosondes, high-frequency radars, Doppler radar systems, dual-frequency altimeter, and radio telescopes. In our research, we studied the ionospheric detection of UNEs using GPS and the Very Large Array (VLA) radio telescope.
The VLA is a world-class UHF/VHF interferometer 50 miles west of Socorro, New Mexico. It consists of 27 dishes in a Y-shaped configuration, each one 25 meters in diameter, cycled through four configurations (A, B, C, D) spanning 36, 11, 3.4, and 1 kilometers, respectively. The instrument measures correlations between signals from pairs of antennas, used to reconstruct images of the sky equivalent to using a much larger single telescope. While conducting these observations, the VLA provides 27 parallel lines of sight through the ionosphere toward cosmic sources.
Past studies have shown that interferometric radio telescopes like the VLA can be powerful tools for characterizing ionospheric fluctuations over a wide range of amplitudes and scales. We used these new VLA-based techniques and a GPS-based approach to investigate the signature of a TID originated by a UNE jointly observed by both GPS and the VLA. For this case study, we selected one of the 1992 U.S. UNEs for which simultaneous GPS and VLA data were available.
Table 1. Characteristics of the analyzed events (UNEs).
Experimental Results
We summarize here the test studies performed by the OSU group in collaboration with Miami University and the U.S. Naval Research Laboratory on detection and discrimination of TIDs resulting from UNEs using the GNSS-based and VLA-based techniques. Table 1 lists the UNE events that have been analyzed to date. As of March 2013, the results of the 2013 North Korean UNE were not fully completed, so they are not included here.
In the 2006 and 2009 North Korean UNE experiments, STEC data from six and 11 nearby GNSS stations, respectively, were used. Within about 23 minutes to a few hours since the explosion, the GNSS stations detected the TIDs, whose arrival time for each station formulated the linear model with respect to the distance to the station. TIDs were observed to propagate with speeds of roughly 150–400 m/s at stations about 365 km to 1330 km from the explosion site. Considering the ionospheric wind effect, the wind-adjusted TIDs located the UNE to within about 2.7 km of its seismically determined epicenter (for the 2009 event; no epicenter location was performed for the 2006 event due to insufficient data). The coordinates estimated by our algorithm are comparable to the seismically determined epicenter, with the accuracy close to the seismic method itself. It is important to note that the accuracy of the proposed method is likely to improve if the stations in better geometry are used and more signals affected by a TID can be observed. An example geometry of UNE detection is shown in Figure 5.
Figure 5. Locations of the underground nuclear explosion (UNE) in 2009 and GNSS stations C1 (CHAN), C2 (CHLW), D1 (DAEJ), D2 (DOND), I1 (INJE), S1 (SUWN), S2 (SHAO), S3 (SOUL), U1 (USUD), Y1 (YANP), Y2 (YSSK) on the coastline map around Korea, China, and Japan. The TID waves are highlighted for stations C1, D1, D2, I1. The bold dashed line indicates the ground track for satellite PRN 26 with dots that indicating the arrival times of the TIDs at their IPPs. All time labels in the figure are in UTC.
For the Hunters Trophy and the Divider UNE tests, the array signature of TIDs at the vicinity of GPS stations was observed for each event. By applying the first-order polynomial model to compute the approximate velocity of TID propagation for each UNE, the data points — that is the TID observations — were fit to the model within the 95 percent confidence interval, resulting in the propagation velocities of 570 m/s and 740 m/s for the Hunters Trophy and the Divider, respectively.
The VLA has observing bands between 1 and 50 GHz, and prior to 2008 had a separate VHF system with two bands centered at 74 and 330 MHz. A new wider-band VHF system is currently being commissioned. The VHF bands and L-band (1.4 GHz) are significantly affected by the ionosphere in a similar way as the GPS signal. In this study, we used VLA observations at L-band of ionospheric fluctuations as an independent verification of the earlier developed method based on the GNSS TID detection for UNE location and discrimination from TIDs generated by other types of point sources.
The VLA, operated as an interfer-ometer, measures the correlation of complex voltages from each unique pair of antennas (baselines), to produce what are referred to as visibilities. Each antenna is pointed at the same cosmic source; however, due to spatial separation, each antenna’s line of sight passes through a different part of the ionosphere. Consequently, the measured visibilities include an extra phase term due to the difference in ionospheric delays, which translates to distortions in the image made with the visibilities. This extra phase term is proportional to the difference in STEC along the lines of sight of the two telescopes that form a baseline. Thus, the interferometer is sensitive to the STEC gradient rather than STEC itself, which renders it capable of sensing both temporal and spatial fluctuations in STEC.
The spectral analysis was performed on the STEC gradients recovered from each baseline that observed the Hunters Trophy event. Briefly, a time series of the two-dimensional STEC gradient is computed at each antenna. Then, a three-dimensional Fourier transform is performed, one temporal and two spatial, over the array and within a given time period (here ~15 minutes). The resulting power spectrum then yields information about the size, direction, and speed of any detected wavelike disturbances within the STEC gradient data.
Roughly 20 to 25 minutes after the UNE, total fluctuation power increased dramatically (by a factor of about 5×103). At this time, the signature of waves moving nearly perpendicular to the direction from Hunters Trophy (toward the northeast and southwest) was observed using the three-dimensional spectral analysis technique. These fluctuations had wavelengths of about 2 km and inferred speeds of 2-8 m s-1. This implies that they are likely due to small-scale distortions moving along the wavefront, not visible with GPS. Assuming that these waves are associated with the arrival of disturbances associated with the Hunters Trophy event, a propagation speed of 570–710 m/s was calculated, which is consistent with the GPS results detailed above.
In addition, a TID, possibly induced by the February 12, 2013, North Korean UNE, was also detected using the nearby IGS stations, by the detection algorithm referred to earlier. Eleven TID waves were found from ten IGS stations, which were located in South Korea, Japan, and Russia. Due to the weakness of the geometry, the epicenter and the ionospheric wind velocity were not determined at this point. The apparent velocity of TID was roughly about 330–800 m/s, and was calculated using the arrival time of the TID after the UNE epoch and the slant distance between the corresponding IPP and the epicenter. The reported explosion yield was bigger, compared to the 2009 North Korean UNE, which possibly affected the propagation velocity by releasing a stronger energy. However, more in-depth investigation of this event and the corresponding GPS data is required.
Conclusions
Research shows that UNEs disturb the ionosphere, which results in TIDs that can be detected by GNSS permanent tracking stations as well as the VLA. We have summarized several GNSS-based TID detections induced by various UNEs, and verified the GNSS-based technique independently by a VLA-based method using the 1992 U.S. UNE, Hunters Trophy. It should be noted that VLA observation was not available during the time of the Divider UNE test; hence, only the Hunters Trophy was jointly detected by GPS and the VLA. Our studies performed to date suggest that the global availability of GNSS tracking networks may offer a future UNE detection method, which could complement the International Monitoring System (IMS).
We have also shown that radio-frequency arrays like the VLA may also be a useful asset for not only detecting UNEs, but for obtaining a better understanding of the structure of the ionospheric waves generated by these explosions. The next generation of HV/VHF telescopes being developed (such as the Lower Frequency Array in the Netherlands, the Long Wavelength Array in New Mexico, the Murchison Widefield Array in Australia) utilize arrays of dipole antennas, which are much cheaper to build and operate and are potentially portable.
It is conceivable that a series of relatively economical and relocatable arrays consisting of these types of dipoles could provide another valuable supplement to the current IMS in the future, particularly for low-yield UNEs that may not be detectable with GPS.
Acknowledgment
This article is based on a paper presented at the Institute of Navigation Pacific PNT Conference held April 22–25, 2013, in Honolulu, Hawaii.
Dorota A. Grejner-Brzezinska is a professor and chair, Department of Civil, Environmental and Geodetic Engineering, and director of the Satellite Positioning and Inertial Navigation (SPIN) Laboratory at The Ohio State University.
Jihye Park recently completed her Ph.D. in Geodetic Science program at The Ohio State University. She obtained her B.A. and M.S degrees in Geoinformatics from The University of Seoul, South Korea.
Joseph Helmboldt is a radio astronomer within the Remote Sensing Division of the U.S. Naval Research Laboratory.
Ralph R.B. von Frese is a professor in the Division of Earth and Planetary Sciences of the School of Earth Sciences at Ohio State University.
Thomas Wilson is a radio astronomer within the Remote Sensing Division of the U.S. Naval Research Laboratory.
Yu (Jade) Morton is a professor in the Department of Electrical and Computer Engineering at Miami University.
Esri and PCI Geomatics announced they have selected 24 organizations to develop and apply innovative methods for using GIS to analyze imagery for land-use management. Through the Esri Natural Resources Imagery Grant Program, Esri, the world leader in GIS, and PCI Geomatics, the world leader in geoimaging, provide each grant recipient with software and data valued at $100,000.
“Esri is committed to the development of tools and processes that advance the use of imagery for geospatial analysis,” said Lawrie Jordan, Esri’s imagery solutions director. “The applications that participants design will offer proof-of-concept models useful to imagery analysts worldwide.”
According to the announcement, participants are required to improve efficiency, productivity, or accuracy for detecting and analyzing land-cover change using MDA synthetic aperture radar (SAR) imagery from RADARSAT-2 and 5 m multispectral imagery from RapidEye. They will use Esri and PCI software to process and analyze imagery. Grant participants, project titles, and organizations are listed on the Esri Natural Resources Imagery Grant Program web page.
“Imagery provides a cost-efficient means to monitor and measure what is happening on the ground and can be integrated with GIS to make better decisions,” said Terry Moloney, president and CEO of PCI Geomatics. “Our partnership with Esri on this program will significantly change the GIS approach participants will apply to land-use management, planning, and policy making.
Esri, PCI Geomatics, MDA, and RapidEye announced their new Natural Resources Imagery Grant Program. The grant program will provide software, data, and training for detecting and analyzing land-cover change through the combined use of geographic information system (GIS), image processing, and remote-sensing technologies.
According to the announcement, the program is designed to foster innovative approaches that solve natural resources management problems, the Natural Resources Imagery Grant Program will provide 20 grants valued at $100,000 each. The grant includes the following:
Esri GIS software and training
PCI Geomatics imagery processing and analysis software and training
“GIS and image processing are mission-critical technologies in natural resources management,” said Jack Dangermond, president, Esri. “This grant opportunity will help organizations expand their existing imagery or GIS infrastructure and more efficiently support sustainable land-use management.”
