A two-moment cloud microphysics parameterization for mixed-phase clouds. Part 1: Model description

Seifert, Axel; Beheng, K.D.

doi:10.1007/s00703-005-0112-4

Cited by 576 publications

(688 citation statements)

References 53 publications

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“…Along with the mass concentration, the number concentration is often used as a prognostic variable in two-moment bulk microphysics schemes that predict the evolution of clouds in recent climate and 15 numerical weather prediction models (Khain et al, 2000;Seifert and Beheng, 2006). An absence of global observational constraints therefore limits the evaluation of model predictions to sparser in situ measurements (e.g.…”

Section: Introductionmentioning

confidence: 99%

Ice crystal number concentration estimates from lidar-radar satellite remote sensing. Part 1: Method and evaluation

Sourdeval¹,

Gryspeerdt²,

Krämer³

et al. 2018

Preprint

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Abstract. The number concentration of cloud particles is a key quantity for understanding aerosol-cloud interactions and describing clouds in climate and numerical weather prediction models. In contrast with recent advances for liquid clouds, few observational constraints exist on the ice crystal number concentration (N i ). This study investigates how combined lidar-radar measurements can be used to provide satellite estimates of N i , using a methodology that constrains moments of a parameterized particle size distribution (PSD). The operational liDAR-raDAR (DARDAR) product serves as an existing base for this method, 5 which focuses on ice clouds with temperatures T c < -30°C.Theoretical considerations demonstrate the capability for accurate retrievals of N i , apart from a possible bias in the concentration in small crystals when T c -50°C, due to the assumption of a monomodal PSD shape in the current method. This is verified by comparing satellite estimates to co-incident in situ measurements, which additionally demonstrates the sufficient sensitivity of lidar-radar observations to N i . Following these results, satellite estimates of N i are evaluated in the context of a 10 case study and a preliminary climatological analysis based on 10 years of global data. Despite of a lack of other large-scale references, this evaluation shows a reasonable physical consistency in N i spatial distribution patterns. Notably, increases in N i are found towards cold temperatures and, more significantly, in the presence of strong updraughts, such as those related to convective or orographic uplifts. Further evaluation and improvements of this method are necessary but these results already constitute a first encouraging step towards large-scale observational constraints for N i . Part two of this series uses this new 15 dataset to examine the controls on N i .

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Section: Introductionmentioning

confidence: 99%

Ice crystal number concentration estimates from lidar-radar satellite remote sensing. Part 1: Method and evaluation

Sourdeval¹,

Gryspeerdt²,

Krämer³

et al. 2018

Preprint

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“…In this study, the COSMO model has been configured in a non-convection-permitting mode with a uniform horizontal grid resolution of 0.25 • (≈ 28 km). The two-moment cloud microphysics scheme in the COSMO model (Seifert and Beheng, 2006) distinguishes between five hydrometeor classes, namely cloud droplets, rain, ice crystals, snow, and graupel. Processes in the warm (liquid) phase considered by this scheme include the nucleation of cloud droplets, autoconversion of cloud droplets to form rain, accretion, and self-collection of rain droplets.…”

Section: The Cosmo-muscat Model and Revised Cloud Activationmentioning

confidence: 99%

“…Seifert et al (2012) investigated the influence of substantially perturbing C ccn from 100 to 3200 cm −3 (see above for a brief discussion of this paper). Accordingly, the grid-scale explicit nucleation rate is calculated from the time derivative of the activation relation (Seifert and Beheng, 2006):…”

Section: The Cosmo-muscat Model and Revised Cloud Activationmentioning

confidence: 99%

“…However, this approach is numerically very expensive especially when applied for regional atmospheric models. As a compromise between these two approaches, two-moment microphysical schemes can predict the number concentration of the liquid and ice hydrometeors in addition to mass variables (Cotton et al, 1986;Meyers et al, 1997;Seifert and Beheng, 2006). Furthermore, numerous studies have shown that using twomoment schemes is a promising avenue in future operational forecast models (Reisner et al, 1998;Tao et al, 2003;Seifert and Beheng, 2006) and is also computationally efficient.…”

