• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Identification and Removal of Non-meteorological Echoes in Dual-polarization Radar Data Based on a Fuzzy Logic Algorithm

    2015-06-09 21:30:01BoYoungYEGyuWonLEEandHongMokPARK
    Advances in Atmospheric Sciences 2015年9期

    Bo-Young YE,GyuWon LEE?,,and Hong-Mok PARK

    1Department of Astronomy and Atmospheric Sciences,Research and Training Team for Future Creative Astrophysicists and Cosmologists,Kyungpook National University,Daegu 702–701,Korea

    2Center for Atmospheric REmote sensing(CARE),Kyungpook National University,Daegu 702–701,Korea

    Identification and Removal of Non-meteorological Echoes in Dual-polarization Radar Data Based on a Fuzzy Logic Algorithm

    Bo-Young YE1,GyuWon LEE?1,2,and Hong-Mok PARK2

    1Department of Astronomy and Atmospheric Sciences,Research and Training Team for Future Creative Astrophysicists and Cosmologists,Kyungpook National University,Daegu 702–701,Korea

    2Center for Atmospheric REmote sensing(CARE),Kyungpook National University,Daegu 702–701,Korea

    A major issue in radar quantitative precipitation estimation is the contamination of radar echoes by non-meteorological targets such as ground clutter,chaff,clear air echoes etc.In this study,a fuzzy logic algorithm for the identification of non-meteorological echoes is developed using optimized membership functions and weights for the dual-polarization radar located at Mount Sobaek.For selected precipitation and non-meteorological events,the characteristics of the precipitation and non-meteorological echo are derived by the probability density functions of five fuzzy parameters as functions of reflectivity values.The membership functions and weights are then determined by these density functions.Finally,the nonmeteorological echoes are identified by combining the membership functions and weights.The performance is qualitatively evaluated by long-term rain accumulation.The detection accuracy of the fuzzy logic algorithm is calculated using the probability of detection(POD),false alarm rate(FAR),and clutter–signal ratio(CSR).In addition,the issues in using filtered dual-polarization data are alleviated.

    dual-polarization radar,non-meteorological echo,quality control,fuzzy logic algorithm

    1.Introduction

    The weather radar is commonly used to estimate rainfall at high spatial and temporal resolution over large regions.However,radarmomentdata are contaminated by nonmeteorological echo(NME)such as ground clutter,anomalous propagation(AP),chaff etc.Smith et al.(1996)showed that AP echoes lead to systematic overestimation of rainfall. In addition,chaff echo can lead to radar precipitation on clear days.Thus,quality control of radar data is essential for improving radar quantitative precipitation estimation.

    Several methods have been used to identify and remove NME:(1)average ground echo mask;(2)time domain filter;(3)frequency domain filter;and(4)moment-based suppression using the temporal and spatial variability of moment data.Moment-based suppression is widely known and accepted for operational application with various statistical techniques(e.g.neural network,Bayes classifier,and fuzzy logic).

    The neural network is one of the most commonly used techniques in identifying NME(Haykin and Deng,1991; Grecu and Krajewski,2000;Krajewski and Vignal,2001; Lakshmanan et al.,2007).Grecu and Krajewski(2000)proposed the use of neural networks to identify AP echoes by applying the spatial fluctuation of the reflectivity.Moszkowicz et al.(1994)suggested the Bayes classifier to detect NME by applying discriminating functions of Bayesian theorem. Rico-Ramirez and Cluckie(2008)compared the Bayes classifier with the fuzzy classifier for C-band dual-polarization radardata.TheirBayesclassifiergave a slightly betterperformance than the fuzzy classifier.Recently,Nicol et al.(2011) showed that ground clutter identification is likely to benefit from measurements of the power ratio or clutter phase alignment even when dual-polarization parameters are available in the Bayes classifier.

    Fuzzy logic algorithmshave been widely used forthe mitigation of NME due to their simplicity.Cho et al.(2006)derived the normalized frequency distributions of three feature parameters(vertical gradient of reflectivity,standard deviation of reflectivity,and absolute value of radial velocity)to define the characteristics of ground clutter echo,AP echo, and precipitation echo(PRE).Membership functions(MFs) and their weights are derived from these characteristics for given parameter and reflectivity intervals.Their algorithm identifies NME using the total membership value calculatedfrom each MF and weight.The performance of their algorithm is comparable with that from a polarimetric approach. Berenguer et al.(2006)also used a fuzzy logic algorithm that employs the echo top,vertical gradient of reflectivity,spin change(Steiner and Smith,2002),and texture of reflectivity.

    Many ground clutter detection algorithms based on dualpolarimetric radar data have also been developed using fuzzy logic algorithms.Gourley et al.(2007)used an algorithm that derives the MFs of the correlation coefficient,standard deviation of differential reflectivity,and standard deviation of differential phase using Gaussian kernel density estimation proposed by Silverman(1981)for C-band dual-polarization radar data.They evaluated the performance using accumulation maps from PRE and NME.In addition,the clutter mitigation decision(CMD)combines three discriminants(clutter phase alignment,texture of reflectivity,and spin)using the fuzzy logic algorithm to identify NME(Hubbert et al., 2009a,2009b)and then applies an adaptive frequency domain filter.The hydrometeor classification algorithm(HCA), which discriminates hydrometeor type as well as ground clutter,also uses a fuzzy logic classifier(Liu and Chandrasekar, 2000;Park et al.,2009;Mahale et al.,2014).

    Recently,several ground clutter filters using spectral parameters have been demonstrated in the radar research community.Warde and Torres(2014)developed CLEAN-AP (Clutter Environment Analysis using Adaptive Processing) using the phase of the auto-correlation spectral density.It shows better results than CMD(Torres et al.,2012).STEP (Spectrum-Time Estimation and Processing)can improve the quality of polarimetric radar data using three novel algorithms:Spectrum Clutter Identification(SCI),bi-Gaussian clutter filter,and multi-lag moment estimation(Cao et al., 2012).The SCI algorithm has four discriminants(spectral power distribution,spectral phase fluctuations,spatial texture of echo power,and spatial texture of spectrum width)based on the Bayesian method(Li et al.,2013).Li et al.(2014)also detected ground clutter using a Bayesian classifier for polarimetric radar.

    The Ministry of Land,Infrastructure and Transport (MOLIT)has been operating the Mount Sobaek S-band polarimetric radar,situated in complex terrain,since November 2011.In this paper,a moment-based fuzzy logic algorithm is developed by optimizing the MFs and weights for Mount Sobaek dual-polarization radar measurements.Our approach is similar to Cho et al.(2006),except for the fuzzy parameters.However,their approach is adapted into dualpolarization data.In addition,this radar does not archive unfiltered polarimetric parameters and only unfiltered reflectivity is obtained.Thus,the algorithm is further adapted to resolve issues with an absence of unfiltered polarimetric measurements.The detection accuracy of the proposed method is evaluated using accumulated rainfall,POD,FAR,and CSR.

