The Numerical Weather prediction (NWP) stress-equivalent 10-m wind (U10S) forecasts are used as a common forcing for ocean models, however, these forecasts present local and systematic biases when compared to the observational data. The scatterometer wind observations are being assimilated by European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) but even after the assimilation the sea-surface wind biases are still present. A previous approach to reduce such biases was based on correcting the forecasts with the mean differences between scatterometer observations and the NWP output accumulated over a certain period of time. However, this approach shows performance degradation for the periods when fewer scatterometers are available and in the operational framework. To overcome these limitations, we propose the use of machine learning (ML) to predict such biases using other atmospheric and oceanic NWP variables as inputs, so that the observational data is only required during the training. In this work we show the results for the preliminary ML models trained on a small subset of data that use U10S scatterometer – NWP differences as the target. The predicted corrections applied to ECMWF fifth reanalysis dataset ERA5 show error variance reduction up to 9.86% on a test subset globally when compared to Advanced scatterometer (ASCAT-A) and up to 6.25% against independent scatterometer HSCAT-B, thereby reducing the local biases. The best performance is seen in the extra-tropics with error variance reduction up to 10.6%.

The C-band Advanced Scatterometer (ASCAT) has the advantages of good spatial-temporal coverage and low sensitivity to nonextreme rainfall. While the perceived wind speed underestimation issues of ASCAT sea surface wind (SSW) retrievals can be mitigated using appropriate high wind speed scalings, the low spatial resolution in ASCAT remains a challenge, which implicitly leads to the blurring effect in tropical cyclone (TC) inner-core regions. To overcome this issue, the 2-D variational (2DVAR) analysis method is modified from 12.5 to 1.8 km grid size, where the latter allows super-resolution (SR) spatial structure functions, empirically trained on synthetic aperture radar (SAR) data, to enhance TC structure retrievals of ASCAT. The method first employs triple collocation analysis to estimate observation and background errors under different TC categories. After that, the relevant spatial parameters during the data assimilation process are determined and linked to TC features. These analyses contribute to constructing SAR-learned structure functions, complementing ASCAT-observed TC characteristics, and then achieving TC vortex reconstruction and wind field SR. Validation studies demonstrate that the SR products possess the correct small-scale properties of TC inner-core structures, such as radius of maximum wind (RMW), TC asymmetry, and wind variability. Notably, the proposed SR approach can achieve a significant reduction in error standard deviations (SDs) of ($l,t$) wind components (by 37% and 33%, respectively) when compared to spatial interpolated results. The encouraging results suggest the feasibility of the method in enhancing the abundant but lower resolution scatterometer winds, potentially contributing to future advancements in TC advisories.

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 work addresses the need for modelling and correcting the persistent Numerical Weather Prediction (NWP) local biases of the ocean surface wind forecasts. For such purpose, several NWP and ocean model output parameters are used as inputs to the machine learning and neural network models to generate the corrections of the NWP forecasts. The results show that such models are able to substantially reduce NWP local biases and therefore its overall error variance, opening the door for both its operational use as well the development of long-term data series of valuable ocean forcing datasets.

Accurate knowledge of Tropical Cyclone (TC) inner-core structures contributes to a better understanding of TC thermodynamics. The Advanced Scatterometer (ASCAT) can measure ocean surface winds at a good spatial-temporal coverage, but the TC inner structures are largely blurred by its 20-km footprint. In this study, the Two-Dimensional Variational (2DVAR) scheme is considered to enhance the TC inner-core structure, by "learning" background spatial error covariances from high-resolution Synthetic Aperture Radar (SAR) winds. We find that the length scales of the stream function are close to the radii of maximum wind speeds and length scales of the velocity potential are dependent on TC asymmetry scales. All these parameters can be provided by ASCAT data. Experimental results prove that the proposed method can enhance TC inner-core structures and thus achieve super-resolution. The promising results contribute to our long-term goal of developing a general method for providing TC inner-core structures from all scatterometer winds available for nowcasting, allowing temporal monitoring of TC winds.

Rain affects on the Ku-band wind scatteroemters and are generally labelled in the Quality Control (QC) procedures applying metrics quantifying the QC indicators such as MLE, representing normalized Euclidian distance summaries from the scatterometer observed Normalized Radar Cross-Sections (NRCS) in a Wind Vector Cell and the wind-NRCS mapping model determined surface, and Jossindicating the heterogeneity. Yet the complex rainy scenes are not analyzed specifically. In this research, the wave energy spectra from buoy measurements are collocated with scatterometer observations. Then the rain effects in the wind scatterometers are addressed in terms of energy distribution with references from the energy governing equation and rain affected spectra. Wind, wave and rain induced features in spectra are obtained applying the rain screening ability from MLE and Joss. The possibility for rain contaminated wind correction in a parameterized spectrum is discussed for the combined application of wind and wave measurements that are obtained from CFOSAT and its follow-on missions.

In polar regions, sea ice introduces deviations of scatterometer observations from the empirical wind model, i.e., the Geophysical Model Functions (GMF) that map them to the ocean surface wind fields and sea ice GMFs have been developed to aid in the Bayesian estimation of sea ice presence to discriminate between water and ice. In this research, collocations of C- and Ku-band scatterometer products from ASCAT-A, ASCAT-B and OSCAT-2 are registered with SIC products from AMSR-2 in the Northern hemisphere, and with rain rates from the global precipitation mission (GPM). Then the ice screening ability in the wind QC indicators MLE and Jossare investigated for sea ice identification as an extension to their QC ability of rain in the tropical regions. Finally, simultaneous wind and SIC estimation is discussed.

