MSPA-CSE: Statistical Methods for Precipitation Nowcasting and Verification

MSPA-CSE：降水临近预报和验证的统计方法

• 批准号: 0434213
• 负责人: Christopher Wikle
• 金额: $ 75万
• 依托单位: University of Missouri-Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-10-01 至 2009-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0434213&HistoricalAwards=false
• 关键词: MSPA; CSE; Statistical; Methods; Precipitation

This project seeks to develop a new approach to short-term forecasts of heavy precipitation based on a multi-resolution statistical representation of the relevant atmospheric dynamics incorporated within a Bayesian hierarchical modeling framework. The primary input data will be reflection data from weather radars and the dynamics of the evolution of the precipitation will be represented as a low-dimensional stochastic process. One set of research questions includes the nature of the map between the hidden dynamics and the precipitation field, of the propagator determining the evolution of the hidden dynamics and its relation to atmospheric physics, and of the parametric dependence of the map and propagator functions on meteorological variables. A variety of linear and nonlinear maps, conditioned on meteorological parameters, between the hidden low-dimensional process and the multi-scale precipitation field will be tested. The propagator will be approximated by a quasi-stationary first-order Markov process where the limited time dependence is represented as a parametric dependence on the meteorological regime. The model will be applied to and tested with radar and other meteorological data collected during the THOR observing project over Illinois, Ohio and Indiana. The method will be compared with short-term forecasts for this region obtained from existing prognostic tools. Later in the project, the approach will be extended to the development of a sequential prediction algorithm using sequential Monte Carlo techniques. A second set of research questions relate to the development of effective verification techniques. Methods for automatically identifying storm cells in data and forecasts and making cell-by-cell comparisons of their shape, location and intensity will be explored. Finally the project will include research into hypothesis testing techniques for storm cell predictions. The methodology developed could lead to improvements in flood forecasting and a quantitative estimate of the uncertainty in these forecasts with consequent benefit to emergency services.

该项目旨在开发一种基于贝叶斯分层建模框架内相关大气动力学的多分辨率统计表示的强降水短期预报新方法。 主要输入数据将是来自天气雷达的反射数据，降水演化的动态将表示为低维随机过程。 一组研究问题包括隐藏动力学和降水场之间的映射的性质、确定隐藏动力学的演化及其与大气物理的关系的传播器的性质、以及映射和传播器函数对气象的参数依赖关系。变量。 将测试隐藏的低维过程和多尺度降水场之间以气象参数为条件的各种线性和非线性图。 传播器将通过准稳态一阶马尔可夫过程来近似，其中有限时间依赖性表示为对气象状况的参数依赖性。 该模型将应用于伊利诺伊州、俄亥俄州和印第安纳州 THOR 观测项目期间收集的雷达和其他气象数据并进行测试。 该方法将与从现有预测工具获得的该地区的短期预测进行比较。 在项目后期，该方法将扩展到使用顺序蒙特卡罗技术开发顺序预测算法。 第二组研究问题涉及有效验证技术的开发。 将探索自动识别数据和预报中的风暴单元并对其形状、位置和强度进行逐个单元比较的方法。 最后，该项目将包括对风暴单元预测的假设检验技术的研究。 所开发的方法可以改进洪水预报，并对这些预报的不确定性进行定量估计，从而有利于应急服务。