The fuel moisture content (FMC) affects the probability of ignition and the rate of spread of fires, the two main components in estimating the fire risk. As a result, accurate monitoring of dead and live FMC can help to reduce the adverse impacts associated with wildland fires. During the last years we have developed machine learning models to estimate the FMC combining predictors from numerical prediction models and remote sensing instruments. These include VIIRS and ABI instruments on board circumpolar and geostationary satellites, respectively. In this presentation I will summarize our retrieval methods, their performance, and our ongoing real-time demonstration of the FMC retrievals over the continental U.S., Alaska, and Hawaii available at https://fmc.ral.ucar.edu/. The retrievals should prove useful in mitigating the adverse impacts of wildland fires.
Presenter
Dr. Pedro Jimnez, Senior Scientist, NSF National Center for Atmospheric Research (NCAR)
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