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Improved Statistical Models and Methods for Atmospheric Science Measurements

改进的大气科学测量统计模型和方法

基本信息

批准号：
1723117
负责人：
Jenny Brynjarsdottir
金额：
$ 12.5万
依托单位：
Case Western Reserve University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1723117&HistoricalAwards=false
关键词：
Improved Statistical Models Methods Atmospheric

项目摘要

In atmospheric science, many remote sensing instruments make indirect measurements by observing readily-accessible phenomena and using mathematical models to infer the quantities of interest. A common example is the use of satellites to measure the spectrum of reflected sunlight and subsequent use of a procedure called optimal estimation to invert a physical model that relates radiances and atmospheric properties. This project focuses on developing statistical methods that more accurately capture the uncertainty in this inference. This research has the potential to greatly improve accuracy of current and future remote sensing efforts, particularly those that utilize spectrometers. Inferring the atmospheric state from spectra is called a retrieval. In statistical parlance, this is an estimation of parameters in a statistical model where a physical forward model defines the mean structure. Since the parameters have a particular physical meaning, it is essential that model error is properly accounted for. This project aims to extend the current framework through statistical advances that include efficient low-rank representation of model-discrepancy and targeted use of independent ground measurements for prior distributions. Developments will be performed within a test-bed created by an uncertainty quantification group. There a surrogate forward model has been developed that includes the essential physics but is simpler and computationally faster than the forward models currently used. The results of the project are anticipated to increase the accuracy of atmospheric measurements and enable advances in several areas of science.

在大气科学中，许多遥感仪器通过观察容易获得的现象并使用数学模型来推断感兴趣的数量来进行间接测量。一个常见的例子是使用卫星测量反射太阳光的光谱，随后使用一种称为最佳估计的程序来反演将辐射和大气特性联系起来的物理模型。该项目的重点是开发统计方法，更准确地捕捉这种推断的不确定性。这项研究有可能大大提高当前和未来遥感工作的准确性，特别是那些利用光谱仪的工作。从光谱中推断大气状态称为反演。在统计术语中，这是对统计模型中的参数的估计，其中物理正演模型定义了平均结构。由于这些参数具有特定的物理意义，因此必须适当考虑模型误差。该项目旨在通过统计方面的进展来扩展现有框架，包括模型差异的有效低秩表示和有针对性地使用独立地面测量值进行先验分布。开发将在不确定性量化小组创建的试验台内进行。有一个替代的前向模型已经开发，包括基本的物理，但更简单，计算速度比目前使用的前向模型。该项目的成果预计将提高大气测量的准确性，并推动若干科学领域的进步。