Home – Home – Jornals – Atmospheric and Oceanic Optics 2026 number 2

This issue of the journal "Atmospheric and Oceanic Optics" includes articles based on papers presented at the International Symposium on Atmospheric Radiation and Dynamics (ISARD-2025).

Atmospheric aerosols are a significant factor of variations in the radiative balance, particularly for such regions as Central Siberia, where there are many anthropogenic and biogenic aerosol sources. However, the parameters and seasonal dynamics of aerosol radiative forcing in this region remain understudied. The aim of this work is to estimate the efficiency of aerosol radiative forcing ( RFE ) for the atmosphere of Central Siberia based on measurements of aerosol scattering and absorption coefficients at background ZOTTO station in 2007-2024. The atmospheric and underlying surface characteristics required for calculating RFE were taken from MERRA-2 reanalysis data. The resulting time series of RFЕ for ZOTTO station show strong day-to-day variability and a clearly pronounced seasonal cycle. Although the maximal concentrations of absorbing (soot) aerosol and, consequently, the maximal values of the aerosol absorption coefficient are observed in summer, the efficiency of aerosol forcing during this period is negative, with the characteristic RFЕ = -30 W/m². In winter, when aerosol concentrations and aerosol optical coefficients are substantially lower, the efficiency of aerosol forcing is positive and amounts to approximately +25 W/m²; the measurement-period mean RFE = -5 W/m². The change in the sign of aerosol forcing from positive to negative occurs in early May, and vice versa, in late October, which is primarily due to the seasonal change in the albedo of the underlying surface. The results can be used to refine predictions of regional climate changes in Siberia.

Atmospheric waves on a planetary scale play the main role in the formation of the atmosphere regime, and a polar vortex forms in the stratosphere in winter. A striking example of the interannual variability of the stratospheric polar vortex caused by stationary planetary waves is sudden stratospheric warming (SSW). The internal dynamics of major sudden stratospheric warming accompanied by displacement and splitting of the stratospheric polar vortex (SPV) was studied based on the MERRA-2 reanalysis data from the point of view of explicitly calculating nonlinear interactions of planetary waves with each other and with the mean flow to identify similar trends in the formation of SSW of various types. In particular, it is shown that the formation of a SSW with SPV splitting is not always accompanied by dominant variations in the wave activity of stationary planetary wave with zonal wavenumber 2 (SPW2), but is determined by the maximum interactions of SPW2 with the mean flow. It was obtained that the wave-wave interactions during the generation of secondary stationary planetary wave with zonal wavenumber 1 (SPW1) are maximal 1-2 weeks before, and during the generation of secondary SPW2, 5-10 days before a SSW with SPV displacement. The results aimed at identifying predictors of SSW formation are important due to the fact that SSW significantly affects the entire middle atmosphere, ionosphere, as well as weather conditions in the troposphere and the formation of extreme weather events.

Studying the spatial distribution and temporal variation in CH₄ concentration as a greenhouse gas is a relevant but difficult scientific task in the Arctic. Reanalysis data can serve an additional source of information, but they require regular validation. This study presents the results of an assessment of the reproduction of surface CH₄ concentration by CAMS global greenhouse gas reanalysis database version EGG4 in the Arctic region. Reanalysis data on the surface CH₄ concentrations variations are compared with continuous measurements at the research station “Ice Base Cape Baranova" (79°16' N, 101°45' E) in 2016-2020 on different time scales (interannual, seasonal, daily). It is found that reanalysis data reflect the interannual variability of surface CH₄ concentrations the worst. The seasonal variability of the CH₄ concentration is well described by the reanalysis data, the model amplitudes of the seasonal cycle are slightly higher than the actual ones. The comparison of the model and actual values of surface temperature and wind speed and direction are also carried out. Such verification of the CAMS database is useful before its subsequent using in regional-scale numerical modeling and other applied problems.

