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Climatic and geographic factors characterizing local conditions, as well as the ecological state of the external environment, have a direct effect on human health, morbidity, and life expectancy. The paper considers the basic concepts of medical ecology, a pathogenetic role of environmental atmospheric pollution in the development of environmentally induced and environmentally dependent diseases associated with exposure to meteorological factors. In addition to the major hygienic risk factors affecting the public health (a level of air, water, and soil pollution), the levels of acoustic and electromagnetic background, the type of human nutrition, architectural and planning conditions of human life are of significant importance. Global climate warming causes numerous environmental changes, primarily an increase in surface air temperature, which in turn entails global changes in weather conditions that lead to various weather anomalies. Human health is directly dependent on environmental conditions and a degree of adaptation to them. The paper highlights the issues of diagnosing environmentally induced diseases and measures to counteract their spread. Based on the system diagnosis using physiological indicators of human health and the integration of the adaptation and nosological approaches, a hardware-software complex has been developed and introduced into medical practice. Corrective technologies of regenerative medicine that are used for ecologically dependent pathologies are described. Their application in the process of the complex sanatorium and resort treatment is most effective. [ABSTRACT FROM AUTHOR]

The paper examines the influence of seasonal ice and prevalent type of recharge on the formation and functioning of zoobenthos communities in the coastal shallow zone of water bodies located north of 35° N. It was revealed that in the coastal shallow zone, zoobenthos species do not form communities but are represented by seasonal ephemeral groups. These habitations reach the maximum species diversity and qualitative development in July. Recommendations are given on the frequency of zoobenthos sampling to determine the water quality and an ecological status of a water body. [ABSTRACT FROM AUTHOR]

The paper investigates the sensitivity of evapotranspiration in the INM RAS–MSU land surface model to the changes in the weights of land surface classes in the cells of the latitude-longitude grid and the leaf area index LAI. It is demonstrated that the refinement of the values of the mentioned parameters based on modern observational data significantly reduces an error in evapotranspiration. The annual sum of evapotranspiration averaged over 10 years (2002–2012) from the surface of medium-sized (2−50×103 km2) watersheds of northern European Russia is used for the analysis. The model error has been calculated against the empirical estimates of evapotranspiration obtained from the watershed water balance equation. As an intermediate task, the accuracy of satellite data on terrestrial water storage used in the calculations is assessed by the comparison with the data of snow route surveys. [ABSTRACT FROM AUTHOR]

The paper examines the implementation of neural network methods for predicting peak water levels during the period of spring ice drift by the example of the Sukhona, Northern Dvina, and Pechora rivers. All considered neural network methods have shown high efficiency according to the criteria recommended by the Hydrometcenter of Russia and surpassed regression dependencies in the skill of forecasts. When using the method of training artificial neural networks, the standard error of prediction is reduced by approximately 10–20% as compared with regression dependencies. [ABSTRACT FROM AUTHOR]

The paper describes an algorithm to differentiate leads from sea ice using the dual polarization synthetic aperture radar (SAR) data from the Sentinel-1 satellite in an extrawide swath mode. The algorithm uses the polarimetric features of the sea surface signal obtained in the SAR images: the ratio between co- and cross-polarization. A technique is proposed for classifying the SAR images to identify discontinuities (cracks, leads) in drifting sea ice using the ratio and difference of polarizations together with texture features and the neural network implementation. The method was tested using the satellite data obtained over the Arctic seas in the Russian Federation. [ABSTRACT FROM AUTHOR]

The paper presents an experience of using artificial intelligence techniques, in particular, neural networks to solve relevant problems of hydrometeorology. The results of the investigations at the Planeta State Research Center on Space Hydrometeorology in detecting clouds and snow cover from the Himawari, Electro-L, and Meteor-M satellite data, as well as on classifying cloud types according to the AHI instrument data (Himawari-8) are reported. The findings of the work on retrieving values of total ozone and water vapor according to the infrared sensing devices are demonstrated. The work on detecting the boundaries of the ice cover and river floods from medium- and high-resolution satellite instruments, as well as the technologies for temperature and humidity sensing in the microwave spectrum are considered. The studies have shown that the use of neural network technologies provides the required accuracy of the received hydrometeorological information and high speed of processing incoming data. [ABSTRACT FROM AUTHOR]

