Abstract
Abstract. A technique for the tracking of individual clouds in a Large Eddy Simulation (LES) is presented. We use this technique on an LES of a shallow cumulus cloud field based upon the Barbados Oceanographic and Meteorological Experiment (BOMEX) to calculate statistics of cloud height, lifetime, and other physical properties for individual clouds in the model. We also examine the question of nature versus nurture in shallow cumulus clouds: do properties at cloud base determine the upper-level properties of the clouds (nature), or are cloud properties determined by the environmental conditions they encounter (nurture). We find that clouds which ascend through an environment that has been pre-moistened by previous cloud activity are no more likely to reach the inversion than clouds that ascend through a drier environment. Cloud base thermodynamic properties are uncorrelated with upper-level cloud properties, while mean fractional entrainment and detrainment rates display moderate correlations with cloud properties up to the inversion. Conversely, cloud base area correlates well with upper-level cloud area and maximum cloud height. We conclude that cloud thermodynamic properties are primarily influenced by entrainment and detrainment processes, cloud area and height are primarily influenced by cloud base area, and thus nature and nurture both play roles in the dynamics of BOMEX shallow cumulus clouds.
Highlights
Shallow cumulus clouds occur over large parts of the tradewind regions (Norris, 1988), where subsiding air creates stable atmospheric conditions
Adopting the language of Romps and Kuang (2010), these results suggest that cloud area and mass flux results from the cloud’s nature, and thermodynamic variables are governed by nurture
We have developed an algorithm for tracking individual shallow cumulus clouds in an Large Eddy Simulation (LES) simulation which generates reasonable statistical distributions for a variety of cloud properties
Summary
Shallow cumulus clouds occur over large parts of the tradewind regions (Norris, 1988), where subsiding air creates stable atmospheric conditions. This algorithm generates output similar to the algorithm created by Plant (2009), but with one significant difference: it can be run off-line, on pre-computed LES model fields. The simplest possible algorithm would identify contiguous regions containing condensed liquid water at each time step with unique ids, and identify regions that overlap spatially in successive time steps This information could be used to construct a graph (in the mathematical sense) of the cloud overlaps, and connected subgraphs of this graph would represent individual tracked clouds. Events, we have developed a more complex method for tracking clouds in time
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
