Classification of polarimetric radar data with texture measure

AbstractA new hydrometeor classification algorithm that combines thermodynamic output from the Rapid Update Cycle (RUC) model with polarimetric radar observations is introduced. The algorithm improves upon existing classification techniques that rely solely on polarimetric radar observations by using thermodynamic information to help to diagnose microphysical processes (such as melting or refreezing) that might occur aloft. This added information is especially important for transitional weather events for which past studies have shown radar-only techniques to be deficient. The algorithm first uses vertical profiles of wet-bulb temperature derived from the RUC model output to provide a background precipitation classification type. According to a set of empirical rules, polarimetric radar data are then used to refine precipitation-type categories when the observations are found to be inconsistent with the background classification. Using data from the polarimetric KOUN Weather Surveillance Radar-1988 Doppler (WSR-88D) located in Norman, Oklahoma, the algorithm is tested on a transitional winter-storm event that produced a combination of rain, freezing rain, ice pellets, and snow as it passed over central Oklahoma on 30 November 2006. Examples are presented in which the presence of a radar bright band (suggesting an elevated warm layer) is observed immediately above a background classification of dry snow (suggesting the absence of an elevated warm layer in the model output). Overall, the results demonstrate the potential benefits of combining polarimetric radar data with thermodynamic information from numerical models, with model output providing widespread coverage and polarimetric radar data providing an observation-based modification of the derived precipitation type at closer ranges.

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Classification of Rainfall Types Using Parsivel Disdrometer and S-Band Polarimetric Radar in Central Korea

Remote Sensing ◽

10.3390/rs12040642 ◽

2020 ◽

Vol 12 (4) ◽

pp. 642 ◽

Cited By ~ 1

Author(s):

Jui Le Loh ◽

Dong-In Lee ◽

Mi-Young Kang ◽

Cheol-Hwan You

Keyword(s):

Vertical Profile ◽

Drop Size ◽

Classification Scheme ◽

Radar Data ◽

Number Concentration ◽

Polarimetric Radar ◽

Retrieval Method ◽

Median Volume ◽

Better Than

Tools to identify and classify stratiform and convective rains at various times of the 12 days from June 2015 to March 2016 in Jincheon, Korea, were developed by using a Parsivel disdrometer and S-band polarimetric (S-POL) radar data. Stratiform and convective rains were identified using three different methods (vertical profile of reflectivity (VPR), the method proposed by Bringi et al. (BR03), and a combination of the two (BR03-VPR)) by using a Parsivel disdrometer for its applications to radar as a reference. BR03-VPR exhibits a better classification scheme than the VPR and BR03 methods. The rain types were compared using the drop size distribution (DSD) retrieved from polarimetric variables and reflectivity only. By using the DSD variables, a new convective/stratiform classification line of the log-normalized droplet number concentration ( log 10 N w ) − median volume diameter ( D 0 ) was derived for this area to classify the rainfall types using DSD variables retrieved from the polarimetric radar. For the radar variables, the method by Steiner et al. (SHY95) was found to be the best method, with 0.00% misclassification of the stratiform rains. For the convective rains, the DSD retrieval method performed better. However, for both stratiform and convective rains, the fuzzy method performed better than the SHY95 and DSD retrieval methods.

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Radar Measurement of Human Polarimetric Micro-Doppler

Journal of Electrical and Computer Engineering ◽

10.1155/2013/804954 ◽

2013 ◽

Vol 2013 ◽

pp. 1-5 ◽

Cited By ~ 2

Author(s):

David Tahmoush ◽

Jerry Silvious

Keyword(s):

Phase Difference ◽

Radar Data ◽

The Body ◽

Polarimetric Radar ◽

Radar Measurement ◽

Planar Surfaces ◽

The Subject ◽

Arm Motion

We use polarimetric micro-Doppler for the detection of arm motion, especially for the classification of whether someone has their arms swinging and is thus unloaded. The arm is often bent at the elbow, providing a surface somewhat similar to a dihedral. This is distinct from the more planar surfaces of the body which allows us to isolate the signals of the arm (and knee). The dihedral produces a double bounce that can be seen in polarimetric radar data by measuring the phase difference between HH and VV. This measurement can then be used to determine whether the subject is unloaded.

