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获得的现象和使用数学模型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，然后使用一种称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。该项目侧重于开发统计方法，以更准确地捕捉这种推断中的不确定性。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遥感工作。从光谱推断大气状态称为反演。用统计学的说法，这是统计模型中参数的估计，其中物理正演模型定义了平均结构。由于参数具有特定的物理含义，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步来扩展当前的框架，包括模型差异的有效低秩表示和有针对性地使用独立的地面测量来进行先验分布。开发将在不确定性量化小组创建的测试平台中执行。已经开发了一种替代正演模型，该模型包括基本物理，但比目前使用的正演模型更简单，计算速度更快。预计该项目的结果将提高大气测量的准确性，并使若干科学领域取得进展。

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.