Doppler Radar Network Research Articles

Characteristics of warm season left-moving supercells over the Highveld of South Africa

A supercell is a type of thunderstorm known to produce severe weather conditions that result in damage to property, injuries, and loss of life. Supercells were previously considered rare in South Africa, however since the implementation of the South African Weather Service's single polarisation S-band Doppler radar network in 2010, numerous cases have been identified. Despite this, there is a lack of research on supercells over South Africa, with very little information on their characteristics specific to the region. The aim of this study is to determine the characteristics of warm season left-moving supercells over the Highveld (Gauteng and Mpumalanga province) of South Africa. This was done using a radar derived database, consisting of 115 left-moving supercells, that occurred during the warm seasons (September to February) of 2010/11 to 2019/20. Left-moving supercells were found to be most common in October and November, peaking in the afternoon between 15:00 and 16:00 South African Standard Time (SAST). A shift in the seasonal distribution was observed, with the most active area over Gauteng in November, moving south-eastwards to the south-eastern parts of Mpumalanga by December. Supercell hotspots were identified over the south-western and eastern Highveld of Gauteng as well as the south-eastern parts of Mpumalanga, of which some areas of occurrence appeared to be influenced by topography. The average track direction was found to be from the south-west, with a southerly shift as the season progressed. On average supercells lasted approximately 1 h 12 min, travelled 49 km and at a speed of 41 kmh−1. Having a better understanding of the characteristics of supercells may provide a heightened awareness of preferred areas and times of occurrence of events, how they travel, how long they last and how fast and far they travel, thereby assisting in producing more accurate forecasts, nowcasts and warnings. More detailed studies are required to understand the influence of topography and the atmospheric conditions on these characteristics.

Bulk Microphysical Characteristics of a Heavy-Rain Complex Thunderstorm System in the Taipei Basin

Abstract Severe rainfall has become increasingly frequent and intense in the Taipei metropolitan area. A complex thunderstorm in the Taipei Basin on 14 June 2015 produced an extreme rain rate (>130 mm h−1), leading to an urban flash flood. This paper presents storms’ microphysical and dynamic features during the organizing and heavy rain stages, mainly based on observed polarimetric variables in a Doppler radar network and ground-based raindrop size distribution. Shallower isolated cells in the early afternoon characterized by big raindrops produced a rain rate > 10 mm h−1, but the rain showers persisted for a short time. The storm’s evolution highlighted the behavior of merged convective cells before the heaviest rainfall (exceeding 60 mm within 20 min). The columnar features of differential reflectivity (ZDR) and specific differential phase (KDP) became more evident in merged cells, which correlated with the broad distribution of upward motion and mixed-phase hydrometeors. The KDP below the environmental 0°C level increased toward the ground associated with the melted graupel and resulted in subsequent intense rain rates, showing the contribution of the ice-phase process. Due to the collision–breakup process, the highest concentrations of almost all drop sizes and smaller mass-weighted mean diameter occurred during the maximum rainfall stage.

Three-Dimensional Multi-Target Tracking Using Dual-Orthogonal Baseline Interferometric Radar.

Multi-target tracking (MTT) generally needs either a Doppler radar network with spatially separated receivers or a single radar equipped with costly phased array antennas. However, Doppler radar networks have high computational complexity, attributed to the multiple receivers in the network. Moreover, array signal processing techniques for phased array radar also increase the computational burden on the processing unit. To resolve this issue, this paper investigates the problem of the detection and tracking of multiple targets in a three-dimensional (3D) Cartesian space based on range and 3D velocity measurements extracted from dual-orthogonal baseline interferometric radar. The contribution of this paper is twofold. First, a nonlinear 3D velocity measurement function, defining the relationship between the state of the target and 3D velocity measurements, is derived. Based on this measurement function, the design of the proposed algorithm includes the global nearest neighbor (GNN) technique for data association, an interacting multiple model estimator with a square-root cubature Kalman filter (IMM-SCKF) for state estimation, and a rule-based M/N logic for track management. Second, Monte Carlo simulation results for different multi-target scenarios are presented to demonstrate the performance of the algorithm in terms of track accuracy, computational complexity, and IMM mean model probabilities.