Companies, educational institutions, nongovernmental organizations (NGOs), state and regional governments, or tribal governments within the United States may apply. Eligible projects are those that focus on remotely sensed imagery beyond the visible spectrum. Preferred projects will also demonstrate increased efficiency, productivity, or accuracy.
“Technology leaders and innovators should be presented with opportunities to advance their resources projects,” said Terry Maloney, president and CEO, PCI Geomatics. “This imagery grant program will bring solutions to the natural resources industries through inventive and operational use of satellite imagery.”
Applications for the Natural Resources Imagery Grant Program will be accepted beginning in September 2012 and ending November 16, 2012. Learn more at esri.com/imagerygrant.
DigitalGlobe, Inc. and GeoEye, Inc. announced that the boards of directors of both companies have unanimously approved a definitive merger agreement under which the companies will combine in a stock and cash transaction valued at approximately $900 million. The combination of DigitalGlobe and GeoEye will create a global leader in earth imagery and geospatial analysis with a more diversified revenue base, a superior financial foundation and significant growth potential.
Under the terms of the agreement, GeoEye shareowners will have the right to elect either 1.137 shares of DigitalGlobe common stock and $4.10 per share in cash, 100% of the consideration in cash ($20.27) or 100% of the consideration in stock (1.425 shares of DigitalGlobe common stock), for each share of GeoEye stock they own, with the amount of cash and stock subject to proration depending upon the elections of GeoEye shareholders, such that aggregate consideration mix reflects the ratio of 1.137 shares of DigitalGlobe common stock and $4.10 per share in cash. Based upon the closing prices of DigitalGlobe and GeoEye as of July 20, 2012, the transaction delivers a premium of 34% to GeoEye’s July 20, 2012 closing price of $15.17 per share. Upon completion of the transaction, DigitalGlobe shareowners are expected to own approximately 64% and GeoEye shareowners are expected to own approximately 36% of the combined company. The transaction structure will allow both DigitalGlobe and GeoEye shareowners to participate in the substantial value creation opportunity resulting from this combination.
According to the announcement, the combined company will be named DigitalGlobe and continue to trade on the NYSE under the symbol DGI. It will have a 10-member board of directors, with six initial members from the current DigitalGlobe board and four initial members from the board of GeoEye. Jeffrey R. Tarr, President and Chief Executive Officer of DigitalGlobe, will serve as President and Chief Executive Officer of the combined company, and General Howell M. Estes III, Chairman of the Board of DigitalGlobe, will serve as Chairman. It is anticipated that, after close, Matt O'Connell, Chief Executive Officer and President of GeoEye, will assist the management of the combined company in an advisory capacity. The company will be headquartered in Colorado, have a large and important presence in Missouri and Virginia, and maintain offices in other locations around the globe.
“The combination of DigitalGlobe and GeoEye creates a global leader in earth imagery and geospatial analysis,” said Mr. Tarr. “Together we will create a more efficient, more diversified and more capable company, better positioned to thrive in a time of unprecedented pressure on our nation’s defense budget. Once the merger is complete, we will emerge as an industry-leading, geospatial information business that does even more to help our customers better understand our changing planet. In so doing, we will further enable our customers to save time, save money and save lives.”
Mr. Tarr continued, “Bringing together the world-class talent and experience of team members from both companies, we will inspire a new wave of innovation and create value for shareowners who have invested their capital in the promise of our industry.”
“We are excited to be joining forces with DigitalGlobe as we believe this transaction represents the best path forward for our shareowners, our customers, and ultimately, the taxpayer,” said Matt O'Connell, Chief Executive Officer and President of GeoEye. “With an impressive constellation of commercial earth imaging satellites and complementary services, the combined company will be well positioned to achieve efficient growth, expand our international reach and create value for all stakeholders. Given the stock component, our shareowners will have the opportunity to participate in the significant growth and value creation potential. I look forward to working closely with the management teams of both companies to support this transaction and establish the foundation for what will be a dynamic and enduring company.”
Benefits of the Transaction
Increased Scale and Customer Diversification
The combined company will conservatively have a pro forma 2012 revenue base of more than $600 million, after adjusting for the currently proposed lower U.S. government fiscal year 2013 EnhancedView funding plan. As a result, the combined company would therefore have better revenue certainty, lower dependence on the U.S. government as a source of revenue, a higher percentage of commercial and international revenue, and be well positioned for future growth.
Substantial Synergy
At close, the combined company is expected to have a constellation of five earth observation satellites and a broad suite of high-value geospatial production and analytic services. The combined company will also have two state-of-the-art satellites under construction, WorldView-3 and GeoEye-2. Over time, the combined company plans to maintain an optimized three-satellite constellation that will meet the needs of the U.S. government, international governments and commercial customers, while delivering better returns to shareowners. Taken together with other operating efficiencies, the net present value of future savings is estimated to be more than $1.5 billion.
Compelling Solution for U.S. Government
By bringing the two companies together, this combination will enable the U.S. government to meet the requirements of the EnhancedView program at substantial savings to the U.S. taxpayer. In addition to the compelling savings, the U.S. government and other customers will benefit from an optimized constellation and better integrated imagery collection, processing and analytics. In return, shareowners should reasonably expect a more stable and predictable funding environment.
Value for All Customers
The combination will deliver extraordinary value to customers around the globe. By bringing together the imagery collection, processing and analytic capabilities of both companies, it will be better able to serve a wide range of customer needs and compete in a high-growth and dynamic global market.
Larger constellation with optimized orbits and coordinated scheduling will collect imagery faster, increase persistence and enhance resilience.
Integrated delivery will simplify access to the industry’s largest imagery archive.
Diverse sensors, including panchromatic, multi-spectral, 8-band and short wave infrared, with high resolution capability will enable customers to solve a wide range of problems.
Extensive archive and collection capacity combined with value added production and advanced analytics will enable new solutions and insights into our changing planet.
Compelling Economics
On a pro forma basis, the combined company will have a robust set of financial attributes and expects to significantly improve its long-term operating model compared with either company on a standalone basis.
High recurring revenue with more than $3 billion in contracted backlog.
More balanced revenue with non-U.S. government revenue accounting for approximately 50% of total pro forma revenue.
Modest leverage with balance sheet flexibility for future investment in growth.
Expected EBITDA margin above 50% by the second half of 2014, net of integration costs.
Improved free cash flow profile from operating and capital efficiencies and enhanced growth prospects.
Financing and Approvals
DigitalGlobe has secured a $1.2 billion fully committed financing from Morgan Stanley Senior Funding, Inc. and The Bank of Tokyo-Mitsubishi UFJ, Ltd. to refinance the combined company’s outstanding debt.
The transaction, which is expected to be completed in the fourth quarter of 2012 or the first quarter of 2013, is subject to the satisfaction of customary closing conditions, including the receipt of requisite regulatory approvals and approval from GeoEye shareowners with respect to the merger and from DigitalGlobe shareowners with respect to the issuance of DigitalGlobe common stock in the merger. GeoEye’s largest shareowner, Cerberus Capital Management, L.P. (“Cerberus”), and its Chairman and CEO each have agreed to vote in favor of the merger, and DigitalGlobe’s largest shareowner, Morgan Stanley Principal Investments, Inc., and its Chairman and CEO each have agreed to vote in favor of the issuance of DigitalGlobe common stock in the merger.
Cerberus intends to continue its investment in the combined company, and may purchase shares of DigitalGlobe in advance of the closing of the transaction. Cerberus has agreed to vote those shares with the board of directors and has entered into a standstill agreement with DigitalGlobe in which their ownership in the combined company will be capped at 19.9%. One of GeoEye’s board designees will be put forth by Cerberus.
GeoEye, Inc. announced that it recently signed seven-figure agreements with two international partners in the Middle East and Asia for both the renewal and expanded use of GeoEye imagery products.
According to the announcement, the Middle East affiliate has signed a new agreement for access to GeoEye-1 sub half-meter imagery, which is the highest resolution commercial imagery available globally. This affiliate has had an ongoing agreement with GeoEye for IKONOS satellite imagery collection and distribution since 2000. The agreement with the government customer in Asia renewed their access to GeoEye-1’s highly precise imagery products. This customer has had an agreement in place for GeoEye-1 satellite imagery collection since 2009. Both partners have indicated they will integrate GeoEye-1’s high-resolution imagery with their own systems to support regional security and peace missions.
“Both the Mideast Regional Affiliate and the Asian government customer have been trusted and valued partners for many years,” said Paolo Colombi, GeoEye’s vice president of International Sales. “We appreciate the extension of our partnerships and the expansion of our global footprint that these agreements represent. We look forward to supporting our partners’ mission critical requirements by delivering superior-quality location intelligence regarding these highly sensitive and dynamic regions of the world.”
TerraGo Technologies Inc. has acquired the complementary software products and technologies of Manhattan Beach, CA-based Geosemble Technologies Inc. Terms between the privately held companies were not disclosed. Both firms are In-Q-Tel portfolio companies.
According to the announcement, Geosemble’s flagship product, GeoXray automates the process of discovering, geospatially visualizing, monitoring and sharing relevant unstructured information from any source. The software mines and processes content from news, blogs and social media and analyzes data by place, time and topic. GeoXray decreases the amount of time analysts spend sifting through big data and produces more germane information specific to an area of interest. The solution, which is used by a number of intelligence agencies, frees analysts to spend more time on quality analysis and enables better collaboration with peers, decision makers and field personnel.
TerraGo reports that the strategic acquisition of Geosemble builds on TerraGo’s growing geospatial intelligence applications and reports expertise. The combined companies’ solutions will now enable users to selectively discover relevant spatial content; compose dynamic, interactive geospatial intelligence applications and reports; and collaborate in online and disconnected environments. The new TerraGo suite of solutions will facilitate enhanced situational awareness and actionable intelligence for better planning, improved decision making and faster response.
