Wind Vector Cell Research Articles

Sea surface wind retrieval from CFOSAT Rotating fan-beam scatterometer in ocean cyclones

ABSTRACT Rotating fan-beam scatterometer (RFSCAT) is a radar scatterometer system for sea surface wind vector measurement. Compared with other available scatterometers, RFSCAT can provide more combination of azimuth angles and incidence angles for a single WVC (wind vector cell), this observation mechanism is more conducive to the sea surface wind direction retrieval. In this paper, the NSCAT-4DS GMF (geophysical model function) with SST (sea surface temperature) correction, and the MSS (Multiple Solution Scheme) combination with a 2DVAR (2-dimensional variational analysis) are adopted to retrieve the sea surface winds from RFSCAT on the CFOSAT (Chinese-French Oceanography Satellite). The retrieved RFSCAT sea surface winds and ASCAT (advanced scatterometer) sea surface winds are compared and tested, and the feasibility of the RFSCAT measuring sea surface winds under high wind speeds is analysed. The results show that the RMSE of the RFSCAT sea surface wind speed using improved algorithm has decreased by 0.292 m s−1, the correlation coefficient has increased by 0.032, and the residual standard deviation has decreased by 0.194 m s−1. The RMSE of RFSCAT sea surface wind direction has decreased by 5.950°, the correlation coefficient has increased by 0.002, and the residual standard deviation has decreased by 5.567°. It is shown that the changes in winds RMSE using pre-improved and improved algorithms have statistical significance. In a word, the spaceborne Ku-band rotating fan-beam scatterometer can capture the winds structure of ocean cyclones. Although there may be high wind speeds saturation phenomena, the detecting sea surface winds at high wind speeds show preferable performance.

Enhanced-resolution reconstruction for the China-France Oceanography Satellite scatterometer

The China-France Oceanography Satellite SCATterometer (CSCAT) can observe radar backscatter values on the same sea surface at multiple incidence angles, and is often used to estimate the ocean near-surface wind. However, CSCAT utilizes a novel scanning mechanism and the wind vector cell has a spatial resolution is 25 km or 12.5 km, which limit the study of high-resolution land and sea ice monitoring. To address this issue, this paper constructs a geometric model of the main lobe-to-ground projection relationship and generates the enhanced-resolution radar images. CSCAT data are applied to three main image reconstruction algorithms (SIR, AART, and MART), and experiments are performed in the Iceland and Hudson Bay, and verified by Sentinel-2 optical remote sensing data. The experiments show the geometric model for CSCAT improves the spatial resolution from traditional 25 km to 5 km, and the SIR-reconstructed images are characterized by higher accuracy and better suppression of noise than are those obtained with the AART and MART methods. Therefore, this study extends the application of domestic remote sensors and provides data support for high-resolution applications, such as land and sea ice monitoring.

A Conceptual Rain Effect Model for Ku-Band Scatterometers

Satellite scatterometer wind retrieval is affected by rain. Both the precipitating clouds in the atmosphere and the sea surface rain effects can enhance or reduce the backscatter signal. Ku-band scatterometer retrievals suffer more rain effects than C-band scatterometer due to the shorter wavelength. Because of the lack of understanding of the potential physical mechanism, the current Geophysical Model Functions (GMF) don’t include rain effects, which leads to wind field retrieval biases in rainy areas. The usual method to avoid rain effects is flagging the possible rain-contaminated data in the quality control procedure and removing these flagged data in the processing. However, rain is often associated with extreme weather events, where accurate wind (and rain) retrieval is particularly relevant. Therefore, the authors propose a conceptual model which describes the relationship between Ku-band scatterometer measured normalized radar cross-section (NRCS) biases and the sea surface wind-induced NRCS and rain rates. The model assumes that the area-weighted rain rate in each wind vector cell (WVC) is a function of the rain coverage area fraction. The received NRCS is constituted by a wind and rain contribution. Model parameters are fitted based on Haiyang-2C scatterometer measurements, collocated ASCAT measurements, and the Level 3 Integrated Multi-satellitE Retrievals average area-weighted rain rates. Scatterometer measured NRCS biases are much reduced by comparing the original measured NRCS biases and the residual NRCS biases after correction. The model can help to better understand rain effects on scatterometers and paves the way towards a Ku-band scatterometer wind retrieval method considering rain effects.

