Wednesday, 6 May 2015: 12:00 PM
Great Lakes Ballroom (Crowne Plaza Minneapolis Northstar)
In situ observation networks more typically than not have inhomogeneities in time and space. Thus, there are some considerable barriers to basing a climatology on long-term meteorological observations. For wildfire applications, this is especially relevant because fire weather station observations can be sparse and not fully representing a region in complex terrain. Dynamical downscaling utilizing a mesoscale weather forecast model can provide regional high-resolution temporal (i.e., hourly) and spatially gridded fire weather and fire danger datasets. These outputs are homogeneous in that data are produced by a consistent methodology, though not without potential model bias and error that needs to be assessed and corrected where possible. Recently, two downscaled datasets have been produced for wildfire management and research purposes. The first is a 40-year (1972-2013) 4-km hourly gridded dataset for Victoria, Australia. The second is a 10-year (2004-2013) 2-km hourly gridded dataset for California and Nevada USA. Both were constructed using the Weather Research and Forecast (WRF) model, and include surface fire weather (e.g., temperature, humidity, wind) and upper-air variables (e.g., mixing height). The datasets provide baseline climatology information for risk management assessments, applied research and climate change adaptation planning (in particular for the longer Victoria dataset). This presentation describes the generation of the datasets, shows examples of output, and highlights use and relevance for fire management and research. The methodologies described have relevance globally where it is desired to provide hi-resolution gridded fire weather and climatology data.
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