Solar thermal tides have a significant impact on global atmospheric circulation, making it important to study the various external factors that can influence their generation and propagation throughout the atmosphere. Using the mechanistic nonlinear numerical general circulation model of the middle and upper atmosphere (MUAM), this paper examines the influence of solar activity (SA) variations on the spatiotemporal structure of tides. Two ensembles of MUAM simulations of the global atmospheric circulation in January are considered, each consisting of 16 runs, corresponding to high and low SA. It is shown that with increasing solar forcing at high SA, the diurnal migrating tide (DW1) weakens in the altitude range 100-150 km and intensifies at higher altitudes. The analysis of the Eliassen-Palm (EP) fluxes demonstrates a significant correlation between changes in the vertical propagation of wave activity and the amplitude of DW1: downward flux increments generally correspond to tide weakening in the range 110-150 km, while upward flux increments correspond to strengthening of the tide above 150 km. The semidiurnal migrating tide (SW2) weakens at high SA at altitudes of up to 140 km in the Southern Hemisphere and 190 km in the Northern Hemisphere in the mid- and high-latitude thermosphere. This is accompanied by mainly weakening of the ascending EP fluxes. Above 200 km, SW2 amplitude in the Northern Hemisphere increases by a factor of 2-3 at high SA. Above 150 km in the thermosphere, the amplitude of the stationary planetary wave with zonal number 1 (SPW1) decreases at high SA, while the amplitude of the migrating tides increases. Taken together, this leads to a complex structure of changes in the amplitudes of non-migrating tides. As an important link in the dynamic relationship between atmospheric layers, tides, in particular, provide the distribution of the effect of changing solar forcing during varying solar activity across all atmospheric layers. Understanding the complex mechanisms of dynamic interactions between tides and atmospheric circulation is important for improving numerical forecasts of changes in atmospheric processes on various time scales, from days to decades.

A detailed study of the spatial distribution of aerosol sources is essential for understanding their impact on air quality and public health. The source fields of various atmospheric aerosol types arriving to the Middle Urals were estimated with the use of the analysis of the potential source contribution function. The initial data comprised information on the aerosol type obtained through the classification of aerosol particles based on the spectral values of atmospheric aerosol optical depth. The results demonstrate a clear spatial differentiation of atmospheric aerosol sources for the classes “dust” and “elevated smoke”. The proposed approach can significantly enhance the information provided by spectral ground-based photometric measurements, thus improving the accuracy of air quality assessments.

During disturbances in near-Earth space, disruptions in ground-based and satellite technological systems, including catastrophic failures, occur. Therefore, the problem of developing methods for real-time analysis and monitoring of the natural environment with acceptable accuracy is particularly pressing. This article explores a new automated method for detecting anomalies in neutron monitor (NM) observations. This method is based on the synthesis of wavelet decompositions with the rules of statistical decision theory; it enables the detection of anomalies in NM observations and the assessment of their intensity. Discrete wavelet decompositions with adaptive threshold functions are used to detect anomalies. The parameters of the threshold functions are automatically estimated (as data entered the processing system) using the rules of statistical decision theory. The application of these rules yielded a solution with an error not exceeding an a priori specified value. The intensity of anomalies in NM observations is then calculated by summing the amplitudes of wavelet coefficients exceeding the estimated thresholds. The paper studies periods of strong and extreme geomagnetic storms in 2024-2025. Correlations between Dst index and the intensity of anomalies in NM observations are analyzed. The results showed a significant increase in the correlation between the Dst index and the intensity of anomalies in neutron monitor observations during geomagnetic storms. The obtained correlations reached their maxima with a delay of several hours, demonstrating the importance of neutron monitor observations and their consideration when solving space weather problems. The results of the work can be used in space weather forecasting for the early detection of sporadic variations in the cosmic ray flux.

