Ku-band scatterometers are sensitive to rain effects due to their cm-scale radar wavelength. The NSCAT-4DS geophysical model function (GMF) corrects for sea surface temperature (SST), whereas it doesn’t consider rain. Rain causes biases in the retrieved wind fields and to prevent these, quality control (QC) flags play an important role in rain identification. Since horizontal polarization and vertical polarization radar beams have a particular sensitivity to rain clouds, a noticeable difference between the rain-dominated backscatter distribution and the wind-dominated backscatter distribution is observed. Employing a Bayesian approach and exploiting these particular wind and rain backscatter characteristics, the authors propose an algorithm to provide the posterior rain probability for each measurement in a Wind Vector Cell and test the method for the Haiyang-2C scatterometer. In a comprehensive comparison between posterior rain probability, KNMI QC flag and Joss flag, for posterior rain probabilities higher than 0.5, the rejection rate is approximately a quarter of that of the KNMI QC flag with better rain detection behavior. While the Joss flag, the difference between the retrieved wind speed and the two-dimensional variational ambiguity removal analysis wind speed, has the best performance in identifying rain in the sweet swath, it comes at the cost of a higher missing rate. The comparison with ASCAT winds also proves the method’s effectiveness. Posterior rain probability has the best rain identification ability in the nadir swath. A combination of different QC flags should be beneficial and applied in wind retrieval.

Satellite scatterometer wind retrieval is affected by rain. Both the precipitating clouds in the atmosphere and the sea surface rain effects can enhance or reduce the backscatter signal. Ku-band scatterometer retrievals suffer more rain effects than C-band scatterometer due to the shorter wavelength. Because of the lack of understanding of the potential physical mechanism, the current Geophysical Model Functions (GMF) don’t include rain effects, which leads to wind field retrieval biases in rainy areas. The usual method to avoid rain effects is flagging the possible rain-contaminated data in the quality control procedure and removing these flagged data in the processing. However, rain is often associated with extreme weather events, where accurate wind (and rain) retrieval is particularly relevant. Therefore, the authors propose a conceptual model which describes the relationship between Ku-band scatterometer measured normalized radar cross-section (NRCS) biases and the sea surface wind-induced NRCS and rain rates. The model assumes that the area-weighted rain rate in each wind vector cell (WVC) is a function of the rain coverage area fraction. The received NRCS is constituted by a wind and rain contribution. Model parameters are fitted based on Haiyang-2C scatterometer measurements, collocated ASCAT measurements, and the Level 3 Integrated Multi-satellitE Retrievals average area-weighted rain rates. Scatterometer measured NRCS biases are much reduced by comparing the original measured NRCS biases and the residual NRCS biases after correction. The model can help to better understand rain effects on scatterometers and paves the way towards a Ku-band scatterometer wind retrieval method considering rain effects.

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.

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.

The unique rotating fan-beam feature of SCAT onboard CFOSA T leads to varying geometries across the swath and furthermore leads to varying wind retrieval performance across the swath. In order to improve the wind retrieval, two kinds of NWP Ocean Calibration (NOC) are applied. One is a NOC as a function of incidence angle (NOCinc). The other one is a newly developed NOC as a function of incidence angle and antenna azimuth angle (NOCant). The results show that NOCant correction improves the wind speed Probability Distribution Function per WVC and reduces the average wind direction bias and the relative wind direction (relative to the satellite motion direction) biases, as compared to NOCinc correction. Overall the performance of the proposed NOCant correction is better than NOCinc and improves the wind statistics.

