Dual-polarization Doppler Weather Radar Research Articles

Quantification of Migration Birds Based on Polarimetric Weather Radar

Weather radar plays an important role in monitoring aerial animal migration, providing a stable data source for biological studies with large-scale coverage and consecutive-time samples. The accurate estimation of bird density from weather radar echoes is fundamental for quantitative biological studies. We analyzed the bird observation model in weather radar, and proposed a method to build the bird quantification model by jointly utilizing dual-polarization Doppler weather radar and scanning bird radar. We designed a detailed process to remove tracks or echoes from non-bird targets, ensuring the effectiveness of bird observations. The field experiments validated the quantification method, showing that the average radar cross section of birds in Jinan was 19.09 dBscm (i.e., 81.19 cm2; 95% confidence interval, CI: 18.92–19.27 dBscm) for the S-band weather radar, with an R2 of 0.79. In addition, through the correlation analysis, we found that the ground terrain may affect the distribution pattern of aerial bird density.

Study on Attenuation Correction for the Reflectivity of X-Band Dual-Polarization Phased-Array Weather Radar Based on a Network with S-Band Weather Radar

X-band dual-polarization phased-array weather radars (X-PARs) have been used in South China extensively. Eliminating the attenuation and system bias of X-band radar data is the key to utilizing the advantage of X-PAR networks. In this paper, the disdrometer raindrop-size distribution (DSD) measurements are used to calculate the radar polarimetric variables and analyze the characteristics of precipitation attenuation. Furthermore, based on the network of S-band dual-polarization Doppler weather radar (S-POL) and X-PARs, an attenuation-correction method for X-PAR reflectivity is proposed with S-POL constraints in view of the radar-mosaic requirements of a multi-radar network. Linear programming is used to calculate the attenuation-correction parameters of different rainfall areas, which realizes the attenuation correction for X-PAR. The results show that the attenuation-correction parameters simulated based on the disdrometer DSD vary with different precipitation classification; the attenuation-corrected reflectivity of X-PARs is consistent with S-POL and can realize a more precise observation of the evolution of the convective system. Compared with previous attenuation-correction methods with constant correction parameters, the improved method can reduce the deviation between X-PAR reflectivity and that of S-POL in heavy rainfall areas and areas of strong attenuation. The method proposed in this paper is stable and effective. After effective quality control, it is found that the X-PAR network deployed in South China observes data accurately and is consistent with S-POL; thus, it is expected to achieve high temporal–spatial resolution within a radar mosaic.

Evaluation of cloud and precipitation processes in regional and global models with ULTIMATE (ULTra-sIte for Measuring Atmosphere of Tokyo metropolitan Environment): a case study using the dual-polarization Doppler weather radars

We describe a collaborative analysis study involving numerical models and observation data for the Tokyo metropolitan area called the ULTra-sIte for Measuring Atmosphere of Tokyo Metropolitan Environment (ULTIMATE) project. It evaluates cloud microphysics schemes of numerical models using extensive observation data for the Tokyo area. We have access to various remote sensing and in situ data for the Tokyo area for operational and research purposes, particularly by enhancing observations for ground validation of the EarthCARE satellite, which is set to launch in 2023. This study focuses on using the dual-polarization Doppler weather radar, operated by the Japan Meteorological Agency. In terms of numerical models, we use and compare multi-models with single-moment (SM) and double-moment (DM) cloud microphysics schemes; the global non-hydrostatic model, Non-hydrostatic ICosahedral Atmospheric Model (NICAM) and the two regional models with A System based on a Unified Concept for Atmosphere (ASUCA) and Scalable Computing for Advanced Library and Environment (SCALE) are used. In particular, because NICAM can be used as both a global and a regional model, we can immediately test the improved scheme on a global scale for its effect on climatology and the evaluation of climate sensitivity. This paper introduces the methodology for evaluating numerical models by the dual-polarization radar using the observation simulator and compares numerical model results with observations. In particular, we evaluate the simulated rain in the lower level near the ground and the large ice particles just above the melting level. The simulation with NICAM-DM reproduces the comparable polarimetric radar characteristics of rain as the observation. However, the simulations with NICAM-SM and ASUCA-SM show larger raindrop sizes in stronger rain areas compared to the observation. For the larger ice particles just above the melting level around 4 km, NICAM-DM and ASUCA-SM overestimate particle sizes of graupel or snow, while NICAM-SM has a similar size of the ice particles. In future studies, we will use the present results to improve the cloud microphysics scheme, which will be tested on a global model.

