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.

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.

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.

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.

Solution for polarimetric radar cross section measurement and calibration

The exact radar cross-section （RCS） measurement is difficult when the scattering of targets is low. Ful polarimetric cali-bration is one technique that offers the potential for improving the accuracy of RCS measurements. There are numerous polarimetric calibration algorithms. Some complex expressions in these algo-rithms cannot be easily used in an engineering practice. A radar polarimetric coefficients matrix （RPCM） with a simpler expression is presented for the monostatic radar polarization scattering matrix （PSM） measurement. Using a rhombic dihedral corner reflector and a metal ic sphere, the RPCM can be obtained by solving a set of equations, which can be used to find the true PSM for any target. An example for the PSM of a metal ic dish shows that the proposed method obviously improves the accuracy of cross-polarized RCS measurements.

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.

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.

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.

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.

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.

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.

A New Radar Target Recognition Method Based on Polarimetric Processing and Neural Learning

The new millimeter-wave(MMW) radar target recognition method proposed uses polarmetric information to obtain stable amplitudes of range profiles and neural learning to extract angle-invariant features of range profiles and polarimetric processing reduces speckle to enhance ability to discriminate targets, and in comparison with conventional approaches, subclass features obtained by the neural learning carries more information and thus makes the correctness of target classification higher and simulation results vended the validity of this approach.

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.

Polarimetric whitening filter for POLSAR image based on subspace decomposition

Speckle filtering is an indispensable pre-processing step for applications of polarimetric synthetic aperture radar （POLSAR）, such as terrain classification, target detection, etc. As one of the most typical methods, the polarimetric whitening filter （PWF） can be used to produce a minimum-speckle image by combining the complex elements of the scattering matrix, but polarimetric information is lost after the filtering process. A polarimetric filter based on subspaze decomposition which was proposed by Cu et al specializes in retrieving principle scattering characteristics, but the corresponding mean value of an image after filtering is not kept well. A new filter is proposed for improving the disadvantage based on subspace decomposition. Under the constraint that a weighted combination of the polarimetric SAR images equals to the output of the PWF, the Euclidean distance between an unfiltered parameter vector and a signal space vector is minimized so that noises can be reduced. It is also shown that the proposed method is equivalent to the subspace filter in the case of no constraint. Experimental results with the NASA/JPL airborne polarimetric SAR data demonstrate the effectiveness of the proposed method.

Dry/wet snow mapping based on the synergistic use of dual polarimetric SAR and multispectral data

We propose a multi-sensor multi-spectral and bi-temporal dual-polarimetric Synthetic Aperture Radar(SAR) data integration scheme for dry/wet snow mapping using Sentinel-2 and Sentinel-1 data which are freely available to the research community. The integration is carried out by incorporating the information retrieved from ratio images of the conventional method for wet snow mapping and the multispectral data in two different frameworks. Firstly, a simple differencing scheme is employed for dry/wet snow mapping, where the snow cover area is derived using the Normalized Differenced Snow Index(NDSI). In the second framework, the ratio images are stacked with the multispectral bands and this stack is used for supervised and unsupervised classification using support vector machines for dry/wet snow mapping. We also investigate the potential of a state of the art backscatter model for the identification of dry/wet snow using Sentinel-1 data. The results are validated using a reference map derived from RADARSAT-2 full polarimetric SAR data. A good agreement was observed between the results and the reference data with an overall accuracy greater than 0.78 for the different blending techniques examined. For all the proposed frameworks, the wet snow was better identified. The coefficient of determination between the snow wetness derived from the backscatter model and the reference based on RADARSAT-2 data was observed to be 0.58 with a significantly higher root mean square error of 1.03 % by volume.

Modified version of three-component model-based decomposition for polarimetric SAR data

A new hybrid Freeman/eigenvalue decomposition based on the orientation angle compensation and the various extended volume models for polarimetric synthetic aperture radar(PolSAR) data are presented. There are three steps in the novel version of the three-component model-based decomposition.Firstly, two special unitary transform matrices are applied on the coherency matrix for deorientation to decrease the correlation between the co-polarized term and the cross-polarized term.Secondly, two new conditions are proposed to distinguish the manmade structures and the nature media after the orientation angle compensation. Finally, in order to adapt to the scattering properties of different media, five different volume scattering models are used to decompose the coherency matrix. These new conditions pre-resolves man-made structures, which is beneficial to the subsequent selection of a more suitable volume scattering model.Fully PolSAR data on San Francisco are used in the experiments to prove the efficiency of the proposed hybrid Freeman/eigenvalue decomposition.

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.