Development and evaluation of an advanced microwave radiance data assimilation system for estimating snow water storage at the continental scale

Snow cover modulates the Earth's surface energy and water fluxes, and snowmelt runoff is the principal source of water for humans and ecosystems in many of the middle to high latitudes in the Northern Hemisphere. Understanding spatial and temporal variation in snowpack is crucial for climate studies and water resource management and thus the climate and hydrological research communities have invested in improving large-scale snow estimates. This dissertation aims to develop an advanced snow radiance assimilation (RA) system to improve continental-scale snow water storage estimates. The RA system is comprised of the Community Land Model version 4 (CLM4) (for snow energy and mass balance modeling), radiative transfer models (RTMs) (for brightness temperature estimates), and the Data Assimilation Research Testbed (DART) (for ensemble-based data assimilation). Two snowpack RTMs, the Microwave Emission Model for Layered Snowpacks (MEMLS) and the Dense Media Radiative Transfer--Multi Layers model (DMRT-ML), are used to simulate T[subscript B] of a multi-layered snowpack. Through an error characterization study, this dissertation presents that the correlations between snow water equivalent (SWE) error and brightness temperature (T[subscript B]) error and subsequent RA performance in estimating snow are significantly affected by all physical properties of soil and snow involved in estimating T[subscript B]. Based on the error characterization results, it is hypothesized that the continental-scale RA performance in estimating snow water storage can be improved by simultaneously updating all model physical states and parameters determining T[subscript B] based on a rule, in which prior estimates are updated depending on their correlations with a prior T[subscript B]. The results of a series of RA experiments show that the improved continental-scale snow estimates are obtained by applying the hypothesis. This dissertation also shows that further improvement of the performance of the RA system can be achieved, especially for vegetated areas, by assimilating the best-performing frequency channels (i.e., 18.7 and 23.8 GHz) and by considering the vegetation single scattering albedo to represent the vegetation effect on T[subscript B] at the top of the atmosphere.Geological Science

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Department of Materials Science and EngineeringSunlight is an essential energy source that profoundly affects our lives (e.g., photosynthesis), and as light is regarded as one of the next-generation energy sources owing to its economic and environmental benefits, there have been attempted to utilize light as an energy source. To handle the sunlight in organic chemistry, in the beginning, sunlight was employed just as a heat source for distillation. Today, beyond thermal energy source, photochemistry has attracted much academic interest for generation of new reactive species (i.e., photoexcited species) in organic synthesis and polymerization. Consequently, in more future-oriented perspectives, those synthetic advances have been expanding into the utilization of the visible-light or near-infrared regions over the ultraviolet light region. In the present dissertation, the design strategies for highly efficient photoredox catalysis under visible-light irradiation was studied. In addition, designed purely organic photoredox catalysts (PCs) based on cyanoarenes, one of the classes of thermally activated delayed fluorescence (TADF) compounds, were prepared to perform highly efficient organic (i.e., reductive dehalogenation) and polymeric (i.e., pressure-sensitive adhesive, PSA) syntheses. Chapter 1 presents an introduction to photoredox catalysis and a synopsis of the dissertation. In Chapter 2, the design strategies for highly efficient formation of radical anion of cyanoarene-based PCs (PC??????) were studiedunder visible-light irradiation, photoexcited PCs which have long-lived triplet excited state (T1) become one-electron-reduced species in the presence of sacrificial reducing agents. Furthermore, the photodegradation behavior of PCs, which would reduce their catalytic efficiencies, was studied using the density functional theory (DFT) calculations and detailed structural analysis of photodegraded products. Subsequently, based on the understanding of formation and degradation of PC??????, the highly efficient photoredox-mediated reductive dehalogenation combined with both ultra-low PC loading (ca. 0.005 mol%) and oxygen tolerance was realized. In Chapter 3, UV-blocking acrylic PSAs were successfully prepared with cyanoarene-based PCs under visible-light irradiation. In particular, PC design strategies were studied to efficiently generate the PC?????? with various donor moieties to control their electrochemical properties. Bulk polymerization of the prepolymer was mechanistically studied combined with DFT calculations. Oxygen tolerance behavior in bulk polymerization was also observed, and an oxygen tolerance mechanism was proposed. The overall conclusion of this dissertation and additional supplementary contents are presented in Chapter 4 and the Appendix, respectively.ope

Analyzing Machine Learning Predictions of Passive Microwave Brightness Temperature Spectral Difference Over Snow-Covered Terrain in High Mountain Asia

