PlanetiQ has been awarded a $15 million, 48-month Strategic Funding Increase (STRATFI) contract by the U.S. Air Force. The program will support the development and launch of spacecraft equipped with next-generation GNSS radio occultation (GNSS-RO) and GNSS polarimetric radio occultation (GNSS-PRO) instruments and the delivery of high-value weather data to the U.S. Air Force.
The mission will focus on advancing GNSS-RO, GNSS-PRO and GNSS reflectometry (GNSS-R) capabilities. The program includes the development of advanced data assimilation techniques to integrate enhanced GNSS-PRO data into numerical weather prediction (NWP) models, improving forecast accuracy and enabling new insights into atmospheric conditions.
After spacecraft commissioning, PlanetiQ will provide on-orbit data delivery during the contract period. This will support multiple applications across the Department of the Air Force, including artificial intelligence (AI) model training, data assimilation, and performance evaluation.
As the largest commercial provider of GNSS-RO data, PlanetiQ operates a global constellation of satellites, including spacecraft equipped with advanced receivers capable of capturing high signal-to-noise ratio (SNR) GNSS-RO and GNSS-PRO measurements. GNSS-PRO has demonstrated strong efficacy for measuring precipitation, a key capability for improving severe weather forecasting.
This STRATFI award will enable the development of a next-generation receiver that adds GNSS-R capabilities, supporting new applications such as ocean surface wind measurement, sea state characterization, and soil moisture monitoring over land.
“This award represents a major step forward in delivering more advanced, actionable weather information to the warfighter,” said Ira Scharf, CEO of PlanetiQ. “By combining GNSS-RO, PRO and R measurements in a single platform, we are unlocking a more complete picture of the atmosphere and Earth’s surface. We are proud to partner with the U.S. Air Force to accelerate these capabilities and bring next-generation environmental data into operational use.”
In recent years, scientists have shown that detecting changes in navigation signals from GPS and Galileo after they bounce off Earth’s surface (GNSS reflectometry, or GNSS-R) can deliver valuable information on sea ice. Now research drawing on data from Spire Global has enabled the generation of Arctic-wide sea ice maps, marking a major step forward for the emerging technique.
Spire Global‘s sea ice freeboard maps use data captured by Spire’s GNSS-reflectometry multipurpose listening constellation.
The research — enabled by the Third Party Missions (TPM) programme of the European Space Agency (ESA) — suggests that harnessing reflected navigation signals could become an important complement to established ice-monitoring altimetry missions.
The study leveraged Spire’s GNSS-R data to retrieve sea ice freeboard measurements across an entire winter season. The results show strong alignment with established altimetry datasets, including the ESA’s CryoSat mission, validating the complementary role of commercial satellite data alongside government missions.
Arctic-wide sea ice freeboard map for January 2024. (Credit: ESA)
The study was led by Felix Müller at the Technical University of Munich (DGFI-TUM) and Robert Ricker at the Norwegian Research Centre, experts in GNSS-R.
“The primary purpose of signals emitted from GNSS is to fix the location of a device at any point on Earth,” Müller explained. “However, when these signals bounce off Earth’s surface, their properties change. By analyzing these changes, we can infer information about the characteristics of Earth’s surface.”
“Previous research has shown that this technique works well experimentally,” Ricker added. “Using the Spire constellation, we aimed to demonstrate whether it would hold up on a larger scale by generating an Arctic-wide map of sea ice freeboard, which is a measure of how far ice protrudes above the waterline.”
Spire’s GNSS-R constellation
Spire’s constellation was first used to sample the atmosphere for weather forecasting. Then scientists began exploring other applications. Spire started collecting reflected signals arriving at shallow angles using a technique called grazing-angle GNSS-R. This method is particularly well suited for ice monitoring.
The research team analyzed data detected over the Arctic Ocean and surrounding seas between October 2023 and July 2024. The data was obtained via the TPM program, through which ESA disseminates data from a range of commercial and institutional partners on a free basis for research and development purposes.
The team focused on one of the most critical challenges in sea ice altimetry: reliably identifying narrow openings in the ice pack, known as leads. These openings are reference points for determining sea surface height and, ultimately, sea ice freeboard.
In turn, sea ice freeboard can be used to infer sea ice thickness — an essential parameter for tracking climate change, estimating sea level, and modeling ocean and weather patterns.
Identifying leads in sea ice with GNSS-R data. (Credit: ESA)
Classifying surface properties
“In the initial phase of the project, we used two complementary methods to identify surface properties based on GNSS-R data, with the aim of identifying leads,” Müller said.
The first — known as the adaptive threshold technique — involved measuring the power of the reflected navigation signal to classify surface type as either water or ice. This method allows rapid processing of the entire GNSS-R dataset, while remaining robust to changes in signal conditions.
The second method — known as unsupervised clustering — offers a more complex approach to classifying surface conditions. In addition to signal power, it considers multiple other signal features that tease out more nuanced information on surface type, including identifying thin or refrozen ice.
Both methods were compared with co-located CryoSat surface-type classifications and Sentinel-1 imagery, confirming that the GNSS-R classifications were largely comparable against conventional satellite products.
Mapping sea ice freeboard
“Building on this classification work, we then took the research to the next step by producing Arctic-wide sea ice freeboard maps from GNSS-R data,” Ricker said.
The team corrected ice surface height measurements generated from GNSS-R data for tidal variations, sea surface height, and atmospheric delays, which is standard practice in altimetry. A refined algorithm then identified where leads in the ice were likely to occur, with the lowest points in these areas revealing estimated sea surface height. Sea surface height estimates were then subtracted from ice surface heights to retrieve freeboard. Using this approach, monthly gridded freeboard products were generated for the full winter season.
The team reported that the GNSS-R datasets showed strong agreement with CryoSat freeboard datasets across much of the Arctic, confirming that GNSS-R can reproduce large-scale patterns previously observed by dedicated altimetry missions. Independent validation against upward-looking sonar measurements in the Beaufort Sea further supported the accuracy of the retrieved freeboard values.
However, as expected, the GNSS-R estimates became less reliable during spring, when surface melt alters reflection characteristics. This limitation is consistent with earlier GNSS-R and radar altimetry studies and remains an active area of research.
The contribution of commercial data
While GNSS signals have long been used for positioning, this research highlights how reflected signal analysis can extend their value into large-scale Earth observation applications, delivering persistent coverage independent of sunlight or weather conditions, said Theresa Condor, Spire Global CEO.
“Advances in miniaturization, digital signal processing, and machine learning have fundamentally changed what’s possible in RF sensing,” Condor said. “Commercial constellations can now deliver persistent, high-quality RF data that complements traditional government systems with greater flexibility and cost efficiency.
