The Hydrometeor Classification Algorithm for the Polarimetric WSR-88D: Description and Application to an MCS

2009 ◽  
Vol 24 (3) ◽  
pp. 730-748 ◽  
Author(s):  
Hyang Suk Park ◽  
A. V. Ryzhkov ◽  
D. S. Zrnić ◽  
Kyung-Eak Kim
Keyword(s):  
Fuzzy Logic ◽  
Classification Scheme ◽  
Mesoscale Convective System ◽  
Radar Data ◽  
Classification Algorithm ◽  
Convective System ◽  
Polarimetric Radar ◽  
The Matrix ◽  
Mesoscale Convective ◽  
Radar Measurements

Abstract This paper contains a description of the most recent version of the hydrometeor classification algorithm for polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D). This version contains several modifications and refinements of the previous echo classification algorithm based on the principles of fuzzy logic. These modifications include the estimation of confidence factors that characterize the possible impacts of all error sources on radar measurements, the assignment of the matrix of weights that characterizes the classification power of each variable with respect to every class of radar echo, and the implementation of a class designation system based on the distance from the radar and the parameters of the melting layer that are determined as functions of azimuth with polarimetric radar measurements. These additions provide considerable flexibility and improve the discrimination between liquid and frozen hydrometeors. The new classification scheme utilizes all available polarimetric variables and discerns 10 different classes of radar echoes. Furthermore, a methodology for the new fuzzy logic classification scheme is discussed and the results are illustrated using polarimetric radar data collected with the Norman, Oklahoma (KOUN), WSR-88D prototype radar during a mesoscale convective system event on 13 May 2005.

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

2014 ◽  
Vol 142 (1) ◽  
pp. 141-162 ◽  
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.

Ensemble Probabilistic Prediction of a Mesoscale Convective System and Associated Polarimetric Radar Variables Using Single-Moment and Double-Moment Microphysics Schemes and EnKF Radar Data Assimilation

2017 ◽  
Vol 145 (6) ◽  
pp. 2257-2279 ◽  
Author(s):  
Bryan J. Putnam ◽  
Ming Xue ◽  
Youngsun Jung ◽  
Nathan A. Snook ◽  
Guifu Zhang
Keyword(s):  
Mesoscale Convective System ◽  
Radar Data ◽  
Convective Precipitation ◽  
Convective System ◽  
Ensemble Forecasts ◽  
Verification Methods ◽  
Probabilistic Forecasts ◽  
Mesoscale Convective ◽  
Single Moment ◽  
Double Moment

Abstract Ensemble-based probabilistic forecasts are performed for a mesoscale convective system (MCS) that occurred over Oklahoma on 8–9 May 2007, initialized from ensemble Kalman filter analyses using multinetwork radar data and different microphysics schemes. Two experiments are conducted, using either a single-moment or double-moment microphysics scheme during the 1-h-long assimilation period and in subsequent 3-h ensemble forecasts. Qualitative and quantitative verifications are performed on the ensemble forecasts, including probabilistic skill scores. The predicted dual-polarization (dual-pol) radar variables and their probabilistic forecasts are also evaluated against available dual-pol radar observations, and discussed in relation to predicted microphysical states and structures. Evaluation of predicted reflectivity (Z) fields shows that the double-moment ensemble predicts the precipitation coverage of the leading convective line and stratiform precipitation regions of the MCS with higher probabilities throughout the forecast period compared to the single-moment ensemble. In terms of the simulated differential reflectivity (ZDR) and specific differential phase (KDP) fields, the double-moment ensemble compares more realistically to the observations and better distinguishes the stratiform and convective precipitation regions. The ZDR from individual ensemble members indicates better raindrop size sorting along the leading convective line in the double-moment ensemble. Various commonly used ensemble forecast verification methods are examined for the prediction of dual-pol variables. The results demonstrate the challenges associated with verifying predicted dual-pol fields that can vary significantly in value over small distances. Several microphysics biases are noted with the help of simulated dual-pol variables, such as substantial overprediction of KDP values in the single-moment ensemble.