Founded in December of 2004, TerraGo reported that Geosemble is a spin-off from the University of Southern California (USC). Its founders are computer science faculty members and originally developed the company’s core artificial intelligence and geospatial data analysis algorithms at USC. The technology has since been strengthened and refined to apply to a range of government and commercial user needs focused on automatically discovering and integrating information into satellite and aerial imagery and maps. The Geosemble business and technical team will be brought into TerraGo as the Geosemble solutions group, and the office in Manhattan Beach will be expanded to accommodate additional engineering staff as well as support resources to serve TerraGo’s growing West Coast customer base. Both firms are In-Q-Tel portfolio companies.
“The strategic acquisition of Geosemble will enable our customers to discover, visualize, monitor and share geospatial intelligence relevant to their operations and areas of interest,” said TerraGo Pres. and CEO Rick Cobb. “We warmly welcome our new colleagues to the TerraGo team, which, as always, remains committed to our tradition of product innovation and dedication to customer success.”
“This union is a perfect fit for Geosemble since TerraGo brings valuable distribution, implementation and customer support capability to Geosemble’s products, in addition to TerraGo’s own valuable suite of complementary technology. The combined resources of our companies will enable us to further develop advanced geospatial intelligence solutions for our existing defense and intelligence customers as well as others in crisis management, public safety and a wide range of commercial businesses,” said Andre Doumitt, former Geosemble CEO and now TerraGo vice president of business development for Geosemble solutions.
Hexagon AB announced it has acquired all shares in the Norwegian company My Virtual Reality Software AS (myVR).
According to the announcement, myVR provides software that offers a solution for 2D, 3D and 360-degree viewing for desktop and mobile. The company has developed a patented technology platform that enables high-resolution real-time viewing of interactive maps over networks with limited bandwidth. The platform makes it possible to view large-scale models on any 3D hardware-supported client platform, including mobile phones and tablets.
myVR 3D Map
"The acquisition of myVR will be of great value for Hexagon's current offerings. Everything is going mobile, including our customer offerings," said Ola Rollén, President and CEO, Hexagon AB. "In the past, the problem with displaying 3D data on a mobile device such as a tablet has been size limitations of data transfers, and also the ability to handle the transfer in a real-time environment. myVR has a unique solution to this problem, and Hexagon will make use of its technology in all of our divisions."
Founded in 2003, myVR provides real-time, 3D virtual technology to the Oil & Gas, Building & Construction, Government and Web Portals industries, as well as other traditional and emerging digital markets.
Hexagon announced that myVR will be fully consolidated as of today. The acquisition will not have any visible impact on Hexagon's earnings in the short-term.
Using a large network of GPS stations, a team of researchers has found that the Rio Valley Rift in the Southwest United States — previously suspected to be dead — is slowly expanding, at a rate of about 0.1 millimeter per year.
The Rio Grande Rift extends from Colorado’s central Rocky Mountains to Mexico.
The study was conducted by scientists at the Cooperative Institute for Research in the Environmental Sciences (CIRES) at the University of Colorado at Boulder, in collaboration with the University of New Mexico, New Mexico Tech, Utah State University, and UNAVCO.
“We don’t expect to see a lot of earthquakes, or big ones, but we will have some earthquakes,” said study author Anne Sheehan, CIRES Fellow and associate director of CIRES Solid Earth Sciences Division. “We use continuous measurements of GPS sites from across the Rio Grande Rift, Great Plains, and Colorado Plateau to estimate present-day surface velocities and strain rates,” Sheehan said.
Using GPS instruments at 25 sites in Colorado and New Mexico, the team tracked the rift’s miniscule movements from 2006 to 2011. The team found an average strain rate of 1.2 nanostrain each year across the experimental area. A nanostrain is a change in length of one part per billion, thus 1.2 nanostrain per year is equivalent to 1.2 millimeter per year extension over a 1000-kilometer length.“If you picked two points in New Mexico, and one of them lies 100 kilometers to the west of the other, then they would be moving apart at a rate of 0.1 millimeter per year,” explained researcher Henry Berglund.
Researchers used data from 25 continuous GPS stations installed as part of the EarthScope Rio Grande Rift GPS experiment, supplemented by data from other GPS monuments in the southwestern U.S., resulting in a data set of daily position estimates of 284 GPS monuments for the years 2006 through 2010.
“It is lower than we thought but it does exist,” Sheehan said. “Some people thought it was zero but we are seeing things are extending slowly.”
The slow rates of motion made previous attempts to determine tectonic activity difficult. Previously, geologists had estimated the rift had spread apart by up to 5 millimeters each year but the errors introduced by the measuring instrumentations were significant. “The GPS has reduced the uncertainty dramatically,” Sheehan said. “This is the most comprehensive and accurate set of geodetic measurements in this area to date.”
The extensional deformation is not concentrated in a narrow zone centered on the Rio Grande Rift. Instead, it is distributed broadly from the western edge of the Colorado Plateau into the western Great Plains — a span of more than 370 miles. “This unexpected pattern of broadly distributed deformation at the surface has important implications for our understanding of how low strain-rate deformation within continental interiors is accommodated,” Sheehan said. “Questions we wanted to answer are: how is the Rio Grande Rift deforming? Is it alive or dead? Is it opening or not?”
Along the rift, spreading motion in the crust has caused magma to rise to the surface, creating long basins susceptible to earthquakes. “The rift is still active,” Sheehan said.
The team plans to continue monitoring the Rio Grande Rift, and may attempt to determine vertical as well as horizontal activity to determine whether the Rocky Mountains are still uplifting.
University of Colorado (Boulder) student Henry Berglund services GPS site RG20 west of Silverton, Colorado.
The study’s findings shed light on how continents deform away from plate boundaries, Sheehan said. At plate boundaries scientists can clearly see what is going on. “Things move past each other and crash into each other. At active plate boundaries, the rates of motion detected by GPS can be centimeters per year. Compare that with the fraction of a millimeter per year that we have measured for the Rio Grande Rift.”
“Present day measurements of deformation within continental interiors have been difficult to capture due to the typically slow rates of deformation within them,” Berglund said. “Now, with the recent advances in space geodesy, we are finding some very surprising results in these previously unresolved areas.”
The National Science Foundation funded the study. EarthScope and UNAVCO provided instruments, equipment, and engineering services. Results of the study were published in the January 2012 issue of Geology magazine.
GPS monuments in the vicinity of the Rio Grande Rift and southern Rocky Mountains. The study included construction of 25 GPS monuments (blue circles) in Colorado and New Mexico in 2006 and 2007. Regional EarthScope Plate Boundary Observatory and Continuously Operating Reference Station monuments are shown by gray triangles.
Can GPS be used to detect underground nuclear explosions?
A research team is developing a software program that uses GPS to analyze the ionospheric effect of nuclear explosions. Results would show when and where a country has conducted a secret underground nuclear test. Team members are Jihye Park, Ralph. R. B. von Frese, and Dorota A. Grejner-Brzezinska from The Ohio State University and Jade Yu Morton from Miami University.
The Comprehensive Nuclear-Test-Ban Treaty was adopted by the United Nations General Assembly in 1996, but not all nuclear countries have ratified it, including the United States, China, Egypt, Indonesia, Iran, and Israel. Also, India, North Korea, and Pakistan have not signed the treaty.
Park, a doctoral student in geodetic science at Ohio State, created the computer program to detect changes in the ionosphere from nuclear weapons testing.
A previous study showed that the ionosphere was disturbed by underground nuclear testing conducted by Russia in 1990. GPS is capable of precisely measuring the total electron content (TEC) of the ionosphere along the path between satellite and receiver at a GPS station, so Park and her team decided to begin researching the use of GPS in detecting nuclear explosions.
“Many studies have been done to monitor and model the atmosphere using GPS technology,” Park said. “Research has proven that GPS can detect natural disasters such as earthquakes or tsunamis. This study broadens those areas of study with its capability to detect underground explosions.”
Detonation of a nuclear weapon results in a shockwave that travels through the atmosphere, changing the density of charged particles in the ionosphere. “The explosions can’t hide from the ionosphere,” said von Frese, geophysicist and project leader. “Our technology would be another nail in the structure to detect explosions.”
“One of the arguments is ‘Well, how do you prove that a clandestine explosion occurred?’” said Grejner-Brzezinska, Park’s adviser and GPS World’s Tech Talk blog editor. “Now we can say, ‘Here, we have the data from GPS to show when and where.’”
According to the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) nuclear testing has been carried out in the past by the United States, Russia, the United Kingdom, France, China, India, Pakistan, and North Korea (see Figure 1).
Researchers, and those monitoring treaty violations, are able to target specific geographic areas that are equipped for tests, since development of a nuclear test site requires a lot of technical effort and budget. For example, the North Korean tests carried out in 2006 and 2009 were very close geographically.
“They tend to stick to the same site and reuse their facilities for nuclear testing,” von Frese said. “So a country that has previously conducted underground nuclear testing probably will reuse the site if new testing is needed.”
“They could be monitored using GPS as long as there are GPS stations nearby,” Park said.
The new GPS nuclear-detection technology was presented at the Comprehensive Nuclear-Test-Ban Treaty Organization meeting held June 8–10 in Vienna, Austria, and received press coverage that drew additional interest.
GPS Detection. The team zeroed in on a specific event to test the software, selecting a nuclear test conducted by North Korea in 2009 and using data pulled from nearby South Korean GPS stations.
Traditional detection methods for underground nuclear tests include seismic and other sensors. The CTBTO operates an international monitoring system to detect explosions with a yield of at least one kiloton. Besides seismic sensors, monitoring includes hydroacoustic sensors to monitor for shockwaves on land and in water, infrasound to detect pressure waves, and radionuclide detectors for any gas that may have been generated, though the levels aren’t always detectable.
“Even though there are four different systems available, they sometimes are unable to detect the underground nuclear explosions,” Park said. “GPS technology will make the detection validation stronger since each of them is based on a different theory. In the case of the nuclear test conducted by North Korea in 2009, only seismic and a few infrasound sensors detected the event because of their improved containment technique. Our study tracked down the 2009 event using GPS, and found it coincided with the seismic results.”
Park was able to take advantage of the well-established worldwide infrastructure already in place for GPS for her software test. The team used GPS data recorded by South Korean GPS receivers of the 2009 North Korea test. “There are a few IGS (International GNSS Service) stations in South Korea, China, and Japan. Since South Korea runs their own GPS network, I requested the data so that we could obtain data from more stations located in South Korea,” Park said.