“As environmental monitoring requirements intensify, we’re seeing agencies increasingly integrate commercially sourced RF datasets into operational architectures, reflecting the continued maturation of this market and the growing role of commercial infrastructure in government missions.”
“By producing analysis-ready gridded datasets, this work marks an important milestone in the progress of grazing angle GNSS-R from an experimental method to a reliable technique for mapping Arctic sea ice freeboard at scale,” said Matthieu Talpe, Remote Sensing Product Engineer, Spire Global. “In doing so, it strengthens the case for the grazing angle GNSS-R technique employed by the Spire constellation as a valuable complement to existing ESA and partner missions, helping to close observational gaps in one of Earth’s most rapidly changing regions.”
Divirod and Oki Electric Industry (OKI) have completed a project to monitor landslide risk and slope stability across vulnerable areas in the Fukuoka Prefecture of Japan. The project deployed Divirod’s next-generation ground deformation and anomaly-detection technology to provide continuous, high-resolution monitoring of mountainous terrain prone to extreme rainfall and seismic activity.
The initiative supports Japan’s broader effort to enhance early-warning capabilities and strengthen climate resilience following recent years of severe rainfall disasters and complex terrain-related hazards.
Monitoring with GNSS-R technology
For the project, Divirod designed a system comprised of GNSS-Reflectometry (GNSS-R) sensors and intelligent algorithms and deployed it across three areas of interest collecting continuous, all-weather measurements throughout the monitoring period. Divirod’s proprietary algorithms examined daily GNSS-R measurements to detect even subtle changes in the ground surface.
Divirod’s system successfully classified the observed terrain changes into three key physical categories:
Slope failure events,
Creep/slow-moving landslides, and
Temporary terrain changes (often linked to rainfall or ground moisture variations).
Hundreds of terrain changes were detected across the monitored regions and correlated with rainfall measurements and earthquake events. The results enabled detailed risk mapping and precise identification of active zones.
The technology proved highly sensitive in differentiating short-lived disturbances from long-term geomorphological changes — an essential capability for early intervention and warnings.
Documented landslide at Hakikoga
A significant project highlight was successful detection of a real landslide event in August. While comparison images taken on Aug. 10 and 11 revealed visible changes in the slope during daylight hours, Divirod’s terrain change maps show that the slope movement itself occurred overnight, a time when on-site cameras were unable to observe the event due to darkness.
Despite the lack of visual evidence, Divirod’s GNSS-R sensors registered a distinct spike in ground-movement, accurately detecting the terrain shift and providing clear evidence of a nocturnal landslide that could have otherwise gone unnoticed.
Strengthening the disaster-preparedness ecosystem
Divirod’s collaboration with OKI represents a significant advancement in real-time terrain intelligence for Japan, a region characterized by frequent typhoons, intense rainfall and high seismicity. The successful deployment in the Fukuoka Prefecture presents new opportunities for:
scalable early-warning systems,
automated landslide risk modeling,
and the integration of GNSS-R sensing with existing monitoring infrastructure.
Taiwan will launch its first indigenously developed meteorological satellite in March 2023, reports Taiwan News.
The Triton satellite was designed and made by the National Space Organization (NSPO) over eight years. The satellite has completed testing and is undergoing review.
The 250-kg Triton carries GNSS-reflectometry equipment (GNSS-R) — independently developed by NSPO — to collect GNSS signals reflected by the Earth’s surface. Its mission is to research air-sea interaction to help predict typhoons.
Taiwan is hit by typhoons every year. Information on wave height and sea-surface wind speed, retrieved from the reflected signals, will be useful for research on typhoon intensity and path prediction.
The satellite will be transported to the Guiana Space Center in January. It is scheduled to launch in March aboard a Vega C rocket manufactured by Arianespace.
CICERO-2 satellites will track Earth’s atmosphere, water, surface and interior
Remote sensing company GeoOptics Inc. has upgraded its CICERO constellation of satellites that measure the Earth’s climate. With launches beginning next year, CICERO-2 will form a unified Earth observatory allowing governments, industry and individual stakeholders to monitor and prepare for the impacts of climate change.
“In today’s environment, in which precision Earth sensing is becoming ever more critical, GeoOptics is deploying a flexible observatory made up of dozens of small satellites,” said Alex Saltman, Chief Executive Officer of GeoOptics. “The real time services will satisfy a broad range of needs for government and civil users around the world.”
The first CICERO-2 satellites launched are designed to achieve key milestones in small satellite Earth observation, including:
Advanced GNSS reflectometry (GNSS-R). Advanced GNSS-R measures many phenomena near Earth’s surface, including ocean winds, flooding, land cover (snow, ice, vegetation), soil moisture and topography by means of reflected GNSS signals. NASA’s recent CYGNSS mission demonstrated the broad utility of the GNSS-R technique. GeoOptics is working with NASA’s Jet Propulsion Laboratory (JPL) to deploy an advanced operational version, offering dramatically enhanced performance in a small, low-cost package. This collaboration is funded jointly by GeoOptics, the U.S. Air Force, and NASA.
Triple radio occultation (GNSS-RO). GNSS-RO enables Profiling of atmospheric temperature, pressure, density and other key properties. First proposed by company founder Tom Yunck while he was at JPL, GNSS-RO offers extreme measurement precision and is an essential contributor to global weather forecasting. The CICERO-2 satellites will yield three times the data volume of their predecessors and many times the volume.
Global precipitation watch. The CICERO-2 satellites will monitor heavy precipitation using polarimetric radio occultation (RO), an advanced remote sensing technique pioneered by GeoOptics’ collaborators at JPL and the Spanish PAZ mission.
Measuring weather changes
For GeoOptics’ strategic partner Climavision, a weather data provider, these innovations will enable customers to manage significant risks in a time of global change. “With these new developments in remote-sensing technologies from GeoOptics, we’ll be able to further enhance our climate and weather prediction capabilities,” said Chris Goode, CEO and co-founder of Climavision. “Through the combination of advanced RO profiles, GNSS-R data about surface conditions and our proprietary gap-filling radar network data, we’ll help customers in weather-sensitive industries see weather like never before and give them the tools and data to make informed critical decisions.”
GeoOptics will later extend the system to a range of new applications, including precise mapping of Earth’s gravitational field, which has been named a top NASA Earth science priority for the next decade. This measurement shows the imprint of climate-related movement of water and other key changes in the Earth.
With internal investment and nearly $4 million from NASA, GeoOptics has devised a unique system architecture for daily gravity mapping with clusters of small satellites. This patented technique promises to improve gravity sensing 20-fold over current methods at a fraction of the cost.
