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Fast radiative transfer models are essential for the efficient processing of hyperspectral satellite data in trace gas retrievals, as full multi-stream radiative transfer simulations are computationally demanding. We present FALCAS (Fast Atmospheric Line-by-line Code with Aerosol and Cloud Scattering), a surrogate forward model combining line-by-line radiative transfer with the virtual isotropic scattering layer approximation adopted from FOCAL. FALCAS retains much of the accuracy of full multi-stream calculations while enabling rapid simulations. Previously validated against synthetic spectra from a discrete ordinate radiative transfer model, FALCAS is here applied to real measurements from the TROPOspheric Monitoring Instrument (TROPOMI) to retrieve total column water vapour (TCWV) in the shortwave infrared band around 2.3 μm. Retrieval results are compared to the operational TROPOMI Level-2 TCWV from the CH4 product. As this comparison is performed against an operational product from the same instrument, it represents an intercomparison rather than an evaluation against an independent reference dataset. FALCAS retrievals show a Pearson correlation coefficient greater than 0.99 with the operational data, and after empirical bias correction, the mean absolute bias across all regions is 1.45 mol m−2 (0.12% relative) and the mean RMSE is 39.24 mol m−2 (3.85% relative). These results demonstrate that FALCAS shows strong agreement with the operational TROPOMI Level-2 TCWV product, offering substantial computational advantages for large-scale processing.

The accurate determination of the location, height, and loading of sulfur dioxide (SO2) plumes emitted by volcanic eruptions is essential for aviation safety. The SO2 layer height is also one of the most critical parameters with respect to determining the impact on the climate. Retrievals of SO2 plume height have been carried out using satellite UV backscatter measurements, but, until now, such algorithms are very time-consuming. We have developed an extremely fast yet accurate SO2 layer height retrieval using the Full-Physics Inverse Learning Machine (FP_ILM) algorithm. This is the first time the algorithm has been applied to measurements from the TROPOMI instrument onboard the Sentinel-5 Precursor platform. In this paper, we demonstrate the ability of the FP_ILM algorithm to retrieve SO2 plume layer heights in near-real-time applications with an accuracy of better than 2 km for SO2 total columns larger than 20 DU. We present SO2 layer height results for the volcanic eruptions of Sinabung in February 2018, Sierra Negra in June 2018, and Raikoke in June 2019, observed by TROPOMI.

In this paper, we present neural network methods for predicting uncertainty in atmospheric remote sensing. These include methods for solving the direct and the inverse problem in a Bayesian framework. In the first case, a method based on a neural network for simulating the radiative transfer model and a Bayesian approach for solving the inverse problem is proposed. In the second case, (i) a neural network, in which the output is the convolution of the output for a noise-free input with the input noise distribution; and (ii) a Bayesian deep learning framework that predicts input aleatoric and model uncertainties, are designed. In addition, a neural network that uses assumed density filtering and interval arithmetic to compute uncertainty is employed for testing purposes. The accuracy and the precision of the methods are analyzed by considering the retrieval of cloud parameters from radiances measured by the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR).

A spectral acceleration approach for the spherical harmonics discrete ordinate method (SHDOM) is designed. This approach combines the correlated k-distribution method and some dimensionality reduction techniques applied on the optical parameters of an atmospheric system. The dimensionality reduction techniques used in this study are the linear embedding methods: principal component analysis, locality pursuit embedding, locality preserving projection, and locally embedded analysis. Through a numerical analysis, it is shown that relative to the correlated k-distribution method, PCA in conjunction with a second-order of scattering approximation yields an acceleration factor of 12. This implies that SHDOM equipped with this acceleration approach is efficient enough to perform spectral integration of radiance fields in inhomogeneous multi-dimensional media.

Fast radiative transfer models (RTMs) are required to process a great amount of satellite-based atmospheric composition data. Specifically designed acceleration techniques can be incorporated in RTMs to simulate the reflected radiances with a fine spectral resolution, avoiding time-consuming computations on a fine resolution grid. In particular, in the cluster low-streams regression (CLSR) method, the computations on a fine resolution grid are performed by using the fast two-stream RTM, and then the spectra are corrected by using regression models between the two-stream and multi-stream RTMs. The performance enhancement due to such a scheme can be of about two orders of magnitude. In this paper, we consider a modification of the CLSR method (which is referred to as the double CLSR method), in which the single-scattering approximation is used for the computations on a fine resolution grid, while the two-stream spectra are computed by using the regression model between the two-stream RTM and the single-scattering approximation. Once the two-stream spectra are known, the CLSR method is applied the second time to restore the multi-stream spectra. Through a numerical analysis, it is shown that the double CLSR method yields an acceleration factor of about three orders of magnitude as compared to the reference multi-stream fine-resolution computations. The error of such an approach is below 0.05%. In addition, it is analysed how the CLSR method can be adopted for efficient computations for atmospheric scenarios containing aerosols. In particular, it is discussed how the precomputed data for clear sky conditions can be reused for computing the aerosol spectra in the framework of the CLSR method. The simulations are performed for the Hartley–Huggins, O2 A-, water vapour and CO2 weak absorption bands and five aerosol models from the optical properties of aerosols and clouds (OPAC) database.

