Mesoscale Data Assimilation and Predictability Studies

提出利用国家气象中心ETA模型进行综合中尺度四维资料同化（FDDA）和可预测性研究。 PI 打算继续使用 ETA 模型进行 FDDA 研究，评估同化系统的性能并研究中尺度可预测性问题。 此外，研究还将扩展到中尺度模型中剖面仪数据同化问题，包括评估剖面仪网络在短程天气预报中的有用性。 最后，作为本研究的成果，我们将研究大尺度强迫、模型分辨率和模型物理在中尺度模型中更精细尺度特征演化中的作用。

A comprehensive mesoscale four-dimensional data assimilation (FDDA) and predictability study is proposed using the National Meteorological Center ETA model. The PIs intend to continue FDDA studies using the ETA model, assessing the performance of the assimilation system and examining the problem of mesoscale predictability. In addition, the investigation will be extended to the problem of profiler data assimilation in mesoscale models, including an assessment of the usefulness of the profiler network in short range weather prediction. Finally, as an outgrowth of this study, the role of large-scale forcing, model resolution, and model physics in the evolution of finer-scale features in a mesoscale model will be investigated.