A. James Clark School of Engineering
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The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item INTEGRATION OF SUPERCONDUCTORS INTO WIDE BANDGAP SEMICONDUCTOR ENVIRONMENTS FOR DEPLOYABLE SINGLE PHOTON DETECTORS(2024) Drechsler, Annaliese Grace; Christou, Aristos; Material Science and Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Superconducting nanowire single photon detectors (SNSPDs) are the photon detecting devices of the future. These devices offer exceptional detecting capabilities over a wide range of wavelengths, which will enable next generation systems for optical communications, light detection and ranging, quantum key decryption, and astronomy among others. There are substantial materials, fabrication, and device development challenges that need to be addressed before these devices are ready for large scale deployment in arrays. This dissertation demonstrates novel approach to SNSPD development by monolithically integrating superconducting materials with wide bandgap semiconductor systems to scale these devices. Specifically, this work explores the integration of niobium nitride (NbN) with multi-channel aluminum gallium nitride (AlGaN)/gallium nitride (GaN) superlattice devices to leverage the benefits of materials similarity and lattice matching to provide high quality detector performance in the proposed system. The multichannel superlattice device selected for this work, the superlattice castellated field effect transistor (SLCFET) utilizes a novel δ-doping approach to generate conducting channels. Epitaxial structures were studied between 300K and 4K. This structure exhibits a substantial reduction in epitaxial resistance, determined to be a result of mobility improvement to 4151.5 cm2/Vs through Hall effect analysis. Phonon scattering modelling indicates that the device is limited by polar optical phonon scattering at high temperatures and interface roughness between the channels at cryogenic conditions. Field effect transistors fabricated from this epitaxial structure were tested and shown to exhibit exceptionally high performance at low temperatures, proving feasibility of device integration. A production-scalable NbN deposition process was developed for SNSPD fabrication. Thorough analyses determined the relationship between deposition parameters and the resultant crystallinity, defectivity, and surface morphology. Analysis of ultra-thin films determined that the NbN films grow through a step-flow growth mechanism. This data was used to develop a temperature-dependent empirical model of the kinetics of the surface morphology and growth mechanism evolution based on the Avrami equation. Fabrication processes were developed using these films to pattern SNSPDs with narrow linewidths down to 50 nanometers composing the meander structure for long wavelength performance. Thorough analysis of the impact of electron beam lithography write conditions were conducted to propose ideal fabrication conditions. Methods were proposed and implemented to address defectivity by reducing the impact of elasto-capillary forces on line collapse including chemical surface modification using hexamethyldisilazane and resist thinning using polymethyl methacrylate (PMMA) and ZEP and implementing charge dissipation layers. Additional processes were proposed and implemented to enable integration into the SLCFET fabrication flow. The SLCFET devices and NbN structures were tested and determined to be functional, thus demonstrating the feasibility of integration. An initial integrated device was designed and modelled by combining a SLCFET with NbN SNSPDs, using the RF output as a readout approach. The devices were successfully fabricated using the processes developed within this dissertation. Testing of the devices showed a 30dB signal difference between the normal and detecting states, thus demonstrating the first device of its kind, representing a substantial contribution to the field. This will open the door for full-scale array development using novel on and off chip signal processing approaches proposed in this work.Item Characterizing a Multi-Sensor System for Terrestrial Freshwater Remote Sensing via an Observing System Simulation Experiment (OSSE)(2022) Wang, Lizhao; Forman, Barton A; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Terrestrial freshwater storage (TWS) is the vertically-integrated sum of snow, ice, soilmoisture, vegetation water content, surface water impoundments, and groundwater. Among these components, snow, soil moisture, and vegetation are the most dynamic (i.e., shortest residence time) as well as the most variable across space. However, accurately retrieving estimates of snow, soil moisture, or vegetation using space-borne sensors often requires simultaneous knowledge of one or more of the other components. In other words, reasonably characterizing terrestrial freshwater requires careful consideration of the coupled snow-soil moisture-vegetation response that is implicit in both TWS and the hydrologic cycle. One challenge is to optimally determine the multi-variate, multi-sensor remote sensing observations needed to best characterize the coupled snow-soil moisture-vegetation system. Different types of sensors each have their own unique strengths and limitations. Meanwhile, remote sensing data is inherently discontinuous across time and space, and that the revisit cycle of remote sensing observations will dictate much of the efficacy in capturing the dynamics of the coupled snow-soil moisture-vegetation response. This study investigates different snow sensors and simulates the sensor coverage as a function of different orbital configurations and sensor properties in order to quantify the discontinuous nature of remotely-sensed observations