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

使用大气观测来初始化数值天气预报需要知道观测误差的统计信息。产生误差的原因之一就是“代表性误差”，这是因为在比方说在一个单点处观察到的变量被用来在整个网格单元中初始化该变量，并且该点上的变量的值包括由湍流运动引起的分量，其特性随时间和空间而变化。PI将应用改进的当地观测误差估计，在现实大气条件下同化来自现实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，例如来自多普勒雷达的数据，并将能够准确评估下一代观测系统的效用。观测误差的准确估计也将被用于产生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准确测量，PI将研究来自研究飞机和无线电探空数据的湍流的统计描述。这项研究的广泛影响来自于它对改进天气预报的应用。

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.