C-band scatterometer winds have been adjusted for extreme conditions and in this research extension to Ku-band scatterometers is investigated. With rain rates from the Global Precipitation Measurement mission collocated to the Ku-band scatterometer observations to identify and exclude rain contamination of winds, calibration of the Ku-band observations can be done. Using high-wind cases extracted from collocated C- and Ku-band observations, we develop a calibration model and extend the Ku-band winds to 35 m/s. Validation is obtained from the set not included in the model derivation, indicating a speed error less than 10% for wind speed larger than 30m/s. The modified speed is consistent with the Step Frequency Microwave Radiometer measurements, when collocated with another Ku-band scatterometer. A comparison for the Tropical Cyclone Manyi in 2018 shows the adjustedd wind fits better with the best-track information provided by the Chinese Meteorological Administration, while more details are revealed. Results can be improved after obtaining more collocations with the dual-frequency scatterometer "WindRad" onboard the FY-3E satellite. A method for wind direction enhancement in extreme conditions is also discussed.

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.

We model the sea foam emissivity at frequencies from 1 to 89 GHz. This model is part of the work done by an international science team to develop a radiative transfer model of reference quality for the ocean surface emissivity from L band to infrared frequencies. A study of the sensitivity to different foam properties (foam layer thickness and upper limit of the foam void fraction) guided the effort to tune the foam emissivity model by frequency and polarization. The results show that the differences between simulated and observed brightness temperatures decrease when using the tuned foam model.

The resolution of regional numerical weather prediction (NWP) models has continuously been increased over the past decades, in part, thanks to the improved computational capabilities. At such small scales, the fast weather evolution is driven by wind rather than by temperature and pressure. Over the ocean and in the free troposphere, where global NWP models are not able to resolve wind scales below 150 km, regional models provide wind dynamics and variance equivalent to 25 km or lower. However, although this variance is realistic, it often results in spurious circulation (e.g., moist convection systems), thus misleading weather forecasts and interpretation. An accurate and consistent initialization of the evolution of the 3-dimensional (3-D) wind structure is therefore essential in regional weather analysis. The wind profiles provided by the ESA Aeolus satellite mission will help filling the observational gap in the upper air and hopefully improve regional weather forecast. For a correct assimilation into NWP models, the observations need to be characterized in terms of their spatial scales and measurement errors. To this end, the triple collocation method, widely used in scatterometry, is applied to Aeolus observations collocated with Mode-S aircraft observations and ECMWF model output. An algorithm for collocating 4D wind observations from Aeolus, Mode-S and ECMWF over a region of Western Europe will be presented, along with measurement errors obtained from triple collocation.

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.

Accurate knowledge of wind directions plays a critical role in atmospheric dynamics exploration, numerical weather prediction and Tropical Cyclone (TC) research. This study proposes a new method for wind direction retrieval from TC Synthetic Aperture Radar (SAR) images. Unlike conventional approaches, which estimate wind directions from singlepolarization imagery, the method utilizes dual-polarized (VV and VH) signals to obtain continuous wind directions across moderate and extreme wind speed regimes. The technique is developed based on the Histogram of Oriented Gradient descriptor and the Hann window function. In addition, the neighbouring information is introduced to alleviate sharp directional variations. As case studies, the wind directions in TCs Karl and Maria are derived and subsequently verified by simultaneous dropsonde and ASCAT (ambiguity-removed) measurements. The encouraging results suggest that the wind direction retrieval method based on dual-polarization SAR imagery can be useful in extracting wind direction and contribute to further exploitation of SAR images in TC studies.

In tropical regions, Ku-band scatterometer observations are affected by heavy rain, and affected data are labeled in the quality control (QC) step during wind product generation. This is achieved by setting thresholds for QC indicators. Among the applied indicators, it has been shown that$J_{\mathrm {oss}}$can be beneficially applied for reducing the false alarm rate (FAR) for Ku-band observations for data sets collocated to C-band observations. In this letter, the FAR reduction method based on$J_{\mathrm {oss}}$is further generalized to be tested without collocated C-band observations, which extension is subsequently applied to the CSCAT wind products. Results prove the effectiveness of the FAR reduction method without C-band collocations for Ku-band scatterometers. Verification with rain rates from the Global Precipitation Mission (GPM) proves the capability of the recruited data set to benefit nowcasting applications. Moreover, the results of this research indicate the consistency of products in rainy conditions for the new rotating-fan-beam CSCAT and the existing rotating-pencil-beam Ku-band scatterometers. Further research would be to analyze detailed rain effects in different rain development stages in a wind vector cell (WVC) for tropical regions.

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 airborne Stepped Frequency Microwave Radiometer (SFMR) provides measurements of 10-m ocean-surface wind speed in high and extreme wind conditions. These winds are calibrated using the surface-adjusted wind estimates from the so-called dropsondes. The surface-adjusted winds are obtained from layer-averaged winds scaled to 10-m altitude to eliminate the local surface variability not associated with the storm strength. The SFMR measurements and, consequently, the surface-adjusted dropsonde winds represent a possible reference for satellite instrument and model calibration/validation at high and extreme wind conditions. To this end, representativeness errors that those measurements may introduce need to be taken into account to ensure that the storm variability is correctly resolved in satellite retrievals and modelling. In this work, we compare the SFMR winds with the dropsonde surface-adjusted winds derived from the so-called WL150 algorithm, which uses the lowest 150-meter layer between 10 m to 350 m. We use nine years of data from 2009 to 2017. We focus on the effects of the layer altitude and thickness. Our analysis shows that the layer altitude has a significant impact on dropsonde/SFMR wind comparisons. Moreover, the averaged winds obtained from layers thinner than the nominal 150 m and closer to the surface are more representative of the SFMR surface wind speed than the WL150 speeds. We also find that the surface-adjusted winds are more representative of 10-km horizontally averaged SFMR winds. We conclude that for calibration/validation purposes, the WL150 algorithm can introduce noise and the use of actual 10-m dropsonde measurements should be further investigated.