Snow is an important component of the terrestrial freshwater budget in high mountainAsia (HMA) and contributes to the runoff in Himalayan rivers through snowmelt. Despitethe importance of snow in HMA, considerable spatiotemporal uncertainty exists across the different estimates of snow water equivalent for this region. In order to better estimate snow water equivalent, radiative transfer models are often used in conjunction with microwave brightness temperature measurements. In this study, the efficacy of support vector machines (SVMs), a machine learning technique, to predict passive microwave brightness temperature spectral difference (1Tb) as a function of geophysical variables (snow water equivalent, snow depth, snow temperature, and snow density) is explored through a sensitivity analysis. The use of machine learning (as opposed to radiative transfer models) is a relatively new and novel approach for improving snow water equivalent estimates. The Noah-MP land surface model within the NASALand Information System framework is used to simulate the hydrologic cycle over HMA and model geophysical variables that are then used for SVM training. The SVMsserve as a nonlinear map between the geophysical space (modeled in Noah-MP) andthe observation space (1Tb as measured by the radiometer). Advanced MicrowaveScanning Radiometer-Earth Observing System measured passive microwave brightness temperatures over snow-covered locations in the HMA region are used as training data during the SVM training phase. Sensitivity of well-trained SVMs to each Noah-MP modeled state variable is assessed by computing normalized sensitivity coefficients. Sensitivity analysis results generally conform with the known first-order physics. Input states that increase volume scattering of microwave radiation, such as snow density and snow water equivalent, exhibit a plurality of positive normalized sensitivity coefficients. In general, snow temperature was the most sensitive input to the SVM predictions. The sensitivity of each state is location and time dependent. The signs of normalized sensitivity coefficients that indicate physical irrationality are ascribed to significant cross-correlation between Noah-MP simulated states and decreased SVM prediction capability at specific locations due to insufficient training data. SVM prediction pitfalls do exist that serve to highlight the limitations of this particular machine learning algorithm

Production Planning Forecasting System Based on M5P Algorithms and Master Data in Manufacturing Processes

With the increasing adoption of smart factories in manufacturing sites, a large amount of raw data is being generated from manufacturers’ sensors and Internet of Things devices. In the manufacturing environment, the collection of reliable data has become an important issue. When utilizing the collected data or establishing production plans based on user-defined data, the actual performance may differ from the established plan. This is particularly so when there are modifications in the physical production line, such as manual processes, newly developed processes, or the addition of new equipment. Hence, the reliability of the current data cannot be ensured. The complex characteristics of manufacturers hinder the prediction of future data based on existing data. To minimize this reliability problem, the M5P algorithm, is used to predict dynamic data using baseline information that can be predicted. It combines linear regression and decision-tree-supervised machine learning algorithms. The algorithm recommends the means to reflect the predicted data in the production plan and provides results that can be compared with the existing baseline information. By comparing the existing production plan with the planning results based on the changed master data, it provides data results that help production management determine the impact of work time and quantity and confirm production plans. This means that forecasting data directly affects production capacity and resources, as well as production times and schedules, to help ensure efficient production planning

Data Assimilation Enhancements to Air Force Weathers Land Information System

The United States Air Force (USAF) has a proud and storied tradition of enabling significant advancements in the area of characterizing and modeling land state information. 557th Weather Wing (557 WW; DoDs Executive Agent for Land Information) provides routine geospatial intelligence information to warfighters, planners, and decision makers at all echelons and services of the U.S. military, government and intelligence community. 557 WW and its predecessors have been home to the DoDs only operational regional and global land data analysis systems since January 1958. As a trusted partner since 2005, Air Force Weather (AFW) has relied on the Hydrological Sciences Laboratory at NASA/GSFC to lead the interagency scientific collaboration known as the Land Information System (LIS). LIS is an advanced software framework for high performance land surface modeling and data assimilation of geospatial intelligence (GEOINT) information

Aziridine-Capped Poly(ethylene glycol) Brush Copolymers with Tunable Architecture as Versatile Cross-Linkers for Adhesives

Controlling adhesive and cohesive properties in a refined manner is paramount to developing high-performance adhesives. Therefore, developing and implementing cross-linkers have become a major focus in adhesive research, but most conventional cross-linkers are small molecules or oligomers with limited functionality. Herein, a brush copolymer consisting of varying lengths and densities of the aziridine-capped poly(ethylene glycol) (AzPEG) brush, termed P(AzPEG), is presented for use as a multifunctional polymeric cross-linker for carboxylate-containing acrylic adhesives. The density and length of the AzPEG brush, with respect to the total brush, are controlled conveniently by the concentration and molecular weight of the AzPEG acrylate monomer, respectively. In addition, the use of amphiphilic PEG also makes it possible for use in a variety of solvents. Using poly(EHA-co-AA) and poly(EHA-co-BA-co-MMA-co-AA-co-HEMA) as solvent and waterborne acrylic adhesives, respectively, their mechanical and adhesive properties are effectively controlled by P(AzPEG) with varying densities and lengths of the AzPEG brush without the need for a substantial change in concentration. These findings certainly show the advantages of brush copolymer-based cross-linkers for adhesives

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We investigated mesoporous Nd0.7Sr0.3CoO3–?? electrocatalytic properties for the ORR. Mesoporous Nd0.7Sr0.3CoO3−?? has been prepared with a polymethyl methacrylate (PMMA) hard-template for an energy storage system. The prepared 3-dimensionally ordered mesoporous Nd0.7Sr0.3CoO3-??. (3DOM-NSC) has a well-developed mesoporous structure and has high specific surface area (22.0 m2 g-1). The catalytic activity for the oxygen reduction reaction (ORR) has been studied by a rotating-ring-disk electrode (RRDE). In the ORR test, a limiting current density of 3DOM-NSC was 5.83 mA cm-2 at 0.7 V (V vs. Hg/HgO) with 1600 rpm and the value was good compared with that of Pt/C. Moreover, it shows the stable and excellent performance in a hybrid Li-air battery