Storm-Scale Quantitative Precipitation Forecasting Using Advanced Data Assimilation Techniques:  Methods, Impacts and Sensitivities



Dr. Ming Xue, Principal Investigator

Dr. Keith Brewster, Co-Principal Investigator

Dr. Jidong Gao, Co-Principal Investigator

Dr. Alan Shapiro, Co-Principal Investigator


Center for Analysis and Prediction of Storms

University of Oklahoma


12/15/2005 - 12/31/2008




      Convective storms and associated strong winds and heavy precipitation cause billions of dollars of damage and numerous deaths annually; at the same time, accurate forecasting of severe weather and precipitation amounts are among the most challenging tasks in meteorology. As participants in a former NSF Science and Technology Center, CAPS, whose focus was and remains convective-scale data assimilation (DA) and numerical weather prediction (NWP), this group will develop and apply advanced techniques and tools for predicting localized precipitation, and will study the impact of assimilated data and the sensitivities associated with initial and boundary conditions and model physics. The project will perform detailed analyses of high-resolution numerical simulations in order to understand fundamental physical processes that determine how, when and where convective storms are initiated. It will study the impact of advanced cloud microphysics on quantitative precipitation forecasting (QPF), and understand the microphysical processes responsible for the sensitivities. The knowledge gained from the process and sensitivity studies will be applied to the design and improvement of DA systems. The research will exploit a wealth of observations collected during the 2002 International H2O Project, the largest continental field experiment ever undertaken whose main focus was on the 4-D distribution of atmospheric water vapor and its impact on QPF. Several other intensively observed tornadic thunderstorm cases will also be used. Starting from accurate analyses of these storms, attempts will be made to predict, for the first time, real tornadoes using a numerical model.


First Annual Report, October 2006.