Predictability of Forced, Dissipated and Bounded AtmospheresFor Global, Synoptic, and Mesoscales

在1969年的一篇经典论文中，洛伦兹证明了 在一个简单的（无摩擦和加热）模型的可预测性， 地球的大气层，原则上，限制在大约5 天 具体而言，这意味着预测误差将 超过实际上的可变性（例如风或压力） 在预测验证时的地图，在一些预测之后 期 一个更好的模型和/或一个更完整和准确的集合 在初始时间的观测可能达到这个时间， “无用”的预测，但即使是最好的组合， 必须在五天内到达可预测性，通过 这一措施，在一个实际的业务预测模型，现在使用， ECMWF从1980年的5天增加到1990年的8天。 该操作模型包括加热的影响， 摩擦和地形对水流的干扰， 地球的表面。 有了这些并发症， 洛伦兹所使用的理论治疗方法 但 有可能正是这些复杂的过程 提高业务模式的可预测性。 因此 有效预报的时限尚未确定， 提高可预测性值得进一步探索。 该奖项支持的研究目标是 通过以下方式估计可实现的可预测性增强：i） 改进模型对大尺度流动的处理 预测误差最明显的成分，以及 与两极、赤道和大陆有着最直接的联系 海洋加热和摩擦/地形的差异 干扰; ii）用以下参数拟合模型的研究版本 更详细的计算动量、热量和 与下垫面进行水交换，以及iii）研究 全球尺度预测误差增长率的敏感性 在选定的区域模拟观测误差， 假定需要初始状态的细网格规格。 PI先前在数值天气预报方面的研究，以及 热带、边界层和山区气象学尤其是 与拟议的工作有关。 这些研究将增加我们的 了解影响可预测性的因素， 可能有助于进一步改善数值天气 预测未来十年。

In a classic 1969 paper, Lorenz proved that the predictability in a simple (frictionless and unheated) model of the earth's atmosphere was, in principle, limited to about five days. Specifically, this meant that the forecast error would exceed the variability (say of wind, or pressure) on the actual map at the verification time of the forecast, after some forecast period. A better model and/or a more complete and accurate set of observations at initial time might reach this time of "useless" forecasts later, but even the best combination of these would have to reach it in about five days. The predictability, by this measure, in an actual operational forecast model now used in the ECMWF has gone from five days in 1980 to eight days in 1990. This operational model does include the effects of heating, and frictional and topographic interference with the flow caused by the earth's surface. With these complications it is not amenable to the type of theoretical treatment used by Lorenz. However, it is possible that precisely these complicating processes somehow enhance the predictability of the operational model. Thus the time limit on useful forecasts has yet to be determined, and ways to enhance the predictability deserve extended exploration. The objective of the research supported under this award is to estimate the attainable enhancement of predictability by: i) Improving the model's treatment of the larger-scale flow components, which have the most obvious forecast errors, and which are most directly linked to pole to equator and continent- ocean contrasts in heating and frictional/topographic interference; ii) Fitting a research version of the model with more detailed formulas for the calculation of momentum, heat, and water exchanges with the underlaying surface, and iii) Studying the sensitivity of the global-scale forecast error growth rate to simulated observational errors in selected regions which are presumed to require fine mesh specification of the initial state. The PI's prior research in Numerical Weather Prediction,and tropical, boundary layer, and mountain meteorology is especially relevant to the proposed work. These studies will increase our understanding of the factors affecting predictability, and are likely to contribute to further improvements in Numerical Weather Prediction over the next decade.