Investigation of liquid cloud microphysical properties of deep convective systems: 1. Parameterization raindrop size distribution and its application for stratiform rain estimation

Abstract

To investigate liquid‐phase (T > 3°C) cloud and precipitation microphysical properties within Deep Convective Systems (DCSs), eight DCS cases sampled by the University of North Dakota Citation II research aircraft during Midlatitude Continental Convective Clouds Experiment were selected. A full spectrum of raindrop size distribution (DSD) was constructed from 120 µm to 4000 µm through a combination of two‐dimensional cloud probe (120 to 900 µm) and High Volume Precipitation Spectrometer (900 to 4000 µm) data sets. A total of 1126 five second DSDs have been used to fit to Gamma and Exponential functions within the stratiform rain (SR) regions of DCSs. The Gamma shape μΓ and slope λΓ parameters are then compared with those derived from surface disdrometer measurements. The similar μΓ‐λΓ relationships but different μΓ and λΓ value ranges from two independent platforms at different elevations may represent the real nature of DSD shape information in clouds and at the surface. To apply the exponentially fitted DSD parameters to precipitation estimation using Next Generation Weather Radar (NEXRAD) radar reflectivity factor Ze, the terms N0E and λE have been parameterized as a function of Ze using an empirical N0E‐λE relationship. The averaged SR rain rate retrieved from this study is almost identical to the surface measurements, while the NEXRAD Q2 precipitation is twice as large. The comparisons indicate that the new DSD parameterization scheme is robust, while the Q2 SR precipitation estimation based on Marshall‐Palmer Z‐R relationship, where a constant DSD intercept parameter (N0E) was assumed, needs to be improved for heavy precipitation cases.

Vertical Distributions of Raindrops and Z‐R Relationships Using Microrain Radar and 2‐D‐Video Distrometer Measurements During the Integrative Monsoon Frontal Rainfall Experiment (IMFRE)

“…The vertical variations of the DSDs in Figure suggest different Z‐R relationships at different heights for the LR, SR, and CR, the same results found in Steiner et al () and Wang et al (). Jash et al () showed significant variation in the coefficient ( a ) and exponent ( b ) of the Z‐R relationships with height using MRR measurements.…”

“…At height of 200 m, the estimated Z‐R relationship for the CR category from the MRR measurements is Z = 284 R 1.36 , which is slightly different from the default relationship of Z = 300 R 1.4 for summer convection (Fulton et al, ). The rainfall estimates, using the default relationship, are underestimated when compared with the MRR estimates, which contradicts the findings of Wang et al () over the Southern Great Plains of the United States. Similarly, the traditional Z = 200 R 1.6 used by Marshall and Palmer () for continental SR may slightly underestimate the real rainfall when compared to the new relationship of Z = 154 R 1.67 found in this study.…”

“…). The rainfall estimates, using the default relationship, are underestimated when compared with the MRR estimates, which contradicts the findings ofWang et al (2016) over the Southern Great Plains of the United States. Similarly, the traditional Z = 200 R 1.6 used byMarshall and Palmer (1948) for continental SR may slightly underestimate the real rainfall when compared to the new relationship of Z = 154 R 1.67 found in this study.…”

Understanding Ice Cloud‐Precipitation Properties of Three Modes of Mesoscale Convective Systems During PECAN

“…Note that large uncertainties may be associated with the radar‐only precipitation estimates related to the Z ‐ R parameters (Ahmed & Schumacher, ; Liu & Zipser, ). The results from Zhang et al () and Wang et al () show that the radar‐only MRMS and Q2 (the predecessor of MRMS) estimates do have significant wet biases compared with surface disdrometer and gauge measurements when estimating precipitation related to the convective processes. To address this issue, the hourly local gauge bias‐corrected radar QPE product is used to adjust the minute‐level radar‐only estimations.…”

Simulating a Mesoscale Convective System Using WRF With a New Spectral Bin Microphysics: 1: Hail vs Graupel

“…Stratiform surface rain mass distribution in observations (Parsival distrometer ‐ PD, gray circles) and in simulations (black line, H43; black plus, H33; and black diamond, G33). For the observation, 16 PDs were used, giving rain rate and rain size distribution over 32‐diameter size bins within the interval 0.06–25.2 mm (Wang et al, ). The gray circles represent the mean value in each size bin in each distrometer for the corresponding rain rate between 11 and 13 UTC.…”