An improved wind quality control for the China-France Oceanography Satellite (CFOSAT) scatterometer

Quality control(QC)is an essential procedure in scatterometer wind retrieval,which is used to distinguish good-quality data from poor-quality wind vector cells(WVCs)for the sake of wind applications.The current wind processor of the China-France Oceanography Satellite(CFOSAT)scatterometer(CSCAT)adopts a maximum likelihood estimator(MLE)-based QC method to filter WVCs affected by geophysical noise,such as rainfall and wind variability.As the first Ku-band rotating fan-beam scatterometer,CSCAT can acquire up to 16 observations over a single WVC,giving abundant information with diverse incidence/azimuth angles,as such its MLE statistical characteristics may be different from the previous scatterometers.In this study,several QC indicators,including MLE,its spatially averaged value(MLE_(m)),and the singularity exponents(SE),are analyzed using the collocated Global Precipitation Mission rainfall data as well as buoy data,to compare their sensitivity to rainfall and wind quality.The results show that wind error characteristics of CSCAT under different QC methods are similar to those of other Ku-band scatterometers,i.e.,SE is more suitable than other parameters for the wind QC at outer-swath and nadir regions,while MLE_(m) is the best QC indicator for the sweet region WVCs.Specifically,SE is much more favorable than others at high wind speeds.By combining different indicators,an improved QC method is developed for CSCAT.The validation with the collocated buoy data shows that it accepts more WVCs,and in turn,improves the quality control of CSCAT wind data.

Validation and accuracy analysis of wind products from scatterometer onboard the HY-2B satellite

The Chinese marine dynamic environment satellite HY-2B was launched in October 2018 and carries a Ku-band scatterometer.This paper focuses on the accuracies of HY-2B scatterometer wind data during the period from November 2018 to May 2021.The HY-2B wind data are validated against global moored buoys operated by the U.S.National Data Buoy Center and Tropical Atmosphere Ocean,numerical model data by the National Centers for Environmental Prediction,and the Advanced Scatterometer data issued by the Remote Sensing System.The results showed that the wind speeds and directions observed by the HY-2B scatterometer agree well with these buoy wind measurements.The root-mean-squared errors(RMSEs)of the HY-2B wind speed and direction are 0.74 m/s and 11.74°,respectively.For low wind speeds(less than 5 m/s),the standard deviation of the HY-2B-derived wind direction is higher than 20°,which implies that the HY-2B wind direction for low wind speeds is less accurate than that for moderate to high wind speed ranges.The RMSE of the HY-2B wind speed is slightly larger in high latitude oceans(60°–90°S and 60°–90°N)than in low latitude regions.Furthermore,the dependence of the residuals on the cross-track location of wind vector cells and the stability of the HY-2B scatterometer wind products are discussed.The wind stability assessment results indicate that a clear yearly oscillation is observed for the HY-2B wind speed bias which is due to seasonal weather variations.In general,the accuracy of HY-2B winds meets the operational precision requirement and is consistent with other wind data.

Neural network wind retrieval from ERS-1/2 scatterometer data

A neural network methodology is presented to retrieve wind vectors from ERS - 1/2 scatterometer data. The wind directional ambiguities are eliminated by a circular median filter algorithm. All data come from ERS - 1/2 scatterometer data collocated pairs with CMCD4 vector. Comparing the results with CMCD4 and ECMWF wind vector,they agree well, which indicates that it is possible to extract wind vector from the ERS-1/2 scatterometer with the neural network method.

Astudyofanewretrievalalgorithmformeasurementofoceanicwindvectorsfieldusingsatellitemicrowavescatterometer

The Seasat-A satellite scatterometer(SASS) demonstrated very successfully that scatterometers can makeaccurate synoptic measurements of surface wind vectors field over the ocean. The technology is based on the sensitivityof microwave radar back scatter to the ocean waves in centimeter scale created by the action of the surface wind. More-over, the back scatter is anisotropic, therefore, wind speed and direction can be derived from radar measurements attwo or more different azimuths. Owing to the nonlinear nature of scatter model function and the existence of variousnoise sources in the measurements, the retrieval wind results consist of as many as four wind directions. A new algo-rithm is proposed to recover ocean wind field from the SASS normalized cross-section measurement in this paper. Comparison with those estimated from the SASS surface wind analysed by Peteherych et al . (1984) and other referencesshow agreement largely in the wind direction and more exactly in the wind speed.

