Study on Quantitative Precipitation Estimation by Polarimetric Radar Using Deep Learning

Accurate radar quantitative precipitation estimation(QPE)plays an essential role in disaster prevention and mitigation.In this paper,two deep learning-based QPE networks including a single-parameter network and a multi-parameter network are designed.Meanwhile,a self-defined loss function(SLF)is proposed during modeling.The dataset includes Shijiazhuang S-band dual polarimetric radar(CINRAD/SAD)data and rain gauge data within the radar’s 100-km detection range during the flood season of 2021 in North China.Considering that the specific propagation phase shift(KDP)has a roughly linear relationship with the precipitation intensity,KDP is set to 0.5°km^(-1 )as a threshold value to divide all the rain data(AR)into a heavy rain(HR)and light rain(LR)dataset.Subsequently,12 deep learning-based QPE models are trained according to the input radar parameters,the precipitation datasets,and whether an SLF was adopted,respectively.The results suggest that the effects of QPE after distinguishing rainfall intensity are better than those without distinguishing,and the effects of using SLF are better than those that used MSE as a loss function.A Z-R relationship and a ZH-KDP-R synthesis method are compared with deep learning-based QPE.The mean relative errors(MRE)of AR models using SLF are improved by 61.90%,51.21%,and 56.34%compared with the Z-R relational method,and by 38.63%,42.55%,and 47.49%compared with the synthesis method.Finally,the models are further evaluated in three precipitation processes,which manifest that the deep learning-based models have significant advantages over the traditional empirical formula methods.

Microphysical Characteristics of Extreme-Rainfall Convection over the Pearl River Delta Region, South China from Polarimetric Radar Data during the Pre-summer Rainy Season

During the pre-summer rainy season,heavy rainfall occurs frequently in South China.Based on polarimetric radar observations,the microphysical characteristics and processes of convective features associated with extreme rainfall rates(ERCFs)are examined.In the regions with high ERCF occurrence frequency,sub-regional differences are found in the lightning flash rate(LFR)distributions.In the region with higher LFRs,the ERCFs have larger volumes of high reflectivity factor above the freezing level,corresponding to more active riming processes.In addition,these ERCFs are more organized and display larger spatial coverage,which may be related to the stronger low-level wind shear and higher terrain in the region.In the region with lower LFRs,the ERCFs have lower echo tops and lower-echo centroids.However,no clear differences of the most unstable convective available potential energy(MUCAPE)exist in the ERCFs in the regions with different LFR characteristics.Regardless of the LFRs,raindrop collisional coalescence is the main process for the growth of raindrops in the ERCFs.In the ERCFs within the region with lower LFRs,the main mechanism for the rapid increase of liquid water content with decreasing altitude below 4 km is through the warm-rain processes converting cloud drops to raindrops.However,in those with higher LFRs,the liquid water content generally decreases with decreasing altitude.

A Quality Assurance Procedure and Evaluation of Rainfall Estimates for C-Band Polarimetric Radar

A mobile C-band dual polarimetric weather radar J type （PCDJ）, which adopts simultaneous transmission and simultaneous reception （STSR） of horizontally and vertically polarized signals, was first developed in China in 2008. It was deployed in the radar observation plan in the South China Heavy Rainfall Experiment （SCHeREX） in the summer of 2008 and 2009, as well as in Tropical Western Pacific Ocean Observation Experiments and Research on the Predictability of High Impact Weather Events from 2008 to 2010 in China （TWPOR）. Using the observation data collected in these experiments, the radar systematic error and its sources were analyzed in depth. Meanwhile an algorithm that can smooth differential propagation phase （~Dp） for estimating the high-resolution specific differential phase （KDP） was developed. After attenuation correction of reflectivity in horizontal polarization （ZH） and differential reflectivity （ZDR） of PCDJ radar by means of KDP, the data quality was improved significantly. Using quality-controlled radar data, quantitative rainfall estimation was performed, and the resutls were compared with rain-gauge measurements. A synthetic ZH /KDp-based method was analyzed. The results the traditional ZH-based method when the rain suggest that the synthetic method has the advantage over rate is 〉5 mm h^-1. The more intensive the rain rates, the higher accuracy of the estimation.

