Mesoscale high-resolution modeling of extreme wind speeds over western water areas of the Russian Arctic

International Conference and Early Career Scientists School on Environmental Observations, Modelling and Information Systems

生态环境科学;计算机科学

Platonov, Vladimir S.^1 ; Kislov, Alexander V.^1

Lomonosov Moscow State University, Faculty of Geography, Department of Meteorology and Climatology, Leninskie Gory MGU, 1, GSP-1, Moscow

119991, Russia^1

关键词: Atmospheric modelling;    Extreme weather events;    Extreme wind speed;    Global climate model;    High-resolution models;    Non-hydrostatic;    Observational data;    Wind velocity distributions;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/48/1/012007/pdf
DOI  :  10.1088/1755-1315/48/1/012007

来源: IOP

【 摘 要 】

A statistical analysis of extreme weather events over coastal areas of the Russian Arctic based on observational data has revealed many interesting features of wind velocity distributions. It has been shown that the extremes contain data belonging to two different statistical populations. Each of them is reliably described by a Weibull distribution. According to the standard terminology, these sets of extremes are named 'black swans' and 'dragons'. The 'dragons' are responsible for most extremes, surpassing the 'black swans' by 10 - 30 %. Since the data of the global climate model INM-CM4 do not contain 'dragons', the wind speed extremes are investigated on the mesoscale using the COSMO-CLM model. The modelling results reveal no differences between the 'swans' and 'dragons' situations. It could be associated with the poor sample data used. However, according to many case studies and modeling results we assume that it is caused by a rare superposition of large-scale synoptic factors and many local meso- and microscale factors (surface, coastline configuration, etc.). Further studies of extreme wind speeds in the Arctic, such as 'black swans' and 'dragons', are necessary to focus on non-hydrostatic high-resolution atmospheric modelling using downscaling techniques.