Due to high spatial and temporal resolution, geostationary meteorological satellite imagery is a valuable source of information on the development of deep convective clouds and related severe weather events. Some methods for automatic deep convection detection from satellite data provide a satisfactory probability of detection for independent datasets, but are characterized by a high false alarm rate. The paper gives a description of an algorithm for automatic detection of deep convective clouds with satellite imagery using gradient boosting, logistic regression, and artificial neural network models. The results of validation of the proposed method using dependent and independent data of ground-based observations for the period 2013–2020 are presented. A low false alarm rate and high probability of detection suggest that the algorithm can be used in the operational mode. [ABSTRACT FROM AUTHOR]

The paper presents some methods of satellite data preprocessing for the elimination of atmospheric effects on the electromagnetic radiation detected by the target equipment of a satellite and subsequent detection of floods in the Amur River basin. The atmospheric correction algorithm that has been used for the preprocessing is based on the use of a lookup table obtained by applying the Second Simulation of a Satellite Signal in the Solar Spectrum, which is a model of atmosphere radiative transfer. The subsequent flood detection in the Amur River basin water bodies builds on a neural network algorithm, the core of which is the upgraded U-Net. The developed algorithms for atmospheric correction and subsequent flood detection make it possible to receive information in an automatic near-real-time mode for monitoring flood conditions. Some groundwork has been made for applying the algorithm to the data of the Russian satellite instruments for spacecraft planned for launch. [ABSTRACT FROM AUTHOR]

The paper presents the research work aimed at improving the quality characteristics of information products based on the MSU-GS/VE radiometer aboard the Arktika-M No. 1 satellite, as well as at obtaining data preprocessing products. All described methods are based on using machine learning algorithms, namely, neural networks of various architectures. The results of developing a technology for minimizing the interference that occurs in the channels of the satellite device are provided. The work on detecting cloud formations based on processing the channel data in the visible and infrared ranges is presented. It is shown that the use of neural networks makes it possible to implement automatic algorithms for obtaining thematic products that take into account various factors and have an accuracy that is commensurate with statistical and physical approaches and reduces the time of satellite data processing. [ABSTRACT FROM AUTHOR]

The sum of ions of the basic Lake Ladoga water mass has varied quite significantly from 55.6 to 71.6 mg/L over the past 60 years. In the present paper, separate periods of the mineralization growth are identified. It is shown that the change in the mineralization of Lake Ladoga has been always accompanied by a significant change in the ratio of basic ion concentrations. Any significant trends in the total mineralization of Lake Ladoga over the analyzed period have not been identified, but periods of the growth and decrease in the number of ions have been revealed. Until 1998, the periods of increased mineralization, as a rule, were accompanied by an increase in concentrations of and , most likely coming from the catchment area due to industrial and household pollution. During 2009–2019, the mineralization was growing mainly due to an increase in the absolute and relative concentrations of . At the same time, the concentration of dissolved CO2 in the main water mass of the lake also was increasing (and, consequently, pH was decreasing). Thus, the changes in the bicarbonate ion concentration in the water of Lake Ladoga may be largely determined by an increase in its inflow from the catchment area, whose possible reason is the enhancement of the chemical weathering of carbonate rocks due to the increasing concentrations of dissolved in the water. [ABSTRACT FROM AUTHOR]

Accumulated degree-days are among the most significant indicators in ecological and geographical modeling of the distribution of biological objects. The present paper analyzes several existing methods for calculating accumulated degree-days, proposes an improved calculation method, and presents a set of the global maps of accumulated degree-days constructed using the improved method. The produced maps are publicly available and can be used for ecological and geographical analysis and modeling. [ABSTRACT FROM AUTHOR]

