Evaluating Simulated Raindrop Size Distributions and Ice Microphysical Processes with Polarimetric Radar Observations in a Meiyu Front Event over Eastern China

2021 ◽  
Author(s):  
Gang Chen ◽  
Kun Zhao ◽  
Hao Huang ◽  
Zhengwei Yang ◽  
Yinghui Lu ◽  
...  
Keyword(s):  
Eastern China ◽  
Size Distributions ◽  
Polarimetric Radar ◽  
Radar Observations ◽  
Meiyu Front ◽  
Raindrop Size ◽  
Microphysical Processes

scholarly journals Reflectivity and Liquid Water Content Vertical Decomposition Diagrams to Diagnose Vertical Evolution of Raindrop Size Distributions

2016 ◽  
Vol 33 (3) ◽  
pp. 579-595 ◽  
Author(s):  
Christopher R. Williams
Keyword(s):  
Water Content ◽  
Liquid Water ◽  
Liquid Water Content ◽  
Doppler Velocity ◽  
Size Distributions ◽  
Vertical Column ◽  
Vertical Decomposition ◽  
Air Motion ◽  
Raindrop Size ◽  
Microphysical Processes

AbstractThis study consists of two parts. The first part describes the way in which vertical air motions and raindrop size distributions (DSDs) were retrieved from 449-MHz and 2.835-GHz (UHF and S band) vertically pointing radars (VPRs) deployed side by side during the Midlatitude Continental Convective Clouds Experiment (MC3E) held in northern Oklahoma. The 449-MHz VPR can measure both vertical air motion and raindrop motion. The S-band VPR can measure only raindrop motion. These differences in VPR sensitivities facilitates the identification of two peaks in 449-MHz VPR reflectivity-weighted Doppler velocity spectra and the retrieval of vertical air motion and DSD parameters from near the surface to just below the melting layer.The second part of this study used the retrieved DSD parameters to decompose reflectivity and liquid water content (LWC) into two terms, one representing number concentration and the other representing DSD shape. Reflectivity and LWC vertical decomposition diagrams (Z-VDDs and LWC-VDDs, respectively) are introduced to highlight interactions between raindrop number and DSD shape in the vertical column. Analysis of Z-VDDs provides indirect measure of microphysical processes through radar reflectivity. Analysis of LWC-VDDs provides direct investigation of microphysical processes in the vertical column, including net raindrop evaporation or accretion and net raindrop breakup or coalescence. During a stratiform rain event (20 May 2011), LWC-VDDs exhibited signatures of net evaporation and net raindrop coalescence as the raindrops fell a distance of 2 km under a well-defined radar bright band. The LWC-VDD is a tool to characterize rain microphysics with quantities related to number-controlled and size-controlled processes.

Precipitation microphysics characteristics of a Typhoon Matmo (2014) rainband after landfall over eastern China based on polarimetric radar observations

2016 ◽  
Vol 121 (20) ◽  
pp. 12,415-12,433 ◽  
Author(s):  
Mingjun Wang ◽  
Kun Zhao ◽  
Ming Xue ◽  
Guifu Zhang ◽  
Su Liu ◽  
...  
Keyword(s):  
Eastern China ◽  
Polarimetric Radar ◽  
Radar Observations

Classification of Precipitation Types during Transitional Winter Weather Using the RUC Model and Polarimetric Radar Retrievals

2012 ◽  
Vol 51 (4) ◽  
pp. 763-779 ◽  
Author(s):  
Terry J. Schuur ◽  
Hyang-Suk Park ◽  
Alexander V. Ryzhkov ◽  
Heather D. Reeves
Keyword(s):  
Numerical Models ◽  
Radar Data ◽  
Storm Event ◽  
Model Output ◽  
Polarimetric Radar ◽  
Radar Observations ◽  
Precipitation Type ◽  
Microphysical Processes ◽  
Using Data

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.

scholarly journals Characteristics of Raindrop Size Distributions during Meiyu Season in Mount Lushan, Eastern China

2022 ◽  
Author(s):  
Yi CHANG ◽  
Qianrong MA ◽  
Lijun GUO ◽  
Jing DUAN ◽  
Jun LI ◽  
...  
Keyword(s):  
Eastern China ◽  
Size Distributions ◽  
Raindrop Size ◽  
Meiyu Season

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 Raindrop Size Distribution Parameters Retrieved from Guangzhou S-band Polarimetric Radar Observations

2018 ◽  
Vol 32 (4) ◽  
pp. 571-583 ◽  
Author(s):  
Xiantong Liu ◽  
Qilin Wan ◽  
Hong Wang ◽  
Hui Xiao ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Size Distribution ◽  
Polarimetric Radar ◽  
Radar Observations ◽  
Raindrop Size Distribution ◽  
Distribution Parameters ◽  
Raindrop Size
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