First assessment of Noise-Equivalent Sigma-Zero in GF3-02 TOPSAR mode with sea surface wind speed retrieval

Gaofen-3-02(GF3-02)is the first C-band synthetic aperture radar(SAR)satellite with terrain observation with progressive scans of SAR(TOPSAR)imaging mode in China,which plays an essential role in marine environment monitoring.Given the weak scattering characteristics of the ocean,the system thermal noise superimposed on SAR images has significant interference,especially in cross-polarization channels.Noise-Equivalent Sigma-Zero(NESZ)is a measure of the sensitivity of the radar to areas of low backscatter.The NESZ is defined to be the scattering cross-section coefficient of an area which contributes a mean level in the image equal to the signal-independent additive noise level.For TOPSAR,NESZ exhibits the shape of the SAR scanning gain curve in the azimuth and the shape of the antenna pattern in the range.Therefore,the accurate measurement of NESZ plays a vital role in the application of spaceborne SAR sea surface cross-polarization data.This paper proposes a theoretical calculation method for the NESZ curve in GF3-02 TOPSAR mode based on SAR noise inner calibration data and the imaging algorithm.A method for correcting the error existing in the theoretical curve of NESZ is also proposed according to the relationship between sea surface backscattering and wind speed and the same characteristics of target scattering in the overlapping area of adjacent sub-swaths.According to assessment with wide-swath TOPSAR cross-polarization data,the GF3-02 TOPSAR mode has a very low thermal noise level,which is better than−33 dB at the edge of each beam,and controlled below−38 dB at the center of the beam.The two-dimensional reference curves of the NESZ of each beam are provided to the GF3-02 TOPSAR users.After discussing the relationship between normalized radar cross section(NRCS)and wind speed,we provide a formula for NRCS related to wind speed and radar incidence angle.Compared with the NRCS derived from this formula and the NESZ-subtracted NRCS of SAR images,the bias is−0.0048 dB,the Root Mean Square Error is 1.671 dB and the correlation coefficient is 0.939.

洋面风场的QuikSCAT/SeaWinds遥感探测

QuikSCAT卫星是监测全球气候和海洋环境的重要卫星之一,它已经被世界气象组织列为业务卫星,所得数据对天气预报、气候研究等方面有重要作用。介绍了QuikSCAT卫星的有关背景情况,星载SeaWinds散射测风计观测洋面风的原理和基本算法,说明了利用该资料在热带气旋中心定位及移向预报以及2003年淮河流域暴雨成因分析方面做了尝试性工作。最后,对该数据资料应用中存在的一些问题进行了讨论。

Effect of sea surface wind on the seasonal variation of sea level in the east of China seas

Sea level seasonal variations in the east of China seas from 2004 to 2006 are simulated by the advanced ROMS model. The results show similar sea level spatial features with TOPEX/Poseidon observations, with annual ranges decreasing gradually from the sea coast to the Kuroshio region. By getting rid of wind stress in ROMS model, the simulated sea level results still show obvious seasonal variations. However, the phenomenon of sea level anomaly disappears in Min Zhe Current Coastwise （MZCF） and Su Bei current coastwise （SBCF）, and the change of it from coastal area to ocean recedes. The seal level difference between Bohai, Yellow Sea （BYS） and East China Sea （ECS） becomes weaker in spring and autumn. The annual differences decrease obviously, and the gradual change of annual ranges from seacoast to the Kuroshio almost disappears. The annual ranges in BYS are nearly identical. The annual range ratio without the wind stress to with the wind stress increases gradually from the sea coast to Kuroshio region.

Sea Surface Roughness Derivation from Wind Speed Estimated by Satellite Altimeter

For open sea conditions the sea surface roughness is described as a function of surface stress and wind speed over sea surface by Charnock relation. The sea surface roughnessn in the North-west Pacific Ocean is derived successfully using wind speed data estimated by the TOPEX satellite altimeter. From the results we find that： （1） the mean sea surface roughness in winter is greater than in summer; （2） compared with other sea areas, the sea surface roughness in the sea area east of Japan （ N30°- 40°, E135°- 150°） is larger than in other sea areas; （3） sea surface roughness in the South China Sea changes more greatly than that in the Bohai Sea, Yellow Sea and East China Sea.

