ALBERT

Description: Reliable information on the distribution of precipitation at high temporal resolution (〈l hour) is essential for understanding the characteristics of convection in a region. Cloud-top infrared (IR) brightness temperatures from geostationary platforms have a weak physical connection to precipitation, however, their high sampling frequency makes them attractive in studying the temporal evolution of cloudiness and convection. On the other hand, microwave-based (MW) observations from lower sampling-frequency polar-orbiting platforms can provide a better physical connection to precipitating hydrometeors. A recent invention in rainfall estimation from a combination of these two sensors involves adjustment of IR estimates using co-existing MW-based precipitation data on a monthly basis. These techniques use the MW data to remove systematic errors in IR rain estimates, while retaining the high sampling frequency of IR observations (approximately every 15-30 minutes). Perhaps of even greater importance to climate and hydrometeorological applications is the separation of mesoscale convective systems into a portion of rain associated with deep convection (hereafter called convective precipitation), and to precipitation falling from more widespread anvil clouds. This current work focuses on estimation of tropical convective and stratiform rainfall. We attempt to answer fundamental questions, such as : is estimation of convective and stratiform precipitation from IR feasible? If so, how accurate can this be? What is the scale dependence of the IR algorithm's performance? To address these questions, quantitative comparisons are performed between coincident IR- and MW-based instantaneous rainfall estimates at the MW 85 Ghz resolution (-12.5 km). Our data set spans a three-month period (January to March, 1996) of MW and IR observations over northern South America (15N-15S and 35W-80W), which includes the Amazon river basin.