Storm-Centered Ensemble Data Assimilation for Tropical Cyclones
| dc.contributor.advisor | Hakim, Gregory J | en_US |
| dc.contributor.author | Navarro, Erika Lourdes | en_US |
| dc.date.accessioned | 2013-07-25T17:51:22Z | |
| dc.date.available | 2013-07-25T17:51:22Z | |
| dc.date.issued | 2013-07-25 | |
| dc.date.submitted | 2013 | en_US |
| dc.description | Thesis (Master's)--University of Washington, 2013 | en_US |
| dc.description.abstract | A 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.terms | No embargo | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.other | Navarro_washington_0250O_10469.pdf | en_US |
| dc.identifier.uri | http://hdl.handle.net/1773/23473 | |
| dc.language.iso | en_US | en_US |
| dc.rights | Copyright is held by the individual authors. | en_US |
| dc.subject | Data Assimilation; Intensity Change; Short-Term Predictability; Tropical Cyclone | en_US |
| dc.subject.other | Atmospheric sciences | en_US |
| dc.subject.other | atmospheric sciences | en_US |
| dc.title | Storm-Centered Ensemble Data Assimilation for Tropical Cyclones | en_US |
| dc.type | Thesis | en_US |
