From 2002 to 2012, wave mode of Envisat Advanced Synthetic Aperture Radar (ASAR) collects 7,500,000 SAR vignettes over the global open ocean. These vignettes are acquired every 200 km along track with repeat cycle of 35 days. Footprint of each vignette is 10✕7 km with a nominal spatial resolution of 9✕6 m. This massive ocean SAR dataset provides directional ocean wave spectra. Based on which, a wide range of applications have been carried out, for example, swell tracking, assimilation in the numerical wave model and global wave climate. Yet, all the previous studies focused on swell wave parameters, while statistical characteristics of ocean waves at discrete wavelength are rarely exploited. In this study, we take advantage of the 10-year database to investigate ocean waves through a new radar parameter, called MACS [1]. It is defined by filtering the SAR image cross-spectra around range-traveling waves of given wavelength. This parameter allows us to isolate intermediate (~ 40 m) to large (swell) wavelengths (~ 400 m).
For a given wavelength, the MACS increases with wind speed and reaches saturation when the wind speed is higher than a threshold. The saturation threshold increases with wavelength. This is expected since shorter waves reach equilibrium state at lower wind speed, while longer waves reach at higher wind speed. More encouragingly, global patterns of MACS at various wavelengths are distinct. For intermediate waves at 62 m, the features are representatives of global winds. While the global pattern increasingly differs for MACS with longer wavelengths. Besides, the MACS variation with wavenumber at a given location can help to characterize the dominance of either local generated wind waves or distant swell. The regions on the global ocean that are primarily dominated by ocean swell and the regions that are mostly wind sea can then be accordingly identified. One notable advantage of this method is that the derivation of swell dominance is entirely self-sufficient, without a priori information.
To summarize, the parameter MACS offers a new strategy to enhance the utility of global SAR observations. On one hand, it can help to examine the developing of ocean wave at a given wavelength with wind speed. Also, we are allowed to efficiently quantify regional and seasonal wind wave and swell properties (periodicities and space-time variabilities).
[1]Li. et al, A new ocean SAR spectral parameter: definition and global analysis using Sentinel-1 SAR measurements, submitted, 2018.

The theory of radar imaging of internal waves developed by Alpers (1985) is based on weak hydrodynamic interaction theory and Bragg scattering theory. It is able to describe quite well the observed radar signatures of internal waves at L-band, but underestimates the strength of the modulation (modulation depth) at C-and X-band. Although this theory has also been applied for describing radar signatures of internal solitary waves (ISWs) at X-band (Romeiser and Graber, 2015), one has to use unrealistic values of the relaxation rate, which is not supported by theory. The physical mechanism responsible for the enhanced modulation at C- and X-band is scattering at breaking surface wave, which has not been included in the conventional imaging theory. It is well known that ISWs often manifest themselves on the sea surface as bands of very strong roughness, which include breaking waves with wavelengths of the order of meters. Such bands are observed even when there is no wind and the sea is glassy. Obviously, the generation of roughness bands associated with breaking surface waves cannot be explained by weak hydrodynamic interaction theory, where the variable surface current associated with ISWs modulates “gently” the Bragg waves responsible for the radar backscattering, Furthermore, the radar backscattering at C-band and X-band from these roughness bands cannot be explained by Bragg scattering theory alone; it requires taking into account also radar backscattering from breaking waves, which, in contrast to Bragg scattering, is non-polarized scattering (Kudryavtsev et al., 2005). In this paper we analyze the relative contribution of scattering from breaking surface waves to the total radar backscattering (Bragg and non-polarized scattering). To this end, we have analyzed TerraSAR-X images acquired at HH and VV polarizations over the Strait of Gibraltar and Sentinel-1A images acquired at VV and VH polarizations over the South China Sea and the Alboran Sea at low wind speeds. We show that backscattering from the leading solitary wave in an internal wave packet is dominated by non-polarized scattering, while backscattering from the rear of the wave packet is a mixture of Bragg and non-polarized scattering. Furthermore, we have analyzed a TerraSAR-X image acquired at HH polarization in the Stripmap Mode with a resolution of 3 m over the Amazone Shelf showing sea surface signatures of small-scale strong ISWs propagating upstream. Due the narrow roughness bands and the high resolution of this SAR image, we were able to determine the azimuthal displacement of “sea spikes” (elements of very strongly enhanced radar backscatter) due to the motion of these scatter elements. We have estimated the velocity of these scatter elements associated with wave breaking from the azimuthal shifts in the SAR image and found that they result from breaking of waves with wavelengths of the order of 1 m.

