libcloudph++ 1.0: a single-moment bulk, double-moment bulk, and particle-based warm-rain microphysics library in C++

Arabas, Sylwester; Jaruga, Anna; Pawłowska, Hanna; Grabowski, Wojciech W.

doi:10.5194/gmd-8-1677-2015

Cited by 48 publications

(72 citation statements)

References 60 publications

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“…The key element of the scheme presented here that makes it distinct from the approach used in Andrejczuk et al (2008Andrejczuk et al ( , 2010, Shima et al (2009), Riechelmann et al (2012, Arabas et al (2015), and others, is the way super-droplets are created. The original implementations assume that superdroplets fill the entire computational domain, and they initially represent deliquesced (humidified) CCN in equilibrium with their local environment.…”

Section: Super-droplet Initiationmentioning

confidence: 99%

“…CCN are assumed to be composed of sodium chloride (sea salt; NaCl). Idealized CCN distribution, the same as in Arabas et al (2015), is represented by a sum of two lognormal distributions with number mixing ratio, mean radii, and geometric standard deviations (unitless) 57.33 and 38.22 m g −1 (i.e., per cubic centimeter for the air density of 1 kg m −3 ; these values come from converting the 60 and 40 cm −3 concentrations to the number mixing ratio using air density at the bottom of the computational domain in simulations discussed in Sect. 3), 20 and 75 nm, and 1.4 and 1.6, respectively.…”

Section: Super-droplet Initiationmentioning

confidence: 99%

“…Among those, the particle-based Lagrangian method, referred to as the Lagrangian cloud model (Andrejczuk et al, 2008(Andrejczuk et al, , 2010 or the "super-droplet method" (Shima et al, 2009), is of particular relevance (see also Riechelmann et al, 2012;Arabas et al, 2015;Hoffmann et al, 2015, among others). By representing formation and growth of natural cloud particles using a subset of those particles ("super-particles"), many problems haunting traditional Eulerian approaches are either eliminated or significantly reduced.…”

Section: W W Grabowski Et Al: Lagrangian Condensation With Twomey mentioning

confidence: 99%

“…We assume the same CCN characteristics as in GA17 and Arabas et al (2015). CCN characteristics include the chemical composition, the number mixing ratio of activated CCN for a given supersaturation (the Twomey relationship) and the activation radius.…”

Section: Super-droplet Initiationmentioning

confidence: 99%

“…With a single super-droplet located in the grid cell (see Fig. 3), the horizontal and vertical velocities can be interpolated using a simple scheme similar to that used in Arabas et al (2015):…”

Section: Transport Of Super-droplets In the Physical Spacementioning

confidence: 99%

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Lagrangian condensation microphysics with Twomey CCN activation

2018

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Abstract. We report the development of a novel Lagrangian microphysics methodology for simulations of warm icefree clouds. The approach applies the traditional Eulerian method for the momentum and continuous thermodynamic fields such as the temperature and water vapor mixing ratio, and uses Lagrangian "super-droplets" to represent condensed phase such as cloud droplets and drizzle or rain drops. In other applications of the Lagrangian warm-rain microphysics, the super-droplets outside clouds represent unactivated cloud condensation nuclei (CCN) that become activated upon entering a cloud and can further grow through diffusional and collisional processes. The original methodology allows for the detailed study of not only effects of CCN on cloud microphysics and dynamics, but also CCN processing by a cloud. However, when cloud processing is not of interest, a simpler and computationally more efficient approach can be used with super-droplets forming only when CCN is activated and no super-droplet existing outside a cloud. This is possible by applying the Twomey activation scheme where the local supersaturation dictates the concentration of cloud droplets that need to be present inside a cloudy volume, as typically used in Eulerian bin microphysics schemes. Since a cloud volume is a small fraction of the computational domain volume, the Twomey super-droplets provide significant computational advantage when compared to the original superdroplet methodology. Additional advantage comes from significantly longer time steps that can be used when modeling of CCN deliquescence is avoided. Moreover, other formulation of the droplet activation can be applied in case of low vertical resolution of the host model, for instance, linking the concentration of activated cloud droplets to the local updraft speed. This paper discusses the development and testing of the Twomey super-droplet methodology, focusing on the activation and diffusional growth. Details of the activation implementation, transport of super-droplets in the physical space, and the coupling between super-droplets and the Eulerian temperature and water vapor field are discussed in detail. Some of these are relevant to the original superdroplet methodology as well and to the ice phase modeling using the Lagrangian approach. As a computational example, the scheme is applied to an idealized moist thermal rising in a stratified environment, with the original superdroplet methodology providing a benchmark to which the new scheme is compared.

