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

严重冰雹分析、表示和预测 (SHARP) 项目

• 批准号: 1261776
• 负责人: Ming Xue
• 金额: $ 81.91万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1261776&HistoricalAwards=false
• 关键词: Severe; Hail; Analysis; Representation; Prediction

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.

严重的冰雹，特别是在城市地区，可能造成重大伤害和数百万美元的财产损失。 然而，这些影响可以通过基于0-2小时数值天气预报（NWP）预报的更准确和精确的冰雹预报来显著减轻。 为了提供这样的NWP预报需要在数据同化（DA），创新的集合预报方法的发展，以及应用新的数据挖掘技术来表示和预测冰雹在国家的最先进的NWP模式的进步。 强冰雹分析、表示和预测项目（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模式中的冰雹表示，（2）将先进的数据挖掘技术应用于数值预报集合输出，将改善冰雹大小和覆盖范围的预报。 SHARP的具体目标将是：（1）为各种事件制作和核实0-2小时的严重冰雹集合预报;（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）准确预报冰雹到达地面的地理范围和大小，根据高质量的观测数据集，如美国国家海洋和大气管理局严重灾害分析和验证实验（SHAVE）产生的数据集，验证预报。预报集合输出将与基于雷达的外推方法进行比较。 这一努力的智力价值在于在应用先进的数据同化（使用多矩微物理和双极化雷达数据）和数据挖掘进行短期冰雹预报方面取得进展。 虽然数据挖掘已被应用于大陆尺度的降水预报，更新颖的是它的使用与先进的Enkilman滤波器（EnKF）DA对流尺度NWP。 这项工作建立在以前的工作，由PI在DA，风暴尺度集合预测和数据挖掘领域。预计该项目将推进恶劣天气预测科学，确定一种将数据同化和数据挖掘联系起来的范例，可用于预测下击暴流和龙卷风等其他对流规模的灾害。 有了足够的计算资源，在这个项目中开发的技术和算法可以应用到支持实时恶劣天气预警业务，在NWS的“警告预测”范式的设想。更广泛的影响将包括加强跨学科的联系，学科和潜在的经济利益，包括航空部门，特别容易受到冰雹。 研究结果将有助于改善冰雹预警的准备时间和用户信心，使脆弱行业有更多的机会减轻损失，使个人有更多的时间寻求庇护。与国家强风暴实验室的合作将使结果能够与基于雷达的业务外推方法进行比较和测试，并使知识和工具能够转让给负责恶劣天气警报的国家气象局预报员。 该PI将提供跨学科的培训，研究生，以及本科生，通过ESTA/CAPS研究经验的本科生（REU）计划，其中PI有很强的参与历史。