Monitoring and analysing the dynamics of ionospheric parameters allow one to detect disturbances that negatively impact technical systems. Problems of the timely detection of ionospheric disturbances are associated with a high degree of uncertainty in our prior knowledge about the dynamics of ionospheric processes during disturbed periods and the influence of interference and uneven observation network in certain areas. These issues necessitate the development of data recording and analysis methods that guarantee high accuracy and efficiency. The paper presents a new automated method for estimating the state of the ionosphere using ground-based vertical radiosonde data. This method combines elements of statistical decision theory and threshold estimation with wavelet transform. Numerical solutions constructed using this method are used in the Aurora interactive system (https://lsaoperanalysis.ikir.ru/lsaoperanalysis.html), which was developed at the Institute of Cosmophysical Research and Radio Wave Propagation, Far Eastern Branch, Russian Academy of Sciences. The results of the implemented algorithms provide data on the state of the ionosphere over Kamchatka (calm or perturbed) and the parameters of ionospheric inhomogeneities in perturbed state. The algorithms are adaptive and do not require preliminary training. Data from the Paratunka (Russia, Kamchatka Region) and Wakkanai (Japan) stations were used to evaluate the method. The behaviour of the ionosphere during periods of strong geomagnetic storms in 2023-2024 was studied. The study confirmed the method efficiency in analysing ionospheric data and detecting inhomogeneities. Prior to the analysed events, signs of an anomalous increase in electron density in the ionosphere were identified. This is of significant practical importance. The suggested method can be used in ionospheric data analysis techniques for monitoring and forecasting space weather conditions, with the aim of timely detection of ionospheric disturbances.

A method for remote mapping of the sea surface temperature field (SST) in a cloudless and cloudy atmosphere has been proposed and tested based on SST measurements of MTVZA-GYa microwave radiometer from Meteor M satellite Nos. 2-2 and 2-4. The method includes the preliminary SST estimation using an artificial neural network (ANN) algorithm of multilayer perceptron type and statistical filtering of the preliminary estimates using climatic SST values calculated from the ERA5 reanalysis. The neural network algorithm uses antenna temperatures measured in five scanner channels of the MTVZA-GYa radiometer as predictors. Reference SST values from the open access ICOADS database are used to train the ANN. The statistical filtering procedure makes it possible to reduce the influence of clouds and precipitation in the satellite radiometer field of view and provides a root-mean-square error of the SST estimates on the order of 1.2-1.7 °C and a coefficient of determination of about 0.8-0.9 when compared with in situ observations. The proposed approach is applicable to operational global “all-weather" monitoring of sea surface temperature and can be adapted to analyze SST measurements of MTVZA-GYa type radiometers with improved technical and information characteristics.

This article addresses the problem of cloud detection in hyperspectral satellite images using an interpretable neural network classifier for partial cloudiness. For effective solution, the preliminary selection of spectral channels and derived features is performed using decision trees trained with labeled satellite data of the HYPERION sensor. The selected channels and features are then used for building a convolutional neural network based on a modified Unet architecture. Modifications to the original Unet architecture enable simplifying the network structure, avoiding overfitting, assessing the importance of spatial and spectral features, analyzing classification results, and explaining decision-making processes. Feature selection and evaluation of their importance are critical stages in developing adequate machine learning and deep learning models combined with the analysis of their generalization ability. The suggested feature selection method is based on iterative training of decision trees to identify significant features in terms of classification accuracy. The operation of the convolutional neural network is interpreted and the importance of spatial and spectral features is assessed by evaluating Shapley vectors. The results of testing a neural network with HYPERION images made over three surface types (ocean, vegetation, and urbanized territory) are presented; its accuracy and commission and omission errors are estimated. The model enables semantic segmentation of images with thin clouds with accuracy over 95% in selected spectral bands and with selected features. The importance of input features, caused by their distribution across spectral channels and the relative positions of pixels in an image, for the detection of thick and thin clouds in hyperspectral satellite images is analyzed. The presented neural network model is designed for working with limited data volumes, enables applying augmentation, and can be used to assess the importance of selected spectral channels and spatial features.

