京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18564号 / 工博第3925号 / 新制||工||1603(附属図書館) / 31464 / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 中北 英一, 准教授 KIM Sunmin, 教授 角 哲也 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

The lee shores of the North American Great Lakes are subject to hazardous amounts of snowfall each winter as continental polar air masses are destabilized by the relatively warmer lakes which manifests as pronounced heat and moisture fluxes and subsequent convection and snow generation. This phenomenon, known as lake-effect snow (LES), has been studied by the atmospheric scientific community extensively as the local and mesoscale processes are becoming better understood through the implementation of in situ research projects and high-resolution numerical weather prediction models. However, considerably less research effort has inquired on what large-scale conditions are linked with lake-effect snow. The objective of this dissertation is to develop a more comprehensive understanding of the synoptic-scale conditions associated with lake-effect snowstorms and how they differentiate with non-LES winter storms. Chapter 1 provides a brief introduction to LES and reviews the basic dynamics of LES formation in the form of a comprehensive literature review. Chapter 2 consists of the first synoptic climatologies of lake-effect snowstorms off Lakes Michigan and Superior through statistical analysis of past lake-effect cases off those two lakes. Chapter 3 focuses on developing a synoptic climatology of wintertime cyclonic systems, specifically Alberta Clippers, that traversed the Great Lakes basin but did not result in lake-effect snow formation. Chapter 4 features the development of an objective classification model that differentiates between these two winter weather phenomena by using past LES and non-LES winter storm case repositories to train and test the model. This research effort will focus on wintertime Alberta Clipper systems and LES off Lakes Erie and Ontario. Finally, Chapter 5 reviews the primary results from this research and discusses their significance and implications regarding possible future research.

This text provides an overview of problems in the field of data assimilation. We explore the possibility of recreating unknown data by continuously inserting known data into certain dynamical systems, under certain regularity assumptions. Additionally, we discuss an alternative statistical approach to data assimilation and investigate the utilization of the Ensemble Kalman Filter for assimilating data into dynamical models. A key challenge in numerical weather prediction is incorporating convective precipitation into an idealized setting for numerical computations. To answer this question we examine the modified rotating shallow water equations, a nonlinear coupled system of partial differential equations and further assess if this primitive model accurately mimics phenomena observed in operational numerical weather prediction models. Numerical experiments conducted using a Deterministic Ensemble Kalman Filter algorithm support its applicability for convective-scale data assimilation. Furthermore, we analyze the frequency spectrum of numerical forecasts using the Wavelet transform. Our frequency analysis suggests that, under certain experimental settings, there are similarities in the initialization of operational models, which can aid in understanding the problem of intialization of numerical weather prediction models.

