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dc.contributor.advisorKummerow, Christian D.
dc.contributor.advisorZupanski, Milija
dc.contributor.authorLee, Yoonjin
dc.contributor.committeememberReising, Steven C.
dc.contributor.committeemembervan den Heever, Susan C.
dc.date.accessioned2017-06-09T15:41:09Z
dc.date.available2017-06-09T15:41:09Z
dc.date.issued2017
dc.description2017 Spring.
dc.descriptionIncludes bibliographical references.
dc.description.abstractAssimilation of observation data in cloudy regions has been challenging due to the unknown properties of clouds such as cloud depth, cloud vertical profiles, or cloud drop size distributions. Attempts to assimilate data in cloudy regions generally assume a drop size distribution, but most assimilation systems fail to maintain consistency between models and the observation data, as each has its own set of assumptions. This study tries to retain the consistency between the forecast model and the retrieved data by developing a Bayesian retrieval scheme that uses the forecast model itself for the a-priori database. Through the retrieval algorithm, vertical profiles of three variables related to the development of tropical cyclones, including vertical velocity, latent heating, and hydrometeor water contents are derived from the same reflectivity observation. Vertical velocity and latent heating are variables related to dynamical processes of tropical cyclones, whereas hydrometeors are byproducts of those processes. Each retrieved variable is assimilated in the data assimilation system using a flow dependent forecast error covariance matrix. The simulations are compared to evaluate the respective impact of each variable in the assimilation system. In this study, the three assimilation experiments were conducted for two hurricane cases captured by the Global Precipitation Measurement (GPM) satellite: Hurricane Pali and Hurricane Jimena. Analyses from these two hurricane cases suggest that assimilating latent heating and hydrometeor water contents have similar impacts on the assimilation system while vertical velocity has less of an impact than the other two variables. Using these analyses as an initial condition for the forecast model reveals that the assimilations of retrieved latent heating and hydrometeor water contents were also able to improve the track forecast of Hurricane Jimena.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierLee_colostate_0053N_14081.pdf
dc.identifier.urihttp://hdl.handle.net/10217/181357
dc.languageEnglish
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019 - CSU Theses and Dissertations
dc.rightsCopyright of the original work is retained by the author.
dc.subjecthurricane
dc.subjectlatent heating
dc.subjectvertical velocity
dc.subjecthydrometeors
dc.subjectdata assimilation
dc.subjectreflectivity
dc.titleImpacts of assimilating vertical velocity, latent heating, or hydrometeor water contents retrieved from a single reflectivity data set
dc.typeText
dcterms.rights.dplaThe copyright and related rights status of this Item has not been evaluated (https://rightsstatements.org/vocab/CNE/1.0/). Please refer to the organization that has made the Item available for more information.
thesis.degree.disciplineAtmospheric Science
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)


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