Atmospheric parameters at the 6-m Big Telescope Alt-azimuthal site

Shikhovtsev, A. Yu.; Bolbasova, L. A.; Kovadlo, P. G.; Kiselev, Alexander V.

doi:10.1093/mnras/staa156

Cited by 21 publications

(18 citation statements)

References 18 publications

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“…In fact, the image quality is highly dependent on the turbulence velocity, often determined by the wind speed at a pressure level of 200 hPa (V200). The suitability of a given site for good astronomical observations is often evaluated using the information provided by V200, and this information is useful for making recommendations for AO systems [ 62 ]. Some authors have made astroclimatic characteristics for different observatory sites in order to be used to plan the observing time, as well as to facilitate the development of AO systems [ 63 , 64 ], since, for example, the vertical wind speed profiles are one of the most important characteristics for the determination of the dynamic range of an adaptive optics system [ 62 ].…”

Section: Discussionmentioning

confidence: 99%

“…The suitability of a given site for good astronomical observations is often evaluated using the information provided by V200, and this information is useful for making recommendations for AO systems [ 62 ]. Some authors have made astroclimatic characteristics for different observatory sites in order to be used to plan the observing time, as well as to facilitate the development of AO systems [ 63 , 64 ], since, for example, the vertical wind speed profiles are one of the most important characteristics for the determination of the dynamic range of an adaptive optics system [ 62 ]. For example, Goodwin et al [ 65 ] developed a model of the optical turbulence profile (model-OPT) to be used as input to simulations to evaluate the performance of the AO system of the Giant Magellan Telescope [ 5 ].…”

Section: Discussionmentioning

confidence: 99%

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Compensation Techniques Aimed at Mitigating Vibrations in Optical Ground-Based Telescopes: A Systematic Review

Palacios-Navarro

Martínez

Ferrer

et al. 2021

Sensors

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The objective of this study was to evaluate the performance of the different systems and techniques aimed at suppressing vibrations on optical ground-based telescopes. We identified the studies by searching three electronic databases (Science Direct, IEEE library and Web of Science) from the year 2000 to December 2020. The studies were eligible if they proposed systems focused on mitigating the effects of vibrations in optical telescopes and brought performance data. A total of nine studies met our eligibility criteria. Current evidence confirms the feasibility of adaptative optics (AO) systems based on closed-loop control to mitigate vibrations, although variations and additions should be made depending on their nature and characteristics in order to improve the performance of the proposed techniques. This systematic review was conducted to provide a state-of-the-art of the methods and techniques that have been developed over the past two decades. The review also points out some issues that demand future research.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Compensation Techniques Aimed at Mitigating Vibrations in Optical Ground-Based Telescopes: A Systematic Review

Palacios-Navarro

Martínez

Ferrer

et al. 2021

Sensors

View full text Add to dashboard Cite

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“…As the problem is considered soft classification, each of the target vector elements is considered an independent random variable with Bernoulli distribution. Thus, the loss function, in this case, is just the sum of individual binary cross-entropy loss functions for all the K components of the target vector, as shown in Equation (8).…”

Section: Tcc Retrieval As Ordinal Regressionmentioning

confidence: 99%

“…In satellite observations, clouds mask land and ocean surface, thus significantly decreasing the rate of useful satellite information about the surface in cloud-rich regions. In optical observations in land-based astronomy, clouds are unavoidable obstacles as well [7,8]. Thus, accurate estimating and forecasting of cloud characteristics is crucial for observational time planning [9,10].…”

Section: Introductionmentioning

confidence: 99%

On the Generalization Ability of Data-Driven Models in the Problem of Total Cloud Cover Retrieval

et al. 2021

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Total Cloud Cover (TCC) retrieval from ground-based optical imagery is a problem that has been tackled by several generations of researchers. The number of human-designed algorithms for the estimation of TCC grows every year. However, there has been no considerable progress in terms of quality, mostly due to the lack of systematic approach to the design of the algorithms, to the assessment of their generalization ability, and to the assessment of the TCC retrieval quality. In this study, we discuss the optimization nature of data-driven schemes for TCC retrieval. In order to compare the algorithms, we propose a framework for the assessment of the algorithms’ characteristics. We present several new algorithms that are based on deep learning techniques: A model for outliers filtering, and a few models for TCC retrieval from all-sky imagery. For training and assessment of data-driven algorithms of this study, we present the Dataset of All-Sky Imagery over the Ocean (DASIO) containing over one million all-sky optical images of the visible sky dome taken in various regions of the world ocean. The research campaigns that contributed to the DASIO collection took place in the Atlantic ocean, the Indian ocean, the Red and Mediterranean seas, and the Arctic ocean. Optical imagery collected during these missions are accompanied by standard meteorological observations of cloudiness characteristics made by experienced observers. We assess the generalization ability of the presented models in several scenarios that differ in terms of the regions selected for the train and test subsets. As a result, we demonstrate that our models based on convolutional neural networks deliver a superior quality compared to all previously published approaches. As a key result, we demonstrate a considerable drop in the ability to generalize the training data in the case of a strong covariate shift between the training and test subsets of imagery which may occur in the case of region-aware subsampling.

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“…The ERA5 launched by ECMWF is currently the most powerful global climate atmospheric reanalysis tool, which provides hourly data and uncertainty estimates of various atmospheric, land and ocean state parameters. Additionally, it has also been widely used for atmospheric parameter estimation [36][37][38]. Existing studies [36,[38][39][40] have shown the accuracy and applicability of wind speed and temperature in ERA5 data.…”

Section: Era5 Accuracy Analysismentioning

confidence: 99%

Estimation of PM2.5 Concentration Using Deep Bayesian Model Considering Spatial Multiscale

et al. 2021

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Directly establishing the relationship between satellite data and PM2.5 concentration through deep learning methods for PM2.5 concentration estimation is an important means for estimating regional PM2.5 concentration. However, due to the lack of consideration of uncertainty in deep learning methods, methods based on deep learning have certain overfitting problems in the process of PM2.5 estimation. In response to this problem, this paper designs a deep Bayesian PM2.5 estimation model that takes into account multiple scales. The model uses a Bayesian neural network to describe key parameters a priori, provide regularization effects to the neural network, perform posterior inference through parameters, and take into account the characteristics of data uncertainty, which is used to alleviate the problem of model overfitting and to improve the generalization ability of the model. In addition, different-scale Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite data and ERA5 reanalysis data were used as input to the model to strengthen the model’s perception of different-scale features of the atmosphere, as well as to further enhance the model’s PM2.5 estimation accuracy and generalization ability. Experiments with Anhui Province as the research area showed that the R2 of this method on the independent test set was 0.78, which was higher than that of the DNN, random forest, and BNN models that do not consider the impact of the surrounding environment; moreover, the RMSE was 19.45 μg·m−3, which was also lower than the three compared models. In the experiment of different seasons in 2019, compared with the other three models, the estimation accuracy was significantly reduced; however, the R2 of the model in this paper could still reach 0.66 or more. Thus, the model in this paper has a higher accuracy and better generalization ability.

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Atmospheric parameters at the 6-m Big Telescope Alt-azimuthal site

Cited by 21 publications

References 18 publications

Compensation Techniques Aimed at Mitigating Vibrations in Optical Ground-Based Telescopes: A Systematic Review

Compensation Techniques Aimed at Mitigating Vibrations in Optical Ground-Based Telescopes: A Systematic Review

On the Generalization Ability of Data-Driven Models in the Problem of Total Cloud Cover Retrieval

Estimation of PM2.5 Concentration Using Deep Bayesian Model Considering Spatial Multiscale

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