“Since the stations we chose were permanent reference stations controlled by an international organization (IGS) and a specific country (Republic of Korea or South Korea) respectively, most of them have been running continuously except for unexpected data gaps from time to time,” Park said. Figure 2 shows the GPS stations processed for the project.
With data in hand, Park was able to test her software. The results showed definite peaks from different stations at different times after the 2009 explosion. “We realized that the time of the detected peak was dependent on the distance between underground nuclear explosion and each GPS station,” Park said. Figure 3 shows four different stations’ TIDs (traveling ionospheric disturbances) that the team initially recognized.
Figure 3. Traveling ionospheric disturbances (TIDs) detected at stations INJE (top left), DOND (top right), DAEJ (bottom left), and CHAN (bottom right). Click to enlarge.
Ruling Out Quakes. One big challenge using GPS for ionospheric monitoring is determining the origin of an event. “Since earthquakes also disturb the ionosphere, distinguishing earthquakes from underground nuclear explosions are problematic even with GPS,” Park said. “Indeed, we only focused on examining and isolating TIDs from the nuclear explosions. We are now working to analyze the TIDs from earthquakes and compare them with nuclear TIDs.”
Besides helping to distinguish between earthquakes and nuclear-test explosions, the software may eventually distinguish between nuclear plant fallout and nuclear test fallout.
With this goal in mind, the team is analyzing the ionospheric data gathered from recent nuclear plant accidents such as the one in Japan following the earthquake and tsunami in March. “Since there were data gaps and other data issues, we have as yet nothing more to report. Hopefully, we find the earthquakes’ signature soon.”
A receiver can selectively acquire scattered signals and the resulting measurements can be interpreted to reveal certain characteristics of the source of the scattering. This article discusses the design and application of a GNSS instrument that uses scattered signals for monitoring the level and roughness of inland and coastal water surfaces for the betterment of planet Earth.
By Alejandro Egido and Marco Caparrini
INNOVATION INSIGHTS by Richard Langley
WHY IS THE SKY BLUE? This is an age-old question, interesting to anyone with a curiosity about his or her surroundings. But what has it got to do with global navigation satellite systems? Believe it or not, there is a connection.
Some of you might remember the explanation of the sky’s color from your Physics 101 course but to bring everyone up to the same level, let’s review. Everything we see is the result of the interaction of light and matter. And by matter, we mean the atoms, molecules, and particles making up matter. Light causes matter to vibrate. And vibrating matter (due to its electrical charges) in turn emits light, which combines with the original light. But matter not only re-emits light in the forward direction, it re-emits light in all other directions. This is called scattering.
Now, the light from the sun includes all colors and so if look directly at the sun when it is high in the sky (don’t try this at home), it looks white or slightly yellowish. We are seeing the light propagating directly toward our eyes. When we look at the sky away from the sun, we are seeing scattered light. And this scattered light is predominantly blue. Why? It turns out that scattering is proportional to the fourth power of frequency. Light that is of a higher frequency, say a factor of two, is sixteen times more intensely scattered. So, blue light, which has about twice the frequency of light from the red end of the visible spectrum, is scattered much more than red light. Violet light is scattered even more but our eyes are not as sensitive to violet light as they are to blue light. Hence the sky looks blue.
So what has this got to do with GNSS? As we know, for the best positioning and navigation results, we need the satellite signals to travel along a direct path to the receiver’s antenna. There may be slight changes in the speed and direction of propagation of these direct-path signals caused by the interaction of the electromagnetic waves with the matter making up the ionosphere and the neutral atmosphere, but these are readily accounted for in the position fixes.
However, once they reach the Earth’s surface, the signals can be reflected by buildings, vegetation, the ground, water surfaces, and so on. The signals are actually being scattered by the matter they encounter. A receiver can selectively acquire the scattered signals and the resulting measurements can be interpreted to reveal certain characteristics of the source of the scattering.
In this month’s column, we learn about the design and application of a GNSS instrument that uses scattered signals for monitoring the level and roughness of inland and coastal water surfaces–yet one more use of GNSS signals for the betterment of planet Earth.
Lakes and water reservoirs are the world’s most important sources of accessible fresh water. Despite its paramount importance — not only for a large variety of human activities, but also for the sustainability of ecosystems — fresh water is already scarce in many regions. The problem is envisaged to become worse in the coming decade. In addition, in climatological studies surface water storage is a critical element of the water cycle since the analyses integrate all hydrologic processes (precipitation, runoff, evapotranspiration, and so on) over a given basin; and for hydroelectric companies, it is the main parameter to be kept under observation for efficient energy production. All of these concerns make the monitoring of fresh water resources a prime activity for a wide variety of stakeholders including governments, climate research organizations, and hydroelectric production companies.
Coastal management is also a wide-ranging issue with large social and economic impacts. Care of our coasts includes dealing with threats such as storm surges and flooding, coastal erosion, and conflicting land-use issues. Coastal areas support the greatest concentration of living resources and people on the planet. In the past few decades, these regions have experienced a population density increase, which is envisioned to grow steadily. Furthermore, conflicts between commercial interests, recreational activities, infrastructure development, environment conservation, and exploitation of natural resources will become increasingly important and contentious. In fact, the coastal zone is a peculiar environment in which terrestrial, oceanic, atmospheric, and human inputs of energy and matter converge. Storm surges and coastal flooding events have caused considerable damage and economic loss on European coasts in particular. Such events, possibly linked to the world climate change, are expected to get worse in the near future, due to sea level rise and storm activity.
So, close monitoring of both inland waters and coastal regions is necessary for the well being of the planet. And since the need is so pervasive, monitoring systems should be characterized by a relatively low cost, low maintenance, and easy deployment, to serve the widest possible user community. We have developed a patent-pending solution using signals from global navigation satellite systems (GNSS).
Called Oceanpal, our monitoring system exploits reflected GNSS signals as signals of opportunity for passive remote sensing of the Earth’s water surfaces. These multipath signals are usually considered to be nuisance signals since they reduce the accuracy of GNSS positioning applications. But for monitoring various processes affecting the Earth’s surface, they are very beneficial. The technique is known as GNSS reflectometry (GNSS-R), and during the past decade, its use as a technique for Earth observation purposes has taken root.
GNSS-R is basically a bistatic radar technique. While most radar systems, such as those used for monitoring air space and harbor approaches and for weather forecasting, combine the radar transmitter and receiver at the same site — so-called monostatic radar — bistatic systems use transmitters and receivers separated by a considerable distance. Such systems have been used for studying certain atmospheric phenomena and for military applications where simple line-of-sight reflections from the target of interest are inadequate or insufficient.
The concept of bistatic radar can be extended to satellite signals. Since some of the signal transmitted by a satellite gets reflected off the Earth’s surface, detecting this reflected signal by a separate passive receiver would provide some information about the reflecting surface. While any satellite signal could be used in principle, GPS (and other GNSS) turn out to be particularly useful. The concept of using GPS signal reflections was initially proposed in 1993 by Manuel Martín-Neira, working at the European Space Agency’s European Space Research and Technology Centre in Noordwijk, The Netherlands. Since then, the technique has been successfully implemented by an increasing number of researchers.
We could list several reasons for the continuous growing interest in GNSS as a remote sensing tool, but two main ones stand out: first, the global availability and stability of GNSS signals enables their use as reliable signals of opportunity; and second, GNSS makes use of L-band radiation, which is highly interactive with the natural scattering medium but relatively impervious to atmospheric conditions. Moreover, the passive nature of this concept allows for the production of cost- and resource-effective instruments.
Navigation signals are sensitive to a wide variety of geophysical parameters including topography, surface roughness, surface moisture, ionospheric electron content, tropospheric water vapor, water salinity, and vegetation. Research targeting related geophysical applications has been ongoing for many years, and the first pre-operational services exploiting reflected GNSS signals are now available. In fact, while the scientific community is waiting for a dedicated GNSS-R space mission to confirm the theoretical predictions about the characteristics of reflected signals observed from space, ground-based and airborne sensors have already been developed and validated for a number of applications.
The GNSS-R research area that has been most thoroughly investigated concerns the reflection of navigation signals from water surfaces, given the highly reflective nature of water. However, from water the interest has now moved towards ice and land applications, more specifically to the detection of sea ice and the monitoring of soil moisture. Recently, GNSS-R has also been proposed as a possible tool to monitor vegetation. This article focuses on the presentation of the Oceanpal sensor, and the description of the altimetry algorithms for monitoring the levels of sea (coastal) and inland waters.
Our Instrument
As mentioned above, Oceanpal is a GNSS-R-based sensor designed for operational monitoring of coastal and inland waters. The instrument comprises three subsystems: a radio frequency (RF) section, an intermediate frequency (IF) section, and a data-processing section. The RF section features a pair of low gain L-band antennas. A right-hand circularly polarized (RHCP) zenith-facing antenna collects the direct GNSS signals while a left-hand circularly polarized (LHCP) nadir-facing antenna collects the sea- or lake-surface reflected GNSS signals. (On reflection, the signals become predominantly LHCP.) Data bursts of some minutes’ duration are acquired from each antenna using two GPS L1 receivers (front ends) that down-convert the signals to IF. Within the IF sections, the signals are one-bit sampled and stored on a hard disk.
These direct and reflected raw data are then fed into the processing section of the instrument, where a pair of software GNSS receivers detects and tracks the available signals in the direct channel (which works as a master) and blindly despreads the reflected signals in the reflected (slave) channel. The result of this processing is a set of direct and reflected electromagnetic field time series for each satellite in view, plus some ancillary information, such as the satellite pseudorandom noise code (PRN) numbers and GPS time references, among others. The architecture described above is shown in FIGURE 1.
Figure 1. Basic operation of Oceanpal and the principle of GNSS-R-based sea-surface monitoring. Right-hand and left-hand circularly polarized antennas feed signals to radio frequency (RF) receiver front-ends that, in turn, feed software (SW) receiver back-ends and subsequent processing algorithms.
The data products provided by Oceanpal are so-called “Level-2” or derived products, namely the significant wave height (a statistical measure of trough-to-crest wave height), and the height of the nadir antenna over the mean level of the water surface under observation. To make this data available for the user in a friendly way, the observations are uploaded to a web server and displayed on a web page.