<p>  </p> <p>Rapid intensification of low-level rotation in non-classic tornadic storms in southeastern United States, often at time scales shorter than the volume updates from existing opera- tional radars, calls for a deeper understanding of storm-scale processes. There is growing evidence that the highly nonlinear interactions between vertical wind shear and cold pools regulate the intensity of downdrafts, low- and mid-level updrafts, and thus tornadic poten- tial in supercells. Tornado-strength circulations are more likely associated with cold pools of intermediate strength. The microphysical pathway leading to storm electrification also plays a major role in the regulation of cold pool intensity. Storm electrification and subsequent lightning initiation are a by-product of charging of ice hydrometeors in the mixed-phase updrafts. Lightning flashes frequently initiate along the periphery of turbulent updrafts and total flash rate is controlled by the updraft speed and volume.</p> <p><br></p> <p>In the first part of this work, polarimetric fingerprints like ZDR and KDP columns (proxies for mixed-phase updraft strength) are objectively identified to track rapid fluctuations in updraft intensity. We quantify the volume of ZDR and KDP columns to evaluate their utility in predicting temporal variability in lightning flash characteristics and the onset of severe weather. Using observational data from KTLX radar and Oklahoma Lightning Mapping Array, we had previously found evidence of temporal covariance between ZDR column volume and the total lightning flash rate in a tornadic supercell in Oklahoma. </p> <p><br></p> <p> Here, we extend our analysis to three high-shear low-CAPE (HSLC) cases observed during the 2016-17 VORTEX-SE field campaign in Northern Alabama. In all three scenarios (one tornadic and one nontornadic supercell, and a quasi-linear convective system), the KDP column volume had a stronger correlation with total flash rates than the ZDR column volume. We also found that all three storms maintained a normal tripole charge structure, with majority of the cloud-to-ground (CG) strikes lowering negative charge to the ground. The tornadic storm’s CG polarity changed from negative to positive at the same time it entered a region with higher surface equivalent potential temperature. In contrast to the Oklahoma storm, lightning flash initiations in HSLC storms occurred primarily outside the footprint of ZDR and KDP column objects.</p> <p><br></p> <p>Storm dynamics coupled with microphysical processes such as diabatic heating/cooling and advection/sedimentation of hydrometeors also plays a significant role in electrification of thunderstorms. Simulation of deep convection, therefore, needs to account for the feedback of microphysics to storm dynamics. In the second part of this work, the NSSL microphysics scheme is used to simulate ice mass fluxes, cold pool intensity, and noninductive charging rates. The scheme is run in its triple-moment configuration in order to provide a more realis- tic size-sorting process that avoids pathologies that arise in double-moment representations.</p> <p><br></p> <p>We examine the possible tertiary linkage between noninductive charging rates and cold pool through their dependence on mixed-phase microphysical processes. The Advanced Re- gional Prediction System (ARPS) model is used to simulate the same three HSLC cases from VORTEX-SE 2016-17 IOPs. WSR-88D radar reflectivity and Doppler velocity observations are assimilated in a 40-member ensemble using an ensemble Kalman filter (EnKF) filter.</p> <p><br></p> <p>In all three cases, the simulated charge separation is consistent with the observed normal tripole. Greater updraft mass flux, supercooled liquid water concentration, and nonprecip- itation mass flux explain the nontornadic supercell’s higher total flash rate compared to the tornadic supercell. Positive and negative graupel charging rates were found to have the greatest linear correlation with updraft mass flux, followed by precipitation mass flux in all three cases. At zero time lag, horizontal buoyancy gradients associated with a surface cold pool were not found to be correlated with either the charging rates or the updraft and precipitation mass flux. Total flash rate based on empirical relationships between simulated ice mass fluxes was lower than the observed values.</p>

Atmospheric particulate matter (PM) is linked to various adverse environmental and health impacts. PM in the atmosphere reduces visibility, alters precipitation patterns by acting as cloud condensation nuclei (CCN), and changes the Earth’s radiative balance by absorbing or scattering solar radiation in the atmosphere. The long-range transport of pollutants leads to increase in PM concentrations even in remote locations such as polar regions and mountain ranges. One significant effect of PM on the earth’s climate occurs while light absorbing PM, such as Black Carbon (BC), deposits over snow. In the Arctic, BC deposition on highly reflective surfaces (e.g. glaciers and sea ices) has very intense effects, causing snow to melt more quickly. Thus, characterizing PM sources, identifying long-range transport pathways, and quantifying the climate impacts of PM are crucial in order to inform emission abatement policies for reducing both health and environmental impacts of PM. Chemical transport models provide mathematical tools for better understanding atmospheric system including chemical and particle transport, pollution diffusion, and deposition. The technological and computational advances in the past decades allow higher resolution air quality and weather forecast simulations with more accurate representations of physical and chemical mechanisms of the atmosphere. Due to the significant role of air pollutants on public health and environment, several countries and cities perform air quality forecasts for warning the population about the future air pollution events and taking local preventive measures such as traffic regulations to minimize the impacts of the forecasted episode. However, the costs associated with the complex air quality forecast models especially for simulations with higher resolution simulations make “forecasting” a challenge. This dissertation also focuses on applications, performance analysis, and optimization of meteorology and air quality modeling forecasting models. This dissertation presents several modeling studies with various scales to better understand transport of aerosols from different geographical sources and economic sectors (i.e. transportation, residential, industry, biomass burning, and power) and quantify their climate impacts. The simulations are evaluated using various observations including ground site measurements, field campaigns, and satellite data. The sector-based modeling studies elucidated the importance of various economical sector and geographical regions on global air quality and the climatic impacts associated with BC. This dissertation provides the policy makers with some implications to inform emission mitigation policies in order to target source sectors and regions with highest impacts. Furthermore, advances were made to better understand the impacts of light absorbing particles on climate and surface albedo. Finally, for improving the modeling speed, the performances of the models are analyzed, and optimizations were proposed for improving the computational efficiencies of the models. Theses optimizations show a significant improvement in the performance of Weather Research and Forecasting (WRF) and WRF-Chem models. The modified codes were validated and incorporated back into the WRF source code to benefit all WRF users. Although weather and air quality models are shown to be an excellent means for forecasting applications both for local and hemispheric scale, further studies are needed to optimize the models and improve the performance of the simulations.