Precipitation Microphysical Processes in the Inner Rainband of Tropical Cyclone Kajiki (2019) over the South China Sea Revealed by Polarimetric Radar

Polarimetric radar and 2D video disdrometer observations provide new insights into the precipitation microphysical processes and characteristics in the inner rainband of tropical cyclone(TC)Kajiki(2019)in the South China Sea for the first time.The precipitation of Kajiki is dominated by high concentrations and small(<3 mm)raindrops,which contribute more than 98%to the total precipitation.The average mass-weighted mean diameter and logarithmic normalized intercept are 1.49 mm and 4.47,respectively,indicating a larger mean diameter and a lower concentration compared to the TCs making landfall in eastern China.The ice processes of the inner rainband are dramatically different among different stages.The riming process is dominant during the mature stage,while during the decay stage the aggregation process is dominant.The vertical profiles of the polarimetric radar variables together with ice and liquid water contents in the convective region indicate that the formation of precipitation is dominated by warm-rain processes.Large raindrops collect cloud droplets and other raindrops,causing reflectivity,differential reflectivity,and specific differential phase to increase with decreasing height.That is,accretion and coalescence play a critical role in the formation of heavy rainfall.The melting of different particles generated by the ice process has a great influence on the initial raindrop size distribution(DSD)to further affect the warm-rain processes.The DSD above heavy rain with the effect of graupel has a wider spectral width than the region without the effect of graupel.

Raindrop Size Distribution Parameters Retrieved from Xinfeng C-Band Polarimetric Radar Observations

The objective of this research was to acquire a raindrop size distribution(DSDs)retrieved from C-band polarimetric radar observations scheme for the first time in south China.An observation period of the precipitation process was selected,and the shape-slope(μ-Λ)relationship of this region was statistically analyzed using the raindrop sample observations from the two-dimensional video disdrometer(2DVD)at Xinfeng Station,Guangdong Province.Simulated data of the C-band polarimetric radar reflectivity ZHHand differential reflectivity ZDRwere obtained through scattering simulation.The simulation data were combined with DSD fitting to determine the ZDR-Λand log10(ZHH/N0)-Λrelationships.Using Xinfeng C-band polarimetric radar observations ZDRand ZHH,the raindrop Gamma size distribution parametersμ,Λ,and N0were retrieved.A scheme for using C-band polarimetric radar to retrieve the DSDs was developed.This research revealed that during precipitation process,the DSDs obtained using the C-band polarimetric radar retrieval scheme are similar to the 2DVD observations,the precipitation characteristics of rainfall intensity(R),mass-weighted mean diameter(Dm)and intercept parameter(Nw)with time obtained by radar retrieval are basically consistent with the observational results of the 2DVD.This scheme establishes the relationship between the observations of the C-band polarimetric radar and the physical quantities of the numerical model.This method not only can test the prediction of the model data assimilation system on the convective scale and determine error sources,but also can improve the microphysical precipitation processes analysis and radar quantitative precipitation estimation.The present research will facilitate radar data assimilation in the future.

Assimilation of Polarimetric Radar Data Using an Ensemble Kalman Filter for the Analysis and Forecast of Tropical Storm Ewiniar(2018)

This study explores the potential for directly assimilating polarimetric radar data(including reflectivity Z and differential reflectivity Z_(DR))using an ensemble Kalman filter(EnKF)based on the Weather Research and Forecasting model to improve analysis and forecast of Tropical Storm Ewiniar(2018).Ewiniar weakened but brought about heavy rainfall over Guangdong,China after its final landfall.In the present study,two experiments are performed,one assimilating only Z and the other assimilating both Z and Z_(DR).Assimilation of Z_(DR)together with Z effectively modifies hydrometeor fields,and improves the forecast of the intensity,shape and position of rainbands.Forecast of 24-hour extraordinary rainfall≥250 mm is significantly improved.Improvement can also be seen in the wind fields because of cross-variable covariance.The current study shows the possibility of applying polarimetric radar data to improve forecasting of tropical cyclones,which deserves more research in the future.

