Extreme Values of Wind Speed over the Kara Sea Based on the ERA5 Dataset

Extreme values of wind speed were studied based on the highly detailed ERA5 dataset covering the central part of the Kara Sea. Cases in which the ice coverage of the cells exceeded 15% were filtered. Our study shows that the wind speed extrema obtained from station observations, as well as from modelling results in the framework of mesoscale models, can be divided into two groups according to their probability distribution laws. One group is specifically designated as black swans, with the other referred to as dragons (or dragon-kings). In this study we determined that the data of ERA5 accurately described the swans, but did not fully reproduce extrema related to the dragons;these extrema were identified only in half of ERA5 grid points. Weibull probability distribution function (PDF) parameters were identified in only a quarter of the pixels. The parameters were connected almost deterministically. This converted the Weibull function into a one-parameter dependence. It was not clear whether this uniqueness was a consequence of the features of the calculation algorithm used in ERA5, or whether it was a consequence of a relatively small area being considered, which had the same wind regime. Extremes of wind speed arise as mesoscale features and are associated with hydrodynamic features of the wind flow. If the flow was non-geostrophic and if its trajectory had a substantial curvature, then the extreme velocities were distributed according to a rule similar to the Weibull law.

Combining Monte Carlo and Ensemble Probabilities in Tropical Cyclone Forecasts near Landfall

The Monte Carlo probability(MCP)model,which has been used for official tropical cyclone(TC)warnings to the public by the United States’National Hurricane Center(NHC),can estimate the probability of wind speed in the vicinity of a TC during the forecast period.It has been successful in the operational environment for many years.However,due to its strong dependence on a given forecast track(e.g.,forecast from the NCEP Global Forecast System),the MCP model may generate a poor probability map for TCs near landfall.In this study,we proposed and tested a modified MCP method for TC forecasts near landfall.We first adjusted the MCP model by adding limits to the direction angle and motion distance to deal with the substantial change in TC moving direction and the low wind speeds during landfall.Then,we combined ensemble probability maps generated from ECMWF,United Kingdom Met Office(UKMO),and NCEP ensemble forecasts,obtained from The International Grand Global Ensemble(TIGGE),into the MCP model to configure a modified MCP model.Wind speed probability maps for the 0-120-h forecast from both the original and modified MCP models are compared.It is found that the modified MCP model can provide a better wind speed probability map during landfall,especially at wind speeds of 20-64 kt near TC landfall.The results from this study prove the benefits of combining the MCP model with ensemble forecasting in potential applications for improved TC forecasts.