Observed Bulk Hook Echo Drop Size Distribution Evolution in Supercell Tornadogenesis and Tornadogenesis Failure

2021 ◽  
Author(s):  
Kristofer S. Tuftedal ◽  
Michael M. French ◽  
Darrel M. Kingfield ◽  
Jeffrey C. Snyder
Keyword(s):  
Drop Size ◽  
Thermodynamic Characteristics ◽  
Radar Data ◽  
Number Concentration ◽  
Size Distributions ◽  
Polarimetric Radar ◽  
Dual Polarization ◽  
Near Surface ◽  
Microphysical Processes ◽  
Drop Size Distributions

AbstractThe time preceding supercell tornadogenesis and tornadogenesis “failure” has been studied extensively to identify differing attributes related to tornado production or lack thereof. Studies from the Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX) found that air in the rear-flank downdraft (RFD) regions of non- and weakly tornadic supercells had different near-surface thermodynamic characteristics than that in strongly tornadic supercells. Subsequently, it was proposed that microphysical processes are likely to have an impact on the resulting thermodynamics of the near-surface RFD region. One way to view proxies to microphysical features, namely drop size distributions (DSDs), is through use of polarimetric radar data. Studies from the second VORTEX used data from dual-polarization radars to provide evidence of different DSDs in the hook echoes of tornadic and non-tornadic supercells. However, radar-based studies during these projects were limited to a small number of cases preventing result generalizations. This study compiles 68 tornadic and 62 non-tornadic supercells using Weather Surveillance Radar–1988 Doppler (WSR-88D) data to analyze changes in polarimetric radar variables leading up to, and at, tornadogenesis and tornadogenesis failure. Case types generally did not show notable hook echo differences in variables between sets, but did show spatial hook echo quadrant DSD differences. Consistent with past studies, differential radar reflectivity factor (ZDR) generally decreased leading up to tornadogenesis and tornadogenesis failure; in both sets, estimated total number concentration increased during the same times. Relationships between DSDs and the near-storm environment, and implications of results for nowcasting tornadogenesis, also are discussed.

scholarly journals Drop Size Distributions Measured by a 2D Video Disdrometer: Comparison with Dual-Polarization Radar Data

2001 ◽  
Vol 40 (6) ◽  
pp. 1019-1034 ◽  
Author(s):  
Terry J. Schuur ◽  
Alexander V. Ryzhkov ◽  
Dusan S. Zrnić ◽  
Michael Schönhuber
Keyword(s):  
Drop Size ◽  
Radar Data ◽  
Size Distributions ◽  
Dual Polarization ◽  
Drop Size Distributions

scholarly journals Comparison of dual-polarization radar-derived and disdrometer drop-size distributions with S- and X- band radars

2017 ◽  
Author(s):  
◽  
Jordan A. Wendt
Keyword(s):  
Drop Size ◽  
Radar Data ◽  
Precipitation Event ◽  
Drop Diameter ◽  
Size Distributions ◽  
Dual Polarization ◽  
X Band ◽  
The Difference ◽  
Drop Size Distributions ◽  
Differential Reflectivity

There have been many studies on the evaluations of drop-size distributions and the parameters that affect these distributions, however, few, if any, have directly compared the relationship between the radar-derived parameters and those parameters that are disdrometer-derived. This study focuses on many different features of thunderstorms that changes the structure of the drop-size distribution (DSD) including: Horizontal reflectivity (ZH), differential reflectivity (ZDR), median drop diameter (D0), the shape parameter of the gamma-distributed DSD ([mu]), and the slope parameter of the gamma-distributed DSD (lambda). This work compares data collected by two disdrometers (OTT PARSIVEL and the Campbell Scientific Present Weather Sensor 100) against DSD parameters derived from dual-polarization radar observations. Using the Warning Decision Support System-Integrated Information (WDSS-II), radar data was merged at 1-km resolution to account for the movement of the precipitation systems before comparing to the 10-minute disdrometer data intervals. It was found that to accurately estimate DSDs from the perspective of using a weather radar, a larger precipitation event is needed. At the beginning and end of a precipitation event the difference between the radar retrieved values of D0, [mu], and [lambda] and those sampled by the disdrometer were much greater than during the middle of the event. Throughout the majority of the cases, the radar-derived reflectivity values were consistently lower than those collected by the disdrometers.

Drop deformation estimate with multi-polarization radar

2020 ◽  
Vol 12 (9) ◽  
pp. 870-877
Author(s):  
Yuliya Averyanova ◽  
Anna Rudiakova ◽  
Felix Yanovsky
Keyword(s):  
Drop Size ◽  
Mathematical Expression ◽  
Microwave Remote Sensing ◽  
Radar Signal ◽  
Drop Deformation ◽  
Size Distributions ◽  
Polarimetric Radar ◽  
Energy Redistribution ◽  
Drop Size Distributions ◽  
Radar Antenna

AbstractThis paper considers the ability of polarization measurements for microwave remote sensing of clouds and precipitation. The simulation of reflections from liquid hydrometeors with a multi-polarization radar system is presented. The mathematical expression of energy received by a radar antenna with arbitrary polarization is obtained. The simulation of the energy redistribution of the signal reflected from liquid hydrometeors assembled over the antennas of multi-polarimetric radar for different wind conditions and different drop-size distributions is obtained and analyzed. The simulation results demonstrate the possibility to register wind and wind-related phenomena by polarimetric radar. The results of the paper can also be used to exclude an impact of drop vibration or oscillation into the radar signal to eliminate errors and underestimation during parameter measurements. The approach to segregate the reflected signal magnitude variations due to the wind-related phenomena from other factors is discussed.