Section: Introductionmentioning

confidence: 99%

“…The two-moment cloud microphysical scheme in the COSMO model (Seifert and Beheng, 2006) uses fixed profiles of C ccn . Rather than this simplification, here we use C ccn predicted on the basis of the simulated aerosol from the MUSCAT module.…”

Section: Introductionmentioning

confidence: 99%

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Implementation of aerosol–cloud interactions in the regional atmosphere–aerosol model COSMO-MUSCAT(5.0) and evaluation using satellite data

et al. 2017

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Abstract. The regional atmospheric model Consortium for Small-scale Modeling (COSMO) coupled to the Multi-Scale Chemistry Aerosol Transport model (MUSCAT) is extended in this work to represent aerosol-cloud interactions. Previously, only one-way interactions (scavenging of aerosol and in-cloud chemistry) and aerosol-radiation interactions were included in this model. The new version allows for a microphysical aerosol effect on clouds. For this, we use the optional two-moment cloud microphysical scheme in COSMO and the online-computed aerosol information for cloud condensation nuclei concentrations (C ccn ), replacing the constant C ccn profile. In the radiation scheme, we have implemented a droplet-size-dependent cloud optical depth, allowing now for aerosol-cloud-radiation interactions. To evaluate the models with satellite data, the Cloud Feedback Model Intercomparison Project Observation Simulator Package (COSP) has been implemented. A case study has been carried out to understand the effects of the modifications, where the modified modeling system is applied over the European domain with a horizontal resolution of 0.25 • × 0.25 • . To reduce the complexity in aerosol-cloud interactions, only warm-phase clouds are considered. We found that the online-coupled aerosol introduces significant changes for some cloud microphysical properties. The cloud effective radius shows an increase of 9.5 %, and the cloud droplet number concentration is reduced by 21.5 %.

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Models of Clouds, Precipitation and Storms

Flossmann

2005

Encyclopedia of Hydrological Sciences

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Clouds play an important role for life on earth. Apart from influencing, for example, the radiative balance of the atmosphere and the lifetime of atmospheric trace constituents, they are the essential element in the hydrological cycle. Clouds transport the evaporated water of the oceans to the continents where the precipitation releases the water load. This release of water can be more or less vigorous depending on the energy stored in the cloud in the form of condensed hydrometeors (liquid or solid). This energy depends on the amount of available moisture and the way the vertical lifting necessary for cloud formation proceeds. We distinguish here mainly two different forms with varying extensions on the horizontal scale: the gentle uplift associated to the large-scale lifting, for example at a frontal zone, and the vigorous small-scale ascent associated with convection, knowing that also mixed forms of the lifting occur. This article provides an introduction to the complex subject of modeling clouds, the production of precipitation, and the development of cloud and storm systems. The elements intervening in cloud modeling are exposed, starting from a description of the physical phenomena. On the basis of the occurring scale problem, a number of approaches for simplification are presented. These simplifications concern the dynamics as well as the microphysics. Bulk and bin modeling approaches are explained, as well as cumulus parameterizations. Some numerical problems are discussed. This approach gives an insight into current state-of-the-art cloud modeling and the necessary balance between the degree of parameterization, the number of physical and chemical processes relevant to a particular problem, and the available computing resources. Encyclopedia of Hydrological Sciences. Edited by M. Anderson.

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A two-moment cloud microphysics parameterization for mixed-phase clouds. Part 1: Model description

Cited by 576 publications

References 53 publications

Ice crystal number concentration estimates from lidar-radar satellite remote sensing. Part 1: Method and evaluation

Ice crystal number concentration estimates from lidar-radar satellite remote sensing. Part 1: Method and evaluation

Implementation of aerosol–cloud interactions in the regional atmosphere–aerosol model COSMO-MUSCAT(5.0) and evaluation using satellite data

Models of Clouds, Precipitation and Storms

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