Gazing inside a giant-hail-bearing Mediterranean supercell by dual-polarization Doppler weather radar

Ongoing climate change is considered to be responsible for the intensification and increased frequency of extreme weather events. The Mediterranean basin is not exempted by such modification processes as testified by the recent increase in the number of severe convective storm and mesocyclones. In recent years this kind of events caused many victims and damages in the such area, due to its geomorphological configuration and to the massive urban development of the last decades. Due to their local-scale nature Mediterranean extreme events can hardly be forecast with the use of numerical weather prediction systems and cannot adequately observed by using satellite platforms. These methods are not as straightforward as those based on radar observations because their spatial-temporal resolution is not necessarily adequate to resolve exceptional atmospheric phenomena as a weather radar can indeed offer. This article uses weather radar observations of an exceptional Mediterranean hail-bearing supercell that hit the central-eastern coast of the Adriatic Sea on 10 July 2019 causing flash flood and giant hail. Two operational dual-polarization Doppler C-band radars, managed by the Civil Protection Department of Italy, were able to observe, in an operational framework, the genesis and the evolution of the supercell, allowing for a detailed analysis of the event. Exploiting the unique time-resolved high-resolution three-dimensional available measurements, the combined use of a wind-field retrieval scheme and the interpretation of the dual-polarization radar observed features reveals some unique microphysical processes, rarely documented through radar observations at the Mediterranean latitudes. Results of this work improve the understanding of mechanisms and processes of giant hail formation and it is crucial to improve the nowcasting and forecasting skill for the early detection of hazardous meteorological events.

C-band dual-polarization Doppler weather radar at Thumba (8.537°N, 76.865°E): initial results and validation

Recently, a C-band dual-polarization Doppler weather radar (C-DWR) was installed at Thumba (8.537°N, 76.865°E), a coastal station in the southern peninsula of India known as “Gateway of Indian Summer Monsoon.” The C-DWR operates in the frequency range of 5.6 to 5.65 GHz with a peak transmitting power of 250 kW at 0.004 duty ratio and employs the state-of-the-art technology for both transmitting and receiving subsystems. Apart from providing regular base products such as reflectivity (Z), radial velocity (V), and spectral width (σ), the DWR being equipped with dual-polarization capability provides differential reflectivity (Zdr), differential phase shift (ϕdp), and correlation coefficient (ρhv). This is the first DWR with polarimetric capability developed indigenously in India. The C-DWR was operated during North-East as well as South-West monsoon period to evaluate its performance with special emphasis on the reflectivity and rainfall intensity measurements. Before the commencement of the campaign, the C-DWR was calibrated using a metallic sphere and the appropriate corrections were made to the radar constant. The preliminary results show that the radar products are consistent with present understanding of precipitating cloud systems. The simultaneous observations of precipitating clouds by C-DWR and Global Precipitation Measurement mission are used to evaluate the former. The comparison of spatial pattern of reflectivity from space-based radars and C-DWR shows very good agreement, qualitatively. The C-DWR-derived rainfall observations also show very good agreement with ground-based laser precipitating monitor measurements. The regression analysis between the C-DWR and laser precipitation monitor-derived rainfall shows a correlation coefficient of 0.89. The C-DWR observations during the passage of tropical cyclone Ockhi are also discussed. The significance of this study lies in describing the first indigenously developed C-band dual-polarization radar and its validation using the space- and ground-based observations.

Echo simulation of dual polarization Doppler weather radar based on the physical model

Using the scattering characteristics of particles to simulate the radar echo can supply the test signals close to the real precipitation echo for the weather radar and save the time and cost of the research and development and maintenance of the weather radar. In this paper, the precipitation echo of weather radar is simulated based on the theoretical basis that the falling raindrops have a shape well approximated by an oblate spheroid in the atmosphere. The Marshal-Palmer distribution is applied to describe the raindrop spectrum distribution of precipitation particles. It is assumed that the raindrop particles of different sizes have the random distribution in the radar resolution volume, and then the spatial distribution of precipitation particles in the resolution volume is modeled. The echo signals of horizontal and vertical polarization channels of dual-polarization weather radar are obtained by vector superposition of backscattering echoes of each particle. The experimental results show that this method can describe the microphysical characteristics of precipitation particles more completely and can be used to test the signal processing module of dual-polarization Doppler weather radar.

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 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.

Identification of Precipitation-Clouds Based on the Dual-Polarization Doppler Weather Radar Echoes Using Deep–Learning Method

The dual-polarization Doppler weather radar is a kind of radio-frequency sensor that can provide abundant information about atmospheric particle scattering behavior. The identification of the precipitation cloud type based on dual-polarization Doppler weather radar echoes is a study that classifies precipitation clouds based on the scattering theory of precipitation cloud particles to polarized electromagnetic waves. In recent years, the Doppler weather radar has been widely used in quantitative precipitation estimation, and the accurate identification of precipitation cloud types plays an essential role in improving the accuracy of precipitation estimation. The accuracy of the conventional precipitation cloud identification method relies on the number of features that are identified by human eyes, and it greatly reduces the operation efficiency. In order to improve the accuracy and efficiency of the precipitation cloud identification, a methodology of precipitation cloud identification based on deep learning is proposed in this paper. The method mainly consists of three major parts, which are constant altitude plan position indicator data inversion, zero-layer bright band identification, and precipitation-cloud classification by using the deep learning network model. At last, this paper evaluates the identification effect of this method through a real precipitation process. The results show that this method can distinguish the stratiform clouds and convective cloud precipitation in the precipitation area in real time, and it is in good agreement with the ground observation data. This method is very useful for improving the accuracy of the quantitative precipitation estimation of the Doppler weather radar.