Decades of ocean surface vector wind (OSVW) data acquired from space-based radar scatterometry have been providing short and long-term researches and applications information about ocean surfaces. The main objective of the project, stands and metrics of ocean surface vector wind by space-borne microwave remote sensing, of W orking Group on Calibration and Validation of the Committee on Earth Observation Satellites (CEOS WGCV), is to develop the standard and guideline for the requirement, procedure, processing and assessment for the space borne radar scatterometer measurement calibration, wind retrieval approaches, wind data validation and assessment for OSVW, which will be used to assure the consistency of the data quality of these satellites and instruments are the prerequisite for related scientific researches and applications. This synthesizes calibration, standardized practices of retrieval approaches for ocean surface winds, development of guidelines/standards of validation of ocean surface winds, and identifying and organizing collocation related data. This presentation will provide an overview of the proj ect and the recent progresses.

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 Data Innovation and Science Cluster (DISC) is a core element of ESA's data quality strategy for the Aeolus mission, which was launched in August 2018. Aeolus provides for the first-time global observations of vertical profiles of horizontal wind information by using the first Doppler wind lidar in space. The Aeolus DISC is responsible for monitoring and improving the quality of the Aeolus aerosol and wind products, for the upgrade of the operational processors as well as for impact studies and support of data usage. It has been responsible for multiple significant processor upgrades which reduced the systematic error of the Aeolus observations drastically. Only due to the efforts of the Aeolus DISC team members prior to and after launch, the systematic error of the Aeolus wind products could be reduced to a global average below 1 m/s which was an important pre-requisite for making the data available to the public in May 2020 and for its use in operational weather prediction. In 2020, the reprocessing of earlier acquired Aeolus data, another important task of the Aeolus DISC, also started. In this way, also observations from June to December 2019 with significantly better quality could be made available to the public, and more data will follow this and next year. Without the thorough preparations and close collaboration between ESA and the Aeolus DISC over the past decade, many of these achievements would not have been possible.

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.

In the context of the ocean surface vector wind virtual constellation, the combined wind products from the ongoing operational scatterometers will unprecedentedly increase the spatial and temporal coverage of remote sensing winds, and ease the development of gap-free sea surface wind data of high quality and high spatial/temporal resolution for a variety of applications, including a better marine forecasting and monitoring. However, systematic differences do exist in the wind products derived from different scatterometers, which may result in detrimental impacts in these applications. Therefore, the difference between the retrieved winds from C- and Ku-band scatterometers is further explored in this paper. In particular, sea surface temperature (SST) effects, quality control and high wind sensitivity of both C- and Ku-band scatterometers are analyzed, with the objective to better understand the sources of the inconsistencies, and to provide the wind users with support and recommendations in terms of wind applications.

With the advent of the golden era of scatterometry, with seven scatterometers currently operating in orbit and a few others to be launched in the near future, a wide variety of scientific and operational applications will certainly benefit from consolidated wind retrieval procedures. In particular, an important component of the scatterometer wind processing is the quality control (QC) procedure. Over the last two decades, several QC indicators have been developed for C-band and Ku-band scatterometers, and used in the operational generation of sea surface wind products. Such indicators mostly aim at identifying and filtering retrieved wind quality degradation due to high wind variability and/or rain contamination effects. As such, the different QC indicators may be applied for different oceanographic and meteorological applications. The methods will be presented at the conference to motivate a discussion on their application-dependent use and come up with a consolidated view from the different user communities.

We highlight some of the scientific benefits of the Aeolus Doppler Wind Lidar mission since its launch in August 2018. Its scientific objectives are to improve weather forecasts and to advance the understanding of atmospheric dynamics and its interaction with the atmospheric energy and water cycle. A number of meteorological and science institutes across the world are starting to demonstrate that the Aeolus mission objectives are being met. Its wind product is being operationally assimilated by four Numerical Weather Prediction (NWP) centres, thanks to demonstrated useful positive impact on NWP analyses and forecasts. Applications of its atmospheric optical properties product have been found, e.g., in the detection and tracking of smoke from the extreme Australian wildfires of 2020 and in atmospheric composition data assimilation. The winds are finding novel applications in atmospheric dynamics research, such as tropical phenomena (Quasi-Biennial Oscillation disruption events), detection of atmospheric gravity waves, and in the smoke generated vortex associated with the Australian wildfires. It has been applied in the assessment of other types of satellite derived wind information such as atmospheric motions vectors. Aeolus is already successful with hopefully more to come.

How strong does the wind blow in a hurricane? This proves a question that is difficult to answer, but has far-reaching consequences for satellite meteorology, weather forecasting and hurricane advisories. In the EUMETSAT CHEFS project, KNMI, ICM and IFREMER worked with international colleagues to address this question to prepare for the EPS-SG SCA scatterometer, which introduces C-band cross-polarization measurements to improve the detection of hurricane-force winds. To calibrate the diverse available satellite, airplane and model winds, in-situ wind speed references are needed. Unfortunately, these prove rather inconsistent in the wind speed range of 15 to 25 m/s, casting doubt on the higher winds too. Should we trust dropsondes at high and extreme winds or perhaps put more confidence inthe moored buoy references? This dilemma will be presented to initiate a discussion with the international community gathered at IGARSS ‘21.

