Disclaimer: This material is being kept online for historical purposes. Though accurate at the time of publication, it is no longer being updated. The page may contain broken links or outdated information, and parts may not function in current web browsers. Visit https://espo.nasa.gov for information about our current projects.

 

Use of A‐Train data to estimate convective buoyancy and entrainment rate

Luo, Z. J., G. Y. Liu, and G. Stephens (2010), Use of A‐Train data to estimate convective buoyancy and entrainment rate, Geophys. Res. Lett., 37, L09804, doi:10.1029/2010GL042904.
Abstract: 

This study describes a satellite‐based method to estimate simultaneously convective buoyancy (B) and entrainment rate (l). The measurement requirements are cloud‐top height (CTH), cloud‐top temperature (CTT), cloud profiling information (from radar and lidar), and ambient sounding. Initial results of the new method applied to A‐ Train data are presented. It is observed that tropical oceanic convection above the boundary layer fall into two groups: deep convection (DC) and cumulus congestus (Cg). DC tend to have negative buoyancy near cloud top and l < 10%/km. Cg are further divided into two groups due to the snapshot view of the A‐Train: one has positive buoyancy and l ≤ 10%/km and the other has negative buoyancy and l reaching up to 50%/km. Uncertainty analysis is conducted showing that CTT and CTH are the primary source of errors, but they do not affect our conclusions qualitatively. Brief comparisons with previous studies indicate the results of this study are broadly consistent with these earlier studies. Although most of the initial results are expected, this study represents the first time, to our knowledge, that satellite data are used to estimate convective buoyancy and entrainment rate. This new, space‐ borne method presents an opportunity for a number of follow‐up investigations. For example, it serves as a bridge to connect A‐Train observations (and follow‐up missions) to GCM cumulus parameterizations.

PDF of Publication: 
Download from publisher's website.
Research Program: 
Modeling Analysis and Prediction Program (MAP)