Under the umbrella of the National Oceanographic Partnership Program (NOPP), GeoOptics is also designing a radar instrument to observe ocean vector winds, topography, soil moisture and a variety of other surface properties with patented multi-satellite radar techniques. NOPP is seeking to sponsor a trial flight of GeoOptics’Cellular Ocean Altimetry/Scatterometry Technology (COAST) within the next two years.
Tom Yunck, GeoOptics’ Chief Technology Officer, said, “These advanced remote sensing applications – from basic RO to advanced radar and gravity mapping – exploit shared micro technologies that fit in the palm of one’s hand. Each new function builds naturally upon the previous, yielding prodigious observing capacity in a low-cost system of great simplicity and reliability.”
“CICERO-2 is designed to help provide high-priority NOAA climate and weather monitoring observations, as ranked by the NOAA Space Platform Requirements Working Group (SPRWG),” said Conrad C. Lautenbacher (Vice Admiral, USN ret.), executive chairman of GeoOptics and former National Oceanic and Atmospheric Administration (NOAA) administrator. “It can also play a key role in supporting crucial Defense Department satellite weather data requirements.”
GeoOptics’ CICERO satellites continue to provide precise global profiles of the Earth’s atmosphere. In February, NOAA selected GeoOptics to provide the first commercial satellite data to be included in its operational forecasts.
In 2020, GeoOptics was selected by NOAA to lead an end-to-end design study for its next-generation low-orbiting weather satellite system, planned to come online later this decade, building in part on RO and GNSS-R technologies.
Spire Global, a global provider of space-based data and analytics, has announced the continuation of its participation in NASA’s Commercial Smallsat Data Acquisition (CSDA) Program with a $6 million contract extension.
The contract continuation, Task Order 6 (TO6), is a subscription data solution that includes radio occultation (RO) data, grazing angle GNSS-RO, total electron content (TEC) data, precise orbit determination (POD) data, soil moisture and ocean surface wind speed GNSS-Reflectometry data and magnetometer data.
This data will be available to all federal agencies, NASA-funded researchers and, more broadly, to all U.S. government-funded researchers for scientific purposes.
Under CSDA Program TO6, Spire will deliver a comprehensive catalog of data, associated metadata and ancillary information from its Earth-orbiting small-satellite constellation. The company operates its constellation in low Earth and collects upwards of 10,000 radio occultations per day with consistent global coverage.
For TO6, Spire will provide rolling access to 12 months of radio occultation data with a 30-day latency. This data will be archived and maintained by NASA under the CSDA Program’s SmallSat Data Explorer (SDX) database.
“Programs like CSDA highlight the incredible potential of private-public partnerships in the federal government to drastically accelerate our ability to confront some of the greatest challenges of our time, such as climate change,” said Peter Platzer, CEO of Spire. “With the end-user license agreements, our data is now available to all federal agencies and the larger NASA scientific community to help support Earth observation research across fields.”
The program includes end-user license agreements (EULAs) to enable broad levels of dissemination and shareability. All federal agencies and U.S. government-funded researchers will have access to Spire’s data for scientific purposes under TO6 and will be able to request access to the data via the CSDA Program’s Commercial Datasets webpage.
“At NASA, the CSDA Program has continued to blossom as a valuable resource to our team for our Earth observation research and analysis. We are committed to growing the program as well as continuing the work we have started,” said Will McCarty, project scientist at the CSDA Program and research meteorologist at NASA Global Modeling and Assimilation Office. “Spire has been a valued partner through CSDA’s development since its inception, and with this additional task order, we are excited about the new insights and results that will come not only from within NASA, but also through broader collaboration through the domestic government scientific community.”
NASA has used Spire data in its research on water and sea-ice levels in the polar regions, the height of the planetary boundary layer (PBL), and the day-to-day variability of thermospheric density at flight level.
NASA also noted that Spire data has shown positive benefit to its GEOS Atmospheric Data Assimilation System, which uses space-based data to analyze the Earth’s atmosphere and assimilate the data into its Earth observation systems.
As one of the original vendors for the CSDA Program, Spire provides NASA yearly updates to the scope of work under this agreement to ensure alignment of data to the agency’s needs.
Spire Global, a global provider of space-based data and analytics, has announced the continuation of its participation in NASA’s Commercial Smallsat Data Acquisition (CSDA) Program with a $6 million contract extension.
The contract continuation, Task Order 6 (TO6), is a subscription data solution that includes radio occultation (RO) data, grazing angle GNSS-RO, total electron content (TEC) data, precise orbit determination (POD) data, soil moisture and ocean surface wind speed GNSS reflectometry (GNSS-R) data and magnetometer data.
This data will be available to all federal agencies, NASA-funded researchers and, more broadly, to all U.S. government-funded researchers for scientific purposes.
Under CSDA Program TO6, Spire will deliver a comprehensive catalog of data, associated metadata and ancillary information from its Earth-orbiting small-satellite constellation. The company operates its constellation in low Earth orbit and collects upwards of 10,000 radio occultations per day with consistent global coverage.
For TO6, Spire will provide rolling access to 12 months of radio occultation data with a 30-day latency. This data will be archived and maintained by NASA under the CSDA Program’s SmallSat Data Explorer (SDX) database.
“Programs like CSDA highlight the incredible potential of private-public partnerships in the federal government to drastically accelerate our ability to confront some of the greatest challenges of our time, such as climate change,” said Peter Platzer, CEO of Spire. “With the end-user license agreements, our data is now available to all federal agencies and the larger NASA scientific community to help support Earth observation research across fields.”
The program includes end-user license agreements (EULAs) to enable broad levels of dissemination and shareability. All federal agencies and U.S. government-funded researchers will have access to Spire’s data for scientific purposes under TO6 and will be able to request access to the data via the CSDA Program’s Commercial Datasets webpage.
“At NASA, the CSDA Program has continued to blossom as a valuable resource to our team for our Earth observation research and analysis. We are committed to growing the program as well as continuing the work we have started,” said Will McCarty, project scientist at the CSDA Program and research meteorologist at NASA Global Modeling and Assimilation Office. “Spire has been a valued partner through CSDA’s development since its inception, and with this additional task order, we are excited about the new insights and results that will come not only from within NASA, but also through broader collaboration through the domestic government scientific community.”
NASA has used Spire data in its research on water and sea-ice levels in the polar regions, the height of the planetary boundary layer (PBL), and the day-to-day variability of thermospheric density at flight level.
