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The Cyclone Global Navigation Satellite System (CYGNSS) mission has generated several new ocean surface data products. Because of its high overpass rate and ability to measure surface wind speeds through weather, these data products are typically higher temporal resolution than traditional satellite tropical cyclone (TC) data products. However, the nature of the Global Navigation Satellite Systems reflectometry (GNSS-R) signal, which is essentially a single pixelwide measurement along the CYGNSS specular point (SP) track on the ground, necessitates aggregating the wind speed data over a period of time to accurately characterize TCs in terms of intensity and structure. The standard CYGNSS level 3 (L3) wind products are averaged over 1 h, which typically is not sufficient to produce an accurate characterization. A new L3 storm-centric gridded (SCG) wind speed product is presented here. It has been developed to improve upon the standard L3 algorithm for the purpose of providing better characterization of TC wind fields over their storm life cycle. This paper describes the L3 SCG algorithm, provides examples of L3 SCG wind fields, and discusses the potential uses and limitations of the new data product.

Global Navigation Satellite System Reflectometry (GNSS-R)-based wind retrieval techniques use the global positioning system (GPS) signals scattered from the ocean surface in the forward direction, and can potentially work in all weather conditions. An overview of recent progress made in the Cyclone Global Navigation Satellite System (CYGNSS) level-2 surface wind products is given. To this end, four publicly released CYGNSS surface wind products—Science Data Record (SDR) v2.1, SDR v3.0, Climate Data Record (CDR) v1.1, and science wind speed product NOAA v1.1—are validated quantitatively against high-quality data from tropical buoy arrays. The latest released CYGNSS wind products (e.g., CDR v1.1, SDR v3.0, NOAA v1.1), as compared with these tropical buoy data, significantly outperform the SDR v2.1. Moreover, the uncertainty among these products is found to be less than 2 m s−1 root-mean-squared difference, meeting the NASA science mission level-1 uncertainty requirement for wind speeds below 20 m s−1. The quality of the CYGNSS wind is further assessed under different precipitation conditions in low winds, and in large-scale convective regions. Results show that the presence of rain appears to cause a slightly positive wind speed bias in all CYGNSS data. Nonetheless, the outcomes are encouraging for the recently released CYGNSS wind products in general, and for CYGNSS data in regions with precipitating deep convection. The overall comparison indicates a significant improvement in wind speed quality and sample size when going from the older version to any of the newer datasets.

Ocean microplastic concentrations are known to vary significantly by location, with especially high levels in the North Atlantic and North Pacific gyres. Most direct measurements come from plankton net trawling made in these regions; concentrations in other regions have been estimated by microplastic transport models that depend on large-scale ocean circulation patterns. However, global measurements of microplastic distribution and its temporal variability are lacking. A new method is presented for detecting and imaging the global distribution of ocean microplastics from space. The method uses spaceborne bistatic radar measurements of ocean surface roughness and relies on an assumed reduction in responsiveness to wind-driven roughening caused by surfactants that act as tracers for microplastics near the surface. Annual mean microplastic distributions estimated by the radars are generally consistent with model predictions. The spaceborne observations are also able to detect temporal changes that are not resolved by the models. For example, seasonal dependencies are observed at mid-latitudes in both Northern and Southern Hemispheres, with lower concentrations noted in the winter months. Time lapse images at finer spatial and temporal scales reveal episodic bursts of microplastic tracers in the outflow from major river discharges into the sea. This new method will provide better monitoring of ocean microplastics and will support future model development and validation.

Freeze/Thaw (F/T) surface state retrieval is important to further understand hydrological patterns and climate change. This paper investigates the use of Earth-reflected Global Positioning System (GPS) L-band signals as collected by the National Aeronautics and Space Administration NASA’s CYclone Global Navigation Satellite System (CYGNSS) mission for F/T surface state retrieval over a target area in South America, covering the Andes Mountains and the Argentinian Pampas. In the study, CYGNSS’s responsiveness to changes in surface permittivity is leveraged to detect transitions of F/T surface state, at an improved spatio-temporal sampling as compared to traditional Remote Sensing missions. A Seasonal-Threshold Algorithm (STA) is developed and validated using surface temperature data as provided by the European Centre for Medium-Range Weather Forecast (ECMWF) ERA5-Land numerical reanalysis model. Then, the monthly evolution of CYGNSS-derived F/T surface state maps is evaluated and an inter-comparison with the Soil Moisture Active Passive (SMAP) F/T data product is performed.

