Fire Modelling & Forecasting System (FireMAFS)

就受影响的生物群落的面积和种类而言，火灾是世界范围内最重要的干扰因子，是碳从陆地转移到大气的主要机制，也是气溶胶和许多微量气体物种的全球重要来源。在许多火灾易发环境中进行火灾风险预测，以帮助干旱季节的预先规划，并且在动态植被模型中也需要适当考虑火灾，这些模型旨在检查过去，现在和未来的植被-气候相互作用。目前绘制火灾“风险”、“易感性”或“危险性”的方法使用根据过去天气条件和火灾事件校准的经验火灾危险指数。因此，它们提供的过程信息很少，只适合处理当前的气候、土地利用和土地覆盖变化（LULCC），而且它们的测试能力有限，并受到EO产品或其他观测数据（例如点火“热点”、燃烧面积、热原C释放等）的限制。fireafs的目标是通过开发一种强大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），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，并由提前数月发布的季节性天气预报驱动，从而解决这些限制。具体目标是：(i)开发使用EO和其他植被（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-植被相互作用正演模型的子组件和端到端预测，并在预测模式中使用模型时提供信息、测试和限制模型，以确保它沿着最佳轨迹推进，并在观察期赶上前一个预测期时进一步重置；（ii）通过季节天气预报集合推动改进的正演模式，预测火灾“危险”指数、火灾发生和一系列随后的火灾行为和火灾效应（强度、蔓延速度、燃烧面积、地上/地下碳储量变化和痕迹气体/气溶胶排放）的时空变化，并评估它们在提前1 / 6个月进行季节性火灾预测以及在未来预测气候和LULCC情景下进行预测研究的有用性。

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.