across space and time. The information gleaned from this analysis, coupled with a time-varying snow binary map, is used to evaluate the efficacy of a single sensor (or constellation of sensors) to estimate terrestrial snow on a global scale. A suite of different combinations, and permutations, of different sensors, including different orbital characteristics, is explored with respect to 1-day, 3-day, and 30-day repeat intervals. The results show what can, and what cannot, be observed by different sensors. The results suggest that no single sensor can accurately measure all types of snow, but that a constellation composed of different types of sensors could better compensate for the limitations of a single type of sensor. Even though only snow is studied here, a similar procedure could be conducted for soil moisture or vegetation. To better investigate the coupled snow-soil moisture-vegetation system, an observing system simulation experiment (OSSE) is designed in order to explore the value of coordinated observations of these three separate, yet mutually dependent, state variables. In the experiment, a “synthetic truth” of snow water equivalent, surface soil moisture, and/or vegetation biomass is generated using the NoahMP-4.0.1 land surface model within the NASA Land Information System (LIS). Afterwards, a series of hypothetical sensors with different orbital configurations is prescribed in order to retrieve snow, soil moisture, and vegetation. The ground track and footprint of each sensor is approximated using the Trade-space Analysis Tool for Constellations (TAT-C) simulator. A space-time subsampler predicated on the output from TAT-C is then applied to the synthetic truth. Furthermore, a hypothesized amount of observation error is injected into the synthetic truth in order to yield a realistic synthetic retrieval for each of the hypothetical sensor configurations considered as part of this dissertation. The synthetic retrievals are then assimilated into the NoahMP-4.0.1 land surface model using different boundary conditions from those used to generate the synthetic truth such that the differences between the two sets of boundary conditions serve as a realistic proxy for real-world boundary condition errors. A baseline Open Loop simulation where no retrievals are assimilated is conducted in order to evaluate the added utility associated with assimilation of one (or more) of the synthetic retrievals. The impact of the assimilation of a given suite of one or more retrievals on land surface model estimates of snow, soil moisture, vegetation, and runoff serve as a numeric laboratory in order to assess which sensor(s), either separate or in a coordinated fashion, yield the most utility in terms of improved model performance. The results from this OSSE show that the assimilation of a single type of retrieval (i.e., snow or soil moisture or vegetation) may only improve the estimation of a small part of the snow-soil moisture-vegetation system, but may also degrade of other parts of that same system. Alternatively, the assimilation of more than one type of retrieval may yield greater benefits to all the components of the snow-soil moisture-vegetation system, because it yields a more complete, holistic view of the coupled system. This OSSE framework could potentially serve as an aid to mission planners in determining how to get the most observational “bang for the buck” based on the myriad of different sensor types, orbital configurations, and error characteristics available in the selection of a future terrestrial freshwater mission.Item QUANTIFYING THE ADDED VALUE OF AGILE VIEWING RELATIVE TO NON-AGILE VIEWING TO INCREASE THE INFORMATION CONTENT OF SYNTHETIC SATELLITE RETRIEVALS(2022) McLaughlin, Colin; Forman, Barton A; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Satellite sensors typically employ a “non-agile” viewing strategy in which the boresight angle between the sensor and the observed portion of Earth’s surface remains static throughout operation. With a non-agile viewing strategy, it is relatively straightforward to predict where observations will be collected in the future. However, non-agile viewing is limited because the sensor is unable to vary its boresight angle as a function of time. To mitigate this limitation, this project develops an algorithm to model agile viewing strategies to explore how adding agile pointing into a sensor platform can increase desired information content of satellite retrievals. The synthetic retrievals developed in this project are ultimately used in an observing system simulation experiment (OSSE) to determine how agile pointing has the potential to improve the characterization of global freshwater resources.Item Toward Photonic Orbital Angular Momentum as a Remote Sensing Modality through Random Media(2022) Ferlic, Nathaniel Alexander; Davis, Christopher C; van Iersel, Miranda; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Within the last few decades, the spatial degree of freedom of light has gained significant attention in the form of photonic orbital angular momentum (OAM). The use of OAM for remote sensing has been of significant interest due to its inherent orthogonality that can be used for spatial frequency filtering, coherence filtering, and as both an active or passive sensing modality. Beams with OAM also contain interesting propagation properties that have potential to be more robust than non-OAM counterparts. One application of remote sensing is using OAM to measure the strength of optical phase distortions through random media that can contain turbulence or particulate matter. There has been significant work done on the subject, but there have been difficulties at creating an applicable OAM based sensing technique employed for use in an outdoor environment. This