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  • 1
    Online Resource
    Online Resource
    Machine Learning Models for Approximating Downward Short-Wave Radiation Flux over the Ocean from All-Sky Optical Imagery Based on DASIO Dataset
    MDPI AG ; 2023
    In:  Remote Sensing Vol. 15, No. 7 ( 2023-03-23), p. 1720-
    Details
    In: Remote Sensing, MDPI AG, Vol. 15, No. 7 ( 2023-03-23), p. 1720-
    Abstract: Downward short-wave (SW) solar radiation is the only essential energy source powering the atmospheric dynamics, ocean dynamics, biochemical processes, and so forth on our planet. Clouds are the main factor limiting the SW flux over the land and the Ocean. For the accurate meteorological measurements of the SW flux one needs expensive equipment-pyranometers. For some cases where one does not need golden-standard quality of measurements, we propose estimating incoming SW radiation flux using all-sky optical RGB imagery which is assumed to incapsulate the whole information about the downward SW flux. We used DASIO all-sky imagery dataset with corresponding SW downward radiation flux measurements registered by an accurate pyranometer. The dataset has been collected in various regions of the World Ocean during several marine campaigns from 2014 to 2021, and it will be updated. We demonstrate the capabilities of several machine learning models in this problem, namely multilinear regression, Random Forests, Gradient Boosting and convolutional neural networks (CNN). We also applied the inverse target frequency (ITF) re-weighting of the training subset in an attempt of improving the SW flux approximation quality. We found that the CNN is capable of approximating downward SW solar radiation with higher accuracy compared to existing empiric parameterizations and known algorithms based on machine learning methods for estimating downward SW flux using remote sensing (MODIS) imagery. The estimates of downward SW radiation flux using all-sky imagery may be of particular use in case of the need for the fast radiative budgets assessment of a site.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs15071720
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2513863-7
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  • 2
    Online Resource
    Online Resource
    Machine Learning for Simulation of Urban Heat Island Dynamics Based on Large-Scale Meteorological Conditions
    MDPI AG ; 2023
    In:  Climate Vol. 11, No. 10 ( 2023-10-02), p. 200-
    Details
    In: Climate, MDPI AG, Vol. 11, No. 10 ( 2023-10-02), p. 200-
    Abstract: This study considers the problem of approximating the temporal dynamics of the urban-rural temperature difference (ΔT) in Moscow megacity using machine learning (ML) models and predictors characterizing large-scale weather conditions. We compare several ML models, including random forests, gradient boosting, support vectors, and multi-layer perceptrons. These models, trained on a 21-year (2001–2021) dataset, successfully capture the diurnal, synoptic-scale, and seasonal variations of the observed ΔT based on predictors derived from rural weather observations or ERA5 reanalysis. Evaluation scores are further improved when using both sources of predictors simultaneously and involving additional features characterizing their temporal dynamics (tendencies and moving averages). Boosting models and support vectors demonstrate the best quality, with RMSE of 0.7 K and R2 〉 0.8 on average over 21 years. For three selected summer and winter months, the best ML models forced only by reanalysis outperform the comprehensive hydrodynamic mesoscale model COSMO, supplied by an urban canopy scheme with detailed city-descriptive parameters and forced by the same reanalysis. However, for a longer period (1977–2023), the ML models are not able to fully reproduce the observed trend of ΔT increase, confirming that this trend is largely (by 60–70%) driven by megacity growth. Feature importance assessment indicates the atmospheric boundary layer height as the most important control factor for the ΔT and highlights the relevance of temperature tendencies as additional predictors.
    Type of Medium: Online Resource
    ISSN: 2225-1154
    URL: Article
    DOI: 10.3390/cli11100200
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2720343-8
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  • 3
    Online Resource
    Online Resource
    RAS-NAAD: 40-yr High-Resolution North Atlantic Atmospheric Hindcast for Multipurpose Applications (New Dataset for the Regional Mesoscale Studies in the Atmosphere and the Ocean)
    American Meteorological Society ; 2020
    In:  Journal of Applied Meteorology and Climatology Vol. 59, No. 5 ( 2020-05), p. 793-817
    Details
    In: Journal of Applied Meteorology and Climatology, American Meteorological Society, Vol. 59, No. 5 ( 2020-05), p. 793-817
    Abstract: We present in this paper the results of the Russian Academy of Sciences North Atlantic Atmospheric Downscaling (RAS-NAAD) project, which provides a 40-yr 3D hindcast of the North Atlantic (10°–80°N) atmosphere at 14-km spatial resolution with 50 levels in the vertical direction (up to 50 hPa), performed with a regional setting of the WRF-ARW 3.8.1 model for the period 1979–2018 and forced by ERA-Interim as a lateral boundary condition. The dataset provides a variety of surface and free-atmosphere parameters at sigma model levels and meets many demands of meteorologists, climate scientists, and oceanographers working in both research and operational domains. Three-dimensional model output at 3-hourly time resolution is freely available to the users. Our evaluation demonstrates a realistic representation of most characteristics in both datasets and also identifies biases mostly in the ice-covered regions. High-resolution and nonhydrostatic model settings in NAAD resolve mesoscale dynamics first of all in the subpolar latitudes. NAAD also provides a new view of the North Atlantic extratropical cyclone activity with a much larger number of cyclones as compared with most reanalyses. It also effectively captures highly localized mechanisms of atmospheric moisture transports. Applications of NAAD to ocean circulation and wave modeling are demonstrated.