Bolide fragment detection in Doppler weather radar data using artificial intelligence/machine learning

AbstractUnsupervised machine learning methods present a promising approach for detecting fragments produced from meteors and bolides as distinct signatures within Doppler weather radar data. A method combining principal component analysis (PCA), t‐distributed statistical neighbor embedding (t‐SNE), and data point pruning based on the nearest neighbor algorithm is introduced as a process to detect outlier meteor signatures from terrestrial weather signatures using the national NOAA WSR‐88D Doppler radar network. This method is applied against unlabeled data from four weather radar sites during two bolide events: the KFWS radar for the Ash Creek bolide and the KDAX, KRGX, and KBBX radars for the Sutter’s Mill bolide. The combined algorithm results in an accuracy rate of 99.7% and can classify the data in <8 min for a 121,000 return sized data set. However, the classifier’s recall and precision rates remained low due to difficulties in correctly classifying true‐positive meteorite fall events. This method enables the expedited detection of materials from bolides and meteors that fall within the national radar network, leading to the faster confirmation of meteorite fall events and subsequent dispatch of recovery teams.

Pedestrian detection using Doppler radar and LSTM neural network

<span lang="EN-US">Integration of radar systems as primary sensor with deep learning algorithms in driver assist systems is still limited. Its implementation would greatly help in continuous monitoring of visual blind spots from incoming pedestrians. Hence, this study proposes a single-input single-output based Doppler radar and long short-term memory (LSTM) neural network for pedestrian detection. The radar is placed in monostatic configuration at an angle of 45 degree from line of sight. Continuous wave with frequency of 1.9 GHz are continuously transmitted from the antenna. The returning signal from the approaching subjects is characterized by the branching peaks higher than the transmitted frequency. A total of 1108 spectrum traces with Doppler shifts characteristics is acquired from eight volunteers. Another 1108 spectrum traces without Doppler shifts are used for control purposes. The traces are then fed to LSTM neural network for training, validation and testing. Generally, the proposed method was able to detect pedestrian with 88.9% accuracy for training and 87.3% accuracy for testing.</span>

A Variation Method for Retrieval of Three-Dimensional Wind Field by Bistatic Doppler Weather Radar Network

A variational method for the retrieval of 3D wind field by bistatic Doppler radar network is developed, and its performance is evaluated. This bistatic Doppler weather radar network consists of one X-band Doppler radar and one passive low-gain bistatic receiver. To allow for measurement error, this method uses the two radial velocities as weak constraints and uses the continuity equation as a strong constraint in a cost function. Improvements are brought to the classical upward integration of the continuity equation, using a weighted combination of upward and downward integrations and its adjoint. The wind field retrieval experiments are carried out by the actual observation data, and compared with the wind filed retrieved by the two Doppler radars. The results show that the wind field is very close, and the synchronous observation technology of the bistatic Doppler radar network is reasonable, which has the potential application in real-time detection of 3D wind field, especially for detecting near surface wind field.

Vertical air motion retrievals in deep convective clouds using the ARM scanning radar network in Oklahoma during MC3E

Abstract. The US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program's Southern Great Plains (SGP) site includes a heterogeneous distributed scanning Doppler radar network suitable for collecting coordinated Doppler velocity measurements in deep convective clouds. The surrounding National Weather Service (NWS) Next Generation Weather Surveillance Radar 1988 Doppler (NEXRAD WSR-88D) further supplements this network. Radar velocity measurements are assimilated in a three-dimensional variational (3DVAR) algorithm that retrieves horizontal and vertical air motions over a large analysis domain (100 km × 100 km) at storm-scale resolutions (250 m). For the first time, direct evaluation of retrieved vertical air velocities with those from collocated 915 MHz radar wind profilers is performed. Mean absolute and root-mean-square differences between the two sources are of the order of 1 and 2 m s−1, respectively, and time–height correlations are of the order of 0.5. An empirical sensitivity analysis is done to determine a range of 3DVAR constraint weights that adequately satisfy the velocity observations and anelastic mass continuity. It is shown that the vertical velocity spread over this range is of the order of 1 m s−1. The 3DVAR retrievals are also compared to those obtained from an iterative upwards integration technique. The results suggest that the 3DVAR technique provides a robust, stable solution for cases in which integration techniques have difficulty satisfying velocity observations and mass continuity simultaneously.

