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%.

Ocean currents and winds are crucial parameters to understand ocean-atmosphere interactions, while the simultaneous retrievals using Doppler scatterometry can provide direct observational support. To obtain high-quality airborne Doppler scatterometer wind and current products, accurate estimation of the observed backscatter coefficient and reliable wind field inversion are essential. Based on the Ocean Surface Current Observation Mission (OSCOM) airborne experiment data collected using a Ka-band rotating pencil-beam Doppler scatterometer, we propose two different calibration methods for the backscatter coefficient to account for the larger-than-expected azimuthal modulation of the backscatter signal, as predicted by consolidated Geophysical Model Functions (GMFs) used in Ka-band scatterometry. Both methods are based on the numerical ocean calibration (NOC) approach, which is in turn based on the estimation of the mean backscatter differences between real measurements and simulated ones with the use of the GMF and reference winds. The first method employs an azimuth-dependent calibration, which can be implemented using either an overall ratio or a ratio per flight leg. The second method involves modifying the GMF to match the observed azimuthal modulation, with options for one or two GMF coefficient adjustments. The retrieved wind speeds range from 4 to 7 m/s, with wind directions around 155°. In comparison with collocated ECMWF winds, the wind speed biases of different methods are all lower than 1.2 m/s, and the wind direction standard deviations (SD) are lower than 9.3 °. The azimuth-dependent calibration method yields smaller wind speed biases but larger wind direction SDs compared to the modified GMF method. The azimuth-dependent calibration using leg-dependent ratios leads to the closest retrieved wind speeds and directions to ECMWF. The calibration methods proposed in this study provide data support for future simultaneous retrieval studies of ocean winds and currents. Additionally, these methods can be applied to other airborne Doppler scatterometer experiments.

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.

The spatial spectra of three Soil Moisture and Ocean Salinity (SMOS) soil moisture (SM) datasets, produced by the Barcelona Expert Center (BEC), were assessed in this study along zonal and meridional directions. The datasets are the Level 3 (L3) SM gridded at 25 km, the Level 4 (L4) SM at 1 km and an experimental L4 SM at ~300 m. Since the L4 products are obtained by a downscaling algorithm that uses Normalized Difference Vegetation Index (NDVI), NDVI data from MODIS (1 km) and Sentinel-3 (~300 m) were also analyzed.Both L4 SM products provide useful spatial information of small-scale structures, with estimated effective spatial resolutions of ~2.5 km (for the L4 at 1 km) and ~500 m (for the L4 at ~300 m). The NDVI data used for the downscaling have a significant impact not only on the spatial patterns of the resulting SM product, but also on its spectrum.

Satellite-derived, coincident ocean surface winds and currents are of great importance to enhance our understanding of air-sea interactions. The data from a flight campaign, carrying a Ka-band rotating pencil-beam Doppler scatterometer (i.e., the so-called OSCOM prototype), are exploited in this work. In particular, data calibration and wind/current retrievals are performed. The Normalized Radar Cross-Sections (NRCS, σ0) or backscatter measurements are calibrated using two different methods to account for the larger than expected (by consolidated Geophysical Model Functions or GMFs used in Ka-band scatterometry) azimuthal modulation of the backscatter signal. Both methods are based on the so-called target or numerical ocean calibration (NOC). The first method consists of applying an azimuth-dependent calibration, while the second is based on a modification of the GMF to match the observed modulation after calibration. The retrieved wind speeds range from 3 to 6 m/s, and the wind directions are around 155°. The standard deviations for wind speed and direction against ECMWF winds are lower than 0.9 m/s and 9°. The winds derived using azimuth-dependent calibration present lower wind speed bias and larger wind direction standard deviation with respect to the modified GMF calibration.

