Data bases

Thursday, June 25, 2015 - 2:00pm - 3:30pm
Chris Snyder (National Center for Atmospheric Research)
The problem of state estimation has come to be known as data assimilation in many geophysical applications. I will review data assimilation for the atmosphere, especially for numerical weather prediction. A distinguishing characteristic of atmospheric data assimilation is the diversity and extremely large numbers of observations considered and the extremely large dimension of the state vector.
Monday, November 18, 2013 - 9:45am - 10:25am
James Faghmous (University of Minnesota, Twin Cities)
Our planet is experiencing simultaneous changes in global population, urbanization, and climate. These changes, along with the rapid growth of climate data and increasing popularity of data mining techniques may lead to the conclusion that the time is ripe for data mining to spur major innovations in climate science. However, climate data bring forth unique challenges that are unfamiliar to the traditional data mining literature, and unless they are addressed, data mining will not have the same impact that it has had on fields such as biology or e-commerce.
Monday, January 14, 2013 - 11:30am - 12:20pm
Andrew Stuart (University of Warwick)
The 3DVAR filter is prototypical of methods used to combine observed data with a dynamical system, online, in order to improve estimation of the state of the system. Such methods are used for high dimensional data assimilation problems, such as those arising in weather forecasting. To gain understanding of filters in applications such as these, it is hence of interest to study their behaviour when applied to infinite dimensional dynamical systems. This motivates study of the problem of accuracy and stability of 3DVAR filters for the Navier-Stokes equation.
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