The article compares two ensemble forecasting systems, S1 and S2, using the SLAV072L96 model, in terms of forecasts with a lead time of up to 6 weeks. The first system, S1, uses an ensemble of 61 members, and the ensemble of initial states is generated with the help of the breeding method. The newer system, S2, has 41 members, and the ensemble of initial states is prepared using the data assimilation technique based on the local ensemble Kalman filter. It is shown that despite a smaller number of ensemble members, S2 is not inferior to S1; instead, it has some advantages, which are especially remarkable over long integration intervals (up to 46 days). At the same time, both systems are characterized by inadequate spread of the ensemble and asymmetric distribution of forecasted values and hence require additional adjustment and improvement. It is concluded that the use and further development of the S2 system is preferable, since the same or slightly higher quality of ensemble forecasts is achieved with lower computational costs.
Based on long series (for the period 1958–2024), a study was conducted on the statistical structure of the field of available moisture content in the one-meter layer of soil under crops in European Russia, and the impact of observed climate changes on the soil moisture regime was assessed. It has been shown that in the 21st century, the soil moisture content is higher than that in the period 1958–1999, and the currently observed climatic changes are positive for the agricultural industry of Russia. The curves constructed for seasonal variations of available moisture content according to modern data can be applied in operational agrometeorological practice. The main reasons for the changes were analyzed: an increase in the recurrence of warm winters and the cyclical nature of the climate system.
The paper discusses results of analyzing the correspondence of the empirical probability distribution of daily precipitation’s absolute maxima to the theoretical Pearson and Frechet curves. An application of the method of constructing probability curves for the largest members of the samples has shown that the laws of maximum daily precipitation distribution are satisfactorily described using the Pearson distribution. The use of the Frechet distribution leads to a significant overestimation of distribution quantiles in the region of rare events.
The paper presents the results of developing the Lavinex Russian system for dynamic impact on the snow cover at avalanche sites. The system for artificial avalanche triggering using the gas-air mixture explosions consists of a detonating device installed on a concrete foundation in the upper part of an avalanche site (2–5 m below the top of the avalanche initiation zone) as well as an autonomous gas (oxygen and propane) component storage and supply unit (“shelter”) installed out of the area of possible impact of falling stones or snowslides. The components of the system are connected by gas supply pipelines. The device is controlled remotely. The tests have shown that an explosion of gas mixture consisting of 1/6 of propane and 5/6 of oxygen makes it possible to generate an excessive pressure up to 30 kPa (relative to atmospheric pressure) onto the snow surface at the avalanche site. This significantly exceeds the pressure of 8 kPa generated by the foreign analogue, the GAZ.EX system used in Russia and abroad.
This study employs a machine learning approach based on the Support Vector Machine (SVM) for water body classification from remote sensing imagery. To enhance the model’s performance, generalization ability, and robustness, the Particle Swarm Optimization (PSO) algorithm was used to optimize the SVM parameters, constructing the PSO-SVM model. Using the Gaofen-1 (GF-1) satellite imagery from Poyang Lake, the classification results before and after optimization were compared. The PSO-SVM achieved a 7.3332
The paper presents the results of studying minor sudden stratospheric warmings (SSWs) that were obtained using an original methodology that combines identifying Rossby wave breaking (RWB) with analyzing of potential vorticity fields on the 850 K isentropic surface based on ERA5 reanalysis data. The main focus is on the spatiotemporal features of wave activity in key regions of the Northern Hemisphere. Characteristic scenarios of minor SSW development were revealed, including cases of compensatory interaction between wave disturbances, which leads to stabilization of the stratospheric polar vortex. It is found that as compared to major sudden stratospheric warmings, the role of RWB events formed in the Euro-Atlantic sector is increasing for minor SSWs. The enhanced meridional circulation periods associated with the Ural blocking are on average less stationary than those during major SSWs and do not form persistent cold patterns over Siberia (although individual events may deviate from this pattern). The identified features of the spatial organization of RWB and its relationship with thermal anomalies are important for improving methods for diagnosing and predicting stratosphere–troposphere coupling.
