Institute for Mathematical Sciences Event Archive
Mathematical Science of Understanding and Predicting Regional Climate: A School and Workshop
(28 February - 11 March 2011)
Jointly organized with National Center for Atmospheric Research, USA, Singapore-Delft Water Alliance,
Tropical Marine Science Institute, NUS
Organizing Committee · Visitors and Participants · Overview · Poster · Activities · Venue · Funding for Students/Young Scientists
Chair
- Douglas Nychka (National Center for Atmospheric Research)
Members
- Vladan Babovic (National University of Singapore)
- James Done (National Center for Atmospheric Research)
- Greg Holland (National Center for Atmospheric Research)
- Hans Kuensch (ETH, Zurich)
- Shie-Yui Liong (National University of Singapore)
- David Nott (National University of Singapore)
- Pavel Tkalich (National University of Singapore)
Greenhouse gases released into the atmosphere by human activities have effected the overall climate of the Earth. Although there is high confidence in climate change at a global scale and for surface temperatures, the change in climate for specific regions, such as Southeast Asia, and for other variables, such as rainfall and sea level, have less certainty. Determining changes in climate at local scales is crucial because of its value for making policy decisions, assessing ecological and economic impacts and planning future infrastructure. This program explores from a mathematical and statistical perspective how to improve prediction of regional climate changes. Mathematics can support improvements in physical models, the combination of models with observations and also characterizing the uncertainty of climate predictions. The goal of this program is to bring together mathematical and geophysical scientists to address this problem from a multidisciplinary and collaborative perspective.
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School: 28 Feb - 4 Mar 2011
A week-long training and lab activity that will blend lectures with hands-on data experience using the R statistical language and student teams working on climate data relevant to Singapore. The level will be comparable to a Masters level applied statistics course but will accommodate interested scientists and graduate students outside of statistics. This will include tutorial lectures on geostatistical and Bayesian methods for spatial data.
The materials for the short course will be posted on the IMAGE/NCAR site along with the software that supports the statistical analysis. (In the interim, see http://www.image.ucar.edu/~nychka/NUSIMS.)
Click here for the materials for the school.
Participants are required to bring their own laptop for the course and have the latest R version 2.12.1 software installed.
Installation instructions for Mac users
Installation instructions for Windows users
Principle Lecturer: Stephen Sain, National Center for Atmospheric Research, USA
* Strictly no walk-in
Workshop: 7 - 11 Mar 2011
Three themes will be linked through a week long workshop that encourages discussion among scientists from different disciplines The analysis of climate impacts relevant to Singapore and Southeast Asia through the use of climate model numerical experiments. Statistical models for computer experiments. Dimension reduction techniques for large spatio-temporal fields.
Keynote Lecturer: Linda O. Mearns, Program Director and Senior Scientist, National Center for Atmospheric Research, USA
Students and researchers who are interested in attending these activities and who do not require financial aid are requested to complete the
online registration form.
The following do not need to register:
- Those invited to participate.
- Those applying for financial support.
The Institute for Mathematical Sciences has limited funds to cover living expenses for students and young scientists interested in participating in the program.
We regret that only successful applicants will be notified.
Application for financial support is closed.
For enquiries, please email us at ims(AT)nus.edu.sg.
Organizing Committee · Visitors and Participants · Overview · Poster · Activities · Venue · Funding for Students/Young Scientists