Bayesian Algorithm for Rain Detection in Ku-Band Scatterometer Data

Ku-band scatterometers are sensitive to rain effects due to their cm-scale radar wavelength. The NSCAT-4DS geophysical model function (GMF) corrects for sea surface temperature (SST), whereas it doesn’t consider rain. Rain causes biases in the retrieved wind fields and to prevent these, quality control (QC) flags play an important role in rain identification. Since horizontal polarization and vertical polarization radar beams have a particular sensitivity to rain clouds, a noticeable difference between the rain-dominated backscatter distribution and the wind-dominated backscatter distribution is observed. Employing a Bayesian approach and exploiting these particular wind and rain backscatter characteristics, the authors propose an algorithm to provide the posterior rain probability for each measurement in a Wind Vector Cell and test the method for the Haiyang-2C scatterometer. In a comprehensive comparison between posterior rain probability, KNMI QC flag and Joss flag, for posterior rain probabilities higher than 0.5, the rejection rate is approximately a quarter of that of the KNMI QC flag with better rain detection behavior. While the Joss flag, the difference between the retrieved wind speed and the two-dimensional variational ambiguity removal analysis wind speed, has the best performance in identifying rain in the sweet swath, it comes at the cost of a higher missing rate. The comparison with ASCAT winds also proves the method’s effectiveness. Posterior rain probability has the best rain identification ability in the nadir swath. A combination of different QC flags should be beneficial and applied in wind retrieval.

Bayesian Sea Ice Detection Algorithm for CFOSAT

This paper describes the adaptation of the Bayesian sea ice detection algorithm for the rotating fan-beam scatterometer CSCAT onboard the China–France Oceanography Satellite (CFOSAT). The algorithm was originally developed and applied for fixed fan-beam and rotating pencil-beam scatterometers. It is based on the probability of the wind and ice backscatter distances from the measurements to their corresponding geophysical model functions (GMFs). The new rotating Ku-band fan-beam design introduces very diverse geometry distributions across the swath, which leads to three main adaptations of the algorithm: (1) a new probability distribution function fit for the backscatter distances over open sea; (2) a linear ice GMF as a function of incidence angle; (3) the separation of outer swath wind vector cells ((WVCs) number 1, 2, 41, 42) from the other WVCs to form two sets of probability distribution function fits for these two WVC groups. The results are validated against sea ice extents from the active microwave ASCAT and the passive microwave SSMI. The validation shows good agreement with both instruments, despite the discrepancies with SSMI during the melting season, and this discrepancy is caused by the lower sensitivity of the passive microwave to detect the ice at a low concentration with a mixed water/ice state, while the scatterometer is more tolerant regarding this situation. We observed that the sea-ice GMF regression between HH and VV sea-ice backscatter at low and high incidence angles decorrelates at around −12 dB (28) and −20 dB (50) and an experiment with truncated backscatter values at these incidence angles is executed, which significantly improves the year-long average sea ice extents. In conclusion, the adapted algorithm for CSCAT works effectively and yields consistent sea ice extents compared with active and passive microwave instruments. As such, it can, in principle, contribute to the long-term global scatterometer sea ice record, and as the algorithm was adapted for a rotating fan-beam scatterometer, it also can serve as a guideline for the recently launched, dual-frequency, rotating fan-beam scatterometer WindRAD.

QuikSCAT Climatological Data Record: Land Contamination Flagging and Correction

We develop, utilize, and validate techniques to produce a global data set of accurate coastal ocean surface vector winds. The dataset extends as near to the coast as 5 km and includes 10 years of SeaWinds on QuikSCAT ocean scatterometer data obtained from 1999 to 2009. We demonstrate improved retrievals over other large land-locked bodies of water as well, such as the Caspian Sea and the Great lakes. To determine the coastal winds we quantify the extent of land contamination in each scatterometer backscatter measurement and to the extent possible remove that contamination. After the measurements are thus corrected we retrieve winds with the corrected measurements using a previously published algorithm which has been extensively used for JPL scatterometer wind products. The coastal processing vastly increases the number of wind vector cells near coasts. We have ten times the number of wind vectors within 10 km of coast as without coastal processing, and over twice as many at 20 km from coast. These new wind vectors are high-quality, and have zero effect on non-coastal wind vectors. The effect of residual land contamination is quantified by comparing to buoys at varying distance from the coast and comparing coastal wind vector cells to oceanward neighbors. We show that the non-coastal QuikSCAT processing has very few good wind vectors nearer to the coast than about 22.5 km. In comparison to buoys, and oceanward neighbors, we find a small increase in speed errors of these new coastal wind vectors versus the performance of non-coastal QuikSCAT at 22.5 km, indicating the high-quality of these new coastal wind vectors. A quality control scheme is employed that flags regions where the coastal wind retrieval is poor due to the assumptions inherent in the technique being locally invalid. The coastal winds retrieved in this manner have been publicly distributed to the oceanography community and utilized in other published works.

