NASA’s CYGNSS Constellation of Hurricane Remote Sensing Satellites

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C. Ruf, F. Marsik, A. Lyons, University of Michigan, Ann Arbor, MI, USAlogooo
M. Unwin, STTL, Surrey Research Park, Guilford GU2 7YE, UK
J. Dickinson, R. Rose, Southwest Research Institute, San Antonio, TX, USA
D. Rose, M. Vincent, Southwest Research Institute, Boulder, CO, USA
Due to its tremendous size, Hurricane Sandy (Oct. 22-29, 2012) generated a devastating storm surge that significantly impacted coastal communities in the states of New Jersey and New York. According to the National Hurricane Center (Blake et al. 2013), the storm surge was responsible for 41 of the 72 U.S. deaths associated with the storm. Across the eastern coast of the U.S., at least 650,000 houses were damaged or destroyed as a result of the significant waves and storm surge associated with Hurricane Sandy. Preliminary U.S. damage estimates (approximately $50 billion) suggest that the storm was the second-costliest tropical cyclone (TC) to hit the United States since 1900 (Blake et al. 2013).
The generation of storm surge is greatly influenced by both the track and the intensity of TCs. DeMaria et al. (2007) note that the accuracy of TC track forecasts has improved by about 50 percent since 1990, largely as a result of improved mesoscale and synoptic modeling, including improved data assimilation techniques associated with such models. In that same period, there has been relatively little improvement in the accuracy of TC intensity forecasts. The tracks of TCs are primarily influenced by the large, synoptic scale (100 to 2000 kilometers) atmospheric patterns which are relatively well-characterized through both in-situ and remotely-sensed measurements. In contrast, the intensities of tropical cyclones are more closely related to mesoscale (1 100 km) and microscale (< 1 km) meteorological processes, such as surface fluxes of moisture and heat, which are typically less well-characterized due to inadequacies in the measurements of these processes within the tropical storm environment.
The inadequacy in observations within the TC environment results from two primary causes: (1) much of ocean surface within the inner core of a TC is obscured when using conventional remote sensing instruments by intense precipitation in the eye wall and inner rain bands and (2) the rapidly evolving (genesis and intensification) stages of the TC life cycle are poorly sampled in time by conventional polar-orbiting imagers. A new NASA satellite mission, CYGNSS (Cyclone Global Navigation Satellite System), is specifically designed to address these two limitations by combining the all-weather performance of GNSS bistatic ocean surface scatterometry with the sampling properties of a constellation of satellites [Katzberg et al. 2001, Katzberg et al. 2006]. The use of a constellation of microsatellites will result in spatial and temporal sampling properties which are markedly different from conventional imagers. CYGNSS is led by the University of Michigan, with partners Southwest Research Institute developing the satellite observatories and mission operations and Surrey Satellite Technology Ltd. (SSTL) providing the science payload.
The Science Motivation for the CYGNSS Approach
The Value of Wind Observations in Precipitating Conditions
Mesoscale Convective Systems (MCSs), which are complexes of thunderstorms, contribute more than half of the total rainfall in the tropics and serve as the precursors to TCs. Over the ocean, the moisture and heat fluxes which feed MCSs depends critically on the interaction between ocean surface vector winds, ocean surface properties, moist atmospheric thermodynamics, radiation, and convective dynamics.
Previous spaceborne measurements of the ocean surface vector winds have suffered from degradation of the signal in highly precipitating regimes, such as those observed in the inner core of TCs. As a result, in the absence of reconnaissance aircraft, the accuracy of wind speed estimates in the inner core of TCs often have been highly compromised, limiting our ability to understand the complex surface processes associated with intensification of TCs. For this reason, the added quality and quantity of surface vector wind data to be provided by CYGNSS in high precipitating conditions will significantly improve estimates of the intensity of TCs and our ability to understand and model these ever-changing intensities.
The Value of Frequent Wind Observations
Most current spaceborne active and passive microwave instruments are in polar low earth orbit (LEO). From a spatial sampling standpoint, while these polar LEO orbits maximize global coverage, they also can result in large coverage gaps in the tropics. Schlax et al. (2001) present a comprehensive analysis of the sampling characteristics of conventional polar-orbiting, swath-based imaging systems, including the consideration of so-called tandem missions. The study demonstrated that a single, wide-swath, high-resolution scatterometer system cannot resolve synoptic scale spatial detail everywhere on the globe, and in particular not in the tropics. The irregular and infrequent revisit times (ca. 11-35 hours) are not sufficient to resolve synoptic scale temporal variability. The CYGNSS sampling strategy will overcome the limitations of previous single, wide-swath approaches. The ability to adequately characterize changes in the intensity of TCs prior to landfall will provide an important decision-making tool for emergency managers who are responsible for providing guidance to the general public on the need for and/or timing of evacuation measures.