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

A modification of the Barcelona Expert Center (BEC) algorithm to downscale the Soil Moisture and Ocean Salinity (SMOS) soil moisture (SM) to 300 m spatial resolution is presented. It maintains the same functional relationship as the currently implemented version but employs the following inputs: SMOS brightness temperature (TB) and SM (25 km), European Center for Medium Weather Forecast (ECMWF) skin temperature (9 km), and Sentinel 3 Normalized Difference Vegetation Index (NDVI, 300 m).The performance of the downscaled SMOS SM at 300 m is analyzed by means of a temporal validation with in-situ observations from the Soil Moisture Measurements Stations Network of the University of Salamanca (REMEDHUS) and the Continuous Soil Moisture and Temperature Ground-based Observation Network (RSMN) during the year 2021. No significant differences in correlation, unbiased root mean square difference (ubRMSD) and bias are obtained over both networks compared to the 25 km and 1 km SM products, suggesting the BEC downscaling algorithm could work at hundreds of meters and result in a similar SM accuracy.

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.

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.

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

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

Recent advances in the sea surface wind quality control of the Advanced Scatterometer (ASCAT) show that spatial wind variability within a resolution cell of 25 km × 25 km, namely the subcell wind variability, is highly correlated with the ASCAT quality indicators, such as the wind inversion residual (maximum likelihood estimator, MLE) and the singularity exponent (SE) derived from singularity analysis. This opens up opportunities for quantifying the instantaneous spatial wind variability over the global sea surface. In this paper, it is assumed that the spatial wind variability is linearly proportional to the temporal variation of buoy sea surface winds time-series following the Taylor’s hypothesis. As such, the moored buoy winds with 10-minute sampling are used to examine the subcell wind variability. Then the sensitivity of ASCAT quality indicators to the subcell wind variability is evaluated. The results indicate that although SE is more sensitive than MLE in characterizing the wind variability, they are mainly complementary in flagging the most variable winds. Consequently, an empirical model is derived to relate the buoy wind vector variability to the ASCAT MLE and/or SE values. Although the overall procedure is based on the one-dimensional temporal analysis and such empirical model cannot fully represent the two-dimensional spatial variability, it leads for the first time to the development of an ASCAT-derived local wind variability product. The empirical method presented here is straightforward and can be applied to other scatterometer systems.

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.

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.

The novel Correlated Triple Collocation (CTC) analysis allows to assess three different data sources of similar spatial resolutions, but with two of them being correlated. In this study, the CTC was applied to estimate the unbiased random errors of the global soil moisture (SM) data provided by two L-band satellite missions —the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP)— and one numerical model—the ERA5-Land. The three existing SMOS SM products distributed by different research institutions were also analyzed. Preliminary results revealed that errors of SMOS and SMAP SM are correlated, with correlations of ∼0.5-0.6. Thus, only ERA5-Land can be considered as independent. The lowest error was obtained for SMAP (0.025 m3m−3), followed by ERA5-Land (0.036 m3m−3). Among the SMOS SM, SMOS-IC had the lowest error (0.046 m3m−3), SMOS-BEC showed an intermediate value (0.048 m3m−3), and SMOS-CATDS had the highest error (0.055 m3m−3).

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.

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.

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.

Rain is known to be the most significant phenomenon in degrading the Ku-band scatterometer wind quality. After the decommission of the National Aeronautics and Space Administration scatterometer (NSCAT), little work has been done in characterizing the impact of rain on Ku-band fan beam scatterometer. In this paper, the rain impact on the backscatter measurements as well as the retrieved wind quality of the China-France Oceanography Satellite (CFOSAT) scatterometer (CSCAT) is investigated using the European Centre for Medium-range Weather Forecasts (ECMWF) winds and the Global Precipitation Measurement (GPM) mission's Microwave Imager (GMI) rain data as reference. The dependence of rain effects on the observing incidence angle is studied with the objective to optimize the configurations of wind inversion and quality control (QC). It is shown that the backscatter measurements at low incidence angles (~30°) are much less affected by rain than those at higher incidence angles. The operational CSCAT processing proves to be effective in screening rain-contaminated wind vectors but at the expense of many valuable winds. An adapted wind inversion scheme is proposed to further improve the CSCAT wind quality under rainy conditions.

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).

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.

