Assimilation of Doppler Radar Data with an Ensemble-based Variational Method for Storm-scale Numerical Weather Prediction

大多数目前采用的对流尺度数据同化（DA）方案主要是为应用于大尺度大气流动和天气现象，其中对比鲜明的平衡和约束是相关的。 此外，在对流尺度上，多普勒雷达是唯一广泛可用的手段，可提供足够高的空间和时间分辨率的广泛观测，以促进对强雷暴等影响力大的天气现象进行动态预测。 因此，将多普勒雷达数据有效地同化到对流分辨模式中越来越重要，但利用不足。 这些研究人员将在他们以前关于对流尺度DA的工作的基础上，探索新的方法来最佳地同化从采用双极化技术的新升级的国家网络中获得的WSR-88 D多普勒雷达数据，特别是将：（i）确定如何最好地利用反射率观测以及径向速度数据;（ii）研究背景趋势资料在一个风暴尺度的数据分析系统中的效用;以及（iii）实现一种高效的基于集合的混合三维变分/包络卡尔曼滤波器（3DVAR/EnKF）框架，将现有的中尺度集合预报信息纳入风暴尺度三-三维变分DA系统。这项工作的智力价值集中在开发一种新的DA战略，使雷达反射率和径向速度场的最佳利用，它是唯一适合于指定快速演变的对流尺度流，以提供初始条件的高分辨率风暴尺度NWP模式，如NSF/NCAR天气研究和预报（WRF）模式-一个系统，享有在研究和业务设置使用。 这种方法旨在提高我们对对流风暴尺度动力学的物理理解，并进一步提高雷暴相关灾害的检测和预测能力，以及水文应用所需的更准确的定量降水预报。 这项工作也可能有助于解决对流数值天气预报（NWP）中固有的初始“平衡问题”，但迄今为止在很大程度上被研究界所忽视。 这项工作的更广泛的影响包括通过加速这些数据在业务和基于研究的NWP中的使用，从美国对全国WSR-88 D雷达网络的大量投资中获得最大利益。 该项目将通过指导研究生和博士后研究助理来体现教育效益，新出现的成果将被整合到面向广泛受众的教材和出版物中。

Most currently employed convective-scale data assimilation (DA) schemes were developed primarily for application to larger-scale atmospheric flows and weather phenomena, in which sharply contrasting balances and constraints are relevant. Moreover, at convective scales Doppler radar is the only widely available means of providing extensive observations of sufficiently high spatial and temporal resolution needed to facilitate dynamic prediction of high-impact weather phenomena such as severe thunderstorms. As such, the effective assimilation of Doppler radar data into convection-resolving models is of increasing importance, yet underutilized. Building upon their previous work on convective-scale DA, these researchers will explore new approaches to optimally assimilate operationally-collected WSR-88D Doppler radar data available from a newly-upgraded national network employing dual-polarization technology, and in particular will: (i) Determine how to best use reflectivity observations in addition to radial velocity data; (ii) examine the usefulness of the background tendency information in a storm-scale DA system; and (iii) implement an efficient ensemble-based hybrid three-dimensional variational/Ensemble Kalman Filter (3DVAR/EnKF) framework that incorporates existing mesoscale ensemble forecast information into a storm-scale three-dimensional variational DA system.The Intellectual Merit of this effort centers upon developing a novel DA strategy that makes optimal use of both radar reflectivity and radial velocity fields, which are uniquely suitable for specifying rapidly evolving convective-scale flows, in order to provide initial conditions for high-resolution storm-scale NWP models such as the NSF/NCAR Weather Research and Forecasting (WRF) model--a system that enjoys use in both research and operational settings. This approach seeks to improve our physical understanding of convective storm-scale dynamics and is further aimed toward improved detection and anticipation of thunderstorm-related hazards as well as more accurate quantitative precipitation forecasts needed for hydrological applications. This work may also help to solve the initial "balance problem" that is inherent in convective numerical weather prediction (NWP), but has heretofore been largely overlooked by the research community. Broader Impacts of this work include deriving maximum benefit from the considerable U.S. investment in the nationwide WSR-88D radar network by accelerating the use of these data in both operational and research-based NWP. This project will embody educational benefits through mentoring of graduate students and a postdoctoral research associate, and emerging results will be integrated into teaching materials and publications reaching broad audiences.