STEM Education: Preventing the pipeline from leaking
April 3, 2008 - 11:30 am
The College of Engineering and Tau Beta Pi invites you to a free panel luncheon where you can voice your opinions and learn how to get involved:
Featuring panelists from the UM community:
Jill Andrews – Director, Office of Engineering Outreach and Engagement (OE)^2
Perry Samson – Associate Chair and Professor Department of Atmospheric, Oceanic and Space Sciences
Richard Hill – Graduate Student, Department of Mechanical Engineering
Aaron Santos – Research Fellow, Department of Chemical Engineering
America is at risk of losing its place as the global leader in innovation if we do not address the issues. Other countries are currently outpacing the U.S. in science and technology. In 2004, the NAS study found that China graduated more than 600,000 engineers; India, 350,000 and America, only 70,000. How can we address this trend? What is the current state of public education in regards to science, technology, engineering, and math (the STEM fields)? How can we encourage and prepare students to become leaders in the STEM fields? Are there steps that should be taken or programs that can be implemented to help prevent ‘the STEM pipeline from leaking?’
**Free Middle Eastern food from Jerusalem Garden**
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Tomislava Vukicevic, Atmospheric and Oceanic Sciences, University of Colorado, Boulder
April 3, 2008 - 3:30 pm
A new look at data assimilation and inversion problems in atmospheric sciences. Data assimilation and inversion problems are involved in almost every aspect of quantitative analysis in atmospheric sciences from observing by indirect measurements to prediction and projections by numerical models. Contemporary literature on common data assimilation and inversion techniques typically refers to theoretical basis of the techniques as a straightforward generalization of the Bayesian rule for conditional probabilities when applied under assumptions of errorless linear model and Gaussian statistics. The data assimilation and inversions are however, more often than not done with complex nonlinear models and observations for which these assumptions are not necessarily valid. To help understand impact of model nonlinearities, Gaussian statistics and modeling and observation errors a new approach is used which is based on a generalized formulation of the statistical inverse problem theory that does not make explicit use of the Bayesian rule.
Based on the new approach an analysis of data assimilation and inversion solutions was done by numerical evaluation of the associated probability density functions on examples of two relatively simple but representative dynamical models of atmospheric processes. Relationship between properties of common data assimilation and inversion techniques and the generalized solutions will be discussed with an outlook at furthering benefits from inverse methodology in atmospheric sciences.
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Dr. Hiram Levy, NOAA
April 24, 2008 - 3:30 pm
Title: "Climate Change Science Program: Synthesis and Assessment
Product 3.2 or Climate vs. Air Quality?"
Abstract:
By year 2100, "IPCC" projected decreases in sulfate aerosol and
increases in black carbon aerosol contribute a significant portion of
the simulated A1B surface air warming relative to the year 2000; 0.2oC (southern hemisphere), 0.4oC globally, 0.6oC (northern
hemisphere), 1.5-3oC (wintertime Arctic), and 1.5-2oC (~40% of the
total) in the summertime United States. These changes are also
responsible for a significant decrease in central US late-summer
root-zone soil water and precipitation. These changes in short-lived
air pollutants produce a global average increase in radiative forcing
of ~1 W/m2; over East Asia it exceeds 5 W/m2. However, the resulting
regional patterns of surface temperature warming are uncorrelated to
the forcing pattern (correlation coefficient of -0.172) and similar
to the temperature patterns for well-mixed greenhouse gases (global
pattern-correlation coefficient of 0.8) with the strongest summertime
warming occurring over the continental US, Mediterranean Sea and
southern Europe. It should be noted that the projections of future
pollution emissions are extremely uncertain, particularly looking out
to 2100.
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