scholarly journals Characteristics of Raindrop Size Distribution in Typhoon Nida (2016) before and after Landfall in Southern China from 2D Video Disdrometer Data

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Lu Feng ◽  
Xiantong Liu ◽  
Hui Xiao ◽  
Liusi Xiao ◽  
Feng Xia ◽  
...  
Keyword(s):  
Size Distribution ◽  
Southern China ◽  
Spectral Width ◽  
Average Mass ◽  
Raindrop Size Distribution ◽  
The Pacific ◽  
Before And After ◽  
Precipitation Estimation ◽  
Distribution Parameters ◽  
Raindrop Size

During the passage of Typhoon Nida, the raindrop size distribution parameters, the raindrop spectra, the shape and slope (μ–Λ) relationship, the radar reflectivity factor, and rain rate (Z–R) relationship were investigated based on a two-dimensional (2D) video disdrometer in Guangdong, China, from August 1 to 2, 2016. Due to the underlying surface difference between the ocean and land, this process was divided into two distinct periods (before landfall and after landfall). The characteristics of raindrop size distribution between the period before landfall and the period after landfall were quite distinct. The period after landfall exhibited higher concentrations of each size bin (particularly small drops) and wider raindrop spectral width than the period before landfall. Compared with the period before landfall, the period after landfall had a higher average mass-weighted mean diameter Dm that was smaller than those of other TCs from the same ocean (the Pacific). The μ–Λ relationship and Z–R relationship in this study were also compared with other TCs from the same ocean (the Pacific). This investigation of the microphysical characteristics of Typhoon Nida before landfall and after landfall may improve radar quantitative precipitation estimation (QPE) products and microphysical schemes by providing useful information.

scholarly journals Observed Response of the Raindrop Size Distribution to Changes in the Melting Layer

Atmosphere ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 319 ◽  
Author(s):  
Patrick Gatlin ◽  
Walter Petersen ◽  
Kevin Knupp ◽  
Lawrence Carey
Keyword(s):  
Size Distribution ◽  
Basic Assumption ◽  
Weighted Mean ◽  
Radar Rainfall ◽  
Melting Layer ◽  
Quantitative Precipitation Estimation ◽  
Raindrop Size Distribution ◽  
Precipitation Estimation ◽  
Raindrop Size ◽  
Stratiform Rainfall

Vertical variability in the raindrop size distribution (RSD) can disrupt the basic assumption of a constant rain profile that is customarily parameterized in radar-based quantitative precipitation estimation (QPE) techniques. This study investigates the utility of melting layer (ML) characteristics to help prescribe the RSD, in particular the mass-weighted mean diameter (Dm), of stratiform rainfall. We utilize ground-based polarimetric radar to map the ML and compare it with Dm observations from the ground upwards to the bottom of the ML. The results show definitive proof that a thickening, and to a lesser extent a lowering, of the ML causes an increase in raindrop diameter below the ML that extends to the surface. The connection between rainfall at the ground and the overlying microphysics in the column provide a means for improving radar QPE at far distances from a ground-based radar or close to the ground where satellite-based radar rainfall retrievals can be ill-defined.

scholarly journals STUDY ON SUMMER RAINDROP SIZE DISTRIBUTION AND RETRIEVED POLARIMETRIC RADAR PARAMETER OVER LEIZHOU PENINSULA

2019 ◽  
Vol XLII-3/W9 ◽  
pp. 245-250
Author(s):  
Z. B. Zhou ◽  
J. J. Lv ◽  
S. J. Niu
Keyword(s):  
Size Distribution ◽  
Gamma Distribution ◽  
Least Squares Method ◽  
Convective Precipitation ◽  
Polarimetric Radar ◽  
Leizhou Peninsula ◽  
Raindrop Size Distribution ◽  
Precipitation Estimation ◽  
Raindrop Size ◽  
Subtropical Monsoon Climate

Abstract. Leizhou peninsula is located in the south of Guangdong Province, near South China Sea, and has a tropical and subtropical monsoon climate. Based on observed drop size distribution (DSD) data from July 2007 to August 2007 with PARSIVEL disdrometers deployed at Zhanjiang and Suixi, the characterists of DSDs are studied. Non-linear least squares method is used to fit Gamma distribution. Convective and stratiform averaged DSDs are in good agreement with Gamma distribution, especially in stratiform case. Convective average DSDs have a wider spectrum and higher peak. Microphysical parameter differences between convective and stratiform are discussed, convective precipitation has a higher mass-weighted mean diameter (Dm) and generalized intercepts (Nw) in both areas. The constrained relations between Gamma distribution parameter (μ, Λ, N0) is derived. The retrieved polarimetric radar parameter (KDP, ZDR, Zh) have a good self-consistency, which can be used to improve the accuracy of KDP calculation. R-KDP-ZDR is superior to the R-KDP, R-ZDR-Zh in quantitative precipitation estimation (QPE), with a correlation coefficient higher than 0.98.