Uncertainty Analysis in SAR Sea Surface Wind Speed Retrieval through C-Band Geophysical Model Functions Inversion

The purpose of the study is to assess the suitability of synthetic aperture radar (SAR) data to provide sea surface wind (SSW) fields along with a spatial distribution of both SSW speed and direction uncertainty. A simple methodology based on geophysical model function (GMF) inversion to obtain a spatial distribution of both SSW speed and its uncertainty is proposed. Exploiting a dataset of Sentinel-1 images, a sensitivity analysis of the SSW speed uncertainty is carried out on both the uncertainties and the mean values of SAR normalised radar cross section (NRCS), incidence angle and SSW direction, at different spatial scales. The results show that SSW speed uncertainty significantly increases with wind vector cell (WVC) dimension. Moreover, the dominant contribution to the SSW speed uncertainty due to both NRCS and SSW direction uncertainty must always be taken into account. A better precision and accuracy in the estimation of SSW speed and its uncertainty is evidenced by C-band model 7 (CMOD7) GMF rather than the C-band model 5.N (CMOD5.N). To evaluate the results of SSW retrievals, wind data from the European Centre for Medium-Range Weather Forecasts (ECMWF) model are also exploited for comparisons. Findings indicate a high correlation between the uncertainty from SAR estimations and that from the comparison of SAR vs. ECMWF.

Preliminary Estimate of CFOSAT Satellite Products in Tropical Cyclones

The China France Oceanography Satellite (CFOSAT) was launched on October 29, 2018, and is equipped with two sensors: a space-borne rotating fan beam scatterometer (CSCAT) and a space-borne surface wave investigation and monitoring (SWIM) radar. The CSCAT is dedicated to monitoring sea surface winds (SSWs), and the SWIM radar is designed to measure surface ocean waves. In this study, CSCAT SSW products during global tropical cyclones (TCs) were validated using wind data from multiple sources, including European Center for Medium-Range Weather Forecasts (ECMWFs) reanalysis (ERA5), cross-calibrated multi-platform (CCMP), soil moisture active passive (SMAP), WindSat, HY-2B, advanced scatterometer (ASCAT), and buoy wind. Wave parameters (significant wave height (SWH), dominant direction, and wavelength) were validated using wave data from ERA5, Jason3, and the HY-2B altimeter. The results show the following: 1) errors of the CFOSAT wind speed (WS) and SWH increase with the enhancement of TC intensity; 2) the WS accuracy of wind vector cells (WVCs) near the nadir is better than that at the nadir and the edge of the swath; 3) the wind direction (WD) correlation between the CSCAT and ERA5, ASCAT, and WindSat is more obvious at high WSs (>20 m/s) than at lower WSs (<6 m/s); 4) the accuracy of the SWIM off-nadir SWH product is lower than that of the nadir SWH; and 5) a triple collocation comparison among the CFOSAT SSW, ERA5 SSW, and WindSat SSW indicates that the CFOSAT SSW provides the best performance.

Numerical Weather Prediction Ocean Calibration for the Chinese‐French Oceanography Satellite Wind Scatterometer and Wind Retrieval Evaluation

AbstractThe rotating fan‐beam architecture wind scatterometer Chinese‐French Oceanography Satellite (CSCAT) onboard the Chinese‐French Oceanography Satellite leads to varying geometries and varying wind retrieval performance across the swath. To assess this, the wind vector cells (WVCs) are classified into three groups: outer, sweet, and nadir. The sweet swath WVCs contain the most diverse geometries, which give the best wind retrieval performance, whereas on the opposite the outer swath WVCs have the least diverse geometries, which makes the wind retrieval and ambiguity removal more challenging. NWP Ocean Calibration (NOC) is used to calibrate the radar backscatter prior to the wind inversion. It computes the difference between measured backscatter and simulated backscatter from collocated NWP winds through the geophysical model function. The difference is applied as a correction. An improved NOC method, NOC as a function of incidence angle and azimuth angle (NOCant) is introduced, which takes the orientation of the antenna into account. This method is compared with the widely used NOC as a function of incidence angle only (NOCinc). It shows a better fit with the geophysical model function, except for the outer swath WVCs which have limited geometry diversity. It also corrects the asymmetric wind direction bias distribution across the swath and reduced the nadir swath relative wind direction bias which is caused by the poor azimuth diversity. In conclusion, the rotating fan‐beam architecture of CSCAT leads to unique and varying data characteristics across the swath. Overall, the performance of the proposed NOCant correction is better than NOCinc and improves the wind statistics.

