WIFIRE Data Model and Catalog for Wildfire Data and Tools

Monday, 15 December 2014
Ilkay Altintas, Daniel Crawl, Charles Cowart, Amarnath Gupta, Jessica Block and Raymond de Callafon, University of California San Diego, La Jolla, CA, United States
The WIFIRE project (wifire.ucsd.edu) is building an end-to-end cyberinfrastructure for real-time and data-driven simulation, prediction and visualization of wildfire behavior. WIFIRE may be used by wildfire management authorities in the future to predict wildfire rate of spread and direction, and assess the effectiveness of high-density sensor networks in improving fire and weather predictions. WIFIRE has created a data model for wildfire resources including sensed and archived data, sensors, satellites, cameras, modeling tools, workflows and social information including Twitter feeds. This data model and associated wildfire resource catalog includes a detailed description of the HPWREN sensor network, SDG&E’s Mesonet, and NASA MODIS. In addition, the WIFIRE data-model describes how to integrate the data from multiple heterogeneous sources to provide detailed fire-related information. The data catalog describes ‘Observables’ captured by each instrument using multiple ontologies including OGC SensorML and NASA SWEET. Observables include measurements such as wind speed, air temperature, and relative humidity, as well as their accuracy and resolution. We have implemented a REST service for publishing to and querying from the catalog using Web Application Description Language (WADL). We are creating web-based user interfaces and mobile device Apps that use the REST interface for dissemination to wildfire modeling community and project partners covering academic, private, and government laboratories while generating value to emergency officials and the general public. Additionally, the Kepler scientific workflow system is instrumented to interact with this data catalog to access real-time streaming and archived wildfire data and stream it into dynamic data-driven wildfire models at scale.