Evaluation of Unified Model Microphysics in High-resolution NWP Simulations Using Polarimetric Radar Observations

The UK Met Office Unified Model（UM） is employed by many weather forecasting agencies around the globe. This model is designed to run across spatial and time scales and known to produce skillful predictions for large-scale weather systems. However, the model has only recently begun running operationally at horizontal grid spacings of ～1.5 km [e.g.,at the UK Met Office and the Korea Meteorological Administration（KMA）]. As its microphysics scheme was originally designed and tuned for large-scale precipitation systems, we investigate the performance of UM microphysics to determine potential inherent biases or weaknesses. Two rainfall cases from the KMA forecasting system are considered in this study： a Changma（quasi-stationary） front, and Typhoon Sanba（2012）. The UM output is compared to polarimetric radar observations in terms of simulated polarimetric radar variables. Results show that the UM generally underpredicts median reflectivity in stratiform rain, producing high reflectivity cores and precipitation gaps between them. This is partially due to the diagnostic rain intercept parameter formulation used in the one-moment microphysics scheme. Model drop size is generally both underand overpredicted compared to observations. UM frozen hydrometeors favor generic ice（crystals and snow） rather than graupel, which is reasonable for Changma and typhoon cases. The model performed best with the typhoon case in terms of simulated precipitation coverage.

Parameterized Forward Operators for Simulation and Assimilation of Polarimetric Radar Data with Numerical Weather Predictions

Many weather radar networks in the world have now provided polarimetric radar data(PRD)that have the potential to improve our understanding of cloud and precipitation microphysics,and numerical weather prediction(NWP).To realize this potential,an accurate and efficient set of polarimetric observation operators are needed to simulate and assimilate the PRD with an NWP model for an accurate analysis of the model state variables.For this purpose,a set of parameterized observation operators are developed to simulate and assimilate polarimetric radar data from NWP model-predicted hydrometeor mixing ratios and number concentrations of rain,snow,hail,and graupel.The polarimetric radar variables are calculated based on the T-matrix calculation of wave scattering and integrations of the scattering weighted by the particle size distribution.The calculated polarimetric variables are then fitted to simple functions of water content and volumeweighted mean diameter of the hydrometeor particle size distribution.The parameterized PRD operators are applied to an ideal case and a real case predicted by the Weather Research and Forecasting(WRF)model to have simulated PRD,which are compared with existing operators and real observations to show their validity and applicability.The new PRD operators use less than one percent of the computing time of the old operators to complete the same simulations,making it efficient in PRD simulation and assimilation usage.

Kinematics and Microphysical Characteristics of the First Intense Rainfall Convective Storm Observed by Jiangsu Polarimetric Radar Network

The polarimetric radar network in Jiangsu Province has just been operationalized since 2020.The first intense precipitation event observed by this polarimetric radar network and disdrometer occurred during August 28-29,2020 and caused severe flooding and serious damage in eastern Jiangsu Province.The microphysics and kinetics for this heavy precipitation convective storm is diagnosed in this study,in order to promote the application of this polarimetric radar network.Drop size distribution(DSD)of this event is estimated from measurements of a ground disdrometer,and the corresponding three-dimensional atmospheric microphysical features are obtained from the multiple polarimetric radars.According to features of updraft and lighting,the evolution of the convective storm is divided into four stages:developing,mature with lightning,mature without lightning and dissipating.The DSD of this event is featured by a large number of raindrops and a considerable number of large raindrops.The microphysical characteristics are similar to those of warm-rain process,and ice-phase microphysical processes are active in the mature stages.The composite vertical structure of the convective storm indicates that deep ZDR and KDP columns coincide with strong updrafts during both mature stages.The hierarchical microphysical structure retrieved by the Hydrometeor Identification Algorithm(HID)shows that depositional growth has occurred above the melting level,and aggregation is the most widespread ice-phase process at the-10℃level or higher.During negative lightning activity,the presence of strongest updrafts and a large amount of ice-phase graupel by riming between the 0℃and-35℃layers generate strong negative electric fields within the cloud.These convective storms are typical warm clouds with very high precipitation efficiency,which cause high concentration of raindrops,especially the presence of large raindrops within a short period of time.The ice-phase microphysical processes above the melting layer also play an important role in the triggering and enhancing of precipitation.

Application of X-band Polarimetric Phased-array Radars in Quantitative Precipitation Estimation

The performance of different quantitative precipitation estimation(QPE) relationships is examined using the polarimetric variables from the X-band polarimetric phased-array radars in Guangzhou,China.Three QPE approaches,namely,R(ZH),R(ZH,ZDR) and R(KDP),are developed for horizontal reflectivity,differential reflectivity and specific phase shift rate,respectively.The estimation parameters are determined by fitting the relationships to the observed radar variables using the T-matrix method.The QPE relationships were examined using the data of four heavy precipitation events in southern China.The examination shows that the R(ZH) approach performs better for the precipitation rate less than 5 mm h-1, and R(KDP) is better for the rate higher than 5 mm h-1, while R(ZH,ZDR) has the worst performance.An adaptive approach is developed by taking the advantages of both R(ZH) and R(KDP) approaches to improve the QPE accuracy.