scholarly journals Estimation of Raindrop Size Distribution Parameters from Polarimetric Radar Measurements

2002 ◽  
Vol 59 (15) ◽  
pp. 2373-2384 ◽  
Author(s):  
Eugenio Gorgucci ◽  
V. Chandrasekar ◽  
V. N. Bringi ◽  
Gianfranco Scarchilli
Keyword(s):  
Size Distribution ◽  
Polarimetric Radar ◽  
Raindrop Size Distribution ◽  
Distribution Parameters ◽  
Radar Measurements ◽  
Raindrop Size

A study on raindrop size distribution variability in before and after landfall precipitations of tropical cyclones observed over southern India

2017 ◽  
Vol 159 ◽  
pp. 23-40 ◽  
Author(s):  
Jayalakshmi Janapati ◽  
Balaji Kumar seela ◽  
Venkatrami Reddy M. ◽  
Krishna Reddy K. ◽  
Pay-Liam Lin ◽  
...  
Keyword(s):  
Size Distribution ◽  
Tropical Cyclones ◽  
Southern India ◽  
Raindrop Size Distribution ◽  
Before And After ◽  
Raindrop Size

Raindrop Size Distribution Parameters Retrieved from Xinfeng C-Band Polarimetric Radar Observations

2020 ◽  
Vol 26 (3) ◽  
pp. 275-285
Author(s):  
DING Yan ◽  
WAN Qi-lin ◽  
YANG Ling ◽  
LIU Xian-tong ◽  
XIA Feng ◽  
...  
Keyword(s):  
Size Distribution ◽  
Polarimetric Radar ◽  
Radar Observations ◽  
Raindrop Size Distribution ◽  
Distribution Parameters ◽  
Raindrop Size

An Inverse Mapping Table Method for Raindrop Size Distribution Parameters Retrieval Using X-band Dual-Polarization Radar Observations

2020 ◽  
Vol 58 (11) ◽  
pp. 7611-7632 ◽  
Author(s):  
Yue Sun ◽  
Hui Xiao ◽  
Huiling Yang ◽  
Liang Feng ◽  
Haonan Chen ◽  
...  
Keyword(s):  
Size Distribution ◽  
Dual Polarization ◽  
Inverse Mapping ◽  
Radar Observations ◽  
Table Method ◽  
Raindrop Size Distribution ◽  
X Band ◽  
Distribution Parameters ◽  
Raindrop Size

scholarly journals Measurements and Modeling of the Full Rain Drop Size Distribution

Atmosphere ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 39 ◽  
Author(s):  
Merhala Thurai ◽  
Viswanathan Bringi ◽  
Patrick Gatlin ◽  
Walter Petersen ◽  
Matthew Wingo
Keyword(s):  
Size Distribution ◽  
Small Drop ◽  
Dual Frequency ◽  
Raindrop Size Distribution ◽  
Precipitation Estimation ◽  
Rain Drop ◽  
Double Moment ◽  
Raindrop Size ◽  
Microphysical Processes ◽  
Global Precipitation

The raindrop size distribution (DSD) is fundamental for quantitative precipitation estimation (QPE) and in numerical modeling of microphysical processes. Conventional disdrometers cannot capture the small drop end, in particular the drizzle mode which controls collisional processes as well as evaporation. To overcome this limitation, the DSD measurements were made using (i) a high-resolution (50 microns) meteorological particle spectrometer to capture the small drop end, and (ii) a 2D video disdrometer for larger drops. Measurements were made in two climatically different regions, namely Greeley, Colorado, and Huntsville, Alabama. To model the DSDs, a formulation based on (a) double-moment normalization and (b) the generalized gamma (GG) model to describe the generic shape with two shape parameters was used. A total of 4550 three-minute DSDs were used to assess the size-resolved fidelity of this model by direct comparison with the measurements demonstrating the suitability of the GG distribution. The shape stability of the normalized DSD was demonstrated across different rain types and intensities. Finally, for a tropical storm case, the co-variabilities of the two main DSD parameters (normalized intercept and mass-weighted mean diameter) were compared with those derived from the dual-frequency precipitation radar onboard the global precipitation mission satellite.

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