CYGNSS was launched on 15 December 2016, and it consists of eight microsatellites equally spaced on the same orbital plane, that are providing measurements of wind speeds in tropical cyclones, and in particular in their inner core. It does so through the reception of signals from the Global Positioning System (GPS) satellites reflected off the ocean surface, a technique known as GNSS-Reflectometry or GNSS-R. These reflections are well known to respond to ocean surface roughness, from which the wind speed can be retrieved. As such, CYGNSS outputs winds, and a wind speed product is being produced from CYGNSS data and provided to the user community via the NASA PO.DAAC website. However, the ocean surface roughness, to which GNSS-R signals are primarily sensitive, is determined by both the wind blowing on the scattering surface and the waves that populate such surface [2]. Waves or sea state are usually described through a parameter known as Significant Wave Height or SWH. SWH is particularly useful for weather forecasts, operational oceanography and also climate studies. SWH is measured on a global scale by satellite radar altimeters, with sufficient level of accuracy, but the large spacing between tracks, and the large temporal repeat time still make the satellite observations of SWH suboptimal to capture the full evolution of the wave field. Here we propose a methodology to estimate SWH from CYGNSS data. A CYGNSS-derived SWH has the fundamental advantage of an increased space-time sampling that cannot be achieved with any other existing satellite, and the provision of SWH measurements even in high precipitation conditions, as GPS signals are largely insensitive to rain.
The methodology to derive SWH from CYGNSS stems from the work presented in [3], where a wind speed geophysical model function was derived from altimetry data. The approach relies on the use of an observable derived from the data, and on the assumption that the observable varies monotonically and uniquely with the unknown geophysical parameter to be estimated, and it requires knowledge of the separate, rather than joint, distribution functions of the observable and of the geophysical parameter. In this case, we estimate SWH using: a) the Delay/Doppler Map Average (DDMA) observable, derived from CYGNSS Delay/Doppler Maps and routinely provided in the CYGNSS data files, and b) ground truth SWH data, collocated with the CYGNSS Acquisitions, and output by the WAVEWATCH III model. A Geophysical Model Function to estimate SWH from DDMA is derived, using the Cumulative Distribution Function (CDF) of the observable, and the inverse CDF for SWH, following the same approach as in [3]. The SWH retrievals obtained using this approach are in good agreement with SWH measurements from altimetry and in-situ instruments, as well as with wave model outputs. A discussion on future improvements of the retrieval methodology will be carried out, as well as on the possibility to provide SWH as a further product originating from CYGNSS.




REFERENCES

[1] Ruf, C., R. Atlas, P. Chang, M. Clarizia, J. Garrison, S. Gleason, S. Katzberg, Z. Jelenak, J. Johnson, S. Majumdar, A. O’Brien, D. Posselt, A. Ridley, R. Rose, V. Zavorotny (2015), “New Ocean Winds Satellite Mission to Probe Hurricanes and Tropical Convection,” Bulletin of American Meteorological Society, doi:10.1175/BAMS-D-14- 00218.1.

[2] Clarizia, M. P., & Ruf, C. S. (2017). Bayesian Wind Speed Estimation Conditioned on Significant Wave Height for GNSS-R Ocean Observations. Journal of Atmospheric and Oceanic Technology, v. 34, pp. 1193-1202.

[3] Freilich, M. and P. Challenor, ”A New Approch for determining Fully Empirical Altimeter Wind Speed Model Functions”, Journal of Geophysical Research, Vol. 99, No. C12, December 1994.

CFOSAT is a new satellite mission launched on October 29th, 2018. It is developed since 2006 thanks to a fruitful Chinese-French cooperation involving the French Space Agency CNES, the Chinese agencies CNSA and NSOAS, and scientific support from various research laboratories in France and China.
CFOSAT will provide for the first time colocated and global observations on the surface ocean wind vector, and ocean wave spectral parameters (wave spectra and associated parameters) from a combination of two Ku-Band radar instruments (SWIM and SCAT) working in altimeter and wave spectrometer modes (SWIM) and wind scatterometer mode (SCAT).
The CFOSAT products will offer the opportunity to develop new studies from global observations, such as joint analysis of space evolution of wind and waves, detailed analysis of the spectral properties of the wave field (in particular its directionality) and relationship between long waves (measured by SWIM) and short wave properties (indirect information from the normalized radar cross-section). The data will also be used in data assimilation schemes of wave and atmospheric numerical models (through assimilation) in order to improve wave and atmospheric forecast. The information on peak wavenumber, wave direction, directional spread for several wave spectrum partitions, will also be of great interest to study wave/current interactions and to feed the coastal studies with sea state boundary conditions.
During this conference, less than six months after the launch, the first results on the products generated in near real-time by the CNES mission center will be presented. First the data processing principles of the SWIM data and the main products will be recalled. Then, a first assessment of the normalized radar cross-section at near-nadir incidence and of wave products (significant wave height, dominant direction and dominant wave length, for the whole spectra and separated by wave partitions) will be presented. This first assessment of the SWIM products relies on the work of a CAL/VAL team which carries out comparisons between CFOSAT data, satellite altimeter products, wave forecast model products (ECWAM, MFWAM, WW3), in situ data, and other satellite data (such a Sentinel-1 wave mode data).