    The data used are summarized in section 2 and the methodology is discussed in section 3.Section 4 presents the results of determining the MFs and weights,as well as validation of the proposed algorithm.

    2.Data

    Moment data from the Mount Sobaek S-band dual polarization radar are used.This radar has been operated by MOLIT since 15 November 2011.The peak power of the Klystron transmitter is 750 kW.The Mount Sobaek radar operates a full volume scan composed of six elevation angles (?0.5?,?0.1?,0.3?,0.7?,1.0?,1.3?)in a simultaneous transmitter and receiver(STAR)mode.The unambiguous range is 150 km in the rain mode.The gate size is 125 m and the pulse repetition frequency is 1000 Hz.

    The radar produces unfiltered reflectivity(DZ),filtered reflectivity(CZ),Doppler velocity(Vr,spectrum width,differential reflectivity(ZDR),correlation coefficient(ρHV),differential phase(ΦDP),specific differential phase(KDP),and signal quality index(SQI).Figure 1 shows plan position indicator(PPI)images of all radar parameters for the precipitation case at the elevation angle of?0.5?at 0802 LST(local standard time)6 July 2012.The signal processor of the radar provides one unfiltered parameter,DZ only,and all other parameters are filtered by a Gaussian model adaptive processing(GMAP;Siggia and Passarelli Jr.,2004)clutter filter in the signal processor.Thus,other parameters show areas of“no data”when compared with DZ data.

    Two different data sets are used in this study:one for building MFs and weights,and the other for validating the proposed algorithm.The MFs and weights are obtained from seven NME events(377 volume scans)and four PRE events (375 volume scans),listed in Table 1.The NME events include ground clutter,clear air,and chaff echoes.The precipitation events are composed of widespread rain and monsoon fronts.The validation of the fuzzy logic algorithm is performed for three dominant echo types(chaff,clear air,and precipitation),shown in Table 2.The selection of the various events is due to the increase in diversity in the feature parameters.

    3.Methodology

    3.1.Determination of MFs and weights

    The MFs and weights of the Mount Sobaek radar are necessary to detect NME.The MFs and weights are determined by the same procedure described in Cho et al.(2006).Radar moment data are divided into two areas(the NME and PRE areas)based on the average ground echo map.Then,the probability density functions(PDFs)of the five fuzzy parameters are derived for the two areas to define the characteristics of the NME and PRE.Finally,the MFs and weights are derived from these PDFs.

    The average ground echo map is produced by the mean reflectivity of ground clutter echoes during 0345–0445 LST 8 July 2012.This period is composed of 25 volume scans and is representative of the normal propagation of the radar beam. No precipitation and clouds are present.The derived average ground echo map at the elevation angle of?0.5?shows significant ground echoes in northwest,south,and southeastareas,where several mountain ranges are located(Fig.1). The NME and PRE areas are classified using this average ground echo map as reference.For the PRE events in Table 1,the PRE area is limited to pixels where DZ<0 dB Z in the average ground echo map.On the other hand,the NME area is determined for the NME events(Table 1)with the condition of DZ≥5 dB Z in the average ground echo map.

    For the selected NME and PRE areas,five fuzzy parameters(FPs)are calculated.They are:the standard deviation of reflectivity[SD(DZ)];the standard deviation of differential reflectivity[SD(ZDR)];the standard deviation of the correlation coefficient[SD(ρHV)];the standard deviation of differentialphase[SD(ΦDP)];and the correlation coefficient(ρHV). The standard deviation of a given variable(V:DZ,ZDR,ΦDP, andρHV)is defined as follows:

    Table 1.Events for deriving the MFs and weights of the fuzzy logic algorithm.

    Table 2.Events used for evaluation of the fuzzy logic algorithm.

    Here,the c is the gate number at which the SD is calculated. The i indicates the gate number.

    The PDFs of the five FPs are then calculated with five reflectivity intervals for both the NME and PRE areas.The four intervals of reflectivity(DZ)are:0≤DZ<10;10≤DZ<20;20≤DZ<30;30≤DZ.

    For given fuzzy parameters(FPi)and reflectivity intervals (DZi),the MF isderived using the PDFsofthe NME and PRE with the following equation:

    Here,the FPstands forSD(DZ),SD(ZDR),SD(ρHV),SD(ΦDP), andρHV.

    The MFs of the fuzzy parameters are combined with weights to derive the total membership value as Eq.(3).The total membership value is the same concept as in another study using a fuzzy logic algorithm(Cho et al.,2006):

    Here,W indicates the weight,which is derived below:

    where the A indicates the overlapping area of PDFNME[FPi, DZj]and PDFPRE[FPi,DZj].A point of intersection between PDFNME[FPi,DZj]and PDFPRE[FPi,DZj]is determined.The overlapping area is then calculated by adding smaller values between normalized PDFNME[FPi,DZj]and PDFNME[FPi,DZj]on both sides of the point.Thus,the FPiwith the large overlapping area is considered as a less significant parameter in the detection of NME.

    3.2.Detection of NME using MFs and weights

    The five fuzzy parameters are calculated from radar moment data at each gate.The fuzzy membership values of fuzzy parameters are calculated by predetermined MFs and are combined with weights as in Eq.(3)to lead to the total fuzzy membership value(MFtotal).When MFtotalis larger than the threshold(=0.5),the pixel is identified as the NME, and vice versa for the PRE.

    Since all parameters of Mount Sobaek radar except DZ are filtered,the derived FP does not fully represent the characteristics of NME.Thus,the performance of the algorithm can be significantly undermined.To avoid this problem, the difference between DZ and CZ is additionally applied to eliminate residual NME.The difference between DZ and CZ reflects the degree of contamination from ground clutter. The larger the difference,the more contaminations PRE will receive.The threshold of 5 dB Z is used to further remove echoes from PRE in this study.

    4.Results

    4.1.Determination of MFs and weights

    For the selected NME and PRE areas from pure NME and pure PRE events in Table 1,the PDFs of NME and PRE are calculated for five fuzzy parameters with reflectivity intervals(Fig.2).Figure 2a shows that the SD(DZ)of the PRE is within 5 dB.The higher the values of the reflectivity interval,the broader and the more skewed the distribution of NME.The PDFs of SD(ZDR)show a distinctive discrepancy between PRE and NME.The PDFPRE[SD(ZDR)]appears mostly at SD(ZDR)≤2 dB,except for 0≤DZ<10 dB Z(Fig.2b).The higher value of SD(ZDR)for 0≤DZ<10 dB Z is due to the edge of precipitation echoes.The PDFPRE[SD(ρHV)]shows a smaller value[SD(ρHV)<0.2] (Fig.2c).The most distinctive separation is shown in the PDFs of SD(ΦDP)(Fig.2d).The PDFPRE[SD(ΦDP)]mostly appears at SD(ΦDP)<10?.When DZ≥30 dB Z,the overlap area between the PDFs of NME and PRE is almostnegligible. The higher the value of the reflectivity interval,the larger the value ofρHV.(Fig.2e).The PDFs clearly show that the SD of the three dual-polarimetric variables will play a key role in identifying NME.In particular,the SD(ΦDP)is the most important indicator at higher DZ.

