Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item Optimization of Signal Routing in Disruption-Tolerant Networks(2021) Singam, Caitlyn; Ephremides, Anthony; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Communication networks are prone to disruption due to inherent uncertainties such as environmental conditions, system outages, and other factors. However, current state-of-the-art communication protocols are not yet optimized for communication in highly disruption-prone environments, such as deep space, where the risk of such uncertainties is not negligible. This work involves the development of a novel protocol for disruption-tolerant communication across space-based networks that avoids idealized assumptions and is consistent with system limitations. The proposed solution is grounded in an approach to information as a time-based commodity, and on reframing the problem of efficient signal routing as a problem of value optimization. The efficacy of the novel protocol was evaluated via a custom Monte Carlo simulation against other state-of-the-art protocols in terms of maintaining both data integrity and transmission speed, and was found to provide a consistent advantage across both metrics of interest.Item OMI Tropospheric Sulfur Dioxide Retreival: Validation and Analysis(2007-08-28) McClure, Brittany; Dickerson, Russell R; Chemistry; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)SO2 impacts the radiative balance of the Earth and is the precursor to the major acid and much of the particulate matter in the atmosphere. Improved spectrometer resolution of the Ozone Monitoring Instrument (OMI) enables SO2 retrieval in the planetary boundary layer. OMI has a small spatial resolution of 13 km x 24 km and daily near-global coverage. I have evaluated the accuracy of the OMI by comparing aircraft measurements in Northeast China to the OMI retrieval of three different algorithms: the Band Residual Difference (BRD), the Spectral Fit (SF), and a combination of the two (SF & BRD). The SF algorithm shows the best agreement with a less than 15% difference for high SO2 loading (greater than 1 DU). The SF & BRD has a ~ -0.25 DU bias, the BRD and SF a ~ -0.1 DU bias. The noise of the OMI is reduced to ~0.2 DU by averaging over 100 days and is not improved by increasing the averaging time. The OMI is also able to track SO2 as it moves away from its source region in the PBL and once it is lofted above this layer.Item Assimilating Satellite Observations with a Local Ensemble Kalman Filter(2007-04-25) Fertig, Elana Judith; Hunt, Brian R; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Numerical weather prediction relies on data assimilation to estimate the current state of the atmosphere. Generally speaking, data assimilation methods combine information from observations and from a prior forecast state, taking into account their respective uncertainties. Ensemble-based data assimilation schemes estimate the forecast uncertainty with the sample covariance from an ensemble of forecasts. While these schemes have been shown to successfully assimilate conventional observations of model state variables, they have only recently begun to assimilate satellite observations. This dissertation explores some of the complications that arise when ensemble-based schemes assimilate satellite observations. Although ensemble data assimilation schemes often assume that observations are taken at the time of assimilation, satellite observations are available almost continuously between consecutive assimilation times. In Chapter 2, we formulate a ``four-dimensional'' extension to ensemble-based schemes that is analogous to the operationally used scheme 4D-VAR. Using perfect model experiments with the Lorenz-96 model, we find that the four-dimensional ensemble scheme can perform comparably to 4D-VAR. Many ensemble data assimilation schemes utilize spatial localization so that a small ensemble can capture the unstable degrees of freedom in the model state. These local ensemble-based schemes typically allow the analysis at a given location to depend only on observations near that location. Meanwhile, the location of satellite observations cannot be pinpointed in the same manner as conventional observations. In Chapter 3, we propose a technique to update the state at a given location by assimilating satellite radiance observations that are strongly correlated to the model state there. For satellite retrievals, we propose incorporating the observation error covariance matrix and selecting the retrievals that have errors correlated to observations near the location to be updated. Our selection techniques improve the analysis obtained when assimilating simulated satellite observations with a seven-layer primitive equation model, the SPEEDY model. Finally, satellite radiance observations are subject to state-dependent, systematic errors due to errors in the radiative transfer model used as the observation operator. In Chapter 4 we propose applying state-space augmentation to ensemble based assimilation schemes to estimate satellite radiance biases during the data assimilation procedure. Our approach successfully corrects such systematic errors in simulated biased satellite observations with the SPEEDY model.