A Point Cloud Method for Retrieval of High-Resolution 3D Gridded Reflectivity from Weather Radar Networks for Air Traffic Management

AbstractA prototype high-resolution (1 km, 5 min) multiradar 3D gridded reflectivity product, including a suite of derived 2D vertical column products, has been developed for the Single European Skies Air Traffic Management Research program. As part of this, a new method for mapping radar data to grid points is being used, based on the concept of a binary space partitioning (BSP) tree that treats radar data as a set of points in a 3D point cloud. This allows the resulting analysis to be based on a complete picture of the nearby data from overlapping radars and can easily adapt to irregular grid configurations. This method is used with a Barnes successive corrections technique to retrieve finescale features while avoiding problems of undersmoothing in data-sparse regions. This has been tested using 3D domains enclosing the terminal maneuvering areas surrounding Paris, France, and London, United Kingdom, and using reflectivity plan position indicator scan data from the French and U.K. operational networks, encoded using the standard European Operational Programme for the Exchange of Weather Radar Information (OPERA) Data Information Model format. Quantitative intercomparisons between the new method, in various configurations; a high-resolution version of an existing method, in operational use at Météo-France; and a method that was developed by the National Oceanic and Atmospheric Administration for use with the Weather Surveillance Radar-1998 Doppler radar network, have been done using simulated radar scans derived from 3D synthetic radar reflectivity fields in stratiform and convective regimes.

Significant-Hail-Producing Storms in Finland: Convective-Storm Environment and Mode

Abstract The environmental characteristics and convective mode of significant hailstorms (those storms producing reported hail 5 cm or larger in diameter) in Finland during 1972–2011 were analyzed. Altogether, 23 significant-hail-day environments were analyzed by modifying radiosonde data from proximity soundings in the observed data archives of the Finnish Meteorological Institute. Convective parameters derived from the environmental soundings were compared between a set of significant-hail soundings and a null set of nonsevere-thunderstorm soundings. A subset of 13 significant-hail days was examined using data from a network of Doppler radars during 1999–2011. Convective-storm mode and storm characteristics (e.g., hook echo, bounded weak-echo region) were determined for the 18 significant-hail-producing storms during these days. Most (78%) of these storms producing significant hail in Finland occurred with supercells. Of the significant-hail days, 39% (9 out of 23) did not have the minimum of 15 m s−1 of deep-layer (0–6 km) shear commonly expected for supercells. Convective parameters of significant-hail and thunderstorm-day environments were substantially different from each other. Specifically, significant-hail environments had a mean most-unstable convective available potential energy (MUCAPE) of 1464 J kg−1 and deep-layer shear of 17.5 m s−1, whereas thunderstorm days had a MUCAPE of 593 J kg−1 and deep-layer shear of 10.2 m s−1. Larger hail was associated with higher values of MUCAPE. The lifetimes and track lengths of significant-hail-producing storms were related to the convective mode and storm environment. Specifically, larger deep-layer shear seemed to support longer lifetimes and track lengths. Nonsupercells had shorter lifetimes, shorter stormtrack lengths, and lower speeds than supercells. The value of deep-layer shear was smaller for nonsupercells than for supercells. Discrete supercells had higher speeds, longer lifetimes, and longer track lengths than cluster supercells.