Based on long-term observations, data were obtained on the temperature and salinity of water in different parts of Veresovaya Bay located in Kola Gulf (or the estuary of the Tuloma River), as well as their fluctuations during a tidal cycle. In a long-term cycle, the southern part of Veresovaya Bay (south of the Venzin stream) consists of freshwater, and the northern (north of the Venzin stream) consists of brackish water. In the southern part, the salinity is almost always close to zero, and its fluctuations are insignificant, except for cases of extremely low flow from the Tuloma River. In the northern part, the water salinity is very variable and can fluctuate from almost zero to sea-level one (above 24‰) during a tidal cycle. The temperature regime of Veresovaya Bay is not significantly affected by tidal movements. However, during the year, Kola Gulf has a warming effect in winter and a cooling effect in summer.
The western Pacific Subtropical High (WPSH) and Northern East Asian Low (NEAL) are the main pressure systems influencing summer rainfall (SR) over the Democratic People’s Republic of Korea (DPRK). A new coupling index is constructed which determines the interannual variability in SR over the DPRK. The correlation coefficient (CC) between the interannual variabilities of this coupling index and SR over the DPRK is 0.55. An abrupt change in the coupling index WPSHI + NEALI time series appeared around the early 1970s. During the study, four atmospheric circulation indices for the seasonal prediction of this coupling index were found. Also, the possible mechanisms of the connection between WPSHI + NEALI and these indices in preceding winter were investigated. The back propagation (BP) neural network model was used to obtain and analyze the prediction results of WPSHI + NEALI with different predictor combinations. The prediction results show that the predictor combination of the AOI (Arctic Oscillation Index), SHI (Siberian High Index), and EATI (East Asian Trough Index) gives the best prediction skill with CC = 0.92 ( p<0.001 ) and RMSE = 0.13 for the independent test interval.
A comparative analysis of daily precipitation fields from global gauge-based products (CPC, GPCC), reanalysis data (ERA5, ERA5-Land), and satellite information (IMERG, CMORPH) was performed against weather station data for Russia over the period 2000–2018 from the All-Russian Research Institute of Hydrometeorological Information–World Data Center database. The products assimilating gauge data (CPC, GPCC) have demonstrated the highest accuracy with correlation coefficients of 0.70–0.81 and root-mean-square error of 2.1–2.5 mm/day. The reanalyses have shown moderately high reliability (r = 0.64–0.65). A substantial accuracy degradation of satellite-based products was identified north of 60° N, in mountainous regions and for solid precipitation. Pronounced seasonal differences in data quality were found, with degraded performance in summer due to the high variability of convective precipitation. For hydrometeorological studies over Russia, the use of CPC, GPCC, or ERA5 data is recommended.
The paper presents the results of satellite monitoring of the fuel-oil pollution in the Kerch Strait that was conducted due to the sinking of the Volgoneft-212 and Volgoneft-239 tankers on December 15, 2024. During the monitoring period from December 15, 2024 to December 31, 2025, a total of 120 radar and optical satellite images of the study area were obtained and analyzed at the Planeta State Research Center on Space Hydrometeorology. The detected pollution events, including the remnants of the initial oil spill and the fuel-oil leakage from the sunken broken parts of the tankers or the secondary pollution sources formed by resurfacing fuel-oil fragments, were systematized in a specialized database. The analysis has revealed that the destruction and sinking of the tankers were caused by steep short-period waves generated by the interference of incident and coast-reflected waves (rogue waves), whose heights significantly exceeded those of their constituents. The study also proposes to develop a national satellite observation system for monitoring petroleum pollution in the waters of all Russian inland and marginal seas and outlines its main technical characteristics.
