UMD Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/3

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 given thesis/dissertation in DRUM.

More information is available at Theses and Dissertations at University of Maryland Libraries.

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2007 - 200912010 - 20181

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    Improving Forecasts of Volcanic Clouds: An Analysis of Observations and Emission Source Term Methods
    (2018) Hughes, Eric; Dickerson, Russell R; Krotkov, Nickolay A; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Volcanic eruptions can occur with little or no warning and explosively inject dense ash and sulfur dioxide (SO2) clouds high into the atmosphere. I investigated different types of observations and analysis methods used to monitor and quantify volcanic ash and SO2 clouds. I begin with an analysis of the 2010 eruption of Eyjafjallajökull, employing ash cloud transport modeling capabilities I developed for the Goddard Earth Observing System, Version 5 (GEOS-5). The emission source terms describing the initial state of the Eyjafjallajökull ash clouds were estimated using radar observations of the ash cloud’s initial injection altitude. Results of the initial simulations agreed with operational ash forecasts from the time of the eruption and with many other published studies, but showed notable disagreement with satellite observations. The emission source term was estimated using an alternative approach, yielding simulations that better matched satellite observations. I used the result to highlight limitations of radar observations not accounted for in previous studies of the Eyjafjallajökull ash clouds. UV satellite observations are often used to monitor and quantify volcanic clouds of ash and SO2. I tested the limitations of the OMPS SO2 satellite observations using an Observing System Simulation Experiment (OSSE). The framework used GEOS-5 simulations of the atmospheric composition in the wake of a Pinatubo-like volcanic eruption to generate synthetic top-of-the-atmosphere (TOA) radiances. The TOA radiances served as input to the OMPS SO2 retrieval. In comparing the OMPS retrieval SO2 to the original GEOS-5 SO2, I found that the sulfate aerosols and ash can cause the OMPS SO2 retrieval to underestimate the total SO2 burden. These effects were amplified at increased satellite viewing angles. I finish my analysis by looking at observations from the satellite-based Cloud-Aerosol Transport System (CATS), where I show that even under the time constraints of an operational forecast, the available CATS observations were able to improve forecasts of volcanic SO2 clouds.
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    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.