Forecasting the regional fire radiative power for regularly ignited vegetation fires

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http://urn.fi/URN:NBN:fi-fe2022050533237 http://hdl.handle.net/10138/343480
Title: Forecasting the regional fire radiative power for regularly ignited vegetation fires
Author: Partanen, Tero M.; Sofiev, Mikhail
Contributor organization: Ilmatieteen laitos
Finnish Meteorological Institute
Publisher: Copernicus Publ.
Date: 2022
Language: en
Belongs to series: Natural hazards and earth system sciences
ISSN: 1561-8633
1684-9981
DOI: https://doi.org/10.5194/nhess-22-1335-2022
URI: http://urn.fi/URN:NBN:fi-fe2022050533237
http://hdl.handle.net/10138/343480
Abstract: This paper presents a phenomenological framework for forecasting the area-integrated fire radiative power from wildfires. In the method, a region of interest is covered with a regular grid, whose cells are uniquely and independently parameterized with regard to the fire intensity according to (i) the fire incidence history, (ii) the retrospective meteorological information, and (iii) remotely sensed hightemporal-resolution fire adiative power taken together with (iv) consistent cloud mask data. The parameterization is realized by fitting the predetermined functions for diurnal and annual profiles of fire radiative power to the remote-sensing observations. After the parametrization, the input for the fire radiative power forecast is the meteorological data alone, i.e. the weather forecast. The method is tested retrospectively for south-central African savannah areas with the grid cell size of 1.5◦ × 1.5◦. The input data included ECMWF ERA5 meteorological reanalysis and SEVIRI/MSG (Spinning Enhanced Visible and Infra-Red Imager on board Meteosat Second Generation) fire radiative power and cloud mask data. It has been found that in the areas with a large number of wildfires regularly ignited on a daily basis during dry seasons from year to year, the temporal fire radiative power evolution is quite predictable, whereas the areas with irregular fire behaviour, predictability was low. The predictive power of the method is demonstrated by comparing the predicted fire radiative power patterns and fire radiative energy values against the corresponding remote-sensing observations. The current method showed good skills for the considered African regions and was useful in understanding the challenges in predicting the wildfires in a more general case.
Subject: fire
forecasts
wildfires
Rights: CC BY 4.0


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