Calibration and Data Quality Analysis with Mobile C-Band Polarimetric Radar

A C-band mobile polarimetric radar with simultaneous horizontal and vertical transmission was built in the State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences. It was used in heavy rainfall and typhoon observations in 2008. It is well-known that radar calibration is essential and critical to high quality radar data and products. In this paper, the test and weather signals were used in calibration of reflectivity ZH, differential reflectivity ZDR and differential phase ФDP. Noise effects on correlation coefficient ρHV at low signal-noise-ratio （SNR） were analyzed. The polarimetric radar data for a heavy rain and a snow event were inspected to evaluate the performance of the calibration method and radar data quality, and S-band Doppler radar data were used to validate the refiectivity data quality collected by the polarimetric radar. The results show that the polarimetric and S-band Doppler radars have observed comparable reflectivity values and a similar structure of a heavy rainfall case at middle and low levels. The mismatch of two receivers produce obvious ZDR biases, which were verified by the radar data observed at vertical incidence. The ZDR correction improved the radar data quality. The usage range for PHV was defined. Application of the calibration method introduced in this paper can reduce the system biases caused by the difference of horizontal （H） and vertical （V） channels. After the calibration and correction, the polarimetric parameters observed by the polarimetric radar could be used in further relevant researches.

Raindrop Size Distribution Parameters Retrieved from Guangzhou S-band Polarimetric Radar Observations

According to the statistical shape-slope （μ-A） relationship observed for the first time by several 2D-Video-Distro-meters （2DVD） in southern China, a constrained gamma （C-G） model was proposed for the retrieval of rain drop size distributions （DSDs） from Guangzhou S-band polarimetric radar observations. Two typical precipitation processes were selected to verify the accuracy of the retrieval scheme. The p-A relationship： A = 0.0241μ^2 ＋ 0.867μ ＋ 2.453 was obtained based on the 2DVD observation results from at Huizhou Longmen station, which is a very representat-ive location in the area. Relying on the Guangzhou polarimetric radar measurements of radar reflectivity （ZHH） and differential reflectivity （ZDR）, the gamma （F） size distribution parameters （No, μ, and A） can be retrieved by the C-G model retrieval scheme. The results show that the Guangzhou polarimetric radar retrievals of DSDs were close to the 2DVD observations at Guangzhou Maofengshan station. The rain rate, mass mean diameter, and normalized inter-cept parameter of radar retrievals were in good agreement with the 2DVD observations, and the relative errors were less than 10%. The overall accuracy of the retrieval scheme was high. The retrieval scheme has established the rela-tionship between the polarimetric radar measurements and gamma size distribution parameters. It will be helpful to in-depth research and application of the dual-polarization radar data in microphysical precipitation processes analysis, as well as convection-resolved numerical model data assimilation and prediction effect evaluation.

A new approach to dual-band polarimetric radar remote sensing image classification

It is very important to efficiently represent the target scattering characteristics in applications of polarimetric radar remote sensing. Three probability mass functions are introduced in this paper for target representation： using similarity parameters to describe target average scattering mechanism, using the eigenvalues of a target coherency matrix to describe target scattering randomness, and using radar received power to describe target scattering intensity. The concept of cross-entropy is employed to measure the difference between two scatterers based on the probability mass functions. Three parts of difference between scatterers are measured separately as the difference of average scattering mechanism, the difference of scattering randomness and the difference of scattering intensity, so that the usage of polarimetric data can be highly efficient and flexible. The supervised/unsupervised image classification schemes and their simplified versions are established based on the minimum cross-entropy principle. They are demonstrated to have better classification performance than the maximum likelihood classifier based on the Wishart distribution assumption, both in supervised and in unsupervised classification.