Impact of the Waves on the Sea Surface Roughness under Uniform Wind Conditions:Idealized Cases for Uniform Winds(Part I)

The effect of the surface gravity waves over sea surface roughness length (z0) is investigated from several idealized numerical experiments with the Wave-Watch-III (WW3) model. The WW3 model is combined with a simplified model to estimate z0, CD, u* and U10 as function of the sea state. The impacts related to the presence of the ocean waves over z0 are obtained from conditions of growing (young waves) and mature seas (old waves). The wave spectrum is obtained from WW3 model for each idealized simulation under uniform wind conditions. Uniform wind experiments range from 15 to 45 m/s. The simplified algorithm determines z0, CD, u* and U10 for cases of young waves, old waves and by the Charnock method. The results show that when the ocean is characterized by young waves, both z0 and CD (drag coefficient) increase while U10 is reduced. In Charnock case, the values of z0, CD and U10 have no dependence with the presence of gravity waves. Experiments using winds higher than 30 m/s result in young waves’ CD values higher than the CD value for old waves. Even for young waves CD values are high for cases of strong winds. The results also show that in experiments using winds higher than 30 m/s the dependence between CD and wave age becomes stronger, which is in accordance with other studies.

Evaluation of ENVISAT ASAR data for sea surface wind retrieval in Hong Kong coastal waters of China

The C-band wind speed retrieval models, CMOD4, CMOD - IFR2, and CMOD5 were applied to retrieval of sea surface wind speeds from ENVISAT （European environmental satellite） ASAR （advanced synthetic aperture radar） data in the coastal waters near Hong Kong during a period from October 2005 to July 2007. The retrieved wind speeds are evaluated by comparing with buoy measurements and the QuikSCAT （quick scatterometer） wind products. The results show that the CMOD4 model gives the best performance at wind speeds lower than 15 m/s. The correlation coefficients with buoy and QuikSCAT winds are 0.781 and 0.896, respectively. The root mean square errors are the same 1.74 m/s. Namely, the CMOD4 model is the best one for sea surface wind speed retrieval from ASAR data in the coastal waters near Hong Kong.

Evaluation on monthly sea surface wind speed of four reanalysis data sets over the China seas after 1988

This study investigates the long-term changes of monthly sea surface wind speeds over the China seas from 1988 to 2015. The 10-meter wind speeds products from four major global reanalysis datasets with high resolution are used: Cross-Calibrated Multi-Platform data set(CCMP), NCEP climate forecast system reanalysis data set(CFSR),ERA-interim reanalysis data set(ERA-int) and Japanese 55-year reanalysis data set(JRA55). The monthly sea surface wind speeds of four major reanalysis data sets have been investigated through comparisons with the longterm and homogeneous observation wind speeds data recorded at ten stations. The results reveal that(1) the wind speeds bias of CCMP, CFSR, ERA-int and JRA55 are 0.91 m/s, 1.22 m/s, 0.62 m/s and 0.22 m/s, respectively.The wind speeds RMSE of CCMP, CFSR, ERA-int and JRA55 are 1.38 m/s, 1.59 m/s, 1.01 m/s and 0.96 m/s,respectively;(2) JRA55 and ERA-int provides a realistic representation of monthly wind speeds, while CCMP and CFSR tend to overestimate observed wind speeds. And all the four data sets tend to underestimate observed wind speeds in Bohai Sea and Yellow Sea;(3) Comparing the annual wind speeds trends between observation and the four data sets at ten stations for 1988-1997, 1988–2007 and 1988–2015, the result show that ERA-int is superior to represent homogeneity monthly wind speeds over the China seaes.