High-resolution satellite derived sea surface wind data, such as those from scatterometers, are increasingly required for operational monitoring and forecasting of the ocean. We present an improved version of ERA*, an ocean forcing product which keeps the time and space coverage of atmospheric model fields, but adds the accurately observed local mean and variability of wind scatterometers, to make these datasets suitable for, among others, high-resolution ocean model forcing.

Recent attempts of combining scatterometer data and numerical weather prediction (NWP) outputs, i.e., blended ocean forcing products, allows for an increased temporal resolution (e.g., daily) but generally only resolves NWP spatial scales of about 100-150 km. Therefore, information on the wind-current interaction, the diurnal wind cycle and the wind variability in moist convection areas is lost in such products. Moreover, known systematic NWP model (parameterization) errors are in fact propagated at times and locations where no scatterometer winds are available. The alternative, direct forcing from NWP results in even more extensive physical drawbacks. We propose to maintain the increased temporal coverage in a gridded wind and stress product (ERA*), but also to maintain the most beneficial physical qualities of the scatterometer winds, i.e., 25-km spatial resolution, wind-current interaction, variability due to moist convection, etc., and, at the same time correct the large-scale NWP parameterization and dynamical errors. Additionally, we correct these winds for the effects of atmospheric stability and mass density, using stress equivalent 10 m winds, U10S.

A scatterometer-based correction, using accurate, unbiased, high spatial resolution ocean vector winds from several scatterometers, i.e., the Advanced Scatterometers (ASCATs) on board Metop satellites and the OSCAT scatterometer onboard Oceansat-2, is proposed as a new angle to tackle this problem, i.e., to reduce NWP local wind biases accounting for satellite sampling characteristics (note that the scatterometer temporal sampling is latitudinal dependent and rather poor in the tropics). The correction consists of geolocated (i.e., at every ocean grid point), temporally - averaged differences between the scatterometer and the collocated NWP reanalysis (ERA-interim) U10S. Since ERA local biases are relatively persistent over time but such persistence is regionally dependent (e.g., such persistence is longer in the tropics than in the extratropics), we test different configurations of ERA*, i.e., with different temporal windows (from 1 to 5 days) and varying number of scatterometers (i.e., different combinations of the above mentioned scatterometer systems) in order to find the best quality forcing product.

The new ERA* gridded ocean forcing product is validated against independent scatterometer data, i.e., the 25-km HSCAT (onboard HY-2A) wind product. HSCAT is a good wind reference since the orbit pass (6am/6pm) is very different from that of ASCAT-A/B (9:30am/9:30pm) and OSCAT (12:00am/12:00pm). Globally, there is a reduction of the vector root-mean-square error in ERA* w.r.t. ERA interim of more than 10%. Overall, the 1-day temporal window and the multiple scatterometer correction lead to the best quality ERA* global wind product, although such wind quality depends on the region. In particular, this ERA* product outperforms ERA in the tropics, where moist convection and wind-current interaction are not well resolved by the latter. Scores of the bias and standard deviation error support the above statements. Moreover, in contrast with ERA, ERA* is able to resolve eddy scales similar to those resolved by scatterometers, as shown by wind spectral analysis.

Swells from strong storms can spread over very long distances. Ocean currents alter this propagation, with the possible formation of constructive or destructive interference. This effect, still neglected in current models of atmospheric, oceanic, and even wave prediction, is often traced in current measurements, altimetry or even scatterometry / radiometry at medium and high resolution.
Large-scale currents are indirectly measured by satellite. Since the small-scale currents are generally unknown, we propose to consider them as random in wave dynamics simulations. Specifically, the statistical spatial structure of these currents is inferred from large-scale currents through self-similar assumptions. The temporal correlations of the small-scale currents is neglected due to the short-time wave-current interaction. The dispersion ratio is modified and becomes stochastic. From there, we can derive and simulate the random dynamics of wave group the rays. Analytic and semi-analytic solutions have also been derived for simple – though realistic – cases.
Our results not only improve wave simulation capabilities, but also bring new insights about the large wave’s developments at small scales and the wave-current effects on satellite measurements. At longer term, those type of random dynamics will bring new data assimilation procedures for joint wave-current estimations from space.