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Comparison of Radar-Based Hail Detection Using Single- and Dual-Polarization

Remote Sensing ◽

10.3390/rs11121436 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1436 ◽

Cited By ~ 1

Author(s):

Skripniková ◽

Řezáčová

Keyword(s):

Comparative Analysis ◽

Heavy Rain ◽

Radar Data ◽

Severe Weather ◽

Detection Methods ◽

Polarimetric Radar ◽

Dual Polarization ◽

Rain Events ◽

Single Polarization ◽

Strong Agreement

The comparative analysis of radar-based hail detection methods presented here, uses C-band polarimetric radar data from Czech territory for 5 stormy days in May and June 2016. The 27 hail events were selected from hail reports of the European Severe Weather Database (ESWD) along with 21 heavy rain events. The hail detection results compared in this study were obtained using a criterion, which is based on single-polarization radar data and a technique, which uses dual-polarization radar data. Both techniques successfully detected large hail events in a similar way and showed a strong agreement. The hail detection, as applied to heavy rain events, indicated a weak enhancement of the number of false detected hail pixels via the dual-polarization hydrometeor classification. We also examined the performance of hail size detection from radar data using both single- and dual-polarization methods. Both the methods recognized events with large hail but could not select the reported events with maximum hail size (diameter above 4 cm).

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Crop classification from C-band polarimetric radar data

International Journal of Remote Sensing ◽

10.1080/01431169408954289 ◽

1994 ◽

Vol 15 (14) ◽

pp. 2871-2885 ◽

Cited By ~ 31

Author(s):

G.M. FOODY ◽

M. B. McCULLOCH ◽

W. B. YATES

Keyword(s):

Radar Data ◽

Polarimetric Radar ◽

Crop Classification

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Fuzzy Logic Classification of S-Band Polarimetric Radar Echoes to Identify Three-Body Scattering and Improve Data Quality

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-13-0358.1 ◽

2014 ◽

Vol 53 (8) ◽

pp. 2017-2033 ◽

Cited By ~ 14

Author(s):

Vivek N. Mahale ◽

Guifu Zhang ◽

Ming Xue

Keyword(s):

Fuzzy Logic ◽

Standard Deviation ◽

Radar Data ◽

Classification Algorithm ◽

Radar Reflectivity ◽

Membership Functions ◽

Polarimetric Radar ◽

Radar Echo ◽

Three Body ◽

Reflectivity Factor

AbstractThe three-body scatter signature (TBSS) is a radar artifact that appears downrange from a high-radar-reflectivity core in a thunderstorm as a result of the presence of hailstones. It is useful to identify the TBSS artifact for quality control of radar data used in numerical weather prediction and quantitative precipitation estimation. Therefore, it is advantageous to develop a method to automatically identify TBSS in radar data for the above applications and to help identify hailstones within thunderstorms. In this study, a fuzzy logic classification algorithm for TBSS identification is developed. Polarimetric radar data collected by the experimental S-band Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma (KOUN), are used to develop trapezoidal membership functions for the TBSS class of radar echo within a hydrometeor classification algorithm (HCA). Nearly 3000 radar gates are removed from 50 TBSSs to develop the membership functions from the data statistics. Five variables are investigated for the discrimination of the radar echo: 1) horizontal radar reflectivity factor ZH, 2) differential reflectivity ZDR, 3) copolar cross-correlation coefficient ρhv, 4) along-beam standard deviation of horizontal radar reflectivity factor SD(ZH), and 5) along-beam standard deviation of differential phase SD(ΦDP). These membership functions are added to an HCA to identify TBSSs. Testing is conducted on radar data collected by dual-polarization-upgraded operational WSR-88Ds from multiple severe-weather events, and results show that automatic identification of the TBSS through the enhanced HCA is feasible for operational use.

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Multilag Correlation Estimators for Polarimetric Radar Measurements in the Presence of Noise

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-11-00010.1 ◽

2012 ◽

Vol 29 (6) ◽

pp. 772-795 ◽

Cited By ~ 24

Author(s):

Lei Lei ◽

Guifu Zhang ◽

Richard J. Doviak ◽

Robert Palmer ◽

Boon Leng Cheong ◽

...

Keyword(s):

Theoretical Analysis ◽

Data Quality ◽

Signal To Noise Ratio ◽

Simulated Data ◽

Radar Data ◽

Polarimetric Radar ◽

Signal To Noise ◽

Spectral Moments ◽

Radar Measurements

Abstract The quality of polarimetric radar data degrades as the signal-to-noise ratio (SNR) decreases. This substantially limits the usage of collected polarimetric radar data to high SNR regions. To improve data quality at low SNRs, multilag correlation estimators are introduced. The performance of the multilag estimators for spectral moments and polarimetric parameters is examined through a theoretical analysis and by the use of simulated data. The biases and standard deviations of the estimates are calculated and compared with those estimates obtained using the conventional method.

