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We present a cylindrical dual-polarization phased array antenna for weather surveillance radars that features high isolation, matched copolar beams, low sidelobe levels, and adaptive null steering. We present a crossed dipole antenna element to replace the patch antenna on the currently deployed cylindrical polarimetric phased array radar (CPPAR). The crossed dipole element offers suppressed surface wave and lower coupling between adjacent elements of an array. As a result, we achieved lower backlobe and sidelobe levels, and a higher match between copolar beams of CPPAR compared to the currently deployed multi-layer patch antenna. Further reduction of the sidelobe level and adaptive null steering are obtained using a modified particle swarm optimization. The proposed null steering mitigates the interference among the four concurrent beams of a cylindrical phased array radar. The improvement in CPPAR radiation characteristics has been verified by comparing the presented crossed dipole antenna’s radiation patterns and the existing aperture coupled patch antenna. The proposed crossed dipole phased array can benefit national weather radar networks to provide accurate multiparameter measurements enabling reliable observation of severe weather phenomena.

The design and development of a high-performance, dual-polarization, hybrid aperture-coupled microstrip patch antenna are presented. In the presented design, the horizontal and vertical polarizations are excited using the aperture coupling method. The isolation level of better than 51 dB and cross-polarization level of less than −30 dB are achieved from the measurement results of the designed single element. To achieve better cross-polarization levels, the $2 \times 2$ element subarrays of the proposed unit cell, while the horizontal polarization ports are mirrored, are designed and fabricated, and a lower than −39-dB cross-polarization level is achieved in the measurement results. Finally, to characterize the scan performance of the unit cell and subarray, a $4 \times 10$ element array of the proposed single element is fabricated and a better than −45-dB cross-polarization level is observed while scanning up to 45°.

Polarimetric radar data (PRD) have potential to be used in numerical weather prediction (NWP) models to improve convective-scale weather forecasts. However, thus far only a few studies have been undertaken in this research direction. To assimilate PRD in NWP models, a forward operator, also called a PRD simulator, is needed to establish the relation between model physics parameters and polarimetric radar variables. Such a forward operator needs to be accurate enough to make quantitative comparisons between radar observations and model output feasible, and to be computationally efficient so that these observations can be easily incorporated into a data assimilation (DA) scheme. To address this concern, a set of parameterized PRD simulators for the horizontal reflectivity, differential reflectivity, specific differential phase, and cross-correlation coefficient were developed. In this study, we have tested the performance of these new operators in a variational DA system. Firstly, the tangent linear and adjoint (TL/AD) models for these PRD simulators have been developed and checked for the validity. Then, both the forward operator and its adjoint model have been built into the three-dimensional variational (3DVAR) system. Finally, some preliminary DA experiments have been performed with an idealized supercell storm. It is found that the assimilation of PRD, including differential reflectivity and specific differential phase, in addition to radar radial velocity and horizontal reflectivity, can enhance the accuracy of both initial conditions for model hydrometer state variables and ensuing model forecasts. The usefulness of the cross-correlation coefficient is very limited in terms of improving convective-scale data analysis and NWP.

The measurement error of differential reflectivity (ZDR), especially systematic ZDR bias, is a fundamental issue for the application of polarimetric radar data. Several calibration methods have been proposed and applied to correct ZDR bias. However, recent studies have shown that ZDR bias is time-dependent and can be significantly different on two adjacent days. This means that the frequent monitoring of ZDR bias is necessary, which is difficult to achieve with existing methods. As radar sensitivity has gradually been enhanced, large amounts of online solar echoes have begun to be observed in volume-scan data. Online solar echoes have a high frequency, and a known theoretical value of ZDR (0 dB) could thus allow the continuous monitoring of ZDR bias. However, online solar echoes are also affected by low signal-to-noise ratio and precipitation attenuation for short-wavelength radar. In order to understand the variation of ZDR bias in a C-band polarimetric radar at the Nanjing University of Information Science and Technology (NUIST-CDP), we analyzed the characteristics of online solar echoes from this radar, including the daily frequency of occurrence, the distribution along the radial direction, precipitation attenuation, and fluctuation caused by noise. Then, an automatic method based on online solar echoes was proposed to monitor the daily ZDR bias of the NUIST-CDP. In the proposed method, a one-way differential attenuation correction for solar echoes and a maximum likelihood estimation using a Gaussian model were designed to estimate the optimal daily ZDR bias. The analysis of three months of data from the NUIST-CDP showed the following: (1) Online solar echoes occurred very frequently regardless of precipitation. Under the volume-scan mode, the average number of occurrences was 15 per day and the minimum number was seven. This high frequency could meet the requirements of continuous monitoring of the daily ZDR bias under precipitation and no-rain conditions. (2) The result from the proposed online solar method was significantly linearly correlated with that from the vertical pointing method (observation at an elevation angle of 90°), with a correlation coefficient of 0.61, suggesting that the proposed method is feasible. (3) The day-to-day variation in the ZDR bias was relatively large, and 32% of such variations exceeded 0.2 dB, meaning that a one-time calibration was not representative in time. Accordingly, continuous calibration will be necessary. (4) The ZDR bias was found to be largely influenced by the ambient temperature, with a large negative correlation between the ZDR bias and the temperature.

