Wednesdays 4:30pm-7:00pm at SAMSI, Research Triangle Park, NC, beginning Wednesday, January 13, 2010
No class during week of Spring Break (March 17, 2010)
Last class: Wednesday, April 28, 2010
This course was taught by:
- Noel Cressie, Univerity of Wollongong
- Marco Ferreira, University of Missouri
- Gamerman Dani, Federal University of Rio Grande do Sul
- Chong He, University of Missouri
- Renato Martins Assuncao, Univ Federal Minas Gerais, Brazil
- Bruno Sanso, University of California, Santa Cruz
- Alexandra Schmidt, Federal University of Rio Grande do Sul
- Marian Scott, University of Glasgow
- Dongchu Sun, University of Missouri
- Michelle Bell, Yale University
- Richard Smith, University of North Carolina at Chapel Hill
- Alan Gelfand, Duke University
Much of the case for climate change, weather forecast, and determination of air pollution levels and the impact of all these factors on the ecosystem and human health, has relied on deterministic climate, weather and air pollutions models that embrace physical and chemical modeling. These models are approximate representations of the real world, and, hence must be continually assessed. Model errors in atmospheric models must be identified and characterized to provide statements about confidence in results. The results of climate, weather and air pollution models are extremely multi-dimensional. It is very difficult to present all of this information concisely in a manner that can be understood by decision makers. Dimension reduction and data presentation techniques are needed for contrasting spatial data, explaining what is being presented, and determining how to describe the confidence of projections from non-random samples.
Also available for assessing climate change and pollution levels are observational data from different measurement platforms (satellites, weather balloons, surface thermometers, monitoring stations, etc.). Like the simulated data, these can represent very different spatial scales. Understanding, modeling, and analyzing these spatial and temporal uncertainties, in the context of the massive (but sparse) data and the impact on climate change, requires significant methodological and theoretical advances.
In this course we will introduce the statistical methods to characterize uncertainties in climate, weather, ecological and air pollution deterministic models. We will also present statistical frameworks to combine disparate spatial data, from observations and output of deterministic models, and to measure the agreement between an artificially generated climate signal from a climate model and real data as measured by surface observation stations or satellites. We will cover statistical methods for processing ensembles of climate models. We will introduce different spatial temporal modeling approaches to characterize trends in space and time, as well as to estimate dependency structures, and to do space-time prediction for climate, weather, ecological and air pollution data. We will introduce state-of-the art methods for dimension reduction, spatial extreme events in climate and weather, and impact of climate change on mortality and human health.