The Skill of Cloud Fraction and Condensate Decorrelation Lengths to Reproduce Cloud Field Statistics According to a High Resolution MICROBASE Data Set

Lazaros Oreopoulos NASA
Peter Norris NASA - GMAO/UMBC - GEST

Category: Cloud Properties

Working Group: Cloud Life Cycle

The exponential-random paradigm has been recently proposed as a good representation of cloud fraction overlap. In this paradigm, the degree of overlap can be expressed simply in terms of a decorrelation length (scale). A version of the MICROBASE data set with very high horizontal (10 sec) and vertical (45 m) discretization in cloud condensate and extending seven years allows us to test the validity of this framework at the SGP ARM site. Similarly, the appropriateness of exponential decreases in the condensate rank correlations (a measure of the degree of vertical alignment of relatively thin and thick parts of cloud layers) can also be examined. The decorrelation climatology analysis shows a conspicuous seasonal cycle for both overlap and rank decorrelation lengths with peaks in the summer months and a significantly faster tendency towards randomness for condensate rank correlations. The purpose of the presentation is to examine whether decorrelation length scales are capable of reproducing via cloud generators the statistical properties of the condensate field (e.g., total cloud fraction, profiles of cumulative cloud fraction, variance of water path) with acceptable fidelity. We investigate the level of spatial and temporal detail needed in the specification of decorrelation lengths in order for this capability to be realized while maintaining the simplicity needed for actual implementation in cloud parameterizations. We attempt to address this question with a variety of tests that reveal decorrelation length dependencies and sensitivities to specific choices in the analysis methodology.

This poster will be displayed at ASR Science Team Meeting.

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