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

该项目旨在研究利用偏振雷达数据(PRD)来改进对恶劣天气的理解和预测的最佳可能方法。美国国家天气雷达网最近升级为双极化能力，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)模式中往往表现不佳，这可能会对他们的预测产生负面影响。然而，将这种观测到的极化雷达信息纳入到数值预报模式中以改进其预报的预期好处尚未实现。本项目旨在通过探索先进的风暴尺度同化技术在珠江三角洲地区的应用，促进我们对恶劣天气的理解，并改善对NWP模式中的恶劣天气和微物理特征的预测。这些改进将有助于实现现有雷达网络升级的好处，并随着风暴尺度数值预报模式越来越多地纳入预警决策过程，向公众提供更及时的恶劣天气信息。WSR-88D雷达网最新提供的PRD可以说是风暴规模天气量化和预报的最佳数据来源，因为PRD包含丰富的关于水流星微物理的信息，包括降水粒子的大小、形状、相位和组成，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化，进行了水流星分类和从PRD反演水流星粒度分布。此外，PRD可以直接同化到NWP模式中，以改进模式的初始化，并产生更现实的分析和预报。这项工作的具体目标包括：(1)发展准确和有效的水流星的PRD参数正演算符；(2)量化包括测量和正演算符误差在内的观测误差；(3)基于观测的水流星粒子尺寸分布的反演；(4)在不同环境条件下使用具有先进微物理参数化方案的对流尺度数值预报模式模拟强风暴，并在实际案例中与PRD进行比较；以及(5)使用混合集合变分数据同化程序将PRD同化到NWP模式中，以优化模式初始化和更好地预报恶劣天气。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.