    Type of Medium: Online Resource
    ISSN: 1558-8424 , 1558-8432
    URL: Article
    DOI: 10.1175/JAMC-D-19-0190.1
    RVK:
    UA 4547
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2020
    detail.hit.zdb_id: 2227779-1
    detail.hit.zdb_id: 2227759-6
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  • 4
    Online Resource
    Online Resource
    Detection Uncertainty Matters for Understanding Atmospheric Rivers
    American Meteorological Society ; 2020
    In:  Bulletin of the American Meteorological Society Vol. 101, No. 6 ( 2020-06), p. E790-E796
    Details
    In: Bulletin of the American Meteorological Society, American Meteorological Society, Vol. 101, No. 6 ( 2020-06), p. E790-E796
    Type of Medium: Online Resource
    ISSN: 0003-0007 , 1520-0477
    URL: Article
    DOI: 10.1175/BAMS-D-19-0348.1
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2020
    detail.hit.zdb_id: 2029396-3
    detail.hit.zdb_id: 419957-1
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  • 5
    Online Resource
    Online Resource
    On the Generalization Ability of Data-Driven Models in the Problem of Total Cloud Cover Retrieval
    MDPI AG ; 2021
    In:  Remote Sensing Vol. 13, No. 2 ( 2021-01-19), p. 326-
    Details
    In: Remote Sensing, MDPI AG, Vol. 13, No. 2 ( 2021-01-19), p. 326-
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs13020326
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2513863-7
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  • 6
    Online Resource
    Online Resource
    Towards the Accurate Automatic Detection of Mesoscale Convective Systems in Remote Sensing Data: From Data Mining to Deep Learning Models and Their Applications
    MDPI AG ; 2023
    In:  Remote Sensing Vol. 15, No. 14 ( 2023-07-11), p. 3493-
    Details
    In: Remote Sensing, MDPI AG, Vol. 15, No. 14 ( 2023-07-11), p. 3493-
    Abstract: Mesoscale convective systems (MCSs) and associated hazardous meteorological phenomena cause considerable economic damage and even loss of lives in the mid-latitudes. The mechanisms behind the formation and intensification of MCSs are still not well understood due to limited observational data and inaccurate climate models. Improving the prediction and understanding of MCSs is a high-priority area in hydrometeorology. One may study MCSs either employing high-resolution atmospheric modeling or through the analysis of remote sensing images which are known to reflect some of the characteristics of MCSs, including high temperature gradients of cloud-top, specific spatial shapes of temperature patterns, etc. However, research on MCSs using remote sensing data is limited by inadequate (in size) databases of satellite-identified MCSs and poorly equipped automated tools for MCS identification and tracking. In this study, we present (a) the GeoAnnotateAssisted tool for fast and convenient visual identification of MCSs in satellite imagery, which is capable of providing AI-generated suggestions of MCS labels; (b) the Dataset of Mesoscale Convective Systems over the European Territory of Russia (DaMesCoS-ETR), which we created using this tool, and (c) the Deep Convolutional Neural Network for the Identification of Mesoscale Convective Systems (MesCoSNet), constructed following the RetinaNet architecture, which is capable of identifying MCSs in Meteosat MSG/SEVIRI data. We demonstrate that our neural network, optimized in terms of its hyperparameters, provides high MCS identification quality (mAP=0.75, true positive rate TPR=0.61) and a well-specified detection uncertainty (false alarm ratio FAR=0.36). Additionally, we demonstrate potential applications of the GeoAnnotateAssisted labelling tool, the DaMesCoS-ETR dataset, and the MesCoSNet neural network in addressing MCS research challenges. Specifically, we present the climatology of axisymmetric MCSs over the European territory of Russia from 2014 to 2020 during summer seasons (May to September), obtained using MesCoSNet with Meteosat MSG/SEVIRI data. The automated identification of MCSs by the MesCoSNet artificial neural network opens up new avenues for previously unattainable MCS research topics.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs15143493
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2513863-7
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  • 7
    Online Resource
    Online Resource
    Improved sea surface salinity data for the Arctic Ocean derived from SMAP satellite data using machine learning approaches
    Frontiers Media SA ; 2024
    In:  Frontiers in Marine Science Vol. 11 ( 2024-3-25)
    Details
    In: Frontiers in Marine Science, Frontiers Media SA, Vol. 11 ( 2024-3-25)
    Abstract: Salinity is among the key climate characteristics of the World Ocean. During the last 15 years, sea surface salinity (SSS) is measured using satellite passive microwave sensors. Standard retrieving SSS algorithms from remote sensing data were developed and verified for the most typical temperature and salinity values of the World Ocean. However, they have far lower accuracy for the Arctic Ocean, especially its shelf areas, which are influenced by large river runoff and have low typical temperature and salinity values. In this study, an improved algorithm has been developed to retrieve SSS in the Arctic Ocean during ice-free season, based on Soil Moisture Active Passive (SMAP) mission data, and using machine learning approaches. Extensive database of in situ salinity measurements in the Russian Arctic seas collected during multiple field surveys is applied to train and validate the machine learning models. The error in SSS retrieval of the developed algorithm compared to the standard algorithm reduced from 3.15 to 2.15 psu, and the correlation with in situ data increased from 0.82 to 0.90. The obtained daily SSS fields are important to improve accurate assessment of spatial and temporal variability of large river plumes in the Arctic Ocean.