A Study of Sea Surface Rain Identification Based on HY-2A Scatterometer

Rain affects the wind measurement accuracy of the Ku-band spaceborne scatterometer. In order to improve the quality of the retrieved wind field, it is necessary to identify and flag rain-contaminated data. In this study, an HY-2A scatterometer is used to study rain identification. In addition to the conventional parameters, such as the retrieved wind speed, the wind direction relative to the along-track direction, and the normalized beam difference, the experiment expands the mean deviation of the backscattering coefficient, the beam difference between fore and aft, and the node number of the wind vector cell (WVC) as the sensitive parameters according to the microwave scattering characteristics of rain and the actual measurement situation of the HY-2A. Furthermore, a rain identification model for HY2 (HY2RRM) with the K-Nearest Neighborhood (KNN) algorithm was built. After several tests, the accuracy of the selected HY2RRM approach is found to about 88%, and about 70% of rain-contaminated data can be accurately identified. The research results are helpful for better understanding the characteristics of microwave backscattering and provide a possible way to further improve the wind field retrieval accuracy of the HY-2A scatterometer and other Ku-band scatterometers.

Rain False-Alarm-Rate Reduction for CSCAT

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 Joss 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 Joss 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.

First Results From the Rotating Fan Beam Scatterometer Onboard CFOSAT

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 (σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">θ</sup> ) precision and wind quality are studied using a set of collocated ancillary data. The σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">θ</sup> precision 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 σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">θ</sup> calibration should be improved in the future updates.

Comparison of Ocean Surface Rain Rates From the Global Precipitation Mission and the Meteosat Second-Generation Satellite for Wind Scatterometer Quality Control

Ocean surface rain rate information is crucial for quality control (QC) research in wind scatterometry. High-quality precipitation retrievals from microwave instruments are available from the Global Precipitation Mission (GPM). In addition, rain rates can be estimated with high spatiotemporal resolution from geostationary passive visible-infrared imagers such as the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellites. The two products are complementary in observing time and regions. We compare them at different spatial scales, and show that the best consistency is obtained for grids with a size of 25 km or larger; where 25 km corresponds to a common scatterometer wind vector cell size. The results show that correlation coefficient of rain rates from GPM and MSG products for rain rates less than 5 mm/h is about 0.2 and ranges from 0.2 to about 0.5 for rain rates higher than 5 mm/h, while bias and root mean square deviation fluctuate about 2 and 3 mm/h, respectively. Also, QC indicator performances are analyzed with references to MSG and GPM rain rates respectively, for tropical regions. References to these QC indicators further indicate better consistency with both products at rain rates higher than 5 mm/h. The effectiveness of a newly proposed QC indicator has been confirmed as well. The three-way comparison of rain products and scatterometer QC indicators provides a reliable reference for research and for the application of corrections for rain effects on scatterometers.

A generalized simulation capability for rotating- beam scatterometers

Abstract. Rotating-beam wind scatterometers exist in two types: rotating fan-beam and rotating pencil-beam. In our study, a generic simulation frame is established and verified to assess the wind retrieval skill of the three different scatterometers: SCAT on CFOSAT (China France Oceanography SATellite), WindRad (Chinese Wind Radar) on FY-3E, and SeaWinds on QuikSCAT. Besides the comparison of the so-called first rank solution retrieval skill of the input wind field, other figures of merit (FoMs) are applied to statistically characterize the associated wind retrieval performance from three aspects: wind vector root mean square error, ambiguity susceptibility, and wind biases. The evaluation shows that, overall, the wind retrieval quality of the three instruments can be ranked from high to low as WindRad, SCAT, and SeaWinds, where the wind retrieval quality strongly depends on the wind vector cell (WVC) location across the swath. Usually, the higher the number of views, the better the wind retrieval, but the effect of increasing the number of views reaches saturation, considering the fact that the wind retrieval quality at the nadir and sweet swath parts stays relatively similar for SCAT and WindRad. On the other hand, the wind retrieval performance in the outer swath of WindRad is improved substantially as compared to SCAT due to the increased number of views. The results may be generally explained by the different incidence angle ranges of SCAT and WindRad, mainly affecting azimuth diversity around nadir and number of views in the outer swath. This simulation frame can be used for optimizing the Bayesian wind retrieval algorithm, in particular to avoid biases around nadir but also to investigate resolution and accuracy through incorporating and analyzing the spatial response functions of the simulated Level-1B data for each WVC.