The seasonal variations in the significant wave height and sea surface wind speed of the China's seas

Long-term variations in a sea surface wind speed （WS） and a significant wave height （SWH） are associated with the global climate change, the prevention and mitigation of natural disasters, and an ocean resource exploitation, and other activities. The seasonal characteristics of the long-term trends in China＇s seas WS and SWH are determined based on 24 a （1988-2011） cross-calibrated, multi-platform （CCMP） wind data and 24 a hindcast wave data obtained with the WAVEWATCH-III （WW3） wave model forced by CCMP wind data. The results show the following. （1） For the past 24 a, the China＇s WS and SWH exhibit a significant increasing trend as a whole, of 3.38 cm/（s.a） in the WS, 1.3 cm/a in the SWH. （2） As a whole, the increasing trend of the China＇s seas WS and SWH is strongest in March-April-May （MAM） and December-January-February （DJF）, followed by June-July-August （JJA）, and smallest in September-October-November （SON）. （3） The areal extent of significant increases in the WS was largest in MAM, while the area decreased in JJA and DJF; the smallest area was apparent in SON. In contrast to the WS, almost all of China＇s seas exhibited a significant increase in SWH in MAM and DJF; the range was slightly smaller in JJA and SON. The WS and SWH in the Bohai Sea, the Yellow Sea, East China Sea, the Tsushima Strait, the Taiwan Strait, the northern South China Sea, the Beibu Gull and the Gulf of Thailand exhibited a significant increase in all seasons. （4） The variations in China＇s seas SWH and WS depended on the season. The areas with a strong increase usually appeared in DJF.

A preliminary assessment of the sea surface wind speed production of HY-2 scanning microwave radiometer

A scanning microwave radiometer（RM） was launched on August 16,2011,on board HY-2 satellite.The six-month long global sea surface wind speeds observed by the HY-2 scanning microwave radiometer are preliminarily validated using in-situ measurements and WindSat observations,respectively,from January to June 2012.The wind speed root-mean-square（RMS） difference of the comparisons with in-situ data is 1.89 m/s for the measurements of NDBC and 1.72 m/s for the recent four-month data measured by PY30-1 oil platform,respectively.On a global scale,the wind speeds of HY-2 RM are compared with the sea surface wind speeds derived from WindSat,the RMS difference of 1.85 m/s for HY-2 RM collocated observations data set is calculated in the same period as above.With analyzing the global map of a mean difference between HY-2 RM and WindSat,it appears that the bias of the sea surface wind speed is obviously higher in the inshore regions.In the open sea,there is a relatively higher positive bias in the mid-latitude regions due to the overestimation of wind speed observations,while the wind speeds are underestimated in the Southern Ocean by HY-2 RM relative to WindSat observations.

Satellite SAR observation of the sea surface wind field caused by rain cells

Rain cells or convective rain,the dominant form of rain in the tropics and subtropics,can be easy detected by satellite Synthetic Aperture Radar（SAR） images with high horizontal resolution.The footprints of rain cells on SAR images are caused by the scattering and attenuation of the rain drops,as well as the downward airflow.In this study,we extract sea surface wind field and its structure caused by rain cells by using a RADARSAT-2 SAR image with a spatial resolution of 100 m for case study.We extract the sea surface wind speeds from SAR image by using CMOD4 geophysical model function with outside wind directions of NCEP final operational global analysis data,Advance Scatterometer（ASCAT） onboard European Met Op-A satellite and microwave scatterometer onboard Chinese HY-2 satellite,respectively.The root-mean-square errors（RMSE） of these SAR wind speeds,validated against NCEP,ASCAT and HY-2,are 1.48 m/s,1.64 m/s and 2.14 m/s,respectively.Circular signature patterns with brighter on one side and darker on the opposite side on SAR image are interpreted as the sea surface wind speed（or sea surface roughness） variety caused by downdraft associated with rain cells.The wind speeds taken from the transect profile which superposes to the wind ambient vectors and goes through the center of the circular footprint of rain cell can be fitted as a cosine or sine curve in high linear correlation with the values of no less than 0.80.The background wind speed,the wind speed caused by rain cell and the diameter of footprint of the rain cell with kilometers or tens of kilometers can be acquired by fitting curve.Eight cases interpreted and analyzed in this study all show the same conclusion.