    Type of Medium: Online Resource
    ISSN: 2296-7745
    URL: Article
    DOI: 10.3389/fmars.2024.1358882
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2024
    detail.hit.zdb_id: 2757748-X
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  • 8
    Online Resource
    Online Resource
    Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting
    MDPI AG ; 2022
    In:  Remote Sensing Vol. 14, No. 22 ( 2022-11-17), p. 5837-
    Details
    In: Remote Sensing, MDPI AG, Vol. 14, No. 22 ( 2022-11-17), p. 5837-
    Abstract: Global warming has made the Arctic increasingly available for marine operations and created a demand for reliable operational sea ice forecasts to increase safety. Because ocean-ice numerical models are highly computationally intensive, relatively lightweight ML-based methods may be more efficient for sea ice forecasting. Many studies have exploited different deep learning models alongside classical approaches for predicting sea ice concentration in the Arctic. However, only a few focus on daily operational forecasts and consider the real-time availability of data needed for marine operations. In this article, we aim to close this gap and investigate the performance of the U-Net model trained in two regimes for predicting sea ice for up to the next 10 days. We show that this deep learning model can outperform simple baselines by a significant margin, and we can improve the model’s quality by using additional weather data and training on multiple regions to ensure its generalization abilities. As a practical outcome, we build a fast and flexible tool that produces operational sea ice forecasts in the Barents Sea, the Labrador Sea, and the Laptev Sea regions.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs14225837
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2513863-7
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  • 9
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    Tracking of atmospheric phenomena with artificial neural networks: a supervised approach
    Elsevier BV ; 2021
    In:  Procedia Computer Science Vol. 186 ( 2021), p. 403-410
    Details
    In: Procedia Computer Science, Elsevier BV, Vol. 186 ( 2021), p. 403-410
    Type of Medium: Online Resource
    ISSN: 1877-0509
    URL: Article
    DOI: 10.1016/j.procs.2021.04.209
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2021
    detail.hit.zdb_id: 2557358-5
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  • 10
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    Deep Convolutional Neural Networks Capabilities for Binary Classification of Polar Mesocyclones in Satellite Mosaics
    MDPI AG ; 2018
    In:  Atmosphere Vol. 9, No. 11 ( 2018-10-31), p. 426-
    Details
    In: Atmosphere, MDPI AG, Vol. 9, No. 11 ( 2018-10-31), p. 426-
    Abstract: Polar mesocyclones (MCs) are small marine atmospheric vortices. The class of intense MCs, called polar lows, are accompanied by extremely strong surface winds and heat fluxes and thus largely influencing deep ocean water formation in the polar regions. Accurate detection of polar mesocyclones in high-resolution satellite data, while challenging, is a time-consuming task, when performed manually. Existing algorithms for the automatic detection of polar mesocyclones are based on the conventional analysis of patterns of cloudiness and they involve different empirically defined thresholds of geophysical variables. As a result, various detection methods typically reveal very different results when applied to a single dataset. We develop a conceptually novel approach for the detection of MCs based on the use of deep convolutional neural networks (DCNNs). As a first step, we demonstrate that DCNN model is capable of performing binary classification of 500 × 500 km patches of satellite images regarding MC patterns presence in it. The training dataset is based on the reference database of MCs manually tracked in the Southern Hemisphere from satellite mosaics. We use a subset of this database with MC diameters falling in the range of 200–400 km. This dataset is further used for testing several different DCNN setups, specifically, DCNN built “from scratch”, DCNN based on VGG16 pre-trained weights also engaging the Transfer Learning technique, and DCNN based on VGG16 with Fine Tuning technique. Each of these networks is further applied to both infrared (IR) and a combination of infrared and water vapor (IR + WV) satellite imagery. The best skills (97% in terms of the binary classification accuracy score) is achieved with the model that averages the estimates of the ensemble of different DCNNs. The algorithm can be further extended to the automatic identification and tracking numerical scheme and applied to other atmospheric phenomena that are characterized by a distinct signature in satellite imagery.
    Type of Medium: Online Resource
    ISSN: 2073-4433
    URL: Article
    DOI: 10.3390/atmos9110426
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2605928-9
    SSG: 23
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