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Books on the topic 'High-resolution simulation'

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Author: Grafiati
Published: 11 March 2023

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1

Bahl, Rajendar. Computer model of a high-resolution imaging sonar. Monterey, Calif: Naval Postgraduate School, 1990.

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2

D, Bates Paul, and Lane Stuart N, eds. High resolution flow modelling in hydrology and geomorphology. Chichester: John Wiley, 2000.

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3

1956-, Hamilton Kevin, and Ohfuchi Wataru 1963-, eds. High resolution numerical modelling of the atmosphere and ocean. New York: Springer, 2008.

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4

Kevin, Hamilton, and Wataru Ohfuchi 1963-, eds. High resolution numerical modelling in the atmosphere and ocean. New York: Springer, 2008.

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5

Kevin, Hamilton, and Wataru Ohfuchi 1963-, eds. High resolution numerical modelling in the atmosphere and ocean. New York: Springer, 2008.

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6

1956-, Hamilton Kevin, and Ohfuchi Wataru 1963-, eds. High resolution numerical modelling of the atmosphere and ocean. New York: Springer, 2008.

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7

Warhola, Paul J. An analysis of alternative methods to conduct high-resolution activities in a variable-resolution simulation. Monterey, Calif: Naval Postgraduate School, 1997.

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8

United States. National Aeronautics and Space Administration., ed. Technical development to improve satellite soundings over radiatively complex terrain: Final report to National Aeronautics and Space Administration : for the period of Sept. 1, 1982 - Nov. 30, 1984. Madison, Wis: Space Science and Engineering Center at the University of Wisconsin-Madison, 1985.

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9

Driels, Morris R. Prototype line of sight and target acquisition software for high resolution databases. Monterey, Calif: Naval Postgraduate School, 1995.

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10

Alexander, M. J. The gravity wave response above deep convection in a squall line simulation. [Washington, DC: National Aeronautics and Space Administration, 1995.

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11

Alexander, M. J. The gravity wave response above deep convection in a squall line simulation. [Washington, DC: National Aeronautics and Space Administration, 1995.

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12

United States. National Aeronautics and Space Administration., ed. Studies of planetary scale waves and instabilities in support of geophysical fluid flow cell experiment on USML-2: Final technical report. [Washington, D.C: National Aeronautics and Space Administration, 1995.

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13

H, Bigelow J., McEver Jimmie, United States. Dept. of Defense. Office of the Secretary of Defense., and National Defense Research Institute (U.S.), eds. Effects of terrain, maneuver tactics, and C4ISR on the effectiveness of long-range precision fires: A stochastic multiresolution model (PEM) calibrated to high-resolution simulation. Santa Monica, CA: Rand, 2000.

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14

E, Taylor Gregory, Zack John W, and United States. National Aeronautics and Space Administration., eds. Forecast skill of a high-resolution real-time mesoscale model designed for weather support of operations at Kennedy Space Center and Cape Canaveral Air Station. [Washington, DC: National Aeronautics and Space Administration, 1994.

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15

E, Taylor Gregory, Zack John W, and United States. National Aeronautics and Space Administration., eds. Forecast skill of a high-resolution real-time mesoscale model designed for weather support of operations at Kennedy Space Center and Cape Canaveral Air Station. [Washington, DC: National Aeronautics and Space Administration, 1994.

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16

E, Taylor Gregory, Zack John W, and United States. National Aeronautics and Space Administration., eds. Forecast skill of a high-resolution real-time mesoscale model designed for weather support of operations at Kennedy Space Center and Cape Canaveral Air Station. [Washington, DC: National Aeronautics and Space Administration, 1994.

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17

Fujii, Kozo. Use of high-resolution upwind scheme for vortical flow simulations. Tokyo: National Aerospace Laboratory, 1988.

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18

W, Zack John, Karyampudi V. Mohan, and United States. National Aeronautics and Space Administration., eds. Development of high resolution simulations of the atmospheric environment using the MASS model. Hampton, Va: MESO Inc., 1990.

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19

Turok, Neil. Comment on "High resolution simulations of cosmic strings I: network evolution" by D. Bennett and F. Bouchet. Batavia, IL: Fermi National Accelerator Laboratory, 1990.

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20

Service, Canadian Forest, and Rocky Mountain Research Station (Fort Collins, Colo.), eds. High resolution interpolation of climate scenarios for the conterminous USA and Alaska derived from general circulation model simulations. Fort Collins, CO: U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 2011.

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21

Cheng-I, Yang, Naval Surface Warfare Center (U.S.). Carderock Division, and United States. National Aeronautics and Space Administration., eds. Application of an upwind high resolution finite-differencing scheme and multigrid method in steady-state incompressible flow simulations. Bethesda, MD: Naval Surface Warfare Center, Carderock Division, 1997.

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22

Mazo, Aleksandr, and Konstantin Potashev. The superelements. Modeling of oil fields development. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1043236.

