A Probabilistic Framework for Assessment and Interpretation of Quantitative Precipitation Forecasts from Storm-Scale Models

用于评估和解释风暴规模模型定量降水预报的概率框架

• 批准号: 9714387
• 负责人: Efi Foufoula-Georgiou
• 金额: $ 33.42万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-09-01 至 2002-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9714387&HistoricalAwards=false
• 关键词: Probabilistic; Framework; Assessment; Interpretation; Quantitative

9714387 Foufoula The U.S. Weather Research Program (USWRP) is an interagency activity designed to perform and implement the research necessary to improve the delivery of weather services to the nation. Under this Program, the National Science Foundation, the National Oceanic and Atmospheric Administration (NOAA), the National Aeronautics and Space Administration and the Office of Naval Research are jointly evaluating and supporting research of high priority to the USWRP. Over the past decade, a significant efforts have been directed toward research and data collection initiatives that focus on improving numerical weather prediction. These investments have been somewhat successful and state-of-the-art in numerical weather prediction has truly advanced through a deeper understanding and improved parameterizations of the physical processes involved, new and better data assimilation techniques, and greatly improved numerical schemes. Despite this progress, however, precipitation still remains one of the most difficult variables to predict quantitatively in atmospheric models. Also, the inherent uncertainty associated with numerical predictions often makes the interpretation of forecasts difficult and limits their practical utility. The Principal Investigators have proposed an interdisciplinary approach to the problem of predicting precipitation on the storm-scale and interpreting the numerical forecasts. The approach will involve utilization of an advanced mesoscale meteorological model coupled with advanced statistical techniques. The Investigators will address the following three objectives, with emphasis on the use of storm-scale models, initialized using radar and other fine-scale observations, to generate quantitative precipitation forecasts and associated ensembles to be used in applications such as hydrologic modeling and flood prediction. Formulate and study a suite of multiscale statistical measures of forecast performance which can help to detect and correct deficiencies in microphysical parameterizations and obtain forecast assessments at application-appropriate scales, e.g., basin-average rainfall. Develop a probabilistic framework under which forecast improvements and the limits of predictability of current numerical weather prediction (NWP) models can be assessed while explicitly acknowledging forecast uncertainty and dependence on scale. Develop a probabilistic framework for assessing the practical utility of deterministic forecasts for risk-based decision making. Successful completion of this research will potentially open the doors to improved quantitative precipitation forecasting and better utilization/interpretation of numerical forecasts. ***

小行星9714387 美国天气研究计划（USWRP）是一项跨部门活动，旨在执行和实施必要的研究，以改善向国家提供的天气服务。 根据这一方案，国家科学基金会、国家海洋和大气管理局、国家航空航天局和海军研究办公室正在联合评估和支持美国水资源研究计划高度优先的研究。 在过去的十年中，一个显着的努力已经指向研究和数据收集的举措，重点是提高数值天气预报。 这些投资取得了一定的成功，通过更深入的理解和改进，数值天气预报的最新技术得到了真正的发展。 所涉及的物理过程的参数化，新的和更好的数据同化技术，并大大改善了数值方案。 然而，尽管取得了这些进展，降水仍然是大气模式中最难定量预测的变量之一。 此外，与数值预测相关的固有不确定性往往使预测难以解释，并限制了其实际效用。 主要研究人员提出了一种跨学科的方法来预测风暴尺度上的降水和解释数值预报的问题。 这一方法将涉及利用先进的中尺度气象模型，并结合先进的统计技术。 调查人员将解决以下三个目标，重点是使用风暴尺度模型，使用雷达和其他精细尺度观测进行初始化，以生成定量降水预报和相关集合，用于水文建模和洪水预测等应用。 制定和研究一套多尺度预测统计方法 有助于检测和纠正微物理缺陷的性能 参数化，并在适用时获得预测评估 刻度，例如，流域平均降雨量 制定一个概率框架，根据该框架预测改进情况， 当前数值天气预报模式的可预报性极限 在明确承认预测不确定性的同时， 依赖规模。 制定一个概率框架，用于评估确定性 基于风险的决策预测。 这项研究的成功完成将有可能打开大门，以改善定量降水预报和更好地利用/解释数值预报。 ***