Repository logo
 

Exploring the limits of variational passive microwave retrievals

Simple item page
dc.contributor.authorDuncan, David Ian, author
dc.contributor.authorKummerow, Christian D., advisor
dc.contributor.authorBoukabara, Sid-Ahmed, committee member
dc.contributor.authorO'Dell, Christopher W., committee member
dc.contributor.authorReising, Steven C., committee member
dc.contributor.authorRutledge, Steven A., committee member
dc.contributor.authorSchumacher, Russ S., committee member
dc.date.accessioned2017-09-14T16:04:29Z
dc.date.available2017-09-14T16:04:29Z
dc.date.issued2017
dc.description.abstractPassive microwave observations from satellite platforms constitute one of the most important data records of the global observing system. Operational since the late 1970s, passive microwave data underpin climate records of precipitation, sea ice extent, water vapor, and more, and contribute significantly to numerical weather prediction via data assimilation. Detailed understanding of the observation errors in these data is key to maximizing their utility for research and operational applications alike. However, the treatment of observation errors in this data record has been lacking and somewhat divergent when considering the retrieval and data assimilation communities. In this study, some limits of passive microwave imager data are considered in light of more holistic treatment of observation errors. A variational retrieval, named the CSU 1DVAR, was developed for microwave imagers and applied to the GMI and AMSR2 sensors for ocean scenes. Via an innovative method to determine forward model error, this retrieval accounts for error covariances across all channels used in the iteration. This improves validation in more complex scenes such as high wind speed and persistently cloudy regimes. In addition, it validates on par with a benchmark dataset without any tuning to in-situ observations. The algorithm yields full posterior error diagnostics and its physical forward model is applicable to other sensors, pending intercalibration. This retrieval is used to explore the viability of retrieving parameters at the limits of the available information content from a typical microwave imager. Retrieval of warm rain, marginal sea ice, and falling snow are explored with the variational retrieval. Warm rain retrieval shows some promise, with greater sensitivity than operational GPM algorithms due to leveraging CloudSat data and accounting for drop size distribution variability. Marginal sea ice is also detected with greater sensitivity than a standard operational retrieval. These studies ultimately show that while a variational algorithm maximizes the effective signal to noise ratio of these observations, hard limitations exist due to the finite information content afforded by a typical microwave imager.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierDuncan_colostate_0053A_14270.pdf
dc.identifier.urihttps://hdl.handle.net/10217/183906
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectremote sensing
dc.subjectvariational methods
dc.subjectsatellite meteorology
dc.subjectpassive microwave
dc.titleExploring the limits of variational passive microwave retrievals
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineAtmospheric Science
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Duncan_colostate_0053A_14270.pdf
Size:
21.24 MB
Format:
Adobe Portable Document Format
Download

Collections

2000-2019
Theses and Dissertations