A pilot study was undertaken to determine the spatial and temporal variability of chlorophyll concentrations in the northwestern Gulf of Mexico during 2002. The chlorophyll parameter was obtained from daily Level-3 estimations of Sea-Viewing Wide-Field-of–view Sensor (SeaWiFS) data computed by the Naval Research Laboratory. An empirical eigenfunction (EOF) analysis was performed on the data using the Karhunen-Loeve (KL) algorithm. Ten empirical eigenfunctions, temporal coefficients, and variance spectrum were computed. This analysis revealed that 15% of the variance around the mean is accounted by the first empirical eigenfunction, which is identified with chlorophyll fluctuations around the Mississippi Delta, Lakes Pontchartrain and Borgne, the Mississippi Sound, and the Mobile, Pensacola, and Choctawhatchee Bays. The eigenfunction shows that the chlorophyll in near-shore water is changing more rapidly than the rest of the shelf waters. The second EOF which contained 3% of the variance is found to be related to changes in chlorophyll in bays and estuaries to the east of the delta, exclusively. The third EOF (%) was identified with the waters flowing east from the mouth of the Mississippi into bays and estuaries. The fourth EOF (%) is identified with changes in chlorophyll concentrations at the mouth of the Mississippi River proper, propitiated by the river flow. Because this EOF is also identified with waters of Lakes Pontchartrain and Borgne as well as with the Mississippi Sound, it is possible that the changes observed may not be related to chlorophyll but to increases in dissolved and particulate components brought about by an increase in rain fall.

Interns for the summer of 2004 (May-Aug) will assist in determining the maximum depth of seagrass beds along the Southern Outer Banks. Interns will be responsible for assisting scientists at the NOAA Beaufort Laboratory with this project. The interns will work on georeferencing existing aerial photography, selecting sampling sites using this imagery placed in a GIS, and then going in the field to assist with ground-truthing activities.
The goals of this project are to develop a long-term record of seagrass bed extent, focusing primarily on the historical changes that have occurred to the deep-edge, and tie this in with historical changes in water-quality. The data gathered from this internship project will be used to calibrate a model of light-attenuation for seagrass habitat requirements. This model is being developed as a tool to assist managers with monitoring water-quality to protect seagrasses, a critical estuarine habitat in North Carolina. Future developments for this model aim to include remote-sensing information in near-real time to enable timely and appropriate management actions to be made

Interns for the summer of 2004 will assist with determining whether an unusually high number of strandings of harbor porpoise during the winter of 1999 was due to an unusual juxtaposition of oceanographic features in the western the mid-Atlantic. The goals are to investigate whether a narrow band of cold water near shore followed by a strong warm water front results in higher numbers of stranded harbor porpoise than when the front is further offshore. Further, interns will examine the effects of wind speed and direction on both sea surface temperature and strandings. Positive results may allow for development of a model that predicts relative numbers of harbor porpoise strandings. This question has been a concern because an alternative explanation for unusually high numbers of strandings is entanglement of porpoises in gillnets along the mid-Atlantic coast. The interns will work on compiling extracted sea surface temperature (SST) and wind data, creating graphs and GIS plots, and assisting with analysis of the data. Only one other episode of alarming numbers of strandings of harbor porpoise in North Carolina has occurred in recent times and that was in 1977. Interns will use SST and wind data for years when it was available to ensure that the convergence of oceanographic events seen in 1999 did not occur in other years when high numbers of strandings also did not occur. Although comparable data do not exist for the 1970’s, oceanographic sampling cruises did collect data that may be useful. Access to these results will require a literature search.

Hurricanes, tornados, thunderstorms, and other natural disasters can have many devastating outcomes. Global warming, the prime investigated natural disaster of the PRISM (Polar Radar for Ice Sheet Measurements) project, has a tremendous effect on the sea level rise. Scientists and researchers have theorized that the excess water is being allocated from the polar ice sheets of Greenland and Antarctica due to the long-term results of global warming; however there are few resources to confirm the gain or loss ice.
Scientists and researchers of the PRISM project have applied their expertise on teams based on the areas of robotics, communications, intelligent systems, and radar. These areas were essential in measuring the ice thickness an determining the bedrock below the ice sheets in Greenland and Antarctica. In this research, the investigator worked with the Intelligent Systems team by learning the messaging patterns between the data producing agents and the requesting agents. The investigator also created UML class diagrams of a messaging subsystem to represent the collaboration and communication between these two types of agents using XML with FIPA standard code. The class diagram assisted scientists and researchers in planning new features for the multi-agent system.

Scientists have speculated that due to long-term global climate change there is a surplus of water being released from the polar ice sheets. Although there is much speculation, they have insufficient data to prove this theory. This uncertainty has prompted scientist to explore the interactions between ice sheets, oceans, and atmosphere in an attempt to quantify the role of ice sheets in sea level rise. Scientists and engineers at the University of Kansas are applying their expertise to develop and utilize innovative radar and robotic rovers to measure ice thickness and determine bedrock data conditions below ice sheets in Greenland and Antarctica. This combination of data will help earth scientists determine how quickly the polar ice sheets are melting and to make sure more accurate predictions of the effects of this melting on sea level rise.
The Polar Radar for Ice Sheet Measurements (PRISM) project aims to design and develop an autonomous mobile radar system to measure polar ice sheets. The PRISM team is divided into four primary areas: Communication, Robotics, Intelligent Systems, and Radar. The communication team is creating technologies that enable communication with the rover, in the field and also from the field to the University of Kansas. The Robotics group created a virtual prototype model that has been a guide to the design and operation of the rover, which includes maneuverability limits, speed limits, and antenna towering capability. The Radar team produced radar systems that are required to execute the scientific measurement of the ice sheets, and the intelligent Systems team has designed the specification for an intelligent agency for radar and vehicle control.
We are currently working with the Intelligent Systems team using the PRISM Intelligent System codebase to get an understanding of the issues involved in the design and implementation of multi-agent systems. Agents share some common properties but are also very diverse. Some agents are mobile while others are static, some communicate through messages others don’t communicate at all, and some perform tasks individually while others work cooperatively. The study of multi-agent systems focuses on systems in which many intelligent agents interact with each other. Using the PRISM codebase, we are enhancing our understanding of multi-agent systems and contributing to the PRISM project by creating UML models of key parts of the multi-agent architecture including the MatchMaker, and the underlying RMI system that allows agents to communicate with other agents over networks. Using UML we will develop a class diagram of a messaging subsystem and also a sequence diagram of an agent getting data from a sensor.
Using UML I have developed a sequence diagram of a temperature agent getting data from a remote temperature sensor. The diagram illustrates the various objects and methods that are used when the temperature agent talks to the temperature sensor on the robotic rover. With this model the PRISM Intelligent Systems team can plan modifications and addition to the multi-agent system. The diagram will help plan for additional functionality for next years field test.

Abstract: The scope of this analysis is to measure the impact of the simplifying assumption that the Earth is flat and not round when upward continuing gravity data. Because of the differences in spatial geometry, assuming a flat plane will cause systematic effects as data are recomputed at a higher elevation. These effects will become more exaggerated with elevation and with distance away from the point of tangency between the assumed plane of the observations and the actual curve of the Earth. The first case means that as you go up, the magnitude of the errors should increase. The second case means that as you move away from the point of tangency at the same elevation, the errors should also increase (i.e., systematic and not random errors). Problems arise in both cases, because the observed gravity data are not actually in a flat plane. Hence, the physical relationship has been assumed different than it actually is.