The unique European Space Agency (ESA) Earth Explorer Aeolus Doppler Wind Lidar (DWL) is a success with beneficial impacts in weather and climate. At the IGARSS ‘21 venue we will share our results internationally and present initial studies that look into an operational Aeolus follow-on mission and describe lessons learned on the instrument, data processing and application of the winds. The ESA Aeolus mission now demonstrates the unique benefits of direct measurements of the atmospheric wind fieldon atmospheric dynamical analyses and forecasts. The many lessons learned from the Aeolus instrument development, its operation and data processing development are being exploited to help develop the requirements and design of an Aeolus follow-on mission in order to further enhance its application benefit globally. An overview of preliminary Aeolus and future DWL requirements will be presented to initiate a discussion with the international community gathered at IGARSS ‘21.

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.

The quality control (QC) indicator$J_{\text{oss}}$proposed in 2020 represents the spatial wind speed difference between locally inverted wind speed and neighbor wind speeds, which is associated to rain for tropical regions.$J_{\text{oss}}$is effective in rain screening and hence false alarm rate (FAR) reduction. The research is here extended to higher wind speeds and missed detection rate (MDR) in QC acceptance sets, besides, performance of$J_{\text{oss}}$at the edge and nadir parts of the swath are examined. This allows a more complete evaluation of the$J_{\text{oss}}$rain screening features. On-going research is directed towards extension to non-tropical regions where the MDR, as well as FAR, involves different rain features, while the sea ice complicates the QC at higher latitudes.

Uncertainties in wind inversion from scatterometer observations are contributed by system and geophysical noise. In practice, both can be quantified by the indicators applied in the quality control (QC) procedures during wind processing. In this research, the underlying principles of three reported indicators, MLE, SE and Joss, are discussed for CSCAT. In the observation scenes of this Ku-band scatterometer, one of the major reasons for geophysical noise are rain clouds, which are analyzed specifically with respect to those indicators. Finally, examples for super typhoon Lekima, followed by Krosa in 2019, are discussed. We confirm that the MLE and Jossindicators are relatively independent from each other, and show different features in rain screening. The combined application of them would result in a better result of rain labelling. Another conclusion derived from this research is that SE and Jossare similar indicators of spatial heterogeneity in scatterometer wind fields, but that the wind speed depression measured by Joss is a more unique indicator of rain than SE. This research contributes to improving the quality of wind retrieval from scatterometers.

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.

Space-borne scatterometer observations are well-known ocean surface wind remote sensing instruments, they usually work at C- or Ku-band. Particularly in tropical regions, their observations are affected by rain. Rain-affected data are labeled in a quality control (QC) step during wind product generation. This is achieved by setting threshold of QC indicators. Among the applied QC indicators, a new one namely Joss proposed by Xu & Stoffelen in 2019, is characterized by a higher probability of detection (POD) of rain events. Joss particularly reduces the false alarm rate (FAR) for Ku-band observations during the rain screening as tested with reference to C-band observations., However, it is not possible in tropical regions for the Ku-band rotating fan-beam scatterometer onboard the Chinese-French Oceanographic SATellite (CFOSAT), to obtain C-band collocations with time differences less than 30 min. In our research, first, based on the results of the existing FAR reduction method, collocated C-and Ku- band observations are further investigated. Then the method is generalized, without the assistance of collocated C-band observations. Finally, application to wind product FAR reduction of CSCAT is presented. Results prove the effectiveness of the theory of this paper. Rain references are provided by products from the Global Precipitation Mission (GPM). Further research would be analysis on detailed rain effects to Ku-band scatterometers in different rain processes in tropical and sub-tropical regions to improve the established method.

Scatterometers provide consistent observations of ocean surface wind with reliable quality. In tropical regions, Quality Control (QC) rejects observations effected by rain clouds to guarantee the quality of wind products obtained with references to Geophysical Model Functions (GMFs). GMFs map scatterometer observed normalized radar cross-sections (NRCS) to wind fields without considering rain effects. The rejected groups have information of both rains and winds, and can be utilized for quantitative modelling of rain effects. Wind scatterometers usually operate at C-or Ku-band, they are affected differently by rain clouds due to differences in sensing wavelengths. Collocated observations with both frequencies would enable quantitative evaluation of rain effects. This study exploits these collocated C- and Ku-band measurements and seeks to ultimately develop a method for improving surface wind retrieval using a rain-cloud correction factor, with the preliminary model outline proposed. A vector radiative transfer based layer scattering model for rain clouds above ocean surface that accounts for scattering and attenuation effects of rain column and sea surface roughness will be used in quantitative analysis of rain cloud effects on surface wind retrieval at C- and Ku-band. Rain drop size distribution (DSD) and surface modifications from rains under different wind speed are considered in the model. For rain-free condition, a modified IEM surface scattering model is utilized in the quantitative analysis. Model validation and evaluation of the proposed correction factor are conducted using collocated data obtained from existing C- and Ku-band scatterometer observations, with time lag less than 5 minutes and spatial difference less than 25 km, and references to rain rates from the Meteosat Second Generation (MSG) Satellite.

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.

