Spatial Statistics in Climate, Ecology and Atmospherics - Spring 2010
Coordinator: Montse Fuentes, North Carolina State University
Guest Instructors: Noel Cressie, Marco Ferreira, Gamerman Dani, Chong He, Bruno Sanso, Alexandra Schmidt, Marian Scott, Dongchu Sun, and Richard Smith.
Emails:[email protected],[email protected],[email protected],[email protected], [email protected],[email protected],[email protected],[email protected], [email protected]
Assignments: The course will be taught by multiple instructors. The instructors may, at their discretion, assign exercises and homeworks. In addition, each student will be expected to prepare a written paper (8 pages max.) and a poster to be presented at the end of the course. The deadline for the written paper is April 14. The poster presentation will be held the last week of classes. There will be no formal exams in this course.
Course Day and Time: Course will be hold at SAMSI (driving directions) in RTP.
Wednesdays: 4:30 -7:00
16 lectures, starting January 13, 2010, ending April 28, 2010.
No class on March 17 (Spring break)
April 7 - lecture will be at the Radisson RTP in conjuntion with the Environmental Risk workshop
Course Description:
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.
Lecture 1. January 13. Cressie. Topic: Global mapping of remote sensing data (on space, and space-time). Making inference on spatial quantiles and their exceedance regions.
Reference 1. Reference 2.
Lecture 2. January 20. Schmidt. Nonstationary covariance models for weather and pollution processes.
Class Notes
Lecture 3. January 27. Schmidt. Multivariate spatial models for weather and pollution processes.
Lecture 4. February 3. Gamerman. Spatial dynamic models for weather and pollution processes.
Part 1, Part 2, Part 3
Lecture 5. February 10. Sanso.
Lecture 6. February 17. Sanso. Space time models for weather and climate.
Lecture 7. February 24. Smith. Detection and attribution
Lecture 8. March 3. Smith. Detection and attribution
Lecture 9. March 10. Ferreira. Spatial models for air pollution.
Lecture 10. March 24. Sanso. Statistical methods for processing ensemble of climate models.
Lecture 11. March 31. He. Spatial models in ecology
Lecture 12. April 7. Scott. What can the past climate reconstructions inform us about future climates?
Lecture 13. April 14. Scott. classical design and analysis tools applied to computer models- sensitivity and uncertainty analysis.
Lecture 14. April 21. Sun. Bayesian modeling of climate.
Lecture 15. April 28. Sun. Uncertainty analysis.