Antarctic Wind Engineering deals with the effects of wind on the built environment. The assessment of wind induced forces, wind resource and wind driven snowdrifts are the main tasks for a wind engineer when participating on the design of an Antarctic building. While conventional Wind Engineering techniques are generally applicable to the Antarctic environment, there are some aspects that require further analysis due to the special characteristics of the Antarctic wind climate and its boundary layer meteorology. The first issue in remote places like Antarctica is the lack of site wind measurements and meteorological information in general. In order to complement this shortage of information various meteorological databases have been surveyed. Global Reanalyses, produced by the European Met Office ECMWF, and RACMO/ANT mesoscale model simulations, produced by the Institute for Marine and Atmospheric Research of Utrecht University (IMAU), have been validated versus independent observations from a network of 115 automatic weather stations. The resolution of these models, of some tens of kilometers, is sufficient to characterize the wind climate in areas of smooth topography like the interior plateaus or the coastal ice shelves. In contrast, in escarpment and coastal areas, where the terrain gets rugged and katabatic winds are further intensified in confluence zones, the models lack resolution and underestimate the wind velocity. The Antarctic atmospheric boundary layer (ABL) is characterized by the presence of strong katabatic winds that are generated by the presence of surface temperature inversions in sloping terrain. This inversion is persistent in Antarctica due to an almost continuous cooling by longwave radiation, especially during the winter night. As a result, the ABL is stably stratified most of the time and, only when the wind speed is high it becomes near neutrally stratified. This thesis also aims at making a critical review of the hypothesis underlying wind engineering models when extreme boundary layer situations are faced. It will be shown that the classical approach of assuming a neutral log-law in the surface layer can hold for studies of wind loading under strong winds but can be of limited use when detailed assessments are pursued. The Antarctic landscape, mostly composed of very long fetches of ice covered terrain, makes it an optimum natural laboratory for the development of homogeneous boundary layers, which are a basic need for the formulation of ABL theories. Flux-profile measurements, made at Halley Research Station in the Brunt Ice Shelf by the British Antarctic Survery (BAS), have been used to analyze boundary layer similarity in view of formulating a one-dimensional ABL model. A 1D model of the neutral and stable boundary layer with a transport model for blowing snow has been implemented and verified versus test cases of the literature. A validation of quasi-stationary homogeneous profiles at different levels of stability confirms that such 1D models can be used to classify wind profiles to be used as boundary conditions for detailed 3D computational wind engineering studies. A summary of the wind engineering activities carried out during the design of the Antarctic Research Station is provided as contextual reference and point of departure of this thesis. An elevated building on top of sloping terrain and connected to an under-snow garage constitutes a challenging environment for building design. Building aerodynamics and snowdrift management were tested in the von Karman Institute L1B wind tunnel for different building geometries and ridge integrations. Not only for safety and cost reduction but also for the integration of renewable energies, important benefits in the design of a building can be achieved if wind engineering is considered since the conceptual phase of the integrated building design process.

Snowmelt runoff in the mountainous eastern part of Turkey is of great importance as it constitutes 60-70% in volume of the total yearly runoff during spring and early summer months. Therefore, forecasting the amount and timing of snowmelt runoff especially in the Euphrates Basin, where large dams are located, is an important task in order to use the water resources of the country in an optimum manner. The HBV model, being one of the well-known conceptual hydrological models used more than 45 countries over the world, is applied for the first time in Turkey to a small basin of 242 km2 on the headwaters of Euphrates River for 2002-2004 water years. The input data are provided from the automatic snow-meteorological stations installed at various locations and altitudes in Upper Euphrates Basin operating in real-time. Since ground based observations can only represent a small part of the region of interest, spatially and temporally distributed snow cover data are acquired through the use of MODIS optical satellite. Automatic model parameter estimation methods, GML and SCE_UA, are utilized to calibrate the HBV model parameters with a multi-objective criteria using runoff as well as snow covered area to ensure the internal validity of the model and to generate a Pareto front. Model simulations show that the choice of study years and timing of satellite images affect the results and further suggest that more study catchments and years should be included to achieve more comprehensible conclusions. In the second part of the study, the calibrated HBV model is applied to forecast runoff with a 1-day lead time using gridded input data from numerical weather prediction models of ECMWF and MM5 for the 2004 snowmelt period. Promising results indicate the possible operational use of runoff forecasting using numerical weather prediction models in order to prevent or at least take precautions before flooding ahead of time.

