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A critical element to the accurate prediction of fire/weather behaviour is the knowledge of near-surface weather. Weather variables, such as wind, temperature, humidity and precipitation, make direct impacts on the practice of managing prescribed burns and fighting wild fires. State-of-the-art Numerical Weather Prediction (NWP), coupled with the use of high performance computing, now enable significantly improved short-term forecasting of near-surface weather at a 1-3 km grid resolution. This proof of concept project integrates two complementary model types to aid federal agencies in real-time management of fire. (1) A highly complex, full-physics mesoscale weather prediction model (MM5) which is applied in order to estimate the weather fields up to 72 hours in advance. (2) A diagnostic fire behavior model (FARSITE) takes the near-surface weather fields and computes the expected spread rate of a fire driven by wind, humidity, terrain, and fuels (i.e. vegetation).