Hybrid Ensemble Variational Analysis of Polarimetric Radar Data to Improve Microphysical Parameterization and Short-term Weather Prediction

偏振雷达数据的混合集成变分分析，以改进微物理参数化和短期天气预报

• 批准号: 2136161
• 负责人: Guifu Zhang
• 金额: $ 65.51万
• 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: 2021
• 资助国家: 美国
• 起止时间: 2021-12-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2136161&HistoricalAwards=false
• 关键词: Hybrid; Ensemble; Variational; Analysis; Polarimetric

This project seeks to study the best possible ways to utilize polarimetric radar data (PRD) to improve understanding and prediction of severe weather. The United States’ national weather radar network was recently upgraded to dual-polarization capability, which provides detailed, 4D, real-time data about the observed precipitation particles, such as their shape, phase, and amount. This information is often poorly represented in numerical weather prediction (NWP) models, which can negatively impact their forecasts. However, the expected benefits of incorporating this observed polarimetric radar information into NWP models to improve their forecasts have not yet been realized. This project seeks to advance our understanding of, and improve the prediction of, severe weather and microphysical characterization in NWP models by exploring the application of advanced storm-scale data assimilation techniques to PRD. Such improvements will help realize the benefits of the existing upgrade to the radar network and provide more timely severe weather information to the public as storm-scale NWP models are increasingly incorporated into the warning decision process.The newly available PRD from the WSR-88D radar network are arguably the best source of data for storm-scale weather quantification and forecasts because PRD contain rich information about hydrometeor microphysics, including the size, shape, phase, and composition of precipitating particles, and can be used to characterize the microphysics and radar signatures of severe weather event precursors. Hydrometeor classification and the retrieval of hydrometeor particle size distributions from PRD are performed to better diagnose microphysical states and their evolution. Further, PRD can be directly assimilated into NWP models to improve model initialization and to produce more realistic analyses and forecasts. Specific goals of this work include: (1) development of accurate and efficient parameterized PRD forward operators for hydrometeors; (2) quantification of observation errors that include both measurement and forward operator errors; (3) observation-based retrievals of hydrometeor particle size distributions; (4) simulation of severe storms using convective-scale NWP models with advanced microphysics parameterization schemes under different environmental conditions, and their comparison with PRD for real cases; and (5) assimilation of PRD into NWP models using a hybrid ensemble variational data assimilation routine for optimal model initialization and better prediction of severe weather.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目旨在研究利用偏振雷达数据（PRD）的最佳可能方法，以提高对恶劣天气的理解和预测。美国国家气象雷达网络最近升级为双极化能力，可提供有关观测到的降水粒子的详细4D实时数据，如它们的形状、相位和数量。这些信息在数值天气预报（NWP）模型中通常表现不佳，这可能会对它们的预报产生负面影响。然而，将观测到的极化雷达信息纳入NWP模式以改善其预报的预期效益尚未实现。该项目旨在通过探索先进的风暴尺度数据同化技术在珠三角的应用，提高我们对NWP模式中恶劣天气和微物理特征的理解和预测。随着风暴尺度数值预报模式越来越多地被纳入警报决策过程，这些改进将有助于实现现有雷达网络升级的好处，并为公众提供更及时的恶劣天气信息。WSR-88 D雷达网络新提供的PRD可以说是风暴尺度天气量化和预报的最佳数据来源，因为PRD包含丰富的水文气象微物理信息，包括降水粒子的大小、形状、相态和成分，并可用于表征恶劣天气事件前兆的微物理和雷达特征。为了更好地诊断微物理状态及其演变，对PRD进行了水凝物分类和水凝物粒度分布反演。此外，PRD可以直接同化到NWP模式，以改善模式初始化，并产生更现实的分析和预测。这项工作的具体目标包括：（1）发展准确和有效的参数化PRD水凝物的前向算子;（2）量化观测误差，包括测量误差和前向算子误差;（3）基于观测的水凝物粒度分布反演;（4）利用对流-在不同环境条件下采用先进的微物理参数化方案的尺度数值预报模式及其与真实的个例PRD的比较;以及（5）利用混合集合变分资料同化程序将PRD同化到NWP模式中，以获得最佳模式初始化和更好的恶劣天气预报。该奖项反映了NSF的法定使命，并通过利用基金会的智力价值进行评估而被认为值得支持和更广泛的影响审查标准。

项目成果

Test of Power Transformation Function to Hydrometeor and Water Vapor Mixing Ratios for Direct Variational Assimilation of Radar Reflectivity Data

雷达反射率数据直接变分同化中水凝物和水汽混合比的功率变换函数测试

• DOI: 10.1175/waf-d-22-0158.1
• 发表时间: 2023
• 期刊: Weather and Forecasting
• 影响因子: 2.9
• 作者: Hu, Jiafen;Gao, Jidong;Liu, Chengsi;Zhang, Guifu;Heinselman, Pamela;Carlin, Jacob T.
• 通讯作者: Carlin, Jacob T.

Improving Polarimetric Radar-Based Drop Size Distribution Retrieval and Rain Estimation Using a Deep Neural Network

使用深度神经网络改进基于偏振雷达的水滴尺寸分布检索和降雨估计

• DOI: 10.1175/jhm-d-22-0166.1
• 发表时间: 2023
• 期刊: Journal of Hydrometeorology
• 影响因子: 3.8
• 作者: Ho, Junho;Zhang, Guifu;Bukovcic, Petar;Parsons, David B.;Xu, Feng;Gao, Jidong;Carlin, Jacob T.;Snyder, Jeffrey C.
• 通讯作者: Snyder, Jeffrey C.