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.

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.

Scatterometer radar systems in space prove very stable in calibration, but how do we know this? The most accurate method to determine stability to date is called “cone metrics”. Cone metrics uses the consistency of NRCS measurements in measurement space and methods have been developed that determine the location of NRCS sets associated to a particular wind vector to within 0.05 dB. This number corresponds to an uncertainty of roughly 0.05 m/s of retrieved wind speeds. Therefore, if instrument stability is established to within 0.1 m/s for a scatterometer, then it would globally establish the required decadal stability of 0.1 ‘m/s, which is unique. Furthermore, cone metrics is being used for Geophysical Model Function (GMF) development. Whereas focus has been mainly on European scatterometers, the cone metrics methodology may also be applicable for other scatterometer concepts. Cone metrics will be presented to initiate a discussion on its use as a standard tool with the international community gathered at IGARSS ‘21.

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 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) onboard the Metop-A satellite became operational shortly after launch in 2006, and an absolute calibration using three transponders was achieved in November 2008. In this paper, we describe how the CMOD5.n ocean backscatter geophysical model function (GMF), which was derived using data from previous scatterometers onboard the European Remote Sensing 1 and 2 satellites (ERS-1 and ERS-2), was used to derive backscatter bias correction factors. The purpose is to remove the bias between ASCAT backscatter data and the CMOD5.n GMF output which allows these data to be used in place of ERS data in existing wind processing algorithms. The ASCAT Wind Data Processor, developed at the Royal Netherlands Meteorological Institute (KNMI), applies the bias correction factors to ASCAT data and uses CMOD5.n to retrieve wind vectors in order to produce an operational wind product. This resulted in a stable and high-quality ASCAT wind product since February 2007. We validate this product by comparing it to the European Centre for Medium-range Weather Forecasts (ECMWF) winds and buoy measurements. The bias correction factors indicate that ASCAT data and the GMF differ by roughly 0.3 dB below 55$^{\circ}$and up to 0.8 dB above 55$^{\circ}$. A possible explanation lies in CMOD5.n which has been poorly validated in this incidence angle regime. Validation of ASCAT data using the ocean calibration method confirms this result and also indicates that bias-corrected data are everywhere within 0.3 dB of CMOD5.n. The wind product validation shows an rms error of 1.3$\hbox{m} \cdot \hbox{s}^{-1}$in wind speed and 16$^{\circ}$in wind direction when compared to ECMWF winds. This is better than the results achieved using ERS scatterometer data. Against buoy winds, we find an rms error wind component error of approximately 1.8$\hbox{m} \cdot \hbox{s}^{-1}$. These results show that the ASCAT wind product is of high quality and satisfies its wind component accuracy requirement of 2$\hbox{m} \cdot \hbox{s}^{-1}$.

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.