The content of the ARM website is available to any browser, but for the best experience we highly recommend you upgrade to a standards-compliant browser such as Firefox, Opera or Safari.
VIEW CART
primary link menu HOME SITE INDEX PEOPLE
skip to main content ABOUT ARMABOUT ACRFSCIENCESITESINSTRUMENTSMEASUREMENTSDATAPUBLICATIONSEDUCATIONFORMS

Cover image

MODELING DRIZZLING CLOUDS USING the QUADRATURE METHOD of MOMENTS (QMOM)

Robert McGraw Brookhaven National Laboratory
Yangang Liu Brookhaven National Laboratory

Category: Cloud Properties

This poster will assess the quadrature method of moments (QMOM) as a computationally efficient alternative to sectional methods for simulating drizzle processes from initiation to rainout in warm clouds. In modeling the post-onset stage of drizzle, it is essential to include nonlinear feedback effects from droplet depletion, through the collection process, and resulting precipitation quenching. Such nonlinear feedback is analogous to the coupling that takes place between nucleation and condensation growth processes in aerosol-dynamic models and is readily described using moment methods.

The use of moments to represent the droplet spectrum has several additional advantages. Foremost of these is computational efficiency as only the lower-order moments of the distribution, and not the distribution itself, are tracked in time. This feature makes the QMOM especially attractive for representing cloud microphysical processes in climate models. Furthermore, the lower-order moments have a closer connection with and are more readily retrievable from remote sensing measurements than the distribution itself. For example, radar reflectivity is a direct measure of the 6th radial moment and retrievals of the effective radius give the ratio of the 3rd to 2nd radial moments. The threshold function previously derived for the autoconversion process in atmospheric models is also related to the radar reflectivity, and both are given in terms of moments (Liu et al. 2007a, b). We will investigate both 4-moment and 6-moment tracking for each of the cloud and drizzle droplet populations and compare results with those obtained from a 100-point discrete size grid calculation using a matrix approach.

Liu, Y., B. Geerts, M. Miller, P. H. Daum, and R. McGraw. 2007a. Threshold radar reflectivity for drizzling clouds, Geophys. Res. Letts., revised for publication.

Liu, Y., P. H. Daum, R. McGraw, M. Miller, and S. Niu. 2007b. Theoretical expression for autoconversion rate of cloud droplet number concentration, Geophys. Res. Letts. 34, L16821, doi:10.1029/2007GL030389.

This poster will be displayed at ARM Science Team Meeting.

POSTER in PDF: for proper viewing, it should be viewed with Adobe Acrobat Reader. Download the latest version from the Adobe Reader website.