Oceanpal requires low maintenance compared to its competitors. Standard oceanographic buoys, which use accelerometers and a magnetic compass, or GPS buoys, featuring a conventional GPS receiver, are in contact with water, which implies costly infrastructures and frequent maintenance operations. Pressure sensors and air bubblers, commonly used to monitor the level of water reservoirs, also require frequent maintenance because of sediment accumulation. Compared to the alternatives, our sensor is a less costly and lower maintenance solution.
GNSS-R Altimetry Algorithms
The inland-water/sea-level monitoring is based on the estimation of the height of the Oceanpal antennas above the water/sea surface. This height is retrieved by the comparison of the delay (in time or distance) between the reflected and the direct signals. The reflection geometry is shown in FIGURE 2. Such a delay can be estimated using either the PRN code or the carrier phase of the incoming signals. The phase-based estimation provides more precise values, but it is only available for calm water surfaces where coherent constructive scattering (specular reflection) is predominant. In the case of rougher surfaces, the reflected signal’s coherency is lost, and therefore the code-based algorithm must be used.
Figure 2. The geometry of GNSS signal reflections for altimetry applications.
The basic equation that links the delay of arrival of both signals with the height of the antennas over the surface as a function of time (t) can be written as equation (1):
(1)
where τ represents the lapse between the time of arrival of the reflected and the direct signals (as determined using either phase or code measurements), h is the height to be estimated, e is the elevation angle of the satellite considered, and b is the system bias, which is considered unknown but constant during every estimation. Solving a linear system with many such equations for different satellites over, say, one minute provides the sought estimation of h (and b).
Measuring the Level of a Water Reservoir
As mentioned before, when the water surface is sufficiently flat, the coherency of the reflected signal is maintained, thus its phase can be used to retrieve estimates of the height of the antennas over the surface. This algorithm is the so-called phase altimetry algorithm. The basic observable for this algorithm is the interferometric complex field (ICF), defined as the ratio between the reflected and direct complex correlation waveform peaks:
(2)
where PR and PD represent the time series of waveform peaks for the reflected and direct signals, respectively. In computing this ratio, adverse propagation effects such as the extra delay induced by the ionosphere and troposphere cancel out. Measuring the phase of the ICF, , one is then considering the phase single difference, , between the reflected and direct signals as given in equation (3):
(3)
where k is the wave number of the GPS carrier frequency (the reciprocal of the wavelength), noiseφ is the noise present in the ICF phase and is the unknown integer cycle ambiguity. D is the excess path of the reflected with respect to the direct signal, which can be directly linked to the height of the antennas over the surface. In order to solve for the cycle ambiguities, phase double differences among satellites are calculated, and by means of an ambiguity resolution algorithm (we use the null-space method developed by Manuel Martín-Neira and colleagues) the unknown phase-cycle ambiguities can be determined. It is then a straightforward procedure to work out the excess path of the reflected signals to finally deduce the height of the antennas over the water surface.
La Baells Experiment. An experimental campaign was carried out with an Oceanpal instrument at the La Baells water reservoir (near Berga in Catalonia, Spain) in cooperation with the Catalan Water Agency. This experiment was designed to study the feasibility of accurate altimetry measurements at lakes and reservoirs using our technique.
Within this campaign, one week of data was gathered early in March 2008 to compare the Oceanpal GNSS-R phase-altimetry measurements with those from the La Baells in-situ sensor (a water bubbler known to have centimeter-level accuracy). The results from this campaign are shown in FIGURE 3. After referencing the measurements to the antennas’ position with respect to the mean water level, the accuracy obtained from the Oceanpal measurements with respect to the ground truth (the water bubbler) was better than 2 centimeters (after a five-minute integration time).
Figure 3. Results of a one-week campaign at the La Baells water reservoir near Berga, Spain, in March 2008. Lake height in meters with respect to mean sea level.
Despite the fact that the phase altimetry algorithm is precise, it requires the simultaneous observation of several reflections from different satellites to converge and accurately solve for the phase ambiguities. However, this cannot be done for all scenarios, and in these situations the conventional phase altimetry algorithm cannot be applied.
Lake Laja Experiment. A case where we couldn’t use the phase approach was project Hydro. This was an initiative developed by our organization in collaboration with Pontificia Universidad Católica de Chile (the Pontifical Catholic University of Chile) and funded by ENDESA (Empresa Nacional de Electricidad S.A.), one of the world’s largest electricity companies. An Oceanpal instrument was installed at Lake Laja, in the Biobío Region, Chile, a water reservoir managed by ENDESA Chile. The Hydro project aims to use remote sensing assets to predict and monitor water flow in the Laja River basin. For that, having precise measurements of Lake Laja’s level is a must.
The instrument was installed on the shore of the lake as seen in FIGURE 4. However, the high variability of the lake’s level, more than 10 meters in one year, and the abruptness of the terrain, results in the number of observed reflections from the water surface being quite low. This is especially the case when the level of the lake is low. In this situation, the number of different GPS satellites observed per hour was calculated to be fewer than two for more than 45 percent of the time, and fewer than three for more than 85 percent of the time. Given this scarcity of reflections, we could not use the phase altimetry algorithm as described above.
Figure 4. The Oceanpal installation at Lake Laja, Chile.
We developed a new phase altimetry algorithm, which considers the interferometric phase evolution over time. The resulting relative phase parameter can be linked to the height of the antennas over the water surface by means of the same geometrical relationship as before. Despite the fact that measuring a relative phase increases the measurement noise with respect to the case in which an absolute phase is used, the phase ambiguity and the bias between the direct and reflected receiving channels do not need to be calculated, thus reducing the complexity of the algorithm and its convergence requirements. A Kalman filter is used to smooth the inherently noisy behavior of the relative phase.
The Oceanpal measurements were compared to those of a sensor operated by the Dirección General de Aguas (DGA), the Chilean water management agency. An accuracy better than 9 centimeters was achieved in determining the lake’s level during the austral winter, when the lake is at its minimum level and therefore the satellites’ reflections from the water surface are scarce. The lake level has its maximum during the summer after the melting season. During this period of time, the achieved accuracy of Oceanpal with the new phase algorithm was better than 5 centimeters. A comparison of Oceanpal and DGA’s sensor measurements of the water level is shown in FIGURE 5.
Figure 5A. A comparison of measurements of Lake Laja’s water level by Oceanpal and a water bubbler sensor operated by Dirección General de Aguas (DGA) for two periods of time corresponding to the austral winter (from late April 2009 until early August 2009).Figure 5B. A comparison of measurements of Lake Laja’s water level by Oceanpal and a water bubbler sensor operated by Dirección General de Aguas (DGA) for two periods of time corresponding to the austral summer (from late November 2009 until late January 2010).
Measuring Sea Level
Sea level is obtained from Oceanpal measurements by means of the code altimetry algorithm due to the inherent roughness of the sea surface. This technique derives altimetric information from the displacement of reflected waveforms with respect to the direct ones. Such a displacement can be directly related to the delay between the direct and reflected signals (the so-called lapse), and is used in a similar way to the phase-based method to extract the altimetry information of the water surface being monitored.
Despite the fact that the code altimetry algorithm is not as precise as the phase altimetry algorithm, it is not subject to the coherence requirement for the reflected signal. Therefore, it can be applied to rough, dynamic surfaces such as the open ocean and coastal areas. The use of code altimetry in rough water conditions results in a clear observation of tide dynamics but, as expected, with a higher error range compared to situations where phase altimetry can be applied.
Scheveningen Pier Experiment. The performance of the code-based algorithm was tested during an experimental campaign carried out on Scheveningen Pier in Den Haag (The Hague), The Netherlands. An Oceanpal instrument was installed close to a Radac X-band radar tide gauge. FIGURE 6 shows the tide variation at the installation site estimated by the Radac instrument and by Oceanpal. As can be seen, a good agreement between both estimates is achieved with a standard deviation of the difference of 12 centimeters.
Figure 6. Daily tidal variation at Scheveningen Pier, The Hague, The Netherlands, on May 3-4, 2008, measured by X-band radar and Oceanpal.
To improve this result, a combination of code and phase estimation is being investigated, involving the alignment of the phase using the code information. The combination of these two parameters may provide the best of both worlds. However, with the signals from modernized GPS and those of the forthcoming Galileo system, the code-ranging precision is envisioned to increase by a factor of four or five, which is expected to impact directly on the precision of the code altimetry algorithm.
Conclusion and Outlook
During the past decade, the scientific community’s interest in GNSS-R has grown, leading to the continuous development of new applications and to an increasing relevance in specific market niches. Some of these applications, especially those related to the monitoring of water surfaces, have reached an operational level of maturity, and provide end users with valuable information.
In this brief article, we have described the Oceanpal instrument and outlined its use in altimetric measurements of water surfaces. It was shown that using the phase of reflected signals with respect to that of direct signals, accurate measurements of a lake’s level could be obtained. In addition, we overviewed a new algorithm that incorporates the evolution of this phase in time. This algorithm is suitable for low satellite visibility scenarios. For example, using this algorithm, the level of Lake Laja in Chile was determined with an overall accuracy better than 7 centimeters. Such a level of accuracy meets the monitoring requirements necessary for improving the stream-flow prediction in the Laja River basin. We also showed that code altimetry can be successfully used to monitor sea level variations associated with tides, with a demonstrated accuracy of 12 centimeters.
These encouraging results are expected to be further improved with the evolution of GPS, the refurbishment of the Russian GLONASS system, and the deployment of the European Galileo system. First of all, when all three navigation systems are fully deployed, it is calculated that at least 20 navigation satellites will be visible at the same time. A GNSS-R instrument could take advantage of this large number of available signals. In addition, the quality of these signals is expected to be largely improved in terms of signal-to-noise ratio, bandwidth, and ranging precisions, which will in turn improve the performance of GNSS-R altimetry algorithms. As a result, the prospects for GNSS-R altimetry over water surfaces, not only for ground-based systems, but also airborne and even spaceborne systems, are extremely promising.
Manufacturers
The Oceanpal instrument was developed by Starlab, Barcelona, Spain. The Scheveningen Pier experiment used a Radac, Haarlem, The Netherlands, WaveGuide radar level gauge.
