Assessment of Spaceborne GNSS-R Ocean Altimetry Performance Using CYGNSS Mission Raw Data

This article assesses the ocean altimetry performance of spaceborne Global Navigation Satellite Systems reflectometry (GNSS-R) by processing the raw data sets collected by the Cyclone GNSS (CYGNSS) constellation. These raw data sets, i.e., the intermediate frequency signal streams before any receiver processing, are processed on the ground with a software receiver, from which the reflected waveforms of GPS L1, Galileo E1, and BeiDou-3 B1 band open service (OS) signals are generated following the conventional GNSS-R approach. By using different retracking algorithms, the bistatic delays of the reflected signals are derived from these waveforms, in which the retracking biases are removed with the specular point (SP) delay and power information computed from the corresponding waveform model. After applying a set of standard delay corrections, the bistatic delay observations are converted into sea surface height (SSH) measurements and compared with the mean SSH model. Both the random error (precision) and systematic effects (accuracy) are characterized with intratrack and intertrack analyses of the bistatic delay measurements. The two-way ranging precision can reach up to 3.9 and 2.5 m with 1-s GPS and Galileo group delay measurement (a factor of ~2 better for altimetry solution), and its evolution with the signal-to-noise ratio shows good consistency with the theoretical model. A significant delay dispersion of 3.0 m between different tracks is found, which is mainly attributed to the receiver orbit error and ionospheric correction residuals. These results can provide useful inputs for the development of future GNSS-R missions dedicated to ocean altimetry applications

Application of GNSS Interferometric Reflectometry for Lake Ice Studies

This thesis examines the use of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) for the study of lake ice with a particular focus on the estimation of ice thickness. Experiments were conducted in two lake regions: (1) sub-Arctic lakes located near Yellowknife and Inuvik in the Northwest Territories during March 2017 and 2019, and (2) MacDonald Lake, Haliburton, Ontario, which is known as a mid-latitude lake, during the ice season of 2019-2020. For both regions, GNSS-IR results are compared and validated against in-situ ice and on-ice snow measurements, and also with ice thickness derived from thermodynamic lake ice models. In the first experiment, GNSS antennas were installed directly on the ice surface and the ice thickness at each site was estimated by analyzing the signal-to-noise ratio (SNR) of the reflected GNSS signals. The GNSS-IR capability of ice thickness estimation tested on sub-Arctic lakes results in a root mean square error (RMSE) of 0.07 m, a mean bias error (MBE) of -0.01 m, and a correlation of 0.66. At MacDonald Lake, a GNSS antenna was mounted on a 5-m tower on the shore to collect reflected signals from the lake surface. The Least-Squares Harmonic Estimation (LS-HE) method was applied to retrieve higher SNR frequencies in order to estimate the depths of multiple layers within lake ice and the overlaying snowpack. Promising results were obtained from this experiment; however, ice thickness estimation using GNSS-IR at this mid-latitude lake site was found to be highly dependent on the presence or absence of wet layers such as slush at the snow-ice interface and wet snow above that interface. On colder days, when there was a lower chance for the formation of wet layers, ice thickness could be estimated with a correlation of 0.68, RMSE of 0.07 m, and MBE of -0.02 m. In addition, GNSS-IR showed the potential for determining the freeze-up and break-up timing based on the SNR amplitude of reflected signals. The novel work presented in this thesis points to the potential of using reflected signals acquired by recent (e.g. Cyclone Global Navigation Satellite System (CYGNSS) and TechDemoSat-1 (TDS-1)) and future GNSS-R missions for lake ice investigations

Engineering Calibration and Physical Principles of GNSS-Reflectometry for Earth Remote Sensing