    Figure 3 shows the MFs of each fuzzy parameter that are calculated from the PDFs of NME and PRE.Each line represents the MF of different reflectivity intervals.The MFs become broader and less steep as the reflectivity decreases, except for SD(ZDR).For example,when SD(ρHV)=0.1,MF =0.9 in DZ≥30 dB Z,whereas MF=0.65 in 0≤DZ<10 dB Z.This indicates the MFs are good indicators at higher DZ rather than smaller DZ.

    Table 3 shows the weights of the five fuzzy parameters derived from the overlapping area of the PDFs of NME and PRE.When DZ≥30 dB Z,SD(ZDR)and SD(ΦDP)have higher weights,whereas the weight ofρHVis the lowest.The SD(ΦDP)shows higher values of weights at overall ranges of DZ intervals.The SD(ZDR)is also an important feature parameter for the classification of NME.

    4.2.Determination of NME using MFs and weights

    To detect NME for a selected case,the five fuzzy parameters are first calculated from the radar moment data.The NME and PRE are distinctive in the PPI images of the fuzzy parameters,especially SD(DZ)and SD(ZDR)(Fig.4).In particular,the high value of SD(DZ)and SD(ZDR)is distinctive in the areas of normal ground echoes,while these areas are not present in the other parameters.This is due to the GMAPfiltering of power spectra near zero Doppler velocity.In addition,the values of SD(ρHV)and SD(ΦDP)are larger in the residual areas of normal ground echoes.This indicates that the GMAP filtering is not suf ficient to completely eliminate the contamination of ground echoes.Furthermore,the partial beam blocking areas are clearly identi fied with the SD(ρHV) and SD(ΦDP).

    Table 3.The weights of the five fuzzy parameters for Mount Sobaek radar.

    The value of MFs for each parameter are shown in Fig.5. When MF=1.0(0.0),a favorable condition is indicated for NME(PRE).The membership value of SD(DZ)in the areas of normal ground clutter is close to 1.0,while the membership value for PRE is below 0.6(Fig.5a).The NME and PRE areas are clearly discernible in the membership value of SD(ZDR)(Fig.5b).The membership values of SD(ΦDP) show smaller areas of high MF(Fig.5d).In addition,the value of MF in the areas of partial beam blocking is small, indicating less of an effect of beam blocking in ΦDP.The values of SD(ρHV)andρHVbetter represent the partial beam blockage areas and ground clutter compared with SD(ΦDP). In particular,the high values are shown in the rays along the southwest direction when the value ofρHVis low.The total membership value is higher in the areas of partial beam blocking,residual clutter,and edges.The MFtotalin most PRE areas has a value as small as 0.1.

    4.3.Issues with filtered dual-polarization parameters

    In the dual-polarization fuzzy logic algorithm,the unfiltered parameter(DZ)and filtered parameters(ZDR,ρHV,and ΦDP)are used.Figure 6a is the PPI image of hourly average reflectivity at the elevation angle of?0.5?from 0802 to 0859 LST 6 July 2012.After applying the fuzzy logic algorithm, ground clutter echoes above 40 dB Z still remain in areas of residual clutter due to filtered dual-polarimetric parameters (Fig.6a).This is more prominent for areas of mixed ground echoes and precipitation.After applying GMAP filtering,the NME characteristics of dual-polarimetric parameters become weaker and less distinctive.

    Figure 6c shows the fraction of removed pixels to overall pixels as a function of the difference between DZ and CZ. The minimum fraction exists at DZ?CZ=0 dB.As the values of DZ?CZ increase,the fraction also increases,up to DZ?CZ=25 dB.This indicates that mixed areas of clutter and precipitation echoes are not properly removed with such high values of DZ?CZ.The fraction is then less than 1 at DZ?CZ=25–50 dB.This may be due to residual clutter and precipitation echoes at near zero Doppler velocity.The threshold of 5 dB is further applied to eliminate residual clutters.If the difference between DZ and CZ in a pixel is higher than the threshold,the pixel is removed as NME.After applying the threshold,all residual ground clutter echoes shown in Fig.6a are removed(see Fig.6b).This removed area is similar to the ground echo map above 40 dB Z in Fig.1.In addition,the average reflectivities in the zero isodop area,indicated by the red dashed line in Fig.6b,are also decreased by about 10 dB Z.This is unavoidable due to the use of the DZ?CZ threshold.

    4.4.Verification of the dual-polarization fuzzy logic algorithm

    4.4.1.Verification with rainfall accumulation

    The performance of the dual-polarization fuzzy logic algorithm is validated using rainfall accumulation.The follow-ing rainfall estimator is used for each PPI:

    Then,total rainfall accumulations are obtained for the entire period.The total rainfall amount from the original DZ and CZ,and the DZ after applying the fuzzy algorithm,are compared.

    Mount Sobaek radar produces filtered dual-polarization data using GMAP.This frequency domain filter has a weakness in that all echoes are filtered regardless of their echo types.These filtered data are significantly biased and can lead to systematic bias in rainfall accumulation.Moreover,the broad clutter spectrum cannot be completely filtered.These drawbacks are shown in the verification using rainfall accu-mulation.

    The performance of the dual-polarization fuzzy classifier is validated using rainfall accumulation from 0100 to 1300 LST 22 October 2012(Fig.7).The first column shows accumulated rainfall derived from unfiltered reflectivity(DZ); the second shows that calculated from filtered reflectivity (CZ);and the last column from DZ after applying the dualpolarization fuzzy classifier(FZ).High rainfall above 200 mm associated with ground clutter echoes exists in the rainfall accumulation derived from DZ at all elevation angles. Most of these contaminated pixels are removed in both the rainfall accumulations derived from CZ and FZ.However, R(CZ)and R(FZ)show several different features in terms of residual ground clutter,rainfall amount,bright band,andsecond trip echo.The features are easily discernable in Fig. 7 and Fig 8.High rainfall areas(>100 km),shown in red dashed circles(Fig.7),are residual ground clutter echoes that cannot be removed by GMAP,but they are properly removed by FZ(black dashed circles).