ALEJANDRO EGIDO has a B.Sc. degree in electrical engineering from the University of Zaragoza, Spain. After his studies, he worked on the Sentinel-1 remote sensing satellite project at the European Space Agency (ESA), where he performed the interference analysis of the synthetic aperture radar instrument. Since 2007, he has been a research engineer at Starlab, Barcelona, while pursuing a Ph.D. at the Polytechnic University of Catalonia. His main research field is the use of GNSS signals as sources of opportunity for remote sensing applications, with special interest in estimating terrestrial bio-geophysical parameters.
MARCO CAPARRINI received the “Laurea” degree in electronic engineering — remote sensing from the University “La Sapienza” in Rome. He has worked as a research engineer at ESA’s European Space Research and Technology Centre in Noordwijk, The Netherlands; at the German Aerospace Center in Oberpfaffenhofen, Germany; and at the Swiss Federal Institute of Technology in Zurich. His main research field is the use of GNSS signals as sources of opportunity for remote sensing of planet Earth, and he is the Starlab manager for the space research and development area.
FURTHER READING
• Principles of GNSS Reflectometry (GNSS-R)
“The PARIS Concept: An Experimental Demonstration of Sea Surface Altimetry Using GPS Reflected Signals” by M. Martín-Neira, M. Caparrini, J. Font-Rossello, S. Lannelongue, and C. Serra Vallmitjana in IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, No. 1, January 2001, pp. 142–150, doi: 10.1109/36.898676.
• Overview of GNSS-R Applications
“GNSS Reflectometry and Remote Sensing: New Objectives and Results” by J. Shuanggen and A. Komjathy in Advances in Space Research, Vol. 46, 2010, pp. 111–117, doi:10.1016/j.asr.2010.01.014.
• GNSS-R Experimental Campaigns
“Oceanpal: Monitoring Sea State with a GNSS-R Coastal Instrument” by M. Caparrini, A. Egido, F. Soulat, O. Germain, E. Farrès, S. Dunne, and G. Ruffini in Proceedings of the 2007 International Geoscience and Remote Sensing Symposium, Barcelona, Spain, July 23–28, 2007, pp. 5080–5083.
“The Eddy Experiment: Accurate GNSS-R Ocean Altimetry from Low Altitude Aircraft” by G. Ruffini, F. Soulat, M. Caparrini, O. Germain, M. Martín-Neira in Geophysical Research Letters, Vol. 31, L12306, 4 pp., 2004, doi:10.1029/2004GL019994.
“The Eddy Experiment: GNSS-R Speculometry for Directional Sea- Roughness Retrieval from Low Aircraft” by O. Germain, G. Ruffini, F. Soulat, M. Caparrini, B. Chapron, and P. Silvestrin in Geophysical Research Letters, Vol. 31, L21307, 4 pp., 2004, doi: 10.1029/2004GL020991.
“Wind Speed Measurement Using Forward Scattered GPS Signals” by V. Zavorotny, J. Garrison, A. Komjathy, and S. Katzberg in IEEE Transactions on Geoscience and Remote Sensing, Vol. 40, No. 1, January 2002, pp. 50–65, doi: 10.1109/36.981349.
• GNSS-R for Monitoring Soil Moisture
“The SAM Sensor: An Innovative GNSS-R System for Soil Moisture Retrieval” by A. Egido, C. Martin-Puig, D. Felip, M. Garcia, M. Caparrini, E. Farrés, and G. Ruffini in Proceedings of NAVITEC 2008, the 4th ESA Workshop on Satellite Navigation User Equipment Technologies, Noordwijk, The Netherlands, December 10–12, 2008.
“GPS Ambiguity Resolution and Validation: Methodologies, Trends and Issues” by D. Kim and R.B. Langley in Proceedings of the 7th GNSS Workshop – International Symposium on GPS/GNSS, Seoul, Korea, Nov. 30 – Dec. 2, 2000, Tutorial/Domestic Session, pp. 213–221.
By Simon Lutz, Marc Troller, Donat Perler, Alain Geiger, and Hans-Gert Kahle
A team of Swiss researchers is using data from a network of GPS receivers and the technique of tomography to obtain profiles of how moisture is distributed with height, which might lead to better weather forecasts.
INNOVATION INSIGHTS by Richard Langley
WEATHER FORECASTING IS STILL AN IMPERFECT ART. Humankind has been trying to predict the weather for millennia. Early attempts were based on general observations such as “Red sky at night, shepherd’s delight; Red sky in morning, sailor’s warning.” But it was only with advances in scientific thought and the invention of measuring devices, such as the mercury barometer, that more specific predictions could be made.
Towards the end of the 18th century, the father of modern chemistry, Antoine Laurent Lavoisier, said “It is almost possible to predict one or two days in advance, within a rather broad range of probability, what the weather is going to be; it is even thought that it will not be impossible to publish daily forecasts, which would be very useful to society.”
Forecasting ability has improved over the years as measurement technology, communications, and the understanding of atmospheric processes have improved. Meteorologists use measurements from various types of sensors together with mathematical models describing the physics of the atmosphere to predict its future state. The temporal and spatial density of the measurements and the sophistication of the models have a direct impact on the success of the forecasts. Weather stations on the Earth’s surface, radar installations, radiosondes, and satellite sensors all provide data for modern forecasts. Yet better sampling of the current state of the atmosphere, particularly the distribution of water vapor, is required to produce more accurate and more timely forecasts of its future state. GPS can help.
The signals from the GPS satellites must transit the atmosphere on their way to a receiver on the Earth’s surface. The atmosphere’s atoms and molecules slow down the signals so that they arrive slightly later than they would if the Earth was surrounded by a vacuum, and this effect shows up in the GPS receiver measurements. The receiver or measurement processing software needs to remove or model the effect to obtain accurate receiver positions. On the other hand, if all parameters affecting GPS measurements such as satellite and receiver coordinates are well known, then the delay imparted by the atmosphere can be estimated. It is possible to separate the effect of water vapor from that of the dry gases such as nitrogen, oxygen, and carbon dioxide and to provide a measure of the atmosphere’s moisture content. Several national weather agencies are ingesting such estimates from networks of GPS receivers into experimental or operational numerical weather forecast models. But these values represent an integrated measure of moisture above a receiver. Profiles of how moisture is distributed with height would be more useful and might lead to better weather forecasts. In this month’s column, a team of Swiss researchers discuss how they use data from a network of GPS receivers and the technique of tomography to obtain such profiles.
“Innovation” features discussions about advances in GPS technology, its applications, and the fundamentals of GPS positioning. The column is coordinated by Richard Langley, Department of Geodesy and Geomatics Engineering, University of New Brunswick.
Water vapor plays an essential role in the dynamics and thermodynamics of the atmosphere — especially storm systems — on local, regional, and global scales. It is a precursor of precipitation. Furthermore, a significant fraction of the energy released to the atmosphere comes from water vapor via latent heat. And much of the “greenhouse effect” is caused by the presence of water vapor in the atmosphere.
Beginning in 1992, a number of researchers successfully tested the hypothesis that the Global Positioning System (GPS) could be used to detect long- and short-term global and regional air-mass changes by estimating the amount of water vapor in the air above a GPS receiver. The arrival of GPS signals at a receiver is delayed by the presence of the Earth’s atmosphere. The satellite signals slow down when they encounter the atmosphere’s electrons, atoms, and molecules. In particular, the signals are affected by the presence of water vapor. Through a careful analysis of the GPS receiver’s measurements, the amount of water vapor along the signal path can be estimated. This is an integrated value that depends on the density of the water vapor molecules, or alternatively, the associated humidity at each point along the signal path. But from a single integrated value, there is no way to determine the profile of humidity — how the humidity varies with height above the surface. However, if a network of GPS receivers is deployed over a region, it is possible to determine the three-dimensional structure of humidity in the atmosphere above the receivers using tomography in a similar way to that used for medical imaging — albeit with radio waves rather than X-rays.
At the Swiss Federal Institute of Technology in Zurich (familiarly known by its German abbreviation ETH), we have developed the Atmospheric Water Vapor Tomography Software (AWATOS) for estimating humidity profiles. We have tested it with data from various measurement campaigns, including one in Hawaii. We have also used it to determine 40 humidity profiles over Switzerland with data from the Automated GNSS Network for Switzerland (AGNES) of the Swiss Federal Office of Topography, Swisstopo. And recently, we have implemented it in an operational testbed analyzing AGNES data together with observations from the Automated Swiss Weather Station Network (ANETZ) of the Swiss Federal Office of Meteorology and Climatology, MeteoSwiss.
To assess the potential of ground-based GPS water vapor tomography to support meteorological forecasting systems, the tomographic results must be available within near real-time and must be produced with an accuracy comparable to that of existing meteorological measurement techniques and numerical weather prediction models. With those goals in mind, we have carried out a project to determine humidity profiles in a region of the Swiss Alps. In this article, we outline the project, including the background theory, and discuss how we validated the results by comparing them to radiosonde measurements and weather prediction models.
Theoretical Background
Before looking at the project, we will briefly describe the theory behind our tomographic technique.
Radio Wave Refractivity. The propagation of radio waves through the Earth’s ionosphere and the electrically neutral atmosphere (the air) is accompanied by phase and amplitude variations caused by the varying refractive index of the media. Since the effect of the ionosphere on GPS signals can be removed almost completely by processing measurements on both the L1 and L2 frequencies, we are only concerned with the effect of the neutral atmosphere here. In 1951, Essen and Froome published a general formula for the refractive index of air, n, and the corresponding atmospheric refractivity, N, using the three meteorological parameters: total (barometric) air pressure, p, measured in hectopascals; air temperature, T, in kelvins; and the partial pressure of water vapor, e, in hectopascals (see Equation 1). The associated empirically determined constantsk1, k2,andk3 have been continuously improved over the years.
In the weighted mean formula for non-dispersive radio wave refractivity for air with 0.0375 percent carbon dioxide content, k1 is set to 77.6890 kelvins per hectopascal, k2 to 71.2952 kelvins per hectopascal, and k3 to 375463 kelvins-squared per hectopascal. The k1 term of Equation 1 can be associated with dry refractivity (Ndry), the refractivity of the dry constituents of air, and the second and third terms with the wet part (Nwet), which is proportional to the partial water vapor pressure.
