Ensemble-Based Hurricane State Estimation, Intensity Prediction, and Targeting

该奖项是根据2009年《美国复苏和再投资法》(公法111-5)资助的。虽然人们从观测分析和中尺度模式模拟中了解了许多关于飓风结构和强度变化的知识，但对这些特征的可预测性知之甚少。众所周知，在强度变化的动态预报方面缺乏改进，可以说最重要的贡献是需要动态一致的初始条件。创造初始条件的挑战是同化飓风环境中的观测，而飓风环境具有很大的空间变异性。为了解决这一问题，首席调查员(PI)将对飓风状态估计、可预测性和目标性应用综合方法，重点是结构和强度变化。这项研究建立在PI和合作者使用集合技术进行数据同化、可预测性和目标确定的最新工作基础上。研究活动包括系统地使用集合卡尔曼滤波同化观测数据，首先只更新轴对称结构，然后更新增加的方位波数。将使用标准误差衡量标准和最近开发的基于信息论的方法，包括可预测的成分分析，来评估每个组成部分的可预测性时间尺度。将要调查的风暴包括：来自雨带和强度实验(RAINEX)的丽塔、卡特里娜和奥菲莉亚；来自热带云系统和过程(TCSP)实验的艾米丽；以及邦妮(1998)。风暴的分析将使用两种实验类型：(1)实际观测，和(2)从真实模拟中提取的模拟观测，被约束为近似跟踪实际风暴的轨迹和强度。基于集合的敏感性分析将用于通过在真实数据情况下否认观测来评估观测的影响，包括目标确定。智力优势：该研究计划在观测研究和理论和模型研究之间架起了一座桥梁，前者已确定飓风结构和强度变化的特性，但缺乏动力连续性，后者具有空间和时间连续性，以评估这些特征的动力学，但缺乏与真实风暴观测的密切联系。研究产生的同化数据集提供了数值模拟的空间和时间分辨率，但也应该保持对现有观测的忠实性。这些数据将被用来系统地评估风暴结构和强度的可预测性，这是飓风动力学的一个关键方面，人们对此知之甚少。更广泛的影响：飓风强度预报的改进对社会的更广泛影响是众所周知的。鉴于这项研究的成功实施，过渡到运营似乎是可行的。此外，已证明有能力以观测为目标，定期改进对飓风强度的预测，这将对业务飓风预测产生变革性的影响，从而也对这些风暴的应急规划产生重大影响。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).While much has been learned about hurricane structure and intensity change from observational analyses and mesoscale model simulations, much less is known about the predictability of these features. Arguably the most important contribution to the well-known lack of improvement in dynamical forecasts of intensity change is the need for dynamically consistent initial conditions. The challenge in creating initial conditions is the assimilation of observations in a hurricane environment, which has large spatial variability. To address this issue, the Principal Investigator (PI) will apply an ensemble approach to hurricane state estimation, predictability, and targeting, with an emphasis on structure and intensity change. The research builds upon recent work by the PI and collaborators using ensemble techniques for data assimilation, predictability, and targeting. Research activities include assimilating observations using an ensemble Kalman filter in a systematic manner, starting with updates of only the axisymmetric structure, and proceeding thereafter by including updates to increasing azimuthal wavenumbers. The predictability time scale of each component will be evaluated using standard error metrics and more recently developed methods based on information theory, including predictable component analysis. Storms to be investigated include: Rita, Katrina, and Ophelia from the Rainband and Intensity Experiment (RAINEX); Emily from the Tropical Cloud Systems and Processes (TCSP) experiment; and Bonnie (1998). Storms will be analyzed using two experiment types: (1) actual observations, and (2) simulated observations drawn from truth simulations constrained to approximately follow the track and intensity of the actual storms. Ensemble-based sensitivity analysis will be used to assess the impact of observations, including targeting, through observation denial in the real-data cases. Intellectual merit: The research plan builds a bridge between observational studies, which have established properties of hurricane structure and intensity change but lack dynamical continuity, and theoretical and modeling studies that have the spatial and temporal continuity to assess the dynamics of these features, but lack a close link to the observations of real storms. The assimilated datasets that result from the research offer the spatial and temporal resolution of numerical simulations, but should also remain faithful to available observations. These data will be used to systematically assess the predictability of storm structure and intensity, which is a crucial aspect of hurricane dynamics, for which little is known. Broader impact: The broader impact to society of improved forecasts of hurricane intensity is well known. Given a successful execution of the research, a transition to operations seems feasible. Moreover, demonstrated ability to target observations to routinely improve predictions of hurricane intensity would have a transformational impact on operational hurricane forecasting and thus also on emergency planning for these storms.