Polarimetric radar and disdrometer observations obtained during the 2014 Observation, Prediction, and Analysis of Severe Convection of China (OPACC) field campaign are used in this study to investigate the microphysical characteristics of three primary types of organized intense rainfall events (meiyu rainband, typhoon outer rainband, and squall line) in eastern China. Drop size distributions (DSDs) of these three events on the ground are derived from measurements of a surface disdrometer, while the corresponding three-dimensional microphysical structures are obtained from the Nanjing University C-band polarimetric radar (NJU-CPOL). Although the environmental moisture and instability conditions are different, all three events possess relatively high freezing level favorable for warm-rain processes where the high medium to small raindrop concentration at low levels is consistent with the high surface rainfall rates. Convection is tallest in the squall line where abundant ice-phase processes generate large amounts of rimed particles (graupel and hail) above the freezing level and the largest surface raindrops are present among these three events. The storm tops of both the typhoon and meiyu rainbands are lower than that in the squall line, composed of less active ice processes above the freezing level. The typhoon rainrate is more intense than that of meiyu, enhanced by higher coalescence efficiency. A revised generalized intercept parameter versus mass-weighted mean diameter (Nw-Dm) space diagram is constructed to describe the DSD distributions over the three events and illustrate the relative DSD positions for heavy precipitation. DSDs of these intense rainfall convections observed in this midlatitude region of eastern Asia somewhat represent the typical DSD characteristics in low latitudes, suggesting that the parameterization of microphysical characteristics in eastern China in numerical models needs to be further investigated to improve rain fall forecasts in these heavy rainfall events.

A hydrometeor classification algorithm is developed by applying Bayes’ theorem to C-band polarimetric weather radar measurements. The Bayesian hydrometeor classification algorithm (BHCA) includes eight hydrometeor types: hail, rain, graupel, dry snow, wet snow, crystal, biological scatterers (BS) and ground clutter (GC). The conditional likelihood probability distribution functions (PDFs) for each hydrometeor type are constructed with training data from radar observations. The prior PDFs include not only temperature information but also background information about occurrence frequency of hydrometeor types at each altitude, which is incorporated by a hydrometeor classification algorithm for the first time. The BHCA is evaluated by comparing with the Marzano-Bayesian hydrometeor classification algorithm (MBHC) and NCAR fuzzy logic classifier (NFLC). Results show that wet snow is largely missed in MBHC, while crystals are not adequately identified by NFLC. This may be due to the inappropriate conditional likelihood PDFs or membership functions. The prior PDFs in the MBHC may cause unexpected hail due to unreasonable variation above 0 °C. In addition, the prior PDFs of graupel and dry snow in the MBHC appear below −52 °C, which is not realistic. The BHCA proposed in this study overcomes these shortcomings in the prior PDFs and produces an overall reasonable classification product over the Yangtze-Huaihe River Basin (YHRB), Eastern China.

Fourteen-month precipitation measurements from a second-generation PARSIVEL disdrometer deployed in Beijing, northern China, were analyzed to investigate the microphysical structure of raindrop size distribution and its implications on polarimetric radar applications. Rainfall types are classified and analyzed in the domain of median volume diameter D 0 and the normalized intercept parameter N w . The separation line between convective and stratiform rain is almost equivalent to rain rate at 8.6 mm h−1 and radar reflectivity at 36.8 dBZ. Convective rain in Beijing shows distinct seasonal variations in log 10 N w − D 0 domain. X-band dual-polarization variables are simulated using the T-matrix method to derive radar-based quantitative precipitation estimation (QPE) estimators, and rainfall products at hourly scale are evaluated for four radar QPE estimators using collocated but independent rain gauge observations. This study also combines the advantages of individual estimators based on the thresholds on polarimetric variables. Results show that the blended QPE estimator has better performance than others. The rainfall microphysical analysis presented in this study is expected to facilitate the development of a high-resolution X-band radar network for urban QPE applications.