NASA also noted that Spire data has shown positive benefit to its GEOS Atmospheric Data Assimilation System, which uses space-based data to analyze the Earth’s atmosphere and assimilate the data into its Earth observation systems.
As one of the original vendors for the CSDA Program, Spire provides NASA yearly updates to the scope of work under this agreement to ensure alignment of data to the agency’s needs.
The successful demo consolidates concept designs for a proposed land-sensing ESA Scout mission, HydroGNSS
Surrey Satellite Technology Ltd. (SSTL) has successfully demonstrated GNSS reflectometry (GNSS-R) from its 18-kg DoT-1 satellite. SSTL has pioneered the new field of GNSS-R with successful payloads on board TechDemoSat-1 and the CYGNSS constellation, and is continuing to develop the technology and data analysis in pursuit of this new science.
SSTL’s DoT-1 satellite was launched in 2019. (Photo: SSTL)
The GNSS-R payload onboard the DoT-1 satellite is incorporated within a new small-form-factor Core Avionics module integral to all SSTL’s future satellite platforms. This innovation paves the way for any SSTL satellite that can accommodate a nadir pointing antenna to become part of a GNSS-R small-sat constellation.
GNSS reflectometry is an Earth-observation technique that uses GNSS signals as L-band radar sources, allowing the satellite to take measurements of ocean wind speeds, polar ice and hydrological land parameters, at a higher spatial resolution and an order of magnitude lower cost than other methods.
“GNSS-R is a powerful new science that can contribute valuable data to help monitor changes in our global climate, and I am really pleased with the success of our DoT-1 demonstration.,” said Phil Brownnett, managing director of SSTL. “We now have an advanced instrument design and deep expertise in this new field, and I am hopeful that SSTL’s HydroGNSS concept will progress to a full mission to support the global climate-control challenge.”
HydroGNSS mission
The GNSS-R technology demonstration from SSTL’s DoT-1 satellite is also an important stepping stone for a proposed European Space Agency (ESA) Scout Mission Concept called HydroGNSS, which comprises two 40-kg satellites that collect data continually in near-polar orbits, taking hydrological measurements over the whole globe.
The HydroGNSS mission concept makes advanced use of new GNSS reflectometry techniques such as Galileo signals, dual polarization, and coherent signal acquisition to measure four Essential Climate Variables (ECVs) over land: soil moisture, inundation, permafrost freeze/thaw, and biomass. HydroGNSS has been down-selected by ESA for a concept study; the finally selected Scout mission will commence in 2021.
The below animated video illustrates the HydroGNSS mission.
SSTL’s GNSS-R instrument collects and processes measurements into a “delay Doppler map” (DDM) that can be corrected and inverted into Level 2 products such as ocean wind speed, as has already been successfully demonstrated by the SSTL GNSS-R instruments onboard the TechDemoSat-1 and CYGNSS missions.
The plot below shows results from data gathered by the DoT-1 satellite on Aug. 12, and shows simultaneous DDMs from four separate GPS satellites. As this is a forward scattering technique, the stronger signals with redder peaks indicate a calmer ocean with lower wind speeds. Approximately 40 minutes of data were collected from the Pacific to the Antarctic to the Southern Ocean.
DoT-1 sub-satellite track (white) and GPS reflection tracks (yellow) collected during 40 minute data operation. (Image: SSTL)
Four Delay Doppler Maps (DDMs) collected by DoT-1 that show signal reflections from four GPS satellites. (Image: SSTL)
Soil moisture measurements
Data from the SSTL GNSS-R instruments onboard the CYGNSS mission have demonstrated the potential for soil moisture measurement by GNSS reflectometry, and preliminary work shows that GNSS-R also has good sensitivity for freeze/thaw sensing in the active zone of permafrost. Unseasonal changes in the permafrost cycles could potentially release larger quantities of methane, which risks exacerbating global climate change.
GNSS-R can also be used to assess biomass, a measure of forest density which acts as a vital sink for removing carbon dioxide from the atmosphere. In addition to longer term climate observations, soil moisture and inundation measurements from GNSS reflectometry can provide important information for short-term operational purposes, for example, towards Numerical Weather Prediction and flood warnings following rainfall events.
The development of SSTL’s new GNSS-R instrument was funded in part by the European Space Agency.
Traditional tide gauges are in contact with the water surface and as a result are susceptible to measurement error and damage during extreme weather. An alternative approach is the use of GNSS reflectometry. We learn how this innovative use of satellite navigation signals works in this month’s Innovation column.
Innovation Insights with Richard Langley
Seawater level is conventionally monitored by tide gauges that measure the vertical distance of the water surface from a point on the ground. As the tide gauges provide seamless and highly accurate measurements, many countries operate a tide-gauge network to monitor sea-level changes and to assess flood risk. For example, the National Oceanic and Atmospheric Administration (NOAA) operates a permanent observing system, the National Water Level Observation Network (NWLON), with more than 400 gauges throughout the United States.
However, some challenges of tide gauges can be identified. Firstly, tide-gauge measurements require direct contact with the water, which causes limitations in installing and maintaining the equipment. The equipment requiring direct sensing is highly vulnerable to coastal hazards, such as coastal flooding and tsunamis, resulting in potential measurement errors or even equipment destruction during severe natural events.
Furthermore, tide gauges require maintenance on a regular basis, which is expensive because it requires the use of divers. This greatly limits the operation of tide gauges, especially in extreme environments such as in the Arctic. Alaska, for example, has significant gaps in its available spatially-varying tidal information. However, in the Arctic, it is also very important to constantly and closely monitor the long- and short-term variation of water levels because this area has a significant impact on global climate and ecosystems. Consequently, more support is needed for sea-level monitoring and coastal mapping in this region.
GNSS can serve as an alternative approach for water-level monitoring. GNSS satellites continuously transmit radio signals and ground-based, space-based, and airborne receivers access the signals regardless of weather conditions. Some of the received signals are reflected from obstacles or surfaces near the antenna, a phenomenon referred to as multipath (see FIGURE 1).
FIGURE 1. Schematic drawing of the GNSS-based tide gauge. (Image: Authors)
Multipath tends to be regarded as one of the major error sources for GNSS positioning where it causes unexpected phase delays when compared to the direct signal. Consequently, various procedures have been developed to mitigate the multipath effect. However, the GNSS signals reflected from the Earth’s surface contain information about the geophysical properties of the reflecting surface. The use of these signals is known as GNSS reflectometry (GNSS-R). GNSS-R allows us to monitor the temporal variation of water levels by calculating phase delays of GNSS signals reflected from the water surface. A GNSS-R-based tide gauge does not require direct contact with the water because it measures the water levels based on a remote-sensing technique. Thus, a GNSS-R-based tide gauge can be effectively applied to water-level monitoring.
