This article examines changes in Russia's politics of memory at the turn of the 2020s, the balance offerees between mnemonic actors, external conditions, and internal modalities of the struggle for ...political use of the historical past. The tensions and the conflict potential in Russia's relations with the collective West have now turned into a multi-factor confrontation in which the collective identity of Russians - the key elements of the historical narrative and collective memory that answer the basic questions of identity politics (Who are we? Where have we come from and where are we going?) -are being tested to the extreme. The author shows that while securitization is becoming the dominant trend in the politics of memory, the state-supported historical narrative is wanting in many respects. The information-psychological and mnemonic struggle in the world and inside the country is leading the federal authorities to focus above all on the causes and results of the Second World War, the circumstances of the USSR's entry into that war, its decisive role in the victory over fascism, as well as the history of Russo-Ukrainian relations. Securitization of the official historical narrative includes law-making, drafting amendments to the RF Constitution, adopting "memorial" laws, toughening and expanding legislation on "foreign agents," as well as introducing law-enforcement practices that have brought about a change in the configuration and balance of forces between mnemonic actors. The second part of this article looks at the problems of the political use of the past in the context of the Special Military Operation in Ukraine, including the politics of memory in the regions that have been incorporated into the RF under the October 4, 2022 amendments to the Constitution.
•A cloud tomographic retrieval algorithm has been designed.•The adjoint method has been combined with that of the gradient of a surrogate function.•The retrieval algorithm uses regularization and ...accelerated projected gradient methods.
A cloud tomographic retrieval algorithm relying on (i) the spherical harmonics discrete ordinate method for radiative transfer calculation and (ii) the adjoint radiative transfer theory for computing the gradient of the objective function has been designed. In order to escape local minima and to increase the efficiency of the retrieval algorithm, the computation of the gradient of the objective function by the adjoint method has been combined with that of the gradient of a surrogate function. The retrieval algorithm uses regularization and accelerated projected gradient methods endowed with a step length procedure. The performances of the retrieval algorithm as compared to those of a retrieval algorithm based on the surrogate minimization method are analyzed on a few synthetic problems.
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).
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.
In this article, we present three algorithms for aerosol parameters retrieval from TROPOspheric Monitoring Instrument measurements in the <inline-formula><tex-math notation="LaTeX">\text ...{O}_{2}</tex-math></inline-formula> A-band. These algorithms use neural networks 1) to emulate the radiative transfer model and a Bayesian approach to solve the inverse problem, 2) to learn the inverse model from the synthetic radiances, and 3) to learn the inverse model from the principal-component transform of synthetic radiances. The training process is based on full-physics radiative transfer simulations. The accuracy and efficiency of the neural network based retrieval algorithms are analyzed with synthetic and real data.
Micrometer scale colloidal particles experiencing ∼kT scale interactions and suspended in a fluid are relevant to a broad spectrum of applications. Often, colloidal particles are anisotropic, either ...by design or by nature. Yet, there are few techniques by which ∼kT scale interactions of anisotropic particles can be measured. Herein, we present the initial development of scattering morphology resolved total internal reflection microscopy (SMR-TIRM). The hypothesis of this work is that the morphology of light scattered by an anisotropic particle from an evanescent wave is a sensitive function of particle orientation. This hypothesis was tested with experiments and simulations mapping the scattered light from colloidal ellipsoids at systemically varied orientations. Scattering morphologies were first fitted with a two-dimensional (2D) Gaussian surface. The fitted morphology was parameterized by the morphology’s orientation angle M ϕ and aspect ratio M AR. Data from both experiments and simulations show M ϕ to be a function of the particle azimuthal angle, while M AR was a sensitive function of the polar angle. This analysis shows that both azimuthal and polar angles of a colloidal ellipsoid could be resolved from scattering morphology as well or better than using bright-field microscopy. The integrated scattering intensity, which will be used for determining the separation distance, was also found to be a sensitive function of particle orientation. A procedure for interpreting these confounding effects was developed that in principle would uniquely determine the separation distance, the azimuthal angle, and the polar angle. Tracking these three quantities is necessary for calculating the potential energy landscape sampled by a colloidal ellipsoid.
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.
An algorithm for retrieving aerosol parameters by taking into account the uncertainty in aerosol model selection is applied to the retrieval of aerosol optical thickness and aerosol layer height from ...synthetic measurements from the EPIC sensor onboard the Deep Space Climate Observatory. The synthetic measurements are generated using aerosol models derived from AERONET measurements at different sites, while other commonly used aerosol models, such as OPAC, GOCART, OMI, and MODIS databases are used in the retrieval. The numerical analysis is focused on the estimation of retrieval errors when the true aerosol model is unknown. We found that the best aerosol model is the one with a value of the asymmetry parameter and an angular variation of the phase function around the viewing direction that is close to the values corresponding to the reference aerosol model.
Precise knowledge of the location and height of the volcanic sulphur dioxide (SO
2
) plume is essential for accurate determination of SO
2
emitted by volcanic eruptions. Current SO
2
plume height ...retrieval algorithms based on ultraviolet (UV) satellite measurements are very time-consuming and therefore not suitable for near-real-time applications. In this work we present a novel method called the full-physics inverse learning machine (FP-ILM) algorithm for extremely fast and accurate retrieval of the SO
2
plume height. FP-ILM creates a mapping between the spectral radiance and the geophysical parameters of interest using supervised learning methods. The FP-ILM combines smart sampling methods, dimensionality reduction techniques, and various linear and non-linear regression analysis schemes based on principal component analysis and neural networks. The computationally expensive operations in FP-ILM are the radiative transfer model computations of a training dataset and the determination of the inversion operator - these operations are performed off-line. The application of the resulting inversion operator to real measurements is extremely fast since it is based on calculations of simple regression functions. Retrieval of the SO
2
plume height is demonstrated for the volcanic eruptions of Mt. Kasatochi (in 2008) and Eyjafjallajökull (in 2010), measured by the GOME-2 (Global Ozone Monitoring Instrument - 2) UV instrument on-board MetOp-A.
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