A Data-Driven Decision Support Tool for Anticipating Tropical Storm Impacts to the United States Power Grid
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24 POWER TRANSMISSION AND DISTRIBUTION↗
Engineering topics
Anagnostou, Emmanouil
Publications and source records attributed to Anagnostou, Emmanouil.
A Combined Infrared and Microwave Technique for Studying the Diurnal Variation of Rainfall over Amazonia
Over 10 years of continuous data from the Special Sensor microwave Imager (SSM/I) aboard a series of Defense Department satellites has made it possible to construct regional rainfall climatologies at high spatial resolution. Using the Goddard Profiling Algorithm (GPROF), monthly estimates of precipitation were made over the region of northern Brazil, including the Amazon Basin, for 1987 to 1998. GPROF is a physical approach to passive microwave precipitation retrieval, which uses the Goddard Cumulus Ensemble (cloud) model to establish prior probability densities of precipitation structures. Precipitation fields from GPROF were stratified into morning and evening satellite overpasses, and accumulated at monthly intervals at 0.5 degree spatial resolution. Important diurnal effects were noted in the analysis, the most pronounced being a land/sea breeze circulation along the northern coast of Brazil and a mountain/valley circulation along the Andes. There were also indications of morning rainfall maxima along the major rivers, and evening maxima between the rivers. The addition of simultaneous geosynchronous infrared (IR) data leads to the current technique, which takes advantage of the 30 minute sampling and 4 km spatial resolution of the infrared channel and the better physics of the microwave retrieval. The resultant IR method is subsequently used to derive the diurnal variability of rainfall over the Amazon basin, and further, to investigate the relative contribution from its convective and stratiform components.
Negri, Andrew J.↗
A Satellite Infrared Technique for Diurnal Rainfall Variability Studies
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.
Anagnostou, Emmanouil↗