In the framework of the MAXSS project, a multi-mission (MM) wind product under tropical cyclone conditions has been generated for the period 2010-2020, i.e., a synergistic product that combines the European Center for Medium-range Weather Forecast fifth reanalysis (ERA5) output with several scatterometer and radiometer wind data adjusted to the wind scale of hurricane hunter in situ observations. The errors of the satellite and MM wind products have been estimated with triple collocation analysis, while the different spatial representation of the datasets (i.e., representativeness error r2) is accounted for and computed through spatial variance analysis. The error analysis shows that C-band scatterometers have the lowest standard deviation errors (0.9 m/s) compared to those of the Ku-band scatterometers (1.4-2.1 m/s), while radiometers have the largest errors (2.0-2.9 m/s). Finally, the analysis reveals that the MM wind product has a lower error (1.6 m/s) compared to ERA5 (2.6 m/s) under tropical cyclone conditions.

This study is to discuss the speckle statistics in terms of sea surface roughness and, specifically, in terms of near sea surface wind speed. A data set of Sentinel-1 Synthetic Aperture Radar scenes, acquired under different wind regimes, is processed using normalized intensity moments.

This study is to present the lesson learned during the activities related to the Italian Space Agency (ASI) funded APPLICAVEMARS project which aims at estimating sea surface wind field from L-, C- and X-band Synthetic Aperture Radar (SAR) imagery. The paper focuses on the X-band results and it describes a new approach to estimate ancillary wind direction info from the SAR image itself using neural networks.

This paper describes some preliminary steps to improve the coastal winds retrieved from the Seawinds scatterometer on-board the QuikSCAT satellite platform. In particular, it describes a method for estimating the slice Normalized Radar Cross Section ($\sigma_{0}$) noise. Moreover, it shows a simple method for selecting the best-suited$\sigma_{0}$domain to implement a Land Contribution Ratio (LCR) based$\sigma_{0}$correction scheme. The preliminary results suggest that there are some non-negligible differences between the open sea and the “every kind of surface” noise characteristics, even if such differences are not reported in the QuikSCAT files. The intra-egg$\sigma_{0}$biases may amount to approximately ±0.6 dB for H-Pol acquisitions and half that for V-Pol, but the impact on the noise estimation amounts to less than 2%. Finally, the LCR-based$\sigma_{0}$correction scheme is now being tested and developed in the linear domain.

In this paper, preliminary results obtained within the framework of the Italian Space Agency-funded APPLICAVEMARS project are presented. The project aims at estimating sea surface wind field from L-, C- and X-band Synthetic Aperture Radar (SAR) imagery. In particular, the key element is the adaptation/improvement of geophysical model functions to transform the microwave reflectivity map into an added-value product, i.e., a wind map characterized by a spatial resolution much finer than the one obtainable from scatterometer measurements. First experimental results related to C-band Sentinel-1 SAR imagery are presented.

The ERA* stress-equivalent wind (UI0S) is a correction of the ECMWF Fifth Reanalysis (ERA5) output by means of geo-Iocated scatterometer-ERA5 differences over a 3-day temporal window, in which the combined sampling of the Advanced Scatterometers on board the Metop satellite series (ASCAT-A, -B, and -C) and the SCATSat-l scatterometer (OSCAT2) have been used, for the year 2019. ERA*can correct for local, persistent NWP model output errors associated with physical processes that are absent or misrepresented by the model, e.g., strong current effects (such as western boundary current systems, highly stationary), wind effects associated with the ocean mesoscales (sea surface temperature), coastal effects (land see breezes, katabatic winds), Planetary Boundary Layer parameterization errors, and large-scale circulation effects, e.g., at the inter-tropical convergence zone.

This paper presents the implementation of the Land Contribution Ratio (LCR) methodology for the pencil-beam scat-terometer QuikSCAT, with the aim of improving the coastal sampling of the retrieved winds. This methodology is presented with two different models of the Spatial Response Function (SRF): the analytical model and the parameterized one, which is based on a pre-computed Look-up- Table (LUT) of SRFs provided by the Brigham Young University (BYU). Furthermore, a method to characterize the slice$\sigma_{0}$noise$(K_{p})$is presented and compared to the noise information provided in the full resolution QuikSCAT files. The preliminary results show that despite the overall consistency between the two SRF models, their discrepancies may induce LCR differences up to few percent. Furthermore, the$K_{p}$estimated by means of the slice Normalized Radar Cross Section$(\sigma_{0})$is different from the$K_{p}$provided in the files, while such differencies are larger for certain slices and wind conditions. Such discrepancies can impact the wind field retrievals and, as such, should be further investigated.

Delay-Doppler maps (DDMs) are generally the lowest level of calibrated observables produced from global navigation satellite system reflectometry (GNSS-R). A forward model is presented to relate the DDM, in units of absolute power at the receiver, to the ocean surface wind field. This model and the related Jacobian are designed for use in assimilating DDM observables into weather forecast models. Given that the forward model represents a full set of DDM measurements, direct assimilation of this lower level data product is expected to be more effective than using individual specular-point wind speed retrievals. The forward model is assessed by comparing DDMs computed from hurricane weather research and forecasting (HWRF) model winds against measured DDMs from the Cyclone Global Navigation Satellite System (CYGNSS) Level 1a data. Quality controls are proposed as a result of observed discrepancies due to the effect of swell, power calibration bias, inaccurate specular point position, and model representativeness error. DDM assimilation is demonstrated using a variational analysis method (VAM) applied to three cases from June 2017, specifically selected due to the large deviation between scatterometer winds and European Centre for Medium-Range Weather Forecasts (ECMWF) predictions. DDM assimilation reduced the root-mean-square error (RMSE) by 15%, 28%, and 48%, respectively, in each of the three examples.

