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Local Ensemble Transform Kalman Filter for Earth-System Models: An application to Extreme Events

Abstract Earth-system models describe the interacting components of the climate system and

technological systems that affect society, such as communication infrastructures. Data

assimilation addresses the challenge of state specification by incorporating system

observations into the model estimates. In this research, a particular data

assimilation technique called the Local Ensemble Transform Kalman Filter (LETKF) is

applied to the ionosphere, which is a domain of practical interest due to its effects

on infrastructures that depend on satellite communication and remote sensing. This

dissertation consists of three main studies that propose strategies to improve space-

weather specification during ionospheric extreme events, but are generally... (more)
Created Date 2018
Contributor Durazo, Juan Alberto (Author) / Kostelich, Eric J. (Advisor) / Mahalov, Alex (Advisor) / Tang, Wenbo (Committee member) / Moustaoui, Mohamed (Committee member) / Platte, Rodrigo (Committee member) / Arizona State University (Publisher)
Subject Mathematics / Applied mathematics / Atmospheric sciences / Bias Estimation / Data Assimilation / Earth-System Models / Extreme Events / Ionosphere / Targeted Observations
Type Doctoral Dissertation
Extent 192 pages
Language English
Note Doctoral Dissertation Applied Mathematics 2018
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis