As warming temperatures and reduced snowpack lead to drier conditions in Interior Alaska, fire managers increasingly need tools to forecast wildfire trends at seasonal and yearly intervals. Robust gridded historical weather information is needed to develop and validate these forecasts and such data are currently not available for Alaska. Currently, managers work with data at a Predictive Service Area (PSA) scale so can not distinguish spatial variations within a PSA.
To address these needs, the AK CASC will work with the BLM Alaska Fire Service, NOAA, and the Alaska Department of Natural Resources to identify critical meteorological variables to assess and the needed spatial extent.
Why are we doing this?
- Robust gridded historical observations are needed to develop and validate seasonal wildfire forecasts.
- Higher temperatures and decreasing snowpack are expected to lead to drier conditions and more wildfires in Interior Alaska.
- The network of historical weather stations has limited coverage outside of population centers leaving large data gaps in much of the landscape
- Models used to fill the gaps between stations (i.e. reanalysis) contain uncertainty and biases that make the data difficult to use for diagnosing wildfire danger.
- Current evaluations and development of seasonal wildfire forecasts can only be made at a coarse regional scale due to the limited observational data.
Improving the gridded historical observational is a significant task and the AK CASC will approach this problem as follows:
- In consultation with AFS, AK CASC researchers will identify key meteorological variables needed for wildfire monitoring/forecasting. These will include daily temperature, precipitation, humidity and winds needed to compute the Canadian Forest Fire Danger Rating System used operationally by AFS.
- AK CASC researchers will identify potential reanalysis and other downscaled products to serve as the baseline gridded observations. Likely candidates include the ERA5, MERRA2 and 20-km dynamically downscaled ERA-Interim reanalysis.
- Researchers will explore bias correction techniques to adjust the data to be more accurate relative to the available station data and available remote sensing data. Several possibilities will be considered ranging from the simple delta method to sophisticated statistical/machine learning approaches.
- The researchers will regularly engage with AFS to better understand the needs of the managers to provide a final product that is usable for managers and scientists alike.
- Researchers will aim to identify products that will be updated so they are useful into the future.
BLM Alaska Fire Service, NOAA National Weather Service Alaska Region, State of Alaska Department of Natural Resources
- Principal Investigator(s): Peter Bieniek and Uma Bhatt
- Co-PIs: Scott Rupp