Preprints
https://doi.org/10.5194/nhess-2024-57
https://doi.org/10.5194/nhess-2024-57
31 May 2024
 | 31 May 2024
Status: this preprint is currently under review for the journal NHESS.

Statistical calibration of probabilistic medium-range fire weather index forecasts in Europe

Stephanie Bohlmann and Marko Laine

Abstract. Wildfires are increasing in frequency and severity across Europe, which makes accurate wildfire risk estimation crucial. Wildfire risk is usually estimated using meteorological based fire weather indices such as the Canadian Forest Fire Weather Index (FWI). By using weather forecasts, the FWI can be predicted for several days and even weeks ahead. Probabilistic ensemble forecasts require verification and post-processing in order to provide reliable and accurate forecasts, which are crucial for informed decision making and an effective emergency response. In this study, we investigate the potential of non-homogeneous Gaussian regression (NGR) for statistically post-processing ensemble forecasts of the Canadian Forest Fire Weather Index. The FWI is calculated using medium range ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) with lead times up to 15 days over Europe. The method is tested using a 30 day rolling training period and dividing the European region into three training areas (Northern, Central and Mediterranean Europe). The calibration improves FWI forecast particularly at shorter lead times and in regions with elevated FWI values i.e. areas with a higher wildfire risk.

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Stephanie Bohlmann and Marko Laine

Status: open (until 12 Jul 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2024-57', Anonymous Referee #1, 10 Jun 2024 reply
  • RC2: 'Comment on nhess-2024-57', Anonymous Referee #2, 13 Jun 2024 reply
Stephanie Bohlmann and Marko Laine
Stephanie Bohlmann and Marko Laine

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Short summary
Probabilistic ensemble forecasts of the Canadian Forest Fire Weather Index (FWI) can be used to estimate the possible risk for wildfires but requires post-processing to provide accurate and reliable predictions. We present a calibration method using non-homogeneous Gaussian regression to statistical post-process FWI forecasts up to 15 days. Calibration improves the forecast especially at short lead times and in regions with elevated FWI values.
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