    In addition,the rainfall accumulation of R(FZ)shows higher values than that of R(CZ).Figure 8 shows differences among different accumulations.The difference in rain accumulation is larger over precipitation areas in R(DZ)–R(CZ) than in R(DZ)–R(FZ).This is due to the over-filtering of precipitation echoes by GMAP,whereas the precipitation areas are properly presented in FZ.The relatively larger values of the difference in the northwest and southeast directions(Fig. 8)are due to filtering of precipitation echoes near zero velocity.The dashed circle areas in the right panel show overdeduction of precipitation by FZ.Detailed examination reveals that these areas are due to the second trip echoes(not shown).The SQI values in the second trip echo area are low. The dual-polarization parameters of second trip echo are already removed by the SQI threshold before applying our algorithm in several cases.For this reason,if the measurements of dual-polarization variables are void in these areas,then FZ is removed.Thus,FZ can be used to remove second trip echoes.In addition,the proposed algorithm can reduce the overestimated rainfall due to bright band at>100 km for the reason thatρHVis low and SD(ρHV)is high in the melting layer.The rain accumulation in Figs.7g and h clearly shows overestimation at far ranges,as shown by the rings close to the 150 km.This overestimation is not shown in Fig 7i and the large value of R(DZ)–R(FZ)in the same area supports this argument.

    4.4.2.Verification with skill score

    The detection accuracy of the fuzzy logic algorithm is evaluated using the POD and FAR:

    The PODand FARare derived based on the contingency table shown in Table 4.For the PRE events,the hit(H)is defined as the PRE echoes with SQI>0.7 in actual measurements and DZ<10 dB Z in the average ground echo map.The miss(M)is the eliminated pixel by the fuzzy logic algorithm with the same condition as in H.The false(F)is the remaining pixel in the NME area after applying the fuzzy algorithm.For NME events,the M is the area of DZ<10 dB Z in the average ground echo map that is not detected by the algorithm.The H is the detected area in the algorithm.Thus,the POD in NMEevaluates the removal efficiency of ground clutter,chaff,and AP echoes.For the NME events,the F cannot be calculated because the precipitation pixels do not exist.

    Table 4.Contingency table for the POD and FAR.

    Validation is also performed as functions of CSR for the PRE events only.The CSR is defined as

    where T0is the unfiltered signal power and C0is the filtered signal power.Since the real-time moment data do not provide T0and C0,the CSR is approximated with DZ and CZ.For a given CSR,the fraction(F)of removed pixels to overall pixels is calculated by:

    The term Pallrepresents the overall number of pixels with radar echoes and Preis the number of removed pixels among them.

    The validation of the dual-polarization fuzzy classifier is performed for the events in Table 2.Figure 9a shows the PODs as a function of reflectivity thresholds.The average POD for the clear echo and chaff echo events is about 1.00.The average POD for the PRE events is 0.7–0.85.This slightly lower POD is due to the exclusion of mixed areas of ground clutter and precipitation and the removal of edge regions.The average FAR for the PRE events decreases from 0.06 to 0.02 with reflectivity thresholds.In the case of ground clutter,chaff and precipitation echo,NMEs are properly removed by our algorithm(Fig.10).The spatial variations of ZDRand ΦDPin the chaff echo are large andρHVis less than 0.8.

    The fraction of removed pixels to overall pixels is shown in Fig.9b as a function of CSR.The fraction is about 0.5 at CSR=0 dB.As the CSR increases,more pixels are removed.When the echoes are more contaminated,they are easily eliminated.

    5.Summary and conclusions

    A moment-based fuzzy logic algorithm is developed to identify and remove NME by optimizing membership functions and weights for Mount Sobaek dual-polarization radar data.We use five fuzzy parameters[SD(DZ),SD(ZDR), SD(ρHV),SD(ΦDP),andρHV]and reflectivity intervals from selected NME and PRE cases for determination of MFs and weights.For the selected NME and PRE areas,the PDFs of NME and PRE clearly show that the five fuzzy parameters show discernible features to identify NME,especially ΦDP.The MFs and weights are derived using the PDFs of the NME and PRE.The MFs become broader and less steep as the reflectivity interval decreases.The SD(ΦDP)shows higher values of weights at overall ranges of DZ intervals. The SD(ΦDP)is an important feature parameter for classif ication of NME.The fuzzy membership values at each pixel are calculated using predetermined MFs and are combined with weights to lead to the total membership value.When the total membership value is higher than 0.5,this pixel is identified and removed as NME.

    After applying the fuzzy logic algorithm,ground clutter echoes still remain in areas of mixed ground and precipitation echoes.This is due to the absence of unfiltered dualpolarimetric measurements provided from Mount Sobaek radar.The signal processor of Mount Sobaek radar provides one unfiltered parameter only.All the others,including dualpolarization parameters are filtered.After GMAP filtering, the NME characteristics of dual-polarimetric measurements become weaker or are removed.Thus,the dual-polarimetric fuzzy parameters cannot be identified as NME after applying the fuzzy logic algorithm.To avoid this problem,our algorithm further applies the threshold of difference between DZ and CZ.The DZ–CZ indicates the degree of contamination from ground clutter.Average reflectivity after applying the threshold(5 dB)shows that residual ground clutter echoes.

    The performance of the fuzzy logic algorithm is evaluated using accumulated rainfall,POD,FAR,and CSR.In the comparison of rainfall accumulation,the proposed algorithm has several different results to GMAP filtering of Mount Sobaek radar.The GMAP filters all echoes regardless of echo type. Thus,significant underestimation appears in high rainfall ar-eas compared with the proposed algorithm.Moreover,residual ground clutter echoes still remain and cause high rainfall that cannot be removed by GMAP due to the broad clutter spectrum.These areas are properly removed by R(FZ).In addition,the proposed algorithm can reduce the overestimated rainfall due to bright band and second trip echoes.Values of high SD(ρHV)and lowρHVin the bright band can be detected in the proposed algorithm.Moreover,removal of second trip echo is possible using the SQI threshold.The average POD for NME(PRE)events is about 1.00(0.76).The average FAR for PRE events is 0.05,and decreases as a function of the reflectivity threshold.The fraction of removed pixels to overallpixels increases as a function of CSR and reaches F=1 at CSR=4 dB.

    Acknowledgements.This research was supported by a grant (14AWMP-B079364-01)from Water Management Research Program funded by Ministry of Land,Infrastructure and Transport of Korean government.

    REFERENCES

    Berenguer,M.,D.Sempere-Torres,C.Corral,and R.S′anchez-Diezma,2006:A fuzzy logic technique for identifying nonprecipitating echoes in radar scans.J.Atmos.Oceanic Technol.,23,1157–1180.

    Cao,Q.,G.F.Zhang,R.D.Palmer,M.Knight,R.May,and R. J.Stafford,2012:Spectrum-Time Estimation and Processing (STEP)for imprving weather radar data quality.IEEE Trans. Geosci.Remote Sens.,50,4670–4683.