Polarimetric super-resolution algorithm for radar range imaging via spatial smoothing processing

A full-polarimetric super-resolution algorithm with spatial smoothing processing is presented for one-dimensional（1-D）radar imaging.The coherence between scattering centers is minimized by using spatial smoothing processing（SSP）.Then the range and polarimetric scattering matrix of the scattering centers are estimated.The impact of different lengths of the smoothing window on the imaging quality is mainly analyzed with different signal-to-noise ratios（SNR）.Simulation and experimental results show that an improved radar super-resolution range profile and more precise estimation can be obtained by adjusting the length of the smoothing window under different SNR conditions.

Coastal wind field retrieval from polarimetric synthetic aperture radar

Coastal winds are strongly influenced by topology and discontinuity between land and sea surfaces. Wind assessment from remote sensing in such a complex area remains a challenge. Space-borne scatterometer does not provide any information about the coastal wind field, as the coarse spatial resolution hampers the radar backscattering. Synthetic aperture radar （SAR） with a high spatial resolution and all-weather observation abilities has become one of the most important tools for ocean wind retrieval, especially in the coastal area. Conventional methods of wind field retrieval from SAR, however, require wind direction as initial information, such as the wind direction from numerical weather prediction models （NWP）, which may not match the time of SAR image acquiring. Fortunately, the polarimetric observations of SAR enable independent wind retrieval from SAR images alone. In order to accurately measure coastal wind fields, this paper proposes a new method of using co-polarization backscattering coefficients from polarimetric SAR observations up to polarimetric correlation backscattering coefficients, which are acquired from the conjugate product of co-polarization backscatter and cross-polarization backscatter. Co-polarization backscattering coefficients and polarimetric correlation backscattering coefficients are obtained form Radarsat-2 single-look complex （SLC） data.The maximum likelihood estimation is used to gain the initial results followed by the coarse spatial filtering and fine spatial filtering. Wind direction accuracy of the final inversion results is 10.67 with a wind speed accuracy of 0.32 m/s. Unlike previous methods, the methods described in this article utilize the SAR data itself to obtain the wind vectors and do not need external wind directional information. High spatial resolution and high accuracy are the most important features of the method described herein since the use of full polarimetric observations contains more information about the space measured.This article is a useful addition to the work of independent SAR wind retrieval. The experimental results herein show that it is feasible to employ the co-polarimetric backscattering coefficients and the polarimetric correlation backscattering coefficients for coastal wind field retrieval.

The polarimetric features of oil spills in full polarimetric synthetic aperture radar images

Compared with single-polarized synthetic aperture radar （SAR） images, full polarimetric SAIl images contain not only geometrical and backward scattering characteristics, but also the polarization features of the scattering targets. Therefore, the polarimetric SAR has more advantages for oil spill detection on the sea surface. As a crucial step in the oil spill detection, a feature extraction directly influences the accuracy of oil spill discrimination. The polarimetric features of sea oil spills, such as polarimetric entropy, average scatter angle, in the full polarimetric SAR images are analyzed firstly. And a new polarimetric parameter P which reflects the proportion between Bragg and specular scattering signals is proposed. In order to investigate the capability of the polarimetric features for observing an oil spill, systematic comparisons and analyses of the multipolarization features are provided on the basis of the full polarimetric SAR images acquired by SIR-C/X-SAR and Radarsat-2. The experiment results show that in C-band SAR images the oil spills can be detected more easily than in L-band SAR images under low to moderate wind speed conditions. Moreover, it also finds that the new polarimetric parameter is sensitive to the sea surface scattering mechanisms. And the experiment results demonstrate that the new polarimetric parameter and pedestal height perform better than other polarimetric parameters for the oil spill detection in the C-band SAR images.

A Model for Retrieval of Dual Linear Polarization Radar Fields from Model Simulation Outputs

An algorithm for retrieving polarimetric variables from numerical model fields is developed. By using this technique, radar reflectivity at horizontal polarization~ differential reflectivity, specific differential phase shift and correlation coefficients between the horizontal and vertical polarization signals at zero lag can be derived from rain, snow and hail contents of numerical model outputs. Effects of environmental temperature and the melting process on polarimetric variables are considered in the algorithm. The algorithm is applied to the Advanced Regional Prediction System （ARPS） model simulation results for a hail storm. The spatial distributions of the derived parameters are reasonable when compared with observational knowledge. This work provides a forward model for assimilation of dual linear polarization radar data into a mesoscale model.