To address the growing demand for accurate high-resolution ocean wind forcing from the ocean modeling community, we develop a new forcing product, ERA*, by means of a geolocated scatterometer-based correction applied to the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis or ERA-interim (hereafter referred to as ERAi). This method successfully corrects for local wind vector biases present in the ERAi output globally. Several configurations of the ERA* are tested using complementary scatterometer data [advanced scatterometer (ASCAT)-A/B and oceansat-2 scatterometer (OSCAT)] accumulated over different temporal windows, verified against independent scatterometer data [HY-2A scatterometer (HSCAT)], and evaluated through spectral analysis to assess the geophysical consistency of the new stress equivalent wind fields (U10S). Due to the high quality of the scatterometer U10S, ERA* contains some of the physical processes missing or misrepresented in ERAi. Although the method is highly dependent on sampling, it shows potential, notably in the tropics. Short temporal windows are preferred, to avoid oversmoothing of the U10S fields. Thus, corrections based on increased scatterometer sampling (use of multiple scatterometers) are required to capture the detailed forcing errors. When verified against HSCAT, the ERA* configurations based on multiple scatterometers reduce the vector root-mean-square difference about 10% with respect to that of ERAi. ERA* also shows a significant increase in small-scale true wind variability, observed in the U10S spectral slopes. In particular, the ERA* spectral slopes consistently lay between those of HSCAT and ERAi, but closer to HSCAT, suggesting that ERA* effectively adds spatial scales of about 50 km, substantially smaller than those resolved by global numerical weather prediction (NWP) output over the open ocean (about 150 km).

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.

Recent developments on the wind geophysical model function (GMF) of Ku-band scatterometers include a sea surface temperature (SST) dependent term. It has been found that the SST effects on the radar backscatter are wind speed dependent and more pronounced in vertical polarization (VV) than in horizontal polarisation (HH) at higher incidence angles, and are mainly relevant at radar wavelengths smaller than C-band. The new Ku-band GMF, NSCAT-5, is developed based on a physical model and RapidScat radar backscatter measurements, which are only available at two incidence angles, i.e., 48.8° and 55.2°, for HH and VV beams, respectively. The objective of this paper is to verify the NSCAT-5 GMF at similar incidence angles, using data from the scatterometer onboard Indian SCATSat-1 satellite, which operates at 49.1° (HH) and 57.9° (VV) incidence angles. First, the SCATSat-l backscatter sensitivity to sea surface wind and SST is assessed using the C-band Advance Scatterometer (ASCAT) winds as reference. Second, the approach used to derive the NSCAT-5 GMF for RapidScat is adapted to derive a SST-dependent GMF for SCATSat-l. The new GMF will be used to consolidate the current NSCAT-5 model, and then evaluated for SCATSat-l wind retrieval.

In contrast with scatterometer wind data, Numerical Weather Prediction (NWP) models do not well resolve the mesoscale sea surface wind flow under increased wind variability conditions, such as in the vicinity of low-pressure centers, frontal lines, and moist convection. In this paper, several important issues are addressed in order to improve the impact of scatterometer data assimilation into global and regional NWP models, including model error structure functions, situation-dependent Observation/ Background error estimation, and improved scatterometer wind quality control.

This paper reviews several wind quality-sensitive parameters derived from HY-2A scatterometer data, such as the wind-inversion residual (or Maximum Likelihood Estimator, MLE) and its spatially averaged value, and the singularity exponent (SE) derived from an image processing technique, called singularity analysis. Their sensitivity to data quality is evaluated using the collocated European Centre for Medium-range Weather Forecasting (ECMWF) model output and satellite radiometer rain data. It shows that SE is the best quality indicator, followed by the spatially averaged MLE and the conventional MLE. A set of MLE and SE thresholds are derived from the sensitivity analysis in order to optimize the quality control (QC) for the HY-2A scatterometer.

Closely collocated C and Ku band scatterometer winds are investigated and significant differences occur depending on sea surface temperature (SST). C-band scatterometer winds do not show any significant SST effect against independent wind references, but Ku-band scatterometer winds show a clear SST-dependent effect, but varying as a function of wind speed. Both statistical and physical analyses of these effects are presented.