In this paper, a system-specific phased-array radar system simulator was developed, based on a time-domain modeling and simulation method, mainly for system performance evaluation of the future Spectrum-Efficient National Surveillance Radar (SENSR). The goal of the simulation study was to establish a complete data quality prediction method based on specific radar hardware and electronics designs. The distributed weather targets were modeled using a covariance matrix-based method. The data quality analysis was conducted using Next-Generation Radar (NEXRAD) Level-II data as a basis, in which the impact of various pulse compression waveforms and channel electronic instability on weather radar data quality was evaluated. Two typical weather scenarios were employed to assess the simulator’s performance, including a tornado case and a convective precipitation case. Also, modeling of some demonstration systems was evaluated, including a generic weather radar, a planar polarimetric phased-array radar, and a cylindrical polarimetric phased-array radar. Corresponding error statistics were provided to help multifunction phased-array radar (MPAR) designers perform trade-off studies.

Reflectivity factor bias caused by radar calibration errors would influence the accuracy of Quantitative Precipitation Estimations (QPE), and further result in spatial discontinuity in Multiple Ground Radars QPE (MGR-QPE) products. Due to sampling differences and random errors, the associated discontinuity cannot be thoroughly solved by the single-radar calibration method. Thus, a multiple-radar synchronous calibration approach was proposed to mitigate the spatial discontinuity of MGR-QPE. Firstly, spatial discontinuity was solved by the intercalibration of adjacent ground radars, and then calibration errors were reduced by referring to the Ku-Band Precipitation Radar (KuPR) carried by the Global Precipitation Measurement (GPM) Core Observatory as a standard reference. Finally, Mosaic Reflectivity and MGR-QPE products with spatial continuity were obtained. Using three S-band operational radars covering the lower reaches of the Yangtze River in China, this method was evaluated under four representative precipitation events. The result showed that: (1) the spatial continuity of reflectivity factor and precipitation estimation fields was significantly improved after bias correction, and the reflectivity differences between adjacent radars were reduced by 78% and 82%, respectively; (2) the MGR-QPE data were closer to gauge observations with the normalized absolute error reducing by 0.05 to 0.12.

This paper proposes an inverse model for raindrop size distribution (DSD) retrieval with polarimetric radar variables. In this method, a forward operator is first developed based on the simulations of monodisperse raindrops using a T-matrix method, and then approximated with a polynomial function to generate a pseudo training dataset by considering the maximum drop diameter in a truncated Gamma model for DSD. With the pseudo training data, a nearest-neighborhood method is optimized in terms of mass-weighted diameter and liquid water content. Finally, the inverse model is evaluated with simulated and real radar data, both of which yield better agreement with disdrometer observations compared to the existing Bayesian approach. In addition, the rainfall rate derived from the DSD by the inverse model is also improved when compared to the methods using the power-law relations.

A novel dual-polarization and dual-scan (DPDS) classification algorithm is developed for clutter detection in weather radar observations. Two consecutive scans of dual-polarization radar echoes are jointly processed to estimate auto- and cross-correlation functions. Discriminants are then defined and estimated in order to separate clutter from weather based on their physical and statistical properties. An optimal Bayesian classifier is used to make a decision on clutter presence from the estimated discriminant functions. The DPDS algorithm is applied to the data collected with the KOUN polarimetric radar and compared with the existing detection methods. It is shown that the DPDS algorithm yields a higher probability of detection and lower false alarm rate in clutter detection.

Raindrop size distributions (DSD) and rain rate have been estimated from polarimetric radar data using different approaches with the accuracy depending on the errors both in the radar measurements and the estimation methods. Herein, a deep neural network (DNN) technique was utilized to improve the estimation of the DSD and rain rate by mitigating these errors. The performance of this approach was evaluated using measurements from a two-dimensional video disdrometer (2DVD) at the Kessler Atmospheric and Ecological Field Station in Oklahoma as ground truth with the results compared against conventional estimation methods for the period 2006-17. Physical parameters (mass-/volume-weighted diameter and liquid water content), rain rate, and polarimetric radar variables (including radar reflectivity and differential reflectivity) were obtained from the DSD data. Three methods--physics-based inversion, empirical formula, and DNN--were applied to two different temporal domains (instantaneous and rain-event average) with three diverse error assumptions (fitting, measurement, and model errors). The DSD retrievals and rain estimates from 18 cases were evaluated by calculating the bias and root-mean-squared error (RMSE). DNN produced the best performance for most cases, with up to a 5% reduction in RMSE when model errors existed. DSD and rain estimated from a nearby polarimetric radar using the empirical and DNN methods were well correlated with the disdrometer observations; the rain-rate estimate bias of the DNN was significantly reduced (3.3%in DNN vs 50.1%in empirical). These results suggest that DNN has advantages over the physics-based and empirical methods in retrieving rain microphysics from radar observations. [ABSTRACT FROM AUTHOR]