2026-05-27T09:33:45Zhttps://keep.lib.asu.edu/oai/requestoai:keep.lib.asu.edu:node-1566372024-12-20T18:25:12Zoai_pmh:alloai_pmh:repo_items156637
https://hdl.handle.net/2286/R.I.50478
http://rightsstatements.org/vocab/InC/1.0/
2018
xxi, 170 pages : color illustrations
Doctoral Dissertation
Academic theses
Text
eng
Durazo, Juan, Ph.D
Kostelich, Eric J.
Mahalov, Alex
Tang, Wenbo
Moustaoui, Mohamed
Platte, Rodrigo
Arizona State University
Partial requirement for: Ph.D., Arizona State University, 2018
Includes bibliographical references (pages 137-142)
Field of study: Applied mathematics
Earth-system models describe the interacting components of the climate system and<br/><br/>technological systems that affect society, such as communication infrastructures. Data<br/><br/>assimilation addresses the challenge of state specification by incorporating system<br/><br/>observations into the model estimates. In this research, a particular data <br/><br/>assimilation technique called the Local Ensemble Transform Kalman Filter (LETKF) is<br/><br/>applied to the ionosphere, which is a domain of practical interest due to its effects<br/><br/>on infrastructures that depend on satellite communication and remote sensing. This<br/><br/>dissertation consists of three main studies that propose strategies to improve space-<br/><br/>weather specification during ionospheric extreme events, but are generally applicable<br/><br/>to Earth-system models:<br/><br/>Topic I applies the LETKF to estimate ion density with an idealized model of<br/><br/>the ionosphere, given noisy synthetic observations of varying sparsity. Results show<br/><br/>that the LETKF yields accurate estimates of the ion density field and unobserved<br/><br/>components of neutral winds even when the observation density is spatially sparse<br/><br/>(2% of grid points) and there is large levels (40%) of Gaussian observation noise.<br/><br/>Topic II proposes a targeted observing strategy for data assimilation, which uses<br/><br/>the influence matrix diagnostic to target errors in chosen state variables. This <br/><br/>strategy is applied in observing system experiments, in which synthetic electron density<br/><br/>observations are assimilated with the LETKF into the Thermosphere-Ionosphere-<br/><br/>Electrodynamics Global Circulation Model (TIEGCM) during a geomagnetic storm.<br/><br/>Results show that assimilating targeted electron density observations yields on <br/><br/>average about 60%–80% reduction in electron density error within a 600 km radius of<br/><br/>the observed location, compared to 15% reduction obtained with randomly placed<br/><br/>vertical profiles.<br/><br/>Topic III proposes a methodology to account for systematic model bias arising<br/><br/>ifrom errors in parametrized solar and magnetospheric inputs. This strategy is ap-<br/><br/>plied with the TIEGCM during a geomagnetic storm, and is used to estimate the<br/><br/>spatiotemporal variations of bias in electron density predictions during the<br/><br/>transitionary phases of the geomagnetic storm. Results show that this strategy reduces<br/><br/>error in 1-hour predictions of electron density by about 35% and 30% in polar regions<br/><br/>during the main and relaxation phases of the geomagnetic storm, respectively.
Mathematics
Applied Mathematics
Atmospheric science
Bias Estimation
Data Assimilation
Earth-System Models
extreme events
Ionosphere
Targeted Observations
Sudden ionospheric disturbances
Kalman filtering
Local Ensemble Transform Kalman Filter for earth-system models: an application to extreme events