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＜電子ブック＞
Thermally-driven Mesoscale Flows and their Interaction with Atmospheric Boundary Layer Turbulence / by Jon Ander Arrillaga Mitxelena
(Springer Theses, Recognizing Outstanding Ph.D. Research. ISSN:21905061)

版	1st ed. 2020.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2020
本文言語	英語
大きさ	XXVI, 152 p. 58 illus., 55 illus. in color : online resource
著者標目	*Arrillaga Mitxelena, Jon Ander author SpringerLink (Online service)
件　名	LCSH:Mathematics -- Data processing  全ての件名で検索 LCSH:Earth sciences LCSH:Environmental monitoring FREE:Computational Mathematics and Numerical Analysis FREE:Earth Sciences FREE:Environmental Monitoring
一般注記	Introduction and Objectives -- Data and Methodology -- Characterisation of Sea-Breeze Events Over Complex Terrain -- Impacts of Sea-Breeze Fronts on Local Turbulence and Scalar Transport -- Modelling Sea Breezes and their Interaction with Atmospheric Turbulence -- Weak and Intense Katabatics: Influence on Turbulence and Co2 Transport -- Conclusions and Outlook This book presents developments of novel techniques and applies them in order to understand the interactions between thermally driven mesoscale flows (sea and mountain breezes) and the turbulent exchange within the atmospheric boundary layer. These interactions are not accurately reproduced in the meteorological models currently employed for weather forecasting. Consequently, important variables such as air temperature and wind speed are misrepresented. Also, the concentrations of relevant greenhouse gases such as CO2 are considerably affected by these interactions. By applying a systematic algorithm based on objective criteria (presented here), the thesis explores complete observational databases spanning up to 10 years. Further, it presents statistically significant and robust results on the topic, which has only been studied in a handful of cases in the extant literature. Lastly, by applying the algorithm directly to the outputs of the meteorological model, the thesis helpsreaders understand the processes discussed and reveals the biases in such models HTTP:URL=https://doi.org/10.1007/978-3-030-48579-5

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