Identification and Removal of Ground Echoes and Anomalous Propagation Using the Characteristics of Radar Echoes

Cho, Yo-Han; Lee, GyuWon; Kim, Kyung-Eak; Zawadzki, Isztar

doi:10.1175/jtech1913.1

Cited by 86 publications

(56 citation statements)

References 18 publications

(18 reference statements)

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“…-999.0). The total membership value (MV total ) can be obtained from the weighted sum of individual membership values (MV i ) (Cho et al, 2006) as:…”

Section: Tracking Of Convective Cellsmentioning

confidence: 99%

“…The MF for matched cell tracks was determined by the ratio of the NFD of matched cell tracks and the sum of both NFDs of matched and mismatched cell tracks. The procedure to obtain MFs has been well described by Cho et al (2006). When the MF value was either <0.01 or >0.99, the MF values were replaced by 0.00 or 1.00, respectively.…”

Section: Membership Functions and Weightsmentioning

confidence: 99%

“…A large weight can be assigned to the feature parameter with relatively distinctive characteristics (less overlapping area) between matched and mismatched cell tracks. In this study, the weights for the three MFs have been objectively determined by an overlapping area between the NFDs of matched and mismatched cell tracks with Equation (8) (Cho et al, 2006;Gourley et al, 2007;Kuk et al, 2012):…”

Section: Membership Functions and Weightsmentioning

confidence: 99%

“…Fuzzy logic has been widely applied to quality control algorithms for radar data to remove ground clutter and anomalous propagation (AP) (e.g. Kessenger et al, 1999;Cho et al, 2006;Gourley et al, 2007;Rico-Ramirez and Cluckie, 2008), for hydrometeor classification using polarimetric measurements (e.g. Straka and Zrnic, 1993;Vivekanandan et al, 1999;Keenan, 2003;Lim et al, 2005;Park et al, 2009), for correction of bright bands (e.g.…”

mentioning

confidence: 99%

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Radar‐based cell tracking with fuzzy logic approach

Jung

Lee

2015

Meteorological Applications

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This study presents a new algorithm Fuzzy logic Algorithm for Storm Tracking (FAST) for tracking convective cells in 3D Cartesian co-ordinates. A fuzzy logic approach was applied to track cells with objectively determined membership functions (MFs) and weights that were derived from a large set of convective cell tracks (2326 pairs of convective cells). Three feature parameters (cell motion speed, area change ratio and axis transformation ratio) were selected by Bayesian information criterion and combined through fuzzy logic. FAST was evaluated with three skill scores using 2561 (2456) pairs of convective cells with (without) mergers and splits. The performance was tested in comparison with Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN) as a baseline method. For reference tracks without merger and split, the critical success index (CSI) of FAST reaches about 0.89. FAST shows better skill scores (CSI = 0.92 and 0.89 at speed limitations of 16.1 m s −1 and 25.0 m s −1 , respectively) than those of TITAN (CSI = 0.89 and 0.82 at the same speed limitations) and is independent of the number of convective cells and the speed limitation. When mergers and splits are considered in the reference, the tracking performance of FAST (FAST MS ) with a merger and split process is always better than that of TITAN (TITAN MS ) with a merger and split process and is independent of speed limitations. When FAST (=FAST noMS ) does not include a merger and split process, its tracking performance is better than that of TITAN MS at speed limitation of > 20 m s −1 .