However, several challenges exist in processing GNSS signals observed at high latitudes compared to mid-latitudes. Not only do we have to contend with extreme weather conditions and limited infrastructure availability, but also with problematic satellite geometry and ionospheric effects on the GNSS signals. To overcome these limitations in the use of GNSS-R in the Arctic, we introduce enhanced algorithms to improve the temporal and spatial resolutions of GNSS-R sea-level measurements.
Our approach includes an enhanced spectrum analysis based on multi-frequency signals and statistical reliability verification. Moreover, we include the signals transmitted by the Galileo constellation in addition to GPS to improve the quantity and the quality of GNSS observations in the Arctic. We have tested the proposed method with an experiment in Alaska and validated the results with nearby tide gauges. The experimental results clearly show the feasibility of employing GNSS-R-based tide gauges in the Arctic.
GNSS-R-BASED WATER-LEVEL MONITORING
Martin-Neria first introduced a method of monitoring sea level using the GNSS-R technique in 1993. Thereafter, many studies have been conducted to apply GNSS-R to water level estimation. Anderson proposed a method to estimate sea level using the interference pattern caused by the direct and reflected GNSS signals, which relies on the fact that the spacing between peaks in the interference pattern is almost entirely dependent on the height of the antenna above the reflecting surface.
The phase difference in the GNSS receiver between the direct and the reflected satellite signals varies while the geometry of a GNSS satellite changes (see Figure 1), generating the interference pattern. The interference pattern is particularly noticeable in signal-to-noise ratio (SNR) data. The reflected signals contribute to the SNR data in the form of oscillations, while the smoothly rising overall arc mostly depends on the signal strength and the antenna gain pattern.
The reflected signals can be isolated from the SNR data by removing the main trend — for example, by polynomial fitting — indicative of the direct signal. The frequency of the remaining dSNR oscillations is constant with respect to the sine of the elevation angle, assuming that the water level does not change during the satellite arc and the reflection surface is horizontal. Consequently, the frequency of the oscillation is linearly proportional to the height of the antenna above the reflecting surface.
The frequency can be derived from the dSNR data by spectral analysis. Among a number of spectral-analysis methods, the Lomb-Scargle periodogram (LSP) is commonly applied since it allows for processing of unevenly sampled data.
Determining the frequency of the oscillations. The antenna height above the water surface is directly calculated from the frequency of the oscillations derived from LSP processing. However, it is difficult to determine the dominant frequency because of the roughness of the water surface, especially in extreme environments such as Arctic regions with high currents and strong winds. In addition, the observed SNR data is easily affected by obstacles near the GNSS antenna. Therefore, it is difficult to distinguish the spectral peak of the signal reflected from the water surface from other additional reflected signals, especially when additional and unexpected reflections occur near the sea surface.
To minimize the erroneous determination of the frequency of the oscillations using dSNR, we can take advantage of the multiple frequencies of modern GNSS signals. In our study, we processed signals from both the GPS and Galileo constellations, with GPS transmitting three carrier signals (L1, L2 and L5) and Galileo transmitting five carrier signals (E1, E5a, E5b, E5ab and E6).
By comparing the spectrum peaks from the multiple signals on different frequencies, one can analyze the dominant peaks across the different frequencies on the same raypath. This algorithm is based on the fact that the multiple frequency signals should detect consistent sea-level heights because they are transmitted along the same raypath during the same period. One of the biggest advantages of this approach is that no additional data or equipment is required to accurately determine the frequency of oscillations of the GNSS signals reflected from the water surface.
Statistical Testing of Retrieved Sea Levels. Reflected signals are not necessarily all from the sea surface. To remove erroneous solutions, we conducted a statistical test. Data including measurement errors and/or some noise can be approximated to the model by the least squares method that determines the model parameters by minimizing the sum of squared residuals. However, this method yields an incorrect result when many outliers deviating from the normal distribution are included in the data set.
This problem can be overcome by applying RANdom SAmple Consensus (RANSAC). RANSAC stochastically estimates the model parameters maximizing consensus, that is, the parameter supported by the largest number of sample data through an iterative process. However, the RANSAC results can act differently each time for the same input data because it is essentially a statistical estimation method using random samples. Therefore, we perform RANSAC with rough constraints primarily to remove outliers significantly out of normal range, then the remaining noise in the data can be excluded by performing secondary fitting using tightly constrained least squares. For the least squares procedure, a series was applied for the fitting model, which represents various motions of the sea surface such as ocean tide loading, as a sum of trigonometric functions.
SEA-LEVEL MONITORING IN ST. MICHAEL
The Plate Boundary Observatory (PBO) network operated by UNAVCO (formerly the University NAVSTAR Consortium) is primarily designed to monitor long-term tectonic and volcanic deformation. However, it can also be used for GNSS-R applications. A new PBO station, AT01, was installed in May 26, 2018, in St. Michael, Alaska, which is designed to be suitable as a GNSS-R-based tide gauge with a clear and wide-open view toward the sea covering from 0° to 230° in azimuth (see FIGURE 2). The equipment at this site consists of a Trimble choke-ring geodetic antenna and a Septentrio PolaRx5 receiver that can receive not only GPS signals but also those of Galileo, with data recorded every 15 seconds.
FIGURE 2. The surrounding area of AT01 in St. Michael, Alaska: south view. (Photo: Authors)
We have used this station to assess our technique using one month of SNR data from June 2018. It should be emphasized that not only GPS but also Galileo signals were processed, and the Center for Orbit Determination in Europe’s Multi-GNSS Experiment final orbit and satellite clock products were used to minimize the satellite orbit error. Additionally, NOAA tide gauge stations (9468132 and 9468333) were used for comparison and verification of the water levels measured from the GNSS-R-based tide gauge (see FIGURE 3).
FIGURE 3. Locations of AT01 and two NOAA tide-gauge stations (9468132 in St. Michael and 9468333 in Unalakleet). The red box represents the zoomed area at the bottom right. (Image: Authors)
The 9468132 tide gauge in St. Michael is the nearest tide gauge at approximately 1.5 kilometers from AT01. However, since it is not operational, NOAA only provides water-level predictions (just high and low tides) based on the harmonic constituents, not the actual measurements. On the other hand, the 9468333 tide gauge in Unalakleet is approximately 74 kilometers away from AT01. This makes it difficult to use the tide gauge as ground truth, but it does provide the actual sea-level measurements including any abnormal daily variations during the observation period. Therefore, we used the water-level predictions and measurements from both stations to validate the GNSS-R-based water-level measurements at AT01.