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

2014 ◽  
Vol 53 (8) ◽  
pp. 2017-2033 ◽  
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.

scholarly journals Cloud Ice Crystal Classification Using a 95-GHz Polarimetric Radar

2004 ◽  
Vol 21 (11) ◽  
pp. 1679-1688 ◽  
Author(s):  
K. Aydin ◽  
J. Singh
Keyword(s):  
Fuzzy Logic ◽  
Air Temperature ◽  
Classification Algorithm ◽  
Ice Crystals ◽  
Polarimetric Radar ◽  
Ice Crystal ◽  
Radar Measurements ◽  
Particle Images ◽  
Cloud Ice ◽  
Range Of Values

Abstract Two algorithms are presented for ice crystal classification using 95-GHz polarimetric radar observables and air temperature (T). Both are based on a fuzzy logic scheme. Ice crystals are classified as columnar crystals (CC), planar crystals (PC), mixtures of PC and small- to medium-sized aggregates and/or lightly to moderately rimed PC (PSAR), medium- to large-sized aggregates of PC, or densely rimed PC, or graupel-like snow or small lumpy graupel (PLARG), and graupel larger than about 2 mm (G). The 1D algorithm makes use of Zh, Zdr, LDRhv, and T, while the 2D algorithm incorporates the three radar observables in pairs, (Zdr, Zh), (LDRhv, Zh), and (Zdr, LDRhv), plus the temperature T. The range of values for each observable or pair of observables is derived from extensive modeling studies conducted earlier. The algorithms are tested using side-looking radar measurements from an aircraft, which was also equipped with particle probes producing simultaneous and nearly collocated shadow images of cloud ice crystals. The classification results from both algorithms agreed very well with the particle images. The two algorithms were in agreement by 89% in one case and 97% in the remaining three cases considered here. The most effective observable in the 1D algorithm was Zdr, and in the 2D algorithm the pair (Zdr, Zh). LDRhv had negligible effect in the 1D classification algorithm for the cases considered here. The temperature T was mainly effective in separating columnar crystals from the rest. The advantage of the 2D algorithm over the 1D algorithm was that it significantly reduced the dependence on T in two out of the four cases.

scholarly journals Identifikasi Karakteristik Quasi-Linear Convective System Pada Maret Sampai Mei 2017 Di Wilayah Pangkalan Bun Berbasis Radar Cuaca

2019 ◽  
Vol 4 ◽  
pp. 110
Author(s):  
Meldisa Putri Maulidyah ◽  
Rossian Nursiddiq Islamiardi ◽  
Rezky Fajar Maulana ◽  
Kristian Adi Putra Tamba ◽  
Imma Redha Nugraheni ◽  
...  
Keyword(s):  
Coastal Plain ◽  
Descriptive Analysis ◽  
Mesoscale Convective System ◽  
Heavy Rain ◽  
Radar Data ◽  
Horizontal Wind ◽  
Convective System ◽  
Raw Data ◽  
Mesoscale Convective ◽  
Strong Winds

<p><strong>Abstract: </strong>Quasi Linear Convective System (QLCS) is one of the phenomena of meso-scale convective weather systems (MCS), which are linear in shape with an unspecified leftime and potentially bad weather in the form of heavy rain and strong winds. This research will identify, analyze, and characterize QLCS in the Pangkalan Bun region, Central Kalimantan, as a research location with a period of March to May 2017 using raw data radar data base of Pangkalanbun type C-Band single polarization type Selex SI Gematronik. Method of research was conducted in a descriptive analysis with a description of the QLCS temporally and spatially. The results showed the most duration was 30-60 minutes. The location of the QLCS formation is dominant in the coastal plain or lowland areas. The type of formation of QLCS is dominant broken line.</p><p><strong>Abstrak: </strong>Quasi Linear Convective System (QLCS) merupakan salah satu fenomena dari sistem cuaca konvektif skala meso atau Mesoscale Convective System (MCS) yang berbentuk linear dengan masa hidup tidak ditentukan dan berpotensi cuaca buruk berupa hujan lebat dan angin kencang. Pada penelitian ini akan mengidentifikasi, menganalisis, dan mengarakteristikan QLCS di wilayah cakupan radar Pangkalan Bun, Kalimantan Tengah sebagai lokasi penelitian dengan jangka waktu bulan Maret sampai Mei tahun 2017 menggunakan raw data radar cuaca Pangkalan Bun tipe C-Band jenis polarisasi tunggal Selex SI Gematronik. Metode yang dilakukan dalam penelitian ini adalah analisis deskriptif produk Column Max (CMAX), Combined Moment (CM), Strom Structure Analysis (SSA), Severe Weather Indicator (SWI), dan Horizontal WInd (HWIND). Hasil penelitian menunjukkan durasi pembentukan QLCS terbanyak terjadi dalam rentang 30-60 menit dengan lokasi pembentukan QLCS dominan pada area coastal plain atau dataran rendah. Tipe pembentukan QLCS dominan broken line dan banyak terjadi di pagi hari.</p>

scholarly journals Observation of Quasi-2-Day Convective Disturbances in the Equatorial Indian Ocean during DYNAMO