Singularity analysis has proven to be a complementary tool to the Advanced Scatterometer (ASCAT) inversion residual (or maximum likelihood estimator) in terms of wind quality control (QC). In this paper, a new implementation scheme of singularity exponent (SE) is developed for ASCAT data analysis. It combines the wavelet projections of the gradient measurements of multiple parameters into the analysis, ensuring that the analyzed parameters contribute equally to the final singularity map. Therefore, the underlying geophysical phenomena in the different ASCAT-derived parameters can be effectively revealed simultaneously on a unique map of SEs. The validation using both buoy winds and European Centre for Medium-Range Weather Forecasting forecast wind output shows that the newly derived SE significantly improves the current ASCAT wind QC. In particular, poor-quality ASCAT measurements at low-wind and high-variability conditions (w <; 4 m/s) can be effectively screened using the new SE.

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 current ASCAT Wind Data Processor (AWDP) uses the 2D variational ambiguity removal (2DVAR) scheme to select a unique wind field from a set of retrieved ambiguities. This has led to spatially consistent and accurate ASCAT Level 2 wind products. Nevertheless, recent research shows that 2DVAR picks up the wrong wind direction ambiguities in regions where the background field shows mislocation of fronts (convergence) or misses convective systems. In this paper, the exploitation of complementary information derived from the inversion and from an image processing technique is proposed to improve the current 2DVAR for ASCAT in mesoscale conditions.

The Advanced Scatterometer (ASCAT) onboard the Metop satellite series is designed to measure the global ocean surface wind vector. Generally, ASCAT provides wind products at excellent quality. Occasionally, though, ASCAT-derived winds are degraded by rain. Therefore, identification of rain can help to better understand the rain impact on scatterometer wind quality and to develop a proper quality control (QC) approach for scatterometer data processing. In this letter, an image processing method, known as singularity analysis (SA), is used to detect the presence of rain such that rain-contaminated wind vector cells are flagged. The performance of SA for rain detection is validated using ASCAT Level-2 data collocated with satellite radiometer rain data. The rain probability as a function of SA singularity exponent is calculated and compared with other rain sensitive parameters, such as the wind inversion residual or maximum-likelihood estimator (MLE). The results indicate that the SA is effective in detecting ASCAT rain-contaminated data. Moreover, SA is a complementary rain indicator to the MLE parameter, thus showing great potential for an improved scatterometer QC.

In this study, the rain impact on the ASCAT operational Level 2 retrieved wind quality and the effectiveness of the quality control (QC) are investigated. It is shown that ASCAT is much less affected by direct rain effects, such as ocean splashing, but effects of increased wind variability appear to dominate. The operational QC proves to be effective in screening these artifacts, but at the expense of valuable winds. An image processing method, known as the singularity analysis, is proposed in this study to complement the current QC, and its potential is illustrated.

Calibration procedures over the ocean using forecast winds have the advantage that they may be applied over a large portion of the globe and consequently may provide accurate results over a relatively short period. Ocean calibration has been applied successfully for the European Remote-Sensing Satellite (ERS) and Advanced SCATterometer (ASCAT) C-band fan-beam scatterometer wind products at KNMI. The OceanSat-2 rotating pencil-beam scatterometer (OSCAT) uses a Ku-band radar wavelength (13.515 GHz) which is strongly affected by the presence of rain within a scatterometer wind vector cell (WVC). In this paper, The Indian Space Research Organisation (ISRO) L2A data and 50km resolution L2B data processed by the OSCAT Wind Data Processor (OWDP) software, which is being developed by the KNMI scatterometer group, and include European Centre for Medium-range Weather Forecasts (ECMWF) Numerical Weather Prediction (NWP) equivalent-neutral winds, are successfully used in OSCAT NWP ocean calibration (NOC). With the NOC results obtained, the backscatter is corrected and wind retrieval statistics between ECWMF NWP winds and OSCAT NOC-corrected winds are computed and shown to be improved w.r.t. the OSCAT winds without NOC corrections applied. Based on the KNMI QC(Quality Control) flag, the rain effects to Ku-band NWP ocean calibration are investigated and NOC corrections based on WVCs that pass the QC are about 0.1dB lower than the NOC corrections without the QC applied. The best wind retrieval statistics are thus effectively obtained after that the QC-ed NOC corrections are applied on Ku-band OSCAT scatterometer backscatter data.