Determination of Water Level. The GPS and Galileo SNR data were independently analyzed using our in-house software package (written in MATLAB) using the following procedures.
As a preprocessing step, each SNR data series was examined to filter out the signals reflected from other surfaces surrounding the antenna and to isolate the signals that were reflected by the sea surface. Since AT01 PBO station was installed to investigate the feasibility of its use as a GNSS-R-based tide gauge, the most effective azimuth and elevation ranges were given, which are 0° to 230° and 10° to 25°, respectively.
The azimuth and elevation angle ranges were applied, which effectively removed reflected signals from surfaces other than the sea surface. After identifying the SNR data affected by the reflection from the sea surface, the processing windows were dynamically determined by the continuous path and direction (ascending and descending) of the satellites, and the height of the sea surface was estimated using only a portion of the satellite arc contained within each processing window.
For example, FIGURE 4 shows the processing windows determined for the GPS satellite PRN 1 on June 1, 2018. The red dots in the figure show the parts of the satellite arcs affected by multipath from the sea surface. The data was divided into three processing windows due to the arc discontinuities and satellite path directions. It should be noted that only the processing windows with a data span of 30 minutes or longer were used for water level estimation. This minimum data span duration of 30 minutes was empirically determined by observing the probability of failure of the water -level calculation for shorter spans.
FIGURE 4. An example of the processing window determination for GPS satellite PRN 1 on June 1, 2018. (Image: Authors)
To isolate multipath effects from the SNR observation, we removed the trend in the SNR by a second-order polynomial fitting using only the portion of a satellite arc contained within each window. FIGURE 5 (b) shows the detrended SNR (dSNR) from FIGURE 5 (a), and the impact of the multipath is clearly identified in the form of the oscillation. As discussed earlier, the oscillation frequency is related to the antenna height above the sea surface. Accordingly, the dSNR data was analyzed through an LSP. As shown in FIGURE 5 (c), multiple peaks are founded from the LSP results of each dSNR series, and it is not easy to distinguish the frequency of the reflected signal from the sea level among these peaks.
Since multiple frequency signals from the same satellite must detect the same sea-level height, the final dominant peak was determined by checking the consistency of the resulting heights from each dominant peak among the multi-frequency signals. After that, the dominant frequency was converted to the antenna height above the reflection surface, which was then subtracted from the orthometric height of the antenna (the height above the geoid or, approximately, the height above mean sea level [MSL]) to refer the height of the instantaneous sea surface to MSL.
FIGURE 5. SNR data-analysis procedures with PRN 1 GPS on June 1, 2018: (a) The SNR data affected by the reflection from the sea surface, (b) detrended SNR data through a second-order polynomial, and (c) LSP results and dominant peaks of each frequency. (Image: Authors)
After analyzing all SNR data observed during one day, we carried out the reliability test of the retrieved sea levels to reject erroneous sea-level solutions.
RESULTS AND VALIDATION
The water-level changes from the GNSS-R-based tide gauge at St. Michael were compared to the independently predicted and measured sea levels from the neighboring St. Michael and Unalakleet tide gauges during June 1–30, 2018. Although the tide gauges are considered reliable ground truth, our experimental study must take into account the physical distance between the sites (about 1.5 and 74 kilometers from AT01, respectively) as well as the difference coming from the model versus the actual measurement.
In addition, a vertical offset between the data time series of the GNSS-R-based tide gauge and the standard tide gauges should be considered due to their different datums. Whereas the GNSS-R-derived sea level refers to a geodetic datum — namely the U.S. National Spatial Reference System (NAVD 88) — a standard tide gauge is highly localized with reference to a tidal datum such as local mean sea level. Generally, the difference between the geodetic and tidal datums is provided by NOAA, which allows us to convert between two vertical datums.
However, the vertical datum in Alaska has significant gaps in the spatially varying tidal information because of the difficulties of operating tide gauges there so that accurate information for datum conversion cannot be obtained. Therefore, the averages of the vertical differences were calculated (–6.44 centimeters for the St. Michael tide gauge and 9.54 centimeters for the Unalakleet tide gauge), which were then applied to each of the time series to make the comparisons. In fact, such a problem implies another advantage of a GNSS-R-derived tide gauge: it already returns a water-level height based on the terrestrial datum so that the datum of the land and the ocean can be consistently retained.
FIGURE 6 shows the sea level derived from the GNSS-based tide gauge measurements using GPS (red dots), Galileo (blue dots), the predicted sea level from the St. Michael tide gauge (green dots and lines) and measured sea level from the Unalakleet tide gauge (blue line).
FIGURE 6. Time series of sea level derived by GNSS-R-based tide gauge (AT01) in St. Michael, Alaska, during a month (red and blue dots for GPS and Galileo satellites, respectively; yellow dashed lines for the smoothed time series from two hours’ moving average filter) together with sea-level measurements from the Unalakleet tide gauge (blue solid line) and sea-level predictions from the St. Michael tide gauge (green dots for high- and low-tide predictions and green dashed line for interpolated predictions). (image: Authors)
The overall results show good agreement with the tide predictions at the nearby St. Michael tide-gauge station. It should be noted that the St. Michael tide gauge only provides high- and low-tide predictions so these were interpolated. However, some tidal characteristics not represented in the published predictions were also confirmed. In particular, as shown in the red-shaded segments of the time series marked (a) and (b) in Figure 6, larger and lower amplitudes than the tide predictions for the St. Michael tide gauge were identified on June 3 and 16, respectively.
These inconsistencies can be explained by the comparison with actual sea-level measurements at the Unalakleet tide gauge (solid blue line in Figure 6), which show very similar sea-level changes compared to those of the GNSS-R-based tide gauge. In addition, the overall larger amplitudes in the time series from the Unalakleet tide gauge can be explained by considering the fact that the amplitudes of the water levels vary along the coastline in Alaska and the Unalakleet tide gauge is approximately 74 kilometers from AT01.
To quantitatively investigate the agreement between the GNSS-R-based tide gauge and the standard tide gauges, we computed correlation coefficients. To ensure simultaneous data, the standard tide-gauge measurements and predictions were interpolated to the time tags of the GNSS-R-based time series. The correlation coefficients are 0.87 and 0.81 with the St. Michael and Unalakleet tide gauges, respectively.
The statistical analysis of the comparison result is summarized in TABLE 1. The mean and maximum values were computed using the absolute sea-level differences. From the results, it could be established that the GNSS-R-derived sea level shows better agreement with actual sea-level measurements at the Unalakleet tide gauge even though it is approximately 74 kilometers away from AT01.