Figure 1. (a) GPS signal propagation and scattering geometries for ocean surface bistatic quasi-specular scatterometry. (b) Spatial distribution of the ocean surface scattering measured by the UK-DMC-1 demonstration spaceborne mission – referred to as the Delay Doppler Map (DDM) (Gleason 2007).

Figure 1. (a) GPS signal propagation and scattering geometries for ocean surface bistatic quasi-specular scatterometry. (b) Spatial distribution of the ocean surface scattering measured by the UK-DMC-1 demonstration spaceborne mission – referred to as the Delay Doppler Map (DDM) (Gleason 2007).

Measurement Methodology
The measurement methodology to be employed by CYGNSS will rely on the characterization of the signal propagation from the existing constellation of global positioning system (GPS) satellites located at approximately 20,000 km above the Earth’s surface, as well as on the nature of the scattering of these signals by the ocean surface.
Figure 1a illustrates the propagation and scattering geometries associated with the GNSS approach to ocean surface scatterometry. The direct GPS signal provides a coherent reference for the coded GPS transmit signal. It is acquired by a right-hand circular polarized (RHCP) receive antenna on the zenith side of the spacecraft. The quasi-specular forward scattered signal from the ocean surface is received by a downward looking, left-hand circular polarized (LHCP) antenna on the nadir side of the spacecraft. The scattered signal contains detailed information about ocean roughness statistics, from which local wind speed can be derived (Zavorotny and Voronovich 2000). During the processing of CYGNSS data, a scattering cross-section image such as that produced by the UK-DMC-1 demonstration spaceborne mission (shown in Fig. 1b) will be generated. Variable lag correlation and Doppler shift, the two coordinates of the image, enable the spatial distribution of the scattering cross section to be resolved (Gleason et al. 2005, Gleason 2007). This type of scattering image is referred to as a Delay Doppler Map (DDM). Estimation of the ocean surface roughness and near-surface wind speed is possible from two properties of the DDM. The maximum scattering cross-section (the dark red region in Fig. 1b) can be related to roughness and wind speed. This requires absolute calibration of the DDM. Wind speed can also be estimated from a relatively calibrated DDM by the shape of the scattering arc (the red and yellow regions in Fig. 1b). The arc represents the departure of the actual bi-static scattering from the purely specular case (a perfectly flat ocean surface), which appears in the DDM as a single point scatterer. The latter approach imposes more relaxed requirements on instrument calibration and stability than does the former. However, given that the latter approach derives its wind speed estimate from a wider region of the ocean surface, the approach necessarily has poorer spatial resolution. Development of wind speed retrieval algorithms from DDMs is an active area of research (Gleason 2007, Clarizia et al. 2014).
Mission Design
Microsatellite Observatories
Figure 2. The eight CYGNSS Observatories will orbit at an inclination of 35 degrees and are each capable of measuring four simultaneous reflections, resulting in 32 wind measurements per second across the globe. Ground tracks for 90 minutes (left) and for a full day (right) of wind samples are shown above. The number of CYGNSS Observatories, their orbit altitudes and inclinations, and the alignment of the antennas, will be optimized to provide unprecedented high temporal-resolution wind field imagery of TC genesis, intensification and decay.