A diagnostic study of spectral multiscaling on spatio-temporal accumulations of rainfall fields based on radar measurements over Iowa

Spectral multiscaling postulates a power-law type of scaling of spectral distribution functions of stationary processes of spatial averages or their temporal accumulations, over nested and geometrically similar sub-regions of the spatial parameter space of a given spatio-temporal random field. Presently, the validity of this property is investigated using time series of spatio-temporal accumulations of rain rate fields measured by a network of Doppler radars covering the region of Iowa. Statistical evidence of spectral multiscaling is gathered and discussed through a systematic study of appropriate regression diagnostics, using two records of data. One is a 120-month record of hourly (60-min) accumulations of spatially averaged rain rate on square pixels of side length 4km, comprising a rectangular grid of dimension 80×160 covering almost the entire State of Iowa. The other is a 74-month record of quarterly (15-min) accumulations of spatially averaged rain rate on square pixels of side length 1km, comprising a rectangular grid of dimension 68×106 over the Cedar River basin in eastern Iowa. The diagnostic results indicate frequency-dependent scaling relationships interpreted as evidence of spectral multiscaling across a range of spatial scales.

Detection of hail through the three-body scattering signatures and its effects on radar algorithms observed in Romania

The Romanian National Meteorological Administration (NMA) radar network consists of five S-band and four C-band radars. Observation of convection in Romania through the Doppler radar network offered a new perspective in understanding the climatologic risk of certain regions and mesoscale environments. Highly organized convective systems, such as supercells, are better observed and their subsequent threat can be better anticipated during the nowcasting process using Doppler velocity fields and detection algorithms such as mesocyclones (MESO) and tornadic vortex signature (TVS). However, for warning purposes, these tools cannot be used without a subjective validation because of the associated errors and limitations of radar observations. In this paper several cases are presented where the presence of large hail inside the storm produced a radar artifact named three-body scatter signature (TBSS) that disturbed the Doppler velocity field. The cases presented were observed with S-band radars and were associated with hail reports on the ground. The first case shows a TBSS whose radial Doppler velocities are negative due to the falling hydrometeors. The second case is a less frequent event; there the Doppler velocities in the TBSS region are positive due to the updraft. The third case has both positive and negative values in the TBSS region; it ocurred in a supercell that affected the city of Varna in Bulgaria with large hail. The positive values were associated with the overhang region in the rotational updraft at upper heights, while the negative values in the regions outside the rotational updraft at lower heights, were associated with the downdrafts. Features produced by the TBSS have perturbed the output of the MESO and TVS algorithms by introducing false strong values of wind shear that have been interpreted as rotation. Thus false mesocyclonic and tornadic vortex signatures were generated. In some of the cases large hail and weak tornadoes were reported, so the presence of a TBSS was a challenge for the nowcasters. In the last part of the paper we analyze the link between the TBSS appearance with reports of large hail at the ground in 2009 within the coverage area of the Romanian S-band radars, which also cover parts of Hungary, Bulgaria, the Republic of Moldova and Serbia. The results show that the TBSS artifact is a strong indicator of large-size hail.

Principles and applications of dual-polarization weather radar. Part I: Description of the polarimetric radar variables

The United States Weather Surveillance Radar-1988 Doppler (WSR-88D) radar network has been upgraded to dual-polarization capabilities, providing operational and research meteorologists with a wealth of new information regarding the types and distributions of hydrometeors within precipitating storms, as well as a means for improved radar data quality. In addition to the conventional moments of reflectivity factor at horizontal polarization (ZH), Doppler velocity (Vr), and Doppler spectrum width (W), the new variables available from upgraded radars are the differential reflectivity (ZDR), differential propagation phase shift (ΦDP), specific differential phase (KDP), and the co-polar correlation coefficient (ρhv or CC). In the first part of this review series, a description of the polarimetric radar variables available from the newly polarimetric WSR-88D radars is provided. An emphasis is made on their physical meaning and interpretation in the context of operational meteorology.

