Spaceborne synthetic aperture radar (SAR) provides high-resolution sea surface observations to characterize oceanic and atmospheric variability at submesoscales. Despite significant progress in retrieving wind fields from SAR images, challenges remain particularly for single-antenna measurements where wind speed inversion often requires an external wind direction input. This study introduces and evaluates the use of directional features derived from SAR image sublook cross-spectra to improve wind retrieval. Specifically, we explore the imaginary part of SAR mean cross-spectra (IMACS) and its differential counterpart,$\Delta $IMACS, which capture directional signatures of range-traveling intermediate wind waves. Using systematically collocated Sentinel-1A wave mode SAR images and model winds, we demonstrate that IMACS exhibits a cosine-like dependence on wind direction at given wind speeds at two incidence angles, while$\Delta $IMACS reveals unique sensitivities that complement radar backscattering and can resolve wind direction ambiguities. A novel cost function combining radar backscattering, IMACS, and$\Delta $IMACS is proposed to retrieve wind vectors directly from SAR data without external wind direction inputs. The proposed scheme is evaluated through comparisons with independent model outputs and buoy measurements, showing improved performance. These findings highlight the potential of IMACS and$\Delta $IMACS to enhance self-sufficient wind inversion, offering a promising pathway for single-polarized SAR wind retrieval.

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.

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.

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.

The China-France Oceanography Satellite (CFOSAT) scatterometer (CSCAT) is a rotating range-gated fan-beam scatterometer which is designed to measure the normalized radar backscatter. Because of its high spatial sampling density, CSCAT data is suited for land and ice applications facilitated by resolution enhancement algorithms. Generally, such algorithms require accurate knowledge of the spatial response function (SRF) for the individual measurement. Standard CSCAT L1B products include the locations of the backscatter measurements, but not the SRF. In the paper, the mathematical expression of CSCAT slice SRF is derived, and the shape of the SRF is investigated. It has been found that the SRF can be approximated as a binary function that is 1 inside the −3dB contour of the SRF and 0 outside. The shapes of CSCAT SRF varies with antenna rotation angle and orbit position.

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

Sea surface winds derived from the spaceborne rotating pencil-beam scatterometers, such as the current Chinese Haiyang-2 (HY-2) scatterometers and the Indian SCATSAT-1 scatterometer, are used in a wide range of atmospheric and oceanic applications. One key problem of the pencil-beam scatterometers is the relatively low wind quality in the nadir region, due to the poor azimuth diversity of the nadir-track observations. This article proposes to include the altimeter backscatter measurements in the wind inversion to improve the retrieved wind quality in the nadir region of HY-2B scatterometer (HSCAT). First, empirical model functions are developed to relate the C- and Ku-band altimeter backscatters to the neutral surface winds at 10-m height. Subsequently, the maximum likelihood estimator, which is commonly used to perform wind inversion for satellite scatterometers, is adapted to retrieve winds with simultaneous scatterometer and altimeter measurements. The significance of altimeter measurements in the combined wind inversion is addressed by comparing the cost functions of the combined scatterometer/altimeter wind retrieval and the nominal scatterometer inversion. The experimental results are eventually validated using moored buoy winds and the European Centre for Medium-range Weather Forecasting (ECMWF) model winds, indicating that the inclusion of altimeter backscatters has a positive impact on the wind quality over the HSCAT nadir region. Finally, the potential approaches to further improve the combined scatterometer and altimeter wind inversion are summarized.

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.

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.

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.

Polar sea ice is an important and sensitive indicator of climate change. The scatterometer onboard the China-France Oceanography Satellite (CFOSAT) is a Ku-band rotating fan-beam scatterometer with dual polarization capability and dedicated to the measurement of sea surface wind vectors. Because of the wide swath, it is also a good candidate for sea ice detection in the polar regions. The paper explorers a Bayesian sea ice detection method for the CFOSAT scatterometer. The performance of the approach is analyzed and validated against coincident sea ice concentration product of Special Sensor Microwave/Imager (SSMI). The results shows that the sea ice detection approach is feasible and effective. The resulting agreement between the sea ice maps of the CFOSAT scatterometer and sea ice concentration of SSMI is high.

Respecting the low measurement resolution of scatterometer, the adaption of the linear deconvolution processing for scatterometer is investigated to improve its spatial resolution. Based on the linear deconvolution processing, a specific form of averaging window of the resolution enhanced solution is derived. A quantitative evaluation of resolution enhancement and noise amplification of the reconstruction is conducted. The results show that some specific resolution and noise amplification solution can be obtained by tradeoff parameter selection. Based on the linear deconvolution processing, the resolution enhancement of scatterometer data can be analyzed quantitatively.

