Atmospheric and Oceanic Optics最新文献

We present the results of trajectory analysis of long-term measurements of organic (OC) and elemental (EC) carbon in aerosols sampled on quartz filters at an altitude of 300 m at ZOTTO station. The EC and OC concentrations were determined by the thermo-optical method. The resulted time series were supplemented with the HYSPLIT backward trajectories, and CWT and PSCF functions were calculated on a grid of 150 × 250 cells, which covered the geographical area of 30° × 20° centered at Zotino. These functions characterize the intensity of potential sources of carbon-containing aerosols in a cell. The results make it possible to identify the regions with the strongest organic and elemental carbon emissions and to estimate the seasonal variability of these emissions. In particular, in summer, the main sources of OC and EC are located to the east of Zotino, in the Podkamennaya Tunguska River region, and are most likely associated with wildfires. In cold seasons, most sources of carbonaceous aerosols are in the southwestern part of the geographical region under study, where large cities are located and the bulk of the population is concentrated. The regression analysis of CWT functions of organic and elemental carbon is shown to enable determining the dominant type of carbonaceous aerosol sources in some cases. Our results can be used for estimation of aerosol radiative forcing in Siberia.

Weather conditions are a natural limitation of the use of remote lidar sensing methods of the atmosphere, while the direct method based on an aerological aerosol backscattersonde has no such limitations, and these methods are close in physical principles of measurement. The creation of an all-weather stratospheric aerosol monitoring system can be based on the combination of direct and remote observation methods; however, their consistency should be experimentally confirmed. The results of a lidar-aerological experiment on atmospheric sounding at altitudes of 7–50 and 0–30 km using a ground-based lidar and an aerosol backscattersonde (AZOR), respectively, are presented. The experiment was conducted in Tomsk on March 15–16, 2023. Vertical profiles of backscattering coefficients of radiation from sources with close wavelengths were measured: ground-based 532 nm (in lidar) and balloon-based 528 nm (in AZOR). The obtained consistency of lidar and balloon measurements indicates the possibility of using AZOR as a mobile tool to complement lidar measurements in the case of clouds. The combination of direct and remote sensing of the atmosphere with the aim of improving the quality of measurements in studies of the aerosol composition of the atmosphere is discussed. The possibility of extending two wave (355 and 532 nm) lidar observations by direct measurements of AZOR with an additional set of wavelengths (470, 528, 850, and 940 nm) is shown.

The temporal variability of microphysical parameters of pyrolysis smoke, retrieved by inverting the characteristics of aerosol scattering and extinction, has been studied. The polarization scattering phase functions and spectral extinction coefficients were measured for 65 hours in smoke aerosols produced from thermal decomposition of pine wood during low-temperature pyrolysis in the Big Aerosol Chamber (BAC) of Institute of Atmospheric Optics, Siberian Branch, Russian Academy of Sciences. The microstructure parameters (volume concentration and mean radius of particles with division into fine and coarse fractions) and the complex refractive index of pyrolysis smoke are retrieved following the developed algorithm for inverting optical measurements. The real part of the refractive index is found to be in the vicinity of n = 1.55, and the imaginary part is in the range 0.007 < κ < 0.009; the mean radius of fine particles varies in the narrow range 0.137–0.146 μm. During smoke aging, the particle ensemble-mean radius monotonically increased from 0.19 to 0.6 μm mainly due to a relative increase in the content of coarse aerosol. Results of this work are important for estimation of the radiative forcing of aerosol and improvement of climate models and algorithms of remote optical sounding.

Fourier absorption spectrum of water vapor is studied in the spectral region 14 800–15 500 cm^–1 with a resolution of 0.05 cm^–1 and an optical path length of 3480 cm. A detailed list of 906 identified absorption lines of H₂¹⁶O and a set of 426 levels energies belonging to 19 vibrational states are compiled. Fifty-five energy levels are determined for the first time, and the energies of 64 levels are corrected. The comparison with the data available in the literature is made. The error in the positions of well-resolved, not very weak lines is 0.002 cm^–1, and the error in intensities is 10–15%.

Signal-to-noise ratio (SNR) is a key factor determining the accuracy of pulsed coherent Doppler lidar (PCDL) wind speed measurements. Therefore, information about SNR is important for interpreting measurement results. However, known approaches to determining SNR from PCDL raw data are not applicable to the case of the pulsed coherent Doppler lidar created at the Wave Propagation Laboratory of the Institute of Atmospheric Optics, Siberian Branch, Russian Academy of Sciences, (WPL PCDL) due to significant nonstationarity of the noise component of recorded signals. In this work, a new technique for estimating the signal-to-noise ratio from PCDL measurements accounting for noise nonstationarity is developed. The technique was tested in an experiment with a Stream Line PCDL and the WPL PCDL. The practical applicability of the suggested technique is confirmed by comparing SNR estimates from joint measurements by these lidars.