An Overview of the Urban Boundary Layer Atmosphere Network in Helsinki

The Helsinki Urban Boundary-Layer Atmosphere Network (UrBAN: http://urban.fmi.fi) is a dedicated research-grade observational network where the physical processes in the atmosphere above the city are studied. Helsinki UrBAN is the most poleward intensive urban research observation network in the world and thus will allow studying some unique features such as strong seasonality. The network's key purpose is for the understanding of the physical processes in the urban boundary layer and associated fluxes of heat, momentum, moisture, and other gases. A further purpose is to secure a research-grade database, which can be used internationally to validate and develop numerical models of air quality and weather prediction. Scintillometers, a scanning Doppler lidar, ceilometers, a sodar, eddy-covariance stations, and radiometers are used. This equipment is supplemented by auxiliary measurements, which were primarily set up for general weather and/or air-quality mandatory purposes, such as vertical soundings and the operational Doppler radar network. Examples are presented as a testimony to the potential of the network for urban studies, such as (i) evidence of a stable boundary layer possibly coupled to an urban surface, (ii) the comparison of scintillometer data with sonic anemometry above an urban surface, (iii) the application of scanning lidar over a city, and (iv) combination of sodar and lidar to give a fuller range of sampling heights for boundary layer profiling.

Using a Diagnostic Soil-Plant-Atmosphere Model for Monitoring Drought at Field to Continental Scales

Drought assessment is a complex undertaking, requiring monitoring of deficiencies in multiple components of the hydrologic budget. Precipitation anomalies reflect variability in water supply to the land surface, while soil moisture, groundwater and surface water anomalies reflect deficiencies in moisture storage. In contrast, evapotranspiration (ET) anomalies provide unique yet complementary information, reflecting variations in actual water use by crops – a useful diagnostic of vegetation health. Here we describe a remotely sensed Evaporative Stress Index (ESI) based on anomalies in actual-to-reference ET ratio. Actual ET is retrieved from thermal remote sensing data using a diagnostic soil-plant-atmosphere modeling system forced by measurements of morning land-surface temperature (LST) rise from geostationary satellites. In comparison with vegetation indices, LST is a relatively fast-response variable, with the potential for providing early warning of crop stress reflected in increasing canopy temperatures. Spatiotemporal patterns in ESI have been compared with patterns in the U.S. Drought Monitor and in standard precipitation-based indices, demonstrating reasonable agreement. However, because ESI does not use precipitation as an input, it provides an independent assessment of evolving drought conditions, and is more portable to data-sparse parts of the world lacking dense rain-gauge and Doppler radar networks. Integrating LST information from geostationary and polar orbiting systems through data fusion, the ESI has unique potential for sensing moisture stress at field scale, with potential benefits to yield estimation and loss compensation efforts. The ESI is routinely produced over the continental U.S. using data from the Geostationary Operational Environmental Satellites, with expansion to North and South America underway. In addition drought and ET monitoring applications are being developed over Africa and Europe using land-surface products from the Meteosat Second Generation (MSG) platform.

Objective Optimization of Weather Radar Networks for Low-Level Coverage Using a Genetic Algorithm

Abstract The current Weather Surveillance Radar-1988 Doppler (WSR-88D) radar network is approaching 20 years of age, leading researchers to begin exploring new opportunities for a next-generation network in the United States. With a vast list of requirements for a new weather radar network, research has provided various approaches to the design and fabrication of such a network. Additionally, new weather radar networks in other countries, as well as networks on smaller scales, must balance a large number of variables in order to operate in the most effective way possible. To offer network designers an objective analysis tool for such decisions, a coverage optimization technique, utilizing a genetic algorithm with a focus on low-level coverage, is presented. Optimization is achieved using a variety of variables and methods, including the use of climatology, population density, and attenuation due to average precipitation conditions. A method to account for terrain blockage in mountainous regions is also presented. Various combinations of multifrequency radar networks are explored, and results are presented in the form of a coverage-based cost–benefit analysis, with considerations for total network lifetime cost.