work develops an active OAM sensing modality denoted as Optical Heterodyne Detection of Orthogonal OAM Modes (OHDOOM) to reduce the optical receiver hardware based on a beatnote signal for the first time. The beatnote signal is then hypothesized to return information about the propagation environment by measuring the crosstalk between OAM modes due to channel perturbations. OHDOOM results through an emulated turbulent medium show that our method is highly sensitive to weak and strong turbulence depending on the transmitted OAM mode. Within a turbid medium, OHDOOM is believed to be sensitive to particles larger than the wavelength and insensitive to smaller particles. Experimental results agree well with simulated environmental conditions using wave optic simulations (WOS) implementing phase screens. A WOS for a turbulent medium is derived from turbulent phase statistics based on refractive index fluctuations. However, for the first time, a turbid medium's phase statistics are derived from a solution to the radiative transfer equation within the paraxial approximation.Item Estimation of Terrestrial Water Storage in the Western United States Using Space-based Gravimetry, Ground-based Sensors, and Model-based Hydrologic Loading(2020) Yin, Gaohong; Forman, Barton A; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Accurate estimation of terrestrial water storage (TWS) is critically important for the global hydrologic cycle and the Earth's climate system. The space-based Gravity Recovery and Climate Experiment (GRACE) mission and land surface models (LSMs) have provided valuable information in monitoring TWS changes. In recent years, geodetic measurements from the ground-based Global Positioning System (GPS) network have been increasingly used in hydrologic studies based on the elastic response of the Earth's surface to mass redistribution. All of these techniques have their own strengths and weaknesses in detecting TWS changes due to their unique uncertainties, error characteristics, and spatio-temporal resolutions. This dissertation investigated the potential of improving our knowledge in TWS changes via merging the information provided by ground-based GPS, GRACE, and LSMs. First, the vertical displacements derived from ground-based GPS, GRACE, and NASA Catchment Land Surface Model (Catchment) were compared to analyze the behavior and error characteristics of each data set. Afterwards, the ground-based GPS observations were merged into Catchment using a data assimilation (DA) framework in order to improve the accuracy of TWS estimates and mitigate hydrologic state uncertainty. To the best of our knowledge, this study is the first attempt to assimilate ground-based GPS observations into an advanced land surface model for the purpose of improving TWS estimates. TWS estimates provided by GPS DA were evaluated against GRACE TWS retrievals. GPS DA performance in estimating TWS constituent components (i.e., snow water equivalent and soil moisture) and hydrologic fluxes (i.e., runoff) were also examined using ground-based in situ measurements. GPS DA yielded encouraging results in terms of improving TWS estimates, especially during drought periods. Additionally, the findings suggest a multi-variate assimilation approach to merge both GRACE and ground-based GPS into the LSMs to further improve modeled TWS and its constituent components should be pursued as a new and novel research project.Item Uncertainty quantification of a radiative transfer model and a machine learning technique for use as observation operators in the assimilation of microwave observations into a land surface model to improve soil moisture and terrestrial snow(2020) Park, Jongmin; Forman, Barton A; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Soil moisture and terrestrial snow mass are two important hydrological states needed to accurately quantify terrestrial water storage and streamflow. Soil moisture and terrestrial snow mass can be measured using ground-based instrument networks, estimated using advanced land surface models, and retrieved via satellite imagery. However, each method has its own inherent sources of error and uncertainty. This leads to the application of data assimilation to obtain optimal estimates of soil moisture and snow mass. Before conducting data assimilation (DA) experiments, this dissertation explored the use of two different observation operators within a DA framework: a L-band radiative transfer model (RTM) for soil moisture and support vector machine (SVM) regression for soil terrestrial snow mass. First, L-band brightness temperature (Tb) estimated from the RTM after being calibrated against multi-angular SMOS Tb's showed good performance in both ascending and descending overpasses across North America except in regions with sub-grid scale lakes and dense forest. Detailed analysis of RTM-derived L-band Tb in terms of soil hydraulic parameters and vegetation types suggests the need for further improvement of RTM-derived Tb in regions with relatively large porosity, large wilting point, or grassland type vegetation. Secondly, a SVM regression technique was developed with explicit consideration of the first-order physics of photon scattering as a function of different training target sets, training window lengths, and delineation of snow wetness over snow-covered terrain. The overall results revealed that prediction accuracy of the SVM was strongly linked with the first-order physics of electromagnetic responses of different snow conditions. After careful evaluation of the observation operators, C-band backscatter observations over Western Colorado collected by Sentinel-1 were merged into an advanced land surface model using a SVM and a one-dimensional ensemble Kalman filter. In general, updated snow mass estimates