Soot aerosol from forest fires injected into the stratosphere can influence climate on a global scale, similar to volcanic aerosol. This paper examined the stratospheric loading with soot aerosol from forest fires in Eastern Siberia, as well as the occurrence of volcanic aerosol over Western Siberia. An episode of ground-based lidar observation in July 2022, where aerosol layers were detected in the stratosphere above Tomsk at approximately 11 km and 20-25 km is considered. The origin of these layers is analyzed using air mass trajectories with control of their aerosol content based on data from the CALIPSO satellite lidar and using atmospheric and surface sensing data from the Suomi-NPP and Himawari-8 satellites. It is shown that the sources of in the lower stratosphere aerosol loading at an altitude of about 11 km are the fires in Eastern Siberia, which led to the formation of powerful pyrocumulative clouds. The location and time of formation of these clouds are determined. It is also shown that the aerosol layers at altitudes 20-25 km are associated with the eruption of the Hunga Tonga-Hunga Ha'apai volcano, which erupted in the Southern Hemisphere in January 2022. The results are of significant interest for predicting climate change on regional and global scales.

In order to study the dynamics of the Earth's atmosphere, it is of interest to examine the frequency content of geomagnetic field variations in the range of the sixteen-day atmospheric planetary wave period (from 14.5 to 18 days). The spectra of Earth's magnetic field variations recorded between 2000 and 2023 at three European mid-latitude magnetic observatories, the Belsk Observatory (eastern Europe), the Furstenfeldbruck Observatory (central Europe), and the Dourbes Observatory (western Europe), were analyzed. Using the Lomb-Scargle periodogram method, harmonics associated with the modulation effect of long-period variations and tidal effects were identified in the spectrum in the range from 14.5 to 18 days. The analysis showed that the spectral content of geomagnetic variations does not depend on the longitude of the observation point (the points are located at approximately the same latitude). Spectral harmonics caused by the modulation wave with a semiannual variation of the second harmonic of the sunspot rotation cycle and the declination tidal were identified. For the M_sf tidal wave, harmonics were identified due to the modulation effect of the 11-year solar activity cycle (Schwabe), the fourth harmonic of the 22-year solar activity cycle, and annual and semiannual variations. Spectral harmonics are clearly distinguished in the spectra, whose periods correspond to the modulation effect of the 11-year solar activity cycle, the fourth harmonic of the 22-year solar activity cycle, and annual and semiannual variations on the 16-day planetary wave. The spectral analysis results confirm the influence of processes observed in the lower neutral atmosphere on the dynamics of the upper atmosphere. The results can be used to develop atmospheric dynamics models.

The study of large-scale atmospheric processes, such as planetary waves, plays a crucial role in understanding the coupling between the lower and upper layers of the atmosphere. However, accurate modeling of these waves is challenging due to the heterogeneity and sparsity of observational data (both satellite and ground-based), as well as the high dimensionality of the parameter space when describing wave structures. In this paper, an approach to approximation of planetary waves with the use of the Two Strategy adaptive Artificial Bee Colony (TSaABC) algorithm based heterogeneous satellite and ground-based data is suggested. The TSaABC algorithm is used to optimize the parameters of a nonlinear spatiotemporal model representing atmospheric temperature data obtained from the Aura satellite (MLS) and three ground-based stations measuring hydroxyl OH(3, 1) emission bands. The temperature data are approximated using the sum of planetary wave harmonics with unknown parameters including amplitudes and wavenumbers, which are selected from a dictionary of harmonics. By solving the inverse problem of minimizing the data divergence and the L₁-norm of harmonic amplitudes, the method achieves approximation accuracy and sparsity in a large dictionary of harmonics. To solve the L1-minimization problem, a hard thresholding strategy was developed within the TSaABC algorithm. The use of a hard threshold value allows us to reduce the dimensionality of the solution search, thus inereasing computational efficiency. The results demonstrate the potential of the algorithm for assimilating heterogeneous data and improving the modeling of atmospheric processes.