Dissertations and Theses
The following list contains known dissertations and theses that use DART.
Please contact dart@ucar.edu to add your dissertation or thesis to the list.
2024
Kugler L., 2024: Assimilation of cloud-affected satellite observations for convective-scale numerical weather prediction.
Doctoral dissertation, University of Vienna, Wien, Austria. https://utheses.univie.ac.at/detail/70638
Britt K., 2024: Forcing Quasi-Linear Convective Systems and Mesovortex Tornado Potential Using the Warn-on-Forecast System.
Doctoral dissertation, University of Oklahoma, Norman, OK. https://hdl.handle.net/11244/340490
Capute P., 2024: A Comparison of Arctic and Atlantic Basin Cyclone Predictability from a Climatology and Case Study Perspective.
Doctoral dissertation, State University of New York, Albany, NY. https://scholarsarchive.library.albany.edu/etd/21/
Hu J., 2024: Development of Data Assimilation for Analysis of Ion Drifts During Geomagnetic Storms.
Doctoral dissertation, Illinois Institute of Technology, Chicago, IL. https://repository.iit.edu/islandora/object/islandora%3A1025135
2023
Kurosawa K., 2023: Bridging Gaussian and non-Gaussian Data Assimilation for High-Dimensional Geophysical Models.
Doctoral dissertation, University of Maryland, College Park, MD. http://hdl.handle.net/1903/31721
Sodhi J., 2023: Attempts to extract novel information from radar for convective-scale data assimilation.
Doctoral dissertation, McGill University, Montreal, QC, Canada. https://escholarship.mcgill.ca/concern/theses/zk51vp30z
Chen N., 2023: Development and application of adjoint sensitivity to potential vorticity and unbalanced flows in a numerical weather prediction model.
Doctoral dissertation, University of Wisconsin, Madison, WI. https://minds.wisconsin.edu/handle/1793/85727
Coleman A.A., 2023: Improving Predictability of Severe Weather and its Associated Hazards through Advanced Applications of Ensemble Sensitivity Analysis.
Doctoral dissertation, Texas Tech University, Lubbock, TX. https://ttu-ir.tdl.org/items/4fe923f9-c80e-425a-94f6-d50e2ea1ae88
2022
Dufort J.A., 2022: Improving the Short-Term Forecast Accuracy of Heavy Precipitation Events Using Time-Lagged Ensembles.
Master’s thesis, Texas Tech University, Lubbock, TX. https://hdl.handle.net/2346/96517
Wieringa M., 2022: The Promise of Sea Ice Thickness: A Data Assimilation Application for Modern Arctic Climate.
Master’s thesis, University of Washington, Seattle, WA. http://hdl.handle.net/1773/48820
2021
Schwartz C., 2021: An Evaluation of Convection-Allowing Ensemble Forecast Sensitivity to Initial Conditions.
Doctoral dissertation, University of Maryland, College Park, MD. http://hdl.handle.net/1903/28405
Wu W., 2021: Advancing the application of remote sensing to improve land surface modeling.
Doctoral dissertation, University of Texas, Austin, TX. https://hdl.handle.net/2152/117616
2020
Riedel C., 2020: Tropospheric Polar Vortices and Impacts on Atmospheric Flow from the Arctic to the Mid-Latitudes using a New Global Modeling System.
Doctoral dissertation, University of Oklahoma, Norman, OK. https://shareok.org/handle/11244/324334
Klees A., 2020: An Idealized Modeling Study of the Nontornadic and Tornadic Supercells Intercepted by VORTEX2 on 10 June 2010.
Doctoral dissertation, Pennsylvania State University, University Park, PA. https://etda.libraries.psu.edu/catalog/17533amk5375
Toye H., 2020: Efficient Ensemble Data Assimilation and Forecasting of the Red Sea Circulation.
Doctoral dissertation, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia. http://hdl.handle.net/10754/666074
Hagan D.F.T., 2020: Advancing the improvement of soil moisture estimation from satellite observations and land models.
Doctoral dissertation, Nanjing University of Information Science & Technology, Nanjing, China. https://gjy.nuist.edu.cn/english/main.htm
2019
Wuerth S., 2019: Development and Applications of a Carbon-Weather Data Assimilation System.
Doctoral dissertation, University of California, Berkeley, CA. https://escholarship.org/uc/item/8875f13t
Kodikara T., 2019: Physical understanding and forecasting of the thermospheric structure and dynamics.
Doctoral dissertation, Royal Melbourne Institute of Technology, Melbourne, VIC, Australia. https://researchrepository.rmit.edu.au/esploro/outputs/doctoral/Physical-understanding-and-forecasting-of-the-thermospheric-structure-and-dynamics/9921863942601341
Laine M., 2019: Data assimilation using the Ensemble Adjustment Kalman filter with application to soil organic carbon modelling.