Spaceborne scatterometers for ocean surface winds usually operate in Ku- or C-band. Rather strict quality control (QC) procedures are included in the Ku-band wind retrieval chain for labeling rain-contaminated observations. Existing QC factors represent the deviation of measurements from the wind geophysical model function (GMF) modeled measurement surface. Other QC indicators flag outliers by examining neighborhood consistency. In this article, spatial heterogeneity of rain is further exploited by a new indicator for Ku-band QC, namely, JOSS, the speed component of the observation cost function, JO, of the selected solution (JOS) in the 2-D variational ambiguity removal (2-DVAR) step of the wind retrieval. First, the characteristics of 2-DVAR speeds in rainy condition are analyzed, and then, the ability of JOSS in quality labeling is proposed and verified by applying it to the Ku-band scatterometer on-board ScatSat. Its effectiveness for rain screening is confirmed with collocated references from the C-band scatterometer on-board the MetOp-B satellite, which are much less affected by rain. With reference to collocated rain rates from the Global Precipitation Mission (GPM), the more direct relations to rain and wind speed errors of the newly proposed QC indicator JOSS than existing QC indicators, including JOS, are illustrated by the analysis of its correlation with rain rates. In a novel approach, JOSS is applied to accept (unflag) more than 75% of the data rejected by the widely applied maximum likelihood estimation (MLE) thresholds (i.e., correct false alarms) in the tropics. The promising results open a new opportunity for improving QC of rain in the Ku-band wind scatterometry benefitting scatterometer applications.

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.

CFOSCAT is the first rotating fan-beam scatterometer in space. It is on board the China France Oceanography Satellite (CFOSAT) launched on 29thOctober, 2018. It can provide wind field products in the unit of Wind Vector Cells. One of the advantages for this novel antenna geometry is being able to obtain more observations of the scene, though for edge and nadir WVCs, sampling issues as well as larger noise level hinder wind retrievals. This is first analyzed in this paper and a new procedure is proposed, tailored for those observations of CFOSCAT. In the proposed method, wind speeds are retrieved from Neural Networks trained for specific edge and nadir WVCs, and based on the obtained speed, a modified principle component analysis (PCA) is applied for CFOSCAT wind direction retrieval. The new process chain is verified by simulated data, and further research is soon planned modifying it for real data after they are released.

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.

This paper presents a comprehensive procedure to improve the wind geophysical model function (GMF) for the Global Navigation Satellite System Reflectometry (GNSS-R) instrument onboard the TechDemoSat-1 satellite. The observable used to define the GMF is extracted from the measured delay-Doppler maps (DDMs) by correcting for the nongeophysical effects within the measurements. Besides the instrument and the geometric effects as provided in the bistatic radar equation, a calibration term that accounts for the uncalibrated receiver antenna gain and the unknown transmitter antenna gain is proposed to optimize the calculation of GNSS-R observables. Such calibration term is presented as a function of observing elevation and azimuth angles and is shown to remarkably reduce the measurement uncertainties. First, an empirical wind-only GMF is developed using the collocated Advanced Scatterometer (ASCAT) winds and European Centre for Medium-Range Weather Forecasts (ECMWF) model wind output. This empirical GMF agrees well with the model output. Then, the sensitivity of the observable to waves is analyzed using the collocated ECMWF wave parameters. The results show that it is difficult to include mean square slope (MSS) in the development of an empirical GMF, since the difference between ECMWF MSS and the MSS sensed by GNSS-R varies with incidence angle and wind speed. However, it is relevant to take significant wave height (Hs) in account, particularly for low wind conditions. Consequently, a wind/Hsapproach is proposed for improved wind retrievals.

In[1], (16) in Section IV titled “Sea ice backscatter model at C-band” contains a typographical error. It currently reads as

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.

This paper describes the adaptation of a Bayesian sea ice detection algorithm for the scatterometer on-board the European Remote Sensing (ERS) satellites (ERS-1 and ERS-2). The algorithm is based on statistics of distances to ocean wind and sea ice geophysical model functions (GMFs) and its performance is validated against coincident active and passive microwave data. We furthermore propose a new model for sea ice backscatter at the C-band in vertical polarization based on the sea ice GMFs derived from ERS and advanced scatterometer data. The model characterizes the dependence of sea ice backscatter on the incidence angle and the sea ice type, allowing a more precise incidence angle correction than afforded by the usual linear transformation. The resulting agreement between the ERS, QuikSCAT, and special sensor microwave imager sea ice extents during the year 2000 is high during the fall and winter seasons, with an estimated ice edge accuracy of about 20 km, but shows persistent biases between scatterometer and radiometer extents during the melting period, with scatterometers being more sensitive to summer (lower concentration and rotten) sea ice types.

The assimilation of Advanced Scatterometer (ASCAT) winds has proven to be beneficial for the European Center for Medium Range Weather Forecasting (ECMWF) system, particularly over the Tropics. In this study, several important aspects of the ASCAT data are addressed in order to further test and improve the impact of scatterometer wind data assimilation into ECMWF Integrated Forecasting System (IFS). First, an improved wind quality control (QC) is proposed and used to remove unrepresentative ASCAT winds. Second, a new ASCAT wind product, more representative of the ECMWF model resolved scales, is produced by averaging the relatively-high resolution ASCAT wind vector cells to lower resolution in an aggregation process. Two months of ASCAT low resolution data are then used to evaluate the impact of the refined QC and the aggregation technique on the IFS data assimilation.

