Mesoscale cellular convection over the oceans

Agee, Ernest M.

doi:10.1016/0377-0265(87)90023-6

Cited by 95 publications

(85 citation statements)

References 22 publications

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“…For example, sheets of Sc clouds are often found in cold sectors of midlatitude cyclones (e.g., Field and Wood, 2007) and transient Sc clouds with open or closed MCC are frequently observed in cold-air outbreaks over oceans (Atkinson and Zhang, 1996;Agee, 1987).…”

Section: A Muhlbauer Et Al: Climatology Of Stratocumulus Cloud Morpmentioning

confidence: 99%

Climatology of stratocumulus cloud morphologies: microphysical properties and radiative effects

2014

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Abstract. An artificial neural network cloud classification scheme is combined with A-train observations to characterize the physical properties and radiative effects of marine low clouds based on their morphology and type of mesoscale cellular convection (MCC) on a global scale. The cloud morphological categories are (i) organized closed MCC, (ii) organized open MCC and (iii) cellular but disorganized MCC.Global distributions of the frequency of occurrence of MCC types show clear regional signatures. Organized closed and open MCCs are most frequently found in subtropical regions and in midlatitude storm tracks of both hemispheres. Cellular but disorganized MCC are the predominant type of marine low clouds in regions with warmer sea surface temperature such as in the tropics and trade wind zones. All MCC types exhibit a pronounced seasonal cycle.The physical properties of MCCs such as cloud fraction, radar reflectivity, drizzle rates and cloud top heights as well as the radiative effects of MCCs are found highly variable and a function of the type of MCC. On a global scale, the cloud fraction is largest for closed MCC with mean cloud fractions of about 90 %, whereas cloud fractions of open and cellular but disorganized MCC are only about 51 % and 40 %, respectively. Probability density functions (PDFs) of cloud fractions are heavily skewed and exhibit modest regional variability.PDFs of column maximum radar reflectivities and inferred cloud base drizzle rates indicate fundamental differences in the cloud and precipitation characteristics of different MCC types. Similarly, the radiative effects of MCCs differ substantially from each other in terms of shortwave reflectance and transmissivity. These differences highlight the importance of low-cloud morphologies and their associated cloudiness on the shortwave cloud forcing.

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Section: A Muhlbauer Et Al: Climatology Of Stratocumulus Cloud Morpmentioning

confidence: 99%

Climatology of stratocumulus cloud morphologies: microphysical properties and radiative effects

2014

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“…The definition of CSs continues to be somewhat evasive, but they are commonly viewed as organized long-lived motions that spontaneously arise, trap much energy, and cover the whole spectrum of fluid motions (down to the Kolmogorov scale). Perhaps the most relevant atmospheric examples can be found in organized structures that occur on a variety of spatial scales within convective boundary layers that form during cold air outbreaks over warmer water (Agee, 1987). Meanwhile, turbulent flows with CSs are characterized by non-Gaussian statistics (e.g., Wyngaard and Weil, 1991;Maurizi, 2006) reflected in the values of skewness and kurtosis that are different from those for a normal distribution (0 and 3, respectively).…”

Section: Coherent Structures In Turbulent Flowsmentioning

confidence: 99%

Statistical inference from atmospheric time series: detecting trends and coherent structures

Gluhovsky

2011

Nonlin. Processes Geophys.

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Abstract. Standard statistical methods involve strong assumptions that are rarely met in real data, whereas resampling methods permit obtaining valid inference without making questionable assumptions about the data generating mechanism. Among these methods, subsampling works under the weakest assumptions, which makes it particularly applicable for atmospheric and climate data analyses. In the paper, two problems are addressed using subsampling: (1) the construction of simultaneous confidence bands for the unknown trend in a time series that can be modeled as a sum of two components: deterministic (trend) and stochastic (stationary process, not necessarily an i.i.d. noise or a linear process), and (2) the construction of confidence intervals for the skewness of a nonlinear time series. Non-zero skewness is attributed to the occurrence of coherent structures in turbulent flows, whereas commonly employed linear time series models imply zero skewness.

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“…In most cases, OCP are made visible by clouds forming in the upward branches of the circulations and are documented as cloud streets aligned along the mean wind in the atmospheric boundary layer and as cellular cloud patterns. Review articles on OCP have been written, for example, by Agee [1987], Etling and Brown [1993], and Atkinson and Zhang [1996]. OCP are generated by thermal and dynamical instabilities, and primarily the shape of the wind profile is believed to play an important role in the determination of the patternform.…”

Section: Introductionmentioning

confidence: 99%

Wintertime roll and cell convection over Greenland and Barents Sea regions: A climatology

Brümmer¹,

Pohlmann²

2000

J. Geophys. Res.

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Mesoscale cellular convection over the oceans

Cited by 95 publications

References 22 publications

Climatology of stratocumulus cloud morphologies: microphysical properties and radiative effects

Climatology of stratocumulus cloud morphologies: microphysical properties and radiative effects

Statistical inference from atmospheric time series: detecting trends and coherent structures

Wintertime roll and cell convection over Greenland and Barents Sea regions: A climatology

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