Global Navigation Satellite System (GNSS) Reflectometry uses reflected GNSS signals for Earth remote sensing applications. Absolute calibration of a Delay Doppler Map (DDM) requires an accurate estimate of the effective isotropic radiated power (EIRP) of the GNSS transmitter, e.g., Global Positioning System (GPS). However, variable transmit power by numerous Block II Follow-on (IIF) and II Replenishment-Modernized (IIR-M) GPS space vehicles has been observed due to their flex power mode. Nonuniformity in the GPS antenna gain patterns further complicates EIRP estimation. A dynamic calibration approach is developed to 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 it into the calibration of normalized bistatic radar cross section (NBRCS). Error analyses using Monte Carlo simulations and a root sum of squares (RSS) approach show that the resulting error in NBRCS is about 0.32 dB. 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 NBRCS and geophysical data products.

A comparison of three different electromagnetic scattering models for land surface delay-Doppler maps (DDMs) obtained from global navigation satellite system reflectometry (GNSS-R) along a Cyclone Global Navigation Satellite System (CYGNSS) track in the San Luis Valley, Colorado, USA, is presented. The three models are the analytical Kirchhoff solutions (AKS), the Soil And VEgetation Reflection Simulator (SAVERS), and the improved geometrical optics with topography (IGOT). Common inputs to the three models were defined by using field samples of soil moisture and texture, soil surface roughness measurements, and a digital elevation model (DEM). The resulting peak reflectivity profiles of the models and the CYGNSS data all had a dynamic range of 10 dB along the selected track, mainly due to the influence of topography. The reflectivities obtained from all three models agreed with one another to within 2.4 dB along the full length of the track. The models also showed general agreement with the corresponding CYGNSS data, although the modeled profiles were higher and smoother. Additional characterization of fine-scale surface roughness is identified as an area for future work to improve model fidelity. An intercomparison of DDM structure for three selected acquisitions is also provided.

This paper presents a detailed examination of fluctuating input noise power levels on the analog to digital convertor (ADC) sampling hardware of the NASA CYGNSS instruments and the associated impacts on the level 1 NBRCS estimation performance. The impact of external noise variations on both the CYGNSS science and navigation channels are quantified with respect to how the fluctuating received power impacts the low level ADC sampling distribution and subsequent NBRCS estimation. This work demonstrates that there are clear quantifiable geo-spatially dependent noise variations linked to GNSS space based augmentation systems (notably QZSS and WAAS) and that these additional noise sources significantly alter the digital sampling distribution of the CYGNSS instruments, actively degrading the level 1 NBRCS estimation if not corrected. A derivation of the theoretical correction for both the science and navigation channel ADC sampling variations is presented which is later tuned based on empirical performance metrics in an effort to minimize the induced calibration errors. The impacts of the enhancements outlined in this work on CYGNSS level 1 calibration are evaluated using 1 year of observations before and after the digital sampling corrections, using model ECMWF wind and Wavewatch III MSS surface validation data sets.

This letter investigates the sensitivity of the ocean surface bistatic scattering cross section measured by the Cyclone Global Navigation Satellite System (CYGNSS) to wind direction using the kurtosis of the delay-Doppler map (DDM) samples within a given area. The azimuthal dependence of the kurtosis is modeled by a cosine expansion of the relative wind direction, as is done in scatterometry for the radar cross section. The harmonic coefficients of the model depend on wind speed and incidence angle. Results show a coefficient of determination (R²) between 0.6 and 0.9 for wind speeds between 4 and 10 m/s and negligible sensitivity outside this range. This study opens the door to the potential of using CYGNSS data for wind direction estimation.