Improved Statistical Models and Methods for Atmospheric Science Measurements

在大气科学中，许多遥感仪器通过观察容易接近的现象并使用数学模型来推断感兴趣的量来进行间接测量。一个常见的例子是使用卫星测量反射太阳光的光谱，然后使用一种名为最佳估计的程序来反演与辐射和大气特性有关的物理模型。这个项目的重点是开发统计方法，以便更准确地捕捉这一推断中的不确定性。这项研究有可能极大地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遥感工作。从光谱中推断大气状态称为恢复。在统计术语中，这是对统计模型中的参数的估计，其中物理正向模型定义了平均结构。由于这些参数具有特定的物理意义，因此适当地考虑模型误差是至关重要的。该项目旨在通过统计方面的进展扩展目前的框架，其中包括模型差异的有效低级表示和有针对性地使用独立的地面测量进行先验分布。开发将在由不确定度量化小组创建的试验台内进行。在那里，已经开发了一个包括基本物理的代理正演模型，但比目前使用的正演模型更简单，计算也更快。该项目的结果预计将提高大气测量的准确性，并使几个科学领域取得进展。

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.