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“…-999.0). The total membership value (MV total ) can be obtained from the weighted sum of individual membership values (MV i ) (Cho et al, 2006) as:…”

Section: Tracking Of Convective Cellsmentioning

confidence: 99%

Section: Membership Functions and Weightsmentioning

confidence: 99%

Section: Membership Functions and Weightsmentioning

confidence: 99%

mentioning

confidence: 99%

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Radar‐based cell tracking with fuzzy logic approach

Jung

Lee

2015

Meteorological Applications

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“…The combination of the input measurements obtained with single-and dual-polarization weather radars have enabled automatic classification of ground clutter echoes as well as anomalous propagation (AP) echoes, which are originated when the radar beam hits the ground/sea surface due to changes in the atmospheric index of refraction. Examples of these classifiers are described in Grecu and Krajewski (1999); Schuur et al (2003); Berenguer et al (2006); Cho et al (2006); Gourley et al (2007); Rico-Ramirez and Cluckie (2008). Ground clutter echoes have been utilized in different applications such as to calibrate weather radars at attenuating wavelengths (Serrar et al, 2000) and to control radar signal stability (Sempere-Torres et al, 2001.…”

Section: Introductionmentioning

confidence: 99%

Real‐time monitoring of weather radar antenna pointing using digital terrain elevation and a Bayes clutter classifier

Rico‐Ramirez

Gonzalez-Ramirez

Cluckie

et al. 2008

Meteorological Applications

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This paper presents a novel technique to monitor continuously the azimuthal pointing accuracy of a weather radar antenna. The technique consists of cross-correlating between modelled and measured echoes from ground clutter in real-time at low elevation angles under precipitation and non-precipitation conditions. The azimuthal angle lag with the maximum cross-correlation indicates the adjustment needed in antenna pointing. The modelled ground clutter echoes were obtained using high-resolution digital elevation model (DEM) data whereas the measured ground clutter echoes can be obtained in real-time using a Bayes classifier, which identifies the clutter echoes in the presence of precipitation. The technique has been successfully tested in the Thurnham radar in Southeast England. This method can be used by data users as well as radar operators. It should complement the traditional methods based on sun measurements.

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Uncertainty in radar‐rainfall composite and its impact on hydrologic prediction for the eastern Iowa flood of 2008

Seo

Cunha

Krajewski

2013

Water Resources Research

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[1] This study addresses a significant potential source of error that exists in radar-rainfall maps that are combined using data from multiple WSR-88D radars of the Next Generation Radar (NEXRAD) national network in the United States. This error stems from different radar calibration offsets that create a border within discontinuous rainfall fields at the equidistance zone among radars. The discontinuity in rainfall fields could lead to misestimation of rainfall over basins and subsequently, to significant errors in streamflow predictions through a hydrologic model. In this study, we produce enhanced radar-rainfall estimates (HN3) based on a novel approach that allows us to reduce the effects of the relative radar calibration bias. We use the relative bias information previously presented in a radar reflectivity comparison study. To investigate the effects of the relative bias adjustment, we evaluate the HN3 and Stage IV radar-rainfall by comparing them with rain gauge data and analyzing their ability to simulate streamflow for an extreme flood case. While the HN3 estimates are statistically comparable to the Stage IV estimates in the rain gauge data comparison, the borderline that identifies discontinuous rainfall fields disappears in the HN3 estimates. We performed hydrological simulations using a physically based, data-intensive, calibration-free, hillslope-link hydrologic model called CUENCAS and demonstrated CUENCAS's ability to accurately simulate flows by comparing results with observed and predicted streamflow generated by the Sacramento (SAC) model. SAC is the operational flood forecast model that has been used by the National Weather Service since 1969, and it was extensively calibrated based on historical data. The simulation results show that the adjustment improves streamflow predictions in the regions where the misestimation of rainfall quantity is considerable. We conclude that systematic error arising from different calibration offsets in rainfall fields can significantly affect hydrologic predictions.

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Identification and Removal of Ground Echoes and Anomalous Propagation Using the Characteristics of Radar Echoes

Cited by 86 publications

References 18 publications

Radar‐based cell tracking with fuzzy logic approach

Radar‐based cell tracking with fuzzy logic approach

Real‐time monitoring of weather radar antenna pointing using digital terrain elevation and a Bayes clutter classifier

Uncertainty in radar‐rainfall composite and its impact on hydrologic prediction for the eastern Iowa flood of 2008

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