A routine operational backscattering coefficient regrouping algorithm for a HY-2A scatterometer

The routine operational sigma0 regrouping method is proposed for a HY-2A scatterometer (HSCAT) that maps time-ordered sigma0s and related parameters into a subtrack aligned grid of wind vector cells (WVCs). The regrouping method consists of two critical steps: ground grid generation and sigma0 resampling. The HSCAT uses subtrack swath coordinates, in which the nadir track of the satellite represents the center and the designated positions are specified in terms of a pair of along-track and cross-track coordinates. To calculate the subtrack coordinates for each sigma0, a “triangle marking” resampling method is developed. Three points, including the point of intersection, the center of a pulse footprint, and the origin of the subtrack coordinate system, form a right triangle; the length of the two right-angled sides is used to represent the cross-track and the along-track coordinates in the subtrack coordinate system. In addition, a nadir point interpolation correction is used to ensure the operation of the regrouping algorithm when the nadir point positional information is missing. To illustrate the ability of the proposed regrouping algorithm, the distribution of the WVC positions and wind vector retrieval results are analyzed, which show that the proposed regrouping algorithm meets the requirements for high-quality sea surface wind field retrieval.

The ASCAT 6.25-km Wind Product

The advanced scatterometer (ASCAT) wind data processor (AWDP) produces ocean surface vector winds from radar measurements by the ASCAT on board the Metop satellites. So far, the ASCAT-coastal product with a grid size of 12.5 km has been the one with the highest resolution. Version 2.4 of AWDP, released May 2016, offers the possibility to process wind data on a 6.25 km grid. In this paper, the true spatial resolution and accuracy of that product is assessed using various methods. The crucial parameter is the radius of the area used to aggregate individual backscatter observations to a wind vector cell (WVC) level. A value of 7.5 km, half of that for ASCAT-coastal, appears to be the best compromise between resolution and accuracy. Spatial responses from multiple radar cross-section measurements are combined to cumulative responses, and show that the ASCAT-6.25 product has a spatial resolution of about 17 km, better than the 28 km found for the ASCAT-coastal product. The accuracy of the ASCAT-6.25 product is estimated using comparison with collocated buoys, triple collocation analysis, and a new method based on spatial variances. These methods show consistently that the ASCAT-6.25 product contains about ${\rm{0.2\,m}}^{2}{\rm{/ s}}^{2}$ more noise in the wind components than the ASCAT-coastal product, due to the smaller number of individual measurements contributing to the average radar cross section in a WVC. The ASCAT-6.25 product is intended for applications that demand a spatial resolution as high as possible, like the study of dynamical mesoscale phenomena.

The CMOD7 Geophysical Model Function for ASCAT and ERS Wind Retrievals

A new geophysical model function (GMF), called CMOD7, has been developed for intercalibrated ERS (ESCAT) and ASCAT C-band scatterometers. It is valid for their combined incidence angle range and being used to generate wind climate data records. CMOD7 has been developed in several steps as a successor of CMOD5.n. First, CMOD5.n has been adapted to the ASCAT transponder calibration, which is considered more accurate than any ESCAT gain calibration. This results in a linear scaling of the backscatter values. Second, for low winds, there is a clear mismatch between CMOD5.n and the measurements. An independently developed ASCAT C-band GMF, C2013, which performs particularly well for low winds was adopted to improve low winds for the ASCAT incidence angle range. Third, retrievals with CMOD5.n show wind speed probability distribution functions (pdf) that undesirably depend on wind vector cell (WVC) position across the swath. To overcome this effect, a higher order calibration is applied, which matches the wind speed pdfs for all WVCs of ASCAT and ESCAT. The resulting CMOD7 GMF indeed shows overall improved performance on all relevant quality parameters compared to CMOD5.n. It is found that the standard deviations of error for wind speed and wind direction of ASCAT are improved. The same holds for the maximum-likelihood estimates, showing an 8% improved consistency with the local triplet of backscatter measurements. As a consequence, triple collocation with moored buoy and numerical weather prediction winds results in smaller wind vector components and wind direction retrieval errorsim.

