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A parametric error model is developed to represent the uncertainty in retrieval of hurricane force wind speed by a spaceborne GNSS-R instrument. The functional form of the model is constructed based on a bottom–up consideration of the primary contributing sources of uncertainty. Scaling parameters in the model are tuned in a top–down manner using a large population of wind speed retrievals by the CYGNSS satellite, which are colocated in space and time with HWRF reanalysis hurricane winds in the North Atlantic during 2018–2022. The root-mean-squared difference between CYGNSS and HWRF winds is found to depend on a number of variables, two of which are wind speed and receive antenna gain. The parametrized error model represents these dependencies. The model can be used as a design tool to predict expected performance as a function of instrument design. In particular, the model predicts the antenna gain required to achieve a particular level of wind speed uncertainty at a particular wind speed. A case study is considered in which a receive antenna gain of at least 20 dBi is found to be required to reliably distinguish between a Category 4 and Category 5 hurricane. This has implications for the optimal design of a future GNSS-R instrument intended for hurricane observations.

A new wind speed product has been developed using bistatic radar data from NASA's Cyclone Global Navigation Satellite System (CYGNSS) mission. The product addresses shortcomings in two existing CYGNSS gridded wind speed products, the L3 gridded fully developed seas wind speeds and the L3 storm-centric gridded products, which individually are optimized for global, nonstorm conditions and localized, high wind speed tropical cyclone (TC) conditions, respectively. In order to create a unified gridded wind speed product that captures both the storm force winds and the far-field environmental wind speeds, wind fields from the two products are merged onto a common grid. The algorithm produces global (±40o latitude) windspeeds, averaged over a ±6 h window, and reported on a 0.1 × 0.1o grid. Gridded wind speeds are reported every 6 h for each TC when there are overpasses available during that time interval. The files are output on a storm-by-storm basis. Quadrant-dependent TC 34-knot wind radii are estimated from the merged wind fields and included in the dataset. The performance of the merged wind speed product is validated against Hurricane Weather Research and Forecast model output and soil moisture active passive TC wind products, and the temporal and spatial sampling of the dataset is characterized. The dataset is found to exceed current temporal coverage capabilities compared to other existing TC wind speed remote sensing instruments, although limitations on high wind speed retrievals and variable spatial coverage are notable and can be attributed to weaknesses of the underlying global navigation satellite system-reflectometry modality.

In recent years, Global Navigation Satellite System reflectometry (GNSS-R) has been explored as a methodology for inland water body characterization. However, thorough characterization of the sensitivity and behavior of the GNSS-R signal to inland water bodies is still needed to progress this area of research. In this paper, we characterize the uncertainty associated with Cyclone Global Navigation Satellite System (CYGNSS) measurements on the determination of river width. The characterization study uses simulated data from a forward model that accurately simulates CYGNSS observations of mixed water/land scenes. The accuracy of the forward model is demonstrated by comparisons to actual observations of known water body shapes made at particular measurement geometries. Simulated CYGNSS data are generated over a range of synthetic scenes modeling a straight river subreach, and the results are analyzed to determine a predictive relationship between the peak SNR measured over the river subreaches and the river widths. An uncertainty analysis conducted using this predictive relationship indicates that, for simplistic river scenes, the SNR over the river is predictive of the river width to within +/−5 m. The presence of clutter (surrounding water bodies) within ~500 m of a river causes perturbations in the SNR measured over the river, which can render the river width retrievals unreliable. The results of this study indicate that, for isolated, straight rivers, GNSS-R data are able to measure river widths as narrow as 160 m with ~3% error.

All sensing systems have some inherent error. Often, these errors are systematic, and observations taken within a similar region of space and time can have correlated error structure. However, the data from these systems are frequently assumed to have completely independent and uncorrelated error. This work introduces a correlated error model for GNSS reflectometry (GNSS-R) using observations from NASA’s Cyclone Global Navigation Satellite System (CYGNSS). We validate our model against near-simultaneous observations between two CYGNSS satellites and double-difference our results with modeled observables to extract the correlated error structure due to the observing system itself. Our results are useful to catalog for future GNSS-R missions and can be applied to construct an error covariance matrix for weather data assimilation.

Freeze/Thaw (F/T) surface state detection and imaging has been demonstrated as a capability of NASA's Cyclone Global Navigation Satellite System mission. The objective of the research presented here is the application of an existing F/T retrieval algorithm to multiple target areas over several years in order to demonstrate the ability to provide long term trending of F/T behavior. Metrics are developed and evaluated to characterize F/T trends related to global warming. They include the annual fraction of time spent frozen and thawed as a function of location and year, and the earliest fall freeze and earliest spring thaw as a function of location and year. The results demonstrate the capability of Global Navigation Satellite Systems Reflectometry to provide long term trending of F/T behavior relevant to studies of climate change.

The precision of sea surface altimetry using bistatically reflected signals of the Global Navigation Satellite System (GNSS) is typically one to two orders of magnitude worse than dedicated radar altimeters. However, when the scattering is coherent, the electromagnetic phase of the carrier signal can be tracked, providing precise ranging measurements. Under grazing angle (GA) geometries, the conditions for coherent scattering are maximized, enabling carrier phase-delay altimetric techniques over sea waters. This work presents the first implementation of GA carrier phase sea surface altimetry using data acquired from a spaceborne platform (NASA Cyclone GNSS mission) and transmitted from both GPS and Galileo constellations. The altimetric results show that the measurement system precision is 3/4.1 cm (median/mean) at 20 Hz sampling, cm level at 1 Hz, comparable to dedicated radar altimeters. The combined precision, including systematic errors, is 16/20 cm (median/mean) precision at 50 ms integration (a few cm level at 1 Hz). The wind and wave requirements to enable coherent scattering at GA geometries appear to be below 6 m/s wind and 1.5 m significant wave height, although only 33% of tracks under these conditions present sufficient coherence. Given that this technique could be implemented by firmware updates of existing GNSS radio occultation missions, and given the large number of such missions, the study indicates that the resulting precision and spatio-temporal resolution would contribute to resolving some submesoscale ocean signals.

This article presents the methodology for an improved estimation of the sea surface wind speed measured by the cyclone global navigation satellite system (CYGNSS) constellation of satellites using significant wave height (SWH) information as external reference data. The methodology consists of a correcting 2D look-up table (LUT) with inputs: (1) the CYGNSS wind speed given by the geophysical model function (GMF); and (2) the collocated reference SWH given by the WW3 model, which is forced by winds from the European Centre for Medium-Range Weather Forecasts (ECMWF) organization. In particular, the analyzed CYGNSS wind speeds are the fully developed seas (FDS) obtained with the GMF 3.0, and the forcing winds are the ECMWF forecast winds. Results show an increase in sensitivity to large winds speeds and an overall reduction in the root mean square difference (RMSD) with respect to the ECMWF winds from 2.05 m/s to 1.74 m/s. The possible influence of the ECWMF winds on the corrected winds (due to their use in the WW3 model) is analyzed by considering the correlation between: (1) the difference between the ECMWF winds and those from another reference; and (2) the difference between the corrected CYGNSS winds and those from the same reference. Results using ASCAT, WindSat, Jason, and AltiKa as references show no significant influence.