Variational Optimization Method for Calculation of Cloud Drop Growth in an Eulerian Drop-Size Framework

Liu, Qingfu; Kogan, Yefim L.; Lilly, Douglas K.; Khairoutdinov, M. P.

doi:10.1175/1520-0469(1997)054<2493:vomfco>2.0.co;2

Cited by 16 publications

(16 citation statements)

References 15 publications

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“…Diffusional growth is calculated using a movable mass grid; i.e., the mass of each bin changes according the solution of the diffusional growth equation. This approach eliminates numerical DSD broadening typical of Eulerian large‐eddy simulation (LES) models [ Liu et al ., ; Khain et al ., ].…”

Section: The Model and Experimental Designmentioning

confidence: 99%

About the horizontal variability of effective radius in stratocumulus clouds

2016

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The role of turbulent mixing in formation of low horizontal variability of effective radius near the top of nondrizzling stratocumulus clouds is investigated in simulations of clouds observed during the Second Dynamics and Chemistry of Marine Stratocumulus field experiment. The clouds are simulated using a spectral bin microphysics Lagrangian‐Eulerian model consisting of ~2000 adjacent parcels moving in a turbulence‐like field with observed correlation properties. The parcels interact through drop sedimentation and turbulent mixing. It was found that the effective radius variability in the horizontal direction near cloud top does not exceed ~10% of the averaged value. Three different types of cloud parcels are revealed to be differently influenced by mixing: ascending slightly diluted parcels, cloudy parcels experiencing intense mixing with parcels from inversion, and initially dry parcels. The evolution of droplet size distributions in parcels belonging to these types is investigated. It is shown that in parcels of the first two types the values of effective radii do not change or change only slightly remaining close to the adiabatic value. In initially droplet‐free parcels effective radius rapidly reaches a value close to the adiabatic value, while liquid water content remains low. Therefore, turbulent mixing leads to establishing vertical profiles of effective radius, which are close to the adiabatic profile.

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Section: The Model and Experimental Designmentioning

confidence: 99%

About the horizontal variability of effective radius in stratocumulus clouds

2016

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“…Note that almost all current bulk schemes represent particle distributions using analytic functions, although some earlier schemes did not make any assumptions about the cloud particle distribution and only considered bulk cloud water content. slower than raindrops) and dynamics through the effects of latent heating from freezing and cooling from melting (e.g., Fovell & Ogura, 1988;Gao et al, 2006;Liu, Kogan, et al, 1997;Lord et al, 1984;McCumber et al, 1991; and many others). Including ice microphysics in a realistic way was a major challenge because of the wide variety of ice particle shapes and types in the atmosphere.…”

Section: A Brief History Of Microphysics Scheme Developmentsmentioning

confidence: 99%

“…A major development in the 1970s and 1980s was the inclusion of ice microphysics (e.g., Cotton et al, 1982; Koenig & Murray, 1976; Lin et al, 1983; Rutledge & Hobbs, 1984). This had important effects on simulations owing to large impacts on sedimentation fluxes (for a given particle mass, low density snowflakes fall much slower than raindrops) and dynamics through the effects of latent heating from freezing and cooling from melting (e.g., Fovell & Ogura, 1988; Gao et al, 2006; Liu, Kogan, et al, 1997; Lord et al, 1984; McCumber et al, 1991; and many others). Including ice microphysics in a realistic way was a major challenge because of the wide variety of ice particle shapes and types in the atmosphere.…”

Section: A Brief History Of Microphysics Scheme Developmentsmentioning

confidence: 99%

Confronting the Challenge of Modeling Cloud and Precipitation Microphysics

Morrison

Lier-Walqui

Fridlind

et al. 2020

J Adv Model Earth Syst

247

125

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In the atmosphere, microphysics refers to the microscale processes that affect cloud and precipitation particles and is a key linkage among the various components of Earth's atmospheric water and energy cycles. The representation of microphysical processes in models continues to pose a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models is divided into two parts: (i) how to represent the population of cloud and precipitation particles, given the impossibility of simulating all particles individually within a cloud, and (ii) uncertainties in the microphysical process rates owing to fundamental gaps in knowledge of cloud physics. The recently developed Lagrangian particle‐based method is advocated as a way to address several conceptual and practical challenges of representing particle populations using traditional bulk and bin microphysics parameterization schemes. For addressing critical gaps in cloud physics knowledge, sustained investment for observational advances from laboratory experiments, new probe development, and next‐generation instruments in space is needed. Greater emphasis on laboratory work, which has apparently declined over the past several decades relative to other areas of cloud physics research, is argued to be an essential ingredient for improving process‐level understanding. More systematic use of natural cloud and precipitation observations to constrain microphysics schemes is also advocated. Because it is generally difficult to quantify individual microphysical process rates from these observations directly, this presents an inverse problem that can be viewed from the standpoint of Bayesian statistics. Following this idea, a probabilistic framework is proposed that combines elements from statistical and physical modeling. Besides providing rigorous constraint of schemes, there is an added benefit of quantifying uncertainty systematically. Finally, a broader hierarchical approach is proposed to accelerate improvements in microphysics schemes, leveraging the advances described in this paper related to process modeling (using Lagrangian particle‐based schemes), laboratory experimentation, cloud and precipitation observations, and statistical methods.

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“…Due to the formulation of the problem as number conservation and discretization of the evolution equation using fixed bins, even though the numerical scheme is conservative (up to subtle limitations outlined below), evaluation of other statistical moments of the evolved distribution from the number density introduces an inherent discrepancy from the analytical results (for a discussion on multi-moment formulation of the problem, see e.g. Liu et al, 1997).…”

Section: Notes On Conservativenessmentioning

confidence: 99%

“…Following Liu et al (1997) and Morrison et al (2018), the earliest documented study employing Eulerian numerics for condensational growth of a continuous size distribution representing a population of particles is that of Kovetz and Olund (1969) (whereas several earlier works starting with Howell (1949) utilized the Lagrangian approaches). The numerical scheme proposed in Kovetz and Olund (1969, eq.…”

Variational Optimization Method for Calculation of Cloud Drop Growth in an Eulerian Drop-Size Framework

Cited by 16 publications

References 15 publications

About the horizontal variability of effective radius in stratocumulus clouds

About the horizontal variability of effective radius in stratocumulus clouds

Confronting the Challenge of Modeling Cloud and Precipitation Microphysics

Comment on gmd-2020-404

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