In recent years Cloud Resolving Models (CRMs) have become an increasingly important tool for the study of convective phenomena. CRMs should not be regarded as simply providing surrogates for observations; rather

Plant, R. S.

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R. S. Plant

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Cloud Tracking in Cloud-Resolving Models

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Cloud Tracking in Cloud-Resolving ModelsR. S. Plant11Department of Meteorology, University of Reading, Earley Gate,Reading, RG6 6BB, United Kingdom. r.s.plant@reading.ac.uk(Dated: May 2, 2007)I. INTRODUCTIONIn recent years Cloud Resolving Models (CRMs) havebecome an increasingly important tool for the study ofconvective phenomena. CRMs should not be regardedas simply providing surrogates for observations; rather,they allow idealized but realistic simulations to be pro-duced which provide a laboratory for the careful diagnos-tic analysis of generic convective systems. Such analysisis a distinctive methodology that is necessary to improveour understanding of the basic phenomena and to developimproved parameterization methods for the larger-scalemodels.We will describe a novel analysis technique for CRMdata, which allows one to investigate statistical prop-erties of the lifecycles of the “clouds” produced duringCRM simulations.II. MOTIVATIONMany of the existing analyses of CRM data have fo-cussed on determining and understanding spatial andtemporal average properties of the full ensemble of con-vective clouds that are generated in response to somespecified forcing. Rather less attention has been devotedto the lifecycle behaviour of individual clouds. One ex-ception is Zhao and Austin (2005). That study looked atsix clouds. Thus, there are practical constraints on thenumber of individual clouds that can be investigated indetail, and this hampers our ability to understand therole of the individual clouds within the full convectiveensemble.The philosophy here is to collect statistical informationabout the lifecycle behaviour of individual clouds. Suchinformation could allow simple, straightforward improve-ments to be made to existing convective parameteriza-tions. As an example, first note that from the pioneer-ing work of Arakawa and Schubert (1974), parameteri-zations have been developed which consider a spectrumof convective clouds. Indeed the author has recently de-veloped a scheme (Plant and Craig, 2007) of this generaltype. Second, note that most parameterizations sufferfrom the defect that there is little or no concept of thecloud lifecycle. Convective clouds are usually assumed topersist for precisely one timestep of the large-scale model.An important feature of the popular Kain and Fritschscheme (2004) is that a rudimentary lifecycle is obtainedby giving the clouds a lifetime which extends over mul-tiple timesteps. However, the Kain Fritsch scheme onlyadmits a single cloud type. An improved parameteri-zation could be constructed by simply combining thesetwo features: multiple cloud types and a simple cloudlifecycle. However, no such schemes are available, essen-tially because there is no information currently availableon how the cloud lifecycle varies with cloud type (andforcing regime).Such information can be obtained by constructing au-tomated procedures to first identify and then also totrack the development of individual clouds. By isolat-ing many distinct lifecycles, statistically-significant in-formation can be gathered to build up a picture of cloudevolution and decay across the spectrum of convectiveclouds.III. METHODOLOGYCode which identifies and tracks individual clouds hasbeen implemented within the Met. Office LEM. This hasthree main aspects:1. identify clouds from CRM results at a giventimestep2. identify the relationships between clouds presentat the current timestep and those present at theprevious ones (i.e., establish the continuations, thebirths, the deaths, the splits and the mergers)3. store data about each cloud as a time history thatis output on the death of the cloud.The code tracks the complete time evolution of theclouds. A variety of identification criteria have been pro-posed in the literature and are straightforwardly imple-mented. These are based on connected sets of gridpointssatisfying some criteria (e.g., w > 1ms−1). Thus, thefocus could be on all cloudy air, or just on the up- ordown-draughts.The tracking methodology was developed and exten-sively tested and validated independently of the CRMin a purely artificial system (using a modified game-of-life algorithm). Clouds are tracked on a timestep-by-timestep basis, allowing a simple but robust methodol-ogy to be applied for step 2. Since the model timestepsatisfies the CFL condition, it follows that a cloud at onetimestep must be in the same location, or at an adjacentpoint, at the next timestep. The number of links betweenclouds at one timestep and the next then reveals the evo-lution of each cloud, whether that be a birth or death (0links), a direct continuation (1 link, and by far the most 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45060003000000210018001500120009000600Cloud fractional areaFIG. 1: Fraction of the CRM domain covered by cloud, as afunction of the time of day. The points represent time-meansover 2h centered around the time marked. 0 50 100 150 200 250060003000000210018001500120009000600Number of clouds / Typical lifetime (min)Number of cloudsLifetimeFIG. 2: Number of clouds (red) in the CRM domain andaverage lifetime of those clouds (green), as a function of thetime of day. The points represent time-means over 2h centeredaround the time marked.common possibility) or a split or merger (> 1 link). Dif-ferent options have been implemented and investigatedfor assigning lifecycle properties (e.g., lifetimes) to cloudsthat have undergone splits and mergers.IV. PRELIMINARY RESULTSResults will be presented at the conference. In Figs. 1and 2 we show some preliminary results in order to illus-trate some of the information that is produced.These example results are from a high-resolution(250m) 2D simulation of the idealized diurnal cycle overland. 2048 horizontal grid points were used. For thepurpose of these plots, “cloudy” points were definedas the significant updraughts: specifically, points withw > 1ms−1. Fig. 1 is the fractional area of the domaincovered by cloud, which is strongly peaked around theearly afternoon. Fig. 2 shows the number of clouds in thedomain and their average lifetimes. During the morning,the average updraught size is around 4 gridpoints and theaverage lifetime 10 or 15 minutes. Updraughts becomelarger during the afternoon, occupying 5 or 6 gridpointson the average, while the lifetime peaks at around 30minutes in the early afternoon. During the early eveningand at night there are just a few small updraughts, butthose which do occur appear to be rather persistent.The progression from small, short-lived to larger,longer-lived updraughts as convection develops over theday is hardly a surprise, and similar conclusions couldno doubt be drawn from careful visual inspections of theraw CRM data. However, one of the uses of this cloud-tracking methodology is that it allows such statementsto be made quantitative.V. REFERENCESArakawa, A., Schubert, W. H., 1974: Interaction of aCumulus Cloud Ensemble with the Large-Scale Environ-ment. Part I. J. Atmos. Sci., 31, 674-701.Kain, J. S., 2004: The Kain–Fritsch Convective Parame-terization: An Update. J. Appl. Meteorol., 43, 170-181.Plant, R. S., Craig, G. C., 2007: A Stochastic Param-eterization for Deep Convection Based on EquilibriumStatistics. Submitted to: J. Atmos. Sci.Zhao M., Austin P. H., 2005: Life Cycle of NumericallySimulated Shallow Cumulus Clouds. Part I: Transport.J. Atmos. Sci., 62, 1269-1290.

A Stochastic Parameterization for Deep Convection Based on Equilibrium Statistics. Submitted to:

Interaction of a Cumulus Cloud Ensemble with the Large-Scale Environment. Part I.

Life Cycle of Numerically Simulated Shallow Cumulus Clouds. Part I: Transport.

The Kain–Fritsch Convective Parameterization: An Update.

Cloud tracking in cloud-resolving models

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Cloud tracking in cloud-resolving models

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