Dual-Polarization Radar Retrievals of Coastal Pacific Northwest Raindrop Size Distribution Parameters Using Random Forest Regression

2020 ◽  
Vol 37 (2) ◽  
pp. 229-242 ◽  
Author(s):  
Robert Conrick ◽  
Joseph P. Zagrodnik ◽  
Clifford F. Mass
Keyword(s):  
Random Forest ◽  
Size Distribution ◽  
Pacific Northwest ◽  
Liquid Water Content ◽  
Random Forest Regression ◽  
Raindrop Size Distribution ◽  
Median Volume ◽  
Distribution Parameters ◽  
Rate Improvement ◽  
Raindrop Size

AbstractRadar retrievals of drop size distribution (DSD) parameters are developed and evaluated over the mountainous Olympic Peninsula of Washington State. The observations used to develop retrievals were collected during the 2015/16 Olympic Mountain Experiment (OLYMPEX) and included the NASA S-band dual-polarimetric (NPOL) radar and a collection of second-generation Particle Size and Velocity (PARSIVEL2) disdrometers over the windward slopes of the barrier. Nonlinear and random forest regressions are applied to the PARSIVEL2 data to develop retrievals for median volume diameter, liquid water content, and rain rate. Improvement in DSD retrieval accuracy, defined by the mean error of the retrieval relative to PARSIVEL2 observations, was achieved when using the random forest model when compared with nonlinear regression. Evaluation of disdrometer observations and the retrievals from NPOL indicate that the radar retrievals can accurately reproduce observed DSDs in this region, including the common wintertime regime of small but numerous raindrops that is important there. NPOL retrievals during the OLYMPEX period are further evaluated using two-dimensional video disdrometers (2DVD) and vertically pointing Micro Rain Radars. Results indicate that radar retrievals using random forests may be skillful in capturing DSD characteristics in the lowest portions of the atmosphere.

scholarly journals Wind Effects on the Shape of Raindrop Size Distribution

2017 ◽  
Vol 18 (5) ◽  
pp. 1285-1303 ◽  
Author(s):  
Firat Y. Testik ◽  
Bin Pei
Keyword(s):  
Wind Speed ◽  
Size Distribution ◽  
Liquid Water Content ◽  
Shape Evolution ◽  
Wind Effects ◽  
Breakup Process ◽  
Radar Rainfall ◽  
Wind Speeds ◽  
Raindrop Size Distribution ◽  
Raindrop Size

Abstract The wind effects on the shape of drop size distribution (DSD) and the driving microphysical processes for the DSD shape evolution were investigated using the dataset from the Midlatitude Continental Convective Clouds Experiment (MC3E). The quality-controlled DSD spectra from MC3E were grouped for each of the rainfall events by considering the precipitation type (stratiform vs convective) and liquid water content for the analysis. The DSD parameters (e.g., mass-weighted mean diameter) and the fitted DSD slopes for these grouped spectra showed statistically significant trends with varying wind speed. Increasing wind speeds were observed to modify the DSD shapes by increasing the number of small drops and decreasing the number of large drops, indicating that the raindrop breakup process governs the DSD shape evolution. Both spontaneous and collisional raindrop breakup modes were analyzed to elucidate the process responsible for the DSD shape evolution with varying wind speed. The analysis revealed that the collisional breakup process controls the wind-induced DSD shape. The findings of this study are of importance in DSD parameterizations that are essential to a wide variety of applications such as radar rainfall retrievals and hydrologic models.

Estimation of raindrop size distribution parameters by maximum likelihood and L-moment methods: Effect of discretization

2012 ◽  
Vol 112 ◽  
pp. 1-11 ◽  
Author(s):  
Marzuki ◽  
Walter L. Randeu ◽  
Toshiaki Kozu ◽  
Toyoshi Shimomai ◽  
Michael Schönhuber ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Size Distribution ◽  
Moment Methods ◽  
Raindrop Size Distribution ◽  
Distribution Parameters ◽  
Raindrop Size

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.

Vertical Variability of the Raindrop Size Distribution in Typhoons Observed at the Shenzhen 356-m Meteorological Tower

2020 ◽  
Vol 77 (12) ◽  
pp. 4171-4187
Author(s):  
Baojun Chen ◽  
Jun Yang ◽  
Ruiquan Gao ◽  
Keping Zhu ◽  
Chungen Zou ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Size Distribution ◽  
Weighted Mean ◽  
Meteorological Tower ◽  
Raindrop Size Distribution ◽  
Mean Diameter ◽  
Distribution Parameters ◽  
Low Altitude ◽  
Raindrop Size ◽  
Different Altitudes

AbstractRaindrop size distribution (DSD) characteristics at various altitudes in two landfalling typhoons in 2017 (Hato and Pakhar) were investigated by using laser-optical disdrometers mounted at four altitudes (10, 40, 160, and 320 m) of the Shenzhen 356-m meteorological tower. Significant differences of the DSD and derived parameters, mass-weighted mean diameter (Dm), normalized intercept parameter (NW), and standard deviation of the mass distribution σm, were observed at different altitudes for the two typhoons, while the rainwater content between the four altitudes had no statistically significant differences. The low-altitude DSDs had more midsize drops (1 < D < 3 mm), fewer large drops (D > 3 mm), and narrower distribution widths than the high-altitude ones, while the concentration of small drops varied nonlinearly with height. The value of NW decreased with height, while Dm and σm increased with height. The gamma distribution parameters N0, μ, and Λ are found to increase with decreasing height. Both the derived μ–Λ and Z–R relations were significantly varied in different altitudes.

scholarly journals An Inverse Model for Raindrop Size Distribution Retrieval with Polarimetric Variables

2018 ◽  
Vol 10 (8) ◽  
pp. 1179 ◽  
Author(s):  
Guang Wen ◽  
Haonan Chen ◽  
Guifu Zhang ◽  
Jiming Sun
Keyword(s):  
Size Distribution ◽  
Liquid Water Content ◽  
Radar Data ◽  
Inverse Model ◽  
Training Data ◽  
Training Dataset ◽  
Drop Diameter ◽  
Raindrop Size Distribution ◽  
Raindrop Size ◽  
Nearest Neighborhood

This paper proposes an inverse model for raindrop size distribution (DSD) retrieval with polarimetric radar variables. In this method, a forward operator is first developed based on the simulations of monodisperse raindrops using a T-matrix method, and then approximated with a polynomial function to generate a pseudo training dataset by considering the maximum drop diameter in a truncated Gamma model for DSD. With the pseudo training data, a nearest-neighborhood method is optimized in terms of mass-weighted diameter and liquid water content. Finally, the inverse model is evaluated with simulated and real radar data, both of which yield better agreement with disdrometer observations compared to the existing Bayesian approach. In addition, the rainfall rate derived from the DSD by the inverse model is also improved when compared to the methods using the power-law relations.

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