The quality of simulating freezing precipitation by the INM-CM5 climate model is assessed for Russia. The modeling data were verified using main 3-hour observations and visual observations of atmospheric events during 1979–2014. The INM-CM5 climate model was used to obtain the spatial distribution of the average annual number of days with freezing precipitation for this period. Forecasts of changes in the frequency of freezing precipitation in Russia were prepared for 2015–2100 and 2071–2100 based on three possible socioeconomic climate scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. The greatest changes in the frequency of freezing precipitation are expected under the SSP5-8.5 scenario. It is predicted that the frequency of freezing precipitation in Russia in the northern European part of Russia, southern and central Western Siberia, and the eastern part of the Chukotka Autonomous Okrug will increase by more than five times by the end of the 21st century. A decrease in the frequency of freezing precipitation is predicted in the areas of the islands and coasts of the Arctic Ocean, as well as the Bering and Okhotsk seas

The paper presents an analysis of water temperature based on archived instrumental observations in Aniva Bay from 1949 to 1994, satellite data from 1981 to 2025, and direct measurements made in 2023. According to the weather station from 1969 to 1925, the minimum trend value for air temperature is positive and is 1.2 °C for the entire period and is observed in December. For August, the increase in air temperature is about 1.6 °C, and for March about 3.4 °C. According to satellite data, a positive trend is observed for water temperature, with a value of about 1.6 °C over the entire period. In the interannual variability of the monthly average water temperature, the greatest positive trends are observed in July-August – about 2.1–2.4 °C over the entire period. According to in situ measurements, until 1994, maximum temperatures were observed in August and did not exceed 20.1 °C. According to ship measurements, a temperature of 21.46 °C was recorded in 2023, according to satellite data, the maximum 22.55 °C was observed in July 2021. On average, the trends in almost the entire bay during the study period were positive and amounted to 0.5–1 °C. The maximum warming is observed in Salmon Bay and in the northern part of Aniva Bay

The paper presents the results of testing a surface wind gust nowcasting system conducted in May–September 2024 at the Hydrometcentre of Russia using the machine learning Random Forest algorithm applied to the output products of the pySTEPS statistical nowcasting model and the COSMO-Ru2.2 numerical weather prediction system. The significance of observations from automatic weather stations in the Central Federal District as control data for wind gust nowcasting is assessed. Some systematic features of numerical gust forecasts during the testing period are revealed. The problems of synchronizing data from various sources are discussed, the weather conditions with significant gusts over a large area are analyzed in detail, and the quality assessment is provided using the FSS metric. In the developed version of the nowcasting system, on the basis of accumulated information and according to the FSS skill score, the useful forecast horizon for wind gusts is limited to 30 minutes.

A new specialized SnowDraw snow cover model of intermediate complexity aimed at climate forecasting of glacier dynamics and river runoff in mountainous regions was tested for the Elbrus mountain glacier. The model verification versus direct observation data from the mountain glacier demonstrated a high-quality reproduction of the seasonal dynamics and water content of the snow cover. A comparison of results of the presented model with other available models of different complexity (NoahMP, SNOWPACK) demonstrated that it is a computationally efficient and high-quality tool for forecasting snowpack dynamics and glaciation in the mountains. Using the presented model as the IGRICE parameterization module for mountain glaciation allowed obtaining estimates of a long-term snow cover distribution and a snow component of runoff for the Elbrus glaciation.

An analysis of changes in soil freezing parameters (freezing depth and freezing time) in the Arkhangelsk region for a period of 1961–2020, as well as for 30-years intervals within this period (1961–1990, 1971–2000, 1981–2010, 1991–2020), was conducted. A decrease in the freezing depth was revealed during all periods, except for 1961–1990. The linear trend coefficients for the freezing depth were calculated. A trend towards a shift to later dates was found in the dates of the onset of the first and stable freezing.

Based on the long series of data (for 1958–2024), a statistical structure of the field of available moisture content in the meter layer of soil under grain crops for the European part of Russia was estimated, and an impact of observed climate change on the soil moisture regime was assessed. It is shown that in the 21st century, soil moisture content is higher than in 1958–1999, and currently observed climate changes are mainly positive for the agricultural industry in Russia. Seasonal variations in the available moisture content were built according to modern data, which can be applied in operational agrometeorological practice. The main reasons for the changes were analyzed: an increase in the frequency of warm winters and the cyclical nature of the climate system.

A study was conducted to assess the accuracy of wind wave height forecasts in the Tsemess (Novorossiysk) Bay using the spectral model WAVEWATCH III forced by GFS winds (0.25°). Comparison of model simulations with in situ measurements obtained at the Sheskharis pier during December 2023 – August 2025 showed that the system provides satisfactory forecast quality of wave heights for different lead times up to 72 hours. The root-mean-square error of significant wave height forecasts ranges from 0.17 to 0.22 m, the correlation coefficient is 0.85–0.9, and the systematic bias is negative (–0.09…–0.13 m). The smallest errors were observed for a lead time of 15 hours. Seasonal variability analysis revealed higher forecast accuracy in the autumn–winter period and lower accuracy in the spring–summer season due to the influence of local wind effects. It was found that the model reproduces wave characteristics more accurately during offshore winds (from sea to land) than during onshore winds (from land to sea). The obtained results can be used to improve the accuracy of operational wave forecasting systems and to enhance maritime safety in the Tsemess Bay area.

A set of methods for short- and medium-range forecasting of water levels on Russian rivers is proposed. The methods utilize observation data from stream gages. The forecast is expressed as a linear function of observed water levels and is adjusted by replacing its extreme values with an acceptable minimum or maximum. The first method of hydrograph extrapolation takes into account only water levels observed at the forecast gage. The second, more general method additionally takes into account water levels observed at an upstream gage. The third, even more general method additionally takes into account water levels observed at a tributary gauge. Validation of these methods using independent data showed that each can produce satisfactory forecasts for a great number of river gages. Recommendations for implementing the presented methods in Roshydromet’s operational hydrological forecasting practices are provided.

The current technology for forecasting the meteorological indicator of pollution dispersion (MIPD) used at the Hydrometcentre of Russia is based on the COSMO-Ru6ENA configuration forecasts. Due to the transition to using ICON-Ru system forecasts with a 6 km horizontal grid spacing, model calculations of temperature and wind at the levels of 1000, 925, and 850 hPa for the COSMO-Ru6ENA and ICON-Ru13/6N29 configurations were compared with radiosonde data over the European Part of Russia. An advantage of ICON-Ru forecasts over COSMO-Ru was revealed using the test sample. A joint analysis of ICON-Ru forecasts of meteorological characteristics with observational data from the Ostankino TV Tower and a radiosonde station revealed characteristic features of the forecasts of temperature and wind speed profiles. These results are of practical importance for predicting pollution dispersion conditions. The results of the MIPD forecast verification using pollutant concentration measurements at the TV tower and Mosekomonitoring surface monitoring stations during the episode of adverse meteorological conditions at the end of March 2025 confirmed the connection of type 1 MIPD (weak dispersion) with increased pollutant concentrations at urban-type stations and indicated reasonability of using the ICON-Ru system data for MIPD forecasting.