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.

该项目旨在根据贝叶斯分层建模框架中纳入的相关大气动力学的多分辨率统计表示，开发一种新方法，以对大降水进行短期预测。 主要的输入数据将是来自天气雷达的反射数据，降水演变的动力学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地图的性质，确定隐藏动力学的演化及其与大气物理学的关系以及地图和传播器对气象变量的参数依赖性的关系。 在隐藏的低维过程和多尺度降水场之间，各种以气象参数为条件的线性和非线性图。 传播器将通过准平台一阶马尔可夫过程近似，其中有限的时间依赖性表示为对气象制度的参数依赖性。 该模型将用于在伊利诺伊州，俄亥俄州和印第安纳州的Thor观察项目中收集的雷达和其他气象数据进行测试。 该方法将与从现有预后工具获得的该区域的短期预测进行比较。 在项目的后期，该方法将扩展到使用顺序蒙特卡洛技术的顺序预测算法的开发。 第二组研究问题与有效验证技术的发展有关。 将探索自动识别数据和预测中的风暴细胞的方法，并探索逐个细胞的形状，位置和强度比较。 最后，该项目将包括对风暴细胞预测的假设测试技术的研究。 开发的方法可能会改善洪水预测，并对这些预测的不确定性进行定量估计，从而对紧急服务有利。