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Books on the topic 'Downscaling'

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Author: Grafiati

Published: 4 June 2021

Last updated: 7 July 2024

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1

Inger, Hanssen-Bauer, and Chen Deliang, eds. Empirical-statistical downscaling. New Jersey: World Scientific Pub Co Inc., 2008.

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2

Benestad, Rasmus E. Empirical-statistical downscaling. New Jersey: World Scientific Pub Co Inc., 2008.

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3

Kathy, Babbitt, ed. Downscaling: Simplify and enrich your lifestyle. Chicago: Moody Press, 1993.

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4

Bierkens, Marc F. P., 1965-, Finke Peter A, and Willigen P. de, eds. Upscaling and downscaling methods for environmental research. Dordrecht: Kluwer Academic Publishers, 2000.

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5

Dehn, Martin. Szenarien der klimatischen Auslösung alpiner Hangrutschungen: Simulation durch Downscaling allgemeiner Zirkulationsmodelle der Atmosphäre. Sankt Augustin: In Kommission bei Asgard-Verlag, 1999.

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6

Das, Someshwar. Simulation of seasonal monsoon rainfall over the SAARC region by dynamical downscaling using WRF model. Dhaka: SAARC Meteorological Research Centre, 2012.

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7

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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8

Mearns, Linda, Katharine Hayhoe, and Rao Kotamarthi. Downscaling Techniques for High-Resolution Climate Projections. University of Cambridge ESOL Examinations, 2021.

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9

Singh, Vijay P., and Taesam Lee. Statistical Downscaling for Hydrological and Environmental Applications. Taylor & Francis Group, 2018.

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10

Lee, Taesam, and Vijay P. Singh. Statistical Downscaling for Hydrological and Environmental Applications. CRC Press, 2018. http://dx.doi.org/10.1201/9780429459580.

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11

Statistical Downscaling for Hydrological and Environmental Applications. Taylor & Francis Group, 2018.

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12

Singh, Vijay P., and Taesam Lee. Statistical Downscaling for Hydrological and Environmental Applications. Taylor & Francis Group, 2018.

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13

Singh, Vijay P., and Taesam Lee. Statistical Downscaling for Hydrological and Environmental Applications. Taylor & Francis Group, 2018.

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14

Singh, Vijay P., and Taesam Lee. Statistical Downscaling for Hydrological and Environmental Applications. Taylor & Francis Group, 2018.

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15

Hentz, Sébastien. Nano Electro Mechanical Systems: Downscaling Resonant Sensors. Wiley & Sons, Incorporated, John, 2016.

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16

Maraun, Douglas, and Martin Widmann. Statistical Downscaling and Bias Correction for Climate Research. Cambridge University Press, 2018.

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17

Maraun, Douglas, and Martin Widmann. Statistical Downscaling and Bias Correction for Climate Research. Cambridge University Press, 2017.

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18

Statistical Downscaling and Bias Correction for Climate Research. Cambridge University Press, 2018.

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19

Hey, Franz Georg. Micro Newton Thruster Development: Direct Thrust Measurements and Thruster Downscaling. Springer Vieweg, 2018.

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20

Hey, Franz Georg. Micro Newton Thruster Development: Direct Thrust Measurements and Thruster Downscaling. Springer Vieweg, 2018.

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21

Durdy, Katrina Ann. Enjoy a Simpler Life: An A-Z Guide for Downscaling to the Essential. Global Incorporated Worldwide Marketing & Adv, 1998.

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22

Bierkens, Marc F. P., Peter A. Finke, and P. De Willigen. Upscaling and Downscaling Methods for Environmental Research (Developments in Plant and Soil Sciences). Springer, 2000.

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23

Jacobs, Jennifer, Katharine Hayhoe, Don Wuebbles, Rao Kotamarthi, and Jennifer Jurado. Downscaling Techniques for High-Resolution Climate Projections: From Global Change to Local Impacts. University of Cambridge ESOL Examinations, 2020.

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24

Jacobs, Jennifer, Linda Mearns, Katharine Hayhoe, Don Wuebbles, and Rao Kotamarthi. Downscaling Techniques for High-Resolution Climate Projections: From Global Change to Local Impacts. Cambridge University Press, 2020.

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25

Simulation of the Indian summer monsoon using ISRO vegetation fraction and downscaling by a regional climate model. New Delhi: NCMRWF-IIT Delhi, 2007.

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26

Low Choy, Samantha, Justine Murray, Allan James, and Kerrie Mengersen. Combining monitoring data and computer model output in assessing environmental exposure. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.18.

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This article discusses an approach that combines monitoring data and computer model outputs for environmental exposure assessment. It describes the application of Bayesian data fusion methods using spatial Gaussian process models in studies of weekly wet deposition data for 2001 from 120 sites monitored by the US National Atmospheric Deposition Program (NADP) in the eastern United States. The article first provides an overview of environmental computer models, with a focus on the CMAQ (Community Multi-Scale Air Quality) Eta forecast model, before considering some algorithmic and pseudo-statistical approaches in weather prediction. It then reviews current state of the art fusion methods for environmental data analysis and introduces a non-dynamic downscaling approach. The static version of the dynamic spatial model is used to analyse the NADP weekly wet deposition data.

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27

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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