All Stories

  1. The use of a U-Net neural network for high-resolutionc urban climate modelling

    Article • May 2025, Copernicus GmbH

    Mikhail Krinitskiy

  2. Predicting Characteristics of Salmon Return Migration Using Machine Learning Models

    Article • January 2025, Springer Science + Business Media

    Mikhail Krinitskiy

  3. Estimating downward short-wave solar flux from all-sky RGB imagery using machine learning trained on DASIO dataset

    Article • January 2024, American Institute of Physics

    Mikhail Krinitskiy

  4. Improving Data-Driven Estimation of Significant Wave Height through Preliminary Training on Synthetic X-band Radar Sea Clutter Imagery

    Article • December 2023, MDPI AG

    Mikhail Krinitskiy

  5. Analyzing Urban Heat Island in Moscow with Machine Learning

    Article • Climate, October 2023, MDPI AG

    Mikhail Krinitskiy

  6. Methods of Identification Coherent Structures in Atmospheric Numerical Data

    Article • September 2023, MDPI AG

    Mikhail Krinitskiy

  7. Towards the Accurate Automatic Detection of Mesoscale Convective Systems in Remote Sensing Data: From Data Mining to Deep Learning Models and Their Applications

    Article • Remote Sensing, July 2023, MDPI AG

    Mikhail Krinitskiy

  8. Machine Learning Models for Approximating Downward Short-Wave Radiation Flux over the Ocean from All-Sky Optical Imagery Based on DASIO Dataset

    Article • Remote Sensing, March 2023, MDPI AG

    Mikhail Krinitskiy

  9. Machine Learning Models for Approximating Downward Short-wave Radiation Flux over the Ocean from All-sky Optical Imagery based on DASIO Dataset

    Article • January 2023, MDPI AG

    Mikhail Krinitskiy

  10. Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting

    Article • Remote Sensing, November 2022, MDPI AG

    Mikhail Krinitskiy

  11. Approximation of high-resolution surface wind speed in the North Atlantic using discriminative and generative neural models based on RAS-NAAD 40-year hindcast

    Article • November 2022, Sissa Medialab, SRL

    Mikhail Krinitskiy

  12. A General Neural-Networks-Based Method for Identification of Partial Differential Equations, Implemented on a Novel AI Accelerator

    Article • Supercomputing Frontiers and Innovations, September 2022, Federal State Funded Educational Institution of Higher Professional Education

    Mikhail Krinitskiy

  13. Wind waves in the North Atlantic from ship navigational radar: SeaVision development and its validation with the Spotter wave buoy and WaveWatch III

    Article • Earth System Science Data, August 2022, Copernicus GmbH

    Mikhail Krinitskiy

  14. Projected changes in the near-surface atmosphere over the Barents Sea based on CMIP5 scenarios

    Article • Russian Journal of Earth Sciences, May 2022, Geophysical Center of the Russian Academy of Sciences

    Mikhail Krinitskiy

  15. Approximating downward short-wave radiation flux using all-sky optical imagery using machine learning trained on DASIO dataset.

    Article • March 2022, Copernicus GmbH

    Mikhail Krinitskiy

  16. Applying neural network for identification of land surface model parameters

    Article • March 2021, Copernicus GmbH

    Mikhail Krinitskiy

  17. Artificial neural networks for clustering sediment grains in microphotographs.

    Article • March 2021, Copernicus GmbH

    Mikhail Krinitskiy

  18. Comparison of AI-based approaches for statistical downscaling of surface wind fields in the North Atlantic

    Article • March 2021, Copernicus GmbH

    Mikhail Krinitskiy

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

    Article • Remote Sensing, January 2021, MDPI AG

    Mikhail Krinitskiy

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

    Article • November 2020, MDPI AG

    Mikhail Krinitskiy

  21. Machine learning methods for the detection of polar lows in satellite mosaics: major issues and their solutions

    Article • IOP Conference Series Earth and Environmental Science, November 2020, Institute of Physics Publishing

    Mikhail Krinitskiy

  22. Detection Uncertainty Matters for Understanding Atmospheric Rivers

    Article • Bulletin of the American Meteorological Society, June 2020, American Meteorological Society

    Mikhail Krinitskiy

  23. 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)

    Article • Journal of Applied Meteorology and Climatology, May 2020, American Meteorological Society

    Sergey Gulev, Mikhail Krinitskiy

  24. Downscaling of surface wind speed over the North Atlantic using conditional Generative Adversarial Networks

    Article • March 2020, Copernicus GmbH

    Mikhail Krinitskiy

  25. Tracking of mesoscale atmospheric phenomena in satellite mosaics using deep neural networks

    Article • March 2020, Copernicus GmbH

    Mikhail Krinitskiy

  26. Wave steepness from satellite altimetry for wave dynamics and climate studies

    Article • Russian Journal of Earth Sciences, November 2018, Geophysical Center of the Russian Academy of Sciences

    Mikhail Krinitskiy

  27. Deep Convolutional Neural Networks Capabilities for Binary Classification of Polar Mesocyclones in Satellite Mosaics

    Article • Atmosphere, October 2018, MDPI AG

    Mikhail Krinitskiy

  28. Deep Convolutional Neural Networks Capabilities for Binary Classification of Polar Mesocyclones in Satellite Mosaics

    Article • October 2018, MDPI AG

    Mikhail Krinitskiy

  29. Deep Convolutional Neural Networks Capabilities for Binary Classification of Polar Mesocyclones in Satellite Mosaics

    Article • October 2018, MDPI AG

    Mikhail Krinitskiy

  30. Deep Convolutional Neural Networks Capabilities for Binary Classification of Polar Mesocyclones in Satellite Mosaics

    Article • September 2018, MDPI AG

    Mikhail Krinitskiy

  31. Wave Steepness from Satellite Altimetry for Wave Dynamics and Climate Studies

    Article • December 2017, MDPI AG

    Dr Sergei Badulin, Mikhail Krinitskiy

  32. Application of machine learning methods to the solar disk state detection by all-sky images over the ocean

    Article • Oceanology, March 2017, Pleiades Publishing Ltd

    Mikhail Krinitskiy

  33. Cloud cover estimation optical package: New facility, algorithms and techniques

    Article • January 2017, American Institute of Physics

    Mikhail Krinitskiy

  34. Adaptive algorithm for cloud cover estimation from all-sky images over the sea

    Article • Oceanology, May 2016, Pleiades Publishing Ltd

    Mikhail Krinitskiy

  35. Modification of Globwave satellite altimetry database for sea wave field diagnostics

    Article • Oceanology, March 2016, Pleiades Publishing Ltd

    Mikhail Krinitskiy