Tropospheric Refraction. The speed of propagation of a radio wave is governed by the refractivity or index of refraction along the signal (slant) path. The path itself is determined by Snell’s Law relating angle of incidence to angle of refraction at the boundary of two media with differing refractive indices. As mentioned previously, GPS measurements include the additional or excess delay due to the presence of the neutral atmosphere. Since the bulk of the effect occurs in the lower, denser part of the atmosphere — the troposphere — we commonly refer to it as the tropospheric delay. The tropospheric slant path delay, , between station p and satellite r is defined by the following integral along the signal ray path, s:
By integrating the individual components of N in Equation 2 and applying Equation 1, the tropospheric slant path delay can be written as a function of the meteorological parameters p, T, and e.
Tropospheric delay as a function of the observation zenith angle, , (90° minus the elevation angle) is calculated using appropriate mapping functions. The mapping function, , is defined as the ratio of the electrical path length through the troposphere at a particular geometrical zenith angle to the electrical path length in the zenith direction. Typically, separate mapping functions are used for the dry and wet components. Furthermore, the slant wet delay, , for elevation angles down to 3 degrees can be represented as the sum of the isotropic term, ZWDp (zenith wet delay at station p) with its corresponding mapping function, and a non-isotropic component, :
The Tomographic Voxel Model. Separate slant delays only provide integrated measures of the tropospheric refractivity — a one-dimensional view, if you like. To get the three-dimensional structure of refractivity, we need a different approach. We divide the troposphere into small volume elements or voxels (short for volumetric pixel). With multiple, simultaneous raypaths criss-crossing the model volume, it is possible, in principle, to estimate the refractivity of each voxel and hence get a height profile of refractivity.
The tomographic voxel model is a three-dimensional geometrical structure with ellipsoidal borders. The grid spacing defines the resulting resolution of the tomographic analysis. In the horizontal plane, the voxel model covers the whole catchment area. For each voxel, an unknown but constant refractivity is introduced. Figure 1 illustrates the principle by means of one single observation.
FIGURE 1 Principle of GPS tomography. The refractivity in the atmosphere along the raypath of a GPS satellite signal to a ground-based receiver is discretized by a three- dimensional voxel model.
According to Equation 2, the wet part of the slant path delay ( ) for one observation between station p and satellite r can be expressed as a summation over each individual voxel i of the voxel model with a total of k voxels, through which the GPS signal passes:
The refractivity value, Ni, of each voxel is determined by performing a least-squares adjustment. A priori models and inter-voxel constraints can be introduced into the tomographic inversion system. The a priori tomographic model consists of selected voxels, which have externally estimated refractivity values. Inter-voxel constraints provide a spatially smoothing characteristic, as the actual state (or the refractivity) of the atmosphere is smoothly changing from point to point.
Double-Difference GPS Tomography. The software package AWATOS is based on double-difference GPS observations; that is, the difference of measurements made by a pair of receivers between a pair of satellites. Common errors such as those of satellite and receiver clocks difference out. The remaining errors in the observation equation are primarily just those due to atmospheric refraction. The influence of the ionosphere can be corrected to first order by using a linear combination of dual-frequency data as previously mentioned.
Therefore, in double-difference processing, the tropospheric slant path delay, , can be reconstructed by combining four observations (between two stations p and q and two satellites r and s). Similar to Equation 3, the total double-difference path delay, , can be written as a function of the GPS processing output (the zenith path delay, ZPD, and the double-difference phase residual ):
Usually, the dry and wet path delays are treated individually with appropriate models and mapping functions. This separation is carried out within the software package AWATOS for both the path delays and the phase residuals.
Introducing the double-difference slant path delays, , as well as the estimation of the zenith total delays, ZTD, for each station, a priori refractivity values, N0, and inter-voxel constraints , (with the scalar product condition ), into the tomographic equation system, the final form of the inversion equation for the unknown refractivity, N, according to Equation 4 including the design matrix A of the observations is:
To obtain only the wet part of the resulting refractivity field (values of refractivity and their gradients, , the individual components of the tomographic observation vector (the left-hand side of Equation 7) have to be correspondingly preprocessed. This is done by introducing additional meteorological observations or numerical data as well as tropospheric mapping functions and models.
Data Description
We recently carried out two measurement campaigns to study the feasibility of our method on a non-permanent densification network in the Swiss Alps. We were interested in investigating such a small-scale high-resolution configuration to see how it can help to determine and model water vapor over a local, mountainous catchment area. We also carried out these campaigns with an eye towards the development of a near real-time analysis procedure with a high update rate of less than one hour and the potential to support short- and medium-range weather forecasts and hydrological hazard assessment.
The Project Area. Two field campaigns, each lasting seven days, were carried out in an area of about 50 kilometers by 50 kilometers in the eastern part of the mountainous canton of Valais in the southwest of Switzerland (see Figure 2) in July and October 2005. This region was selected because of its high degree of exposure to hydrological hazards such as flooding of river valleys.
Figure 2. Project area (identified by the rectangle) in the Swiss Alps in the southwest of Switzerland. The elevation of the topography varies from 500 meters to over 4000 meters above mean sea level.
Besides the impact of the fast-changing meteorological situation in the project area, the rough topography is also a challenge for high-precision GPS analysis because of limited fields of view.
GPS Network and Meteorological Data. Ground-based geodetic GPS stations with dual-frequency receivers were deployed for continuous measurement during the period of the two campaigns. The network was complemented by permanent GPS stations from the national network. The ensemble of all stations used in July 2005 is portrayed in Figure 3.
Figure 3. The 21 GPS stations in the project area in the mountainous canton of Valais (see also Figure 2) used during the measurement campaign in July 2005. The stations’ altitudes vary between 527 meters (SION) and 3119 meters (ZER2).
In October 2005, the non-permanent three-dimensional geodetic Turtmann network was operated with six additional stations in the vicinity of the stations BRAE, SUST, and EMSH (see Figure 3). Furthermore, for this second campaign, data from three stations of the permanent geodynamics/tectonics network in Valais, TECVAL, in the northwestern part of the project area was available.
Several GPS stations were collocated with non-permanent meteorological measurement systems collecting surface temperature, humidity, and air pressure data. Also, rainfall was recorded for validation purposes at five ANETZ stations within the project area. The temperature, humidity, and air pressure observations were processed with the software package Collocation of Meteorological Data for Interpolation and Estimation of Tropospheric Path Delays (COMEDIE) developed at the Geodesy and Geodynamics Lab, ETH Zurich. COMEDIE provides a four-dimensional modeling of meteorological data in space and time. It is based on the method of least-squares collocation and interpolation, meaning that the model is described by a functional and a stochastic part. The interpolated data was used for the separation of the total delay (and refractivity) into a dry and a wet part and to obtain a priori values, N0, for the tomographic analysis (see Equation 7).
To compare the results from GPS processing and tomography, independent measurement techniques were used during the measurement campaigns: solar spectrometry, using the Geodetic Mobile Solar Spectrometer (GEMOSS), for integrated path delays as well as weather balloon soundings up to the tropopause for meteorological profile data.
The Numerical Weather Model COSMO-7. MeteoSwiss uses the COSMO-7 model, developed by the Consortium for Small-scale Modelling, for its operational numerical weather forecasts. The model domain is covered by a grid of 383 × 325 points over western and central Europe with a horizontal resolution of 7 kilometers. The model consists of 45 levels vertically distributed between the filtered orography (or mountain topography) and an altitude of 22.5 kilometers.
For comparison and validation, a subset of the reanalyzed COSMO-7 vertical grid point profile data was processed in order to obtain refractivity profiles as well as integrated and interpolated time series of zenith path delays using another of our software packages, Collocation and Interpolation of Tropospheric Path Delays (COITROPA).
Results
We processed the GPS data from the two measurement campaigns and have compared the results with those from GEMOSS, COMEDIE, radiosonde data, and COSMO-7.
GPS Data Processing. The GPS processing yields high-quality receiver coordinates, tropospheric parameter estimates (ZPD), and ionosphere-free double-difference residuals to reconstruct the slant path delays (see Equation 5). International GNSS Service (IGS) precise products, including satellite orbits, were used to analyze the data, and for ray tracing in AWATOS.
Bernese GPS Software, version 5.0, was chosen for the processing of the GPS data due to its flexibility, modular design, and state-of-the-art modeling characteristics. The network solution was obtained by using minimally constrained coordinates of selected stations of the IGS reference frame with baseline lengths of up to 1,000 kilometers. The mean repeatabilities for the north, east, and up components of the daily coordinate solutions for all stations within the project area are given in Table 1. Final as well as ultra-rapid orbits and broadcast ephemerides were used to compare the best possible results with those that could be expected in real time.
The larger number of stations during the October campaign has a positive influence on the mean coordinate repeatabilities in the horizontal plane, whereas the up component remains at the same order of magnitude. Depending on the antenna and receiver types, there was a slightly positive or negative correlation discovered between the trend of the daily coordinates and the ZTD estimates.
Comparison of ZTD Time Series. The time series of zenith total delays (ZTD) from GPS, GEMOSS, integrated ground meteorological data (COMEDIE), and radiosondes coincide well. In particular, the passage of a cold front with heavy rainfall in the middle of the October 2005 campaign is reflected in the two local delay maximums on October 23 (see Figure 4).
Figure 4. Comparison of zenith total delay (ZTD) at station SUST obtained with COMEDIE, GPS, the local radiosondes (RS) and solar spectrometry (GEMOSS) for the October campaign in 2005. The mean values of the ZTD time series and the standard deviation are given for each technique for comparison purposes in parentheses.
The ZTD from the balloon soundings show an almost systematic overestimation. This may be due to an inaccurate self-calibration of the sensors or a lack of data in the upper atmosphere, and the related mismodeling of the zenith path delay. Differences in the COMEDIE time series are due to meteorological inhomogeneities in the lowest part of the troposphere and the influence of distant radiosondes, which were added to get the vertical information in the upper part. The interpolated ZTD values derived from the numerical weather model COSMO-7 are on average smaller than the GPS estimates (see Table 2).
The ZTD time series of both methods, GPS and the numerical weather model, correlate well with rainfall data. There is a slow increase of the zenith path delay before the precipitation event due to the accumulation of atmospheric water vapor and an abrupt decrease afterwards. Usually, the impact of short periods of localized precipitation is more clearly represented in the GPS data of the dense observation network than in the data of the weather model. The COSMO-7 time series seem to be too smooth.