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This monograph presents the basics of super-element modeling method of two-phase fluid flows occurring during the development of oil reservoir. The simulation is performed in two stages to reduce the spatial and temporal scales of the studied processes. In the first stage of modeling of development of oil deposits built long-term (for decades) the model of the global dynamics of the flooding on the super-element computational grid with a step equal to the average distance between wells (200-500 m). Local filtration flow, caused by the action of geological and technical methods of stimulation, are modeled in the second stage using a special mathematical models using computational grids with high resolution detail for the space of from 0.1 to 10 m and time — from 102 to 105 C. The results of application of the presented models to the solution of practical tasks of development of oil reservoir. Special attention is paid to the issue of value transfer in filtration-capacitive properties of the reservoir, with a detailed grid of the geological model on the larger grid reservoir models. Designed for professionals in the field of mathematical and numerical modeling of fluid flows occurring during the development of oil fields and using traditional commercial software packages, as well as developing their own software. May be of interest to undergraduate and graduate students studying in areas such as "Mechanics and mathematical modeling", "Applied mathematics", "Oil and gas".
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23

National Aeronautics and Space Administration (NASA) Staff. High-Resolution Capability for Large-Eddy Simulation of Jet Flows. Independently Published, 2019.

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24

High resolution numerical modelling in the atmosphere and ocean. New York: Springer, 2008.

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25

An Analysis of Alternative Methods to Conduct High-Resolution Activities in a Variable-Resolution Simulation. Storming Media, 1997.

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26

(Editor), Kevin Hamilton, and Wataru Ohfuchi (Editor), eds. High Resolution Numerical Modelling of the Atmosphere and Ocean. Springer, 2007.

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27

DISC-O-TIC: A Discrete-Time Analytical Meta-Model for use in Combat Systems Studies that Utilize High Resolution Simulation Models. Storming Media, 2000.

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28

DISC-O-TIC: A Discrete-Time Analysis Meta-Model for Use in Combat Systems Studies that Utilize High-Resolution Simulation Models. Storming Media, 2000.

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29

Forecast skill of a high-resolution real-time mesoscale model designed for weather support of operations at Kennedy Space Center and Cape Canaveral Air Station. [Washington, DC: National Aeronautics and Space Administration, 1994.

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30

Succi, Sauro. Lattice Boltzmann for Turbulence Modeling. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199592357.003.0024.

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This chapter introduces the main ideas behind the application of LBE methods to the problem of turbulence modeling, namely the simulation of flows which contain scales of motion too small to be resolved on present-day and foreseeable future computers. Many real-life flows of practical interest exhibit Reynolds numbers far too high to be directly simulated in full resolution on present-day computers and arguably for many years to come. This raises the challenge of predicting the behavior of highly turbulent flows without directly simulating all scales of motion which take part to turbulence dynamics, but only those that fall within the computer resolution at hand.
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31

Kolding, James C. Use of high resolution simulations for training development. 1988.

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32

Cook, Kerry H. Climate Change Scenarios and African Climate Change. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.545.

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Accurate projections of climate change under increasing atmospheric greenhouse gas levels are needed to evaluate the environmental cost of anthropogenic emissions, and to guide mitigation efforts. These projections are nowhere more important than Africa, with its high dependence on rain-fed agriculture and, in many regions, limited resources for adaptation. Climate models provide our best method for climate prediction but there are uncertainties in projections, especially on regional space scale. In Africa, limitations of observational networks add to this uncertainty since a crucial step in improving model projections is comparisons with observations. Exceeding uncertainties associated with climate model simulation are uncertainties due to projections of future emissions of CO2 and other greenhouse gases. Humanity’s choices in emissions pathways will have profound effects on climate, especially after the mid-century.The African Sahel is a transition zone characterized by strong meridional precipitation and temperature gradients. Over West Africa, the Sahel marks the northernmost extent of the West African monsoon system. The region’s climate is known to be sensitive to sea surface temperatures, both regional and global, as well as to land surface conditions. Increasing atmospheric greenhouse gases are already causing amplified warming over the Sahara Desert and, consequently, increased rainfall in parts of the Sahel. Climate model projections indicate that much of this increased rainfall will be delivered in the form of more intense storm systems.The complicated and highly regional precipitation regimes of East Africa present a challenge for climate modeling. Within roughly 5º of latitude of the equator, rainfall is delivered in two seasons—the long rains in the spring, and the short rains in the fall. Regional climate model projections suggest that the long rains will weaken under greenhouse gas forcing, and the short rains season will extend farther into the winter months. Observations indicate that the long rains are already weakening.Changes in seasonal rainfall over parts of subtropical southern Africa are observed, with repercussions and challenges for agriculture and water availability. Some elements of these observed changes are captured in model simulations of greenhouse gas-induced climate change, especially an early demise of the rainy season. The projected changes are quite regional, however, and more high-resolution study is needed. In addition, there has been very limited study of climate change in the Congo Basin and across northern Africa. Continued efforts to understand and predict climate using higher-resolution simulation must be sustained to better understand observed and projected changes in the physical processes that support African precipitation systems as well as the teleconnections that communicate remote forcings into the continent.
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33

Gao, Yanhong, and Deliang Chen. Modeling of Regional Climate over the Tibetan Plateau. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.591.

