ИСТИНА

Studying of extrema’s statistical properties, determining their genesis and mechanisms are the basis for construct a model of complex extreme hydrometeorological phenomena. Wind speed extremes in the sub-Arctic realm of the North-East Pacific region were investigated through ex-treme value analysis of wind speed obtained from atmospheric simulations of the COSMO-CLM mesoscale model, as well as using observational data. The analysis showed that the set of wind speed extremes obtained from observations is a mixture of two different subsets each neatly described by the Weibull distribution. Empirical pdfs deviate from the theoretical Weibull line consistently starting with certain large threshold values both for observational and modeling data. This means that the empirical tail diverges from the lin-earized Weibull model, indicating a different model could fit to the most extreme wind speed data well. Thus, each sample was splitted into two different ones, each of them was characterized by parameters k and A, regarded to a coefficient and free terms of linear Weibull model. Using spe-cial metaphoric terminology, these samples were labelled as “Black Swans” (BSs) and “Dragons” (Ds) [Sornette, 2009; Taleb, 2010]. The Ds are responsible for the strongest extremes. It has been shown that both reanalysis and GCM (general circulation model) data have no Ds [Kislov, Mat-veeva, 2016]. This means that such models underestimate wind speed maxima, and the important small-scale circulation processes generating the anomalies are not simulated. Based on these re-sults, mesoscale modelling investigation is needed. The main investigation tool is the regional climate model COSMO-CLM, the climate version of COSMO model [Rockel et al., 2008], applying for long-term (30-year) simulation over the cited region. A detailed hydrodynamic simulation of major meteorological parameters (1985 – 2014) has been performed for the Sea of Okhotsk and the Sakhalin Island with horizontal resolutions of ~13.2, ~6.6 and ~2.2 km (Fig. 1). This dataset was created to help in the investigation of statistical characteristics and physical mechanisms of formation of extreme weather events (primarily wind speed extremes) on small spatio-temporal scales [Platonov et al., 2017]. Given dataset used for ex-treme wind speeds statistical structure investigation. The first important feature is that extreme wind speeds in high-resolution model data (13.2 and 6.6 km) described well using the aforementioned approach, i.e. there are two well splitting samples having its own k and A Weibull distribution coefficients. It refers to ability of the model with a detailed spatial resolution to reproduce the statistical structure and the special mechanism responsible for the generation of the largest of wind extremes. Furthermore, the most extreme wind speed parameters referred to the same stations in observations and model data. Thus, model has reproduced well the fact of increased winter extremes. However, the differences in the param-eters of the cumulative distribution functions are still significant. The ratio between the modelled Dragons and Black Swans can reach up to only 10%. It is much less than 30%, which was the level established for observations. Further analysis was concerned for comparison k and A coefficients, threshold wind speed values and 0.99 quantiles between model and observational data according to BSs and Ds samples separately. A good agreement was shown for BSs, however, the Weibull distribution parameters related to the Ds sample obtained by model are significantly different. Wind speed thresholds and 0.99 quantiles reproduced by model are lower (12 – 22 m/s at most), i.e. the extremity of wind speed maximum is underestimated. Model quality of statistical parameters reproduction is also de-pended on underlying surface characteristics and sea-land spatial distribution. The spread com-monly decreased on the flat seashore stations, increased over inlands and highly indented coastline [Kislov, Platonov, 2019]. In conclusion, we can conclude that the mesoscale atmospheric model with high resolution markedly improves the modelled results for near-surface wind speed, showing the ability of the mesoscale atmospheric model in capturing the specific physical mechanism that generates wind speed extremes. However, model with a given resolution was not able to reproduce some essential parts of wind speed maximum’ statistical properties. Therefore, this gap could be covered as using higher resolution, as by areal estimation techniques and many others. Thus, future investigations will be dedicated to the specific physical mechanisms of Ds formation, scaling problems and reso-lution dependencies.