HR: 0800h
AN: A41A-05    [Abstracts]
TI: Long-term Probabilistic Forecast and T* Distribution
AU: * Chu, S
EM: chu.shao-hang@epa.gov
AF: U.S. Environmental Protection Agency, US EPA/OAQPS (C539-04), 109 Alexander Drive, RTP, NC 27711, United States
AB: Models are great tools to test ideas. Their usefulness, however, depends on their ability to simulate the current reality and predict the future. In this study, I show that a statistical model based on a new t*-distribution of station temporal data is capable of predicting the probability of any future outcome to exceed a specific value using only the currently available sample statistics assuming a normal random variable. In an air quality management application, the model has demonstrated categorically an average success rate of over 80 percent both in simulating the current ozone non-attainment areas and forecasting the rate of future violation of the 8-hour ozone National Ambient Air Quality Standards in the U.S. for up to 12 years. While the predictability of deterministic climate models is still limited by large uncertainties, the probabilistic forecast by this model provides a promising alternative in assessing the climate impact on environment for decades.
DE: 0345 Pollution: urban and regional (0305, 0478, 4251)
DE: 0545 Modeling (4255)
DE: 3245 Probabilistic forecasting (3238)
DE: 3305 Climate change and variability (1616, 1635, 3309, 4215, 4513)
SC: Atmospheric Sciences [A]
MN: 2007 Joint Assembly