    Cho,Y.H.,G.W.Lee,K.E.Kim,and I.Zawadzki,2006:Identification and removal of ground echoes and anomalous propagation using the characteristics of radar echoes.J.Atmos. Oceanic Technol.,23,1206–1222.

    Gourley,J.J.,P.Tabary,and J.P.du Chatelet,2007:A fuzzy logic algorithm for the separation of precipitating from nonprecipitating echoes using polarimetric radar observations.J.Atmos. Oceanic Technol.,24,1439–1451.

    Grecu,M.,and W.F.Krajewski,2000:An efficient methodology fordetection of anomalous propagation echoes in radarreflectivity data using neural networks.J.Atmos.Oceanic Technol.,17,121–129.

    Haykin,S.,and C.Deng,1991:Classification of radar clutter using neural networks.IEEE Transactions on Neural Networks,2,589–600.

    Hubbert,J.C.,M.Dixon,S.Ellis,and G.Meymaris,2009a: Weather radar ground clutter.Part I:Identification,modeling, and simulation.J.Atmos.Oceanic Technol.,26,1165–1180.

    Hubbert,J.C.,M.Dixon,and S.Ellis,2009b:Weather radar ground clutter.Part II:Real-time identification and filtering. J.Atmos.Oceanic Technol.,26,1181–1197.

    Krajewski,W.F.,and B.Vignal,2001:Evaluation of anomalous propagation echo detection in WSR-88Ddata:Alarge sample case study.J.Atmos.Oceanic Technol.,18,807–814.

    Lakshmanan,V.,A.Fritz,T.Smith,K.Hondl,and G.Stumpf, 2007:An automated technique to quality control radar reflectivity data.J.Appl.Meteor.Climatol.,46,288–305.

    Li,Y.,G.Zhang,R.J.Doviak,L.Lei,and Q.Cao,2013:A new approach to detect ground clutter mixed with weather signals. IEEE Trans.Geosci.Remote Sens.,51,2373–2387.

    Li,Y.G.,G.F.Zhang,and R.J.Doviak,2014:Ground clutter detection using the statistical properties of signals received with a polarimetric radar.IEEE Transactions on SignalProcessing,62,597–606.

    Liu,H.P.,and V.Chandrasekar,2000:Classification of hydrometeors based on polarimetric radar measurements:Development of fuzzy logic and neuro-fuzzy systems,and in situ verification.J.Atmos.Oceanic Technol.,17,140–164.

    Mahale,V.N.,G.F.Zhang,and M.Xue,2014:Fuzzy logic classification of S-band polarimetric radar echoes to identify threebody scattering and improve data quality.J.Appl.Meteor.Climatol.,53,2017–2033.

    Moszkowicz,S.,G.J.Ciach,and W.F.Krajewski,1994:Statistical detection of anomalous propagation in radar reflectivity patterns.J.Atmos.Oceanic Technol.,11,1026–1034.

    Nicol,J.C.,A.J.Illingworth,T.Darlington,and J.Sugier,2011: Techniques for improving ground clutter identification.Proc. Symp.Weather Radar Hydrol.,IAHS Press,351 pp.

    Park,H.S.,A.V.Ryzhkov,D.S.Zrni′c and K.-E.Kim,2009: The hydrometeor classification algorithm for the polarimetric WSR-88D:Description and application to an MCS.Wea. Forecasting,24,730–748.

    Rico-Ramirez,M.A.,and I.D.Cluckie,2008:Classification of ground clutter and anomalous propagation using dualpolarization weather radar.IEEE Trans.Geosci.Remote Sens.,46,1892–1904.

    Siggia,A.D.,and R.E.Passarelli Jr.,2004:Gaussian model adaptive processing(GMAP)for improved ground clutter cancellation and moment calculation.Proc.3rd European Conf.on Radar in Meteorol.and Hydrology,Visby,Sweden,67–73.

    Silverman,B.W.,1981:Using kernel density estimates to investigate multimodality.Journal of the Royal Statistical Society: Series B,43,97–99.

    Smith,J.A.,D.J.Seo,M.L.Baeck,and M.D.Hudlow,1996:An intercomparison study of NEXRAD precipitation estimates. Water Resour.Res.,32,2035–2045.

    Steiner,M.and J.A.Smith,2002:Use of three-dimensional reflectivity structure for automated detection and removal of nonprecipitating echoes in radardata.J.Atmos.Oceanic Technol.,19,673–686.

    Torres,S.,D.Warde,and D.Zrnic,2012:Signal Design and Processing Techniques for WSR-88D Ambiguity Resolution: Part 15.The CLEAN-AP Filter,National Severe Storms Lab., Norman,OK,65 pp.

    Warde,D.A.,and S.M.Torres,2014:The autocorrelation spectral density for Doppler-weather-radar signal analysis.IEEE Trans.Geosci.Remote Sens.,52,508–518.

    :Ye,B.-Y.,G.Lee,and H.-M.Park,2015:Identification and removal of non-meteorological echoes in dualpolarization radar data based on a fuzzy logic algorithm.Adv.Atmos.Sci.,32(9),1217–1230,

    10.1007/s00376-015-4092-0.

