Ensemble Kalman Filter Assimilation of Multisensor Observations from Convection for Storm-Scale Analysis

对流多传感器观测的集合卡尔曼滤波器同化用于风暴规模分析

• 批准号: 0333872
• 负责人: Louis Wicker
• 金额: $ 24.94万
• 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: 2003
• 资助国家: 美国
• 起止时间: 2003-11-15 至 2007-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0333872&HistoricalAwards=false
• 关键词: Ensemble; Kalman; Filter; Assimilation; Multisensor

Observational capabilities on the storm scale have increased dramatically during the past decade. The national network of operational Doppler radars provides observations of convective storms and their environments every five minutes. In the near future, rapidly scanning phased array radars, dual polarization data, and regional high-density networks of radars could also be available. Surface data from local surface mesonets have also become increasingly available. The availability of these data will spur the development of new retrieval and data assimilation methods for storm scale phenomena.Observations of Doppler velocity and reflectivity are not often available near the ground. At the same time the convective cold pool and storm wind field plays a critical role in determining significant surface weather, convective initiation, and storm rotation. Therefore retrieval of the wind, temperature, and moisture fields near the surface is both difficult and crucial toward increasing understanding of storm dynamics and improving forecasts and warnings.Recently the ensemble Kalman filter (EnKF) has been shown to be a viable method for obtaining convective-scale atmospheric state estimates from Doppler radar observations. Assimilation experiments using synthetic data sampled from convective storm models indicate that this method can be used to retrieve the unobserved fields within storms. Initial experiments using observed Doppler winds indicate that EnKF has potential for real data as well. An attractive feature of the EnKF is that it is a relatively simple methodology and can be implemented into a forecast model with little effort. The major goal of this research is to assess the potential of EnKF in assimilating real observations of convective storms through the use of a series of observing system simulation experiments. Unique aspects of this work include the use of an ultra-high-resolution numerical cloud model to produce synthetic observations as well as considering the role of model error associated with the microphysical parameterization. Both radar and in situ surface observations will be generated synthetically. The methodology will be to use multiple Doppler radars and surface-based observations to retrieve wind, temperature, and hydrometeor fields at low levels. Retrieval of low-level fields are particularly important toward understanding storm dynamics, since these features play an important role in determining storm evolution and severity.The research increases intellectual knowledge in both the theoretical and applied meteorological communities. The development of robust methods for estimating near-surface wind, temperature, and moisture fields within storms from various observational platforms will greatly enhance theoretical understanding of storm dynamics within more realistic environments. Research results can then be applied in the operational community to enable regional forecasters to focus on smaller regions where the conditions are most conducive for severe storm development. Increased knowledge of storm scale dynamics will also help local forecasters improve the warning process by increasing lead times and reducing false alarms.

在过去的十年中，风暴尺度上的观测能力大幅提高。运行中的国家多普勒雷达网络每五分钟提供对流风暴及其环境的观测。在不久的将来，快速扫描相控阵雷达、双极化数据和区域高密度雷达网络也可能实现。来自当地表面中子网的表面数据也变得越来越容易获得。这些资料的可获得性将促进对风暴尺度现象的新的反演和数据同化方法的发展。同时，对流冷池和风暴风场在决定显著的地面天气、对流启动和风暴旋转方面起着关键作用。因此，对地面附近的风场、温度场和湿度场的反演对于加深对风暴动力学的了解以及改进预报和预警都是困难和关键的。最近，集合卡尔曼滤波(EnKF)被证明是从多普勒雷达观测中获得对流尺度大气状态估计的一种可行的方法。利用对流风暴模式采集的合成资料进行的同化试验表明，该方法可以用来反演风暴中未观测到的场。利用观测到的多普勒风进行的初步实验表明，EnKF也有可能获得真实数据。EnKF的一个吸引人的特点是，它是一种相对简单的方法，可以很容易地实施到预测模型中。这项研究的主要目的是通过使用一系列观测系统模拟实验来评估EnKF在同化真实对流风暴观测方面的潜力。这项工作的独特方面包括使用超高分辨率数值云模式产生综合观测，以及考虑与微物理参数化有关的模型误差的作用。雷达和现场地面观测都将综合生成。该方法将使用多个多普勒雷达和基于地面的观测来反演低层的风、温度和水流星场。低层场的反演对于理解风暴动力学尤为重要，因为这些特征在确定风暴的演变和严重性方面起着重要作用。这项研究增加了理论和应用气象界的知识。从不同的观测平台开发用于估计风暴内近地表风场、温度和湿度场的稳健方法，将极大地增强对更现实环境中风暴动力学的理论理解。然后，研究成果可以应用于业务界，使区域预报员能够将重点放在条件最有利于强风暴发展的较小区域。增加对风暴规模动态的了解，也将通过增加准备时间和减少错误警报来帮助当地预报员改进预警过程。