A51F-0117
Organized precipitating systems in tropics and their environmental characteristics

Friday, 18 December 2015
Poster Hall (Moscone South)
Baohua Chen, Texas A & M University Corpus Christi, Corpus Christi, TX, United States and Chuntao Liu, Texas A&M Univ Corpus Christi, corpus christi, TX, United States
Abstract:
In nature, tropical precipitating systems are often observed to cluster together. Organized rain systems play the leading roles in the heat, moisture and momentum budgets. Although mesoscale organizing has been studied by many previous literatures, it is still difficult to introduce the physics of organization to GCMs due to some unclear complicated processes. To this challenge, an exploring approach is proposed in this study to investigate the interactions between organized systems and background thermodynamic environment. The 16-yr Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) dataset is used to characterize the precipitating features. A feature with large rain area is the proxy of an organized system, which includes convective and stratiform precipitation regions embedded with convection at various stages. ECMWF reanalysis of atmospheric fields are collocated to characterize the immediate environments. The resolution of atmospheric variables is comparable to a grid box in current climate model. The motivation is to investigate the characteristic of grid-box mean environment corresponding to organized rain systems. Following questions will be addressed:
  1. Which model-resolved atmospheric variables are identified to be closely associated with the aggregation of precipitating system, such as moisture, low-level wind shear, moisture convergence, atmospheric instability etc.?
  2. If a particular environmental condition at model grid is given, can we reproduce the fractional rainfall contribution from large precipitating systems?

This study aims to provide some observational supports for the systematic relationships between organized systems and their environments, based on a large number of sample data. Hopefully, this approach could offer useful information to improve the parameterization of convective hierarchies.