Use of Cloud Radar Doppler Spectra to Evaluate Stratocumulus Drizzle Size Distributions in Large-Eddy Simulations with Size-Resolved Microphysics

Rémillard, Jasmine; Fridlind, A. M.; Ackerman, A. S.; Tselioudis, George; Kollias, Pavlos; Mechem, David B.; Chandler, Hannah E.; Luke, E. P.; Wood, Robert; Witte, Mikael; Chuang, P. Y.; Ayers, J. Kirk

doi:10.1175/jamc-d-17-0100.1

Cited by 21 publications

(16 citation statements)

References 78 publications

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“…In the simulations we evaluated here, the total (i.e., combined SGS and numerical) dissipation rate is about 6 times the SGS dissipation rate within the liquid cloud layer. In contrast to this study, Rémillard et al (2017) found that the turbulent broadening from the MMCR observations agreed well with that modeled based on the SGS dissipation rates reported by DHARMA and another LES model simulating a marine stratocumulus case, thus arousing no suspicions. Our diagnosis using DHARMA output from that case study (not shown) indicates that the total dissipation rate was about 3 times the SGS dissipation rate and that the dissipation rate inferred from the power spectra of the resolved vertical velocity was much closer to the SGS dissipation rate than in the case reported here.…”

Section: 1029/2017jd028104contrasting

confidence: 85%

“…Evaluation of model microphysics with retrieved microphysical broadening terms with known uncertainties would facilitate making fewer additional assumptions about the observations. Second, interest in higher moments like skewness and kurtosis is growing (Kollias, Rémillard, et al, , Kollias, Szyrmer, et al, ; Maahn et al, ; Maahn & Lohnert, ; Rémillard et al, ). Assuming that the dynamical factors broaden the Doppler spectra in a Gaussian manner, the deviations between modeled or observed skewness and kurtosis from those expected from a Gaussian distribution contain information on the microphysics.…”

Section: Introductionmentioning

confidence: 99%

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On the Forward Modeling of Radar Doppler Spectrum Width From LES: Implications for Model Evaluation

et al. 2018

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Large‐eddy simulations of an observed single‐layer Arctic mixed‐phase cloud are analyzed to study the value of forward modeling of profiling millimeter wave cloud radar Doppler spectral width for model evaluation. Individual broadening terms and their uncertainties are quantified for the observed spectral width and compared to modeled broadening terms. Modeled turbulent broadening is narrower than the observed values when the turbulent kinetic energy dissipation rate from the subgrid scale model is used in the forward model. The total dissipation rates, estimated with the subgrid scale dissipation rates and the numerical dissipation rates, agree much better with both the retrieved dissipation rates and those inferred from the power spectra of the simulated vertical air velocity. The comparison of the microphysical broadening provides another evaluative measure of the ice properties in the simulation. To accurately retrieve dissipation rates as well as each broadening term from the observations, we suggest a few modifications to previously presented techniques. First, we show that the inertial subrange spectrum filtered with the radar sampling volume is a better underlying model than the unfiltered −5/3 law for the retrieval of the dissipation rate from the power spectra of the mean Doppler velocity. Second, we demonstrate that it is important to filter out turbulence and remove the layer‐mean reflectivity‐weighted mean fall speed from the observed mean Doppler velocity to avoid overestimation of shear broadening. Finally, we provide a method to quantify the uncertainty in the retrieved dissipation rates, which eventually propagates to the uncertainty in the microphysical broadening.

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Section: 1029/2017jd028104contrasting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

On the Forward Modeling of Radar Doppler Spectrum Width From LES: Implications for Model Evaluation

et al. 2018

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“…Entrainment in fine-scale large-eddy simulation models can vary substantially as a function of grid spacing, numerical advection method, and subgrid-scale parametrization (e.g. Stevens et al, 2005;Rémillard et al, 2017; see also the excellent review of the subject by Mellado, 2017). Even the apparently innocuous process of cloud-droplet sedimentation has been demonstrated to influence entrainment (Ackerman et al, 2004;Bretherton et al, 2007).…”

Section: Abstract Boundary Layer Entrainment Stratocumulus Introducmentioning

confidence: 99%

Estimates of entrainment in closed cellular marine stratocumulus clouds from the MAGIC field campaign