    27 September 2014;revised 31 December 2014;accepted 19 January 2015)

    ?Corresponding author:GyuWon LEE

    Email:gyuwon@knu.ac.kr

    成人18禁高潮啪啪吃奶动态图| 国产精品久久久av美女十八| 免费高清视频大片| 久久久精品欧美日韩精品| 麻豆av在线久日| 亚洲欧美激情综合另类| 一级毛片女人18水好多| 侵犯人妻中文字幕一二三四区| 亚洲自拍偷在线| 亚洲久久久国产精品| 视频在线观看一区二区三区| 老司机福利观看| 9191精品国产免费久久| 成人国产一区最新在线观看| 亚洲av日韩精品久久久久久密| 久久精品91无色码中文字幕| 精品久久久久久成人av| 欧美精品亚洲一区二区| 99re在线观看精品视频| 交换朋友夫妻互换小说| 久久久久国产一级毛片高清牌| 日韩大尺度精品在线看网址 | 国产熟女xx| 日本黄色视频三级网站网址| 韩国精品一区二区三区| 精品少妇一区二区三区视频日本电影| 午夜精品在线福利| 黄网站色视频无遮挡免费观看| 很黄的视频免费| 18禁黄网站禁片午夜丰满| 国产极品粉嫩免费观看在线| 后天国语完整版免费观看| 久99久视频精品免费| 97超级碰碰碰精品色视频在线观看| 高清在线国产一区| 精品久久久久久久久久免费视频 | 国产av一区在线观看免费| 日韩大尺度精品在线看网址 | 亚洲av片天天在线观看| 日日摸夜夜添夜夜添小说| 曰老女人黄片| 欧美成人午夜精品| 好男人电影高清在线观看| 亚洲成人免费电影在线观看| 日本欧美视频一区| 亚洲精品中文字幕在线视频| 韩国精品一区二区三区| 91字幕亚洲| 欧美日韩福利视频一区二区| 国产成人精品久久二区二区91| 大香蕉久久成人网| 性欧美人与动物交配| 国产在线精品亚洲第一网站| 欧美 亚洲 国产 日韩一| 国产视频一区二区在线看| 水蜜桃什么品种好| 在线观看66精品国产| 国产精品乱码一区二三区的特点 | 亚洲精品中文字幕在线视频| 一区二区三区国产精品乱码| 成人亚洲精品一区在线观看| 90打野战视频偷拍视频| 多毛熟女@视频| 久9热在线精品视频| 一级,二级,三级黄色视频| 欧美激情高清一区二区三区| 18禁国产床啪视频网站| 久久国产精品男人的天堂亚洲| 每晚都被弄得嗷嗷叫到高潮| 欧美日韩精品网址| 在线观看舔阴道视频| av天堂久久9| 亚洲精品国产色婷婷电影| 午夜老司机福利片| 精品一区二区三区四区五区乱码| 女人精品久久久久毛片| 亚洲免费av在线视频| 久久精品国产99精品国产亚洲性色 | 精品久久久久久久毛片微露脸| 国产欧美日韩一区二区三| 午夜福利,免费看| 免费高清视频大片| 男女床上黄色一级片免费看| 老汉色av国产亚洲站长工具| 亚洲精品国产精品久久久不卡| 亚洲狠狠婷婷综合久久图片| 精品免费久久久久久久清纯| 一个人观看的视频www高清免费观看 | 中文字幕人妻丝袜制服| 国产主播在线观看一区二区| 午夜福利欧美成人| 国产亚洲精品第一综合不卡| 精品电影一区二区在线| 日日夜夜操网爽| 日本三级黄在线观看| 中文欧美无线码| 琪琪午夜伦伦电影理论片6080| av中文乱码字幕在线| 丝袜在线中文字幕| 久久精品91蜜桃| 一二三四在线观看免费中文在| 麻豆成人av在线观看| 亚洲欧美精品综合一区二区三区| a级毛片黄视频| 亚洲av五月六月丁香网| 热99国产精品久久久久久7| 中文字幕精品免费在线观看视频| 好看av亚洲va欧美ⅴa在| 国产熟女xx| 国产成+人综合+亚洲专区| 亚洲专区字幕在线| 不卡av一区二区三区| 欧美不卡视频在线免费观看 | 国产精品1区2区在线观看.| 老司机在亚洲福利影院| 国产亚洲av高清不卡| 99久久久亚洲精品蜜臀av| 一进一出抽搐gif免费好疼 | 99精品久久久久人妻精品| 久久久精品国产亚洲av高清涩受| 亚洲一区高清亚洲精品| 亚洲男人的天堂狠狠| 高清av免费在线| 欧美成人免费av一区二区三区| 精品久久蜜臀av无| 很黄的视频免费| 国产区一区二久久| 国产麻豆69| x7x7x7水蜜桃| 99久久精品国产亚洲精品| 最新在线观看一区二区三区| 久久精品国产99精品国产亚洲性色 | 不卡av一区二区三区| 美女国产高潮福利片在线看| 在线观看66精品国产| 黄网站色视频无遮挡免费观看| 日本黄色视频三级网站网址| 亚洲精品国产区一区二| 久久婷婷成人综合色麻豆| 欧美日韩黄片免| 色哟哟哟哟哟哟| 男人舔女人的私密视频| 神马国产精品三级电影在线观看 | 亚洲 国产 在线| 精品电影一区二区在线| 新久久久久国产一级毛片| 日本五十路高清| 日本欧美视频一区| 精品国产一区二区三区四区第35| 天天躁狠狠躁夜夜躁狠狠躁| 精品一区二区三卡| 婷婷六月久久综合丁香| 一区二区日韩欧美中文字幕| 国产精品免费一区二区三区在线| 日本黄色日本黄色录像| 女性生殖器流出的白浆| 日韩视频一区二区在线观看| 精品久久久久久久毛片微露脸| av有码第一页| 亚洲久久久国产精品| 亚洲精品美女久久av网站| 黑丝袜美女国产一区| 免费女性裸体啪啪无遮挡网站| 中文字幕人妻丝袜制服| 极品教师在线免费播放| 午夜福利在线免费观看网站| 99国产综合亚洲精品| 亚洲精品中文字幕一二三四区| 久久亚洲真实| 中国美女看黄片| 99香蕉大伊视频| 国产亚洲欧美98| 亚洲男人的天堂狠狠| 色尼玛亚洲综合影院| 黄色毛片三级朝国网站| 久久精品亚洲熟妇少妇任你| 国产精品98久久久久久宅男小说| 亚洲中文字幕日韩| 黑人巨大精品欧美一区二区mp4| 999久久久精品免费观看国产| 色播在线永久视频| 在线观看免费高清a一片| 免费一级毛片在线播放高清视频 | 欧美日韩福利视频一区二区| bbb黄色大片| 最新在线观看一区二区三区| 国产精品久久电影中文字幕| 国产单亲对白刺激| 多毛熟女@视频| 国产成人av激情在线播放| 亚洲在线自拍视频| 日日爽夜夜爽网站| 中文字幕高清在线视频| 久久精品国产综合久久久| 亚洲av五月六月丁香网| 一级黄色大片毛片| 