Sea ice extent retrieval with HY-2A scatterometer data and its assessment

A sea ice extent retrieval algorithm over the polar area based on scatterometer data of HY-2A satellite has been established. Four parameters are used for distinguishing between sea ice and ocean with Fisher＇s linear discriminant analysis method. The method is used to generate polar sea ice extent maps of the Arctic and Antarctic regions of the full 2013-2014 from the scatterometer aboard HY-2A （HY-2A-SCAT） backscatter data. The time series of the ice mapped imagery shows ice edge evolution and indicates a similar seasonal change trend with total ice area from DMSP-F17 Special Sensor Microwave Imager/Sounder （SSMIS） sea ice concentration data. For both hemispheres, the HY-2A-SCAT extent correlates very well with SSMIS 15% extent for the whole year period. Compared with Synthetic Aperture Radar （SAR） imagery, the HY-2A-SCAT ice extent shows good correlation with the Sentinel-1 SAR ice edge. Over some ice edge area, the difference is very evident because sea ice edges can be very dynamic and move several kilometers in a single day.

Application of Tikhonov regularization method to wind retrieval from scatterometer data Ⅱ: cyclone wind retrieval with consideration of rain

According to the conclusion of the simulation experiments in paper I, the Tikhonov regularization method is applied to cyclone wind retrieval with a rain-effect-considering geophysical model function （called CMF＋Rain）. The CMF＋Rain model which is based on the NASA scatterometer-2 （NSCAT2） GMF is presented to compensate for the effects of rain on cyclone wind retrieval. With the multiple solution scheme （MSS）, the noise of wind retrieval is effectively suppressed, but the influence of the background increases. It will cause a large wind direction error in ambiguity removal when the background error is large. However, this can be mitigated by the new ambiguity removal method of Tikhonov regularization as proved in the simulation experiments. A case study on an extratropical cyclone of hurricane observed with SeaWinds at 25-km resolution shows that the retrieved wind speed for areas with rain is in better agreement with that derived from the best track analysis for the GMF＋Rain model, but the wind direction obtained with the two-dimensional variational （2DVAR） ambiguity removal is incorrect. The new method of Tikhonov regularization effectively improves the performance of wind direction ambiguity removal through choosing appropriate regularization parameters and the retrieved wind speed is almost the same as that obtained from the 2DVAR.

The study on an Antarctic sea ice identification algorithm of the HY-2A microwave scatterometer data

An Antarctic sea ice identification algorithm on the HY-2A scatterometer（HSCAT） employs backscattering coefficient（σ0） and active polarization ratio（APR） for a preliminary sea ice identification.Then standard deviation（STD） filtering and space filtering are carried out.Finally,it is used to identify sea ice.A process uses a σ0,STD threshold and an APR as sea ice indicators.The sea ice identification results are verified using the sea ice distribution data of the ASMR2 released by the National Snow and Ice Data Center as a reference.The results show very good consistence of sea ice development trends,seasonal changes,area distribution,and sea ice edge distribution of the sea ice identification results obtained by this algorithm relative to the ASMR2 sea ice results.The accuracy of a sea ice coverage is 90.8% versus the ASMR2 sea ice results.This indicates that this algorithm is reliable.

Validation and intercomparison of HY-2A/MetOp-A/Oceansat-2 scatterometer wind products

Sea surface winds are of great significance in scientific research. In the last few years,three series of scatterometers were launched to measure these winds,including the Advanced Scatterometer(ASCAT) aboard Meteorological Operational Satellite A(Met Op-A) and Met Op-B,Oceansat-2 Scatterometer(OSCAT),and HY-2A Scatterometer(HY-2A SCAT). Based on buoy wind data,validation and intercomparison of these scatterometers were performed. Scatterometer-derived wind and buoy wind data were collected only if the spatial difference was less than 0.1 degree and temporal difference less than 5 min. After discarding wind direction data outside five times the standard deviation,ASCAT wind products showed high accuracy in both wind speed and direction,with root-mean-square error(RMSE) 0.86 m/s and 17.97 degrees,respectively. HY-2A SCAT nearly meets the mission requirement,with RMSE for wind speed 1.23 m/s and 22.85 degrees for wind direction. OSCAT had poor performance when compared with the others. RMSE for wind speed was 1.54 m/s and 39.86 degrees for wind direction,which greatly exceeds the mission requirement of 20 degrees. In addition,the RMSE for wind direction shows a high-low pattern on buoy wind speed. However,a wind speed range from 14 to 15 m/s was found to be abnormal,and the reason remains unclear. There was no systematic dependency of both wind speed and direction residuals on buoy wind speed and cross-track location of the wind vector cells across the entire range. No seasonal variation was found for any scatterometer.

