DEPARTMENT OF MATHEMATICAL SCIENCES

APPLIED AND COMPUTATIONAL MATHEMATICS SEMINAR

**Speaker:** Brian Hunt, Mathematics, University of Maryland, College Park

**Title: ***
Determining the initial conditions for a weather forecast
*

**Abstract:**
A key factor limiting the accuracy of weather forecasts is the
accuracy of the initial conditions used. Global weather forecast
models require as input meteorological variables (temperature, wind
speed, etc.) at a regular grid of points throughout the atmosphere.
Accurate measurements of these variables are not available at nor even
near many of the grid points. In practice, the initial conditions are
formed by a statistical interpolation between the available
measurements and a prior forecast. This interpolation, involving
millions of variables, must be done in a computationally efficient
manner. I will describe an approach to this problem that we have
developed at the University of Maryland, which we call a Local
Ensemble Transform Kalman Filter. As with other Ensemble Kalman
Filters, we track an ensemble of solutions of the forecast model in
order to assess the likely states of the atmosphere at a given time,
and periodically adjust the ensemble to select the states that are
most likely in light of newly collected measurements. Unlike other
methods, we do the adjustment in a manner that is explicitly
geographically local, which both improves the accuracy of the results
and allows for a massively parallel implementation. Our method can be
used more generally to estimate the state of a spatiotemporally
chaotic system for which a forecast model is known but only limited
measurements are available.

**Time:** Friday, Feb. 20, 2009, 1:30-2:30 p.m.

**Place:** Science and Tech I, Room 242

Department of Mathematical Sciences

George Mason University

4400 University Drive, MS 3F2

Fairfax, VA 22030-4444

http://math.gmu.edu/

Tel. 703-993-1460, Fax. 703-993-1491