Improvement of X-Band Polarization Radar Melting Layer Recognition by the Bayesian Method and ITS Impact on Hydrometeor Classification

Using melting layer(ML)and non-melting layer(NML)data observed with the X-band dual linear polarization Doppler weather radar(X-POL)in Shunyi,Beijing,the reflectivity(ZH),differential reflectivity(ZDR),and correlation coefficient(CC)in the ML and NML are obtained in several stable precipitation processes.The prior probability density distributions(PDDs)of the ZH,ZDR and CC are calculated first,and then the probabilities of ZH,ZDR and CC at each radar gate are determined(PBB in the ML and PNB in the NML)by the Bayesian method.When PBB>PNB the gate belongs to the ML,and when PBB<PNB the gate belongs to the NML.The ML identification results with the Bayesian method are contrasUsing melting layer(ML)and non-melting layer(NML)data observed with the X-band dual linear polarization Doppler weather radar(X-POL)in Shunyi,Beijing,the reflectivity(ZH),differential reflectivity(ZDR),and correlation coefficient(CC)in the ML and NML are obtained in several stable precipitation processes.The prior probability density distributions(PDDs)of the ZH,ZDR and CC are calculated first,and then the probabilities of ZH,ZDR and CC at each radar gate are determined(PBB in the ML and PNB in the NML)by the Bayesian method.When PBB>PNB the gate belongs to the ML,and when PBB<PNB the gate belongs to the NML.The ML identification results with the Bayesian method are contrasted under the conditions of the independent PDDs and joint PDDs of the ZH,ZDR and CC.The results suggest that MLs can be identified effectively,although there are slight differences between the two methods.Because the values of the polarization parameters are similar in light rain and dry snow,it is difficult for the polarization radar to distinguish them.After using the Bayesian method to identify the ML,light rain and dry snow can be effectively separated with the X-POL observed data.ted under the conditions of the independent PDDs and joint PDDs of the ZH,ZDR and CC.The results suggest that MLs can be identified effectively,although there are slight differences between the two methods.Because the values of the polarization parameters are similar in light rain and dry snow,it is difficult for the polarization radar to distinguish them.After using the Bayesian method to identify the ML,light rain and dry snow can be effectively separated with the X-POL observed data.

Hybrid Method to Identify Second-trip Echoes Using Phase Modulation and Polarimetric Technology

For pulse Doppler radars,the widely used method for identifying second-trip echoes(STs)in the signal processing level yields significant misidentification in regions of high turbulence and severe wind shear.In the data processing level,although the novel algorithm for ST identification does not yield significant misidentification in specific regions,its overall identification performance is not ideal.Therefore,this paper proposes a hybrid method for the identification of STs using phase modulation(signal processing)and polarimetric technology(data processing).Through this approach,most of the STs are removed,whereas most of the first-trip echoes(FTs)remain untouched.Compared with the existing method using a signal quality index filter with an optimized threshold,the hybrid method exhibits superior performance(Heidke skill scores of 0.98 versus 0.88)on independent test datasets,especially in high-turbulence and severe-wind-shear regions,for which misidentification is significantly reduced.

Novel polarimetric SAR speckle filtering algorithm based on mean shift

For better interpretation of synthetic aperture radar（SAR） images,the speckle filtering is an important issue.In the area of speckle filtering,the proper averaging of samples with similar scattering characteristics is of great importance.However,existing filtering algorithms are either lack of a similarity judgment of scattering characteristics or using only intensity information for similarity judgment.A novel polarimetric SAR（PolSAR） speckle filtering algorithm based on the mean shift theory is proposed.As polarimetric covariance matrices or coherency matrices form Riemannian manifold,the pixels with similar scattering characteristics gather closely and those with different scattering characteristics separate in this hyperspace.By using the range-spatial joint mean shift theory in Riemannian manifold,the pixels chosen for averaging are ensured to be close not only in scattering characteristics but also in the spatial domain.German Aerospace Center（DLR） L-Band Experiment SAR（E-SAR） data and East China Research Institute of Electronic Engineering（ECRIEE） PolSAR data are used to demonstrate the efficiency of the proposed algorithm.The filtering results of two commonly used speckle filtering algorithms,refined Lee filtering algorithm and intensity driven adaptive neighborhood（IDAN） filtering algorithm,are also presented for the comparison purpose.Experiment results show that the proposed speckle filtering algorithm achieves a good performance in terms of speckle filtering,edge protection as well as polarimetric characteristics preservation.