GPS World » Remote Sensing

Intergeo 2013

GPS World staff — Tue, 12 Feb 2013 23:52:46 +0000

Intergeo is a leading conference trade fair for geodesy, geoinformation and land management. With over half a million event website users and more than 16,000 visitors from 80 countries at Intergeo itself, it is one of the key platforms for industry dialogue. Intergeo will be held October 8-10 in Essen, Germany.

The 19th Intergeo will look at the significance of changes in economic and political structures, among other things. Few locations have been as greatly affected by these structural changes in the past few decades as the German Ruhr area. Essen itself shows the transformation from a coal and steel region to one whose industry is based on technology and services. Strategic networking with Messe München International and the Intergeo Advisory Board launched in 2012 will bring new impetus to the event.

Intergeo 2013 promises to bring with it many developments that will strengthen its image as a significant source of inspiration for future national and international business. For example, the newly founded Advisory Board with partners Esri, Hexagon and Trimble will deal with strategic issues and discuss the industry’s socio-political role.

The collaboration with Messe München International creates new opportunities for global communication through a worldwide network with six subsidiaries in Europe and Asia, and more than 60 offices abroad that are active in more than 90 countries. Messe München International provides Intergeo with a globally comprehensive portfolio of services for exhibitors, visitors and media representatives.

“The collaborative work between Intergeo and Messe München International will not only have a positive effect in supporting exhibitors and winning new ones. I also expect to see a further increase in the number of international visitors,” said Olaf Freier, Managing Director of HINTE GmbH, which organises INTERGEO on behalf of DVW.

The host, DVW e.V. — the German Society for Geodesy, Geoinformation and Land Management — is expecting 16,000 visitors and 1,400 conference participants from all over the world. At the heart of the conference are the topics of “property valuation” and “intelligent geoinformation — how to gather, process and put it to practical use” and the continuation of the INSPIRE conference under the patronage of the German Federal Ministry of the Interior, the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety, the Steering Committee for Spatial Information Germany, the German Association of Cities, the German Rural District Association and DVW e. V.

Answers will also be given to questions of urban development and the challenges posed by the energy revolution. The event days will be opened by keynote speeches from Garrelt Duin, minister for Economic Affairs for North Rhine-Westphalia, Jürgen Dold, president and CEO of Leica Geosystems AG, and Prof. Reiner Rummel from the Institute for Astronomical and Physical Geodesy at Munich Technical University.

Intergeo covers a wide variety of fields, ranging from surveying, geoinformation, remote sensing and photogrammetry to complementary solutions and technologies. Another field is processing, using and analyzing geodata on the Internet or in the field. Associated solutions will be presented and discussed by experts.

Intergeo will also highlight areas of innovation that are enjoying dynamic growth. Suppliers will showcase these future technologies at venues such as the OSGeoPark and the Innovation Park for young, innovative companies.

Esri Announces 24 Organizations to Receive $2 Million in GIS Imagery Grants

Eric Gakstatter — Tue, 22 Jan 2013 23:24:38 +0000

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.

Detecting Nuclear Testing: Software Under Development by OSU Could Pinpoint Treaty Violations

GPS World staff — Mon, 01 Aug 2011 23:22:26 +0000

By Tracy Cozzens

Figure 1. Worldwide nuclear testing 1945–2009 (CTBTO website).

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.”

Innovation: Friendly Reflections: Monitoring Water Level with GNSS

Richard Langley — Wed, 01 Sep 2010 03:24:27 +0000

INNOVATION INSIGHTS with Richard Langley

By Alejandro Egido and Marco Caparrini

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 P_R and P_D 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), noise_φ 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.

Innovation: Better Weather Prediction Using GPS: Water Vapor Tomography in the Swiss Alps

GPS World staff — Thu, 01 Jul 2010 23:24:11 +0000

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.

By Simon Lutz, Marc Troller, Donat Perler, Alain Geiger, and Hans-Gert Kahle

INNOVATION INSIGHTS with 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 constants k1, k2, and k3 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 (N^dry), the refractivity of the dry constituents of air, and the second and third terms with the wet part (N^wet), 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, ZWD_p (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, N_i, 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 parentheses

Figure 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.