Browsing by Author "Cheney, Margaret, committee member"

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Open Access
A new automotive system architecture for minimizing rear-end collisions
(Colorado State University. Libraries, 2024) Rictor, Andrew, author; Chandrasekaran, Venkatachalam, advisor; Cheney, Margaret, committee member; Herber, Daniel, committee member; Simske, Steven, committee member
Advanced Driver Assistance Systems, more frequently referred to as ADAS, are intelligent systems integrated into newer automotive vehicles to improve safety and minimize accidents. These systems utilize radar, sonar, lidar and camera sensors mounted around the vehicle to maintain situational awareness of the vehicle and the surrounding environment. The majority of ADAS that focus on collision avoidance modify the host vehicle's operation. Some existing ADAS will stop the vehicle, sound an audible alert, initiate internal warning lights or dash warning messages, and prevent lane change operations. The ADAS proposed and detailed here focuses on enabling the host vehicle to communicate with the inbound vehicle's driver via the brake lights so that the driver has the opportunity to modify the inbound vehicle's operation before a collision occurs. This is called the Aft Collision Assist (ACA). This work presents the Model Based System Engineering (MBSE) diagrams, SIMULINK models and simulation of the ACA, data derivation utilized in the simulations, validation with empirical data, and future work for optimizing the ACA's algorithms.
Open Access
A step toward constant time local search for optimizing pseudo boolean functions
(Colorado State University. Libraries, 2013) Chen, Wenxiang, author; Whitley, L. Darrell, advisor; Howe, Adele E., advisor; Cheney, Margaret, committee member
Pseudo Boolean Functions (PBFs) are the objective functions for a wide class of hard optimization problems, such as MAX-SAT and MAX-CUT. Since these problems are NP-Hard, researchers and practitioners rely on incomplete solvers, such as Stochastic Local Search (SLS), for large problems. Best-Improvement Local Search (BILS) is a common form of SLS, which always takes the move yielding the highest improvement in the objective function. Generally, the more runtime SLS is given, the better solution can be obtained. This thesis aims at algorithmically accelerating SLS for PBFs using Walsh Analysis. The contributions of this thesis are threefold. First, a general approach for executing an approximate best-improvement move in constant time on average using Walsh analysis, "Walsh-LS", is described. Conventional BILS typically requires examining all n neighbors to decide which move to take, given the number of variables is n. With Walsh analysis, however, we can determine which neighbors need to be checked. As long as the objective function is epistatically bounded by a constant k (k is the number of variables per subfunctions), the number of neighbors that need to be checked is constant regardless of problem size. An impressive speedup of runtime (up to 449 times) is observed in our empirical studies. Second, in the context of Walsh-LS, we empirically study two key components of SLS from the perspectives of both efficiency and effectiveness: 1) Local optimum escape method: hard random or soft restarts; 2) Local search strategy: first-improvement or best-improvement. Lastly, on average we can perform approximate BILS using the mean over a Hamming region of arbitrary radius as a surrogate objective function. Even though the number of points is exponential in the radius of the Hamming region, BILS using the mean value of points in the Hamming region as a surrogate objective function can still take each move in time independent of n on average. According to our empirical studies, using the average over a Hamming region as a surrogate objective function can yield superior performance results on neutral landscapes like NKq-landscapes.
Embargo
Advanced solutions for rainfall estimation over complex terrain in the San Francisco Bay area
(Colorado State University. Libraries, 2023) Biswas, Sounak Kumar, author; Chandrasekar, V., advisor; Cheney, Margaret, committee member; Gooch, Steven, committee member; James, Susan, committee member
Fresh water is an increasingly scarce resource in the western United States and effective management and prediction of flooding and drought have a direct economic impact on almost all aspects of society. Therefore it is critical to monitor and predict water inputs into the hydrological cycle of the Western United States (US). The complex topography of the western US poses a significant challenge in developing physically realistic and spatially accurate estimates of precipitation using remote sensing techniques. The intricate landscape presents a challenging observing environment for weather radar systems. This is further compounded by the complex microphysical processes during the cool season which are influenced by coastal air-sea interactions, as well as orographic effects along the coastal regions of the West. The placement and density of operational National Weather Service (NWS) radars (popularly known as NEXRAD or WSR-88D) pose a challenge in meeting the needs for water resource management in the western US due to the complex terrain of the region. Consequently, areas like the San Francisco Bay Area could use enhanced precipitation monitoring, in terms of amount and type, along watersheds and surrounding rivers and streams. Shorter wavelength radars such as X-Band radar systems are able to augment the WSR-88D network, to observe better the lower atmosphere with higher temporal and spatial resolution. This research investigates and documents the challenges of precipitation monitoring by radars over complex terrain and aims to provide effective and advanced solutions for accurate Quantitative Precipitation Estimation (QPE) using both WSR-88D and the gap-filling X-Band radar systems over the Bay Area on the US West Coast, with a focus on the cool season. Specifically, this study focuses on a precipitation microphysics perspective, aiming to create an algorithm capable of distinguishing orographically enhanced rainfall from cool-season stratiform rainfall using X-Band radar observations. A radar-based rainfall estimator is developed to increase the accuracy of rainfall quantification. Additionally, various other scientific and engineering challenges have been addressed including radar calibration, attenuation correction of the radar beam, radar beam blockage due to terrain, and correction of measurements of the vertical profiles of radar observables. The final QPE product is constructed by merging the X-Band based QPE product with the operational NEXRAD based QPE product, significantly enhancing the overall quality of rainfall mapping within the Bay Area. Case studies reveal that the new product is able to improve QPE accuracy by ~70% in terms of mean absolute error and root mean squared error compared to the operational products. This establishes the overall need for precipitation monitoring by gap-filling X-Band radar systems in the complex terrain of the San Francisco Bay Area.