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The modeling of climate over the Tibetan Plateau (TP) started with the introduction of Global Climate Models (GCMs) in the 1950s. Since then, GCMs have been developed to simulate atmospheric dynamics and eventually the climate system. As the highest and widest international plateau, the strong orographic forcing caused by the TP and its impact on general circulation rather than regional climate was initially the focus. Later, with growing awareness of the incapability of GCMs to depict regional or local-scale atmospheric processes over the heterogeneous ground, coupled with the importance of this information for local decision-making, regional climate models (RCMs) were established in the 1970s. Dynamic and thermodynamic influences of the TP on the East and South Asia summer monsoon have since been widely investigated by model. Besides the heterogeneity in topography, impacts of land cover heterogeneity and change on regional climate were widely modeled through sensitivity experiments.In recent decades, the TP has experienced a greater warming than the global average and those for similar latitudes. GCMs project a global pattern where the wet gets wetter and the dry gets drier. The climate regime over the TP covers the extreme arid regions from the northwest to the semi-humid region in the southeast. The increased warming over the TP compared to the global average raises a number of questions. What are the regional dryness/wetness changes over the TP? What is the mechanism of the responses of regional changes to global warming? To answer these questions, several dynamical downscaling models (DDMs) using RCMs focusing on the TP have recently been conducted and high-resolution data sets generated. All DDM studies demonstrated that this process-based approach, despite its limitations, can improve understandings of the processes that lead to precipitation on the TP. Observation and global land data assimilation systems both present more wetting in the northwestern arid/semi-arid regions than the southeastern humid/semi-humid regions. The DDM was found to better capture the observed elevation dependent warming over the TP. In addition, the long-term high-resolution climate simulation was found to better capture the spatial pattern of precipitation and P-E (precipitation minus evapotranspiration) changes than the best available global reanalysis. This facilitates new and substantial findings regarding the role of dynamical, thermodynamics, and transient eddies in P-E changes reflected in observed changes in major river basins fed by runoff from the TP. The DDM was found to add value regarding snowfall retrieval, precipitation frequency, and orographic precipitation.Although these advantages in the DDM over the TP are evidenced, there are unavoidable facts to be aware of. Firstly, there are still many discrepancies that exist in the up-to-date models. Any uncertainty in the model’s physics or in the land information from remote sensing and the forcing could result in uncertainties in simulation results. Secondly, the question remains of what is the appropriate resolution for resolving the TP’s heterogeneity. Thirdly, it is a challenge to include human activities in the climate models, although this is deemed necessary for future earth science. All-embracing further efforts are expected to improve regional climate models over the TP.
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34

Busuioc, Aristita, and Alexandru Dumitrescu. Empirical-Statistical Downscaling: Nonlinear Statistical Downscaling. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.770.

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This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Climate Science. Please check back later for the full article.The concept of statistical downscaling or empirical-statistical downscaling became a distinct and important scientific approach in climate science in recent decades, when the climate change issue and assessment of climate change impact on various social and natural systems have become international challenges. Global climate models are the best tools for estimating future climate conditions. Even if improvements can be made in state-of-the art global climate models, in terms of spatial resolution and their performance in simulation of climate characteristics, they are still skillful only in reproducing large-scale feature of climate variability, such as global mean temperature or various circulation patterns (e.g., the North Atlantic Oscillation). However, these models are not able to provide reliable information on local climate characteristics (mean temperature, total precipitation), especially on extreme weather and climate events. The main reason for this failure is the influence of local geographical features on the local climate, as well as other factors related to surrounding large-scale conditions, the influence of which cannot be correctly taken into consideration by the current dynamical global models.Impact models, such as hydrological and crop models, need high resolution information on various climate parameters on the scale of a river basin or a farm, scales that are not available from the usual global climate models. Downscaling techniques produce regional climate information on finer scale, from global climate change scenarios, based on the assumption that there is a systematic link between the large-scale and local climate. Two types of downscaling approaches are known: a) dynamical downscaling is based on regional climate models nested in a global climate model; and b) statistical downscaling is based on developing statistical relationships between large-scale atmospheric variables (predictors), available from global climate models, and observed local-scale variables of interest (predictands).Various types of empirical-statistical downscaling approaches can be placed approximately in linear and nonlinear groupings. The empirical-statistical downscaling techniques focus more on details related to the nonlinear models—their validation, strengths, and weaknesses—in comparison to linear models or the mixed models combining the linear and nonlinear approaches. Stochastic models can be applied to daily and sub-daily precipitation in Romania, with a comparison to dynamical downscaling. Conditional stochastic models are generally specific for daily or sub-daily precipitation as predictand.A complex validation of the nonlinear statistical downscaling models, selection of the large-scale predictors, model ability to reproduce historical trends, extreme events, and the uncertainty related to future downscaled changes are important issues. A better estimation of the uncertainty related to downscaled climate change projections can be achieved by using ensembles of more global climate models as drivers, including their ability to simulate the input in downscaling models. Comparison between future statistical downscaled climate signals and those derived from dynamical downscaling driven by the same global model, including a complex validation of the regional climate models, gives a measure of the reliability of downscaled regional climate changes.
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