Table 1 Statistical analysis of the sea-level differences between the GNSS-R-based tide gauge (AT01) and the standard tide gauges (Unalakleet and St. Michael).
Spectral analysis was additionally conducted to validate the sea levels from the GNSS-R-based tide gauge. Because the St. Michael tide gauge does not provide actual measurements (only predictions), only the Unalakleet tide gauge was used in the spectral comparison. A fast Fourier transform (FFT) was applied to convert the time series of the sea levels to the frequency domain.
The GNSS-R-based tide gauge showed good agreement with the Unalakleet tide gauge overall. In addition, from the corresponding spectral analysis results, we were able to find meaningful harmonic constituents, M2, K1 and O1. The harmonic constituents estimated from the sea-surface measurements of the GNSS-R-based tide gauge have amplitudes most similar to the published harmonic constituents of the nearest St. Michael tide gauge, although the difference in amplitudes of the three harmonic constituents averages 12.3 centimeters.
In fact, the Unalakleet tide gauge also does not exactly match the amplitude of the estimated harmonic constituents and the published harmonic constituents. But by summarizing the corresponding results, we can conclude that the harmonic constituents estimated from the GNSS-R-based tide gauge are reliable.
As mentioned earlier, in our study, we estimated the water-level change by using GPS and Galileo satellite signals to overcome the degradation of GNSS performance due to the satellite geometry in the Arctic. The smoothed time series, calculated from a moving-average filter of two-hour intervals, is shown in Figure 6 (yellow dashed lines). The time series of sea level derived by the GNSS-R-based tide gauge during the whole month were used as ground truth for evaluating the accuracy.
This was done because the Unalakleet tide gauge is approximately 74 kilometers away from AT01 and the St. Michael tide gauge does not provide actual measurements, making it difficult to use as ground truth. As a result, the sea levels determined using the Galileo and GPS signals showed very similar accuracy with an average difference of 0.11 meters. Therefore, even if Galileo is additionally used, the estimated final water levels were at a similar level of accuracy.
However, the number of water-level observations dramatically increased (approximately doubled) when GPS and Galileo signals were both involved, even though the number of Galileo satellites is fewer than the number of GPS satellites. This is because Galileo transmits on five frequencies while GPS transmits on just three, so we can achieve more robust solutions by including Galileo.
We investigated how adding Galileo satellites changes the temporal resolution of the final sea-level measurements. At this time, several sea-level measurements pointing to the same epoch (such as sea levels from several frequency observations of the same satellite arc) were considered as one measurement for the time interval computation.
Overall, sea-level measurements using only Galileo satellites show lower temporal resolution compared to GPS satellites alone, with a mean time interval of 48.97 minutes because Galileo is not fully operational yet and fewer satellites are available. However, combining GPS and Galileo satellites to the sea-level analysis significantly increased the time resolution.
When only GPS satellites were used, the maximum time interval between two water-level measurements was greater than 3 hours, while the maximum time interval was shortened to about 1.5 hours when Galileo satellites were included in the water-level measurement.
However, even if both GPS and Galileo satellites were used, the average time interval was still 14.1 minutes, which is considerably longer than the time resolution of the standard tide gauge of 6 minutes. The lower time resolution of a GNSS-R-based tide gauge is explained by the limited ranges (azimuth and elevation angle ranges of 0° to 230° and 10° to 25°, respectively) toward the ocean at station AT01. It means the time resolution can be improved by securing a wider view of the ocean from the GNSS-R-based tide gauge.
SUMMARY AND CONCLUSION
The purpose of our study was to evaluate and verify the feasibility of using GNSS-R for sea-level monitoring in the Arctic. We used data from a GNSS station in St. Michael, Alaska, and applied an advanced algorithm that accurately determines sea levels through the comparisons of results from multiple GNSS signals along with an effective filtering procedure. Our results were validated through comparisons with measurements and predictions from nearby standard tide gauges.
From the corresponding analysis, we could confirm that the GNSS-R technique overcomes the limitations of standard tide gauges in the Arctic and successfully estimated the sea-level change in St. Michael, Alaska. The results from this study show many promising applications for a GNSS-R-based tide gauge in the Arctic, such as tsunami and flood monitoring and tidal datum determination.
In future studies, additional research should be conducted on how well the GNSS-R-based tide gauge can operate in extreme conditions such as low temperatures, wind gusts, storms, and snow. And, for further improvement of the temporal resolution of the technique, all active GNSS constellations including GPS, GLONASS, Galileo, and BeiDou should be included — that will certainly improve the temporal resolution and also potentially improve the accuracy and reliability. It would be also worth studying the spatial variations of sea-level changes by investigating the specular reflection points of GNSS multipath signals.
ACKNOWLEDGMENTS
This article is based on the paper “Monitoring Sea Level Change in the Arctic Using GNSS-Reflectometry” presented at ION ITM 2019, the 2019 International Technical Meeting of The Institute of Navigation, Reston, Virginia, Jan. 28–31, 2019.
SU-KYUNG KIM is a graduate research assistant at Oregon State University in Corvallis, Oregon. She received her M.Sc. in geoinformation engineering from Sejong University in Seoul, South Korea, in 2013. Her research interests are focused on sea-level change monitoring and crustal deformation studies using GNSS.
JIHYE PARK is an assistant professor of geomatics at Oregon State University. She holds a Ph.D. in geodetic science and surveying from The Ohio State University in Columbus, Ohio. Her research interests include GNSS positioning and navigation, GNSS reflectometry, ionospheric and tropospheric monitoring for natural hazards and artificial events, and other geospatial-related topics.
FURTHER READING
Authors’ Conference Paper
“Monitoring Sea Level Change in the Arctic Using GNSS-Reflectometry” by S.-K. Kim and J. Park in Proceedings of ION ITM 2019, the 2019 International Technical Meeting of The Institute of Navigation, Reston, Virginia, Jan. 28–31, 2019.
Pioneering Work by Manuel Martin-Neira
“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.S. Vallmitjana in IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, No. 1, 2001, pp. 142–150, doi: 10.1109/36.898676.
“Coastal Sea Level Measurements Using a Single Geodetic GPS Receiver” by K.M. Larson, J.S. Löfgren and R. Haas in Advances in Space Research, Vol. 51, No. 8, 2013, pp. 1301–1310, doi: 10.1016/j.asr.2012.04.017.
“Monitoring Coastal Sea Level Using Reflected GNSS Signals” by J.S. Löfgren, R. Haas and J.M. Johansson in Advances in Space Research, Vol. 47, No. 2, 2011, pp. 213–220, doi: 10.1016/j.asr.2010.08.015.