An important part of the scatterometer wind data processing is the quality control (QC). This letter shows the implementation of a new scatterometer QC procedure, based on a comprehensive analysis of the wind inversion residual, which significantly improves the effectiveness of the wind data QC. The method is applied on the Advanced Scatterometer onboard the EUMETSAT Polar System (EPS) Metop-A satellite but is generic and can therefore be applied to any scatterometer system.

The quality of the Ku-band scatterometer-derived winds is known to be degraded by the presence of rain. Little work has been done in characterizing the impact of rain on C-band scatterometer winds, such as those from the Advanced Scatterometer (ASCAT) onboard Metop-A. In this paper, the rain impact on the ASCAT operational level 2 quality control (QC) and retrieved winds is investigated 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. In contrast to Ku-band, it is shown that C-band is much less affected by direct rain effects, such as ocean splash, but effects of increased wind variability appear to dominate ASCAT wind retrieval. ECMWF winds do not well resolve the airflow under rainy conditions. ASCAT winds do but also show artifacts in both the wind speed and wind direction distributions for high rain rates (RRs). The operational QC proves to be effective in screening these artifacts but at the expense of many valuable winds. An image-processing method, known as singularity analysis, is proposed in this paper to complement the current QC, and its potential is illustrated. QC at higher resolution is also expected to result in improved screening of high RRs.

This paper details the construction of a Bayesian sea ice detection algorithm for the C-band Advanced Scatterometer ASCAT onboard MetOp based on probabilistic distances to ocean wind and sea ice geophysical model functions. The performance of the algorithm is validated against coincident active and passive microwave sea ice extents on a global scale across the seasons. The comparison between the ASCAT, QuikSCAT, and AMSR-E records during 2008 is satisfactory during the winter seasons, but reveals systematic biases between active and passive microwave methods during the summer months. These differences arise from their different sensitivities to mixed sea ice and open water conditions, scatterometers being more inclusive regarding the detection of lower concentration and summer ice. The sea ice normalized backscatter observed at C-band shows some loss of contrast between thin and thick ice types relative to the Ku-band QuikSCAT, but offers a better sensitivity to prominent surface features, such as fragmentation and rafting of marginal sea ice.

The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility delivers operational wind products from the Advanced Scatterometer (ASCAT) at 25 km and 12.5 km Wind Vector Cell (WVC) spacing. In these products, based on the backscatter processing performed at EUMETSAT, data closer than ~ 70 km (25 km products) or ~ 35 km (12.5 km products) to the coast are flagged because of land contamination. An alternative wind product is presented here which uses a different way of averaging the full resolution (FR) backscatter measurements from ASCAT. The FR backscatter measurements are screened for land contamination in the coastal zone, thus allowing the construction of WVCs that follow the coast line. The implied alternative spatial averaging allows good quality winds over sea as close as 15-20 km to the shore. The alternative (coastal) and nominal products are compared, and the resulting winds are validated with buoy winds, both in coastal and open sea regions. In regions far away from the coast, the ASCAT coastal and nominal products appear to be of identical quality, but fewer WVCs pass the quality control steps for the nominal product, indicating that the coastal product better resolves sub-WVC wind variability. In the coastal region, we anticipate enhanced wind variability due to katabatic and sea breeze effects, among others. However, the quality of the coastal winds in terms of buoy wind component difference standard deviation is almost as good as for the open sea winds.

