General Circulation Studies

这个正在进行的研究项目的主要目标是提高我们对超过一周时间尺度的大气变化的理解和预测。研究包括对观测数据集和模式生成数据集的诊断，使用简单合成、时域数字滤波、主成分分析和典型相关分析等统计技术，并强调根据我们目前对大尺度大气动力学的理解对结果进行动力学解释。这项工作与NCAR和NOAA/GFDL的环流模拟项目以及NOAA/NMC和ECMWF的业务和实验数值天气预报项目密切相关。周期为几周或更长时间的波动是由大气内部的动力过程和大气与底层介质（主要是海洋和陆地表面水文）之间的相互作用产生的。相关的动力学包括各种低频过程，以及与斜压不稳定相关的高频瞬变的强迫。通过对时间过滤气象场的空间模式和时间序列的仔细分析，pi希望分离出与各种过程相关的特征，并记录它们如何对总变率做出贡献。这项研究很重要，因为它直接有助于我们在与人类活动相关的时间尺度上预测大气变化的能力。

The primary objective of this ongoing research project is to improve our understanding and prediction of atmospheric variability with time scales longer than a week. The research involves the diagnosis of observational and model generated data sets, using statistical techniques such as simple compositing, digital filtering in the time domain, principal component analysis and canonical correlation analysis, and emphasizing the dynamical interpretation of the results in light of our current understanding of large-scale atmospheric dynamics. The work is closely linked to projects in general circulation modeling at NCAR and NOAA/GFDL and to operational and experimental numerical weather prediction at NOAA/NMC and ECMWF. Fluctuations with periods on the order of weeks and longer are generated both by dynamical processes internal to the atmosphere and by interactions between the atmosphere and the underlying media (primarily the oceans and the land-surface hydrology). The relevant dynamics include a variety of processes that operate on low-frequencies, as well as the forcing by high frequency transients associated with baroclinic instability. By careful analysis of the spatial patterns and time sequences of time-filtered meteorological fields, the PIs hope to isolate the signatures associated with the various processes and document how they contribute to the total variability. This research is important because it contributes in a direct way toward our ability to predict atmospheric variability on time-scales relevant to human activities.