2018 ◽  
Vol 75 (9) ◽  
pp. 2867-2888 ◽  
Author(s):  
Hungjui Yu ◽  
Richard H. Johnson ◽  
Paul E. Ciesielski ◽  
Hung-Chi Kuo
Keyword(s):  
Indian Ocean ◽  
System Development ◽  
Equatorial Indian Ocean ◽  
Mesoscale Convective System ◽  
Radar Data ◽  
Distinct Pattern ◽  
Convective System ◽  
Zonal Scale ◽  
Central Indian Ocean ◽  
Mesoscale Convective

Abstract This study examines the westward-propagating convective disturbances with quasi-2-day intervals of occurrence identified over Gan Island in the central Indian Ocean from mid- to late October 2011 during the Dynamics of the Madden–Julian Oscillation (DYNAMO) field campaign. Atmospheric sounding, satellite, and radar data are used to develop a composite of seven such disturbances. Composites and spectral analyses reveal that 1) the quasi-2-day convective events comprise westward-propagating diurnal convective disturbances with phase speeds of 10–12 m s−1 whose amplitudes are modulated on a quasi-2-day time scale on a zonal scale of ~1000 km near the longitudes of Gan; 2) the cloud life cycle of quasi-2-day convective disturbances shows a distinct pattern of tropical cloud population evolution—from shallow to deep to stratiform convection; 3) the time scales of mesoscale convective system development and boundary layer modulation play essential roles in determining the periodicity of the quasi-2-day convective events; and 4) in some of the quasi-2-day events there is evidence of counterpropagating (westward and eastward) cloud systems along the lines proposed by Yamada et al. Based on these findings, an interpretation is proposed for the mechanisms for the quasi-2-day disturbances observed during DYNAMO that combines concepts from prior studies of this phenomenon over the western Pacific and Indian Oceans.

Morphology, Intensity, and Rainfall Production of MJO Convection: Observations from DYNAMO Shipborne Radar and TRMM

2015 ◽  
Vol 72 (2) ◽  
pp. 623-640 ◽  
Author(s):  
Weixin Xu ◽  
Steven A. Rutledge
Keyword(s):  
Mesoscale Convective System ◽  
Tropical Rainfall Measuring Mission ◽  
Radar Data ◽  
Convective System ◽  
Precipitation Radar ◽  
Convective Systems ◽  
Trmm Pr ◽  
Mesoscale Convective ◽  
Trmm Precipitation

Abstract This study uses Dynamics of the Madden–Julian Oscillation (DYNAMO) shipborne [Research Vessel (R/V) Roger Revelle] radar and Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) datasets to investigate MJO-associated convective systems in specific organizational modes [mesoscale convective system (MCS) versus sub-MCS and linear versus nonlinear]. The Revelle radar sampled many “climatological” aspects of MJO convection as indicated by comparison with the long-term TRMM PR statistics, including areal-mean rainfall (6–7 mm day−1), convective intensity, rainfall contributions from different morphologies, and their variations with MJO phase. Nonlinear sub-MCSs were present 70% of the time but contributed just around 20% of the total rainfall. In contrast, linear and nonlinear MCSs were present 10% of the time but contributed 20% and 50%, respectively. These distributions vary with MJO phase, with the largest sub-MCS rainfall fraction in suppressed phases (phases 5–7) and maximum MCS precipitation in active phases (phases 2 and 3). Similarly, convective–stratiform rainfall fractions also varied significantly with MJO phase, with the highest convective fractions (70%–80%) in suppressed phases and the largest stratiform fraction (40%–50%) in active phases. However, there are also discrepancies between the Revelle radar and TRMM PR. Revelle radar data indicated a mean convective rain fraction of 70% compared to 55% for TRMM PR. This difference is mainly due to the reduced resolution of the TRMM PR compared to the ship radar. There are also notable differences in the rainfall contributions as a function of convective intensity between the Revelle radar and TRMM PR. In addition, TRMM PR composites indicate linear MCS rainfall increases after MJO onset and produce similar rainfall contributions to nonlinear MCSs; however, the Revelle radar statistics show the clear dominance of nonlinear MCS rainfall.