Long-Term Characterization of Sea Conditions in the East China Sea Using Significant Wave Height and Wind Speed

In this study, the statistical characterization of sea conditions in the East China Sea(ECS) is investigated by analyzing a significant wave height and wind speed data at a 6-hour interval for 30 years(1980–2009), which was simulated and computed using the WAVEWATCH Ⅲ(WW3) model. The monthly variations of these parameters showed that the significant wave height and wind speed have minimum values of 0.73 m and 5.15 ms^(-1) and 1.73 m and 8.24 ms^(-1) in the month of May and December, respectively. The annual, seasonal, and monthly mean sea state characterizations showed that the slight sea generally prevailed in the ECS and had nearly the highest occurrence in all seasons and months. Additionally, the moderate sea prevailed in the winter months of December and January, while the smooth(wavelets) sea prevailed in May. Furthermore, the spatial variation of sea states showed that the calm and smooth sea had the largest occurrences in the northern ECS. The slight sea occurred mostly(above 30%) in parts of the ECS and the surrounding locations, while higher occurrences of the rough and very rough seas were distributed in waters between the southwest ECS and the northeast South China Sea(SCS). The occurrences of the phenomenal sea conditions are insignificant and are distributed in the northwest Pacific and its upper region, which includes the Southern Kyushu-Palau Ridge and Ryukyu Trench.

Evaluation of reanalysis and satellite-based sea surface winds using in situ measurements from Chinese Antarctic Expeditions

Sea surface winds from reanalysis （NCEP-2 and ERA-40 datasets） and satellite-based products （QuikSCAT and NCDC blended sea winds） are evaluated using in situ ship measurements from the Chinese National Antarctic Research Expeditions （CH1NAREs） from 1989 through 2006, with emphasis on the Southern Ocean （south of 45°S）. Compared with ship observations, the reanalysis winds have a positive mean bias （0.32 m·s-1 for NCEP-2 and 0.13 m·s-1 for ERA-40）, and this bias is more pronounced in the Southern Ocean （0.57 m·s-1 and 0.45 m·s-1, respectively）. However, mean biases are negative in the tropics and subtropics. The satellite-based winds also show positive mean biases, larger than those of the reanalysis data. All four wind products overestimate ship wind speed for weak winds （〈4 m·s-1） but underestimate for strong winds （〉10 m·s-1）. Differences between the reanalysis and satellite winds are examined to identify regions with large discrepancies.

MODELING AND QUANTITATIVE RETRIEVAL OF FINITE FIELD FOR THE TROPICAL SEA SURFACE WIND SPEED OF THE FY-3B MICROWAVE IMAGER

The purpose of this study is to select a suitable sea wind retrieval method for FY-3B(MWRI). Based on the traditional empirical model of retrieving sea surface wind speed, and in the case of small sample size of FY-3B satellite load regression analysis, this paper analyzes the channel differences between the FY-3B satellite microwave radiation imager(MWRI) and TMI onboard the TRMM. The paper also analyzes the influence of these differences on the channel in terms of receiving temperature, including channel frequency, sensitivity and scaling precision. Then, the limited range of new model coefficient regression analysis is determined(in which the channel range settings include the information and features of channel differences), the regression methods of the finite field are proposed, and the empirical model of wind speed retrieval applicable to MWRI is obtained, which achieves robust results. Compared to the TAO buoy data, the mean deviation of the new model is 0.4 m/s, and the standard deviation is 1.2 m/s. In addition,the schematic diagram of the tropical sea surface wind speed retrieval is provided.

