Assessing Uncertainty in Mesoscale Numerical Weather Prediction

                         Montserrat Fuentes
                     Department of Statistics
                 North Carolina State University

Current methods of meteorological forecasting
produce predictions with unknown levels of uncertainty,
particularly in regions with few observational assets.
Forecast errors and uncertainties also arise from
shortcomings in model physics. With the ability to estimate
the uncertainty in predictions, forecasters would have a
powerful tool to make decisions and to judge the likelihood
of mission success.

The goals of this work are to develop methods for evaluating
the uncertainty of mesoscale meteorological model
predictions, and to create methods for the integration and
visualization of multisource information derived from model
output, observations and expert knowledge.

We also develop a new approach to assess the performance of
mesoscale numerical models, and show how it can also be used
to remove the bias in model output. We specify a simple
model for both numerical model predictions and  observations
in terms of the  unobserved ground truth, and estimate it in
a Bayesian way.

 Montserrat Fuentes
                                  Office: Patterson Hall 210 C
 Box 8203 NCSU                    Tel: 919-515-1921
 Department of Statistics         Fax: 919-515-1169
 North Carolina State University  Email: fuentes@stat.ncsu.edu
 Raleigh, NC 27695

 Home Page: http://www4.stat.ncsu.edu/~fuentes/


 Street address for Fedex: 2501 Founder's Drive, Room 209 Patterson Hall.
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