国产99白浆流出| 成人av一区二区三区在线看| 国产精品综合久久久久久久免费 | 国产97色在线日韩免费| 亚洲男人的天堂狠狠| 午夜成年电影在线免费观看| 一级毛片女人18水好多| 中文字幕高清在线视频| 一级a爱视频在线免费观看| 动漫黄色视频在线观看| 日韩免费av在线播放| 在线观看一区二区三区激情| 精品人妻在线不人妻| 久久天堂一区二区三区四区| 久久久水蜜桃国产精品网| 视频区欧美日本亚洲| av在线天堂中文字幕 | 操出白浆在线播放| 精品久久久久久电影网| 欧美另类亚洲清纯唯美| 精品国产乱子伦一区二区三区| 国产成人免费无遮挡视频| 黑人操中国人逼视频| 亚洲一区中文字幕在线| 在线观看www视频免费| 涩涩av久久男人的天堂| 国产真人三级小视频在线观看| 婷婷六月久久综合丁香| 另类亚洲欧美激情| 十八禁网站免费在线| 免费观看人在逋| 高清黄色对白视频在线免费看| 亚洲人成电影观看| 色婷婷av一区二区三区视频| 久久草成人影院| 91老司机精品| 一本大道久久a久久精品| 激情在线观看视频在线高清| 夜夜看夜夜爽夜夜摸 | 亚洲成人久久性| 在线观看一区二区三区激情| 两个人免费观看高清视频| 国产欧美日韩精品亚洲av| 亚洲人成77777在线视频| 亚洲精品中文字幕一二三四区| 电影成人av| 男人的好看免费观看在线视频 | 免费搜索国产男女视频| 久久九九热精品免费| bbb黄色大片| 女人被狂操c到高潮| 国产在线精品亚洲第一网站| 免费一级毛片在线播放高清视频 | 欧美日韩瑟瑟在线播放| 国产av在哪里看| 天堂动漫精品| 最近最新免费中文字幕在线| aaaaa片日本免费| 黑人欧美特级aaaaaa片| 免费日韩欧美在线观看| 韩国精品一区二区三区| 亚洲熟妇中文字幕五十中出 | 久久精品亚洲熟妇少妇任你| 日韩欧美国产一区二区入口| 99精品欧美一区二区三区四区| 电影成人av| 国产激情久久老熟女| 一本综合久久免费| 999精品在线视频| 91麻豆精品激情在线观看国产 | 好看av亚洲va欧美ⅴa在| 韩国精品一区二区三区| 88av欧美| 91国产中文字幕| 一级片'在线观看视频| 精品高清国产在线一区| 欧美日韩黄片免| 精品人妻在线不人妻| 中文字幕人妻丝袜制服| 很黄的视频免费| 人妻丰满熟妇av一区二区三区| 黄网站色视频无遮挡免费观看| 80岁老熟妇乱子伦牲交| 亚洲黑人精品在线| 欧美另类亚洲清纯唯美| 国产午夜精品久久久久久| 国产精品成人在线| 人人妻人人添人人爽欧美一区卜| 国产精品免费视频内射| 国产精品久久久av美女十八| 男女之事视频高清在线观看| 十分钟在线观看高清视频www| 成人精品一区二区免费| 欧美亚洲日本最大视频资源| 精品国产美女av久久久久小说| 18禁美女被吸乳视频| 亚洲成a人片在线一区二区| 成人三级做爰电影| 欧美在线一区亚洲| 欧美av亚洲av综合av国产av| netflix在线观看网站| 免费人成视频x8x8入口观看| 国产野战对白在线观看| 国产无遮挡羞羞视频在线观看| 午夜福利影视在线免费观看| 日本黄色视频三级网站网址| 亚洲视频免费观看视频| 亚洲精品中文字幕一二三四区| 久久天躁狠狠躁夜夜2o2o| 中文字幕色久视频| 国产日韩一区二区三区精品不卡| 国产视频一区二区在线看| 亚洲成人国产一区在线观看| 亚洲一区二区三区欧美精品| 在线播放国产精品三级| 成人永久免费在线观看视频| 村上凉子中文字幕在线| 亚洲一码二码三码区别大吗| 精品午夜福利视频在线观看一区| 亚洲va日本ⅴa欧美va伊人久久| 亚洲第一av免费看| 亚洲一码二码三码区别大吗| 午夜成年电影在线免费观看| 老司机在亚洲福利影院| 在线永久观看黄色视频| a在线观看视频网站| 成人18禁高潮啪啪吃奶动态图| av免费在线观看网站| 免费av毛片视频| 国产精品久久视频播放| 色尼玛亚洲综合影院| 欧美成人午夜精品| 欧美黄色片欧美黄色片| 亚洲一卡2卡3卡4卡5卡精品中文| 一级毛片女人18水好多| 男人舔女人的私密视频| 国内毛片毛片毛片毛片毛片| 欧美一级毛片孕妇| 老司机深夜福利视频在线观看| 国产真人三级小视频在线观看| 日韩人妻精品一区2区三区| 国产精品一区二区在线不卡| 亚洲三区欧美一区| 国产精品九九99| 国产精品秋霞免费鲁丝片| 高清欧美精品videossex| 80岁老熟妇乱子伦牲交| 中亚洲国语对白在线视频| 欧美久久黑人一区二区| 国产高清视频在线播放一区| 成人免费观看视频高清| 精品欧美一区二区三区在线| 精品少妇一区二区三区视频日本电影| 欧美黄色片欧美黄色片| 99香蕉大伊视频| 国产熟女xx| 午夜a级毛片| 久久性视频一级片| 麻豆一二三区av精品| 亚洲少妇的诱惑av| 丝袜美腿诱惑在线| 亚洲欧美精品综合久久99| 在线观看免费视频日本深夜| 久久精品影院6| 一进一出抽搐动态| 91九色精品人成在线观看| 在线视频色国产色| 这个男人来自地球电影免费观看| 制服人妻中文乱码| 午夜精品国产一区二区电影| 大陆偷拍与自拍| 夫妻午夜视频| 色哟哟哟哟哟哟| 久久久国产精品麻豆| 狂野欧美激情性xxxx| 亚洲色图av天堂| 欧美老熟妇乱子伦牲交| 99久久国产精品久久久| 欧美日韩国产mv在线观看视频| 亚洲国产精品999在线| 免费搜索国产男女视频| 国产日韩一区二区三区精品不卡| 国产精品综合久久久久久久免费 | 亚洲熟女毛片儿| 长腿黑丝高跟| 欧美成狂野欧美在线观看| 99久久99久久久精品蜜桃| 男女下面进入的视频免费午夜 | 久久午夜综合久久蜜桃| 久久久久久人人人人人| 日本免费a在线| 91大片在线观看| 精品电影一区二区在线| 欧美成人性av电影在线观看| 日韩大尺度精品在线看网址 | av天堂在线播放| 大陆偷拍与自拍| 多毛熟女@视频| 国产精品综合久久久久久久免费 | 国产精品久久久av美女十八| 精品福利永久在线观看| 在线播放国产精品三级| 三上悠亚av全集在线观看| 欧美激情高清一区二区三区| svipshipincom国产片| 不卡一级毛片| av在线天堂中文字幕 | 久久久久国产精品人妻aⅴ院| 