Figure 2. The eight CYGNSS Observatories will orbit at an inclination of 35 degrees and are each capable of measuring four simultaneous reflections, resulting in 32 wind measurements per second across the globe. Ground tracks for 90 minutes (left) and for a full day (right) of wind samples are shown above. The number of CYGNSS Observatories, their orbit altitudes and inclinations, and the alignment of the antennas, will be optimized to provide unprecedented high temporal-resolution wind field imagery of TC genesis, intensification and decay. Image Credit: Southwest Research Institute.

The CYGNSS mission will employ a constellation of eight microsatellite Observatories in LEO (approximately 500 km above the surface of the Earth). Each CYGNSS Observatory will consist of a microsatellite platform hosting a GPS receiver modified to measure surface reflected signals. Similar GPS-based instruments have been demonstrated on both airborne and spaceborne platforms to retrieve wind speeds as high as 60 meters per second (a Category 4 hurricane) through all levels of precipitation, including the intense levels experienced in a TC eyewall (Katzberg et al. 2001).
Each observatory simultaneously tracks scattered signals from up to four independent transmitters in the operational GPS network. The number of Observatories and orbit inclination are chosen to optimize the TC sampling properties. As shown in Fig. 2, the result is a dense cross-hatch of sample points on the ground that cover the critical latitude band between ±35 degrees.
GPS Reflectometry and UK-DMC
For some years, GPS receivers have been used to provide position, velocity and time knowledge to satellite platforms in LEO in a similar way to ground-based satellite navigation receivers. In addition to navigation, GPS signals have also been increasingly used for remote sensing. Signals at L-band with a 2-20 megahertz bandwidth are being broadcast globally from a 20,000 km altitude and can be used to measure, amongst other things, tectonic plate motion and ionospheric and tropospheric parameters. Furthermore, signals from other GNSS are becoming available, and there will soon be more than 120 signal sources in space.
Spaceborne GNSS reflectometry uses GNSS signals that have been scattered by the Earth surface to measure geophysical parameters. The potential for GNSS reflectometry was demonstrated by the UK-DMC mission in 2003. The mission included a GNSS-R sensor with a nadir-pointing antenna (gain just under 12 dBiC, 3 dB field of view approximately 20x 70 degrees) permitting collection of as many as three reflected signals simultaneously. The primary mode of operation on the first experiment was the collection of sampled IF data into a data-recorder, typically 20 seconds, and downloading for post processing on the ground. The raw data were processed on the ground into DDMs using software receiver techniques to allow analysis of signal returns off ocean, land and ice. An example DDM is shown in Fig. 3. DDMs represent a measure the spread in energy away from the specular point (initial point of contact of the signal with the ocean surface), with the spread of energy growing as the surface becomes rougher.
Figure 3. Example Delay-Doppler Map of ocean reflection from UK-DMC GPS-R Experiment.

Figure 3. Example Delay-Doppler Map of ocean reflection from UK-DMC GPS-R Experiment (Ruf et al 2013).

