Climate, Assimilation of Data and Models - When Data Fail Us
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Thu, 01/12/2011 14:00 |
Prof Juan Restrepo (University of Arizona) |
Computational Mathematics and Applications  |
Gibson Grd floor SR |
| The fundamental task in climate variability research is to eke out structure from climate signals. Ideally we want a causal connection between a physical process and the structure of the signal. Sometimes we have to settle for a correlation between these. The challenge is that the data is often poorly constrained and/or sparse. Even though many data gathering campaigns are taking place or are being planned, the very high dimensional state space of the system makes the prospects of climate variability analysis from data alone impractical. Progress in the analysis is possible by the use of models and data. Data assimilation is one such strategy. In this talk we will describe the methodology, illustrate some of its challenges, and highlight some of the ways our group has proposed to improving the methodology. | |||