Evaluation of wind vectors observed by HY-2A scatterometer using ocean buoy observations,ASCAT measurements,and numerical model data

The first Chinese microwave ocean environment satellite HY-2A was launched successfully in August, 201 I. This study presents a quality assessment of HY-2A scatterometer （HYSCAT） data based on comparison with ocean buoy data, the Advanced Scatterometer （ASCAT） data, and numerical model data from the National Centers for Environmental Prediction （NCEP）. The in-situ observations include those from buoy arrays operated by the National Data Buoy Center （NDBC） and Tropical Atmosphere Ocean （TAO） project. Only buoys located offshore and in deep water were analyzed. The temporal and spatial collocation windows between HYSCAT data and buoy observations were 30 min and 25 km, respectively. The comparisons showed that the wind speeds and directions observed by HYSCAT agree well with the buoy data. The root-mean-squared errors （RMSEs） of wind speed and direction for the HYSCAT standard wind products are 1.90 m/s and 22.80°, respectively. For the HYSCAT-ASCAT comparison, the temporal and spatial differences were limited to 1 h and 25 km, respectively. This comparison yielded RMSEs of 1.68 m/s for wind speed and 19.1° for wind direction. We also compared HYSCAT winds with reanalysis data from NCEP. The results show that the RMSEs of wind speed and direction are 2.6 m/s and 26°, respectively. The global distribution of wind speed residuals （HYSCAT-NCEP） is also presented here for evaluation of the HYSCAT-retrieved wind field globally. Considering the large temporal and spatial differences of the collocated data, it is concluded that the HYSCAT-retrieved wind speed and direction met the mission requirements, which were 2 rn/s and 20° for wind speeds in the range 2-24 m/s. These encouraging assessment results show that the wind data obtained from HYSCAT will be useful for the scientific community.

The Performance of Dual-Frequency Polarimetric Scatterometer in Sea Surface Wind Retrieval

The wind retrieval performance of HY-2 A scanning scatterometer operating at Ku-band in HH and VV polarizations has been well evaluated in the wind speed range of 0–25 m s^-1.In order to obtain more accurate ocean wind field,a potential extension of dual-frequency(C-band and Ku-band)polarimetric measurements is investigated for both low and very high wind speeds,from 5 to 45 m s^-1.Based on the geophysical model functions of C-band and Ku-band,the simulation results show that the polarimetric measurements of Ku-band can improve the wind vector retrieval over the entire scatterometer swath,especially in nadir area,with the wind direction root-mean-square error(RMSE)less than 12?in the wind speed range of 5–25 m s^-1.Furthermore,the results also show that C-band cross-polarization plays a very important role in improving the wind speed retrieval,with the wind speed retrieval accuracy better than 2 m s^-1 for all wind conditions(0–45 m s^-1).For extreme winds,the C-band HH backscatter coefficients modeled by CMOD5.N(H)and the ocean co-polarization ratio model at large incidence are used to retrieve sea surface wind vector.This result reveals that there is a big decrease of wind direction retrieval RMSE for extreme wind fields,and the retrieved result of C-band HH polarization is nearly the same as that of C-band VV polarization for low-to-high wind speed(5–25 m s^-1).Thus,to improve the wind retrieval for all wind conditions,the dual-frequency polarimetric scatterometer with C-band and Ku-band horizontal polarization in inner beam,and C-band horizontal and Ku-band vertical polarization in outer beam,can be used to measure ocean winds.This study will contribute to the wind retrieval with merged satellites data and the future spaceborne scatterometer.

Analysis on monthly-averaged distribution of sea surface wind and wave over the seas southeast of Asia using ERS-2 scatterometer data

variation. In the area of 2 The wind system over the seas southeast of Asia （SSEA） plays an important role in China＇s climate this paper, ERS scatterometer winds covering the period from January 2000 to December 2000 and 41°N, 105 130°E were analyzed with a distance-weighting interpolation method and the monthly mean distribution of the sea surface wind speed were given, The seasonal characteristics of winds in the SSEA were analyzed. Based on WAVEWATCH Ⅲ model, distribution of significant wave height was calculated.