Local systematic differences between scatterometer and global numerical weather prediction (NWP) model stress equivalent winds (SEW) are due to unresolved geophysical processes by the model, e.g., ocean currents and moist convection. A scatterometer-based correction, which contains the mesoscale informationpresent in the Advanced Scatterometer (ASCAT) observations, sets the grounds for a high-resolution ocean forcing product. To assess the effectiveness of such correction, a Monte Carlo simulation procedure is applied to NWP SEW. It allows for a thorough evaluation of the NWP error reduction, which depends on the scatterometer sampling. The local NWP biases are reduced at the cost of a somewhat increased variance, and the total error mitigation is constrained to regions covered by the scatterometer at least 3 times over 5 days. Despite the limited sampling in the tropics, the real NWP corrected SEW over the West African coast show areas of increased wind variability associated to moist convection.

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.

The Koninklijk Nederlands Meteorologisch Instituut (KNMI) is since long involved in the development of wind processing software and wind products for European Users. This experience is exploited for the development of wind retrieval for rotating fan-beam scatterometers, such as the China-France Oceanography SATellite (CFOSAT) scatterometer. KNMI has a long experience in the development of software and the operational provision of scatterometer wind products, both in near real time and as climate data records. A large part of this work is done in the context of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) and Numerical Weather Prediction Satellite Application Facility (NWP SAF). Both data products and software are freely available upon registration at the respective SAFs. KNMI offers to use these facilities for CFOSAT, as further detailed below.

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.

The present paper proposes an innovative mission concept for a C-band fan-beam wind scatterometer with both ocean wind vector and ocean current vector measurement capability on a global scale.

Microwave backscattering from the ocean surface is closely related to the wind-generated ocean surface roughness. This property is used for obtaining global ocean surface vector winds. The deployed scatterometers so far do not use the cross-polarized sea return (VH, representing either vertical transmit horizontal receive or horizontal transmit vertical receive) because of its weak signal level. The copolarized returns (VV or HH), however, may saturate in high wind speeds especially for low incidence angles.

This paper investigates the potential of cross polarization (VH) to extend the upper dynamic range of the wind measurements from ocean scatterometry. An analytical model for the VH polar scattering of the microwave radiation from ocean is proposed. The model combines the second-order small-slope approximation theory with the vector radiative transfer theory to obtain a statistical expression of the ocean scattering in presence of foam. Cross-polarized backscatter signals from RADARSAT-2 C-band synthetic aperture radar imagery, which were acquired during severe weather events, and collocated/time-coincident stepped-frequency microwave radiometer wind measurements by the National Oceanic and Atmospheric Administration's hurricane-hunter aircraft are used to verify the model. The validity of the model has been also proven against European Centre for Medium-Range Weather Forecasts forecasted winds. The results suggest that the present scattering model can be a valuable tool for understanding VH at very high wind speeds and for interpreting the data collected by the future dual polarimetric wind scatterometer (SCA), which will be flown on the Second Generation Meteorological Operational satellite program (MetOp-SG) as an evolution of the Advanced Scatterometer instrument on board MetOp.

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 growing interest in achieving a better understanding of the physics that governs the cross-polar scattering of microwave radiation from ocean is triggered by recent measurement campaigns over hurricanes performed by NOAA Hurricane-Hunter winds and RADARSAT-2 [1]. From this data set the cross-polarized signals showed no evident loss of sensitivity as the wind-speed increased from 20 m/s up to 45 m/s. On the contrary C-band co-polar backscatter suffered from problems of incidence and azimuth angle-dependent signal saturations and dampening which makes it weakly sensitive above 25 m/s. On the basis of these considerations there are good reasons to think that the cross-polarized data can be a valuable tool for the retrieval of strong-to-severe wind speeds for future scatterometers. In this paper we present a physical scattering model based on the Small Slope Approximation theory [2] in conjunction with the Vector Radiative Transfer Theory [3] to describe the behavior of cross-polar scattering from ocean as function of the wind-speed and direction. Numerical results will be compared with real data from RADARSAT-2 and the brand new empirical Geophysical Model Function GMF-VH [1].

In this paper, we present a survey of some of the most common analytical approximate models that are used to describe the microwave sea surface scattering. The main strengths and weaknesses of the various methods are identified and critically discussed. Such models combine an adequate sea surface description with advanced electromagnetic theories to simulate both monostatic and bistatic scattering for a wide range of wind speeds, radar frequencies, incidence angles, different polarizations and arbitrary radar look direction with respect to the wind direction. Theoretical calculations for co-polar signals in C-band and Ku-band are in good overall agreement with the experimental data represented by the empirical models, CMOD5 and NSCAT, with the exception of HH-polarization at high incidence angles (above 40°). A parametric analysis on the sea surface spectrum, will demonstrate that the discrepancy between the measured and the simulated HH normalized radar cross-sections, is only in part due to the inefficiency of the spectrum and mostly due to additional scattering contribution from breaking waves, not taken into account by the most common analytical scattering theories.

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.