Effect of Voxel Model Resolution. In order to assess the quality of the results obtained by applying the high-resolution GPS tomographic technique, special time series contour plots were created. They consist mainly of the wet refractivity profiles for each voxel model column between mean sea level and an altitude of 10 kilometers. The height of the nearest GPS station is given by a dashed line.
Figure 5 and Figure 6 give two examples from station SUST in the northwest of the project area (see Figure 3) during the October 2005 campaign. Figure 5 shows the wet refractivity variation from a 16-layer voxel model with 5-kilometer horizontal grid spacing, whereas Figure 6 was calculated with 32 layers with the same horizontal resolution.
Figure 5. Vertical wet refractivity distribution (in parts per million) from a 16-layer voxel model (the increasing vertical distances with height of the voxels are given by black tick marks on the left-hand side) in October 2005. The time series of integrated wet delays (ZWD) from 15 radiosondes (RS), the interpolated profiles from the numerical weather model COSMO-7, and the GPS tomographic results (AWATOS) are shown for comparison purposes with a corresponding scale on the right-hand side. Mean values and their standard deviations are shown in parenthesesFigure 6. Vertical wet refractivity distribution (in parts per million) from a 32-layer voxel model over the timespan of the October campaign in 2005 and ZWD time series of integrated AWATOS, COSMO-7, and the corresponding radiosonde (RS) profiles for comparison purposes.
The integrated wet refractivity profiles and the reference radiosonde measurements agree better the more layers that are introduced into the tomographic voxel model. The largest differences between the results with different numbers of layers can be detected in the middle troposphere between 4- and 6-kilometers altitude (see Figure 7). It is also recognizable that in the lower troposphere, voxel models with a large number of layers are even able to resolve refractivity inversions.
Figure 7. Tomographic wet refractivity profiles (in parts per million) from 16-, 26-, and 43-layer voxel models, and that of the corresponding radiosonde (RS) launched at station GRUB at 1844 meters above mean sea level on July 13, 2005, 17:04 UTC.
We analyzed tomographic voxel models with horizontal resolutions of 15, 10, 7.5, 5, 3.75, and 3 kilometers. Increasing the horizontal resolution of the model leads to an increase in the estimated wet refractivity above an altitude of 6 kilometers compared to both the radiosondes and the numerical weather model. Due to the mean distance of about10 kilometers between the ground-based GPS stations in the project area, the best results were obtained with a similar resolution.
Table 3 gives the results of the comparison between the wet refractivity profiles of the tomographic analysis and the radiosondes launched within the project area.
Effect of Temporal Resolution. The tomographic results shown here are based on one-hour time windows for the GPS double-difference data. Higher update rates are also possible without changing the input options of AWATOS. Figure 8 shows the wet refractivity variation based on a 10-minute window together with rainfall data at the ANETZ station Evolène in the western part of the project area.
Figure 8. Wet refractivity distribution at station Evolène (EVOL) in the western part of the project area from a 26-layer tomographic voxel model with an update interval of 10 minutes. Rainfall data in millimeters per 10 minutes is shown as vertical bars with the corresponding scale on the right-hand side.
Even though the wet refractivity profiles are affected by higher-frequency variations in the upper troposphere, precipitation and weather changes are still recognizable in the 10-minute time series.
Although the Bernese GPS Software is not designed for real-time parameter estimation, near real-time conditions can be simulated by introducing specific input files. Thus, the sensitivity of AWATOS to real-time conditions can be assessed. In terms of coordinate repeatability, the results of the horizontal components degrade by about 30 percent when using the predicted part of the ultra-rapid products (see also Table 1). Using broadcast ephemerides, the three-dimensional accuracy suffers even more.
Implications. We collected input data for both the spatially and temporally high-resolution GPS tomographic analysis and the validation of the results. The inhomogeneous distribution of rainfall in the local project area would necessitate even more rain gauges in the meteorological measurement network to perform a hydrological hazard assessment.
The comparison of independent techniques showed that the ZTD time series agree within 2 centimeters on average; that is, to better than 1 percent. The correlation of the GPS data and the data derived from the numerical weather model is greater than 70 percent. However, local rain showers are sometimes more clearly represented by the data of the dense GPS network than by COSMO-7.
It is possible to increase the spatial and temporal resolution in GPS tomography, so it can enhance numerical weather models. The better agreement of the tomographic profiles with radiosonde data, compared to the COSMO-7 estimates, indicates that the numerical weather prediction models will benefit from additional information on the vertical distribution of water vapor provided by high-resolution GPS tomography.
To assess the potential of near real-time GPS tomography, IGS satellite products with short latency and fast update rates were tested in the GPS processing. With ultra-rapid orbits, we obtained satisfactory results for the tropospheric parameters in almost real-time mode. The use of predicted orbits in the tomographic processing degrades the results of the wet refractivity profiles by 20 percent compared to using final (that is, best available) products.
Conclusions
In this brief article, we have shown that high-resolution GPS tomography is well suited for application in mountainous regions, especially in view of its potential to contribute to hydrological hazard assessment. We have been able to estimate the wet refractivity field with a spatial
and temporal resolution comparable with the current and the next generation of numerical weather models (COSMO-2 with 2-kilometer horizontal resolution).
We have been successful in illustrating several beneficial aspects of GPS tomography in supporting high-resolution numerical weather prediction models. We would also point out that tomographically determined wet refractivity fields may also be used in conjunction with directly estimated integrated slant path delays to adjust the GPS observations, especially those obtained at low elevation angles. Implemented in GPS processing software, GPS tomography could provide completely anisotropic tropospheric corrections for very high-accuracy positioning applications.
Acknowledgments
The research discussed in this article was financially supported by the Swiss National Science Foundation and the Swiss Geodetic Commission.
The Swiss Federal Office of Meteorology and Climatology, MeteoSwiss, and the Swiss Federal Office of Topography, Swisstopo, provided necessary data sets for processing, analyzing, and validating the results.
Furthermore, O. Heller and Dr. A. Somieski supported the field work and several residents or public organizations in the canton of Valais offered their premises for temporary mounting of the campaign measurement systems.
SIMON LUTZ is a research fellow at the Astronomical Institute of the University of Bern, Switzerland, and a member of the Center for Orbit Determination in Europe analysis center team. He received M.S. and Ph.D. degrees in geodesy and geodynamics from the Swiss Federal Institute of Technology (ETH), Zurich, Switzerland.
MARC TROLLER is a communications, navigation, and surveillance (CNS) expert at Swiss Air Navigation Services Ltd., Skyguide, Switzerland. He received M.S. and Ph.D. degrees in geodesy and geodynamics from ETH Zurich.
DONAT PERLER is a Ph.D. candidate at ETH Zurich. He received an M.S. degree in computer science from ETH Zurich.
ALAIN GEIGER is a professor in the Geodesy and Geodynamics Lab of the Institute of Geodesy and Photogrammetry at ETH Zurich. He received an M.S. degree in physics and a Ph.D. in geodesy and geodynamics, both from ETH Zurich.
HANS-GERT KAHLE is professor emeritus of geodesy and geodynamics at ETH Zurich and was leader of the Geodesy and Geodynamics Lab from 1979 to 2009. He received a Ph.D. degree from the University of Kiel, Germany, and the habilitation in geophysics from ETH Zurich.
FURTHER READING
• Seminal Paper on Use of GPS for Meteorology
“GPS Meteorology: Remote Sensing of Atmospheric Water Vapor Using the Global Positioning System” by M. Bevis, S. Businger, T.A. Herring, C. Rocken, R.A. Anthese, and R.H. Ware in Journal of Geophysical Research, Vol. 97, No. D14, 1992, pp. 15787–15801, doi:10.1029/92JD01517.
• Other Studies on Using GPS to Monitor the Atmosphere
“Using the Global Positioning System to Study the Atmosphere of the Earth: Overview and Prospects” by J.L. Davis, M.L. Cosmo, and G. Elgered in GPS Trends in Precise Terrestrial, Airborne, and Spaceborne Applications edited by G. Beutler, G.W. Hein, W.G. Melbourne, and G. Seeber, editors, Volume 115 of the International Association of Geodesy Symposia, Springer Verlag, Berlin, 1996, pp. 233–242.
“GPS Meteorology: Direct Estimation of the Absolute Value of Precipitable Water” by J. Duan, M. Bevis, P., Fang, Y. Bock, S. Chiswell, S. Businger, C. Rocken, F. Solheim, T. van Hove, R. Ware, S. McClusky, T.A. Herring, and R.W. King in Journal of Applied Meteorology, Vol. 35, No. 6, 1996, pp. 830–838, doi:10.1175/1520-0450(1996)035<0830:GMDEOT>2.0.CO;2.
• Effect of the Atmosphere on GPS Positioning
“Atmospheric Modelling in GPS Analysis and Its Effect on the Estimated Geodetic Parameters” by T.R. Emardson and P.O.J. Jarlemark in Journal of Geodesy, Vol. 73, No. 6, 1999, pp. 322–331, doi:10.1007/s001900050249.
“Determination of the Spatial and Temporal Variation of Tropospheric Water Vapour Using CGPS Networks” by M. Troller, A. Geiger, E. Brockmann, and H.-G. Kahle in Geophysical Journal International, Vol. 167, No. 24, 2006, pp. 509–520, doi:10.1111/j.1365-246X.2006.03101.x.
“Diagnosis of Three-Dimensional Water Vapor Using a GPS Network” by A.E. MacDonald, Y. Xie, and R.H. Ware in Monthly Weather Review, Vol. 130, No. 2, 2002, pp. 386–397, doi:10.1175/1520-0493(2002)130<0386:DOTDWV>2.0.CO;2.
“3-D Refractivity Field from GPS Double Difference Tomography” by M. Troller, B. Bürki, M. Cocard, A. Geiger, and H.-G. Kahle in Geophysical Research Letters, Vol. 29, No. 24, 2149, 2002, 4 pp. doi:10.1029/2002GL015982.
• Radio Wave Refractivity of Air
“Refractive Index Formulae for Radio Waves” by J.M. Rüeger in Proceedings of the XXII International Federation of Surveyors (FIG) International Congress, Washington, D.C., April 19–26, 2002.
• Previous GPS World Articles on Tropospheric Propagation Delay
