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.

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.

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 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 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.

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.

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.

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.

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 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.

We present one of the first studies on ocean current retrievals from interferometric synthetic aperture radar (InSAR) data acquired during the Shuttle Radar Topography Mission (SRTM) in February 2000. The InSAR system of SRTM was designed for high-resolution topographic mapping of the Earth's land surfaces, using two SAR antennas on a Space Shuttle with a cross-track separation of 60 m. An additional along-track antenna separation of 7 m resulted in an effective time lag of about 0.5 ms between the two images, which could theoretically be exploited for target velocity retrievals. However, the feasibility of ocean current measurements with SRTM has been questionable, since the time lag was much shorter than the theoretical optimum (about 3 ms at X-band) and the signal-to-noise ratio over water was quite low. Nevertheless, some X-band InSAR images of coastal areas exhibit clear signatures of tidal flow patterns. As an example, we discuss an image of the Dutch Wadden Sea. We convert the InSAR data into a line-of-sight current field, which is then compared with results of the numerical circulation model KUSTWAD. For tidal phases close to the conditions at the time of the SRTM overpass; we obtain correlation coefficients of up to 0.6 and rms differences on the order of 0.2 m/s. Furthermore we find that SRTM resolves current variations down to spatial scales on the order of 1 km. This is consistent with predictions of a numerical InSAR imaging model. Remaining differences between SRTM- and KUSTWAD-derived currents can be attributed mainly to residual motion errors in the SRTM data as well as to a limited representation of the conditions at the time of the SRTM overpass in the available KUSTWAD results.

Latest concepts for the TerraSAR-X hardware design include the availability of a split antenna mode, in which the fore and aft halves of the SAR antenna array with a total length of 4.8 m can act as separate receiving antennas for along-track interferometry (ATI). The effective ATI time lag is 0.17 ms. We discuss in this paper whether the split antenna mode is suitable for ocean current measurements. Typical (tidal) currents in coastal areas are on the order of 1 to 2 m/s. To measure them, the ATI time lag should ideally be 20 times longer than the time lag of TerraSAR-X. Furthermore, the backscattered power from the ocean at low wind speeds can be below the noise floor of the instrument. However, the high resolution of TerraSAR-X permits averaging over many pixels to reduce noise. Simulated data products suggest that TerraSAR-X is capable of measuring currents with satisfactory accuracy at a spatial resolution of about 1 km, which is sufficient for many applications.

The feasibility of exploiting the along-track separation of 7 m between the two X band antennas of SRTM for ocean current measurements was already demonstrated by us at IGARSS 2002. We presented an SRTM image of the Dutch Waddenzee with clear phase variations in water-covered areas, which could be converted into line-of-sight current variations by about 1.3 m/s. A comparison with a current map from a tidal atlas showed good qualitative agreement, but digital reference data for a quantitative validation of the SRTM results were not available. In this work we present a detailed comparison of the SRTM data with simulated current fields from the numerical circulation model KUSTWAD. We obtain an overall correlation of 0.558, a regression coefficient of 1.011, and an rms difference between SRTM and KUSTWAD currents of 0.24 m/s. Furthermore, we analyze the correlation of spatial variations in the measured and simulated current fields on different length scales. We find that SRTM can resolve the current variations which are relevant to KUSTWAD down to scales on the order of 1 km.