Summer 2003
Summer 2003 Abstracts

Donald Charity

Willie Gilchrist
L. Creekmore
Vincent Davis
Danielle Graves
Carl Seward
Eunice Smith
Nelson Veale
A. Anderson
Zaccheus Eley
Cory Hill
Karitsa Williams
Tracey Ward
Golar Newby
 

Zaccheus Eley Karitsa Williams
email: kgwilliams@mail.ecsu.edu

Mentor:
Dr. S. Raj Chaudhury, NSU
Internship:
Digital Earth Group. REESS Summer Internship Program, Norfolk State University
Title:
Disaster Agency Readiness: Predicting And Preparing For El Nino, La Nina Southern Oscillation (ENSO), And North Pacific Oscillation (NPO)

Digital Earth is a program created by Vice-president Al Gore to view vast quantities of geo-referenced data on a multi-resolution 3-dimensional representation of the Earth. This program was designed to offer mass amounts of data to the public in a user-friendly format. The program is designed to teach through easy-to-use classroom modules. To demonstrate the usefulness of this program, we used satellite data to show/predict trends in El Nino Southern Oscillation (ENSO). We created a user-friendly, classroom ready module that will make this data easier to understand. El Nino Southern Oscillation (ENSO) is a disruption of normal atmospheric flow patterns in the tropical and southern hemisphere areas of the Pacific Ocean. ENSO can create substantial changes in sea-surface temperatures (SST) of the eastern and central equatorial Pacific. The ability to predict El Nino trends will have great societal and economical impacts. These impacts include increase awareness of environmental destruction and a decrease in economic loss. In order to create a working module for students and faculty to use, we utilized information on ENSO, North Pacific Oscillation (NPO), El Nino/La Nina, sea-surface temperature (SST), vegetation index data, and Heavy Precipitation Frequency (HPF) data. Also by using this information we searched for trends that will help us to determine whether the year to come will have an El Nino or a La Nina, high or low NPO, and high or low HPF. We also presented our findings in an easy to understand multi-dimensional model.