The sea surface winds from the CFOSAT scatterometer (CFOSCAT) are retrieved using the maximum likelihood estimator, and the inversion residual is used to sort the good-quality winds from the poor-quality ones. A two-dimensional variational analysis ambiguity removal (2DVAR) scheme is then applied over the CFOSCAT swath such that a unique wind field is selected from the available local scatterometer wind vector ambiguities. The preliminary results of CFOSCAT Level 2 (L2) processing show that the retrieved wind speed is overestimated under low-wind conditions (w15 m/s). Moreover, the inversion residual for the sweet swath (where there are more than 10 views) is generally higher than that for the nadir/outer swath. These imply that observations with different geometries (views) at the same WVC are inconsistent with respect to the geophysical model function, and thus a comprehensive calibration is highly demanded. A more detailed assessment of the CFOSCAT wind quality will be carried out after calibration and validation campaign.

The quality of the Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) maps has been noticeably improved in the last two years, in particular those produced at the Barcelona Expert Center (BEC). However, the BEC SSS maps are still affected by a latitudinal and seasonal bias. In this work, we comprehensively characterize the residual latitudinal and seasonal biases, which are used to correct de retrieved SSS, leading to a new generation of higher-quality SSS maps. The shape and regularity of this bias suggests that the effect, which produces this error, is not a poor characterization of the galaxy, some residual Total Electron Content (TEC) effect, or a poor characterization of the systematic Sea Surface Temperature (SST) effects on the SSS retrieval. It appears to be related to a geometrical effect associated to the relative position between the SMOS antenna, the Sun and the Earth.

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

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

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

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

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.

After more than eight years of Soil Moisture and Ocean Salinity (SMOS) aquisitions, an empirical characterization of the biases and the computation of an effective brightness temperature uncertainty is possible. In this work we show that both parameters strongly depend on the geographical location of the acquisition. Metrics based on the differences between expected and theoretical values of the bias and uncertainty are developed and used for a quantitative assessment of the locations where SMOS errors are currently being worse characterized. This characterization can be used for the definition of an empirical bias correction and a more accurate cost function which are expected to provide a better SMOS SSS product.

Typhoon is one of the most powerful and destructive natural disasters. Accurate forecasting of Typhoon track and intensity is very important to disaster prevention and reduction. Satellite observations can effectively compensate for the shortcomings of traditional methods of sea surface measurement and provide all-weather observation over the sea surface, which is of great significance to improve the numerical prediction of strong convective weather over ocean. The spaceborne radar observes the backscattering caused by the sea surface roughness, and then, the sea surface wind can be retrieved. The Synthetic Aperture Radar (SAR) is an important data source for sea surface monitoring. A variety of meteorological hydrological elements can be retrieved by SAR observation, and it has been used in data assimilation in recent years [1]. SAR imagery is also used to monitor strength and structure of typhoons [2]. The accuracy of sea surface winds retrieved from SAR has been found to be comparable to that of scatterometer data [3], and these wind fields can be used with a data assimilation system to provide the initial conditions for the numerical weather prediction (NWP) model [4].

The triple collocation (TC) technique allows the simultaneous calibration of three independent, collocated data sources, while providing an estimate of their accuracy. In this paper, the TC is adapted to validate different salinity data products along the tropical band. The representativeness error (the true variance resolved by the relatively high-resolution systems but not by the relatively low-resolution system) is accounted for in the validation process. A method based on the intercalibration capabilities of TC is used to estimate the representativeness error for each triplet, which is found to impact between 15% and 50% the error estimation of the different products. The method also sorts the different products in terms of their resolving spatiotemporal scales. Six salinity products (sorted from smaller to larger scales) used were: the in situ data from the Global Tropical Moored Buoy Array (TAO), the GLORYS2V3 ocean reanalysis output provided by Copernicus, the satellite-derived Aquarius Level 3 version 4 (AV4) and Soil Moisture and Ocean Salinity (SMOS) objectively analyzed (SOA) maps, and the climatology maps provided by the World Ocean Atlas (WOA). This calibration study is limited to the year 2013, a year when all the products were available. This validation approach aims to assess the quality of the different salinity products at the satellite-resolved spatiotemporal scales. The results show that, at the AV4 resolved scales, the Aquarius product has an error of 0.17, and outperforms TAO, GLORYS2V3, and the SOA maps. However, at the SOA resolved scales (which are coarser than those of the Aquarius product because of the large OA correlation radii used), the SMOS product has an error of 0.20, slightly lower than that of GLORYS2V3, Aquarius, and TAO. The WOA products show the highest errors. Higher order calibration may lead to a more accurate assessment of the quality of the climatological products.