Relationships between Lightning Location and Polarimetric Radar Signatures in a Small Mesoscale Convective System

2009 ◽  
Vol 137 (12) ◽  
pp. 4151-4170 ◽  
Author(s):  
Nicole R. Lund ◽  
Donald R. MacGorman ◽  
Terry J. Schuur ◽  
Michael I. Biggerstaff ◽  
W. David Rust
Keyword(s):  
Mesoscale Convective System ◽  
Convective System ◽  
Polarimetric Radar ◽  
Mature Phase ◽  
Mesoscale Convective ◽  
Mapping Array ◽  
Lightning Initiation ◽  
Convective Cells ◽  
Differential Reflectivity ◽  
Kinematic Properties

Abstract On 19 June 2004, the Thunderstorm Electrification and Lightning Experiment observed electrical, microphysical, and kinematic properties of a small mesoscale convective system (MCS). The primary observing systems were the Oklahoma Lightning Mapping Array, the KOUN S-band polarimetric radar, two mobile C-band Doppler radars, and balloonborne electric field meters. During its mature phase, this MCS had a normal tripolar charge structure (lightning involved a midlevel negative charge between an upper and a lower positive charge), and flash rates fluctuated between 80 and 100 flashes per minute. Most lightning was initiated within one of two altitude ranges (3–6 or 7–10 km MSL) and within the 35-dBZ contours of convective cells embedded within the convective line. The properties of two such cells were investigated in detail, with the first lasting approximately 40 min and producing only 12 flashes and the second lasting over an hour and producing 105 flashes. In both, lightning was initiated in or near regions containing graupel. The upper lightning initiation region (7–10 km MSL) was near 35–47.5-dBZ contours, with graupel inferred below and ice crystals inferred above. The lower lightning initiation region (3–6 km MSL) was in the upper part of melting or freezing layers, often near differential reflectivity columns extending above the 0°C isotherm, which is suggestive of graupel formation. Both lightning initiation regions are consistent with what is expected from the noninductive graupel–ice thunderstorm electrification mechanism, though inductive processes may also have contributed to initiations in the lower region.

scholarly journals Evaluation of Different Storm Parameters as the Proxies for Gridded Total Lightning Flash Rates: A Convection-Allowing Model Study

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 95
Author(s):  
Xinyao Qian ◽  
Haoliang Wang
Keyword(s):  
Data Assimilation ◽  
Mesoscale Convective System ◽  
Radar Data ◽  
Skill Score ◽  
Convective System ◽  
Test Case ◽  
Lightning Flash ◽  
Simulation Accuracy ◽  
Mesoscale Convective ◽  
Total Lightning

Lightning simulation is important for a variety of applications, including lightning forecast, atmospheric chemical simulation, and lightning data assimilation. In this study, the potential of five storm parameters (graupel volume, precipitation ice mass, radar echo volume, maximum updraft, and updraft volume) to be used as the proxy for the diagnosis of gridded total lightning flash rates has been investigated in a convection-allowing model. A mesoscale convective system occurred in the Guangdong province of China was selected as the test case. Radar data assimilation was used to improve the simulation accuracy of the convective clouds, hence providing strong instantaneous correlations between observed and simulated storm signatures. The areal coverage and magnitude of the simulated lightning flash rates were evaluated by comparing to those of the total lightning observations. Subjective and the Fractions Skill Score (FSS) evaluations suggest that all the five proxies tested in this study are useful to indicate general tendencies for the occurrence, region, and time of lightning at convection-allowing scale (FSS statistics for the threshold of 1 flash per 9 km2 per hour were around 0.7 for each scheme). The FSS values were decreasing as the lightning flash rate thresholds used for FSS computation increased for all the lightning diagnostic schemes with different proxies. For thresholds from 1 to 3 and 16 to 20 flashes per 9 km2 per hour, the graupel contents related schemes achieved higher FSS values compared to the other three schemes. For thresholds from 5 to 15 flashes per 9 km2 per hour, the updraft volume related scheme yielded the largest FSS. When the thresholds of lightning flash rates were greater than 13 flashes per 9 km2 per hour, the FSS values were below 0.5 for all the lightning diagnostic schemes with different proxies.

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