Fire Modelling & Forecasting System (FireMAFS)

就受影响的生物群的面积和种类而言，火是全世界最重要的干扰剂，是碳从陆地转移到大气的主要机制，也是全球重要的气溶胶和许多微量气体物种的来源。火灾风险预测是在许多易发生火灾的环境中进行的，以帮助旱季预先规划，在旨在研究过去、现在和未来植被-气候相互作用的动态植被模型中也需要适当地考虑火灾。目前绘制火灾“风险”、“易感”或“危险”图的方法使用的是根据过去的天气状况和火灾事件校准的经验火灾危险指数。因此，它们提供的关于过程的信息很少，只适合处理当前的气候、土地利用和土地覆盖变化(LULCC)，并且它们受到EO产品或其他观测数据(例如点火‘热点’、燃烧面积、热源C释放等)的测试和约束的能力有限。FireMAFS的目标是通过开发一种可靠的方法来预测火灾活动(火灾危险指数、着火概率、燃烧面积、火灾强度等)，从而解决这些限制，该方法通过基于过程的火灾-植被相互作用模型，使用最先进的EO数据产品进行测试、改进和约束，并以发布了数月提前期的季节性天气预报为驱动。具体目标是：(I)开发利用EO和其他关于植被(燃料)状况、火灾活动和火灾影响的观测数据的方法，以测试、改进和约束火-植被相互作用正向模型的子组件和端到端预测，并在预测模式中使用该模型时通知、测试和限制该模型，以确保它沿着最优轨迹推进，并在观察期赶上前一预测期时进一步重置；(Ii)通过季节性天气预报集合，预测火灾“危险”指数、火灾发生和一系列后续火灾行为和火灾影响(强度、蔓延速度、燃烧面积、地面/地下碳储量变化和微量气体/气溶胶排放)的时空变异性，并评估其对提前1/6个月的季节性火灾预测以及在未来预测气候和LULCC情景下进行的预测研究的有用性。

Fire is the most important disturbance agent worldwide in terms of area and variety of biomes affected, a major mechanism by which carbon is transferred from from the land to the atmosphere, and a globally significant source of aerosols and many trace gas species. Forecasting of fire risk is undertaken in many fire-prone environments to aid dry season pre-planning, and appropriate consideration of fire is also required within dynamic vegetation models that aim to examine vegetation-climate interactions in the past, present and future. Current methods of mapping fire 'risk', 'susceptabilty' or 'danger' use empirical fire danger indexes calibrated against past weather conditions and fire events. As such, they provide little information on process, are appropriate to deal only with current climate, land use and land cover change (LULCC), and are limited in their ability to be tested and constrained by EO products or other observational data (e.g. ignition 'hotspots', burned area, pyrogenic C release etc). The objective of FireMAFS is to resolve these limitations by developing a robust method to forecast fire activity (fire danger indices, ignition probabilities, burnt area, fire intensity etc) via a process-based model of fire-vegetation interactions, tested, improved, and constrained using state-of-the-art EO data products and driven by seasonal weather forecasts issued with many months lead-time. Specific aims are to: (i) develop the methodology for using EO and other observational data on vegetation (fuel) condition, fire activity and fire effects to test, improve and constrain sub-components and end-to-end predictions of a forward model of fire-vegetation interactions and to inform, test and restrict the model when used in forecast mode to ensure it is nudged along the optimum trajectory, and is furthermore reset when the observation period catches up with the prior period of prediction; (ii) drive the improved forward model by seasonal weather forecast ensembles, predicting spatio-temporal variability in fire 'danger' indices, fire occurrence and a range of subsequent fire behaviour and fire effects (intensity, rate of spread, burned area, above/below ground C stock change, and trace gas/aerosol emissions) and evaluate their usefulness for seasonal fire prediction at 1 / 6 months lead time and for prognostic studies run under future projected climate and LULCC scenarios.