This study examines the impact of drought on the agricultural productivity in Pakistan, focusing on wheat yield prediction. Using the Long Short-term Memory (LSTM) model, the research integrates the Standardized Precipitation Evapotranspiration Index (SPEI) and water availability data to assess the effects of seasonal and annual droughts. Drought trends from 1990 to 2020 reveal severe dry periods, particularly those in 2001, which led to crop failures despite a steady increase in wheat yields. Water availability has remained stagnant, highlighting the challenge of maintaining growth with limited resources. A correlation analysis shows a strong negative relationship between drought severity, water availability (–0.95), and wheat yield (–0.98). The LSTM model outperforms other ones, including ARIMA, Linear Regression, and Random Forest, with an R^2 score of 0.8, explaining 80
The study presents a neural network based approach for retrieving atmospheric temperature profiles from measurements of the Microwave Temperature Sounder (MWTS) onboard satellites of the FengYun series. The retrieval algorithm is implemented as a fully connected feedforward neural network. The training data include the MWTS channel brightness temperatures simulated with the RTTOV fast radiative transfer model and the corresponding temperature profiles from the ECMWF ERA5 reanalysis. The performance of the proposed method was evaluated against the data of radiosonde observations over the Russian Far East for the summer and winter seasons of 2025. The results have demonstrated that the root-mean-square error (RMSE) of temperature retrieval does not exceed 3.5 K in the near-surface layer in summer and 5.5 K in winter while remaining below 3 K throughout the troposphere and lower stratosphere. In addition, the results obtained from the MWTS data using the developed method were compared with those obtained from the AMSU-A radiometer based on the physical 1D-Var algorithm. It was found that in summer, the deviation of temperature retrieved from the MWTS data was smaller than the one from the AMSU-A data: by approximately 1 K near the surface and by 0.5 K in the mid-troposphere. An additional analysis of errors relative to radiosonde data was carried out for coastal and mountainous stations as well as for winter temperature inversion conditions, which make the greatest contribution to an increase in retrieval errors.
The paper analyzes the regional long-term temperature variability in the Northern Hemisphere using quantile regression. This method allows assessing not only changes in average values but also those in the entire distribution, including extreme low and high temperatures. The study is based on ERA5 reanalysis data and historical forecasts of the SLAV072L96 and INM-CM6 hydrodynamic models. It is found that warming is distributed unevenly: in some regions (for example, in southern European Russia), extreme low temperatures are increasing most significantly, reducing overall climate extremity, while in others (for example, in northern Asia), the most significant increase is associated with extreme high temperatures rise, increasing climate contrasts and risks. A comparison with reanalysis data showed that both models systematically underestimate warming in key Arctic regions. The INM-CM6 has demonstrated a higher quality of simulating changes in seasonal averages, but both models struggle with simulating extreme high/low temperatures in summer/winter. The results of the study can be used in forecasting activities by the Hydrometeorological Research Center of the Russian Federation/North Eurasia Climate Center.
This study investigates water pollution by petroleum products also known as Total Petroleum Hydrocarbons (TPH) in the highly productive southern reaches of the Volga, Don, and Temernik rivers, near the major urban agglomerations of Volgograd and Rostov-on-Don. The assessment was performed using infrared (IR) spectrometry and fluorimetric analysis, which are widespread techniques in environmental monitoring. Concurrently, chromatographic methods were employed to analyze specific TPH components, namely polycyclic aromatic hydrocarbons and alkanes, to identify their origins based on diagnostic ratios. The most heavily polluted river sections were determined, where TPH concentrations significantly exceeded maximum permissible concentrations alongside elevated levels of benzo[a]pyrene. While IR spectrometry and fluorimetric methods yielded comparable TPH concentrations when petrogenic sources dominated, their results differed significantly in cases where biogenic (natural) hydrocarbons prevailed.
The review was compiled based on the operation results of the total ozone (TO) monitoring system in the CIS and Baltic countries that functions in an operational mode at the Central Aerological Observatory (CAO). The monitoring system uses data from the national network equipped with M-124 filter ozonometers, which operates under the methodological supervision of the Main Geophysical Observatory. The performance of the entire system is operationally controlled in CAO by comparison with the OMI satellite observations (NASA, USA). Basic TO observation data are generalized for each month of the first quarter of 2026 and for the entire first quarter. The data of routine surface ozone observations in Moscow region are also summarized.