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.

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

The first rotating fan beam scatterometer onboard China-France Oceanography Satellite (CFOSAT) was successfully launched on October 29, 2018. CFOSAT SCATterometer (CSCAT) is dedicated to the monitoring of sea surface wind vectors but also provides valuable data for the applications over land and Polar Regions. This article provides an overview of the relevant procedures of CSCAT data processing, including onboard signal processing and operational ground processing. Then a post-launch analysis is carried out to evaluate the first results of CSCAT L1 and L2 products. It shows that the CSCAT instrument is generally stable in terms of noise measurements and internal calibration, unless there is any important change in the system configuration. Specifically, the CSCAT backscatter (σθ) precision and wind quality are studied using a set of collocated ancillary data. The σθprecision degrades as wind speed decreases, and it is relatively low at high incidence angles (e.g., θ >46°). In particular, backscatter estimation of the horizontally polarized beam should be further improved by correcting the noise subtraction factor. The retrieved CSCAT winds are in good agreement with the European Centre for Medium Range Weather Forecasts (ECMWF) winds, the Advanced Scatterometer (ASCAT) winds, as well as the buoy winds. However, due to unresolved calibration and interbeam consistency problems, the wind quality degrades remarkably for the out-swath and the nadir-region wind vector cells, implying that the σθcalibration should be improved in the future updates.

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

The new Ku-band scatterometer (HSCAT-B) onboard the HY-2B satellite was launched on October 25, 2018, and soon after the C-band scatterometer (Advanced Scatterometer (ASCAT)-C) onboard the MetOp-C satellite was launched on November 6, 2018. This article aims to validate the new sea surface wind products from them, and also to summarize the common issues in current scatterometer wind products. Thus, other scatterometer data are also used for comparisons, including the C-band MetOp-B/ASCAT, and Ku-band SCATSAT-1/OSCAT2 and HY-2A/SCAT winds. In this study, the C-band and Ku-band scatterometer wind products were each reproduced using the same procedures, in terms of backscatter calibration, wind retrieval, and quality control. The scatterometer winds are compared to the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 winds or buoy winds, and the results show that the quality of ASCAT-C winds is almost the same as the well-known ASCAT-B; the HSCAT-B winds show quite good quality and similar validating statistics as ASCAT winds. Noticeable wind-speed-dependent biases are found in all Ku-band scatterometer winds, which suggests that refinements are needed for the NSCAT-4 geophysical model function, especially in terms of wind speed dependence for all incidence angles.

After the launching of CFOSAT (China-France Oceanography Satellite), calibrations of the SCAT (scatterometer onboard the CFOSAT) are carried out to modify the bias of the observed NRCS (normalized radar cross section). In this paper, some preliminary calibration results of CFOSAT SCAT are presented, including both internal calibration onboard the CFOSAT and external calibration on the ground. First, the onboard calibration signals and noise signals are analyzed to ensure the SCAT works in a steady status. Then, calibrations on NRCS are carried out by using the SCAT data from Amazon rainforest area. Furthermore, Calibrations using transponders will be carried out at Inner-Mongolian. Preliminary calibration results show that the NRCS of CFOSAT SCAT are compliable with the NRCS of NSCAT.

The scatterometer (SCAT) onboard the China-France Oceanography Satellite (CFOSAT) is the first rotating fan-beam scatterometer (RFSCAT) in space for global ocean surface vector wind measurement. After the CFOSAT launched in Oct. 29, 2018, the main performances of the CFOSAT scatterometer (CFOSCAT) are being tested in orbit. In this paper, the preliminary data processing results of CFOSCAT are presented, including the calibration signal analyses, system noise analyses, normalized radar cross section (NRCS) processing results and wind retrieval results. The results show that CFOSCAT works properly in orbit, the main system specifications of scatterometer system satisfies the design. Preliminary results also validates the wind retrieval performance.

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.

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.

The Ku-band rotating fan-beam scatterometer (RFSCAT) onboard the China-France Oceanography Satellite (CFOSAT) is dedicated to the measurement of sea surface wind vectors. The flight model of RFSCAT has been assembled and tested, and the data processing algorithms have been developed. This paper provides an overview of the design and characteristics of RFSCAT instrument, and then a prelaunch analysis is carried out to estimate the scatterometer backscatter and wind accuracy. The overall study includes two parts: first, a forward model is developed to simulate the ocean backscatter signals under certain wind conditions; second, a wind inversion processor is used to retrieve sea surface wind vectors from the outputs of the forward model. Simulations show that the backscatter accuracy and the retrieved wind quality of RFSCAT are quite promising and meet the scientific requirements of the mission.