Master’s thesis, University of Helsinki, Helsinki, Finland. https://helda.helsinki.fi/items/URN:NBN:fi:hulib-201905292226
Elmer N., 2019: Using satellite observations of river height and vegetation to improve National Water Model initialization and streamflow prediction.
Doctoral dissertation, University of Alabama, Huntsville, AL. https://louis.uah.edu/uah-dissertations/166
Moker Jr. J., 2019: Investigating the Performance of Convection-Allowing Hindcast Simulations during the North American Monsoon with and without GPS-PWV Data Assimilation.
Doctoral dissertation, University of Arizona, Tucson, AZ. https://repository.arizona.edu/handle/10150/633162
Robinson G.A., 2019: Beating The Curse of Dimensionality of Sequential Monte Carlo For Bayesian Inverse Problems In Nonlinear Fluids.
Doctoral dissertation, University of Colorado, Boulder, CO. https://scholar.colorado.edu/concern/graduate_thesis_or_dissertations/pk02cb67g
Kurzrock F., 2019: Assimilation de données satellitaires géostationnaires dans des modèles atmosphériques à aire limitée pour la prévision du rayonnement solaire en région tropicale.
Doctoral dissertation, Université de la Réunion, Réunion, France. https://theses.hal.science/tel-02495080/
Coleman A.A., 2019: Convective-scale ensemble subsetting with ensemble sensitivity analysis.
Master’s thesis, Texas Tech University, Lubbock, TX. https://hdl.handle.net/2346/85334
2018
Matus S.A., 2018: Using ensemble precipitation forecasts to improve hydrologic risk assessment at river crossings.
Master’s thesis, University of Illinois, Urbana-Champaign, IL. https://hdl.handle.net/2142/102508
Kuroski A., 2018: An Investigation of the Conditional Practical Predictability of the 31 May 2013 Heavy-Rain-Producing Mesoscale Convective System.
Master’s thesis, University of Milwaukee, Milwaukee, WI. https://dc.uwm.edu/etd/1853
Li Y., 2018: Applications of regional ocean Ensemble Kalman Filter data assimilation.
Doctoral dissertation, Imperial College London, London, UK. https://core.ac.uk/outputs/195780835/
Sutherland B., 2018: Verification of cloud production in the Community Atmosphere Model: A comparison of two data assimilation techniques.
Master’s thesis, University of Washington, Seattle, WA. http://hdl.handle.net/1773/42184
2017
Aydogdu A., 2017: Advanced modeling and data assimilation methods for the design of sustained marine monitoring networks.
Doctoral dissertation, Università Ca’ Foscari Venezia, Venezia VE, Italy. http://dspace.unive.it/handle/10579/10343
McNicholas C., 2017: Advanced Approaches for the Collection, Quality Control, and Bias Correction of Smartphone Pressure Observations and Their Application in Numerical Weather Prediction.
Master’s thesis, University of Washington, Seattle, WA. http://hdl.handle.net/1773/39940
Burghardt B.J., 2017: Performance characteristics of convection-allowing ensemble forecasts with varied physics parameterizations.
Doctoral dissertation, Texas Tech University, Lubbock, TX. http://hdl.handle.net/2346/72721
Pan S., 2017: Simultaneous Assimilation of Radar and Satellite Data for Covective Scale NWP Using Hybrid Ensemble Variational Data Assimilation Approach.
Master’s thesis, University of Oklahoma, Norman, OK. https://shareok.org/handle/11244/51870
Kerr C., 2017: Analysis of environmental modifications by deep convection during the Mesoscale Predictability Experiment.
Doctoral dissertation, University of Oklahoma, Norman, OK. https://shareok.org/handle/11244/50449
Gallo B., 2017: Deriving Operationally Relevant Tornado Probabilities from Convection-Allowing Ensembles.
Doctoral dissertation, University of Oklahoma, Norman, OK. https://shareok.org/handle/11244/52733
2016
Attia A., 2016: Advanced Sampling Methods for Solving Large-Scale Inverse Problems.
Doctoral dissertation, Virginia Polytechnic Institute and State University, Blacksburg, VA. http://hdl.handle.net/10919/73683
Kwon Y., 2016: Development and evaluation of an advanced microwave radiance data assimilation system for estimating snow water storage at the continental scale.
Doctoral dissertation, University of Texas, Austin, TX. http://hdl.handle.net/2152/45944
Reinhart A., 2016: Effects of Microphysical Parameterizations and Drop Breakup on Supercells and Supercell Cold Pools.
Doctoral dissertation, Texas Tech University, Lubbock, TX. https://hdl.handle.net/2346/82253
2015
Islam S.U., 2015: Ensemble simulation and forecasting of South Asian Monsoon.
Doctoral dissertation, University of Northern British Columbia, Prince George, BC, Canada. https://unbc.arcabc.ca/islandora/object/unbc:16990
Garcia M., 2015: Data assimilation unit for the general curvilinear environmental model | San Diego State University Digital Collections.