An algorithm for the retrieval of total column amount of trace gases in a multi-dimensional atmosphere is designed. The algorithm uses (i) certain differential radiance models with internal and external closures as inversion models, (ii) the iteratively regularized Gauss–Newton method as a regularization tool, and (iii) the spherical harmonics discrete ordinate method (SHDOM) as linearized radiative transfer model. For efficiency reasons, SHDOM is equipped with a spectral acceleration approach that combines the correlated k-distribution method with the principal component analysis. The algorithm is used to retrieve the total column amount of nitrogen for two- and three-dimensional cloudy scenes. Although for three-dimensional geometries, the computational time is high, the main concepts of the algorithm are correct and the retrieval results are accurate.

Introduction. The use of nanomaterials and nanotechnology represents one of the most important areas in global scientific and technological development. Nanotechnology involves the controlled regulation of the properties of objects at the molecular and supramolecular level, which determine most of the fundamental parameters and properties of physical objects, based on the targeted manipulation of their atoms and molecules. In hydraulic engineering, this involves the use of nanomaterials and technologies that improve the properties of building materials and structures, increasing their durability and resistance to external influences. Methods and materials. The Novosibirsk Reservoir is a unique multi-purpose water facility. For more than 60 years, it has been intensively used for national economic purposes - it is a source of water supply, the main recreation area for residents of Novosibirsk, the Novosibirsk Region, and the Altai Territory, and is used for navigation and fisheries. There are 41 settlements located within a two-kilometer zone of the reservoir's coastal strip, including the cities of Kamen-na-Obi, Berdsk, Iskitim, and Ordynskoe. The forested coastal area is home to health resorts, cottage and dacha settlements, and gardening communities, as well as a place for short-term recreation for the population. In this regard, the recreational load on the coastal zone of the reservoir is very high, partly due to the steepness of the banks. Various methods are used to calculate the design of shore protection hydraulic structures on water bodies, which take into account, first of all, the natural conditions and characteristics of the water body's shoreline. The results of the calculations are verified and refined on the basis of field studies and, if necessary, laboratory tests and experiments. Results. The paper presents calculations for determining the design parameters of the structure, such as: wind surge height, wave run-up height, top elevation of the structure, scour in front of the stone bank, design composition of the stone bank, and parameters of the stone bank. Discussion. Currently, there are many varieties of nanoscale additives and nanomodified materials. The possibilities for implementing modification mechanisms are determined by the type, characteristics, and dosage of nanoscale particles. As a suggestion, the authors propose to pay attention to the use of geogrids with different cell sizes and nanomodified concretes. However, it should be noted that at present, even with a low required content of nanomodifying additives (2-3% of the total mass of concrete), the addition of such additives will significantly increase the cost of the material. A comparison of technical and economic indicators in this case will clearly indicate this disadvantage and, as a result, the impossibility of using this option for economic reasons. Conclusion. One of the most important criteria for assessing the prospects for the introduction of nanotechnological innovations in the construction industry is their final cost. Nanomodifiers for concrete and building mortars at a price of $100 per gram, even though their strength properties increase by 30%, are unlikely to be in demand. Most of the experts agree that nanostructuring should be applied to widely used materials, including concrete, metals, and fiber-based composites. These breakthrough technologies can be applied in many areas, including hydraulic engineering, strengthening concrete foundations of gas transmission systems, creating flexible plastic geogrids, selecting and creating new high-quality fillers for them, etc. Also, thanks to new nanomaterials, it is possible to produce metal that will last an order of magnitude longer than modern samples. There are sufficient scientific developments in this area. Now it is necessary to find practical applications for them. However, this vector of development entails the need for production re-equipment, staff training, and so on.

Linearizations of the spherical harmonic discrete ordinate method (SHDOM) by means of a forward and a forward-adjoint approach are presented. Essentially, SHDOM is specialized for derivative calculations and radiative transfer problems involving the delta-M approximation, the TMS correction, and the adaptive grid splitting, while practical formulas for computing the derivatives in the spherical harmonics space are derived. The accuracies and efficiencies of the proposed methods are analyzed for several test problems.

The retrieval of aerosol and cloud properties such as their optical thickness and/or layer/top height requires the selection of a model that describes their microphysical properties. We demonstrate that, if there is not enough information for an appropriate microphysical model selection, the solution’s accuracy can be improved if the model uncertainty is taken into account and appropriately quantified. For this purpose, we design a retrieval algorithm accounting for the uncertainty in model selection. The algorithm is based on (i) the computation of each model solution using the iteratively regularized Gauss–Newton method, (ii) the linearization of the forward model around the solution, and (iii) the maximum marginal likelihood estimation and the generalized cross-validation to estimate the optimal model. The algorithm is applied to the retrieval of aerosol optical thickness and aerosol layer height from synthetic measurements corresponding to the Earth Polychromatic Imaging Camera (EPIC) instrument onboard the Deep Space Climate Observatory (DSCOVR) satellite. Our numerical simulations show that the heuristic approach based on the thesolution minimizing the residual, which is frequently used in literature, is completely unrealistic when both the aerosol model and surface albedo are unknown.

To retrieve aerosol properties from satellite measurements, micro-physical aerosol models have to be assumed. Due to the spatial and temporal inhomogeneity of aerosols, choosing an appropriate aerosol model is an important task. In this paper, we use a Bayesian algorithm that takes into account model uncertainties to retrieve the aerosol optical depth and layer height from synthetic and real TROPOMI O2A band measurements. The results show that in case of insufficient information for an appropriate micro-physical model selection, the Bayesian algorithm improves the accuracy of the solution.