Storm-Centered Ensemble Data Assimilation for Tropical Cyclones

dc.contributor.advisorHakim, Gregory Jen_US
dc.contributor.authorNavarro, Erika Lourdesen_US
dc.date.accessioned2013-07-25T17:51:22Z
dc.date.available2013-07-25T17:51:22Z
dc.date.issued2013-07-25
dc.date.submitted2013en_US
dc.descriptionThesis (Master's)--University of Washington, 2013en_US
dc.description.abstractA significant challenge for tropical cyclone ensemble data assimilation is that storm-scale observations tend to make analyses that are more asymmetric than the prior forecasts. Compromised structure and intensity, such as an increase of amplitude across the azimuthal Fourier spectrum, are a routine property of ensemble-based analyses, even with accurate position observations and frequent assimilation. Storm dynamics in subsequent forecasts evolve these states toward axisymmetry, creating difficulty in distinguishing between model-induced and actual storm asymmetries for predictability studies and forecasting. To address this issue, we propose here a novel algorithm using a storm-centered approach. The method is designed for use with existing ensemble filters with little or no modification, facilitating its adoption and maintenance. The algorithm consists of: (1) an analysis of the environment using conventional coordinates, (2) a storm-centered analysis using storm-relative coordinates, and (3) a merged analysis that combines the large-scale and storm-scale fields together at an updated storm location. The storm-centered method is evaluated for two sets of experiments: no-cycling tests of the update step for idealized, three-dimensional storms in radiative--convective equilibrium, and full cycling tests of data assimilation applied shallow-water model for a field of interacting vortices. In both cases results are compared against a control based on a conventional ensemble Kalman filter scheme. Results show that storm-relative assimilation yields vortices that are more symmetric and exhibit finer inner-core structure than for the control, with errors reduced by an order of magnitude as compared to a control with prior spread similar to the National Hurricane Center's 12~h mean track error in 12~h forecasts. Azimuthal Fourier error spectra exhibit much-reduced noise associated with data assimilation as compared to the conventional EnKF scheme. An assessment of the affect of the merge step on balance reveals a similar, balanced trend in free-surface height tendency between the storm-centered and conventional EnKF approaches, with storm-centered values more closely resembling the reference state.en_US
dc.embargo.termsNo embargoen_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.otherNavarro_washington_0250O_10469.pdfen_US
dc.identifier.urihttp://hdl.handle.net/1773/23473
dc.language.isoen_USen_US
dc.rightsCopyright is held by the individual authors.en_US
dc.subjectData Assimilation; Intensity Change; Short-Term Predictability; Tropical Cycloneen_US
dc.subject.otherAtmospheric sciencesen_US
dc.subject.otheratmospheric sciencesen_US
dc.titleStorm-Centered Ensemble Data Assimilation for Tropical Cyclonesen_US
dc.typeThesisen_US

Files