An advanced wind vector retrieval algorithm for the rotating fan-beam scatterometer

The rotating fan-beam scatterometer (RFSCAT) is a new type of satellite scatterometer that is proposed approximately 10 a ago. However, similar to other rotating scatterometers, relatively larger wind retrieval errors occur in the nadir and outer regions compared with the middle regions of the swath. For the RFSCAT with the given parameters, a wind direction retrieval accuracy decreases by approximately 9 in the outer regions compared with the middle region. To address this problem, an advanced wind vector retrieval algorithm for the RFSCAT is presented. The new algorithm features an adaptive extension of the range of wind direction for each wind vector cell position across the whole swath according to the distribution histogram of a retrieved wind direction bias. One hundred orbits of Level 2A data are simulated to validate and evaluate the new algorithm. Retrieval experiments demonstrate that the new advanced algorithm can effectively improve the wind direction retrieval accuracy in the nadir and outer regions of the RFSCAT swath. Approximately 1.6 and 9 improvements in the wind direction retrieval are achieved for the wind vector cells located at the nadir and the edge point of the swath, respectively.

Russian scatterometer: discussion of the concept and the numerical simulation of wind field retrieval

Orbital scatterometry is briefly overviewed and its trends are indicated. Two scatterometer concepts are currently considered for trade-offs: with fixed and rotating antenna systems. The concept with a rotating antenna system was selected and SeaWinds was chosen as the prototype for the first Russian scatterometer. The scatterometer concept was then further developed and instead of two pencil beams, a fan-beam antenna was proposed (about 1° × 6°). The fan-beam antenna allows successive measurements for horizontal and vertical polarization in each wind vector cell (WVC). This increases the number of observations of the WVC at different incidence and azimuth angles during flight. The scatterometer parameters required to implement the proposed measurement geometry for an orbit altitude of 650 km and a swath width of 1525 km are discussed. A numerical scatterometer model that accounts for both the specifications and the observation geometry is developed. The scatterometer performance, with subsequent formation of a swath and splitting into WVCs, is simulated. The procedure of wind vector retrieval includes two stages: 1) determining wind speed and wind direction in a single WVC; and 2) using the information from adjacent WVCs to correct wind direction. It is shown that the accuracy of wind direction retrieval by a WVC can be increased by simultaneous radar cross-section (RCS) measurements at vertical and horizontal polarization. The basic error in determining wind direction is due to a 180° wind direction ambiguity caused by the form of RCS azimuth dependence. Two-dimensional median filtering is commonly employed in scatterometry to increase the accuracy of wind direction retrieval. In this study, two-dimensional angular median filtering was employed and it is shown that the error in wind direction retrieval significantly decreased. The results of the research indicate that wind field can be retrieved by the new scatterometer with the level of precision required.

A Preliminary Study of the Calibration for the Rotating Fan-Beam Scatterometer on CFOSAT

The first rotating fan-beam scatterometer (RFSCAT) will be launched onboard the Chinese-French Oceanic Satellite (CFOSAT) in 2018. It provides a set of radar cross-section (σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> ) measurements at different azimuth/incidence angles over a wind vector cell (WVC), in order to determine the near-surface wind field using the backscatter model, i.e., the so-called geophysical model function (GMF). The accuracy of the retrieved wind vector is a sensitive function of the radiometric accuracy of the σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> measurements. Therefore, in-flight calibration, including the loop-back (internal) calibration and the external calibration performed with natural extended-area targets, is studied in this paper. Several homogeneous areas over land are first analyzed to check the stability and azimuthal dependence of the σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> over these areas. A new calibration mask of the homogeneous land areas is generated and will be used by RFSCAT calibration. Then a simple method of external calibration is proposed to eliminate the azimuthal-dependent σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> errors induced by the insertion loss of the rotating joint, which can be applied to both the rotating pencil-beam scatterometers and the coming RFSCAT. The “observed” σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> of RFSCAT is simulated using the SeasatA scatterometer (SASS) measurements and the “perturbed” azimuthal-dependent σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> errors. The latter is then tracked by the proposed external calibration. The results show that the accuracy of gain corrections is up to 0.2 dB, ensuring consistency between different azimuthal measurements.