A substantial effort into the modeling of signal returns has been undertaken using data from the first UK-DMC experiment with the intention to assess inversion of sea state parameters (Gleason et al. 2005, Gleason 2007, Clarizia et al. 2014) and the retrieval of directional roughness information (Clarizia et al. 2009a, Clarizia et al. 2009b). The retrieval of near-surface wind speed has been demonstrated with an accuracy of better than 2 meters per second at low to moderate wind speeds (Clarizia et al. 2014).
The Space GNSS Receiver – Remote Sensing Instrument (SGR-ReSI) and Delay Doppler Maps
The UK-DMC experiment demonstrated that a microsatellite-compatible passive instrument was able to make scientifically relevant geophysical measurements using GPS reflectometry. The mission flew a new GNSS-R instrument for this purpose, the Space GNSS Receiver – Remote Sensing Instrument (SGR-ReSI).
A schematic of the SGR-ReSI (Unwin et al. 2010) is shown in Fig. 4. The SGR-ReSI in effect fulfills in one module what might be handled by three separate units on previous spacecraft:
a)      It performs all the core functions of a space GNSS receiver, with front-ends supporting up to eight single or four dual frequency antenna ports
b)      It is able to store a quantity of raw sampled data from multiple front-ends or processed data in its 1 GByte solid state data recorder
c)      It has a dedicated reprogrammable FPGA co-processor (Virtex 4).
The co-processor was specifically included for the real-time processing of the raw reflected GNSS data into DDMs. However, it has flexibility to be programmed in orbit as required for different purposes, for example to track new GNSS signals, or to apply spectral analysis to received signals.
Figure 4. GNSS reflectometry instrument configuration.

Figure 4. GNSS reflectometry instrument configuration (Ruf et al. 2013).

For the co-processor to generate DDMs of the sampled reflected data, it needs to be primed with the PRN (pseudorandom noise) code of the transmitting GPS satellite, and the estimated time delay and Doppler of the reflection as seen from the satellite. These are calculated by the processor in conjunction with the main navigation solution – the data flow for this is shown in Fig. 5. Direct signals (received by the zenith antenna) are used to acquire and track GNSS signals. From the broadcast ephemerides, the GNSS satellite positions are known. Then, from the geometry of the position of the transmit and receive satellites, the reflectometry geometry can be calculated.
The processing of the DDM is performed on the co-processor using data directly sampled from the nadir antenna. In common with a standard GNSS receiver, the local PRN is generated on-board the co-processor. As an alternative to synchronizing and decoding the reflected signal in a standalone manner, the direct signals can be used to feed the navigation data sense, and assist the synchronization. The sampled data is multiplied by a replica carrier and fed into a matrix that performs an FFT on a row-by-row basis to form the DDM, to achieve in effect a 7,000 channel correlator, integrating over one millisecond. Each point is then accumulated incoherently over hundreds of milliseconds to bring the weak signals out of the noise.
pic 5

Figure 5. GNSS reflectometry dataflow (Ruf et al. 2013).

This processing is performed in real-time on-board the satellite, which greatly reduces the quantity of data required to be stored and for the satellite’s downlink. CYGNSS plans to use the SGR-ReSI primarily in an autonomous manner generating DDMs at a low data rate continuously, which will provide gap-free measurements of the ocean roughness throughout the tropical oceans.
Microsatellite Structure
The microsatellite structure (Fig. 6) requirements are driven by physical accommodation of the Delay Doppler Mapping Instrument (DDMI) antennas, the solar arrays, and launch configuration constraints. Our design uses the same principles as our heritage avionics chassis, using milled Al piece parts bolted together to provide an integrated, mass efficient solution for CYGNSS. Close tolerance pins/holes ensure repeatability of structural alignment. The microsatellite’s shape is specifically configured to allow clear nadir and zenith fields of view for the DDMI antennas, while its structure integrates the microsatellites and instrument electronic boards directly by creating avionics and Delay Mapping Receiver (DMR) “bays.” The avionics and DMR bays form the core of the microsatellites; all other components are mounted to this backbone with structural extensions included to accommodate the Al honeycomb-based solar arrays and DDMI nadir antenna assemblies. The structural configuration allows easy access to all observatory components when the nadir DDMI antenna panel assemblies and microsatellites endplates are removed for Observatory AI&T.
Figure 6. CYGNSS Observatory structure.

Figure 6. CYGNSS Observatory structure. Image Credit: Southwest Research Institute.

