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Optimal Estimation Retrievals and Their Uncertainties: What Every Atmospheric Scientist Should Know

Abstract

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, 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’ 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 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.

Article / Publication Data
Active/Online
YES
Volume
101
Available Metadata
Accepted On
May 21, 2020
DOI ↗
Fiscal Year
NOAA IR URL ↗
Peer Reviewed
YES
Publication Name
Bulletin of The American Meteorological Society
Published On
September 01, 2020
Publisher Name
American Meteorological Society
Print Volume
101
Print Number
9
Page Range
E1512–E1523
Issue
9
URL ↗

Authors

Authors who have authored or contributed to this publication.