The National Observation, Prediction and Analysis of Severe Convection of China (OPACC) 973 Project and Research Highlights

Conference: 
ICMCS-X
Presentation Type: 
Oral
Author(s): 
Ming Xue (University of Oklahoma and Nanjing University)
Abstract: 

The national Observation, Prediction and Analysis of severe Convection of China (OPACC) project started in January 2013 is introduced. It is a 5-year project funded by the Ministry of Science and Technology of China as part of the National Fundamental Research 973 Program, and is consisted of scientists from 8 institutions that include Nanjing University, Beijing University, the Institute of Atmospheric Physics, National Meteorological Center, with Nanjing University serving as the lead organization. The project includes 29 core scientists plus many more participants, and will focus on meso-g and meso-b scale convective weather systems of China, with emphasis on the effective utilization of high-resolution, modern operational observing networks as well as the utilization and deployment of special experimental fixed and mobile observing facilities, and in particular dense networks of polarimetric Doppler radars, during intensive observing periods (IOPs). The primary goals of the project include the understanding of the dynamic, thermodynamic and microphysical processes supporting and within specific types of mesoscale convective systems of China, processes leading to severe weather including damaging winds, heavy localized precipitation, hail, tornadoes, etc. Developing new data assimilation and prediction capabilities that will enhance the forecasting capabilities and accuracy of severe convective weather at the national and regional levels is also a central goal. Results obtained from field data collections during 2013 and 2014, and from realtime forecasts during the rainy seasons of the two year over China at a convection-permitting resolution will be presented. Key research findings on convective processes and events, and the development of new data assimilation and prediction capabilities will also be highlighted.

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