The Soil Moisture and Ocean Salinity (SMOS) and Aquarius satellite missions have produced the first sea-surface salinity (SSS) maps from space. The quality of the retrieved SSS must be assessed, in terms of its validation against sparse ground truth, but also in terms of its ability to detect and characterize geophysical processes, such as mesoscale features. Such characterization is sometimes elusive due to the presence of noise and processing artifacts that continue to affect state-of-the-art remote sensing SSS maps. A new method, based on singularity analysis, is proposed to contribute to the assessment of the geophysical characteristics of such maps. Singularity analysis can be used to directly assess the spatial consistency of the SSS fields and to improve the estimation of the wavenumber spectra slope through a new method, the singularity power spectra (SPS). To demonstrate the SPS performance and utility, we applied SPS to different gridded SSS maps, such as SMOS and Aquarius high-level products, the output of a numerical simulation, in situ reanalysis, and climatology, as well as to other sea-surface temperature products for reference. The singularity analysis and SPS methods reveal that both the SMOS level 4 and the Aquarius combined active passive products are both able to describe the geometry of the existing geophysical structures and provide consistent spectral slopes. This paper demonstrates that beyond the remaining sources of uncertainty in remote sensing SSS products, valuable dynamical information on the ocean state can be extracted from these SSS products.

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.

Quality control (QC) is an essential part of the scatterometer wind retrieval. In the current pencil-beam scatterometer wind processor (PenWP), a maximum likelihood estimator (MLE)-based QC is used to discern between good- and poor-quality winds. MLE QC is generally effective in flagging rain contamination and increased subcell wind variability in the ocean surface wind vectors derived from Ku-band pencil-beam scatterometers, such as the RapidScat (RSCAT) installed on the International Space Station. However, the MLE is not an effective quality indicator over the outer swath where the inversion is underdetermined due to the lack of azimuthal diversity (including lack of horizontal polarized measurements). Besides, it is challenging to discriminate rain contamination from “true” high winds. This paper reviews several wind quality-sensitive indicators derived from the RSCAT data, such as MLE and its spatially averaged value (MLEm), and the singularity exponents (SE) derived from an image processing technique, called singularity analysis. Their sensitivities to data quality and rain are evaluated using collocated Advanced Scatterometer wind data, and global precipitation measurement satellite's microwave imager rain data, respectively. It shows that MLEm and SE are the most effective indicators for filtering the poorest-quality winds over RSCAT inner and outer swath, respectively. A simple combination of SE and MLEm thresholds is proposed to optimize RSCAT wind QC. Comparing to the operational PenWP QC, the proposed method mitigates over-rejection at high winds, and improves the classification of good- and poor-quality winds.

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

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.

New ocean products from the Soil Moisture and Ocean Salinity (SMOS) mission are being developed at the Barcelona Expert Centre. Besides the already operational 9-day and monthly sea surface salinity (SSS) products, two additional daily SSS products have been recently become operational: a simple user-friendly product containing all swath-based Level 2 data for each day, and a more elaborated product that uses multifractal fusion techniques to increase the spatial and temporal resolution. Finally, experimental BEC products are also presented which provide SSS values in regions strongly affected by radio-frequency interference (RFI). Recent progress on Land-Sea contamination mitigation has been applied to the BEC products.

This work is devoted to describe the new processing techniques that are being conceived, developed and implemented at the Barcelona Expert Centre (BEC) for the generation of sea surface salinity (SSS) maps from the Soil Mooisture and Ocean Salinity (SMOS) mission. Several algorithms to mitigate the ripples and sidelobes present in the SMOS brightness temperature (TB) images, to characterize the spatial correlations in the SMOS antennas, to correct for the systematic SSS-derived biases, and to improve the spatial and temporal resolution of the SSS products, have been recently developed and are presented in this paper.

