A Technique to Censor Biological Echoes in Radar Reflectivity Data

2010 ◽  
Vol 49 (3) ◽  
pp. 453-462 ◽  
Author(s):  
Valliappa Lakshmanan ◽  
Jian Zhang ◽  
Kenneth Howard
Keyword(s):  
Biological Species ◽  
Radar Reflectivity ◽  
Local Characteristic ◽  
Vertical Profiles ◽  
Biological Targets ◽  
Local Texture ◽  
Ground Clutter ◽  
Biological Contaminants ◽  
Anomalous Propagation ◽  
Reflectivity Data

Abstract Existing techniques of quality control of radar reflectivity data rely on local texture and vertical profiles to discriminate between precipitating echoes and nonprecipitating echoes. Nonprecipitating echoes may be due to artifacts such as anomalous propagation, ground clutter, electronic interference, sun strobe, and biological contaminants (i.e., birds, bats, and insects). The local texture of reflectivity fields suffices to remove most artifacts, except for biological echoes. Biological echoes, also called “bloom” echoes because of their circular shape and expanding size during the nighttime, have proven difficult to remove, especially in peak migration seasons of various biological species, because they can have local and vertical characteristics that are similar to those of stratiform rain or snow. In this paper, a technique is described that identifies candidate bloom echoes based on the range variance of reflectivity in areas of bloom and uses the global, rather than local, characteristic of the echo to discriminate between bloom and rain. Every range gate is assigned a probability that it corresponds to bloom using morphological (shape based) operations, and a neural network is trained using this probability as one of the input features. It is demonstrated that this technique is capable of identifying and removing echoes due to biological targets and other types of artifacts while retaining echoes that correspond to precipitation.

scholarly journals An Automated Technique to Quality Control Radar Reflectivity Data

2007 ◽  
Vol 46 (3) ◽  
pp. 288-305 ◽  
Author(s):  
Valliappa Lakshmanan ◽  
Angela Fritz ◽  
Travis Smith ◽  
Kurt Hondl ◽  
Gregory Stumpf
Keyword(s):  
Neural Network ◽  
Object Identification ◽  
Radar Reflectivity ◽  
Biological Targets ◽  
Ground Clutter ◽  
The Neural Network ◽  
Automated Technique ◽  
Anomalous Propagation ◽  
The Individual ◽  
Reflectivity Data

Abstract Echoes in radar reflectivity data do not always correspond to precipitating particles. Echoes on radar may result from biological targets such as insects, birds, or wind-borne particles; from anomalous propagation or ground clutter; or from test and interference patterns that inadvertently seep into the final products. Although weather forecasters can usually identify and account for the presence of such contamination, automated weather-radar algorithms are drastically affected. Several horizontal and vertical features have been proposed to discriminate between precipitation echoes and echoes that do not correspond to precipitation. None of these features by themselves can discriminate between precipitating and nonprecipitating areas. In this paper, a neural network is used to combine the individual features, some of which have already been proposed in the literature and some of which are introduced in this paper, into a single discriminator that can distinguish between “good” and “bad” echoes (i.e., precipitation and nonprecipitation, respectively). The method of computing the horizontal features leads to statistical anomalies in their distributions near the edges of echoes. Also described is how to avoid presenting such range gates to the neural network. The gate-by-gate discrimination provided by the neural network is followed by more holistic postprocessing based on spatial contiguity constraints and object identification to yield quality-controlled radar reflectivity scans that have most of the bad echo removed while leaving most of the good echo untouched. A possible multisensor extension, utilizing satellite data and surface observations, to the radar-only technique is also demonstrated. It is demonstrated that the resulting technique is highly skilled and that its skill exceeds that of the currently operational algorithm.

scholarly journals General Application of the Relative Calibration Adjustment (RCA) Technique for Monitoring and Correcting Radar Reflectivity Calibration

2015 ◽  
Vol 32 (3) ◽  
pp. 496-506 ◽  
Author(s):  
David B. Wolff ◽  
David A. Marks ◽  
Walter A. Petersen
Keyword(s):  
Real Time ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Absolute Calibration ◽  
Ground Clutter ◽  
Original Application ◽  
Using Data ◽  
Anomalous Propagation ◽  
The Stability ◽  
Reflectivity Data

AbstractAccurate calibration of radar reflectivity is integral to quantitative radar measurements of precipitation and a myriad of other radar-based applications. A statistical method was developed that utilizes the probability distribution of clutter area reflectivity near a stationary, ground-based radar to provide near-real-time estimates of the relative calibration of reflectivity data. The relative calibration adjustment (RCA) method provides a valuable, automated near-real-time tool for maintaining consistently calibrated radar data with relative calibration uncertainty of ±0.5 dB or better. The original application was to S-band data in a tropical oceanic location, where the stability of the method was thought to be related to the relatively mild ground clutter and limited anomalous propagation (AP). This study demonstrates, however, that the RCA technique is transferable to other S-band radars at locations with more intense ground clutter and AP. This is done using data from NASA’s polarimetric (NPOL) surveillance radar data during the Iowa Flood Studies (IFloodS) Global Precipitation Measurement (GPM) field campaign during spring of 2013 and other deployments. Results indicate the RCA technique is well capable of monitoring the reflectivity calibration of NPOL, given proper generation of an areal clutter map. The main goal of this study is to generalize the RCA methodology for possible extension to other ground-based S-band surveillance radars and to show how it can be used both to monitor the reflectivity calibration and to correct previous data once an absolute calibration baseline is established.