Ghate

Mechem

Cadeddu

et al. 2019

Quart J Royal Meteoro Soc

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Entrainment of warm, dry air from above the boundary layer into the cloud layer has a significant impact on stratocumulus clouds in the marine boundary layer. During the MAGIC field campaign, the Atmospheric Radiation Measurement (ARM) mobile facility was deployed aboard a container ship that made regular transects between Los Angeles, California and Honolulu, Hawaii. Observations made during MAGIC transects were collocated with observations from the Geostationary Operational Environmental Satellite (GOES‐15) and European Centre for Medium‐range Weather Forecasting (ECMWF) reanalysis model. From these data, hourly estimates of entrainment velocities in closed cellular stratocumulus cloud conditions were calculated from the mixed‐layer mass budget equation, modified to accommodate observations sampled from a moving platform. The technique is demonstrated using observations collected during Leg 15A (46 h) and then extended to 178 h of data. The average entrainment velocity was 7.83 ± 5.23 mm/s, and the average large‐scale vertical air motion at cloud top (obtained from reanalysis) was −2.56 ± 3.31 mm/s. The vertical air motion at cloud top was positive (upward) during 36 h (∼20%) with a mean of 2.68 mm/s. Entrainment velocity is highly variable and on average the MAGIC observations show no dependence of entrainment velocity on longitude or any pronounced diurnal cycle. When binned by inversion strength, the mean entrainment velocity and mean large‐scale vertical air motion mirrored each other, with both exhibiting substantial variability. Collectively, our results suggest a mean entrainment‐velocity behaviour associated with the background state, with large changes in entrainment velocity forced by strong variability in internal boundary‐layer properties like turbulence, radiation, and inversion strength. This cautions against using climatological mean estimates of entrainment velocities or neglecting instances with upward large‐scale vertical air motion.

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“…Other more sophisticated simulators are additionally capable of reproducing Doppler moments (e.g., Lamer et al 2018), polarimetry (e.g., Dolan et al 2017;Matsui et al 2019;Oue et al 2020) and complete Doppler spectra observables (Oue et al 2020). These forward operators allow us to investigate the relationships between observed parameters both in the observational and in the model world and facilitate a more complete evaluation of modeled microphysical processes, such as ice formation (van Diedenhoven et al 2009) and drizzle size distributions (Rémillard et al 2017).…”

Section: E603mentioning

confidence: 99%

The ARM Radar Network: At the Leading Edge of Cloud and Precipitation Observations

Kollias

Bharadwaj

Clothiaux

et al. 2020

Bulletin of the American Meteorological Society

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Improving our ability to predict future weather and climate conditions is strongly linked to achieving significant advancements in our understanding of cloud and precipitation processes. Observations are critical to making these advancements because they both improve our understanding of these processes and provide constraints on numerical models. Historically, instruments for observing cloud properties have limited cloud–aerosol investigations to a small subset of cloud-process interactions. To address these challenges, the last decade has seen the U.S. DOE ARM facility significantly upgrade and expand its surveillance radar capabilities toward providing holistic and multiscale observations of clouds and precipitation. These upgrades include radars that operate at four frequency bands covering a wide range of scattering regimes, improving upon the information contained in earlier ARM observations. The traditional ARM emphasis on the vertical column is maintained, providing more comprehensive, calibrated, and multiparametric measurements of clouds and precipitation. In addition, the ARM radar network now features multiple scanning dual-polarization Doppler radars to exploit polarimetric and multi-Doppler capabilities that provide a wealth of information on storm microphysics and dynamics under a wide range of conditions. Although the diversity in wavelengths and detection capabilities are unprecedented, there is still considerable work ahead before the full potential of these radar advancements is realized. This includes synergy with other observations, improved forward and inverse modeling methods, and well-designed data–model integration methods. The overarching goal is to provide a comprehensive characterization of a complete volume of the cloudy atmosphere and to act as a natural laboratory for the study of cloud processes.

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Use of Cloud Radar Doppler Spectra to Evaluate Stratocumulus Drizzle Size Distributions in Large-Eddy Simulations with Size-Resolved Microphysics

Cited by 21 publications

References 78 publications

On the Forward Modeling of Radar Doppler Spectrum Width From LES: Implications for Model Evaluation

On the Forward Modeling of Radar Doppler Spectrum Width From LES: Implications for Model Evaluation

Estimates of entrainment in closed cellular marine stratocumulus clouds from the MAGIC field campaign

The ARM Radar Network: At the Leading Edge of Cloud and Precipitation Observations

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