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.

中文翻译：

---

定期点燃植被火灾的区域火灾辐射功率预测

摘要。本文提出了一个用于预测野火区域综合火灾辐射功率的现象学框架。在该方法中，感兴趣的区域被规则网格覆盖，根据 (i) 火灾发生历史，(ii) 回顾性气象信息，以及 (iii)遥感高时间分辨率火辐射功率与 (iv) 一致的云掩模数据一起使用。参数化是通过将火辐射功率的日和年剖面的预定函数拟合到遥感观测来实现的。参数化后，火灾辐射功率预测的输入是单独的气象数据，即天气预报。该方法在网格单元大小为 1.5° × 1.5° 的中南部非洲大草原地区进行了回顾性测试。输入数据包括 ECMWF ERA5 气象再分析和 SEVIRI/MSG 火辐射功率和云面罩。研究发现，在年复一年旱季每天有规律地发生大量野火的地区，火灾辐射功率的时间演变具有较好的可预测性，而不规则火灾行为的可预测性较低。通过将预测的火灾辐射功率模式和火灾辐射能量值与相应的遥感观测值进行比较，证明了该方法的预测能力。