The paper proposes an approach to the numerical stochastic modeling of the time series of the wind chill index and equivalent effective temperature at weather stations located in the Arctic zone of the Russian Federation. The approach is based on the use of a specially designed stochastic weather generator. It is shown that the approach allows developing the models of the time series of bioclimatic indices that very accurately reproduce various statistical properties of real processes related, in particular, to their daily variations and specific features of the study area. [ABSTRACT FROM AUTHOR]

The paper deals with studying the influence of air pressure fluctuations on the health of patients. Since August 2008, the Obukhov Institute of Atmospheric Physics of Russian Academy of Sciences and the Central Clinical Hospital of the Russian Academy of Sciences (CCH RAS) observed air pressure fluctuations with a simultaneous registration of patients who were urgently hospitalized to the CCH RAS and classified as weather-sensitive. Weather-sensitive patients included patients diagnosed with myocardial infarction, angina, hypertension, extrasystole, stroke, and cerebrovascular disease. The data on 6078 patients over a four-year period (January 1, 2009–November 31, 2012) were processed. The results have shown an increase in the cases of patients' hospitalization with an enhancement of the external effects of air pressure. [ABSTRACT FROM AUTHOR]

The authors explore the trends in the human health risks caused by air pollution and changing levels of weather and climate comfort on the territory of Russia in 2020–2050 under two climate change scenarios. Generally, the dynamics of risks is expected to be moderate. It will presumably be characterized by inter-scenario variability and dispersion by the country's climate zones. In certain areas, primarily in Siberia, very noticeable trends have been revealed. The paper shows a need for additional attention when planning the measures of adaptation to climate change in southern and central regions of European Russia, most of Western Siberia, the Magadan oblast and Kamchatka. [ABSTRACT FROM AUTHOR]

The paper examines the relationship of meteorological factors with the development of cardiovascular complications. Statistically significant seasonal fluctuations in the level and variability of blood pressure, heart rate, cholesterol, glucose, and body mass have been revealed. The variability of blood pressure is maximal in winter and minimal in summer. It is known that heat and cold waves are associated with an increase in mortality and cardiovascular complications, including hypertensive crises. Their frequency depends on the level of cardiovascular risk of a patient and is maximal in people with coronary heart disease, cerebrovascular disease, and type 2 diabetes. The influence of such poorly predictable factors as temperature waves and magnetic storms has been less studied. It seems promising to examine the relationship of the variability of blood pressure and complications of arterial hypertension with meteorological factors using the method for remote blood pressure monitoring, which is currently actively implemented in the regions of the Russian Federation. [ABSTRACT FROM AUTHOR]

The paper provides an overview of the methods for hydrometric streamflow computation in the regime and real-time variants developed at the State Hydrological Institute and intended for inclusion in the technologies for automated processing and generalization of hydrological observations, which were included in the Roshydromet regulatory documents put into effect in 2017–2022. The results of further development of the methodological basis for water discharge calculations in the conditions of variable backwater are given on the example of the reach of the Ob River from Barnaul to Kamen-on-Ob. To control the reliability of streamflow computation at gauging sections, it is proposed to use the computation and analysis of dynamic (short-term) channel water balances as the most effective tool. The effectiveness of using the improved methods for streamflow computation and coordination is shown through examples. [ABSTRACT FROM AUTHOR]

Over the past two decades, the volume and quality of information received from satellites to determine the water level of large lakes and reservoirs have significantly increased. At the same time, due to insufficient accuracy, the results of satellite measurements need an adjustment based on the comparison with ground-based observations. The latter not only allow evaluating errors in satellite data, but also serve as a kind of a benchmark for the development and adaptation of the methods of their correction. Such a methodology is being developed for several years jointly by the Laboratory of Space Geophysical and Oceanographic Studies (LEGOS, France) and the State Hydrological Institute. The paper analyzes the causes and sources of errors in satellite and ground data, the techniques for comparing and analyzing long-term series of these data, and the methods for adapting the results of satellite observations in order to improve their accuracy. The directions of further development of satellite altimetry are considered to increase the reliability of satellite data on the water level of inland water bodies. [ABSTRACT FROM AUTHOR]