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Section: Super-droplet Initiationmentioning

confidence: 99%

Section: Super-droplet Initiationmentioning

confidence: 99%

Section: W W Grabowski Et Al: Lagrangian Condensation With Twomey mentioning

confidence: 99%

Section: Super-droplet Initiationmentioning

confidence: 99%

Section: Transport Of Super-droplets In the Physical Spacementioning

confidence: 99%

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Lagrangian condensation microphysics with Twomey CCN activation

2018

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Roles of Drop Size Distribution and Turbulence in Autoconversion Based on Lagrangian Cloud Model Simulations

Oh¹,

Noh

2022

Preprint

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The roles of the drop size distribution (DSD) and turbulence in the autoconversion rate A are investigated by analyzing Lagrangian cloud model (LCM) data for shallow cumulus clouds. The correlations of DSD and turbulence with other cloud parameters are estimated, and they are applied to parameterize their effects on A. A new parameterization of A is proposed based on it, as A = αqc7/3Nc-1/3H(Rc-Rc0) with α = aNc-X(Rc-Rc0)(1+bε), where qc, Nc, and Rc are the mixing ratio, the number concentration, and the volume mean radius of cloud droplets, respectively. ε is the dissipation rate, Rc0 is the threshold value of Rc, H is the Heaviside step function, and X, a, and b are constants. Here, Nc-X(Rc-Rc0) represents the effect of DSD, via its correlation with Nc and qc, while A [?] qc7/3Nc-1/3 represents the effect of the gravitational collisional growth for given DSD and turbulence. The correlation between turbulence and DSD makes b larger than expected based on turbulence-induced collision enhancement. The effects of DSD and turbulence and their correlations with qc and Nc explain a wide range of exponent values of qc and Nc in many existing parameterizations of A. The new parameterization is compared with the LCM data and applied to a bulk cloud model (BCM) while clarifying the difference between the cloud droplet mixing processes of the LCM and BCM. The importance of DSD and turbulence in the raindrop formation in shallow cumulus clouds are shown by comparing the results from A with and without these effects.

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Persistent mixed‐phase states in adiabatic cloud parcels under idealised conditions

Abade,

Albuquerque

2024

Quart J Royal Meteoro Soc

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By assuming that liquid droplets and ice crystals within a computational grid box grow under the same conditions, mean‐field representations of mixed‐phase clouds in numerical models favour a quick cloud glaciation driven by the Wegener–Bergeron–Findeisen process. Consequently, maintenance of mixed‐phase states under the mean‐field approximation is conditioned to external dynamical forcing, such as sufficiently strong updraughts. In this work we go beyond the mean‐field representation and investigate the maintenance of mixed‐phase states in adiabatic (non‐entraining) cloud volumes by accounting for local variability in a particle's growth conditions in the turbulent cloud environment. This is done by using a Lagrangian microphysical scheme, where temperature and vapour mixing ratio are stochastic attributes attached to each cloud particle. Different dynamic scenarios show that microphysical variability and parametrised turbulence effects may significantly reduce cloud glaciation rates, resulting in much more resilient mixed‐phase states in idealised parcels containing non‐sedimenting and non‐aggregating cloud particles. We have stated a more refined criterion for the Wegener–Bergeron–Findeisen process activity in the bulk of a mixed‐phase cloud parcel (or computational grid box). This criterion is stated in terms of the conditional average supersaturations that are experienced by specific cloud particle types (liquid or ice), and not in terms of unconditional averages corresponding to parcel‐ or grid‐mean values of supersaturations. Formulation of a relation between conditional and unconditional average supersaturations poses an interesting closure problem in mixed‐phase cloud microphysics. Our stochastic microphysical model provides a Lagrangian closure to this problem and gives insights towards the development of a prognostic stochastic subgrid‐scale scheme for condensation/deposition in numerical models of mixed‐phase clouds.

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libcloudph++ 1.0: a single-moment bulk, double-moment bulk, and particle-based warm-rain microphysics library in C++

Cited by 48 publications

References 60 publications

Lagrangian condensation microphysics with Twomey CCN activation

Lagrangian condensation microphysics with Twomey CCN activation

Roles of Drop Size Distribution and Turbulence in Autoconversion Based on Lagrangian Cloud Model Simulations

Persistent mixed‐phase states in adiabatic cloud parcels under idealised conditions

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