Collaborative Research: Adaptive Sampling and Ensemble-Based Data Assimilation

该项目代表了伊曼纽尔博士和宾夕法尼亚州立大学的毕晓普博士之间的合作，毕晓普博士的资金来自ATM-9814376。该项目将扩大主要调查人员在大气层适应性取样方面开展的工作。 因此，对大气的常规原位观测要么是在固定时间从固定地点进行的（例如，通过装有仪器的气象气球）或从诸如船舶和商用飞机的机会平台。 自适应采样利用关于大气当前和（估计）未来状态的信息，例如对分析不确定性和预报敏感性分布的估计，以确定观测将导致预报最大改进的位置。 可编程的观测平台，如有人驾驶或无人驾驶飞机，然后被引导到这些特殊的位置进行观测。 主要研究人员的工作数据已经开发，完善和测试了几个自适应抽样策略。 他们将继续这些研究，并扩展到飓风路径预测问题，这是我们今天面临的最重要的预测问题之一。 这项研究的一个关键组成部分将是探索数据同化及其在天气分析中最佳利用这些新观测结果的作用。 特别是，Bishop和Emanuel博士将研究同化过程中合奏的使用，并将这种方法与当前的技术进行比较。 在这项工作的过程中，主要研究者将采用一系列模型。

This project represents a collaboration between Dr. Emanuel and Dr. Bishop of Penn State University who is funded under ATM-9814376. The project will expand work undertaken by the principal investigators on adaptive sampling of the atmosphere. Heretofore, routine in-situ observations of the atmosphere have been made either from fixed locations at fixed times (e.g., by instrumented weather balloons) or from platforms of opportunity such as ships and commercial aircraft. Adaptive sampling uses information about the current and (estimated) future state of the atmosphere, such as estimates of the distribution of analysis uncertainty and forecast sensitivity, to determine locations where observations would lead to the greatest improvements in forecasts. Programmable observation platforms, such as manned or unmanned aircraft, are then directed to these special locations to make the observations. The principal investigators' work to data has developed, refined, and tested several adaptive sampling strategies. They will continue with these studies and extending to the problem of hurricane track prediction, one of the most important forecasting problems facing us today. A key component of this study will be the exploration of data assimilation and its role in optimally employing these new observations in the weather analyses. In particular, Drs. Bishop and Emanuel will be looking at the use of ensembles for the assimilation process and comparing this approach with current techniques. In the course of this work, the principal investigators will employ a hierarchy of models.