Including Atmospheric Turbulence in Numerical Weather Prediction, Data Assimilation, Ensemble Forecasts, and Error Evaluation

使用大气观测初始化数值天气预报需要已知观测误差的统计。误差的来源之一是“代表性误差”，这是因为对一个变量的观测，比如说，在一个单一的点上，被用来初始化整个网格单元中的变量，并且该点上的变量值包括由于湍流运动而产生的分量，其属性随空间和时间而变化。PI将应用改进后的观测误差局部估计，在实际大气条件下同化来自实际观测系统的大气数据。这将导致充分利用来自高质量观测的数据，如来自多普勒雷达的数据，并将能够准确评价下一代观测系统的效用。观测误差的准确估计也将用于生成集合预报的扰动初始条件的真实集合。由于这些应用依赖于湍流统计的精确测量，PI将研究研究来自研究飞机和无线电探空仪数据的湍流统计描述。这项研究的更广泛影响来自其应用于改进天气预报。

Using an atmospheric observation to initialize a numerical weather forecast requires that the statistics of the observation error be known. Among the sources of error is the "error of representativeness," which arises because an observation of a variable at, say, a single point, is used to initialize that variable throughout a grid cell, and the value of the variable at that point includes a component due to turbulent motions, the properties of which vary over space and time. The PI will apply improved local estimates of observation error to the assimilation of atmospheric data from realistic observing systems under realistic atmospheric conditions. This will lead to full use being made of data from high quality observations, such as from Doppler radar, and it will permit an accurate evaluation of the utility of next-generation observing systems. Accurate estimates of observation error will also be applied to generating realistic ensembles of perturbed initial conditions for ensemble forecasts. Because these applications depend on accurate measurements of turbulent statistics, the PI will investigate the statistical description of turbulence from research aircraft and from radiosonde data.Broader impacts of this research arise from its application to improving weather forecasts.