using the Sentinel-1 DA framework showed modest improvements in comparison to ground-based measurements of snow water equivalent (SWE) and snow depth. These results motivate further application of the outlined assimilation schemes over larger regions in order to improve the characterization of the terrestrial hydrological cycle.Item DETECTION OF RAIN-ON-SNOW EVENTS AND ITS IMPACT ON PASSIVE MICROWAVE-BASED SNOW WATER EQUIVALENT RETRIEVAL(2018) Ryan, Elizabeth Meghan; Forman, Barton A; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Rain-on-snow (ROS) events can impact snow stratigraphy via generation of wet snow and ice crust(s) within the snowpack. Considering the assumptions of most passive microwave-based snow water equivalent (SWE) retrievals, which include a dry and homogenous snowpack, ROS events could significantly impact SWE retrieval accuracy. This study explored the feasibility of various approaches to detect ROS events using multiple data types (i.e., satellite observations, model output, and in-situ measurements). Agreement in ROS events detected varied among the different data types. Only ~10% of suspected ROS events were flagged using the satellite-based algorithm. Alternatively, ~50% of suspected ROS events were flagged using the model-based algorithm, whereas ~40% of suspected ROS events were flagged using the in-situ measurements-based algorithm. Findings were unable to speak to the impact of ROS events on SWE retrieval accuracy due to the lack of in-situ SWE measurements; however, a slight pattern in local fluctuations was observed.Item ASSIMILATION OF PASSIVE MICROWAVE BRIGHTNESS TEMPERATURES FOR SNOW WATER EQUIVALENT ESTIMATION USING THE NASA CATCHMENT LAND SURFACE MODEL AND MACHINE LEARNING ALGORITHMS IN NORTH AMERICA(2017) Xue, Yuan; Forman, Barton A.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Snow is a critical component in the global energy and hydrologic cycle. It is important to know the mass of snow because it serves as the dominant source of drinking water for more than one billion people worldwide. To accurately estimate the depth of snow and mass of water within a snow pack across regional or continental scales is a challenge, especially in the presence of dense vegetations since direct quantification of SWE is complicated by spatial and temporal variability. To overcome some of the limitations encountered by traditional SWE retrieval algorithms or radiative transfer-based snow emission models, this study explores the use of a well-trained support vector machine to merge an advanced land surface model within a variant of radiance emission (i.e., brightness temperature) assimilation experiments. In general, modest improvements in snow depth, and SWE predictability were witnessed as a result of the assimilation procedure over snow-covered terrain in North America when compared against available snow products as well as ground-based observations. These preliminary findings are encouraging and suggest the potential for global-scale snow estimation via the proposed assimilation procedure.Item SENSITIVITY ANALYSIS OF MACHINE LEARNING IN BRIGHTNESS TEMPERATURE PREDICTIONS OVER SNOW-COVERD REGIONS USING THE ADVANCED MICROWAVE SCANNING RADIOMETER(2014) Xue, Yuan; Forman, Barton; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Snow is a critical component in the global energy and hydrologic cycle. Further, it is important to know the mass of snow because it serves as the dominant source of drinking water for more than one billion people worldwide. Since direct quantification of snow water equivalent (SWE) is complicated by spatial and temporal variability, space-borne passive microwave SWE retrieval products have been utilized over regional and continental-scales to better estimate SWE. Previous studies have explored the possibility of employing machine learning, namely an artificial neural network (ANN) or a support vector machine (SVM), to replace the traditional radiative transfer model (RTM) during brightness temperatures (Tb) assimilation. However, we still need to address the following question: What are the most significant parameters in the machine-learning model based on either ANN or SVM? The goal of this study is to compare and contrast sensitivity analysis of Tb with respect to each model input between the ANN- and SVM-based estimates. In general, the results suggest the SVM (relative to the ANN) may be more beneficial during Tb assimilation studies where enhanced SWE estimation is the main objective.Item Impacts of Satellite-derived Fractional Snow-Covered Area Observations on Operational Streamflow Predictions via Direct Insertion(2013) Bender, Stacie; Brubaker, Kaye L; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Snowmelt is a primary driver of spring and early summer streamflow in the western United States. Improved predictions of snowmelt-driven streamflow benefit a wide variety of users. In this study, the snow model used in the National Weather Service's hydrologic operations, SNOW17, is run with and without consideration of fractional snow covered area (fSCA) observations from the National Aeronautics and Space Administration's MODerate Resolution Imaging Spectroradiometer (MODIS). Because computationally frugal methods are desirable in an operational environment, the updating scheme evaluated is a simple direct insertion method. Resulting predictions of snowmelt-driven streamflow for water years 2000 to 2010 are compared to observed flow and a control simulation (using the model without snow cover input). Results indicate that use of MODIS fSCA in SNOW17, with no adjustments, via direct insertion, degrades the streamflow predictions, compared to control simulations. Future research directions include advanced data assimilation and use of different snow models.