Open Access
Advanced spectral processing for dual polarization weather radars
(Colorado State University. Libraries, 2020) Dutta, Amit, author; Chandrasekaran, V., advisor; Cheney, Margaret, committee member; Siller, Thomas, committee member
This thesis focuses on the importance of spectral-domain processing and analysis in weather radar applications such as sea-clutter mitigation and the study of rain-hail mixtures in severe storms. An advanced spectral filtering technique has been proposed that helps in obtaining precipitation spectrum thus helping us to filter sea clutter and also carefully study the spectrum of different rain and hail cases in severe storms. Traditionally, time-domain auto-correlation techniques are used for the estimation of dual-polarization radar moments from the time series data. With the advent of low cost high-speed modern signal processors, frequency-domain processing techniques are feasible to be implemented in real-time. Hence spectral processing can be used for radar moments estimation. Previously, researchers have concluded that spectral filtering has improved the calculation of dual-polarization radar moments. Many algorithms have been implemented in real-time for clutter mitigation and data quality control. In this thesis, various existing frequency and time domain algorithms, such as standard notch filters, Gaussian Model Adaptive Processing (GMAP), and Parametric Time-Domain Method (PTDM) have been used for sea clutter mitigation, and their performances are studied. Spectral Signal Quality Index (SSQI), which is dependent on the auto-correlation spectral density of the signals, has been used to threshold noisy spectrum to obtain a clean precipitation spectrum. Next, using the results from PTDM along with the SSQI thresholding technique, the Polarimetric Spectral Filter for Adaptive Clutter and Noise Suppression [1] has been implemented. The combination of these spectral filtering techniques is regarded as Advanced Spectral Filter (ASF). The algorithms are applied to the observations recorded by the CSU-SEAPOL (Colorado State University - Sea-Going Polarimetric) radar data to identify and filter sea clutter. The ASF has been observed to perform better in terms of sea clutter suppression and identification. In general, spectral analysis of radar time-series data reveals various characteristics of different hydrometeors. Incorporating Doppler information along with polarimetric measurements in dual-polarization weather radar can unveil various microphysical properties in relation to the dynamics of storms in a radar resolution volume. This study is regarded as Spectral Polarimetry. Spectral analysis has been done on observations that were collected during the RELAMPAGO (Remote Sensing Of Electrification, Lightning, And Mesoscale/Microscale Processes With Adaptive Ground Observations) campaign in Argentina by the CSU-CHIVO (Colorado State University-C-band Hydro-meteorological Instrument for Volumetric Observation) radar. Spectral polarimetry revealed various spectral features such as bi-modal power spectrum, slopes in the spectral differential reflectivity, lowering of co-pol correlation spectrum, etc. from the observations. These features essentially helped to characterize and determine the microphysical properties of different storms. Thus the main goal of this thesis is to show the importance of spectral domain processing and analysis in relation to clutter mitigation and micro-physical study of storms.