The Cyclone Global Navigation Satellite System (CYGNSS) is a NASA mission that uses 32 Global Positioning System (GPS) satellites as active sources and 8 CYGNSS satellites as passive receivers to measure ocean surface roughness and wind speed, as well as soil moisture and flood inundation over land. This dissertation addresses two major aspects of engineering calibration: (1) characterization of the GPS effective isotropic radiated power (EIRP) for calibration of normalized bistatic radar cross section (NBRCS) observables; and (2) development of an end-to-end calibration approach using modeling and measurements of ocean surface mean square slope (MSS). To estimate the GPS transmit power, a ground-based GPS constellation power monitor (GCPM) system has been built to accurately and precisely measure the direct GPS signals. The transmit power of the L1 coarse/acquisition (C/A) code of the full GPS constellation is estimated using an optimal search algorithm. Updated values for transmit power have been successfully applied to CYGNSS L1B calibration and found to significantly reduce the PRN dependence of CYGNSS L1 and L2 data products. The gain pattern of each GPS satellite’s transmit antenna for the L1 C/A signal is determined from measurements of signal strength received by the 8-satellite CYGNSS constellation. Determination of GPS patterns requires knowledge of CYGNSS patterns and vice versa, so a procedure is developed to solve for both of them iteratively. The new GPS and CYGNSS patterns have been incorporated into the science data processing algorithm used by the CYGNSS mission and result in improved calibration performance. Variable transmit power by numerous Block IIF and IIR-M GPS space vehicles has been observed due to their flex power mode. Non-uniformity in the GPS antenna gain patterns further complicates EIRP estimation. A dynamic calibration approach is developed to further address GPS EIRP variability. It uses measurements by the direct received GPS signal to estimate GPS EIRP in the specular reflected direction and then incorporates them into the calibration of NBRCS. Dynamic EIRP calibration instantaneously detects and corrects for power fluctuations in the GPS transmitters and significantly reduces errors due to GPS antenna gain azimuthal asymmetry. It allows observations with the most variable Block IIF transmitters (approximately 37% of the GPS constellation) to be included in the standard data products and further improves the calibration quality of the NBRCS. A physics-based approach is then proposed to examine potential calibration errors and to further improve the Level 1 calibration. The mean square slope (mss) is a key physical parameter that relates the ocean surface properties (wave spectra) to the CYGNSS measurement of NBRCS. An approach to model the mss for validation with CYGNSS mss data is developed by adding the contribution of a high frequency tail to the WAVEWATCH III (WW3) mss. It is demonstrated that the ratio of CYGNSS mss to modified WW3 mss can be used to diagnose potential calibration errors that exist in the Level 1 calibration algorithm. This approach can help to improve CYGNSS data quality, including the Level 1 NBRCS and Level 2 ocean surface wind speed and roughness. The engineering calibration methods presented in this dissertation make significant contributions to the spatial coverage, calibration quality of the measured NBRCS and the geophysical data products produced by the NASA CYGNSS mission. The research is also useful to the system design, science investigation and engineering calibration of future GNSS-reflectometry missions.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168052/1/wangtl_1.pd

New Applications of Satellite-Measured Tropical Cyclone Wind Speeds

Major hurricanes are becoming more frequent due to climate change and the warming of the oceans. Now more than ever, the ability to model hurricanes accurately and provide advanced warning to affected areas is crucial. However, while hurricane track forecasting has greatly improved, there has been little improvement in intensity forecasting, partly due to inadequate observations and modeling of the inner core of the storm. CYGNSS, an experimental NASA satellite mission launched in December 2016, is designed to frequently measure surface wind speeds in hurricanes via GPS reflections. These additional measurements should improve forecasting of hurricane rapid intensification. This dissertation looks at new ways to use and interpret remotely sensed wind speeds in hurricanes. First, we look at how estimated storm intensity is affected by the spatial resolution of the satellite. Satellite-measured wind speeds are not point measurements—a single measurement is of a broad area on the surface where contributions from each part of the surface are given weight according to the antenna pattern. Hurricane intensity is usually determined by the maximum wind speed (Vm). Because satellite-measured wind speeds are effectively averages over a large area, the satellite-measured Vm is lower than the true Vm. Unless this is corrected for, hurricane wind fields from satellites will systematically underestimate storm intensity. This work explores how information from any satellite-measured wind field can be used to improve the estimated storm intensity via a scale factor correction. Next, hurricane parametric wind speed models are examined. Parametric wind models are established using meteorological principles combined with simplifying assumptions about the environment or storm structure. These models tune several free parameters to wind speed measurements by minimizing the difference between the measurements and the wind field “seen” by the model. Parametric wind models are especially useful for filling in gaps between measurements—once the free parameters are optimized, the model can report a wind speed estimate everywhere in the storm. Hurricane wind field characteristics such as Vm, radial distance to Vm, azimuthal information, and more are easily determined from a full wind field but are more difficult to estimate from a gap-filled wind field. Also, many modeling applications are enabled by having a full wind field. This dissertation discusses limitations of existing parametric wind models, and new parameters are added to allow for improved representation of a wider variety of storms. A parametric wind model is then used to find the center location of a hurricane using the principle that the model fit is best when the correct storm center is assumed. The storm center is the location that optimizes the model fit. Most storm center fixes are done manually—this is one of few automated storm center fix techniques and the only one not using cloud structure seen in satellite imagery. Accurate storm center locations are necessary for forecasting hurricanes, hurricane research, and historical record keeping. Lastly, a new CYGNSS data product is described which reports gridded surface wind speeds with increased convenience and reliability for users of hurricane-specific data. This product, which will be released to the public in early 2021, processes the CYGNSS wind speeds in a way that allows for self-consistency checks of the data. This new CYGNSS hurricane wind speed product is shown to be in excellent agreement with wind speeds from models and a well-established satellite.PHDApplied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168082/1/drmayers_1.pd