The Advanced Scatterometer (ASCAT) wind data processor (AWDP) currently uses the so called CMOD5n geophysical model function (GMF), which was originally derived for the European Remote Sensing (ERS) scatterometers. In order to deliver a high-quality ASCAT wind product, the operational AWDP uses backscatter measurement corrections that are estimated visually (VOC) for each wind vector cell. We propose an alternative and previously established method for estimating correction tables based on numerical weather prediction ocean calibration residuals (NOC). It embodies a smooth incidence-angle dependent part that could serve as an appropriate ASCAT GMF correction, and a radar-beam-dependent residual. The incidence-angle-dependent part of these correction tables is due to differences in calibration procedure of the ERS and ASCAT scatterometers. For the high ASCAT incidence angles for which the GMF has not been assessed by ERS data, the modification is quite large, almost 1 dB. The incidence angle-dependent part is derived by fitting the OC residuals of all beams obtained over one year of data. It is subsequently used to adapt the GMF (yielding CMOD5na). The remaining radar-beam-dependent residual (NOCa) shows a wiggle pattern as function of incidence angle that is very persistent over time, apart from a seasonally varying offset. Both the effects of the GMF modification and the beam-dependent residual on the wind retrieval quality are investigated in this paper. Overall, the performance of NOC is better than that obtained with the previously used VOC calibration method, and the wind statistics show a much better symmetry of the left and right swath for NOC. The beam-dependent corrections improve the quality of the retrieved winds. NOC may thus be used for the intercalibration of the ERS and ASCAT scatterometers.

The Advanced scatterometer, ASCAT, on MetOp-A was launched on 19 October 2006 as the third wind scatterometer currently in space joining up with the ERS-2 and the SeaWinds scatterometers. Scatterometers measure the radar backscatter from wind-generated cm-size gravity-capillary waves and provide high-resolution wind vector fields over the sea with high quality. In this paper we show progress in high resolution processing and its verification and in processing closer to the coast.

A new scatterometer, the so-called Advanced scatterometer (ASCAT), onboard MetOp-A satellite was successfully launched on October 19 2006. During the commissioning phase one of the main goals is to accurately calibrate the instrument. The radar backscatter has been calibrated using three ground-based transponders in February 2008. Calibration of the ASCAT retrieved winds over the ocean is done by comparing the backscatter measurement with backscatter values derived from collocated NWP winds. Using calibration corrections, ASCAT winds are produced routinely at KNMI since March 2007 as the first MetOp-A geophysical product. Ocean calibration results and scatterometer wind speed statistics show that the Ocean and Sea Ice Satellite Application Facility (OSISAF) wind product is of high quality.

The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) is responsible for the absolute calibration of the new Advanced scatterometer (ASCAT), onboard MetOp-A, which mainly relies on the use of transponders. An alternative calibration method, which uses scatterometer measurements over the ocean, is presented here. The method is based on the knowledge of the backscatter signal modulation by the ocean surface, which is derived from previous C-band scatterometer missions, and on the use of numerical weather prediction wind output as calibration reference. The method proves to be very useful in providing guidance to EUMETSAT calibration efforts and provides continuity of the C- band scatterometers. Moreover, the ocean calibration results in very good quality winds. As such, within the framework of the EUMETSAT Ocean & Sea Ice Satellite Application Facility, the Royal Netherlands Meteorological Institute has released a demonstration ASCAT 25-km wind product, which is available at http ://www.knmi.nl/scatterometer since 28 March 2007.

In scatterometry, the wind vector retrieval problem is ambiguous, i.e., the inversion procedure does not result in a unique wind solution. To remove such ambiguity, a spatial filter is applied over the ambiguous wind field. Such filtering methods succeed in most of the cases. However, as the resolution increases both the noise and the direction ambiguity in retrieved winds increases, leading to arbitrary local minima wind solutions. Exploiting the full wind vector probability density function of the wind inversion, and adopting spatial meteorological balance constraints in a 2D-Var ambiguity removal (AR) alleviates the problem of arbitrary minima and noise, and provides a spatially consistent scatterometer wind field at high resolution. In other words, the method has the advanced filtering properties needed for maintaining small-scale meteorological information in scatterometers, while reducing noise. The method can be adopted in the context of 3D- or 4D-Var data assimilation systems. Moreover, these findings will be used to develop a high resolution (12.5-km sampled) coastal wind product from the new ASCAT scatterometer.