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.

Based on [1], a quantitative comparison of resolution enhancement performance for three deconvolution approaches (L2 norm, L1 norm and TV) to DPS scatterometer over nadir swath are made. The spatial resolution and reconstruction accuracy/precision properties of these methods are analyzed and compared. Results show that resolution enhancement is achieved by the price of decreased accuracy/precision of reconstructed backscatter, and the reconstructed backscatter becomes a more smoothed version of truth backscatter with decrease of spatial resolution.

The scatterometer (SCAT) onboard China France Oceanography Satellite (CFOSAT) will be the first scanning scatterometer with rotating fan-beams. Wind retrieving methods for existed scatterometers, which are with rotating pencil beams or fixed fan beams, are needed to be modified considering efficiency in information extraction from the numerous observations acquired in this working mode. This was achieved in the research of this paper by applying Principle Component Analysis (PCA) method. Firstly, vectors for applying PCA are composed in two different ways for analysis of the effectiveness of information extraction for the SCAT data and for wind retrieving respectively. Then the description of simulated SCAT data considering observing geometry and SCAT working mode was given. Then experiments of PCA based wind retrieving method was carried out with conclusion that it was effective in information extraction for preparing data sets for wind retrieving. Finally, further research has been discussed.

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 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.

Dual-Frequency Polarized Scatterometer (DPS) is a new system utilizing Doppler Beam Sharpening (DBS) technology for resolution enhancement. Respecting the low azimuth resolution over nadir swath, the Backus-Gilbert method is investigated to improve the resolution. According to the precision definition of scatterometer measurement, a Backus-Gilbert method for scatterometer is derived. Using the method, the tradeoff of resolution and precision of resolution-enhanced backscatter (normalized radar cross section, σ0) is established in a quantitative manner. The results show that resolution enhancement is achieved by the price of greatly decreased precision of reconstructed backscatter.

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.

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

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

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

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

Rotating fan-beam scatterometer (RFSCAT) is a new kind of radar system designed to measure the normalized radar backscatter coefficients (σ°) to derive the near ocean surface wind vectors (OSWV). In this paper, some considerations about the in-orbit calibration of a Ku-band RFSCAT are presented. A simple method based on ground Calibration Ground Stations (CGS) is proposed and the method is verified by simulations. Spacecraft attitude errors are estimated and RFSCAT antenna gain pattern is able to be monitored using Calibration Ground Stations (CGS).

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.

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.

Rotating fan-beam scatterometer (RFSCAT) is a new kind of spaceborne scatterometer for ocean surface vector wind (OSVW) measurement. In this paper, some consideration about the calibration of a Ku-band RFSCAT is presented. Both internal calibration and external calibration are described. Preliminary analyses about the internal calibration precision and external calibration accuracy are presented. Based on the method of Long, et al, external calibration method for RFSCAT is proposed. Some simulation results for the external calibration with natural extended targets, such as Amazon forest, are presented.

In this paper, the status and progress of the scatterometer (SCAT) of the Chinese-French Oceanography Satellite (CFOSAT) will be reported. SCAT/CFOSAT will be the first rotating fan-beam scatterometer (RFSCAT) ever flown on a satellite for global ocean vector wind measurement. The mission schedule, some design and development status and simulation results of its performances will be presented. It is shown that the RFSCAT will have some advantages for wind vector retrieval for high wind speed situations.

In this paper, a conceptual design for a dual-frequency Rotating Fanbeam SCATterometer (RFSCAT) to fly onboard the future satellite (e.g. FY-3) with altitude of 836km is studied. System configurations of the studied C- and Ku-band RFSCAT are introduced respectively. Then an end-to-end simulation process is adopted to analyze the backscatter measurement accuracy, ant to assess the wind retrieval quality of the proposed RFSCAT. For a wind vector cell with spatial resolution of 25 km, the communication noise ranges from 4% to 90% for the Ku-band measurement, and from 3% to 25% for the C-band one, depending on ocean surface wind speed. The simulation results indicate that the proposed system would promote the wind retrieval qualities significantly by combining the C- and Ku-band microwave measurement.

A transponder system is developed to fulfill the in-orbit calibration of the HY-2 satellite altimeter (ALT) and scatterometer (SCAT). The interaction of a calibration transponder and radar (altimeter / scatterometer) is rather complicated. After a brief description of the two active payloads on HY-2 satellite, several key issues are investigated in detail, including time arrangement and power budget. There are some innovations in calibration strategy, including the pre-distortion technology in ALT calibration and the Single-Input-Multiple- Output (SIMO) technology in the SCAT calibration.

