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

基于集合的飓风状态估计、强度预测和目标确定

• 批准号: 0842384
• 负责人: Gregory Hakim
• 金额: $ 39.5万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-06-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0842384&HistoricalAwards=false
• 关键词: Ensemble; Based; Hurricane; State; Estimation

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.

该奖项是根据2009年的《美国回收与再投资法》（Priblaw 111-5）资助的。虽然已经从观察性分析和中尺度模型模拟中了解了许多关于飓风结构和强度变化的了解，但对这些特征的可预测性知之甚少。可以说，对强度变化的动力学预测的众所周知缺乏改善的最重要贡献是需要动态一致的初始条件。 创造初始条件的挑战是在飓风环境中的观测值同化，飓风环境具有很大的空间可变性。 为了解决这个问题，首席研究员（PI）将采用整体方法来飓风国家的估计，可预测性和目标，并着重于结构和强度变化。 该研究基于PI和合作者的最新工作，使用集合技术来吸收数据，可预测性和靶向。研究活动包括以系统的方式使用集合卡尔曼过滤器来吸收观察结果，从仅轴对称结构的更新开始，然后通过将更新以增加方位角波数来进行。 每个组件的可预测性时间尺度将使用标准误差指标以及基于信息理论（包括可预测组件分析）的最近开发的方法评估。 要研究的风暴包括：雨带和强度实验的丽塔，卡特里娜飓风和奥菲莉亚（Rainex）；来自热带云系统和过程（TCSP）实验的Emily；和Bonnie（1998）。 将使用两种实验类型对风暴进行分析：（1）实际观察结果，以及（2）依靠实际风暴的轨道和强度，从真实模拟中得出的模拟观察结果。 基于整体的灵敏度分析将用于评估观察结果的影响，包括靶向，通过在实际数据中的观察否认。智力优点：该研究计划在观察性研究之间建立了桥梁，这些研究已经确立了飓风结构和强度变化的特性，但缺乏动力的连续性以及具有空间和时间连续性以评估这些特征动态的理论和建模研究，但缺乏与真实风暴的观察结果的紧密联系。该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，但也应忠于可用观察结果。这些数据将用于系统地评估暴风雨结构和强度的可预测性，这是飓风动力学的关键方面，对此几乎没有知名度。更广泛的影响：众所周知，对改善飓风强度预测的社会的更广泛影响。鉴于该研究成功执行，对操作的过渡似乎是可行的。 此外，表现出靶向观察以定期改善飓风强度预测的能力将对运营飓风预测以及这些风暴的紧急计划产生变革影响。