The wind retrieval performance of a fixed fan-beam scatterometer operating at C-band in VV polarization [Advanced SCATterometer (ASCAT) type] is well established in the range of 0-25 m/s. This work evaluates prospective extensions with HH- and VH-polarized beams aimed at improving retrievals at extreme wind speeds, from 25 to 65 m/s. The geophysical model functions for C-band VV-, HH-, and VH-polarized backscatter used in the wind retrieval simulations are defined, along with an objective assessment of the quality scores ascribed to the optional beam configurations. Our study finds that the introduction of a VH capability in the midbeam improves the determination of wind speed over the entire scatterometer swath, with wind speed root-mean-square (rms) errors of about 0.5 m/s. The introduction of HH beams in the fore/aft antennas improves the determination of the ocean surface wind vector (wind speed and direction) but over a more limited portion of the outer swath, with wind vector rms errors as low as 6 m/s, but conditioned to a priori information to resolve the directional ambiguity. If the determination of wind speed primes over the determination of wind vectors at high wind speeds, then the introduction of a single VH capability in the midbeam antenna comes forth as the most simple and cost-effective manner to extend the wind retrieval capabilities of an ASCAT-type scatterometer into the domain of extreme winds.

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 EUMETSAT Polar System-Second Generation (EPS-SG) mission will be deployed in the 2019-2020 timeframe in order to ensure continuity of the EPS observation missions, currently realized with the MetOp satellite series, to support operational meteorology and oceanography; in particular, for numerical weather prediction (NWP), climate monitoring and to develop new environmental services. The scatterometer (SCA) is one of the high-priority payload instruments to provide vector surface wind observations over the ocean, which constitute an important input to NWP, as well as valuable information for tracking of extreme weather events. The EPS-SG SCA shall offer observations with higher spatial resolution than those provided by ASCAT on board MetOp, operating at C-band and with VV polarization. Furthermore, addition of HH or VH polarization is considered as an option. Phase 0 industrial studies, addressing the complete system design, have taken place from 2008 to 2009. Two study teams, constituted, respectively by Astrium SAS and Thales Alenia Space Italy, have performed comprehensive analyses of the system requirements, tradeoffs of various concepts, and preliminary design of the selected concepts, which included both the single and dual satellite configurations. Three distinct SCA concepts were initially considered for tradeoffs: 1) fixed fan-beam concept with six fixed antennas; 2) rotating fan-beam concept with a single rotating antenna; 3) rotating pencil-beam concept. The first two concepts were further elaborated during Phase 0, and the fixed fan-beam concept was selected as baseline after a final tradeoff. For supporting the above instrument concept elaboration by the industrial study teams during Phase 0, the Royal Dutch Meteorological Institute (KNMI) has developed retrieval algorithms tailored to those concepts, derived from the ASCAT operational algorithms, and specific metrics to characterize the associated retrieval performance. The metrics used for the present performance assessment were: 1) wind vector root-mean-square error; 2) ambiguity susceptibility; and 3) wind biases. The end-to-end performance evaluation makes use of an ensemble of wind fields as input having the mean climatology distribution, generates the output wind-fields which account for the measurement system imperfections and geophysical noise, and computes the performance metrics for comparisons. This paper describes the three SCA concepts as analysed in Phase 0 studies by the industrial study teams and summarizes the technical tradeoffs carried out. The performance metrics are described and applied to two of the concepts in order to compare their respective merits. It is shown that both concepts are able to meet the observation requirements of EPS-SG.

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.

Wind vectors derived from scatterometer measurements are spatially detailed as compared to global numerical weather prediction (NWP) model fields. Since the Advanced Scatterometer (ASCAT)'s wind vector ambiguities are, in general, well defined, ambiguity removal results in accurate wind fields. The dense and regular spatial sampling of ASCAT winds represents a unique resource to study the NWP model field spatial error structure. The current level 2 ASCAT data processor employs 2-D variational ambiguity removal (2DVAR), in which an analysis is made from the ambiguous wind solutions and a prior NWP wind field using a variational technique, and, subsequently, the ambiguity closest to the analysis is selected as best wind. 2DVAR will yield an optimal analysis when the structure functions (background error correlations in the potential domain) are well specified. In this paper, a new method is presented to calculate structure functions from autocorrelations of observed scatterometer wind components minus NWP model predictions (O-B). It is based on direct integration of the differential equations relating structure functions and observed autocorrelations. Reprocessing ASCAT data at 12.5-km grid size with structure functions obtained this way shows a considerable increase in the spectral density of the analysis for scales from about 800 to about 100 km, with the largest effect at scales of around 250 km. In line with this finding, it is shown in a case study that a more detailed analysis leads to fewer ambiguity removal errors for ASCAT data recorded over a frontal zone with rapidly varying wind direction.

The authors propose a new sea ice detection method for a rotating Ku-band scatterometer with dual-polarization capability, such as SeaWinds on the Quick Scatterometer (QuikSCAT), based on probabilistic distances to ocean wind and sea ice geophysical model functions (GMFs) and evaluate its performance against other active and passive microwave algorithms. All the methods yield similar results during the sea ice growth season but show substantial differences during the spring and summer months. A detailed comparison based on high-resolution synthetic aperture radar and optical imagery shows that major discrepancies relate to newly formed, low-concentration, and water-saturated sea ice species. The new GMF-based algorithm for sea ice detection with QuikSCAT improves on the misclassification scores that affect other algorithms and provides daily sea ice masks at a 25-km resolution for use in ground processors that require the effective removal of sea ice contaminated pixels all year round.

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.

