Collaborative Research: From Fine-Scale Mixing to the Mesoscale--Assessing the Climatic Impact of Trade-Wind Cumulus with RICO Data and Modeling

点击翻译按钮获取中文摘要

Further analysis of datasets acquired during the Rain in Cumulus over Ocean (RICO) experiment will be performed. These datasets include scanning K-band cloud radar data acquired on the R/V Seward Johnson, aircraft in-situ microphysical observations, land-based S-Pol polarimetric weather radar data, and satellite imagery. These analyses will be used to constrain and improve the numerical modeling of shallow tropical cumulus clouds. One focus is a statistical assessment of both the macrophysical and microphysical attributes of populations of trade-wind cumuli. Statistics will include cloud top height, cloud depth, horizontal dimensions and spacing, precipitation, and liquid water content. Cloud lifecycles will be examined using simultaneous K-band and S-Pol radar data. The statistical relationships derived from observations will be compared to those derived from numerical model output. This will provide observational constraints on the model, and allow improvement of parameterizations in the model of the physical properties and effects of shallow cumulus clouds on the tropical lower atmosphere. Satellite data for the region, including satellite radar observations, obtained during and after the RICO experiment, will be examined to assess representativeness of joint reflectivity-height distributions observed during RICO, and the relationships of these joint distributions to regional surface winds, precipitable water vapor, and liquid water path. A second focus of the investigation will be validation of model-computed three-dimensional radiative transfer through these cloud fields. The ultimate goals are to improve capability to parameterize precipitation from shallow tropical cumulus, and to provide an accurate parameterization to account for their radiative effects suitable for use in larger-scale climate models. The broader impact of the work is the integration of RICO observations with numerical modeling, leading to generalization of RICO results into lessons useful to the much broader atmospheric science community. Much of the requested funding will be used to support the education and training of two graduate students.