欧美黄色片欧美黄色片| 丝袜人妻中文字幕| 精品久久久久久成人av| 久久中文字幕人妻熟女| 1024视频免费在线观看| 精品午夜福利视频在线观看一区| 一级毛片精品| 黑人巨大精品欧美一区二区mp4| 亚洲国产毛片av蜜桃av| 琪琪午夜伦伦电影理论片6080| 久久精品91无色码中文字幕| 色综合欧美亚洲国产小说| 91九色精品人成在线观看| 999久久久精品免费观看国产| 他把我摸到了高潮在线观看| 人成视频在线观看免费观看| 精品久久蜜臀av无| 日韩中文字幕欧美一区二区| 黄色视频,在线免费观看| 免费久久久久久久精品成人欧美视频| 激情视频va一区二区三区| 久久人妻熟女aⅴ| 黄色怎么调成土黄色| 国产一区在线观看成人免费| 欧美日韩视频精品一区| 欧美性长视频在线观看| 啦啦啦 在线观看视频| 久久久久国产一级毛片高清牌| 动漫黄色视频在线观看| 久久久国产精品麻豆| 级片在线观看| 日本五十路高清| 亚洲精华国产精华精| 99国产精品99久久久久| 欧美日韩av久久| 亚洲欧美日韩高清在线视频| 男女午夜视频在线观看| 久久天躁狠狠躁夜夜2o2o| 热re99久久国产66热| 亚洲精品一二三| 性欧美人与动物交配| 日韩中文字幕欧美一区二区| 欧美日韩亚洲综合一区二区三区_| 两个人看的免费小视频| 一级a爱片免费观看的视频| 久久香蕉激情| 成年版毛片免费区| 久久精品人人爽人人爽视色| 男女高潮啪啪啪动态图| 国产av在哪里看| 香蕉国产在线看| 岛国在线观看网站| 日韩三级视频一区二区三区| 大码成人一级视频| 国产一区二区三区视频了| 免费搜索国产男女视频| 99久久99久久久精品蜜桃| 国产精品一区二区三区四区久久 | 淫妇啪啪啪对白视频| 久久国产精品男人的天堂亚洲| 久久性视频一级片| 亚洲av五月六月丁香网| 欧美日韩中文字幕国产精品一区二区三区 | 黄色 视频免费看| 国产成人精品无人区| 啦啦啦在线免费观看视频4| 欧美黑人精品巨大| 十分钟在线观看高清视频www| 欧美日韩亚洲高清精品| 免费看十八禁软件| 精品久久久精品久久久| 亚洲精品粉嫩美女一区| 欧美大码av| 国产精品久久久av美女十八| 欧美日韩福利视频一区二区| 亚洲人成网站在线播放欧美日韩| 久久人妻熟女aⅴ| 精品高清国产在线一区| 岛国视频午夜一区免费看| 久久久国产成人精品二区 | 一个人观看的视频www高清免费观看 | 咕卡用的链子| 亚洲精品国产精品久久久不卡| www.精华液| 亚洲成人精品中文字幕电影 | 我的亚洲天堂| 午夜a级毛片| 久久久久国产一级毛片高清牌| 成人国产一区最新在线观看| 久9热在线精品视频| 亚洲五月色婷婷综合| 人成视频在线观看免费观看| 国产伦一二天堂av在线观看| 亚洲熟妇熟女久久| 9色porny在线观看| 男女做爰动态图高潮gif福利片 | 香蕉丝袜av| 日日爽夜夜爽网站| 日本精品一区二区三区蜜桃| 最近最新中文字幕大全电影3 | 成人黄色视频免费在线看| 亚洲精品成人av观看孕妇| 亚洲七黄色美女视频| 韩国av一区二区三区四区| 天堂√8在线中文| 久久人妻av系列| 久久精品国产清高在天天线| 少妇的丰满在线观看| 国产av在哪里看| 国产麻豆69| 日韩精品青青久久久久久| 欧美日韩视频精品一区| 日韩中文字幕欧美一区二区| 亚洲国产欧美一区二区综合| 不卡av一区二区三区| 叶爱在线成人免费视频播放| 亚洲av成人一区二区三| 亚洲中文av在线| 窝窝影院91人妻| 国产黄a三级三级三级人| 丰满人妻熟妇乱又伦精品不卡| 少妇被粗大的猛进出69影院| 国产97色在线日韩免费| 嫩草影视91久久| bbb黄色大片| 每晚都被弄得嗷嗷叫到高潮| 国产色视频综合| 精品国产超薄肉色丝袜足j| 丝袜人妻中文字幕| 国产精品1区2区在线观看.| 亚洲aⅴ乱码一区二区在线播放 | 国产亚洲精品久久久久久毛片| 男女下面插进去视频免费观看| 在线十欧美十亚洲十日本专区| 男女之事视频高清在线观看| 国产熟女xx| 极品教师在线免费播放| 久久久久久久久中文| 午夜福利免费观看在线| 在线观看免费高清a一片| 国产三级黄色录像| 亚洲精品av麻豆狂野| 美女高潮到喷水免费观看| 久久 成人 亚洲| 亚洲久久久国产精品| 人人妻人人添人人爽欧美一区卜| 婷婷六月久久综合丁香| 久久久久国产一级毛片高清牌| 成人三级黄色视频| 少妇的丰满在线观看| 村上凉子中文字幕在线| 两人在一起打扑克的视频| 亚洲精品在线观看二区| 国产麻豆69| 亚洲精品在线观看二区| 老司机午夜十八禁免费视频| 天天躁夜夜躁狠狠躁躁| av福利片在线| 大型黄色视频在线免费观看| 国产成人一区二区三区免费视频网站| www.自偷自拍.com| 亚洲成a人片在线一区二区| 亚洲自拍偷在线| 可以免费在线观看a视频的电影网站| 午夜影院日韩av| 国产精品亚洲av一区麻豆| 亚洲美女黄片视频| 久久久国产成人精品二区 | 久久久久久亚洲精品国产蜜桃av| 免费看a级黄色片| 桃色一区二区三区在线观看| 免费看a级黄色片| 国产97色在线日韩免费| 亚洲精品一区av在线观看| 女人被狂操c到高潮| 一a级毛片在线观看| av天堂久久9| 国产精品一区二区三区四区久久 | 日韩精品免费视频一区二区三区| 人妻久久中文字幕网| 国产精品国产av在线观看| 人人澡人人妻人| videosex国产| 精品久久久精品久久久| 满18在线观看网站| av免费在线观看网站| 久久午夜亚洲精品久久| 老熟妇仑乱视频hdxx| 视频区图区小说| 国产精品影院久久| 久久久久久亚洲精品国产蜜桃av| 丁香欧美五月| 亚洲精品国产一区二区精华液| 久久精品国产清高在天天线| 久久久久久久久免费视频了| 亚洲熟妇熟女久久| 看黄色毛片网站| 久久人妻熟女aⅴ| av免费在线观看网站| 视频在线观看一区二区三区| 久久婷婷成人综合色麻豆| 无人区码免费观看不卡| 免费看a级黄色片| 色综合欧美亚洲国产小说| 精品国产美女av久久久久小说| 久久久久久久久免费视频了| 视频区欧美日本亚洲| 欧美 亚洲 国产 日韩一| 亚洲五月天丁香| 身体一侧抽搐| 国产精品秋霞免费鲁丝片|