Wave effects on the retrieved wind field from the advanced scatterometer(ASCAT)

To improve retrieval accuracy, this paper studies wave effects on retrieved wind field from a scatterometer. First, the advanced scatterometer （ASCAT） data and buoy data of the National Data Buoy Center （NDBC） are collocated. Buoy wind speed is converted into neutral wind at 10 m height. Then, ASCAT data are com- pared with the buoy data for the wind speed and direction. Subsequently, the errors between the ASCAT and the buoy wind as a function of each wave parameter are used to analyze the wave effects. Wave param- eters include dominant wave period （dpd）, significant wave height （swh）, average wave period （apd） and the angle between the dominant wave direction （dwd） and the wind direction. Collocated data are divided into sub-datasets according to the different intervals of each wave parameter. A root mean square error （RMSE） for the wind speed and a mean absolute error （MAE） for the wind direction are calculated from the sub-datasets, which are considered as the function of wave parameters. Finally, optimal wave conditions on wind retrieved from the ASCAT are determined based on the error analyses. The results show the ocean wave parameters have correlative relationships with the RMSE of the retrieved wind speed and the MAE of the retrieved wind direction. The optimal wave conditions are presented in terms of dpd, swh, apd and angle.

Application of the Tikhonov regularization method to wind retrieval from scatterometer data I.Sensitivity analysis and simulation experiments

Scatterometer is an instrument which provides all-day and large-scale wind field information, and its application especially to wind retrieval always attracts meteorologists. Certain reasons cause large direction error, so it is important to find where the error mainly comes. Does it mainly result from the background field, the normalized radar cross-section （NRCS） or the method of wind retrieval？ It is valuable to research. First, depending on SDP2.0, the simulated ＇true＇ NRCS is calculated from the simulated ＇true＇ wind through the geophysical mode] function NSCAT2. The simulated background field is configured by adding a noise to the simulated ＇true＇ wind with the non-divergence constraint. Also, the simulated ＇measured＇ NRCS is formed by adding a noise to the simulated ＇true＇ NRCS. Then, the sensitivity experiments are taken, and the new method of regularization is used to improve the ambiguity removal with simulation experiments. The results show that the accuracy of wind retrieval is more sensitive to the noise in the background than in the measured NRCS; compared with the two-dimensional variational （2DVAR） ambiguity removal method, the accuracy of wind retrieval can be improved with the new method of Tikhonov regularization through choosing an appropriate regularization parameter, especially for the case of large error in the background. The work will provide important information and a new method for the wind retrieval with real data.

Comparison of sea surface wind field measured by HY-2A scatterometer and WindSat in global oceans

In this study, we present a comprehensive comparison of the sea surface wind ?eld measured by scatterometer(Ku-band scatterometer) aboard the Chinese HY-2 A satellite and the full-polarimetric radiometer WindSat aboard the Coriolis satellite. The two datasets cover a four-year period from October2011 to September 2015 in the global oceans. For the sea surface wind speed, the statistical comparison indicates good agreement between the HY-2 A scatterometer and WindSat with a bias of nearly 0 m/s and a root mean square error(RMSE) of 1.13 m/s. For the sea surface wind direction, a bias of 1.41° and an RMSE of 20.39° were achieved after excluding the data collocated with opposing directions. Furthermore,discrepancies in sea surface wind speed measured by the two sensors in the global oceans were investigated.It is found that the larger dif ferences mainly appear in the westerlies in the both hemispheres. Both the bias and RMSE show latitude dependence, i.e., they have signi?cant latitudinal ?uctuations.

Soil Moisture Content Monitoring Based on ERS Wind Scatterometer Data

The ERS-1/2 wind scatterometer （WSC） has a low resolution cell of about 50 km but provides a high repetition rate （〈4 d） and can make measurements at multiple incidence angles. In order to estimate effective surface reflectivity （related to soil moisture content） over bare soil Using WSC data, an original methodology based on the advance integral equation model （AIEM） is presented, which takes advantage of its multiple view angular characteristics. This method includes two steps. First, a simplified two-parameter surface scattering model is calibrated by AIEM simulated-database over a wide parameter space. Second, regression analyses are carded out using the simulated database to build the relation between those parameters of our model at different incident angles from two observations of Mid and Fore beams. From the model simulated database, our technique works quite well in estimating Г^0. The possibility of applying the model to retrieve soil moisture is investigated using a set of data collected from the Intensive Observation Period field campaign in 1998 of the Asian Monsoon Experiment Tibet （GAME-Tibet）. The retrieved values obtained for the bare land surface are consistent with ground measurements collected in these areas and the correlation coefficient between retrieved soil moisture and the measured one reaches 0.65.