A Maximum Likelihood Approach to Estimation of Vector Velocity in Doppler Radar Networks

In this paper, a vector velocity estimation approach based on the maximum likelihood technique operating on moment data within the overlapping area of a network of Doppler radars is presented. The relationships between the estimated vector velocity, the statistics of the measured signal, the characteristics of the observing geometry and volume, and the hardware and signal processing parameters are all derived. The most relevant error sources to the network measurements are derived and incorporated into the overall estimation process. The relationship between the measurement and estimation errors is identified, and exploited, so that estimation performance can be measured and, if necessary, improved through the means of error norm minimization. Techniques for mitigating errors in the synthesized reflectivity and velocity folding are presented as well. Results with error metrics are shown for several typical weather observation scenarios that include error sources and simulated data. Finally, it is shown how the technique may be used to provide useful information for the design and intercomparison of various Doppler radar network geometries.

Assimilation of the dual‐polarization Doppler radar data for a convective storm with a warm‐rain radar forward operator

In this paper, a data assimilation scheme is built to utilize the dual‐polarization Doppler radar observations collected by the C band Advanced Radar for Meteorological and Operational Research and is tested with a case study for a mesoscale convective system (MCS) over northern Alabama in the afternoon of 15 March 2008. Three variables, horizontal reflectivity, differential reflectivity, and radial velocity are assimilated into the advanced research version of the Weather Research and Forecasting model. A warm rain radar operator is constructed to assimilate the horizontal reflectivity and differential reflectivity data with the three‐dimensional variational data assimilation (3DVAR) system. The main goals of this paper are to, first, demonstrate the improvements in short‐term forecasts of the moist convection when fields unique to the dual‐polarization radar are assimilated into a regional model and, second, to understand how these fields may best be integrated in a data assimilation system like 3DVAR to improve short‐term storm forecast. The results show that the horizontal reflectivity, differential reflectivity, and radial velocity data can be successfully assimilated using our data assimilation scheme. The assimilation of the horizontal reflectivity and radial velocity data has a significant positive impact on the reflectivity distribution of the MCS. An additional improvement (10%–20%) in the initial condition is found when the differential reflectivity data are further assimilated. Moreover, the improved initial condition leads to apparent improvement in short‐term forecast of the MCS. The best forecast is produced when both horizontal and differential reflectivity and radial velocity data are assimilated. This improvement can be attributed to the improved estimate of liquid water content by using the radar operator with combined information of both horizontal reflectivity and differential reflectivity. This study demonstrates the benefit of utilizing the observations from single dual‐polarization radar. With the forthcoming upgrade of the current U. S. “NEXRAD” radar system (set to begin in ∼2010), a dual‐polarization radar network will be available. Assimilating observations from the dual‐polarization Doppler radar network could be a potential technique to further improve the short‐term numerical forecasts of convective storms.

Importance of Horizontally Inhomogeneous Environmental Initial Conditions to Ensemble Storm-Scale Radar Data Assimilation and Very Short-Range Forecasts

Abstract The assimilation of operational Doppler radar observations into convection-resolving numerical weather prediction models for very short-range forecasting represents a significant scientific and technological challenge. Numerical experiments over the past few years indicate that convective-scale forecasts are sensitive to the details of the data assimilation methodology, the quality of the radar data, the parameterized microphysics, and the storm environment. In this study, the importance of horizontal environmental variability to very short-range (0–1 h) convective-scale ensemble forecasts initialized using Doppler radar observations is investigated for the 4–5 May 2007 Greensburg, Kansas, tornadic thunderstorm event. Radar observations of reflectivity and radial velocity from the operational Doppler radar network at 0230 UTC 5 May 2007, during the time of the first large tornado, are assimilated into each ensemble member using a three-dimensional variational data assimilation system (3DVAR) developed at the Center for Analysis and Prediction of Storms (CAPS). Very short-range forecasts are made using the nonhydrostatic Advanced Regional Prediction System (ARPS) model from each ensemble member and the results are compared with the observations. Explicit three-dimensional environmental variability information is provided to the convective-scale ensemble using analyses from a 30-km mesoscale ensemble data assimilation system. Comparisons between convective-scale ensembles with initial conditions produced by 3DVAR using 1) background fields that are horizontally homogeneous but vertically inhomogeneous (i.e., have different vertical environmental profiles) and 2) background fields that are horizontally and vertically inhomogeneous are undertaken. Results show that the ensemble with horizontally and vertically inhomogeneous background fields provides improved predictions of thunderstorm structure, mesocyclone track, and low-level circulation track than the ensemble with horizontally homogeneous background fields. This suggests that knowledge of horizontal environmental variability is important to successful convective-scale ensemble predictions and needs to be included in real-data experiments.

