The Severe Hail Analysis, Representation, and Prediction (SHARP) Project

严重的冰雹，特别是在城市地区，可以造成重大伤害和数百万美元的财产损失。然而，基于0-2小时数值天气预报（NWP）的更准确和精确的冰雹预报可以显著减轻这些影响。为了提供这样的NWP预报，需要在数据同化（DA）方面取得进展，开发创新的集合预报方法，并应用新的数据挖掘技术来表示和预测最先进的NWP模型中的冰雹。强冰雹分析、表征和预测项目（SHARP）将重点回答两个科学假设：(1)同化来自新的和多个数据源的数据（包括单极化和双极化多普勒雷达、风廓线仪、探测和地面观测）将改善NWP模型中的冰雹表征；(2)将先进的数据挖掘技术应用于NWP集成输出将改善冰雹大小和覆盖范围的预测。SHARP的具体目标是：(1)生成和验证各种事件的0-2小时强冰雹整体预报；(2)评估这些预报正确反映双极化多普勒雷达和其他仪器观测到的冰雹的能力；(3)准确预测地面冰雹的地理范围和大小，并利用NOAA严重灾害分析与验证实验（SHAVE）等高质量观测数据集对预报结果进行验证。预报集合输出将与基于雷达的外推方法进行比较。这项工作的智力价值在于先进的数据同化（使用多时刻微物理和双极化雷达数据）和数据挖掘在短期冰雹预报中的应用进展。虽然数据挖掘已经应用于大陆尺度的降水预报，但更新颖的是它与先进的集成卡尔曼滤波（EnKF）数据分析一起用于对流尺度的NWP。这项工作建立在pi先前在数据分析、风暴尺度集合预测和数据挖掘领域的工作基础上。该项目预计将推动恶劣天气预报科学的发展，定义一个将数据同化和数据挖掘联系起来的范例，该范例可用于预测其他对流尺度的灾害，如暴雨和龙卷风。有了足够的计算资源，本计划所开发的技术和算法可应用于支持实时恶劣天气预警行动，正如国家气象局“预报预警”模式所设想的那样。更广泛的影响将包括加强跨学科联系，以及对航空业等特别容易受到冰雹影响的部门的潜在经济效益。结果将有助于改善冰雹预警的交货时间和用户信心，为脆弱的行业提供更多机会减轻损失，并为个人提供更多时间寻找避难所。与国家强风暴实验室的合作将使结果与基于雷达的操作外推方法进行比较和测试，并使知识和工具能够转移到负责恶劣天气警报的国家气象局预报员。私人学院将通过OU/CAPS本科生研究经验（REU）项目为研究生和本科生提供跨学科培训，私人学院在该项目中有着悠久的参与历史。

Severe hail, particularly in urban areas, can cause significant injury and millions of dollars in property damage. These impacts, however, could be significantly mitigated with more accurate and precise hail predictions based on 0-2 hour numerical weather prediction (NWP) forecasts. To provide such NWP forecasts requires advances in data assimilation (DA), the development of innovative ensemble forecast methods, and the application of novel data mining techniques to represent and predict hail within state-of-the-art NWP models. The Severe Hail Analysis, Representation, and Prediction project (SHARP) will focus on answering two scientific hypotheses: (1) assimilation of data from new and multiple data sources (including single- and dual-polarization Doppler radars, wind profilers, soundings, and surface observations) will improve hail representation within a NWP model, and (2) application of advanced data-mining techniques to NWP ensemble output will improve predictions of hail size and coverage. The specific goals of SHARP will be to: (1) produce and verify 0-2 hour ensemble forecasts of severe hail for a variety of events; (2) assess the ability of these forecasts to correctly represent hail as observed by dual-polarization Doppler radars and other instruments; and (3) accurately predict the geographic extent and size of hail reaching the surface, verifying the forecasts against high-quality observational data sets such as those produced by the NOAA Severe Hazards Analysis and Verification Experiment (SHAVE). Forecast ensemble output will be compared against radar-based extrapolation methods. The intellectual merit of this effort lies in progress toward application of advanced data assimilation (using multiple-moment microphysics and dual-polarization radar data) and data mining for short-term hail prediction. Though data mining has been applied to precipitation forecasts at the continental scale, far more novel is its use alongside advanced Ensemble Kalman Filter (EnKF) DA for convective-scale NWP. This effort builds upon previous work by the PIs in the fields of DA, storm-scale ensemble prediction, and data mining. This project is expected to advance the science of severe weather prediction, defining a paradigm linking data assimilation and data mining that could be applied to predict other convective-scale hazards such as downbursts and tornadoes. With sufficient computing resources, the techniques and algorithms developed in this project could be applied to support real-time severe weather warning operations, as envisioned in the NWS "Warn-on-Forecast" paradigm. Broader Impacts of will include enhanced cross-disciplinary connections disciplinary and potential economic benefits to sectors including aviation that are especially vulnerable to hail. Results will aid in improving lead-time and user confidence in hail warnings, giving vulnerable industries increased opportunity to mitigate damage and individuals additional time to seek shelter. Collaboration with the National Severe Storms Laboratory will enable comparison and testing of results against operational, radar-based extrapolation methods and enable transfer of knowledge and tools to NWS forecasters responsible for severe weather warnings. The PIs will provide interdisciplinary training to graduate students, as well as undergraduate students through the OU/CAPS Research Experiences for Undergraduates (REU) program, with which the PIs have a strong history of involvement.