Open Access
An integrated retrieval framework for multiple polarization, multiple frequency radar networks
(Colorado State University. Libraries, 2015) Hardin, Joseph C., author; Chandrasekar, V., advisor; Jayasumana, Anura P., committee member; Mielke, Paul, committee member; Cheney, Margaret, committee member
Radar networks form the backbone of severe weather and remote sensing in throughout most of the world. These networks provide diverse measurements of weather phenomenon, but ultimately are measuring indirect parameters rather than detecting the physics of the situation. One of the long standing goals of weather remote sensing is to relate the measurements from the various instruments to the physics that give rise to the measurements. Weather radar networks give both a better spatial coverage than single radars, as well as providing multiple looks at the environment. Newly developed radar networks have started to incorporate multiple frequencies and multiple polarizations to take advantage of attributes of different radar frequencies. Raindrops occupy different scattering regimes based on the frequency of the radar being used. Based on this, multiple radars at different wavelengths provide unique information about the microphysical characteristics of the atmosphere. Nonetheless, very little work has been conducted on fusing multiple radar measurements at heterogeneous frequencies to improve microphysical retrievals. This work presents a forward variational algorithm for multiple radar fusion that retrieves microphysical parameters from the atmosphere. The single radar case and the multiple radar case will both be addressed. Ground instrumentation will be used for verification, and the spatial and temporal variability of precipitation microphysics will be discussed.
Open Access
An inverse problem and multi-compartment lung model for the estimation of lung airway resistance throughout the bronchial tree
(Colorado State University. Libraries, 2022) Heavner, Emily, author; Mueller, Jennifer, advisor; Shipman, Patrick, committee member; Cheney, Margaret, committee member; Rezende, Marlis, committee member
Mechanical ventilation is a vital treatment for patients with respiratory failure, but mechanically ventilated patients are also at risk of ventilator-induced lung injury. Optimal ventilator settings to prevent such injury could be guided by knowledge of the airway resistance throughout the lung. While the ventilator provides a single value estimating the total airway resistance of the patient, in reality the airway resistance varies along the bronchial tree. Multiple literature sources reveal a wide range of clinically used values for airway resistance along the bronchial tree, motivating an investigation to estimate the values of airway resistance in the alveolar tree and the relationship to disease state. In this work, we introduce a multi-compartment asymmetric lung model based on resistor-capacitor circuits by using an analogy between electric circuits and the human lungs. A method for solving the inverse problem of computing the vector of airway resistance values in the alveolar tree is presented. The method uses a linear least squares optimization approach with several constraints. First, a symmetric lung model that makes use of parameters supplied by the mechanical ventilator of patients with acute respiratory distress syndrome (ARDS) is used. We then generalize the model to an asymmetric lung model. The asymmetric model takes regional information data from electrical impedance tomography, a medical imaging technique, and converts them to time dependent lung airway volumes. The linear least squares optimization inverse problem is embedded in an iterative method to update unknown parameters of the forward problem for the asymmetric case.
Open Access
Automated event detectors utilized for continental intraplate earthquakes: applications to tectonic, induced, and magmatic sequences
(Colorado State University. Libraries, 2018) McMahon, Nicole D., author; Aster, Richard C., advisor; Schutt, Derek L., committee member; Cheney, Margaret, committee member; Benz, Harley M., committee member
Event detection is a crucial part of the data-driven science of seismology. With decades of continuous seismic data recorded across thousands of networks and tens of thousands of stations, and an ever-accelerating rate of data acquisition, automated methods of event detection, as opposed to manual/visual inspection, allow scientists to rapidly sift through enormous data sets extracting event information from background noise for further analysis. Automation naturally increases the numbers of detected events and lowers the minimum magnitude of detectable events. Increasing numbers and decreasing magnitudes of detected events, particularly with respect to earthquakes, enables the construction of more complete event catalogs and more detailed analysis of spatiotemporal trends in earthquake sequences. These more complete catalogs allow for enhanced knowledge of Earth structure, earthquake processes, and have potential for informing hazard mitigation. This study utilizes automated event detection techniques, namely matched filter and subspace detection, and applies them to three different types of continental intraplate earthquake sequences: a tectonic aftershock sequences in Montana, an induced aftershock sequence in Oklahoma, and a magmatic swarm sequence in Antarctica. In Montana, the combination of matched filtering and multiple-event relocation techniques provided a more complete picture of the spatiotemporal evolution of the aftershock sequence of the large intraplate earthquake that occurred near Lincoln, Montana in 2017. The study reveals movement along an unmapped fault that is antithetical to the main fault system trend in the region and demonstrates the hazards associated with a highly faulted and seismically active region encompassing complex and hidden structures. In Oklahoma, subspace detection methodology is used in combination with multiple-event relocation techniques to reveal movement along three different faults associated with the 2011 Prague, Oklahoma induced earthquake sequence. The study identifies earthquakes located in both the sedimentary zone of wastewater injection as well as the underlying crystalline basement indicating that faults traverse the unconformity. Injecting fluid into the overlying sediment can easily penetrate to the basement where larger earthquakes nucleate. In Antarctica, subspace detection is again used in a very remote intraplate region with sparse station coverage to detail the sustained and ongoing magmatic deep, long-period earthquake swarm occurring beneath the West Antarctic Ice Sheet and Executive Committee Range in Marie Byrd Land, Antarctica. These earthquakes indicate the present-day location of magmatic activity, which appears appear to have increased in intensity over the last few years. This dissertation contributes to the growing bodies of literature around three distinctly interesting types of seismicity that are not associated to the first order with plate tectonic boundaries. Large tectonic intraplate earthquakes are relatively uncommon. Induced seismicity has only drastically increased in the central US during the last decade and created new insights into this process. Deep, long-period, magmatic earthquakes are still a poorly understood type of seismicity in volcanic settings.
