Advanced Climate Simulation and Observation

Global climate changes, particularly extreme events, affect terrestrial carbon, water, and energy exchanges between the atmosphere, biosphere, and lithosphere, thus controlling freshwater availability, floods, and droughts. Therefore, it is urgent and necessary to develop advanced climate simulation...

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Bibliographic Details
Other Authors: Hu, Zengyun (Editor), Tang, Xuguang (Editor), Xin, Qinchuan (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
Subjects:
AQI
HDI
WRF
n/a
Online Access:DOAB: download the publication
DOAB: description of the publication
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520 |a Global climate changes, particularly extreme events, affect terrestrial carbon, water, and energy exchanges between the atmosphere, biosphere, and lithosphere, thus controlling freshwater availability, floods, and droughts. Therefore, it is urgent and necessary to develop advanced climate simulation and observation approaches and models related to extreme climate events. Advanced climate simulation and observation can improve the accurate prediction of climate change and long-term trends, which can mitigate climate events' impacts on human society. Under these conditions, this reprint aims to introduce advanced climate simulation and observation approaches to various practical studies related to climate variations, including the global climate models (GCMs) and regional climate models (RCMs), mitigation studies of high-impact climate events, predictions of climate variations, and some new artificial intelligence. Twenty-two papers have been collected in this reprint, with eight original research articles reporting on climate change and six papers reporting on climate change's impact on society and the economy. Meanwhile, three papers reported climate change's impact on agriculture, and climate change's impact on human health was studied in five articles. 
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650 7 |a Research & information: general  |2 bicssc 
650 7 |a Meteorology & climatology  |2 bicssc 
653 |a hydrological modeling 
653 |a gridded datasets 
653 |a sensitivity analysis 
653 |a water balance 
653 |a snowmelt 
653 |a SWAT 
653 |a Upper Vakhsh River Basin 
653 |a economic loss prediction 
653 |a machine learning 
653 |a input-output model 
653 |a flooding 
653 |a regional climate model 
653 |a RegCM4.5 
653 |a western Tianshan Mountains 
653 |a parameterization scheme 
653 |a air quality satisfaction 
653 |a quality of life 
653 |a binomial logistic regression 
653 |a health utility value 
653 |a experienced utility 
653 |a elevated [CO2] 
653 |a warming 
653 |a SPAD 
653 |a leaf nitrogen monitoring 
653 |a nitrogen management 
653 |a Issyk-Kul 
653 |a accumulated temperature 
653 |a yield per unit area of beans 
653 |a climate change 
653 |a panel spatial error model 
653 |a air pollution 
653 |a respiratory disease 
653 |a generalized additive model 
653 |a scenario analysis 
653 |a assessment of economic losses 
653 |a arid climate 
653 |a geothermal energy 
653 |a underground temperature 
653 |a greenhouse 
653 |a heat exchanger 
653 |a agricultural air pollution 
653 |a labor migration 
653 |a mediation effect 
653 |a income effect 
653 |a economy of scale 
653 |a collective effect 
653 |a haze pollution 
653 |a scale effect 
653 |a special spillover effect 
653 |a urban population agglomeration 
653 |a AQI 
653 |a visual analysis 
653 |a heat map 
653 |a ARIMA model 
653 |a neural network model 
653 |a pulmonary tuberculosis 
653 |a penalized distributed lag non-linear model 
653 |a meteorological factors 
653 |a apparent temperature 
653 |a cumulative risk 
653 |a HDI 
653 |a decoupling index 
653 |a carbon emission performance 
653 |a LMDI 
653 |a 10 m wind speed 
653 |a cumulus parameterization schemes 
653 |a sensitivity of physical processes 
653 |a WRF 
653 |a mainland China 
653 |a environmental regulation 
653 |a green innovation efficiency 
653 |a SBM of super-efficiency 
653 |a system GMM estimation 
653 |a model evaluation 
653 |a rainfall simulation 
653 |a interannual variation 
653 |a IAP-AGCM 
653 |a Thailand 
653 |a China 
653 |a environmental Kuznets curve 
653 |a geographically weighted regression 
653 |a haze 
653 |a spatial heterogeneity 
653 |a air pollutants 
653 |a sustained exposure to pollution 
653 |a respiratory and cardiovascular diseases 
653 |a CiteSpace 
653 |a co-occurrence keywords 
653 |a burst words 
653 |a mountain-type zoonotic visceral leishmaniasis 
653 |a climate variables 
653 |a environmental variables 
653 |a ecological niche model 
653 |a transmission risk prediction 
653 |a drought 
653 |a cropland 
653 |a CMIP6 
653 |a exposure 
653 |a scPDSI 
653 |a weather radar nowcasting 
653 |a generative adversarial network (GAN) 
653 |a Temporal and Spatial GAN (TSGAN) 
653 |a heavy precipitation 
653 |a n/a 
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856 4 0 |a www.oapen.org  |u https://directory.doabooks.org/handle/20.500.12854/128649  |7 0  |z DOAB: description of the publication