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Parametric Convolutional Neural Network for Radar-based Human Activity Classification Using Raw ADC Data

10.36227/techrxiv.12896108 ◽

2020 ◽

Author(s):

Thomas Stadelmayer ◽

Avik Santra

Keyword(s):

Neural Network ◽

Human Activity ◽

Recognition System ◽

Radar Data ◽

Fast Time ◽

Activity Classification ◽

Radar Sensors ◽

Equivalent State ◽

Machine Interface

Radar sensors offer a promising and effective sensing modality for human activity classification. Human activity classification enables several smart homes applications for energy saving, human-machine interface for gesture controlled appliances and elderly fall-motion recognition. Present radar-based activity recognition system exploit micro-Doppler signature by generating Doppler spectrograms or video of range-Doppler images (RDIs), followed by deep neural network or machine learning for classification. Although, deep convolutional neural networks (DCNN) have been shown to implicitly learn features from raw sensor data in other fields, such as camera and speech, yet for the case of radar DCNN preprocessing followed by feature image generation, such as video of RDI or Doppler spectrogram, is required to develop a scalable and robust classification or regression application. In this paper, we propose a parametric convolutional neural network that mimics the radar preprocessing across fast-time and slow-time radar data through 2D sinc filter or 2D wavelet filter kernels to extract features for classification of various human activities. It is demonstrated that our proposed solution shows improved results compared to equivalent state-of-art DCNN solutions that rely on Doppler spectrogram or video of RDIs as feature images.

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The Analysis and Prediction of Microphysical States and Polarimetric Radar Variables in a Mesoscale Convective System Using Double-Moment Microphysics, Multinetwork Radar Data, and the Ensemble Kalman Filter

Monthly Weather Review ◽

10.1175/mwr-d-13-00042.1 ◽

2014 ◽

Vol 142 (1) ◽

pp. 141-162 ◽

Cited By ~ 43

Author(s):

Bryan J. Putnam ◽

Ming Xue ◽

Youngsun Jung ◽

Nathan Snook ◽

Guifu Zhang

Keyword(s):

Kalman Filter ◽

Ensemble Kalman Filter ◽

Mesoscale Convective System ◽

Radar Data ◽

Cold Pool ◽

Convective System ◽

Polarimetric Radar ◽

Microphysics Scheme ◽

Mesoscale Convective ◽

Double Moment

Abstract Doppler radar data are assimilated with an ensemble Kalman Filter (EnKF) in combination with a double-moment (DM) microphysics scheme in order to improve the analysis and forecast of microphysical states and precipitation structures within a mesoscale convective system (MCS) that passed over western Oklahoma on 8–9 May 2007. Reflectivity and radial velocity data from five operational Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band radars as well as four experimental Collaborative and Adaptive Sensing of the Atmosphere (CASA) X-band radars are assimilated over a 1-h period using either single-moment (SM) or DM microphysics schemes within the forecast ensemble. Three-hour deterministic forecasts are initialized from the final ensemble mean analyses using a SM or DM scheme, respectively. Polarimetric radar variables are simulated from the analyses and compared with polarimetric WSR-88D observations for verification. EnKF assimilation of radar data using a multimoment microphysics scheme for an MCS case has not previously been documented in the literature. The use of DM microphysics during data assimilation improves simulated polarimetric variables through differentiation of particle size distributions (PSDs) within the stratiform and convective regions. The DM forecast initiated from the DM analysis shows significant qualitative improvement over the assimilation and forecast using SM microphysics in terms of the location and structure of the MCS precipitation. Quantitative precipitation forecasting skills are also improved in the DM forecast. Better handling of the PSDs by the DM scheme is believed to be responsible for the improved prediction of the surface cold pool, a stronger leading convective line, and improved areal extent of stratiform precipitation.

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Improved fuzzy logic method to distinguish between meteorological and non-meteorological echoes using C-band polarimetric radar data

Atmospheric Measurement Techniques ◽

10.5194/amt-13-537-2020 ◽

2020 ◽

Vol 13 (2) ◽

pp. 537-551

Author(s):

Shuai Zhang ◽

Xingyou Huang ◽

Jinzhong Min ◽

Zhigang Chu ◽

Xiaoran Zhuang ◽

...

Keyword(s):

Fuzzy Logic ◽

Radar Data ◽

Weak Signal ◽

Post Processing ◽

Algorithm Performance ◽

Radar Echoes ◽

Fuzzy Logic Method ◽

Anomalous Propagation ◽

Logic Method

Abstract. To obtain better performance of meteorological applications, it is necessary to distinguish radar echoes from meteorological and non-meteorological targets. After a comprehensive analysis of the computational efficiency and radar system characteristics, we propose a fuzzy logic method that is similar to the MetSignal algorithm; the performance of this method is improved significantly in weak-signal regions where polarimetric variables are severely affected by noise. In addition, post-processing is adjusted to prevent anomalous propagation at a far range from being misclassified as meteorological echo. Moreover, an additional fuzzy logic echo classifier is incorporated into post-processing to suppress misclassification in the melting layer. An independent test set is selected to evaluate algorithm performance, and the statistical results show an improvement in the algorithm performance, especially with respect to the classification of meteorological echoes in weak-signal regions.

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