Open Access
Bayesian approach to the anisotropic EIT problem and effect of structural changes on reconstruction algorithm using 2-D D-bar algorithm
(Colorado State University. Libraries, 2018) Murthy, Rashmi, author; Mueller, Jennifer L., advisor; Cheney, Margaret, committee member; Pinaud, Oliver, committee member; Buchanan, Kristen, committee member
Electrical Impedance Tomography (EIT) is a relatively new imaging technique that is non-invasive, low-cost, and non-ionizing with excellent temporal resolution.In EIT, the unknown electrical conductivity in the interior of the medium is determined from the boundary electrical measurements. In this work, we attempt to find a direct reconstruction algorithm to the anisotropic EIT problem based on the well-known Calderón's method. The non-uniqueness of the inverse problem is dealt with assuming that the directions of anisotropy are known. We utilize the quasi-conformal map in the plane to accomplish Calderóns approach. Additionally, we derive a probability distribution for the anisotropic conductivity values using a Bayesian formulation, where the direction of anisotropy is encoded as the prior information. We show that this results in the generalized Tikhonov regularization, where the prior information about the direction of anisotropy is incorporated in the regularization operator. The computations of the anisotropic EIT problem using the Bayesian formulation is conducted on simulated data and the resulting reconstructions for the data are shown. Finally, the work of this thesis is concluded by implementing dynamic changes in boundary of a human data during respiration process successfully in the D-bar algorithm.
Open Access
Calibration of CSU CHIVO radar during the RELAMPAGO campaign
(Colorado State University. Libraries, 2022) Kim, Juhyup, author; Chandrasekaran, V., advisor; Ray, Indrakshi, committee member; Cheney, Margaret, committee member
Colorado State University C-band Hydrometeorological Instrument for Volumetric Observation (CSU CHIVO) radar is a dual-polarization weather radar operated by Colorado State University. CHIVO radar is easy to be transported and deployed compared to conventional S-band radars. CHIVO radar can be disassembled, shipped, and re-assembled to be deployed to observe weather phenomena at different locations in the world. During the Remote Sensing of Electrification, Lightning, and Mesoscale/Microscale Process with Adaptive Ground Observations (RELAMPAGO) field campaign, CHIVO radar was deployed to Córdoba & Mendoza provinces in Argentina and operated during two observing periods: one from November 10, 2018, to December 22, 2018, and another from December 27, 2018, to January 31, 2019. Any high-quality research radar requires proper calibration to ensure high data quality. To address the requirements associated with high-quality weather radar, this thesis presents 3 aspects of radar calibrations namely a) azimuth, which indicates the horizontal position of targets, b) reflectivity (Z), which indicates the returned power at horizontal polarization, and c) differential reflectivity (ZDR) which indicates the ratio of the horizontal to vertical polarizations of the Z. The calibration techniques presented in this thesis utilizes the sun as a calibration source, ground targets, and meteorological targets. These three techniques are applied appropriately to analyze and calibrate the radar data sets. The goal of the radar calibrations was to improve the data quality to provide researchers with accurate data sets so that weather phenomena under different geological and climatic conditions can be properly studied and understood.
