Spatial Statistics (3-credit PhD-level course): Stat 829
The course is taught in the Autumn quarter, (next taught in Autumn 2008). The lectures present topics that include exploratory spatial data analysis, (multivariate) spatial prediction, spatial hierarchical modeling (empirical Bayesian and fully Bayesian), and the incorporation of a temporal component in spatial models.

Environmental Statistics (3-credit MS-level course): Stat 662
The course is taught every other Spring quarter (next taught in Spring quarter 2010). The lectures present topics that include standard statistics used in environmental settings, bioassay, censoring, spatial statistics, and hierarchical models.

Discussions In Spatial and Environmental Statistics (DISES)
The SSES Program organizes DISES on an occasional basis. Previous manifestations were DIGES (DIscussion Group in Environmental Statistics) and brown-bag seminars. If you would like more information, please e-mail us at sses@stat.osu.edu.

Tutorial on Bayesian Statistics for Geophysicists
A web-based instructional tool, Tutorial on Bayesian Statistics for Geophysicists, has been developed in conjunction with the Web-Project, Ice Streams.

Tutorial on Hierarchical Bayesian Modeling for Exposure to Arsenic
A web-based instructional tool, Tutorial on Hierarchical Bayesian Modeling for Exposure to Arsenic, has been developed in conjunction with the research project, From Sources to Biomarkers.

Some of the science and engineering problems to which this approach has been applied are given below.

Remote Sensing
The Terra Spacecraft, the first of a series of satellites to gather definitive data on global conditions, was launched by NASA in December 1999 as part of the Earth Observing System. Enormous amounts of time and money have been spent on obtaining multi-resolution satellite data with pinpoint accuracy. Data-production algorithms currently use rudimentary statistical methods, such as the sample mean and the sample variance, to interpolate these "level 2" data (irregularly distributed in space and time), in order to obtain regularly distributed "level 3" data. In doing so, the obvious spatial and temporal dependencies present in remote-sensing data gathered from satellites have been largely ignored. We address this problem through an empirical Bayesian methodology that yields a dynamic, spatial, change-of-resolution Kalman filter. The methodology has been applied to Total Column Ozone (TCO) data. Further details are available on the Web-Projects page. A more recent empirical Bayesian methodology, called Fixed Rank Kriging, is also being applied to very large remote-sensing data sets.

Geophysical Modeling
The dramatic world-wide shifts in weather patterns and associated societal impacts caused by the 1997-98 El Nino brought unprecedented attention to the role of inter-annual climate variability in our daily lives. Climate processes exhibit substantial spatio-temporal variability that is often non-stationary, non-linear, and dependent. Although it can be quite difficult to model such variability with traditional spatio-temporal approaches, we have demonstrated that it is possible to use highly structured (hierarchical), Bayesian, spatial-temporal models. Physical knowledge of the system is used wherever possible in constructing the models resulting in what has been called physical statistical modeling. Two projects that take this approach are the study of sea surface temperature in the equatorial Pacific Ocean in order to forecast El Nino Southern Oscillation (ENSO) events, and a study of ice-stream dynamics in Antarctica and Greenland. Further details are available on the Web-Projects page.

Human Exposure to Metals
The project, "From Sources to Biomarkers: A Hierarchical Bayesian Approach for Human Exposure Modeling", seeks to characterize multi-pollutant (arsenic, lead, cadmium, and chromium) human exposures by linking sources to biomarkers using a multi-scale hierarchical Bayesian statistical model. Further details are available on the Research page.