O uso de clusters e grids tem beneficiado durante anos a comunidade de computação de alto desempenho (HPC). O uso deste tipo de sistemas tem permitido aos cientistas usar conjuntos de dados maiores para executar cálculos mais complexos. A computação de alto desempenho tem ajudado para obter aqueles resultados em menos tempo, mas aumentou o custo das despesas de capital nesta área da ciência. Como alguns projetos de e-science são realizados também em ambientes de rede altamente distribuídos, ou usando conjuntos de dados imensos que muitas vezes requerem computação em grade, eles são muito bons candidatos para as iniciativas de computação em nuvem. O paradigma Cloud Computing surgiu como uma solução prática com foco comercial para realizar computação científica em larga escala. A elasticidade da nuvem e o modelo pay-as-you-go apresenta uma oportunidade interessante para aplicações comumente executados em supercomputadores ou clusters. Esta tese apresenta e avalia os desafios da migração e execução da previsão numérica de tempo (NWP) numa infra-estrutura de computação em nuvem. Foi realizada a migração desta aplicação HPC e foi avaliado o desempenho em um cluster local e na nuvem utilizando diferentes tamanhos de instâncias virtuais. Analisamos as principais características da aplicação executando na nuvem. As experiências demonstram que, embora o processamento e a rede criam um fator limitante, o armazenamento dos conjuntos de dados de entrada e saída na nuvem apresentam uma opção atraente para compartilhar resultados e facilitar a implantação de um ambiente de ensaio para investigação meteorológica. Os resultados mostram que a infraestrutura de nuvem pode ser usada como uma alternativa viável de HPC para software de previsão numérica do tempo. / The usage of clusters and grids has benefited for years the High Performance Computing (HPC) community. These kind of systems have allowed scientists to use bigger datasets and to perform more intensive computations, helping them to achieve results in less time but has also increased the upfront costs associated with this area of science. As some e-Science projects are carried out also in highly distributed network environments or using immense data sets that sometimes require grid computing, they are good candidates for cloud computing initiatives. The Cloud Computing paradigm has emerged as a practical solution to perform large-scale scientific computing. The elasticity of the cloud and its pay-as-you-go model presents an attractive opportunity for applications commonly executed in clusters or supercomputers. In this context, the user does not need to buy infrastructure, the resources can be rented from a provider and used for a period of time. This thesis presents the challenges and solutions of migrating a numerical weather prediction (NWP) application to a cloud computing infrastructure. We performed the migration of this HPC application and evaluated its performance in a local cluster and the cloud using different instance sizes. We analyzed the main characteristics of the application running in the cloud. The experiments demonstrate that, although processing and networking create a limiting factor, storing input and output datasets in the cloud presents an attractive option to share results and ease the deployment of a test-bed for a weather research platform. Results show that cloud infrastructure can be used as a viable HPC alternative for numerical weather prediction software.