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.

Remotely sensed measurements acquired by the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite are processed in a uniform equal-area grid, the Icosahedral Snyder Equal Area (ISEA) 4H9. Brightness temperature measurements are projected onto that grid (the so-called Level 1C), as well as sea surface salinity and soil moisture estimates (Level 2). The ISEA grid has been chosen for its characteristics of equal area and almost uniform intercell spacing. Nevertheless, when considering the SMOS viewing geometry, the measurement footprint size, and the processing applied to those measurements, this choice may be revisited. With this objective, the ISEA 4H9 grid is compared to other equal-area grids with different sizes and orientations with respect to the satellite track. The best configuration resulted to be a 25-km-width grid symmetrical with respect to satellite track. This grid appeared to be better suited for improving SMOS Level 2 retrieval algorithms as well as to serve as input for higher level data production, since it best accounts for the instrument's viewing geometry and substantially reduces the correlation between adjacent grid cells.

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

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

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

The European Space Agency Soil Moisture and Ocean Salinity mission aims at estimating, over the oceans, sea surface salinity (SSS) with spatial and temporal coverage adequate for large-scale oceanography. Spatiotemporal averaging of the retrieved SSS [level-3 (L3) product] has to be properly performed in order to meet the challenging mission requirements. At high latitudes, the generally low sea surface temperature (SST) characterizing the ocean degrades the brightness temperature sensitivity to SSS, but conversely, an improvement in the L3 retrieved SSS performances should be expected due to an increased pixel sampling. This tradeoff between geophysical effects in cold seawater and the concomitant temporal oversampling has been addressed by analyzing the latitudinal trend of the retrieved salinity performances, in various retrieval configurations and settings, once a conservative and optimal data filtering strategy is applied. Quantitative rate of changes of the SSS retrieval performance with the SST variability is provided, together with the net oversampling contribution to the L3 SSS accuracy. The experiments carried out demonstrate that the high-latitude oversampling does not compensate for the SST-driven latitudinal degradation of the L3 SSS product quality.

A sounding of the capabilities of a novel salinity retrieval strategy by means of Support Vector Regression (SVR) has been performed. SMOS brightness temperatures measurements and additional auxiliary parameters have been co-located with salinity data collected by ARGO buoys, which represented the ground-truth to be matched by the algorithm. Salinity fields estimated by the SVR are in good agreement with the ground-truth, suggesting that the chosen approach can be promising, despite its robustness and versatility are under further assessment over wider areas and time lags, and in various combinations of SMOS features.

The local oscillators (LOs) of the Soil Moisture and Ocean Salinity mission payload are used to shift the operating frequency of the 72 receivers to an optimal intermediate frequency needed for the signal processing. The LO temperature variations produce phase errors in the visibility, which result in a blurring of the reconstructed brightness temperature (Tb) image. At the end of the commissioning phase, it was decided to calibrate the LO every 10 min while waiting for a more in-depth analysis. During short periods of time, the LO calibration has been performed every 2 min to assess the impact of a higher calibration rate on the quality of the data. In this paper, by means of a decimation experiment, the relative errors of 6- and 10-min calibration interval data sets are estimated using the 2 min as a reference. A noticeable systematic across- and along-track pattern of amplitude ±0.3 K is observed for Tb differences between 10 and 2 min, whereas this is reduced between 6 and 2 min. A simulation experiment confirms that the nature of such systematic pattern is due to the visibility phase errors induced by the LO calibration rate. Such pattern is propagated into the sea surface salinity (SSS) retrievals. Overall, the SSS error increase (relative to the 2 min SSS data) is about 0.39 and 0.14 psu for the 10- and 6-min data sets, respectively. This paper shows that a LO calibration rate of at least 6 min would noticeably improve the SSS retrievals.