The paper presents the results of a three-year experiment on reducing risks of wildfire occurrence in the Republic of Sakha (Yakutia), which was conducted by the Central Aerological Observatory. The reduction of landscape and forest fires was achieved by the aviation technology of intended rainfall enhancement aimed at preventive moistening of fire-prone areas.
Due to the constant increase in anthropogenic pressure in the Arctic in recent years, various components of the Arctic natural environment are subjected to growing risks of pollution both from local sources and through transboundary transport. The present paper deals with studying concentrations of heavy metals in Arctic frozen ground, in surface and sea water, and in snow samples of Wrangel Island. The study results have shown that the maximum permissible concentrations (MPCs) for chromium, manganese, nickel, cobalt, lead, and cadmium are exceeded in the soil samples. Some of the revealed excesses could be associated both with anthropogenic impact and with the naturally elevated background of these elements in the natural environment of these areas. It was revealed that the MPC levels for cadmium, lead, and nickel were exceeded in inland surface water bodies. In coastal sea water, the values of cadmium and lead were above the MPCs at some stations. Lithium and barium were also detected in the sea water samples. In the snow samples, the MPC for aluminum, manganese, iron, lead, and cadmium were exceeded in varying degrees. There is a need in further research to understand the natural and anthropogenic influence on the heavy metal concentrations in the Arctic ecosystem in general and on the islands in particular, as well as in elimination of local pollution sources on Wrangel Island.
The paper deals with an assessment of the anthropogenic impact in the Schirmacher Oasis, Queen Maud Land, East Antarctica. Emissions of the main pollutants and greenhouse gases from diesel generators used at Antarctic stations are estimated for the period from the beginning of the oasis development. It is shown that SO2 emissions have decreased by almost 15 times as compared to the peak values of the late 1980s–early 1990s, which is due to a significant decrease in sulfur content in the fuel. Emissions of other pollutants have generally increased. Surface air pollution by NO2, SO2, PM10, and black carbon is characterized using the AERMOD dispersion model. It has been revealed that the exceeding of hygienic standards for atmospheric air quality can be observed for nitrogen dioxide. The calculated concentrations of pollutants in the atmospheric air are compared with the measured ones, as well as with the simulated air pollutant concentrations in other Antarctica oases. Deposition fluxes of PM10 and black carbon are estimated, which can reach the maximum values of 57 and 43 mg/m2 per year, respectively, and are most intense to the northwest and north of the stations.
The spatial and temporal variability of the atmospheric boundary layer (ABL) height over regions with complicated terrain is still poorly studied due to the fact that its value varies significantly with topography. With the help of the data from radar wind profilers obtained in 2022, the study investigates characteristics of the ABL height variations over a typical mountainous rural site (Yanqing) and plain suburban site (Nanjiao) in Beijing, China in clear-sky conditions by using a normalized signal-to-noise ratio threshold. The results point to strongly contrasting features on the diurnal and seasonal variations. It is interesting to note that at the plain suburb site, the ABL is repressed in the morning and develops rapidly in the afternoon in spring, while it evolves more quickly in winter. An investigation of seasonal variations in wind profiles has led to a deeper understanding of the evolution of local plain-to-mountain circulation in this region, as well as of potential impacts on seasonal changes in the ABL, especially in spring and summer. Furthermore, the steady ABL caused by aerosols and heightened urban heat accumulation may be linked to the rigid ABL development over plain suburb canopy in autumn and winter. The results are helpful for improving knowledge of meteorological conditions contributing to air pollution in mountainous areas.
The paper presents an algorithm for estimating wind speed and direction based on computing cloud motion vectors and water vapor fields from satellite data using the differential optical flow method, namely, a modified Brox method with normalized data constraints. The algorithm utilizes infrared channel data from the MSU-GS instrument on board the Arktika-M and Elektro-L satellite series. The paper also describes the data fusion algorithm for deriving global estimates of wind vectors. The results of the research demonstrate that, in most cases, errors in the computed wind vectors meet the requirements established by the World Meteorological Organization. Furthermore, the achieved accuracy is comparable to that of the similar algorithms developed for foreign satellites.