Numerical simulation of raindrop scattering for C-band dual-polarization Doppler weather radar parameters

The dual-polarization Doppler weather radar plays an important role in precipitation estimation and weather monitoring. For radar applications, the retrieval of precipitation microphysical characteristics is of great importance, and requires assumed scattering properties of raindrops. This study numerically investigates the scattering properties of raindrops and considers the capability of numerical models for raindrop scattering simulations. Besides the widely used spherical and oblate spheroid models, a non-spheroidal model based on realistic raindrop geometries with a flattened base and a smoothly rounded top is also considered. To study the effects of scattering simulations on radar applications, the polarization radar parameters are modeled based on the scattering properties calculated by different scattering models (i.e. the extended boundary condition T-matrix (EBCM) method and discretize dipole approximation (DDA)) and given size distributions, and compared with observations of a C-band dual-polarization radar. Note that, when the spatial resolution of the DDA simulation is large enough, the DDA results can be very close to those of the EBCM. Most simulated radar variables, except copolar correlation coefficient, match closely with radar observations, and the results based on different non-spheroidal models considered in this study show little differences. The comparison indicates that, even for the C-band radar, the effects of raindrop shape and canting angle on scattering properties are relatively minor due to relatively small size parameters. However, although more realistic particle geometry model may provide better representation on raindrop shape, considering the relatively time-consuming and complex scattering simulations for those particles, the oblate spheroid model with appropriate axis ratio variation is suggested for polarization radar applications.

Study on deep-learning-based identification of hydrometeors observed by dual polarization Doppler weather radars

Hydrometeor classification for dual polarization Doppler weather radar echo is a procedure that identifies hydrometeor types based on the scattering properties of precipitation particles to polarized electromagnetic waves. The difference in shape, size, or spatial orientation among different types of hydrometeor will produce different scattering characteristics for the electromagnetic waves in a certain polarization state. Moreover, the polarimetric measurements, which are calculated from the radar data and closely associated with these characteristics, are also different. The comprehensive utilization of these polarimetric measurements can effectively improve the identification accuracy of the phase of various hydrometeors. In this paper, a new identification method of the hydrometeor type based on deep learning (DL) and fuzzy logic algorithm is proposed: firstly, the feature extraction method based on deep learning is used for training the correlation among multiple parameters and extracting the relatively independent features. Secondly, the Softmax classifier is applied to classify the precipitation patterns, including rain, snow, and hail, and it is based on the features extracted by deep learning algorithm. Finally, the fuzzy logic algorithm is adopted to identify the hydrometeor types in various precipitation patterns. In order to test the accuracy of the classification results, the hydrometeor classifier has been applied to a stratiform cloud precipitation process, and it is found that the classification results agree well with the other polarimetric products.

The Narrow Beam C-Band Dual-Polarization Doppler Weather Radar of NUIST and Its Performance Analysis

The Narrow Beam C-Band Dual-Polarization Doppler Weather Radar of NUIST and Its Performance Analysis

Combining spatial modelling and radar to identify and protect avian migratory hot-spots

Abstract Migrating landbirds are known to follow coast lines and concentrate on peninsulas prior to crossing water bodies, especially during daylight but also at night, creating enhanced potential collision hazards with man-made objects. Knowing where these avian migration “hot-spots” occur in time and space is vital to improve flight safety and inform the spatial planning process (e.g. environmental assessments for offshore windfarms). We developed a simple spatial model to identify avian migration hot-spots in coastal areas based on prevailing migration orientation and coastline features known, from visual and radar observations, to concentrate migrating landbirds around land masses. Regional scale model validation was achieved by combining nocturnal passerine movement data gathered from two tier radar coverage (long-range dual-polarization Doppler weather radar and short-range marine surveillance radar) and standardised bird ringing. Applied on a national scale, the model correctly identified the ten most important Danish coastal hot-spots for spring migrants and predicted the relative numbers of birds that concentrated at each site. These bird numbers corresponded well with historical observational data. Here, we provide a potential framework for the establishment of the first three-dimensional avian airspace sanctuaries, which could contribute to more effective conservation of long-distance migratory birds.

The Rainfall Measurement Using Dual-Polarization Doppler Weather Radar

In order to play a part in evaluation and manage of water resource for weather radar, the quantitative rainfall measurement of dual polarization doppler weather radar is developed. And based on the observed values of ombrometer, the differences of the measured values derived from different methods are analysed. The results show: The rainfall using KDP to calculate, i.e. R(KDP) method, is proximal to the data of ombrometer; R(ZH) and R(ZH, ZDR) method is underestimate rainfall obviously; R(ZDR, KDP) method is overestimate rainfall. With further analysis, rainfall measured by dual-polarization radar, especially from R(KDP) method, has higher precision, and provide the spatial distribution of the rainfall and the position of strong center well.