Toward Applications of Stochastic Dynamic Prediction

本研究旨在推进随机动态预测的理论和应用。SDP方程预测了相空间中无限多个点集合的平均值和其他统计性质。目前的确定性预报模型提供了对大气未来状态的预报，但由于初始条件和模式动力学中的不确定性，这些预报并不完美。理想情况下，人们还希望能够估计预报的质量。SDP可能会提供一种方法来实现这一点。好处是显而易见的。由于涉及的计算量和大气物理的复杂性，可行性尚不确定。PI计划使用他和国家大气研究中心的菲利普·汤普森博士开发的几种有希望的方法来探索问题的某些方面。这项研究的结果在涉及流体运动的各种领域具有潜在的应用价值。

The purpose of this research is to advance the theory and application of stochastic dynamic prediction (SDP). SDP equations predict the mean and other statistical properties of an infinite ensemble of points in phase space. Current deterministic prediction models provide forecasts of the future state of the atmosphere, but these forecasts are less than perfect due to uncertainties in the initial conditions and in the model dynamics. Ideally, one also would like to be able to estimate the quality of the forecast. SDP may provide a way to do so. The benefits are obvious. The feasibility is uncertain due to the magnitude of the computations involved and the complexity of the physics of the atmosphere. The PI plans to explore certain aspects of the problem using several promising approaches developed by himself and Dr. Philip Thompson of the National Center for Atmospheric Research. The results of this research have potential application in a wide variety of fields involving fluid motions.