Open Access
Characterizing uncertainties in Doppler measurements using a displaced phase center antenna configuration on spaceborne weather radars
(Colorado State University. Libraries, 2021) Graniello, Sergio Daniel, author; Chandrasekar, V, advisor; Cheney, Margaret, committee member; Chavéz, José, committee member
The work presented in this thesis explores a new antenna configuration for accurately obtaining Doppler measurements from a spaceborne weather radar. Spaceborne weather radars have the potential to observe the dynamic process of the atmosphere at a global scale. Unlike ground and airborne radar, spaceborne radars have a unique challenge due to high orbital velocities, which result in a decorrelation of successive pulses, adversely affecting the ability to accurately measure the vertical Doppler velocity of clouds and precipitation [1]. A Displaced phase center antenna (DPCA) configuration has been proposed to mitigate the effects that high platform velocity have on single antenna system on a spaceborne weather radar. This work describes the concept of DPCA and its benefits over a single antenna system. This thesis characterizes the performance and its uncertainty for Doppler velocity estimation associated with the technique by simulating a spaceborne weather radar with DPCA. Through this process it was determined that DPCA removes the decorrelation effect that is associated with high platform velocities, resulting in a high accuracy in Doppler estimates in both homogeneous and non-homogeneous conditions.
Embargo
Coexistence of weather radars and communication systems: model to identify interfering sources and mitigation solutions
(Colorado State University. Libraries, 2023) Vaccarono, Mattia, author; Chandrasekaran, Chandra V., advisor; Cheney, Margaret, committee member; Jayasumana, Anura, committee member; Popat, Ketul, committee member
Electromagnetic spectrum is a finite resource. Weather radars are one of the many sources which use electromagnetic waves. The availability of spectrum bands that can be assigned to a specific user is limited. Consequently, the electromagnetic spectrum is shared by different application in the same frequency band. This is the specific case of C-band weather radars, which operate in the 5.6GHz band, sharing the same frequencies with Radio Local Area Networks, Wireless Local Area Networks and HiperLAN systems. These telecommunication systems are continuously increasing in rural areas as broadband Internet access points. The coexistence of C-band weather radar and such systems is nowadays a primary topic in the weather radar community. The amount of interference received by weather radars are affecting the data quality, especially for polarimetric observations. Electromagnetic interference may also appear at higher frequencies, such as the X-band located around 9.3GHz. These frequencies are used by weather radars for hydrological purposes. The dense radar network deployed in Dallas Fort Worth area and the mobile radar managed by Arpa Piemonte operate at X-band and they receive interfering signals. These signals have been detected during a field measurement campaign using both the mobile weather radar and a vector signal analyzer able to perform real time analysis. A technique to identify the likely interfering sources is discussed, which can be used by the National Regulatory Authorities or Regional Agencies, such as the Physics and Industrial Risk Department of Arpa Piemonte, Italy, in charge of the telecommunication authorization processes. The model may be applied to a telecommunication tower transmitting at the same frequency of a given radar and in case of likely interference, mitigation strategies could be set during the tower installation, i.e. changing the antenna direction or tilt. Over the years, many RFI removal and mitigation tools have been discussed in the literature, but only few are currently implemented on operational weather radars. This work, instead, aims to implement mitigation solutions that can be implemented by National Weather Services. The electromagnetic interference may be removed at different levels: from the received signals to the processed radar products, such as reflectivity maps that are shown to general public. In order to make possible the interference removal also to those National Weather Services, or radar management services, which are not able to act on the radar signal processor to implement deeper mitigation tools, a RFI mitigation solution based on image processing is shown. This method does not require to access the radar signal processor, but it does not mitigate the effect of interference overlapped with weather echoes. Then, based on the interfering signals features, a mitigation solution has been developed. The interfering signals are removed before received signals are processed to obtain radar moments. The proposed method has been tested with good performances in clear air echoes at both C and X-bands. A study case has been selected to evaluate its performances during precipitation events. The proposed mitigation solution is applied to the received signals to remove interfering signals and to reconstruct the residual information. The radar reflectivity is computed and it is compared to the operational radar Z product. A Swiss C-band radar is selected as reference to validate the mitigation solution. The interfering signals are properly removed and the missing data in the received radar pulses are computed by smoothing from adjacent range gates and pulses. Actually, removing only the interfering signals the proposed solution is able to preserve the meteorological echoes which lead to a better estimate of the reflectivity values, especially in case of weak echoes (i.e. light rain or drizzle). The Interference to Signal Ratio (ISR) is considered the metric to quantitatively evaluate the mitigation performance as ISR difference between processed and received signals. The proposed mitigation solution can achieve up to 20dB suppression.