Analysis of ERS-2 scatterometer winds and wind-wave calculation for eastern China seas

The wind system over the China seas plays an important role in climate variation there. In this paper, ERS-2 scatterometer winds covering the period of 1998 and the area of 25-41°N, 117-130°E were analyzed and compared to NCEP winds and buoy winds in the same period and location, to assess how well the ERS-2 data reflect the real wind regime, at least for this area. The results indicated that ERS-2 scatterometer winds are closer to buoy observations than NCEP winds, In addition, a new wind-wave growth relation was applied to calculate wave parameters.

Numerical simulation of scatterometer assimilated wind and ocean wave in eastern China seas and adjacent waters

Using the latest version of Mesoscale Modeling System （MMSv3）, we assimilated wind data from the scatterometer and built a model to assimilate the wind field over eastern China seas and adjacent waters and applied the wave model WAVEWATCH-Ⅲ to test the sea area with assimilative wind and blended wind of QSCAT and NCEP as driving forces. High precision and resolution numerical wave results were obtained. Analysis indicated that if we replace the model wind result with the blended wind, better sea surface wind results and wave results could be obtained.

A new model to estimate significant wave heights with ERS-1/2 scatterometer data

A new model is proposed to estimate the significant wave heights with ERS-1/2 scatterometer data. The results show that the relationship between wave parameters and radar backscattering cross section is similar to that between wind and the radar backscattering cross section. Therefore, the relationship between significant wave height and the radar backscattering cross section is established with a neural network algorithm, which is, if the average wave period is ≤7s, the root mean square of significant wave height retrieved from ERS-1/2 data is 0.51 m, or 0.72 m if it is >7s otherwise.

The first quantitative joint observation of typhoon by Chinese GF-3 SAR and HY-2A microwave scatterometer

The typhoon, as a mature tropical cyclone that develops in the western part of the North Pacific Ocean with high wind speed and heavy rainfall, is one of the most lethal and costly of natural disasters for the densely populated countries of East Asia. It can be easily detected by space-borne sensors operated at microwave, visible or infrared bands （Liu et al., 2014）. Synthetic Aperture Radar （SAR） is a kind of active imaging radar, which can detect the targets with high resolution at one-meter level. SARs can be used to extract the sea surface wind and the eyes of typhoons or hurricanes （Friedman and Li, 2000; Zhang and Perrie, 2012; Li, 2015; Jin et al., 2014; Liu et al., 2014）. As a pioneer project of Haiyang-3 （HY-3）, the Chinese C-band SAR satellite of Gaofen-3 （GF-3） was launched in August 2016 under China High-resolution Earth Observation System （CEOS）. GF-3 carries a multi-polarized C-band SAR with a highest spatial resolution of one meter, the most imaging modes in the word of twelve and a long designed lifespan of 8 years. Haiyang-2A （HY-2A）, which was launched in August 2011, is the first Chinese marine dynamic environment satellite with a main payload of Ku-band microwave scatterometer （Jiang et al., 2012; Ye et al., 2015）. One of the objectives of HY-2A scatterometer （HY-2A SCAT） is monitoring sea surface wind field of global ocean.

Assessment of wind products obtained from multiple microwave scatterometers over the China Seas

Sea surface winds （SSWs） are vital to many meteorological and oceanographic applications, especially for regional study of short-range forecasting and Numerical Weather Prediction （NWP） assimilation. Spaceborne seatterometers can provide global ocean surface vector wind products at high spatial resolution. However, given the limited spatial coverage and revisit time for an individual sensor, it is valuable to study improvements of multiple microwave scatterometer observations, including the advanced scatterometer onboard parallel satellites MetOp-A （ASCAT-A） and MetOp-B （ASCAT-B） and microwave scatterometers aboard Oceansat-2 （OSCAT） and HY-2A （HY2-SCAT）. These four scatterometer-derived wind products over the China Seas （0°-40°N, 105°-135°E） were evaluated in terms of spatial coverage, revisit time, bias of wind speed and direction, after comparison with ERA-Interim forecast winds from the European Centre for Medium-Range Weather Forecasts （ECMWF） and spectral analysis of wind components along the satellite track. The results show that spatial coverage of wind data observed by combination of the four sensors over the China Seas is about 92.8% for a 12-h interval at 12：00 and 90.7% at 24：00, respectively. The analysis of revisit time shows that two periods, from 5：30-8：30 UTC and 17：00-21：00 UTC each day, had no observations in the study area. Wind data observed by the four sensors along satellite orbits in one month were compared with ERA-Interim data, indicating that bias of both wind speed and direction varies with wind speed, especially for speeds less than 7 m/s. The bias depends on characteristics of each satellite sensor and its retrieval algorithm for wind vector data. All these results will be important as guidance in choosing the most suitable wind product for applications and for constructing blended SSW products.