Global Navigation Satellite System Reflectometry (GNSS-R) observations from the Cyclone Global Navigation Satellite System (CYGNSS) mission are expected to improve numerical weather prediction (NWP) models. Level 1 GNSS-R observables, delay-Doppler maps (DDMs), contain information that is lost in producing the Level 2 wind speed retrievals at the specular point. DDMs could, therefore, prove to be a better observable for data assimilation. In this study, assimilation of GNSS-R DDMs into both global and regional NWP models is demonstrated. In the global case, DDM assimilation shows improvements on the European Centre for Medium-Range Weather Forecasts (ECMWF) background for one month of data. In the regional case, DDM assimilation shows its impact on the hurricane structure and intensity. Results using a two-dimensional variation analysis method (VAM) are presented. A plan for an Observation System Experiment (OSE) experiment is proposed.

This paper presents an assessment of the effects of specular point (SP) estimation inaccuracies on the Signal to Noise Ratio peak (SNRPEAK) used for ocean wind speed retrievals from TechDemoSat-1 (TDS-1) Delay Doppler Maps (DDMs). Results show that the more inaccurate the estimated Doppler frequency at the SP, the lower the intensity of the SNRPEAK. Differences may be up to 2 dB.

In this paper, the synthetic aperture radar (SAR) azimuth cutoff method is thoroughly revised and a new and general implementation is proposed. The key roles of the pixel spacing, the size of the image box, and the texture of the SAR scene are analyzed and optimized in terms of azimuth cutoff (λc) estimation. The reliability of the λcestimation is analyzed by measuring the distance between the measured and fitted autocorrelation functions. This analysis shows that it is of paramount importance to filter unfeasible/unreliable λcvalues. To identify those values in an objective way, a criterion that is based on the χ2 test performed over a large data set of Sentinel1 SAR imagery is defined and proven to be effective. The new robust implementation of the λcestimation at about 1-km grid spacing is then used to produce averaged λcat about 10-km grid spacing. The performance of the new estimation procedure, analyzed using a λc-to-wind-speed forward model, is shown to provide improved wind speed retrievals, with a root-mean-square error of 1.8-2 m/s when verified against independent numerical weather prediction model output and scatterometer winds.

A quality control scheme for TechDemoSat 1 (TDS-1) and Cyclone Global Navigation Satellite System (GNSS) delay-Doppler maps (DDMs) is presented and the results of its application to a data set of more than 700 000 DDMs are discussed. This scheme is proven to be effective for such purpose and its output indices can be successfully used as quality indicators of the DDM. This paper shows that most of the TDS-1 DDMs are affected by some distortions that are attributable to an insufficiently accurate estimation of the specular point location. The errors, moreover, can severely alter the symmetry of the isodelay lines with respect to the iso-Doppler lines leading to an asymmetry in the arrival time of the waveforms. Furthermore, these errors may affect the convolution of the GNSS reflected signal with the Woodward ambiguity function, leading to an unwanted redistribution of the incoming echo energy among the DDM bins. Such distortions may, in turn, affect the accuracy of the wind field retrieval using either the stare processing approach or the more consolidated methods of inverting a Geophysical Model Function based on the DDM peak and/or leading edge slope.

In this study, the azimuth cut-off approach, which is typically adopted to estimate wind speed from Synthetic Aperture Radar (SAR) imagery collected under nominal wind conditions, is discussed with respect to high wind regime cases. First, the key roles played by the pixel spacing, the size of the boxes used to partition the SAR imagery and the image texture (homogeneity) are discussed in terms of their effects on the azimuth cut-off (λc) estimation. Then, the reliability of the λc estimation is analyzed by measuring the distance between the measured and fitted autocorrelation functions (ACFs). This analysis shows that it is of paramount importance to filter unfeasible/unreliable λc values. To identify those values in an objective way a criterion is proposed that is based on the χ2 test performed over a large dataset of Sentinel-l SAR imagery. The effectiveness of the χ2 test is verified by correlating the accepted estimates against auxiliary significant wave height data.

The empirical dependence of the azimuth wavelength cut-off on the significant wave height and on the wind speed have been studied. The azimuth cut-off is estimated on the fitting of a Gaussian function to the azimuth autocorrelation function of the radar cross section. The feasibility of estimating the significant wave height and/or the wind speed has been investigated as well. We use SAR images acquired by the European Sentinel 1 from the beginning of November 2014 to the end of April 2015 co-located with the scatterometer winds acquired by the Chinese sensor HSCAT and the significant wave height from ECMWF forecasts. The correlation between the azimuth cut-off and the significant wave height is rather strong. A linear geophysical model function is fitted in order to estimate it. The dependence on the wind speed is secondary and becomes remarkable only when the sea state is fully developed. A significant wave height retrieval exercise is proposed and the results are compared with the buoy measurements of the National Data Buoy Center (NDBC) network.