To improve the accuracy of weather and climate forecasts, a deeper understanding of atmospheric processes and phenomena, which are determined, among other things, by high-level clouds (HLCs), is required. The experimental results on polarization laser sensing of high-level clouds are presented. The data of systematic (from December 2009 to present) lidar measurements performed with the high-altitude matrix polarization lidar developed at the Tomsk State University are combined. Optical (backscattering phase matrix, optical depth, and scattering ratio) and geometric (lower and upper boundary altitudes and vertical thickness) characteristics of clouds are determined from the lidar measurements. The dataset is supplemented with corresponding vertical profiles of meteorological quantities (temperature, relative and specific humidity, and wind direction and speed) obtained from radiosonde observations and ERA5 reanalysis. The frequency of lidar detection of HLCs and those of them which are characterized by the preferential horizontal orientation of nonspherical ice particles is estimated. The results were combined into a database and used to create a software product based on neural networks to retrieve the dependences between the atmospheric meteorological parameters and HLC optical characteristics. The database can be used for various training options in solving problems of atmospheric optics including independent ones.

Cloud detection in satellite imagery is one the most important problems of satellite meteorology. The accuracy of cloud detection significantly determines the quality of other hydrometeorological products. The paper presents an algorithm for detecting clouds in satellite images, which is based on a convolutional neural network with a modified U-Net architecture. Multispectral satellite imagery from the MSU-GS instrument operating onboard Arktika-M No 1 satellite are used as input data. The algorithm accuracy was estimated through machine learning metrics and comparison with reference masks compiled via manual decryption of the satellite images by an experienced image interpreter. In addition, the results are compared with similar products based on data of SEVIRI and VIIRS instruments. The accuracy of a cloud mask obtained following the suggested algorithm is 92% compared to a reference mask for sun-illuminated areas and 89% for dark areas.

Possibilities of astronomical millimeter and submillimeter observations strongly depend on the precipitable water vapor (PWV), which determines the radiation absorption. The precise estimation of the PWV within large regions is one of key astroclimate problems. In this work, we refine estimates of the PWV for different sites based on processing ERA5 reanalysis and radiosounding data and test the previously suggested technique for correcting PWV values taking into account the characteristic water vapor vertical scale and the relative difference in grid node altitudes. In addition, the spatial distribution of the nighttime atmospheric optical thickness at a wavelength of 3 mm averaged over December–February 2013–2022 was derived for the first time for Russia and the adjacent territory. Our results can serve the basis for selecting an astronomic site for a new large millimeter telescope within the Eurasian Sub-Millimeter and Millimeter Telescope Project.

Thermodynamic and chemical processes inside the stratospheric polar vortex which decrease the ozone content in this region are studied. The winter-spring seasons in the Arctic, with the strongest stratospheric vortices and, hence, maximal ozone loss, are considered. The vortex-averaged variations in ozone and ozone-active components are studied on the basis of an ensemble of backward trajectories inside the vortex and M2-SCREAM stratospheric reanalysis data, which includes some chemical components that affect the ozone concentration. The record ozone depletion in winter 2020 was shown to be due to not only the long-lived stable stratospheric polar vortex, but also the earlier transformation of chlorine reservoirs into the active form and stronger denitrification and dehydration of air masses. The approach suggested can be used to analyze dynamic and chemical processes in the polar stratosphere over past winters and to validate chemical-climate models.

Problem of developing algorithm based upon neutral networks and machine learning to find clouds on hyperspectral images are under consideration. It is required that the network is not a “black box,” but allows an analysis of the reasons for decision making and classification results. Presented hybrid model includes decision tree trained to overcast recognition (model 1) on pre-selected features of an image in combination with convolutional neural network (model 2). Model 2 uses the result of model 1 and brightness in a selected band of an image. Model 1 finds cloud cores, and model 2 finds cloud edges. Results of testing the hybrid model on data of HYPERION sensor are presented. Data obtained over three surface types (ocean, plant, and urban region) are considered. Overall accuracy, as well as commission and omission errors are assessed. It is shown that the hybrid model can find 85% cloud pixels, only if the neural network is trained on an image where the contrast attains a maximum in the same spectral band. The results of this work can be applied to solve the general problem of analyzing and processing multispectral satellite images and further in environmental science and monitoring of changes in vegetation, ocean and glaciers.