Short
Course at 2002 AMS Meeting
"Neural
Network Applications to Environmental Sciences"
The
course starts with the neural network (NN) tutorial, which does not require
any prior familiarity with the technique. Its major topics include: NN
components, architectures, training, relationship to statistics and regression
technique.Numerous generic environmental
NN applications will be introduced and discussed, many of which are closely
related to the topic of this AMS meeting.Topics
include NN applications for intelligent processing of observations, nonlinear
multivariate data analysis, fast direct assimilation of satellite data,
accurate retrieving geophysical information from satellite data, prediction,
scene classification, discrimination between clouds and snow in satellite
imagery, improving computational efficiency of numerical models, nonlinear
time series analysis, data fusion, and so on.Many
practical NN solutions developed and implemented by instructors for atmospheric
and oceanic applications will be introduced.A
practical approach to development of NN applications will be outlined.Some
applications involving fuzzy logic will also be discussed.Computer
demonstrations will enhance and illustrate the course.Interactive
sessions will help students communicate closely with instructors and obtain
advice about applications of NN to various projects.
Program
Outline
Instructors:
Dr.
Bryan
A. Baum, (NASA Langley Research Center)
Prof.,
Dr. William W.
Hsieh (University of British Columbia)
Dr.
Vladimir
Krasnopolsky, (SAIC at NCEP/NWS/NOAA)
Prof.,
Dr. Caren Marzban,(University
of Washington)