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 high-temporal-resolution fire radiative 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.

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, which 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 high temporal resolution fire radiative 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 grid cell size of 1.5° × 1.5°. The input data included ECMWF ERA5 meteorological reanalysis and SEVIRI/MSG Fire Radiative Power and Cloud Mask. It has been found that in the areas with large numbers 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.

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<p>In recent years, large forest fires in Fennoscandia have shown that wildfires can have a strong impact on society also in northern Europe. In the future, meteorological conditions are expected to become increasingly favorable for wildfires due to climate change. An important aspect in fire management are the national forest management strategies that play a crucial role in controlling e.g. fuel availability in forests, and further areal coverage of burned area. In addition, the effectiveness of rescue services is crucial. Thus, the development of fire risk prediction and fire detection systems, as well as, modeling of spread of fires and emissions of harmful ingredients, such as black carbon are urgently required to improve the societies preparedness to the increasing thread. In this presentation we synthetize the current state-of-the-art understanding of wildfires in Fennoscandia from a wide range of key perspectives: historical fire regimes, monitoring using in-situ and remote-sensing technologies, integrated modeling (e.g. climate models, spatial fire propagation models forced with operational weather forecast model) and fire suppression. In addition, we assess the amount of black carbon emissions released from recent wildfires in Fennoscandia. These results will help northern societies to tackle against the negative impacts of climate change and to support the development of efficient mitigation strategies. In the upcoming decades the effective management of wildfires is especially relevant, as wildfires greatly affect regional carbon budgets and mitigation efforts. </p>