Collaborative Research: Scale-Recursive Estimation of Precipitation for Applications to Quantitative Precipitation Forecast (QPF) Verification and Multisensor Estimation

合作研究：降水的尺度递归估计应用于定量降水预报（QPF）验证和多传感器估计

• 批准号: 0130396
• 负责人: Kelvin Droegemeier
• 金额: $ 18.67万
• 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: 2002
• 资助国家: 美国
• 起止时间: 2002-03-15 至 2005-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0130396&HistoricalAwards=false
• 关键词: Collaborative; Research; Scale; Recursive; Estimation

Atmospheric precipitation, whether in convective storms or quasi-uniformly stratified cloud layers, generally is highly inhomogeneous and exhibits considerable natural variability at scales ranging from a few meters to several hundreds of kilometers. A variety of sensors (e.g. rain gauges, radars, and satellites) are used to monitor precipitation rate and total accumulation and provide both direct and indirect measurements at different scales based upon instrument resolution and sampling or analysis strategies. Physically-based computer models, both of the atmosphere and solid earth, rely upon these observed data for initialization/assimilation as well as forecast validation. However, owing to the tremendous scale-dependent variability of precipitation and the discrepancies in scale and resolution among different types/sources of data, merging or comparing observations at different scales, or comparing model outputs to observations, is difficult. Yet, quantitative precipitation estimation (QPE) and model forecast verification are foundational aspects of both atmospheric and hydrologic prediction.In an effort to address issues associated with both scale variability and scale discrepancy in merging or comparing information from multiple sources, the Principal Investigators seek to use a recently-developed scale-recursive estimation (SRE) framework. They will utilize the SRE framework for (1) Quantitative Precipitation Forecast (QPF) verification when observations are available at one or more scales different than the scale of the numerical model; (2) derivation of products or analyses in situations where observations and model outputs at different scales are to be merged to produce a single field; and (3) estimation of background error covariances from fields produced via the comparison of observations and model outputs at different scales. The problems to be addressed require combined expertise in statistical multi-scale analysis of precipitation, optimal estimation theory, radar data analysis and interpretation, data assimilation, and numerical weather prediction modeling. This collaborative team involves two statistical hydrologists and two meteorologists having demonstrated expertise in the above areas, and builds upon a previous successful collaboration in the analysis of the spatio-temporal structure of forecasted and observed precipitation at the scale of individual convective storms. In previous collaborative research, an extensive analysis was made of the spatio-temporal structure of forecasted and observed precipitation with an emphasis on one fundamental question: Do storm-resolving forecast models produce precipitation fields that exhibit the same scale invariant structures as observations, and if not, why? The shortcomings of typical distance-based deterministic interpolators (or averaging operators) for converting data from one scale to another, i.e., downscaling (up-scaling), were documented, and the need for a rigorous methodology capable of handling scale-dependent variability and uncertainty in observations was demonstrated.The present interdisciplinary proposal builds upon this body of previous work and proposes to explore a framework within which issues of variability and scale-dependency can be properly addressed for the purpose of QPF verification and multi-sensor rainfall estimation.

大气降水，无论是在对流风暴或准均匀分层的云层，通常是高度不均匀的，并表现出相当大的自然变化范围从几米到几百公里。 各种传感器（如雨量计、雷达和卫星）用于监测降水率和总累积量，并根据仪器分辨率和取样或分析策略提供不同尺度的直接和间接测量结果。 基于物理的计算机模型，无论是大气层还是固体地球，都依赖于这些观测数据进行初始化/同化以及预测验证。 然而，由于降水的巨大尺度依赖性变化以及不同类型/来源的数据在尺度和分辨率上的差异，很难合并或比较不同尺度的观测结果，或将模型输出与观测结果进行比较。 然而，定量降水估计（QPE）和模式预报验证是大气和水文预报的基础方面，在努力解决合并或比较来自多个来源的信息时与尺度变化和尺度差异相关的问题，首席研究员试图使用最近开发的尺度递归估计（SRE）框架。 他们将利用SRE框架进行以下工作：（1）当观测结果在一个或多个不同于数值模式尺度的尺度上可用时，进行定量降水预报（QPF）验证;（2）在不同尺度的观测结果和模式输出将合并产生一个场的情况下，进行产品或分析的推导;以及（3）通过比较不同尺度下的观测值和模型输出来估计来自所产生的场的背景误差协方差。要解决的问题，需要结合降水的统计多尺度分析，最佳估计理论，雷达数据分析和解释，数据同化，数值天气预报建模的专业知识。 这个合作小组包括两名统计水文学家和两名在上述领域具有专门知识的气象学家，并建立在以前成功合作分析预测和观测到的单个对流风暴降水的时空结构的基础上。 在以前的合作研究中，广泛的分析预测和观测到的降水的时空结构的一个基本问题的重点：风暴解析预报模型产生降水场，表现出相同的尺度不变的结构作为观测，如果不是，为什么？ 用于将数据从一个尺度转换到另一个尺度的典型的基于距离的确定性插值器（或平均算子）的缺点，即，降尺度（升尺度），被记录在案，需要一个严格的方法，能够处理尺度相关的变异性和不确定性的observations.The本跨学科的建议建立在这个机构以前的工作，并建议探讨一个框架内的变异性和尺度依赖性的问题，可以适当地解决QPF验证和多传感器降雨估计的目的。