Maahn, Maximilian ORCID: 0000-0002-2580-9100, Turner, David D., Loehnert, Ulrich, Posselt, Derek J., Ebell, Kerstin, Mace, Gerald G. and Comstock, Jennifer M. (2020). Optimal Estimation Retrievals and Their Uncertainties What Every Atmospheric Scientist Should Know. Bull. Amer. Meteorol. Soc., 101 (9). S. E1512 - 12. BOSTON: AMER METEOROLOGICAL SOC. ISSN 1520-0477

Full text not available from this repository.

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, 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.

Item Type: Journal Article
Creators:
CreatorsEmailORCIDORCID Put Code
Maahn, MaximilianUNSPECIFIEDorcid.org/0000-0002-2580-9100UNSPECIFIED
Turner, David D.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Loehnert, UlrichUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Posselt, Derek J.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Ebell, KerstinUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Mace, Gerald G.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Comstock, Jennifer M.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
URN: urn:nbn:de:hbz:38-320164
DOI: 10.1175/BAMS-D-19-0027.1
Journal or Publication Title: Bull. Amer. Meteorol. Soc.
Volume: 101
Number: 9
Page Range: S. E1512 - 12
Date: 2020
Publisher: AMER METEOROLOGICAL SOC
Place of Publication: BOSTON
ISSN: 1520-0477
Language: English
Faculty: Unspecified
Divisions: Unspecified
Subjects: no entry
Uncontrolled Keywords:
KeywordsLanguage
LIQUID WATER; MICROWAVE RADIOMETER; OPTICAL-THICKNESS; ABSORPTION MODEL; PRECIPITATION; TEMPERATURE; PROFILES; HUMIDITY; CLOUDSMultiple languages
Meteorology & Atmospheric SciencesMultiple languages
URI: http://kups.ub.uni-koeln.de/id/eprint/32016

Downloads

Downloads per month over past year

Altmetric

Export

Actions (login required)

View Item View Item