After 2.5 years of the Soil Moisture and Ocean Salinity (SMOS) mission, the characterization of residual instrumental systematic errors in the measured brightness temperatures (TB) is still rather poor. This, in turn, negatively impacts the sea surface salinity retrievals and, as such, notably limits the mission's success. The error mitigation methodology currently used operationally, the so-called Ocean Target Transformation (OTT), mixes both instrumental and model-induced errors. In this paper, it is proposed to distinguish errors by their type of impact on the TB images: mean brightness level, incidence angle dependence, and azimuth angle dependence. A new approach to characterize the azimuth-dependent errors is proposed. First, a careful data selection strategy is applied. Then, an empirically fitted model, which only accounts for the TB incidence angle dependence, is subtracted from the mean TB images of the selected data sets to estimate the systematic antenna-frame errors. The robustness of this methodology is assessed through the estimated anomaly pattern stability when computed for different geophysical conditions, periods of time, and latitudinal bands. The residual variability ranges from 0.03 K to 0.14 K, whereas the OTT variability is about 0.5 K. The new method is forward model independent and generic. It can therefore be applied to estimate the antenna-frame systematic errors over land and ice. Moreover, it proves to be very effective in separating different sources of error and can therefore be used to further characterize other error components and improve the various SMOS forward model terms.

The Local Oscillators (LO) of the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) onboard the Soil Moisture and Ocean Salinity (SMOS) satellite are used to maintain the operating frequency of the 69 receivers. The phase of the LO drifts over time, in turn blurring the MIRAS brightness temperature (TB) measurements. After a pre-launch assessment, it was decided to calibrate the LO every 10 minutes to reduce the phase drifts. During short periods of the first 2.5 years of SMOS mission, the LO calibration has been performed every 2 minutes to assess the impact of a higher calibration frequency on the quality of the data. In this study, relative differences (10-min TBs versus 2-min TBs) of about 0.3 K are shown, which lead to non-negligible relative differences of about 0.2-0.3 practical salinity units (psu) in the retrieved sea surface salinity (SSS). However, when performing independent validation against Argo float SSS data at Level 3 (spatio-temporally averaged SSS products), no significant differences are found between 10-min and 2-min data. This is due to the fact that current SMOS SSS accuracy (relative to Argo) is about 0.6-0.8 psu, thus masking the relatively smaller LO calibration frequency effect.

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.

A prospective sounding of the capabilities of a novel salinity retrieval by means of Support Vector Regression has been performed. Co-located SMOS measurements and additional auxiliary parameters have been considered, whilst salinity data collected by ARGO buoys represented the ground-truth to be matched by the algorithm. Salinity fields estimated by the SVR are in good agreement with the ground-truth, suggesting that the chosen approach can be promising, despite its robustness and versatility needs to be assessed over wider areas and time lags, and in various combinations of SMOS features.

The Soil Moisture and Ocean Salinity (SMOS) mission was launched on November 2nd, 2009 aiming at providing sea surface salinity (SSS) estimates over the oceans with frequent temporal coverage. The detection and mitigation of residual instrumental systematic errors in the measured brightness temperatures are key steps prior to the SSS retrieval. For such purpose, the so-called ocean target transformation (OTT) technique is currently used in the SMOS operational SSS processor. In this paper, an assessment of the OTT is performed. It is found that, to compute a consistent and robust OTT, a large ensemble of measurements is required. Moreover, several effects are reported to significantly impact the OTT computation, namely, the apparent instrument (temporal) drift, forward model imperfections, auxiliary data (used by forward model) uncertainty and external error sources, such as galactic noise and Sun effects (among others). These effects have to be properly mitigated or filtered during the OTT computation, so as to successfully retrieve SSS from SMOS measurements.

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.

A prerequisite for the successful retrieval of geophysical parameters from remote sensing measurements is the development of an accurate forward model. The European Space Agency Soil Moisture and Ocean Salinity (SMOS), carrying onboard an L-band interferometric radiometer (Microwave Interferometric Radiometer using Aperture Synthesis), was launched on November 2009. Due to the lack of L-band passive ocean measurements from space, several prelaunch forward models were developed and initially used in the SMOS ocean salinity operational processor. In this paper, an update of the prelaunch semi-empirical forward model is presented, using for the first time, real SMOS data. In particular, the ocean surface emissivity modulation at L-band due to rough sea surface is reviewed and reanalyzed. A new model definition is provided with the help of a simple neural network. The improvement is quantified in terms of retrieved salinity accuracy compared with the climatology and concerns essentially the range of wind speeds higher than 12 m·s-1.