A Variational Correction Method as an Alternative to Forced Rejection of Sidelobe-Contaminated Bistatic Doppler Measurements

Abstract The problem of sidelobe contamination of bistatic apparent Doppler velocity measurements involved in a bistatic Doppler radar network is examined. So far in the context of 3D wind field analysis, by combining a traditional Doppler radar with one or more bistatic receivers, identification and hence removal of regions of high degrees of contamination were necessarily crucial steps to obtaining reliable wind fields. This study proposes an alternative solution to the forced rejection of bistatic Doppler data suspected to be contaminated by sidelobe echoes, on the basis of restoring the nonmeasured “actual” (i.e., noncontaminated) bistatic Doppler velocity from both monostatic radar and bistatic receiver measurements. The correction method is based on a modeled expression of the observed bistatic apparent Doppler velocity defined as the reflectivity-weighted average of actual Doppler velocity of particles within individual volume samples, including the antenna gain pattern of both transmitting and receiving radars. The searched actual Doppler velocity is a solution of an underdetermined inverse problem that can be handled as a constrained linear inversion problem, through a variational least squares analysis method. The performances of the proposed method are analyzed, using simulated radar observations involving one remote receiver. An example of application to experimental data collected by the Deutsches Zentrum für Luft und Raumfahrt (DLR) bistatic Doppler radar network within a moderate precipitation system observed on 8 May 2000 in Germany is also presented. Pseudo-Doppler observations of a tropical squall-line system are used to quantify the effective improvement of the correction method on the bistatic Doppler velocity and hence the retrieved 3D wind field. Statistics of the differences are presented between observed and idealized (sidelobe free) velocity structures on the one hand, and corrected and idealized velocity structures on the other hand. Clearly shown is the very low level of the corrected minus idealized differences (mean and standard deviation) against the significantly high level of the observed minus idealized differences. As previously observed, maximum correction occurs in regions of potentially high gradients of reflectivity. It is also found that regions of low observed minus idealized differences remain unchanged after correction, which means that the sidelobe-correction method only acts on needed regions and does not introduce any artificial modification.

Using the Existing Spectral Clutter Filter With the Nonuniformly Spaced Time Series Data in Weather Radar

The National Weather Service (NWS) has a network of weather surveillance Doppler radars, WSR-88D, that is routinely upgraded to reflect current technological and engineering advances. During the last decade, the staggered pulse repetition time (SPRT) was proposed to mitigate range/velocity ambiguity in WSR-88D; however, it could not be used at lowest elevation tilts due to the inability to filter ground clutter from the nonuniformly spaced time series. A complicated spectral procedure was developed to address this issue, but the procedure was derived for a specific set of data acquisition parameters and used a lookup table to provide a value for the clutter spectrum width. Attempts to use SPRT with different sets of acquisition parameters always led to the degradation of filtering. It is proposed here that the clutter spectrum width be estimated by using Gaussian model adaptive processing (GMAP) that is currently used by NWS for uniformly sampled time series. GMAP is an adaptive ground clutter filter that performs an iterative fit of a Gaussian curve to the spectral coefficients identified as being due to ground clutter. GMAP cannot be used directly with the SPRT spectrum because of multiple clutter replicas that are due to staggered nonuniform sampling. However, the elements of GMAP can be exploited and used in the SPRT procedure. This letter presents how the elements of GMAP can be used with the SPRT data to provide successful clutter filtering at lowest elevation tilts.