scholarly journals An Application of Reflectivity Climatology in Constructing Radar Hybrid Scans over Complex Terrain

2009 ◽  
Vol 26 (7) ◽  
pp. 1315-1327 ◽  
Author(s):  
Pao-Liang Chang ◽  
Pin-Fang Lin ◽  
Ben Jong-Dao Jou ◽  
Jian Zhang
Keyword(s):  
Seasonal Variations ◽  
Complex Terrain ◽  
Radar Reflectivity ◽  
The Real ◽  
Ground Clutter ◽  
Precipitation Estimates ◽  
Real Atmosphere ◽  
Reflectivity Data ◽  
Quantitative Precipitation Estimates

Abstract Three years’ worth of radar reflectivity data from four radars in an area of complex terrain (Taiwan) from 2005 to 2007 were analyzed and a reflectivity climatology was developed. The climatology was applied in the construction of new hybrid scans to minimize the impacts of ground clutter and beam blockages. The reflectivity climatology showed significant seasonal variations and captured distributions of ground/sea clutters, beam blockages, and anomalous propagations in addition to precipitation systems in the radar domains. By comparing the reflectivity climatology with gauge observations, it was found that 15 (20) dBZ was a good approximation for rain/no-rain segregation during cool (warm) seasons. Comparisons between the standard (i.e., based on terrain and scan strategies only with the assumption of standard propagations) and nonstandard (i.e., standard plus the clutter and blockage mitigation using the reflectivity climatology) hybrid scans showed that the former did not accurately reflect the clutter and blockage distributions in the real atmosphere. The application of the reflectivity climatology was shown to significantly reduce the impacts of clutter and blockages and provided improved radar quantitative precipitation estimates (QPEs) in the complex terrain.

scholarly journals Estimating Rain Rates from Tipping-Bucket Rain Gauge Measurements

2008 ◽  
Vol 25 (1) ◽  
pp. 43-56 ◽  
Author(s):  
Jianxin Wang ◽  
Brad L. Fisher ◽  
David B. Wolff
Keyword(s):  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Radar Reflectivity ◽  
Time Shift ◽  
Estimation Errors ◽  
Rate Estimation ◽  
Reflectivity Data ◽  
Rain Rates ◽  
Event Definition

Abstract This paper describes the cubic spline–based operational system for the generation of the Tropical Rainfall Measuring Mission (TRMM) 1-min rain-rate product 2A-56 from tipping-bucket (TB) gauge measurements. A simulated TB gauge from a Joss–Waldvogel disdrometer is employed to evaluate the errors of the TB rain-rate estimation. These errors are very sensitive to the time scale of rain rates. One-minute rain rates suffer substantial errors, especially at low rain rates. When 1-min rain rates are averaged over 4–7-min intervals or longer, the errors dramatically reduce. Estimated lower rain rates are sensitive to the event definition whereas the higher rates are not. The median relative absolute errors are about 22% and 32% for 1-min rain rates higher and lower than 3 mm h−1, respectively. These errors decrease to 5% and 14% when rain rates are used at the 7-min scale. The radar reflectivity–rain-rate distributions drawn from the large amount of 7-min rain rates and radar reflectivity data are mostly insensitive to the event definition. The time shift due to inaccurate clocks can also cause rain-rate estimation errors, which increase with the shifted time length. Finally, some recommendations are proposed for possible improvements of rainfall measurements and rain-rate estimations.

scholarly journals Bayesian Echo Classification for Australian Single-Polarization Weather Radar with Application to Assimilation of Radial Velocity Observations

2015 ◽  
Vol 32 (7) ◽  
pp. 1341-1355 ◽  
Author(s):  
S. J. Rennie ◽  
M. Curtis ◽  
J. Peter ◽  
A. W. Seed ◽  
P. J. Steinle ◽  
...  
Keyword(s):  
Weather Radar ◽  
Training Data ◽  
Training Dataset ◽  
Bayes Classifier ◽  
Threshold Method ◽  
Ground Clutter ◽  
Radar Network ◽  
Routine Monitoring ◽  
Single Polarization ◽  
Anomalous Propagation