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.

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.

SMOS salinity inversion consists of minimizing the residual between measured and modeled brightness temperatures. The minimization procedure is a great challenge and crucial step, but its success depends on the quality of the forward model. Consequently, we present an empirical update of pre-launch L-band emissivity forward models, where the essential improvement is related to the emissivity by a rough sea surface. The improvement is quantified in terms of retrieved salinity accuracy compared to the climatology.

SMOS (Soil Moisture and Ocean Salinity), launched in November 2, 2009 is the first satellite mission addressing the salinity measurement from space through the use of MIRAS (Microwave Imaging Radiometer with Aperture Synthesis), a new two-dimensional interferometer designed by the European Space Agency (ESA) and operating at L-band. This paper presents a summary of the sea surface salinity retrieval approach implemented in SMOS, as well as first results obtained after completing the mission commissioning phase in May 2010. A large number of papers have been published about salinity remote sensing and its implementation in the SMOS mission. An extensive list of references is provided here, many authored by the SMOS ocean salinity team, with emphasis on the different physical processes that have been considered in the SMOS salinity retrieval algorithm.

Preliminary results obtained during the commissioning phase of the Soil Moisture and Ocean Salinity (SMOS) mission are described, devoting special attention to the characterization of the systematic errors found in the measurements and the corresponding impact in the retrieved salinity product. The identified issues and objectives to consolidate and improve the processing chain are also described.

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$\hbox{m} \cdot \hbox{s}^{-1}$.

As part of the preparation for the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite mission, empirical sea-surface emissivity (forward) models have been used to retrieve sea-surface salinity from L-band brightness-temperature (TB) measurements. However, the salinity inversion is not straightforward, and substantial effort is required to define the most appropriate cost function. Various Bayesian-based configurations of the cost function are examined, depending on whetheraprioriinformation is used in the inversion. A sensitivity analysis ofTBto several geophysical parameters has been performed and has shown that the instrument has low sensitivity to the parameters that modulate theTB(including salinity). The SMOS end-to-end simulator is used to test the accuracy of different cost-function configurations. Currently, the general opinion in the SMOS community is that a partially constrained cost function, in which the salinity constraint is effectively removed, is the most appropriate for salinity retrieval. The purpose of this letter is to show that we found no evidence that such a configuration performs better than a fully constrained or a nonconstrained one. Moreover, in contrast to previous results, we found that the fully constrained inversion does not converge to the reference or auxiliary salinity value and produces the most accurate salinity retrievals of the tested configurations. Therefore, such a configuration should not be disregarded for future tests.

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.

As part of the preparation for the European Space Agency SMOS (soil moisture and ocean salinity) satellite mission, empirical sea surface emissivity (forward) models have been applied to retrieve sea surface salinity from L-band brightness temperature (TB) measurements. However, the salinity inversion is not straightforward and an important effort is required to define the most appropriate cost function (inversion algorithm). Different Bayesian-based configurations of the cost function are examined, depending on whether prior information is used in the inversion or not. It is important to properly balance all the terms of the cost function, as well as to have a good knowledge of the quality of the prior information. A sensitivity analysis shows that the instrument has low sensitivity to the geophysical parameters that modulate the Tb (including salinity). As such, the inversion needs to be constrained with prior information. Simulations are also performed using the SMOS simulator to assess the retrieval errors produced by the different cost function configurations. In line with the sensitivity analysis, the errors are very large when no prior information is used in the cost function. The lowest errors are obtained when the inversion is constrained with the full prior information, i.e., information from all the auxiliary (geophysical) parameters. As such, it is concluded that the use of prior information is essential for a successful salinity retrieval from SMOS measurements.

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.

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.