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

2020 ◽  
Vol 12 (4) ◽  
pp. 642 ◽  
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.

scholarly journals Testing the Drop-Size Distribution-Based Separation of Stratiform and Convective Rain Using Radar and Disdrometer Data from a Mid-Latitude Coastal Region

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 392
Author(s):  
Merhala Thurai ◽  
Viswanathan Bringi ◽  
David Wolff ◽  
David Marks ◽  
Charanjit Pabla
Keyword(s):  
Drop Size ◽  
Coastal Region ◽  
Size Distributions ◽  
Weighted Mean ◽  
Convective Rain ◽  
Microphysical Processes ◽  
Drop Size Distributions ◽  
Using Data ◽  
Improved Technique ◽  
Different Characteristics

Stratiform and convective rain are associated with different microphysical processes and generally produce drop-size distributions (DSDs) with different characteristics. Previous studies using data from (a) a tropical coastal location, (b) a mid-latitude continental location with semi-arid climate, and (c) a sub-tropical continental location, found that the two rain types could be separated in the NW–Dm space, where Dm is the mass-weighted mean diameter and NW is the normalized intercept parameter. In this paper, we investigate the same separation technique using data and observations from a mid-latitude coastal region. Three-minute DSDs from disdrometer measurements are used for the NW- versus Dm-based classification and are compared with simultaneous observations from an S-band polarimetric radar 38 km away from the disdrometer site. Specifically, RHI (range-height indicator) scans over the disdrometer were used for confirmation. Results show that there was no need to modify the separation criteria from previous studies. Three-minute DSDs from the same location were used as input to scattering calculations to derive retrieval equations for NW and Dm for the S-band radar using an improved technique and applied to the RHI scans to identify convective and stratiform rain regions. Two events are shown as illustrative examples.

scholarly journals Testing the Drop-Size Distribution Based Separation of Stratiform and Convective Rain Using Radar and Disdrometer Data from a Midlatitude Coastal Region

2020 ◽  
Vol 4 (1) ◽  
pp. 13
Author(s):  
Merhala Thurai ◽  
Viswanathan Bringi ◽  
David Wolff ◽  
David Marks ◽  
Charanjit Pabla
Keyword(s):  
Drop Size ◽  
Coastal Region ◽  
Size Distributions ◽  
Weighted Mean ◽  
Convective Rain ◽  
Mean Diameter ◽  
Microphysical Processes ◽  
Drop Size Distributions ◽  
Using Data ◽  
Different Characteristics

Stratiform and convective rain are associated with different microphysical processes and generally produce drop-size distributions (DSDs) with different characteristics. A previous study, using data from a tropical coastal location found that the two rain types could be separated in the NW–Dm space, where Dm is the mass-weighted mean diameter and NW is the normalized intercept parameter. The separation method has also been tested using data and observations from a midlatitude continental location with semiarid climate, and a subtropical continental location. In this paper, we investigate the same separation technique using data and observations from a midlatitude coastal region. Three-minute DSDs from disdrometer measurements were used for the NW versus Dm based classification and were compared with simultaneous observations from an S-band polarimetric radar 38 km away from the disdrometer site. Specifically, range-height indicator (RHI) scans over the disdrometer were used for confirmation. The results showed that there was no need to modify the separation criteria from previous studies. Scattering calculations using the three-minute DSDs were used to derive retrieval equations for Nw and Dm for the S-band radar and applied to the RHI scans to identify convective and stratiform rain regions. Two events are shown as illustrative examples.

scholarly journals Sources of Errors in Rainfall Measurements by Polarimetric Radar: Variability of Drop Size Distributions, Observational Noise, and Variation of Relationships between R and Polarimetric Parameters

2006 ◽  
Vol 23 (8) ◽  
pp. 1005-1028 ◽  
Author(s):  
Gyu Won Lee
Keyword(s):  
Measurement Errors ◽  
Drop Size ◽  
Measurement Noise ◽  
Size Distributions ◽  
Polarimetric Radar ◽  
Local Climate ◽  
Temporal Smoothing ◽  
Temporal Averaging ◽  
Observational Noise ◽  
Drop Size Distributions

Abstract Using a set of long-term disdrometric data and of actual radar measurements from the McGill S-band operational polarimetric radar, several sources of errors in rain measurement with polarimetric radar are explored in order to investigate their relative importance and the feasibility of a polarimetric technique for estimating R in the context of the McGill S-band operational radar that performs a full volume scan of 24 plan position indicators (PPIs) every 5 min. The sources of errors considered are the variability of drop size distributions (DSDs), observational noise, and systematic variation of the relationships between R and polarimetric parameters at different climate regimes. Additional polarimetric parameters dramatically reduce the effect of the DSD variability on rain estimates by radar. The effectiveness of various multiparameter relationships is investigated. The relationships from the literature that are derived from the DSD model and measured DSDs at a different climate regime differ from those derived from the disdrometric dataset herein. An application of these relationships to the Montreal dataset results in a bias (about 10%–20%) and the significant random error resulting from the DSD variability. These errors should be eliminated by using a relationship suitable for the local climate. Assuming a measurement noise as expected from a slow scanning polarimetric radar [∼1 rotation per minute (rpm)] and a 10-min smoothing, the R − (Zh, ZDR) relationship outperforms the conventional R − Zh because of the combined effect of the DSD variability and measurement errors. In addition, the marginal measurement noise that is required to have the same accuracy of R − Zh and R − (Zh, ZDR) algorithms is obtained as a function of temporal smoothing. The quantified measurement noise of the McGill S-band fast scanning operational radar (∼6 rpm) is significantly larger than that of a slow scanning radar, implying that a temporal averaging of ZDR of 1 h is needed to achieve some gain with R − (Zh, ZDR).

Sign in / Sign up

Export Citation Format

Share Document