AbstractThe Australian Bureau of Meteorology’s operational weather radar network comprises a heterogeneous radar collection covering diverse geography and climate. A naïve Bayes classifier has been developed to identify a range of common echo types observed with these radars. The success of the classifier has been evaluated against its training dataset and by routine monitoring. The training data indicate that more than 90% of precipitation may be identified correctly. The echo types most difficult to distinguish from rainfall are smoke, chaff, and anomalous propagation ground and sea clutter. Their impact depends on their climatological frequency. Small quantities of frequently misclassified persistent echo (like permanent ground clutter or insects) can also cause quality control issues. The Bayes classifier is demonstrated to perform better than a simple threshold method, particularly for reducing misclassification of clutter as precipitation. However, the result depends on finding a balance between excluding precipitation and including erroneous echo. Unlike many single-polarization classifiers that are only intended to extract precipitation echo, the Bayes classifier also discriminates types of nonprecipitation echo. Therefore, the classifier provides the means to utilize clear air echo for applications like data assimilation, and the class information will permit separate data handling of different echo types.

scholarly journals Mobile Radar Observations of the Evolving Debris Field Compared with a Damage Survey of the Shawnee, Oklahoma, Tornado of 19 May 2013

2020 ◽  
Vol 148 (5) ◽  
pp. 1779-1803 ◽  
Author(s):  
Roger M. Wakimoto ◽  
Zachary Wienhoff ◽  
Howard B. Bluestein ◽  
David J. Bodine ◽  
James M. Kurdzo
Keyword(s):  
Cross Correlation ◽  
Vertical Structure ◽  
Leading Edge ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Vertical Profiles ◽  
Damage Survey ◽  
X Band ◽  
First Time ◽  
Rapid Scanning

Abstract A detailed damage survey is combined with high-resolution mobile, rapid-scanning X-band polarimetric radar data collected on the Shawnee, Oklahoma, tornado of 19 May 2013. The focus of this study is the radar data collected during a period when the tornado was producing damage rated EF3. Vertical profiles of mobile radar data, centered on the tornado, revealed that the radar reflectivity was approximately uniform with height and increased in magnitude as more debris was lofted. There was a large decrease in both the cross-correlation coefficient (ρhv) and differential radar reflectivity (ZDR) immediately after the tornado exited the damaged area rated EF3. Low ρhv and ZDR occurred near the surface where debris loading was the greatest. The 10th percentile of ρhv decreased markedly after large amounts of debris were lofted after the tornado leveled a number of structures. Subsequently, ρhv quickly recovered to higher values. This recovery suggests that the largest debris had been centrifuged or fallen out whereas light debris remained or continued to be lofted. Range–height profiles of the dual-Doppler analyses that were azimuthally averaged around the tornado revealed a zone of maximum radial convergence at a smaller radius relative to the leading edge of lofted debris. Low-level inflow into the tornado encountering a positive bias in the tornado-relative radial velocities could explain the existence of the zone. The vertical structure of the convergence zone was shown for the first time.

scholarly journals Impact of multiple radar reflectivity data assimilation on the numerical simulation of a flash flood event during the HyMeX campaign

2017 ◽  
Vol 21 (11) ◽  
pp. 5459-5476 ◽  
Author(s):  
Ida Maiello ◽  
Sabrina Gentile ◽  
Rossella Ferretti ◽  
Luca Baldini ◽  
Nicoletta Roberto ◽  
...  
Keyword(s):  
Hydrological Cycle ◽  
Initial Conditions ◽  
Flash Flood ◽  
Central Italy ◽  
Heavy Precipitation ◽  
Radar Reflectivity ◽  
Precipitation Event ◽  
Italian Regions ◽  
Reflectivity Data ◽  
The Impact

Abstract. An analysis to evaluate the impact of multiple radar reflectivity data with a three-dimensional variational (3-D-Var) assimilation system on a heavy precipitation event is presented. The main goal is to build a regionally tuned numerical prediction model and a decision-support system for environmental civil protection services and demonstrate it in the central Italian regions, distinguishing which type of observations, conventional and not (or a combination of them), is more effective in improving the accuracy of the forecasted rainfall. In that respect, during the first special observation period (SOP1) of HyMeX (Hydrological cycle in the Mediterranean Experiment) campaign several intensive observing periods (IOPs) were launched and nine of which occurred in Italy. Among them, IOP4 is chosen for this study because of its low predictability regarding the exact location and amount of precipitation. This event hit central Italy on 14 September 2012 producing heavy precipitation and causing several cases of damage to buildings, infrastructure, and roads. Reflectivity data taken from three C-band Doppler radars running operationally during the event are assimilated using the 3-D-Var technique to improve high-resolution initial conditions. In order to evaluate the impact of the assimilation procedure at different horizontal resolutions and to assess the impact of assimilating reflectivity data from multiple radars, several experiments using the Weather Research and Forecasting (WRF) model are performed. Finally, traditional verification scores such as accuracy, equitable threat score, false alarm ratio, and frequency bias – interpreted by analysing their uncertainty through bootstrap confidence intervals (CIs) – are used to objectively compare the experiments, using rain gauge data as a benchmark.

Sign in / Sign up

Export Citation Format

Share Document