Open Access
Computational advancements in the D-bar reconstruction method for 2-D electrical impedance tomography
(Colorado State University. Libraries, 2016) Alsaker, Melody, author; Mueller, Jennifer L., advisor; Cheney, Margaret, committee member; Notaros, Branislav, committee member; Pinaud, Olivier, committee member
We study the problem of reconstructing 2-D conductivities from boundary voltage and current density measurements, also known as the electrical impedance tomography (EIT) problem, using the D-bar inversion method, based on the 1996 global uniqueness proof by Adrian Nachman. We focus on the computational implementation and efficiency of the D-bar algorithm, its application to finite-precision practical data in human thoracic imaging, and the quality and spatial resolution of the resulting reconstructions. The main contributions of this work are (1) a parallelized computational implementation of the algorithm which has been shown to run in real-time, thus demonstrating the feasibility of the D-bar method for use in real-time bedside imaging, and (2) a modification of the algorithm to include \emph{a priori} data in the form of approximate organ boundaries and (optionally) conductivity estimates, which we show to be effective in improving spatial resolution in the resulting reconstructions. These computational advancements are tested using both numerically simulated data as well as experimental human and tank data collected using the ACE1 EIT machine at CSU. In this work, we provide details regarding the theoretical background and practical implementation for each advancement, we demonstrate the effectiveness of the algorithm modifications through multiple experiments, and we provide discussion and conclusions based on the results.
Open Access
Cracking open the black box: a geometric and topological analysis of neural networks
(Colorado State University. Libraries, 2024) Cole, Christina, author; Kirby, Michael, advisor; Peterson, Chris, advisor; Cheney, Margaret, committee member; Draper, Bruce, committee member
Deep learning is a subfield of machine learning that has exploded in recent years in terms of publications and commercial consumption. Despite their increasing prevalence in performing high-risk tasks, deep learning algorithms have outpaced our understanding of them. In this work, we hone in on neural networks, the backbone of deep learning, and reduce them to their scaffolding defined by polyhedral decompositions. With these decompositions explicitly defined for low-dimensional examples, we utilize novel visualization techniques to build a geometric and topological understanding of them. From there, we develop methods of implicitly accessing neural networks' polyhedral skeletons, which provide substantial computational and memory savings compared to those requiring explicit access. While much of the related work using neural network polyhedral decompositions is limited to toy models and datasets, the savings provided by our method allow us to use state-of-the-art neural networks and datasets in our analyses. Our experiments alone demonstrate the viability of a polyhedral view of neural networks and our results show its usefulness. More specifically, we show that the geometry that a polyhedral decomposition imposes on its neural network's domain contains signals that distinguish between original and adversarial images. We conclude our work with suggested future directions. Therefore, we (1) contribute toward closing the gap between our use of neural networks and our understanding of them through geometric and topological analyses and (2) outline avenues for extensions upon this work.
Open Access
Cross validation of observations from the GPM dual-frequency precipitation radar and dual-polarization S-band ground radars
(Colorado State University. Libraries, 2018) Biswas, Sounak Kumar, author; Chandrasekar, V., advisor; Cheney, Margaret, committee member; Mielke, Paul W., committee member
This research presents a comparative study of observations and various products of the Global Precipitation Measurement (GPM) Mission Satellite with dual polarization S-Band Ground Radars. The GPM mission is a joint venture by the NASA and the JAXA. The radar on board the core observatory is a dual-frequency precipitation radar (DPR) capable of simultaneously operating at 13.6 GHz (Ku band) and 35.5 GHz (Ka band). The DPR is expected to revolutionize the way precipitation is measured from space through its dual-frequency observations. Ground Validation is one of the most critical aspects of the GPM mission. The best way of doing this is by direct comparison of the space-based observations with well calibrated dual polarization ground radar measurements. Before any direct comparisons can be made, volume matching of the data is necessary due to the difference in observation geometry and resolution volume of both the system. In this study, a methodology developed by Bolen and Chandrasekar (2001) for aligning TRMM satellite data with ground radar data is followed. This technique was extended by Schwaller and Morris (2011). Radar reflectivity and rainfall rate product comparison study have been performed in detail. Vertical profiles have been studied thoroughly. Various case studies of simultaneous GPM-DPR and ground radar observations have been carefully chosen. Ground validation operational NEXRAD sites have been considered from all over the USA. Comparison studies with research radars such as CSU-CHILL and NASA N-POL have also been conducted. The GPM satellite's profile classification module's products are also evaluated. Results from Hydrometeor classification method by Bechini and Chandrasekar (2015) for ground radars have been extensively used for validating DPR's melting layer detection capability in different types of precipitation system. In this study, a new method developed by Le et al (2017) for identification of snow falling on the ground has been considered. Ground validation comparisons have been performed with observations from ground radars and the results are presented.