Deployment Module Structure
The Observatories will be released in pairs opposite each other, balancing deployment forces and keeping disturbance torques well within LV (launch vehicle) capabilities. They will be released by the deployment module (DM, Fig. 7), which serves as the constellation carrier during launch and then deploys the CYGNSS Observatories into their proper orbital configuration once on orbit.
The DM consists of two AL cylindrical sections or tiers, each with four mounting/separation assemblies. The mounting/separation assemblies are positioned 90 degrees apart to release the Observatories in pairs. Tier 2 is clocked 45 degrees from Tier 1 to provide proper orbital dispersal vectoring.
Ground Segment and Mission Operations
Concept of Operations
The CYGNSS Observatories were designed to ensure safety without ground intervention. Providing on-board systems, which minimize the need to develop time-tagged command sequences for Observatory routine operations, also supports a simplified operational cadence for maintaining the constellation.
Launch through Commissioning
Each Observatory is deployed with solar arrays stowed and the Observatories can remain in this “stowed” configuration indefinitely. After deployment from the launch vehicle, each Observatory transitions automatically through the initial three states to reach the Standby Mode where it can safely remain indefinitely. The physical deployment sequence for a CYGNSS Observatory is presented in Fig. 8.
The Observatories are independent; therefore it is not necessary to complete all commissioning tasks on one Observatory before moving on to another one. It is also unnecessary to ensure each Observatory is at the same “step” in a commissioning sequence. This independence allows a flexible scheduling approach to be used in setting up commissioning passes.
Figure 7. CYGNSS deployment module configuration without (left) and with (right) CYGNSS Observatories attached.

Figure 7. CYGNSS deployment module configuration without (left) and with (right) CYGNSS Observatories attached. Image Credit: Southwest Research Institute.

Nominal Operations
Every 1.5 to two days, a communication pass will occur for one of the CYGNSS Observatories in which science data will be retrieved. Science and engineering data files are generated, stored on-board, and automatically added into an on-board downlink file list. On-board microsatellites data storage provides storage for greater than 10 days of science data allowing flexibility in pass scheduling and supporting recovery from loss of communications during a pass. The Observatories within the CYGNSS constellation will be visible to three ground stations within the Universal Space Network (USN) – located in Hawaii, Australia, and Santiago, Chile – for periods which average 470-500 seconds visibility per pass. Each Observatory will pass over each of the three ground stations six to seven times each day, thus providing a large pool of scheduling opportunities for communications passes.
Figure 8. Sequential deployment of CYGNSS solar arrays following Observatory detachment from deployment module.

Figure 8. Sequential deployment of CYGNSS solar arrays following Observatory detachment from deployment module. Image Credit: Southwest Research Institute.

Concluding Remarks
The mission has completed two milestones so far in 2014. The first was NASA’s preliminary design review (PDR) in January. This review validated the design of the new hurricane and extreme weather prediction system.
To successfully complete the PDR, the CYGNSS team had to demonstrate that the overall design of the satellites, ground operations, and science data analysis algorithms met all system requirements within acceptable risk limitations.
The team worked tirelessly since the project began in December 2012 to produce a detailed baseline design for the mission.
“Our design clearly demonstrates that CYGNSS has the potential to fundamentally improve the forecasting of tropical cyclones,” said Dr. Chris Ruf, CYGNSS principal investigator. “Indeed, the full tropical coverage, coupled with the higher sampling frequency of acquisitions and their ability to penetrate unattenuated through the heaviest precipitation, overcome the limitation of the present means of observation.”
The second milestone was NASA’s Key Decision Point-C in March 2014, formally confirming the mission to move forward with implementation of the instrumentation, satellite vehicle and, ultimately, launch of the eight microsatellites comprising the mission.
The mission is scheduled to launch in October 2016, which should give the team enough time to prepare for science operations during the 2017 hurricane season.
Figure 9. CYGNSS Ground System Overview

Figure 9. CYGNSS Ground System Overview (Ruf et al. 2013).

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