Large-scale and local control of severe weather: Towards adaptive ensemble forecasting (ADENS)

恶劣天气的大规模和局部控制：走向自适应集合预报（ADENS）

• 批准号: 60884417
• 负责人: Professor Dr. George Craig
• 金额: --
• 依托单位: Meteorologisches Institut
• 依托单位国家: 德国
• 项目类别: Research Units
• 财政年份: 2008
• 资助国家: 德国
• 起止时间: 2007-12-31 至 2015-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/60884417?language=en
• 关键词: Large; scale; local; control; severe

The development of high impact weather, like heavy precipitation or intense wind gusts, is partially controlled by the synoptic flow, and partially by processes on smaller scales. Likewise, the predictability, or forecast uncertainty, of such events is influenced by all these scales. The relative amount of uncertainty created on each scale will, however, depend on the meteorological situation. In the first phase of the project, a multi-scale ensemble forecasting system is being developed, that represents the various sources of uncertainty through a hierarchy of models with different resolutions and newly developed stochastic physical parameterizations. In the second phase, the multi-scale ensemble will be used to investigate sources of forecast uncertainty through experiments that track the upscale growth of small-scale perturbations and their impact on the balanced, large-scale flow. Variance analysis will be used to quantify the contributions of different scales to ensemble spread. Finally the potential for efficient sampling of uncertainty by adaptively choosing the ensemble configuration as a function of meteorological situation will be assessed.

强降水或强阵风等高影响天气的发展，部分受天气流控制，部分受较小尺度过程控制。同样，这些事件的可预测性或预测不确定性也受到所有这些尺度的影响。然而，每个尺度上产生的不确定性的相对量将取决于气象情况。在该项目的第一阶段，正在开发一个多尺度集合预报系统，该系统通过具有不同分辨率和新开发的随机物理参数化的一系列模型来代表各种不确定性来源。在第二阶段，将通过跟踪小规模扰动的大规模增长及其对平衡的大规模气流的影响的实验，使用多尺度集合来调查预报不确定性的来源。方差分析将用于量化不同尺度对集合传播的贡献。最后，将评估通过自适应地选择集合配置作为气象情况的函数来有效地采样不确定性的潜力。