The assessment and validation of the quality of satellite scatterometer vector winds is challenging under increased subcell wind variability conditions, since reference wind sources such as buoy winds or model output represent very different spatial scales from those resolved by scatterometers (i.e., increased representativeness error). In this paper, moored buoy wind time series are used to assess the correlation between subcell wind variability and several Advanced Scatterometer (ASCAT)‐derived parameters, such as the wind‐inversion residual, the backscatter measurement variability factor, and the singularity exponents derived from an image processing technique, called singularity analysis. It is proven that all three ASCAT parameters are sensitive to the subcell wind variability and complementary in flagging the most variable winds, which is useful for further application. A triple collocation (TC) analysis of ASCAT, buoy, and the European Centre for Medium‐range Weather Forecasting (ECMWF) model output is then performed to assess the quality of each wind data source under different variability conditions. A novel approach is used to compute the representativeness errors, a key ingredient for the TC analysis. The experimental results show that the estimated errors of each wind source increase as the subcell wind variability increases. When temporally averaged buoy winds are used instead of 10 min buoy winds, the TC analysis results in smaller buoy wind errors (notably at increased wind variability conditions) while ASCAT and ECMWF errors do not significantly change, further validating the proposed TC approach. It is concluded that at 25 km resolution, ASCAT provides the best quality winds in general.

In this paper, anomalous spatial gradients are investigated by an image processing method, known as singularity analysis, which is proposed to complement the current Advanced Scatterometer (ASCAT) quality control (QC) by using the singularity exponent (SE). The quality of ASCAT winds is known to be generally degraded, with increasing values of the inversion residual or maximum-likelihood estimator (MLE). In the current ASCAT Wind Data Processor (AWDP), an MLE-based QC is adopted to filter poor-quality winds, which has proven to be effective in screening artifacts in the ASCAT winds, associated with increased subcell wind variability and other phenomena such as confused sea state. However, some poorly verifying winds, which appear in areas with moist convection, are not screened by the operational QC. The extension of the QC procedure with SEs is investigated, based on a comprehensive analysis of quality-sensitive parameters, using the European Centre for Medium-range Weather Forecasts (ECMWF) model winds, the Tropical Rainfall Measuring Mission's (TRMM) Microwave Imager (TMI) rain data, and tropical buoy wind and precipitation data as reference, taking into account their spatial and temporal representation. The validation results show that the proposed method indeed effectively removes ASCAT winds in spatially variable conditions. It filters three times as many wind vectors as the operational QC, while preserving verification statistics with local buoys. We find that not the rain itself, but the extreme local wind variability associated with rain appears to generally decrease the consistency between ASCAT, buoy, and ECMWF winds.

The China-France Oceanography Satellite (CFOSAT) to be launched in October 2018 will carry two innovative payloads, i.e., the surface wave investigation and monitoring instrument and the rotating fan-beam scatterometer [CFOSAT scatterometer (CFOSCAT)]. Both instruments, operated in Ku-band microwave frequency, are dedicated to the measurement of sea surface wave spectra and wind vectors, respectively. This paper provides an overview of the system definition and characteristics of the CFOSCAT instrument. A prelaunch analysis is carried out to estimate the scatterometer backscatter and wind quality based on the developed CFOSCAT simulator prototype. The overall simulation includes two parts: first, a forward model is developed to simulate the ocean backscatter signals, accounting for both instrument and geophysical noise. Second, a wind inversion processor is used to retrieve wind vectors from the outputs of the forward model. The benefits and challenges of the novel observing geometries are addressed in terms of the CFOSCAT wind retrieval. The simulations show that the backscatter accuracy and the retrieved wind quality of CFOSCAT are quite promising and meet the CFOSAT mission requirements.

Accurate high and extreme sea surface wind observations are essential for the meteorological, ocean, and climate applications. To properly assess and calibrate the current and future satellite-derived extreme winds, including those from the C-band scatterometers, building a consolidated high and extreme wind reference data set is crucial. In this work, a new approach is presented to assess the consistency between moored buoys and stepped-frequency microwave radiometer (SFMR)-derived winds. To overcome the absence of abundant direct collocations between these two data sets, the reprocessed Advanced Scatterometer (ASCAT)-A winds at the 12.5-km resolution, from 2009 to 2017, have been used to perform an indirect SFMR/buoy winds’ intercomparison. The ASCAT/SFMR analysis reveals an ASCAT wind underestimation for winds of above 15 m/s. SFMR measurements are calibrated using GPS drop-wind-sondes (dropsondes) data and averaged along-track to represent ASCAT spatially. On the other hand, ASCAT and buoy winds are in good agreement up to 25 m/s. The buoy high-wind quality has been confirmed using a triple collocation approach. Comparing these results, both SFMR and buoy winds appear to be highly correlated with ASCAT at the high-wind regime; however, they show a very different wind speed scaling. An SFMR-based recalibration of ASCAT winds is proposed, the so-called ASCAT dropsonde-scale winds, for use by the extreme wind operational community. However, further work is required to reconcile dropsonde (thus, SFMR) and buoy wind measurements under extreme wind conditions.

The new Ku-band scatterometer (HSCAT-B) onboard the HY-2B satellite was launched on October 25, 2018, and soon after the C-band scatterometer (Advanced Scatterometer (ASCAT)-C) onboard the MetOp-C satellite was launched on November 6, 2018. This article aims to validate the new sea surface wind products from them, and also to summarize the common issues in current scatterometer wind products. Thus, other scatterometer data are also used for comparisons, including the C-band MetOp-B/ASCAT, and Ku-band SCATSAT-1/OSCAT2 and HY-2A/SCAT winds. In this study, the C-band and Ku-band scatterometer wind products were each reproduced using the same procedures, in terms of backscatter calibration, wind retrieval, and quality control. The scatterometer winds are compared to the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 winds or buoy winds, and the results show that the quality of ASCAT-C winds is almost the same as the well-known ASCAT-B; the HSCAT-B winds show quite good quality and similar validating statistics as ASCAT winds. Noticeable wind-speed-dependent biases are found in all Ku-band scatterometer winds, which suggests that refinements are needed for the NSCAT-4 geophysical model function, especially in terms of wind speed dependence for all incidence angles.