University of North Florida—Advanced Weather Information Systems Lab

Application areas for the Advanced Weather Information Systems (AWIS) Lab at the University of North Florida involve the GIS mapping of weather data and phenomena and modeled physical parameter fields onto geospatial entities such as roadways, waterways, parks, construction sites, and individual neighborhoods. Since current weather conditions are constantly changing, near real-time data, analysis, and Web mapping are emphasized. The graphic represents several applications overlaid on the Florida Road Weather Information System. They were developed during the last few years primarily at the AWIS Lab with university, state and federal agency, and private sector partnerships. These applications range from fog modeling to area rainfall and damage estimation.

Meteorological modeling, data ingest, and forecast systems are mature from a technical perspective because the National Oceanic and Atmospheric Administration National Weather Service, federal agencies, universities, and the private sector (referred to as the “Weather Enterprise”) have developed a robust national infrastructure in this domain. However, to date, GIS visualization, geolocation, and spatial analysis tools for weather data have been less well developed.

It is envisioned that in the near term, both weather models and meteorological monitoring systems will dramatically increase in number and resolution. Currently, the state forecasts involve answers to questions such as, What is the chance of rain in my county? Soon, answers to questions such as, What is the chance my ballgame will be rained out? or Is it a good day to plan an afternoon of golf on my local course? will become the norm. Nationally, there is a move toward mesoscale (neighborhood) weather forecasts that will impact the public in many new ways.

Another area where rapid development is forecast is in the high-order coupling of various environmental models (storm water, coastal inundation, plankton growth, fire threat, agriculture, air quality) with atmospheric models. Atmospheric inputs such as rain, wind, and temperature are often the primary force functions for a host of environmental processes, and as such, the coupling of atmospheric model outputs and real-time data with various other environmental model inputs should yield significant improvements in the output quality of these diverse environmental models. The use of GIS as a model integration and output framework is very powerful.

Integrating disparate datasets for hurricane construction damage assessment is being tested with GIS. Using postevent GIS analysis, varying construction types and the building codes that were in effect at the time of construction are compared, along with wind speeds (both forecast and measured) and durations, to assess the probability and extent of damage experienced. As more data is collected and the model matures, a reasonable damage prediction will be obtained. Using a Bayesian approach, expected hurricane damage will emerge based on factors such as building code requirements at the time of construction, building type, forecasted (or expected) hurricane severity, geographic position, and building orientation to the wind.