Wind retrieval from scatterometer backscatter measurements is not trivial. A good assessment of the different measurement uncertainties inherent in scatterometer systems is very important for successful wind retrieval and quality control. One source of these uncertainties, i.e., geophysical noise, is dominated by the subcell wind variability. Although the latter is known to dominate the total measurement noise at low winds, no attempt to fully model such effect has yet been performed. In this paper, a simple method to derive a model of geophysical noise for the European Remote Sensing Satellite (ERS) scatterometer is proposed. It is assumed that this noise is mainly due to the spatial distribution of the backscatter footprints and the wind variability within the wind vector cell. In a simulation experiment these parameters were varied, and the values for which the simulation compares best to real data in the three-dimensional measurement space were selected. The resulting geophysical noise model is dependent on wind speed and across subsatellite track location. The empirical method presented here is straightforward and could be applied to other scatterometer systems

In a quest for a generic unbiased scatterometer wind inversion method, the different inversion procedures currently in use are revisited in this paper. A careful examination of both the errors in the wind and in the measurement domain, combined with the nonlinear shape of the geophysical model function (GMF), leads to a generic and novel Bayesian wind retrieval approach in the measurement domain. In this approach the shape of the GMF solution manifold in measurement space is more important than the specified noise. This shape is related to the system wind direction sensitivity, and when this sensitivity is uniform, realistic and precise wind direction distributions are retrieved, even when measurements lie far from the GMF manifold. A simplified measurement space transformation that produces such uniform sensitivity for the European Remote Sensing Satellite (ERS) scatterometer is presented and shown to have reduced wind direction bias compared to the more traditional (measurement-noise normalized) inversion for ERS. Moreover, the simplified wind inversion reveals a similar performance to the current operational ERS wind inversion, but is potentially more generally applicable. The simplified method is then applied to SeaWinds but is ineffective. In this case the instrument geometry results in a low sensitivity to wind direction at a few specific directions. As a consequence, certain wind direction solutions remain favored in the SeaWinds inversion.

In a recent paper, Younis et al. propose an apparently interesting methodology for the computation of the interference received by a spaceborne passive sensor from terrestrial interferences. However, the paper seems to require some clarifications, as some of the conclusions are questionable. This comments paper aims at identifying the most outstanding issues of the publication.

Recent work has shown the important properties of the wind inversion residual or maximum-likelihood estimator (MLE) for quality Control (QC) of QuikSCAT Hierarchical Data Format (HDF) observations. Since March 2000, the QuikSCAT near-real-time (NRT) Binary Universal Format Representation (BUFR) product is available. As this product is used for numerical weather prediction (NWP) assimilation purposes, a QC procedure for the BUFR product is needed. We study the behavior of the MLE in order to determine whether the HDF QC procedure is appropriate for BUFR data. A comparison using real HDF and BUFR data reveals that the MLE distributions of HDF and BUFR differ and are actually poorly correlated. One important difference between BUFR and HDF is the amount of signal averaging prior to wind inversion. The averaging reduces the number of observations used in the wind retrieval for the BUFR product as compared to HDF. We show with a simple example that different MLE distributions are indeed expected due to this averaging. We also run a simulation in order to link theory and reality and better understand the behavior of the MLE. Despite the different MLE behavior in BUFR and HDF, the quality of the retrieved winds, as compared with the European Centre for Medium-Range Weather Forecasts winds, is very similar. We develop an MLE-based QC procedure for BUFR, similarly to the one in HDF, and we compare both. The skill of the QC in BUFR is again very similar to the one in HDF, showing that despite the different MLE behavior in both formats, the properties of the MLE as a QC indicator remain very similar.

Following the successful assimilation of European remote sensing satellite (ERS) scatterometer winds for weather analysis and forecasting, the authors further develop this methodology for the assimilation of the NASA scatterometer (NSCAT) and QuikSCAT Ku-band scatterometer data. Besides retrieval problems in cases of a confused sea state, the quality control (QC) developed identifies cases with rain on a wind vector cell (WVC) by WVC basis. The elimination of such geophysical conditions is a prerequisite to arrive at a successful assimilation of Ku-band scatterometer data. Moreover, the authors propose to assimilate ambiguous winds rather than radar backscatter measurements, as is being done at most meteorological centers assimilating ERS scatterometer data. After their quality assessment, NSCAT winds still have more difficult ambiguity removal properties than ERS winds. A further testing of the data assimilation method proposed is being carried out at the European Center for Medium-range Weather Forecasts in NSCAT impact experiments. A normalized wind inversion residual is used for QC. In order to determine a threshold for the rejection of poor quality wind solutions, the inversion residual and the wind vector departure from the ECMWF model are correlated. They end up rejecting around 7.4% of wind vector solutions and 4.2% of the NSCAT WVCs. In order to perform a qualitative assessment of this rejection, comparisons to collocated SSM/I rain and ECMWF winds are used. Confused sea state and presence of rain seem to be the most likely causes for the rejection of WVCs.

This paper describes the positive impact of ERS scatterometer data on tropical cyclone analyses and forecasts at the European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, U.K. ERS scatterometer data is especially valuable because sparse genesis regions of tropical cyclones are available in the data, and they are available in cloudy and rainy conditions. In November 1997, ECMWF introduced a four-dimensional variational assimilation system (4D-Var) in operational use. This system benefits from a better utilization of ERS scatterometer wind data, ECMWF is using ERS-2 scatterometer wind data in the daily operational assimilation system. In order to understand and investigate the impact of ERS scatterometer wind data, assimilations with and without the use of scatterometer data have been performed for the most intense part of the 1995 Atlantic hurricane season. A comparison with the 1995 operational ECMWF optimum interpolation (OI) assimilation system's performance has also been done. Both intensity and positional errors of tropical cyclones are investigated for analyses and forecasts. The 4D-Var assimilation system show's great improvements compared to the previous OI assimilation system, and the best results are obtained when ERS scatterometer data are used in 4D-Var.