Retrieval of sea surface winds under hurricane conditions from GNSS-R observations

Reflected signals from global navigation satellite systems（GNSSs） have been widely acknowledged as an important remote sensing tool for retrieving sea surface wind speeds.The power of GNSS reflectometry（GNSS-R）signals can be mapped in delay chips and Doppler frequency space to generate delay Doppler power maps（DDMs）,whose characteristics are related to sea surface roughness and can be used to retrieve wind speeds.However,the bistatic radar cross section（BRCS）,which is strongly related to the sea surface roughness,is extensively used in radar.Therefore,a bistatic radar cross section（BRCS） map with a modified BRCS equation in a GNSS-R application is introduced.On the BRCS map,three observables are proposed to represent the sea surface roughness to establish a relationship with the sea surface wind speed.Airborne Hurricane Dennis（2005） GNSS-R data are then used.More than 16 000 BRCS maps are generated to establish GMFs of the three observables.Finally,the proposed model and classic one-dimensional delay waveform（DW） matching methods are compared,and the proposed model demonstrates a better performance for the high wind speed retrievals.

Retrieval algorithm of sea surface wind vectors for WindSat based on a simple forward model

WindSat/Coriolis is the first satellite-borne polarimetric microwave radiometer, which aims to improve the potential of polarimetric microwave radiometry for measuring sea surface wind vectors from space. In this paper, a wind vector retrieval algorithm based on a novel and simple forward model was developed for WindSat. The retrieval algorithm of sea surface wind speed was developed using multiple linear regression based on the simulation dataset of the novel forward model. Sea surface wind directions that minimize the difference between simulated and measured values of the third and fourth Stokes parameters were found using maximum likelihood estimation, by which a group of ambiguous wind directions was obtained. A median filter was then used to remove ambiguity of wind direction. Evaluated with sea surface wind speed and direction data from the U.S. National Data Buoy Center （NDBC）, root mean square errors are 1.2 rn/s and 30~ for retrieved wind speed and wind direction, respectively. The evaluation results suggest that the simple forward model and the retrieval algorithm are practicable for near-real time applications, without reducing accuracy.

Evaluation of Wind Retrieval from Co-Polarization Gaofen-3 SAR Imagery Around China Seas

Gaofen-3(GF-3) is the first Chinese space-borne satellite to carry the C-band multi-polarization synthetic aperture radar(SAR). Marine applications, i.e., winds and waves retrieved from GF-3 SAR images, have been operational since January 2017. In this study, we have collected more than 1000 quad-polarization(vertical-vertical(VV); horizontal-horizontal(HH); vertical-horizontal(VH); horizontal-vertical(HV)) GF-3 SAR images, which were acquired around the China Seas from September 2016 to September 2017. Wind streaks were visible in these images in co-polarization(VV and HH) channel. Geophysical model functions(GMFs), including the CMOD5N together with polarization ratio(PR) model and C-SARMOD, were used to retrieve winds from the collected co-polarization GF-3 SAR images. Wind directions were directly obtained from GF-3 SAR images. Then, the SAR-derived wind speeds were compared with the measurements at a 0.25? grid from the Advanced Scatterometer on board the Metop-A/B and microwave radiometer WindSAT. Based on the analysis, empirical corrections are proposed to improve the performance of the two GMFs. Results of this study show that the standard deviation of wind speed is 1.63 m s^(-1) with a 0.19 m s^(-1) bias and 1.71 m s^(-1) with a 0.26 m s^(-1) bias for VV-and HH-polarization GF-3 SAR, respectively. Our work not only systematically evaluates wind retrieval by using the two advanced GMFs and PR models but also proposes empirical corrections to improve the accuracy of wind retrievals from GF-3 SAR images around the China Seas and thus enhance the accuracy of near real-time operational SAR-derived wind products.

