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: 0130394
• 负责人: Efi Foufoula-Georgiou
• 金额: $ 28.63万
• 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: 2002
• 资助国家: 美国
• 起止时间: 2002-03-15 至 2006-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0130394&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，无论是在对流风暴还是准均匀分层的云层中，通常都是高度不均匀的，在几米到几百公里的尺度上表现出相当大的自然变异性。各种传感器(如雨量计、雷达和卫星)用于监测降雨率和总累积，并根据仪器分辨率和采样或分析策略在不同的尺度上提供直接和间接测量。基于物理的计算机模型，无论是大气还是固体地球，都依赖于这些观测数据进行初始化/同化以及预报验证。然而，由于降水量随尺度变化很大，而且不同类型/来源的数据在尺度和分辨率上存在差异，很难合并或比较不同尺度上的观测值，或比较模型输出与观测值。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本方面。为了解决在合并或比较来自多个来源的信息时与尺度可变性和尺度差异相关的问题，首席调查人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)在与数值模式尺度不同的一个或多个尺度上可获得观测值时，进行定量降水预报核实；(2)在将不同尺度上的观测值和模型输出合并为一个场的情况下，对产品或分析进行推导；(3)通过比较不同尺度上的观测值和模型产出，估计产生的场的背景误差协方差。要解决的问题需要在降水统计多尺度分析、最佳估计理论、雷达数据分析和解释、数据同化和数值天气预报建模方面的综合专门知识。这一合作小组由两名统计水文学家和两名气象学家组成，他们在上述领域表现出专业知识，并建立在先前成功合作的基础上，在单个对流风暴的尺度上分析预报和观测降水的时空结构。在以前的合作研究中，人们对预报和观测降水的时空结构进行了广泛的分析，并强调了一个基本问题：风暴分解预报模式产生的降水场是否表现出与观测相同的尺度不变结构，如果不是，为什么？记录了典型的基于距离的确定性插值器(或平均算子)在将数据从一个尺度转换到另一个尺度时的缺点，即缩小尺度(放大尺度)，并论证了能够处理观测中尺度相关的可变性和不确定性的严格方法的必要性。当前的跨学科建议建立在前人工作的基础上，并建议探索一种框架，在该框架内可以适当地解决变异性和尺度相关性问题，以达到QPF验证和多传感器降雨估计的目的。