“Three Months of Local Sea Level Derived from Reflected GNSS Signals” by J.S. Löfgren, R. Haas, H.-G. Scherneck and M.S. Bos in Radio Science, Vol. 46, No. 6, 2011, RS0C05, doi:10.1029/2011RS004693.
“Determination of Water Level and Tides Using Interferometric Observations of GPS Signals” by K.D. Anderson in Journal of Atmospheric and Oceanic Technology, Vol. 17, No. 8, 2000, pp. 1118-1127, doi: 10.1175/1520-0426(2000)017<1118:DOWLAT>2.0.CO;2.
Earlier Innovation Columns Dealing with GNSS Refectometry
Tides, Surges and Mean Sea-Level by D. Pugh, published originally by J. Wiley & Sons, Chichester, U.K., 1987, reprinted with corrections in 1996 and subsequently issued in e-print form by NERC Open Research Archive.
Random Sample Consensus
“Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography” by M.A. Fischler and R.C. Bolles in Communications of the ACM, Vol. 24, No. 6, 1981, pp. 381–395, 10.1145/358669.358692.
Surrey Satellite Technology’s Space GNSS Receiver Remote Sensing Instrument (SGR-ReSI) is the primary payload onboard NASA’s CYGNSS constellation, launched today, Dec. 15, from Cape Canaveral Air Force Station in Florida.
The Cyclone Global Navigation Satellite System (CYGNSS) mission is part of the NASA Earth System Science Pathfinder Program that aims to improve extreme weather prediction by studying how tropical cyclones form.
Artist’s concept of one of the eight CYGNSS satellites in orbit. (Image: NASA/University of Michigan)
The CYGNSS space segment consists of a constellation of eight micro satellites, each carrying the Surrey SGR-ReSI as the observatory payload in the form of a delay Doppler mapping instrument (DDMI). Making use of reflected global positioning signals, the DDMI collects ocean surface roughness data using a technique called GNSS reflectometry, providing CYGNSS with a new method for looking inside hurricanes. Wind speed will be estimated from this reflectometry data.
“At the end of last year, we delivered the SGR-ReSI flight models, low-noise amplifiers, and antennas to Southwest Research Institute for final integration into the CYGNSS observatories — marking a significant hardware shipment out of our Englewood, Colorado, manufacturing facility,” said Clare Martin, vice president of programs at Surrey Satellite Technology U.S.. “All of us at Surrey are proud that our instrument is playing an integral role in this mission, and we will watch with great interest as the satellites are put to work.”
The CYGNSS team is made up of the University of Michigan Department of Climate and Space Sciences and Engineering, Southwest Research Institute (SwRI), Surrey Satellite Technology and Sierra Nevada Corporation.
Surrey Satellite Technology demonstrated the concept of GNSS reflectometry for the first time on its UK-DMC mission launched in 2003, and subsequently developed the SGR-ReSI, which is currently flying on Surrey’s TechDemoSat-1 mission.
CYGNSS is NASA’s first Earth science small satellite constellation, designed to help improve forecasting hurricane intensity, hurricane tracks and storm surges.
CYGNSS will measure previously unknown details crucial to accurately understanding the formation and intensity of tropical cyclones and hurricanes.
“This is a first-of-its-kind mission,” said Thomas Zurbuchen, associate administrator for NASA’s Science Mission Directorate at the agency’s headquarters in Washington. “As a constellation of eight spacecraft, CYGNSS will do what a single craft can’t in terms of measuring surface wind speeds inside hurricanes and tropical cyclones at high time-resolution, to improve our ability to understand and predict how these deadly storms develop.”
The CYGNSS mission is expected to lead to more accurate weather forecasts of wind speeds and storm surges — the walls of water that do the most damage when hurricanes make landfall.
Using the same GPS technology that allows drivers to navigate streets, CYGNSS will use a constellation of eight micro satellite observatories to measure the surface roughness of the world’s oceans. Mission scientists will use the data collected to calculate surface wind speeds, providing a better picture of a storm’s strength and intensity.
Unlike existing operational weather satellites, CYGNSS can penetrate the heavy rain of a hurricane’s eyewall to gather data about a storm’s intense inner core. The eyewall is the thick ring of thunderstorm clouds and rain that surrounds the calm eye of a hurricane. The inner core region acts like the engine of the storm by extracting energy from the warm surface water via evaporation into the atmosphere.
The latent heat contained in the water vapor is then released into the atmosphere by condensation and precipitation. The intense rain in eyewalls blocks the view of the inner core by conventional satellites, however, preventing scientists from gathering much information about this key region of a developing hurricane.
“Today, we can’t see what’s happening under the rain,” said Chris Ruf, professor in the University of Michigan’s Department of Climate and Space Sciences and Engineering and principal investigator for the CYGNSS mission. “We can measure the wind outside of the storm cell with present systems. But there’s a gap in our knowledge of cyclone processes in the critical eyewall region of the storm — a gap that will be filled by the CYGNSS data. The models try to predict what is happening under the rain, but they are much less accurate without continuous experimental validation.”
The CYGNSS small satellite observatories will continuously monitor surface winds over the oceans across Earth’s tropical hurricane-belt latitudes. Each satellite is capable of capturing four wind measurements per second, adding as many as 32 wind measurements per second for the entire constellation.
CYGNSS is the first complete orbital mission competitively selected by NASA’s Earth Venture program. Earth Venture focuses on low-cost, rapidly developed, science-driven missions to enhance our understanding of the current state of Earth and its complex, dynamic system and enable continual improvement in the prediction of future changes.
The National Oceanography Centre (NOC) has developed global wind speed products based on reflected GPS signals, using data from the UK TechDemoSat-1 satellite, reports Hydro International.
The TechDemoSat-1 satellite, built by Surrey Satellite Technology Ltd. (Photo: SSTL)
The achievement demonstrates the potential of GNSS reflectometry (GNSS-R) to improve sampling of ocean surface winds, as well as improve weather monitoring and forecasting by complementing existing satellite measurements from scatterometers and radiometers.
The GNSS-R receiver on TechDemoSat-1 is a precursor to eight similar receivers to be flown as a constellation for the NASA CYGNSS mission. CYGNSS — Cyclone Global Navigation Satellite System — will be launched Dec. 12 and will observe winds within cyclones, hurricanes and typhoons with unprecedented spatial and temporal sampling.
CYGNSS will launch aboard the Pegasus XL rocket from Cape Canaveral Air Force Station in Florida. It will make frequent and accurate measurements of ocean surface winds throughout the life cycle of tropical storms and hurricanes.