Open Access
Data-driven methods for compact modeling of stochastic processes
(Colorado State University. Libraries, 2024) Johnson, Mats S., author; Aristoff, David, advisor; Cheney, Margaret, committee member; Pinaud, Olivier, committee member; Krapf, Diego, committee member
Stochastic dynamics are prevalent throughout many scientific disciplines where finding useful compact models is an ongoing pursuit. However, the simulations involved are often high-dimensional, complex problems necessitating vast amounts of data. This thesis addresses two approaches for handling such complications, coarse graining and neural networks. First, by combining Markov renewal processes with Mori-Zwanzig theory, coarse graining error can be eliminated when modeling the transition probabilities of the system. Second, instead of explicitly defining the low-dimensional approximation, using kernel approximations and a scaling matrix the appropriate subspace is uncovered through iteration. The algorithm, named the Fast Committor Machine, applies the recent Recursive Feature Machine of Radhakrishnan et al. to the committor problem using randomized numerical linear algebra. Both projects outline practical data-driven methods for estimating quantities of interest in stochastic processes that are tunable with only a few hyperparameters. The success of these methods is demonstrated numerically against standard methods on the biomolecule alanine dipeptide.
Open Access
Deep neural network based rain/no-rain classification and rain rate estimation
(Colorado State University. Libraries, 2022) Potnis, Jay U., author; Chandrasekar, V., advisor; Cheney, Margaret, committee member; Siller, Thomas, committee member
Quantitative Precipitation Estimation is the process of computing rainfall rate or rainfall accumulation based on the state of the atmosphere. Atmospheric conditions can be described by using observations from meteorological instruments. Extreme weather events caused due to high rainfall can be dangerous in terms of loss of property and life. To prevent such disasters, accurate QPE algorithms that analyze and estimate the amount of rainfall observed in a region are critical. Moreover, rain rate estimates are crucial products in making management decisions in water, energy, construction infrastructure, and many other institutions. Researching state-of-the-art rainfall estimation techniques that make use of reliable remote sensing equipment such as satellites and radars is important as deploying rain gauges everywhere is not possible and is not a viable option. As rain precipitation is a complicated phenomenon, depending on multiple factors in the atmosphere, research is being done in this domain for many decades and the goal is to improve the accuracy of estimation by using new state-of-the-art methods. Weather radars are reliable remote sensing instruments that are used to capture the different properties of weather in form of products called moments. The goal of this work is to use weather radars in conjunction with Deep Neural Networks to provide solutions to multiple tasks in the QPE domain. Neural networks can be used for precipitation flagging such as classifying rain and no rain events. They can also be used for estimating the rain rates at specific coordinates or along regions. Though multiple empirical relationships between radar moments and rain rate already exist, this work provides good state-of-the-art alternatives to these equations and can even achieve comparable accuracy.
Open Access
Design, deployment, and cost considerations for DARMA; a low-cost and lightweight FMCW radar
(Colorado State University. Libraries, 2022) Bruner, Marshall, author; Chandrasekar, V., advisor; Cheney, Margaret, committee member; Gooch, Ryan, committee member
The capability of frequency-modulated continuous wave (FMCW) radar to operate in low-power environments has made it a good choice for many mobile systems including automobile radars. While specialized FMCW radars have seen an increase in production recently, there is a lack of general-purpose FMCW radars with the ability to be used in a multitude of applications, especially for volume targets such as precipitation. This thesis presents design considerations for the Dual-polarization phased Array Radar for Measurement of the Atmosphere (DARMA), a low-cost, medium range (km) radar with the versatility to operate mounted on an unmanned aircraft system (UAS) or ground platform. The radar features modular subsystems which allow for easy swapping to support different application requirements as well as upgrades due to rapidly changing technology. Signal processing methods are also introduced, and implemented on COTS systems, to allow for noise mitigation, target detection, and estimation of weather products.
