Wildland Fire Simulation with Sensor Network Data Correction

摘要

We report on an ongoing effort to build a Dynamic Data Driven Application System (DDDAS) for short time frame prediction of weather and wildfire behavior from real time weather data, images, and sensor streams. The system changes its predictions as new data is received. We use a single long term running simulation that corrects itself using out of order, imperfect sensor data. The DDDAS version replaces a code that was previously run using data only with static initial conditions. DDDAS entails the ability to dynamically incorporate additional data into an executing application, and in reverse, the ability of an application to dynamically steer the measurement process. Visualization in the field is an important aspect.