论文标题
一种新的低成本技术可改善世界各地的天气预报
A new low-cost technique improves weather forecasts across the world
论文作者
论文摘要
计算机生成的预测将地球的表面分为网格箱，每个网格盒现在约为伦敦大小的25％，并预测每个网格箱的一个值。如果在特定站点的网格箱预测中天气明显变化，则不可避免地会失败。一种全新的统计后处理方法，使用集合预测作为输入，可以预期两个网格盒 - 依赖性依赖性因素：每个网格箱的变化程度，以及在网格盒刻度上的偏差。在全球范围内，技能大大提高。例如，对于极端降雨，有用的预测延长了5天。不进行后处理，此限制<1天。相对于历史预测，这一进步构成了开创性的进步。在校准过程中合并的主要驱动因素是气象学的理解，并放弃了仅使用本地数据的经典概念。取而代之的是，我们简单地认识到“淋浴是淋浴，无论发生在全球范围内的任何地方》，它可以大大增加校准数据集的大小。许多多方面的应用程序包括改进的山洪警告，对模型弱点的物理相关的见解以及全球重新分析。
Computer-generated forecasts divide the earth's surface into gridboxes, each now ~25% of the size of London, and predict one value per gridbox. If weather varies markedly within a gridbox forecasts for specific sites inevitably fail. A completely new statistical post-processing method, using ensemble forecasts as input, anticipates two gridbox-weather-dependant factors: degree of variation in each gridbox, and bias on the gridbox scale. Globally, skill improves substantially; for extreme rainfall, for example, useful forecasts extend 5 days ahead. Without post-processing this limit is < 1 day. Relative to historical forecasting advances this constitutes ground-breaking progress. The key drivers, incorporated during calibration, are meteorological understanding and abandoning classical notions that only local data be used. Instead we simply recognise that "showers are showers, wherever they occur worldwide" which delivers a huge increase in calibration dataset size. Numerous multi-faceted applications include improved flash flood warnings, physics-related insights into model weaknesses and global pointwise re-analyses.