abstract: Land surface fluxes of energy and mass developed over heterogeneous mountain landscapes are fundamental to atmospheric processes. However, due to their high complexity and the lack of spatial observations, land surface processes and land-atmosphere interactions are not fully understood in mountain regions. This thesis investigates land surface processes and their impact on convective precipitation by conducting numerical modeling experiments at multiple scales over the North American Monsoon (NAM) region. Specifically, the following scientific questions are addressed: (1) how do land surface conditions evolve during the monsoon season, and what are their main controls?, (2) how do the diurnal cycles of surface energy fluxes vary during the monsoon season for the major ecosystems?, and (3) what are the impacts of surface soil moisture and vegetation condition on convective precipitation? Hydrologic simulation using the TIN-based Real-time Integrated Basin Simulator (tRIBS) is firstly carried out to examine the seasonal evolution of land surface conditions. Results reveal that the spatial heterogeneity of land surface temperature and soil moisture increases dramatically with the onset of monsoon, which is related to seasonal changes in topographic and vegetation controls. Similar results are found at regional basin scale using the uncoupled WRF-Hydro model. Meanwhile, the diurnal cycles of surface energy fluxes show large variation between the major ecosystems. Differences in both the peak magnitude and peak timing of plant transpiration induce mesoscale heterogeneity in land surface conditions. Lastly, this dissertation examines the upscale effect of land surface heterogeneity on atmospheric condition through fully-coupled WRF-Hydro simulations. A series of process-based experiments were conducted to identify the pathways of soil moisture-rainfall feedback mechanism over the NAM region. While modeling experiments confirm the existence of positive soil moisture/vegetation-rainfall feedback, their exact pathways are slightly different. Interactions between soil moisture, vegetation cover, and rainfall through a series of land surface and atmospheric boundary layer processes highlight the strong land-atmosphere coupling in the NAM region, and have important implications on convective rainfall prediction. Overall, this dissertation advances the study of complex land surface processes over the NAM region, and made important contributions in linking complex hydrologic, ecologic and atmospheric processes through numerical modeling. / Dissertation/Thesis / Doctoral Dissertation Civil and Environmental Engineering 2016

O uso de clusters e grids tem beneficiado durante anos a comunidade de computação de alto desempenho (HPC). O uso deste tipo de sistemas tem permitido aos cientistas usar conjuntos de dados maiores para executar cálculos mais complexos. A computação de alto desempenho tem ajudado para obter aqueles resultados em menos tempo, mas aumentou o custo das despesas de capital nesta área da ciência. Como alguns projetos de e-science são realizados também em ambientes de rede altamente distribuídos, ou usando conjuntos de dados imensos que muitas vezes requerem computação em grade, eles são muito bons candidatos para as iniciativas de computação em nuvem. O paradigma Cloud Computing surgiu como uma solução prática com foco comercial para realizar computação científica em larga escala. A elasticidade da nuvem e o modelo pay-as-you-go apresenta uma oportunidade interessante para aplicações comumente executados em supercomputadores ou clusters. Esta tese apresenta e avalia os desafios da migração e execução da previsão numérica de tempo (NWP) numa infra-estrutura de computação em nuvem. Foi realizada a migração desta aplicação HPC e foi avaliado o desempenho em um cluster local e na nuvem utilizando diferentes tamanhos de instâncias virtuais. Analisamos as principais características da aplicação executando na nuvem. As experiências demonstram que, embora o processamento e a rede criam um fator limitante, o armazenamento dos conjuntos de dados de entrada e saída na nuvem apresentam uma opção atraente para compartilhar resultados e facilitar a implantação de um ambiente de ensaio para investigação meteorológica. Os resultados mostram que a infraestrutura de nuvem pode ser usada como uma alternativa viável de HPC para software de previsão numérica do tempo. / The usage of clusters and grids has benefited for years the High Performance Computing (HPC) community. These kind of systems have allowed scientists to use bigger datasets and to perform more intensive computations, helping them to achieve results in less time but has also increased the upfront costs associated with this area of science. As some e-Science projects are carried out also in highly distributed network environments or using immense data sets that sometimes require grid computing, they are good candidates for cloud computing initiatives. The Cloud Computing paradigm has emerged as a practical solution to perform large-scale scientific computing. The elasticity of the cloud and its pay-as-you-go model presents an attractive opportunity for applications commonly executed in clusters or supercomputers. In this context, the user does not need to buy infrastructure, the resources can be rented from a provider and used for a period of time. This thesis presents the challenges and solutions of migrating a numerical weather prediction (NWP) application to a cloud computing infrastructure. We performed the migration of this HPC application and evaluated its performance in a local cluster and the cloud using different instance sizes. We analyzed the main characteristics of the application running in the cloud. The experiments demonstrate that, although processing and networking create a limiting factor, storing input and output datasets in the cloud presents an attractive option to share results and ease the deployment of a test-bed for a weather research platform. Results show that cloud infrastructure can be used as a viable HPC alternative for numerical weather prediction software.