Spatio-temporal Statistical Framework for the Evaluation of Global Change in the Ionosphere

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。地球表面和对流层温度升高是大气温室气体增加的一个众所周知的后果。 但是，温室气体在高层大气中增加的预期后果，包括平流层、中间层和热层，是冷却而不是变暖。 高层大气的冷却导致热收缩，这应该伴随着电离层的降低，其中自由电子密度达到其峰值（F层）。 虽然既定理论预测了电离层的降低，而且计算机模拟也知道会发生这种情况，但观测尚未令人信服地记录电离层降低的趋势及其对电离层特性的影响。 由于电离层数据稀少以及背景电离层变化量大，其中大部分与太阳周期和地磁效应有关，电离层趋势探测受到阻碍，本项目将制定一种统计方法，用于评价过去五十年来电离层参数的全球变化，目前已有足够的全球分布和组织良好的数据。该项目开发的建模框架将考虑到电离层测量的空间分布，并将使用比以往研究中使用的更现实的测量误差分布假设。 确定五十年来的趋势，并对其不确定性进行严格的估计，将为改进目前所有的电离层模型和减少其残余误差提供统计基础，在该项目中进行的研究通过试图证实电离层趋势的理论预测，为基础高层大气科学作出贡献。 更一般地说，该项目将开发新的统计工具，用于检测数据集中的趋势和其他信号，这些数据集中的观测结果是稀疏的，并且包含各种快速和慢速时间尺度上的可变性。 这些数据集是许多难以观察的地球物理和生态变量的标准，如土壤湿度，雪深和净初级生产力。 此外，该项目的研究将具有实用价值，因为电离层的状况对天基通信网络有着重大影响。 对电离层观测结果的分析将指导改进电离层模型的努力，从而改进与通信有关的空间天气预报。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Increased temperatures at the earth's surface and in the troposphere are a well-known consequence of increases in atmospheric greenhouse gases. But the expected consequence of greenhouse gas increases in the upper atmosphere, including the stratosphere, mesosphere and thermosphere, is cooling rather than warming. The cooling of the upper atmosphere results in thermal contraction, which should be accompanied by a lowering of the ionospheric layer in which the density of free electrons reaches its peak value (the F layer). While the lowering of ionospheric layers is predicted by established theory and is known to occur in computer simulations, lowering trends and their impacts on ionospheric properties have yet to be convincingly documented in observations. Ionospheric trend detection is hampered by the sparseness of ionsospheric data as well as the large amount of background ionospheric variability, much of which is associated with solar cycles and geomagnetic effects.This project will develop a statistical methodology for the evaluation of global changes in ionospheric parameters over the last five decades, for which sufficient globally distributed and well-organized data are now available. The modeling framework developed in the project will take into account the spatial distribution of ionospheric measurements, and will use more realistic assumptions on the distribution of measurement errors than those used in previous studies. The determination of trends over five decades, together with rigorous estimates of their uncertainty, will provide a statistical basis for improving all present day ionospheric models and reducing their residual error.Research conducted in this project contributes to basic upper-atmospheric science by attempting to confirm theoretical predictions of ionospheric trends. More generally, the project will develop new statistical tools for detecting trends and other signals in datasets in which the observations are sparse and contain variability on a variety of fast and slow time scales. Such datasets are the norm for many hard-to-observe geophysical and ecological variables like soil moisture, snow depth, and net primary productivity. In addition, research in this project will have practical value, as the state of the ionosphere has a significant impact on space-based communication networks. The analysis of ionsopheric observations will guide attempts to improve ionospheric models, resulting in improved forecasts of communication-relevant space weather.