Open Access
Detection and relocation of earthquakes in the sparsely instrumented Mackenzie Mountains region, Yukon and Northwest Territories, Canada
(Colorado State University. Libraries, 2020) Heath, David C., author; Schutt, Derek L., advisor; Aster, Richard C., committee member; Wald, David J., committee member; Cheney, Margaret, committee member
The Mackenzie Mountains are an actively uplifting and seismogenic arcuate thrust belt lying within the Northwest Territories and Yukon, Canada. Seismic activity in the region is poorly constrained due to a historically sparse seismograph distribution. In this study, new data are analyzed from the 40-station, ~875 km-long Mackenzie Mountains temporary network (Baker et al., 2020) crossing the Cordillera-Craton region adjacent to and within the Mackenzie Mountains, in conjunction with Transportable Array and other sparsely distributed arrays in the region. Data from approximately August 2016 – August 2018 are processed and compared to the sparse-network earthquake catalog records maintained by the USGS and Natural Resources Canada. Using algorithms developed by Kushnir et al. (1990), Rawles and Thurber (2015), and Roecker et al. (2006), signals are identified and subsequently associated across the network to note potential events, estimate phase onsets, and resolve hypocenter locations. This study improves the regional earthquake catalog by detecting smaller-magnitude earthquakes and lowering the regional magnitude of completeness from Mc = 2.5 to 1.9. Within the Mackenzie Mountains and immediately surrounding areas we find 524 new events and additionally recommend an updated location for 185 previously cataloged events. Our b-value computation for the updated catalog (0.916 ± 0.08) likely indicates a relatively high level of regional differential stress. We identify the spatial distribution of earthquakes in the Mackenzie Mountains as diffuse, and offer far-field stress transfer as a mechanism for producing widespread reverse faulting observed in the region. Further, we associate regional seismicity with tectonic activity in the context of known faults and orogenic provinces such as the Richardson Mountains.
Open Access
Electrical impedance tomography with Calderón's method in two and three dimensions
(Colorado State University. Libraries, 2020) Shin, Kwancheol, author; Mueller, Jennifer L., advisor; Cheney, Margaret, committee member; Pinaud, Olivier, committee member; Hussam, Mahmoud, committee member
Electrical impedance tomography (EIT) is a non-invasive imaging technique in which electrical measurements on the electrodes attached to the boundary of a subject are used to reconstruct the electrical properties of the subject. That is, voltage data arising from currents applied on the boundary are used to reconstruct the conductivity distribution in the interior. Calderón's method is a direct linearized reconstruction method for the inverse conductivity problem with the attributes that it can provide absolute images with no need for forward modeling, reconstructions can be computed in real-time, and both conductivity and permittivity can be reconstructed. In this three-paper dissertation, first, an explicit relationship between Calderón's method and the D-bar method is provided, facilitating a "higher-order" Calderón's method in which a correction term is included, derived from the relationship to the D-bar method. Furthermore, a method of including a spatial prior is provided. These advances are demonstrated on tank data collected with the ACE1 EIT system. On the other hand, it has been demonstrated that various EIT reconstruction algorithms are very sensitive to the measurement and incorrect modeling of the boundary shape. Calderón's method has been implemented with correct boundary shape, but the exact location of the electrodes are disregarded as they are assumed to be spaced uniformly in angle. In the second body of work, Calderón's method is implemented with a new expansion technique which enables the use of the correct location of the electrodes as well as the shape of the boundary resulting in improved absolute images. We test our new algorithm with experimental data collected with the ACE1 EIT system. Finally, the first implementation of Calderón's method on a 3-D cylindrical domain with data collected on a portion of the boundary is provided. The effectiveness of the method to localize inhomogeneities in the plane of the electrodes and in the z-direction is demonstrated on simulated and experimental data.
Open Access
Event detection and analysis of a dense three-component near-summit seismic array deployed at Erebus volcano
(Colorado State University. Libraries, 2022) Jaski, Erika, author; Aster, Richard C., advisor; Schutt, Derek, committee member; Cheney, Margaret, committee member
Erebus volcano on Ross Island, Antarctica has maintained an erupting phonolitic lava lake for at least five decades. During active periods, the lava lake hosts large (up to ~10-m diameter) gas slugs rising through the conduit that create impulsive Strombolian eruptions and produce very long period (VLP) signals on broadband seismograms. We combine near-summit broadband observations and reanalyze data from a 100-station three-component short-period (4.5 Hz geophones) network deployed in an approximately 3 by 3 km region around the Main Crater during December 2008. Lava lake eruption template events are identified on broadband seismograms from their characteristic and repeating VLP spectral signature of nonharmonic modes between 0.033 and 0.2 Hz. Multi-channel and multi-station waveform matched filter correlations are performed across the short-period network using template events and correlation values that are three or more standard deviations are extracted into a working Inner Crater event catalogue, yielding 819 event detections over 19 days. While 94% of the signals in this catalogue are unique, 17 "families" of repeating lava lake events can also be identified through similar waveforms determined by Ward clustering on 5 stations, which are further interpreted for trends in location, size, and occurrence. We observe time-varying quasi-Poissonian interevent times and an approximately power-law size-frequency distribution with an excess of small events. Investigating the various event families that transpire in the Inner Crater region contributes to improved characterization and understanding of the seismogenic behavior of the lava lake degassing system and assists in the creation of a workflow that can be applied in volcanic and other circumstances that generate prolific low-level impulsive seismicity.