Sea surface wind speed retrieval from Sentinel-1 HH polarization data using conventional and neural network methods

Conventional retrieval and neural network methods are used simultaneously to retrieve sea surface wind speed(SSWS)from HH-polarized Sentinel-1(S1)SAR images.The Polarization Ratio(PR)models combined with the CMOD5.N Geophysical Model Function(GMF)is used for SSWS retrieval from the HH-polarized SAR data.We compared different PR models developed based on previous C-band SAR data in HH-polarization for their applications to the S1 SAR data.The recently proposed CMODH,i.e.,retrieving SSWS directly from the HHpolarized S1 data is also validated.The results indicate that the CMODH model performs better than results achieved using the PR models.We proposed a neural network method based on the backward propagation(BP)neural network to retrieve SSWS from the S1 HH-polarized data.The SSWS retrieved using the BP neural network model agrees better with the buoy measurements and ASCAT dataset than the results achieved using the conventional methods.Compared to the buoy measurements,the bias,root mean square error(RMSE)and scatter index(SI)of wind speed retrieved by the BP neural network model are 0.10 m/s,1.38 m/s and 19.85%,respectively,while compared to the ASCAT dataset the three parameters of training set are–0.01 m/s,1.33 m/s and 15.10%,respectively.It is suggested that the BP neural network model has a potential application in retrieving SSWS from Sentinel-1 images acquired at HH-polarization.

Spatio-temporal variability in sea surface wind stress near and off the east coast of Korea

Sea surface wind stress variabilities near and off the east coast of Korea, are examined using 7 kinds of wind datasets from measurements at 2 coastal (land) stations and 2 ocean buoys,satellite scatterometer (QuikSCAT), and global reanalyzed products (ECMWF,NOGAPS,and NCEP/NCAR). Temporal variabilities are analyzed at 3 frequency bands; synoptic (2-20 d), intra-seasonal (20-90 d),and seasonal (>90 d).Synoptic and intra-seasonal variations are predominant near and off the Donghae City due to the passage of the mesoscale weather system. Seasonal variation is caused by southeastward wind stress during Asian winter monsoon. The sea surface wind stress from reanalyzed datasets.QuikSCAT and KMA-B measurements off the coast show good agreement in the magnitude and direction,which are strongly aligned with the alongshore direction.At the land-based sites,wind stresses are much weaker by factors of 3-10 due to the mountainous landmass on the east parts of Korea Peninsula.The first EOF modes(67%-70%) of wind stresses from reanalyzed and QuikSCAT data have similar structures of the strong southeastward wind stress in winter along the coast but show different curl structures at scales less than 200 km due to the orographic effects.The second EOF modes (23%-25%) show southwestward wind stress in every September along the east coast of the North Korea

Climatology of Wind-Seas and Swells in the China Seas from Wave Hindcast

The wind-sea and swell climates in the China Seas are investigated by using the 27-yr Integrated Ocean Waves for Geophysical and other Applications(IOWAGA)hindcast data.A comparison is made between the significant wave height from the IOWAGA hindcasts and that from a jointly calibrated altimetry dataset,showing the good performance of the IOWAGA hindcasts in the China Seas.A simple but practical method of diagnosing whether the sea state is wind-sea-dominant or swell-dominant is proposed based on spectral partitioning.Different from the characteristics of wind-seas and swells in the open ocean,the wave fields in the enclosed seas such as the China Seas are predominated by wind-sea events in respect of both frequencies of occurrences and energy weights,due to the island sheltering and limited fetches.The energy weights of wind-seas in a given location is usually more significant than the occurrence probability of wind-sea-dominated events,as the wave energy is higher in the wind-sea events than in the swell events on average and extreme wave heights are mostly related to wind-seas.The most energetic swells in the China Seas(and other enclosed seas)are‘local swells’,having just propagated out of their generation areas.However,the swells coming from the West Pacific also play an important role in the wave climate of the China Seas,which can only be revealed by partitioning different swell systems in the wave spectra as the energy of them is significantly less than the‘local swells’.

Comparison among four kinds of data of sea surface wind stress in the South China Sea

By using remote sensing (ERS) data, FSU data, COADS data and Hellerman & Rosen-stein objective analysis data to analyze the sea surface wind stress in the South China Sea, it is found that the remote sensing data have higher resolution and more reasonable values. Therefore we suggest that remote sensing data be chosen in the study of climatological features of sea surface wind stress and its seasonal variability in the South China Sea, especially in the study of small and middle scale eddies.