This paper introduces the design and simulation results of a Ku-band rotating fan-beam radar scatterometer for ocean surface wind vector measurement. It will be flown on a small satellite dedicated to provide data for investigation of the ocean wave and ocean surface wind vector interactions, along with another payload for measurement of directional ocean wave spectra by a real-aperture radar with multiple scanned pencil beams. Key issues about the design of a Ku-band rotating fan-beam radar scatterometer, and results of performance simulations are provided as well. The performance of the system is simulated by the absolute and relative specifications. For the absolute specifications, retrieval performances for both wind speed and wind direction are evaluated, with the maximum likelihood method being employed. For the relative specifications, the figures of merit (FOM) is simulated, for comparison with other Ku-band scatterometers and optimization of system parameters. Simulation results of both the σ° precision and wind retrieval accuracies for different wind speed from 4m/s to 24m/s will be provided, which shows that SCAT can satisfy the performance requirements within most part of the swath.

This paper introduces an on-board signal processing subsystem of a spaceborne rotating fan-beam scatterometer in China. The subsystem processes returned signal after downconversion to intermediate frequency. To reduce the data stream downlinked from the satellite, the final data bins are summed into 34 energy slices, each with a range resolution of 10 km. Then a simulation process is adopted to analyze the Doppler effects due to the motion of the spacecraft, and to generate the Doppler compensation table and the bin summation table. This method is also used to analyze the measurement variance Kpcof each detected slice. Finally, the coefficients of the expression of Kpcare presented.

Spaceborne Rotating Fanbeam Scatterometer, RFSCAT, has a wider continuous swath that can provide a large number of independent samples of Sigma0 for wind speed and direction retrieving. But the large swath and footprint result in a wideband Doppler frequency shift. For a low earth orbiting satellite, the maxim Doppler bandwidth between forward and backward echoes will be about 500 KHz. Even in a single echo of RFSCAT, the Doppler bandwidth is about 90 KHz, while in a pencil beam scatterometer the Doppler bandwidth is almost a single tone. A method of Doppler frequency compensation both on center frequency of transmitted pulses and signal processing section of the echoes are carried out and evaluated.

In this paper, a simulation method is presented to analyze and to optimize the performance of a real aperture radar system for measuring oceanic directional wave spectra, taking into account of data processing parameters. The results show that spaceborne radar ocean wave spectrometer (SB-ROWS) can measure spectra of fully developed waves with significant wave heights (SWH) over approximately 2.0 meter and swell surface under low wind conditions. The minimum detectable wavelength in the study is about 40 m. The wavelength resolution for a wavelength of 200 m is better than 30 m, and the directional resolution after averaging is better than 20o.

A spaceborne Ku-band rotation fan-beam scatterometer (RFSCAT) for sea surface wind field measurement is introduced. The system will be operated in Ku-band frequency so that the dimension of its antenna can be shortened enough for a small satellite platform. Then a simulation method is adopted to analyze the performance of this system. The results show that the proposed scatterometer has a good performance in wind retrieval.

It is well known, that the backscattering radiometric accuracy of radar scatterometers for ocean surface wind and precipitation measurements is decided by the signal-to-noise ratio and number of independent samples for non-coherent average. For sea wind scatterometers and meteorological radars, pulse compression can be employed to improve the range resolution and increase the number of independent numbers. But for airborne and spaceborne scatterometers, the peak power of the transmitter and the pulse length are usually decided by the platform capability, orbit parameter and observation geometry. As a result, compromise and trade-off between the signal-to-noise ratio (SNR) for measurement of each resolution cell and the total number of independent samples of each pulse are usually required. In this paper, based on the radiometric accuracy model, an optimization scheme for design of the independent samples numbers and the corresponding system bandwidth are presented. Based on the presented optimization scheme, the bandwidth of a Ku-band rotating fan-beam radar scatterometer for sea surface wind measurement is design. Simulation validates the optimization and design results.

Pulse compression with very low sidelobes is one of challenges in the design of spaceborne weather radar. To detect the weak echoes of clouds occurring near the strong echoes of sea surface, sidelobe performance below -60db is needed in the pulse compression system. Based on the traditional ways of pulse compression, double Kaiser Windows are applied here for sidelobe suppression and the Peak Sidelobe Level (PSL) can reach -75db. To avoid power and SNR loss, the time domain Kaiser Window weighted on transmitter was replaced by the frequency domain window weighted on receiver equally. A real time signal processor and digital predistortion method based on successive overrelaxation (SOR) arithmetic is carried out. It can be used for verifying the arithmetic and modifying the nonlinear and noise character of the system.