Doctoral dissertation, San Diego State University and Claremont Graduate University, San Diego, CA. https://digitalcollections.sdsu.edu/do/7aa98069-99b1-4ffa-8bb1-06b3af46db68
Zhang Y., 2015: Multivariate land snow data assimilation in the Northern Hemisphere : development, evaluation and uncertainty quantification of the extensible data assimilation system.
Doctoral dissertation, University of Texas, Austin, TX. http://hdl.handle.net/2152/32613
2014
Wu T., 2014: Understanding the Influence of Assimilating Satellite-Derived Observations on Mesoscale Analyses and Forecasts of Tropical Cyclone Track and Structure.
Doctoral dissertation, University of Miami, Miami, FL. https://scholarship.miami.edu/esploro/outputs/doctoral/Understanding-the-Influence-of-Assimilating-Satellite-Derived-Observations-on-Mesoscale-Analyses-and-Forecasts-of-Tropical-Cyclone-Track-and-Structure/991031448085102976
Jewtoukoff V., 2014: Etude des ondes de gravité dans l’atmosphère au moyen de ballons et de simulations.
Doctoral dissertation, Université Paris 6, Paris, France. https://theses.hal.science/tel-01176779
Vincente V., 2014: Ensemble-based analysis of Front Range severe convection on 6-7 June 2012: forecast uncertainty and communication of weather information to Front Range decision-makers.
Master’s thesis, Colorado State University, Fort Collins, CO. http://hdl.handle.net/10217/82539
Mitchell M.C., 2014: Impacts of potential aircraft observations on forecasts of tropical cyclones over the western North Pacific.
Master’s thesis, Naval Postgraduate School, Monterey, CA. https://hdl.handle.net/10945/44619
2013
Hollt T., 2013: Visual Workflows for Oil and Gas Exploration.
Doctoral dissertation, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia. http://hdl.handle.net/10754/287324
Morozov A.V., 2013: Adaptive Control and Data Assimilation for Nonlinear Systems.
Doctoral dissertation, University of Michigan, Ann Arbor, MI. http://deepblue.lib.umich.edu/handle/2027.42/97826
Homan P.B., 2013: Ensemble sensitivity analysis of a severe downslope windstorm in complex terrain: implications for forecast predictability scales and targeted observing networks.
Doctoral dissertation, Naval Postgraduate School, Monterey, CA. https://hdl.handle.net/10945/37639
Wile S.M., 2013: A Further Examination of Potential Observation Network Design with Mesoscale Ensemble Sensitivities in Complex Terrain.
Master’s thesis, Naval Postgraduate School, Monterey, CA. https://hdl.handle.net/10945/32917
Hollan M.A., 2013: Ensemble mean storm-scale performance in the presence of nonlinearity and best member techniques for improved prediction.
Master’s thesis, Texas Tech University, Lubbock, TX. http://hdl.handle.net/2346/58479
Bednarczyk C.N., 2013: Ensemble sensitivity analysis applied to Southern Plains convection.
Master’s thesis, Texas Tech University, Lubbock, TX. http://hdl.handle.net/2346/50308
2012
Kashawlic E., 2012: Comparing observation impact between variational and ensemble data assimilation schemes on short-term, low-level wind forecasting.
Master’s thesis, Texas Tech University, Lubbock, TX. http://hdl.handle.net/2346/46963
Chilcoat K.H., 2012: The Potential Observation Network Design with Mesoscale Ensemble Sensitivities in Complex Terrain.
Master’s thesis, Naval Postgraduate School, Monterey, CA. https://hdl.handle.net/10945/6774
Williams J., 2012: Building a better wind forecast: a stochastic forecast system using a fully-coupled hydrologic-atmospheric model.
Doctoral dissertation, Colorado School of Mines, Golden, CO. https://repository.mines.edu/handle/11124/76832
2011
Mahajan R., 2011: Applying Ensemble Data Assimilation to Understand Tropical Cyclogenesis.
Doctoral dissertation, University of Washington, Seattle, WA. https://www.proquest.com/openview/2affd5621513f489a1e4e62d0fa48650/1?cbl=18750&pq-origsite=gscholar&parentSessionId=Kcys7YyCSe9d3x7MSN2WQgrpr9monQM9LbgfOXLRcJQ%3D
Tanamachi R.L., 2011: Multiple Cyclic Tornado Production Modes in the 5 May 2007 Greensburg, Kansas Supercell Storm.
Doctoral dissertation, University of Oklahoma, Norman, OK. https://shareok.org/handle/11244/319425
Lei L., 2011: A Hybrid Nudging-Ensemble Kalman Filter Approach to Data Assimilation.
Doctoral dissertation, Pennsylvania State University, University Park, PA. https://etda.libraries.psu.edu/catalog/12441