气候变化领域集成服务门户(CCPortal): Optimal estimation retrievals and their uncertainties

CCPortal

DOI	10.1175/BAMS-D-19-0027.1
	Optimal estimation retrievals and their uncertainties
	Maahn M.; Turner D.D.; Löhnert U.; Posselt D.J.; Ebell K.; Mace G.G.; Comstock J.M.
发表日期	2020
ISSN	00030007
起始页码	E1512
结束页码	E1523
卷号	101 期号:9
英文摘要	Remote sensing instruments are heavily used to provide observations for both the operational and research communities. These sensors do not provide direct observations of the desired atmospheric variables, but instead, retrieval algorithms are necessary to convert the indirect observations into the variable of interest. It is critical to be aware of the underlying assumptions made by many retrieval algorithms, including that the retrieval problem is often ill posed and that there are various sources of uncertainty that need to be treated properly. In short, the retrieval challenge is to invert a set of noisy observations to obtain estimates of atmospheric quantities. The problem is often complicated by imperfect forward models, by imperfect prior knowledge, and by the existence of nonunique solutions. Optimal estimation (OE) is a widely used physical retrieval method that combines measurements, prior information, and the corresponding uncertainties based on Bayes's theorem to find an optimal solution for the atmospheric state. Furthermore, OE also allows the relative contributions of the different sources of error to the uncertainty in the final retrieved atmospheric state to be understood. Here, we provide a novel Python library to illustrate the use of OE for inverse problems in the atmospheric sciences. We introduce two example problems: how to retrieve drop size distribution parameters from radar observations and how to retrieve the temperature profile from ground-based microwave sensors. Using these examples, we discuss common pitfalls, how the various error sources impact the retrieval, and how the quality of the retrieval results can be quantified. © 2020 American Meteorological Society. All rights reserved.
语种	英语
scopus关键词	Microwave sensors; Remote sensing; Uncertainty analysis; Atmospheric variables; Drop size distribution; Non-unique solutions; Optimal estimation retrieval; Physical retrieval method; Relative contribution; Remote sensing instruments; Sources of uncertainty; Inverse problems
来源期刊	Bulletin of the American Meteorological Society
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/177823
作者单位	Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, NOAA/Physical Sciences Lab, Boulder, CO, United States; NOAA/Global Systems Lab, Boulder, CO, United States; Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany; Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States; Department of Atmospheric Science, University of Utah, Salt Lake City, UT, United States; Pacific Northwest National Laboratory, Richland, WA, United States
推荐引用方式 GB/T 7714	Maahn M.,Turner D.D.,Löhnert U.,et al. Optimal estimation retrievals and their uncertainties[J],2020,101(9).
APA	Maahn M..,Turner D.D..,Löhnert U..,Posselt D.J..,Ebell K..,...&Comstock J.M..(2020).Optimal estimation retrievals and their uncertainties.Bulletin of the American Meteorological Society,101(9).
MLA	Maahn M.,et al."Optimal estimation retrievals and their uncertainties".Bulletin of the American Meteorological Society 101.9(2020).