Severe convective windstorms and tornadoes regularly hit the territory of Russia causing substantial damage and fatalities. An analysis of the climatology and formation environments of these events is essential for risk assessments, forecast improvements and identifying of links with the observed climate change. In this paper, we present an analysis of severe convective windstorms, i.e., squalls and tornadoes reported between 1984 and 2020 in the Perm region (northeast of European Russia), where a local maximum in the frequency of such events was previously found. The analysed database consists of 165 events and includes 100 squalls (convective windstorms), 59 tornadoes, and six cases with both tornadoes and squalls. We used various information to compile the database including weather station reports, damage surveys, media reports, previously presented databases, and satellite images for windthrow. We found that the satellite images of damaged forests are the main data source on tornadoes, but their role is substantially lower for windstorm events due to the larger spatial and temporal scale of such events. Synoptic-scale environments and associated values of convective indices were determined for each event with a known date and time. Similarities and differences for the formation conditions of tornadoes and windstorms were revealed. Both squalls and tornadoes occur mostly on rapidly moving cold fronts or on waving quasi-stationary fronts, associated with low-pressure systems. Analyses of 72-h air parcel backward trajectories shows that the Caspian and Aral Seas are important sources of near-surface moisture for the formation of both squalls and tornadoes. Most of these events are formed within high CAPE and high shear environments, but tornadic storms are generally characterised by a higher wind shear and helicity. We also differentiated convective storms that caused forest damage and those did not. We found the composite parameter WMAXSHEAR is the best discriminator between these two groups. In general, storm events causing windthrow mainly occur under conditions more favourable for deep well-organised convection. Thus, forest damage can be considered as an indicator of the storm severity in the Perm region and in adjacent regions with forest-covered area exceeding 50%.

ПРОГНОЗ СИЛЬНЫХ СНЕГОПАДОВ НА УРАЛЕ С ИСПОЛЬЗОВАНИЕМ ЧИСЛЕННЫХ МОДЕЛЕЙ АТМОСФЕРЫ

Heavy snowfall events in the Ural region have drawn significant attention due to their substantial frequency, the region’s relatively high population density and its developed network of roads and power lines. This study summarizes the main characteristics of the hazardous heavy snowfall (HHS) events (≥20 mm 12 h⁻¹) that have occurred in the Ural region between 1981 and 2025, as well as in related synoptic-scale environments, for the first time. The dataset consists of 116 HHS reports, with 12-hourly snowfall intensities ranging from 20 mm to 47.6 mm. The main characteristics of these events (snowfall amount, spatial distribution, inter-annual and seasonal variability and trends, associated weather phenomena, and related damage) are examined based on the data from weather stations, the ERA5 reanalysis, scientific literature, and media reports. While there is no statistically significant trend in HHS events, the frequency of the most damaging late spring and early autumn snowfalls has decreased. Using 72 h backward trajectories according to the NOAA HYSPLIT model and the ERA5 reanalysis, we classified the HHS events into five types according to air mass origin, and performed a composite analysis for each type. The main finding is that 46% of HHS reports are related to cyclones forming over the Caspian and Aral seas, resulting in a higher frequency of HHS events to the east of the Ural Mountains compared to the western part of the region.

The type and nature of precipitation in the catchment area of the Votkinsk reservoir are considered with taking into account its hydrological zoning. Intra-annual frequency, duration and intensity of precipitation are shown. It is determined that 321 days are observed with precipitation per year on average. Daily amount does not exceed 1 mm. In the catchment area, widespread and rain precipitations are observed with a frequency of 51 and 53% respectively. The daily duration of precipitation depending on its type was obtained, which equals 4–6 h for rain and mixed precipitations and 8–10 h for solid precipitation. High precipitation intensity is observed in the northern part of the catchment area, where the terrain contributes to the development of cloudiness and an increase in precipitation intensity.

This paper presents an assessment of short-term numerical forecasts of precipitation in the cold period of the Urals. The use of global (GEM and GFS) and mesoscale (WRF) models in forecasting of heavy snowfalls is considered. The reliability levels of the 15-h and 27-h forecasts with the GEM, GFS, and WRF models are approximately equal. The 39-h forecasts with the WRF model are least accurate. The dependences of the quality of model forecasting on synoptic-scale environments, seasonal patterns, geographical location, and topography are obtained. The heavy snowfall events formed by a warm front, a cold front, and in the northern part of a cyclone are predicted with satisfactory accuracy. The least successful numerical forecasts are obtained for non-frontal precipitation in the warm sector of a cyclone. All three models tend to overestimate the precipitation amount during the cold period: the number of false alarms exceeds the number of missed events. The numerical forecasts of precipitation during the cold period have higher reliability levels for the territory of Western Urals than for the Eastern Urals region.