Mikhail Krinitskiy

Research scientist

Research interests

Applied machine learning, applied deep learning, image processing, pattern recognition, unsupervised learning, sea-air interactions, climate research, remote sensing, mesoscale atmospheric phenomena, polar vortex activity, meteorological measurements, high resolution statistical atmospheric modeling, statistical downscaling, neural differential equations. Current research is focused on applications of deep learning methods in problems of environmental science, atmospheric science, and oceanology.

Field experience

  • Research mission #44 of R/V “Akademik Ioffe” in North Atlantic from Gdansk (Poland) to Iqaluit (Canada)
  • Research mission #45 of R/V “Akademik Ioffe” in North Atlantic from Reykjavik (Iceland) to Rotterdam (Netherlands)
  • Research mission #49 of R/V “Akademik Ioffe” in North Atlantic from Gdansk (Poland) to Halifax (Canada)
  • (Head of the scientific mission) Research mission #31 of R/V “Akademik Nikolaj Strakhov” from Colombo (Sri Lanka) to Kaliningrad (Russia)
  • Research mission #52 of R/V “Akademik Ioffe” in North Atlantic
  • (Head of the scientific mission) Research mission #42 of R/V “Akademik Boris Petrov” from Singapore to Kaliningrad (Russia)
  • Research missions ##57,58 of R/V “Akademik Ioffe” in North Atlantic, Arctic. Kaliningrad – Archargelsk – Kaliningrad (Russia)

Publications

2024
Estimating Significant Wave Height from X-Band Navigation Radar Using Convolutional Neural Networks
ISSN 0027-1349, Moscow University Physics Bulletin, 2023, Vol. 78, Suppl. 1, pp. S128–S137.
M.A. Krinitskiy, V.A. Golikov, N.N. Anikin, A.I. Suslov, A.V. Gavrikov, and N.D. Tilinina
DOI: 10.3103/S0027134923070159
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Improvement of the AI-Based Estimation of Significant Wave Height Based on Preliminary Training on Synthetic X-Band Radar Sea Clutter Images
Moscow University Physics Bulletin, 2023, Vol. 78, Suppl.1, pp. S188–S201. © Allerton Press, Inc., 2023
V. Yu. Rezvov, M. A. Krinitskiy, V. A. Golikov & N. D. Tilinina
DOI: 10.3103/S0027134923070275
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2023
Machine Learning for Simulation of Urban Heat Island Dynamics Based on Large-Scale Meteorological Conditions
Climate 2023, 11(10), 200
Mikhail Varentsov, Mikhail Krinitskiy, Victor Stepanenko
DOI: 10.3390/cli11100200
Methods of Identification Coherent Structures in Atmospheric Numerical Data
Environmental Sciences Proceedings, 2023, 26(1), 198
Vasilisa Koshkina, Alexander Gavrikov and Mikhail Krinitskiy
DOI: 10.3390/environsciproc2023026198
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Towards the Accurate Automatic Detection of Mesoscale Convective Systems in Remote Sensing Data: From Data Mining to Deep Learning Models and Their Applications
Remote Sensing (MDPI). 2023, 15(14), 3493
Mikhail Krinitskiy, Alexander Sprygin, Svyatoslav Elizarov, Alexandra Narizhnaya, Andrei Shikhov and Alexander Chernokulsky
DOI: 10.3390/rs15143493
Machine Learning Models for Approximating Downward Short-Wave Radiation Flux over the Ocean from All-Sky Optical Imagery Based on DASIO Dataset
Remote Sensing (MDPI). 2023, 15(7), 1720
Mikhail Krinitskiy, Vasilisa Koshkina, Mikhail Borisov, Nikita Anikin, Sergey Gulev and Maria Artemeva
DOI: 10.3390/rs15071720
2022
Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting
Remote Sensing (MDPI). 2022, 14(22), 5837
Timofey Grigoryev, Polina Verezemskaya, Mikhail Krinitskiy, Nikita Anikin, Alexander Gavrikov, Ilya Trofimov, Nikita Balabin, Aleksei Shpilman, Andrei Eremchenko, Sergey Gulev, Evgeny Burnaev and Vladimir Vanovskiy
DOI: 10.3390/rs14225837
2021
Artificial Neural Networks for the Identification of Partial Differential Equations of LandSurface Schemes in Climate Models
Proceedings of Science, Vol. 410, PoS(DLCP2021)005, 2021, The 5th International Workshop on Deep Learning in Computational Physics (DLCP2021), Moscow, 28-29 June 2021
M. Krinitskiy, V. Stepanenko and R. Chernyshev
DOI: 10.22323/1.410.0005
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Tracking of atmospheric phenomena with artificial neural networks: A supervised approach
Procedia Computer Science, Vol. 186, 2021, p.p. 403-410
Mikhail Krinitskiy, Kirill Grashchenkov, Natalia Tilinina, Sergey Gulev
DOI: 10.1016/j.procs.2021.04.209
2020
Response of the atmospheric rivers and storm tracks to the Sudden Stratospheric Warming events on the basis of North Atlantic Atmospheric Downscaling (1979+)
2020 IOP Conf. Ser.: Earth Environ. Sci. 606 012011
A V Gavrikov, M Krinitsky, N Tilinina, Y Zyulyaeva, A Dufour and S K Gulev
DOI: 10.1088/1755-1315/606/1/012011
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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)
J. Appl. Meteor. Climatol., 59, 793–817,
Alexander Gavrikov, Sergey K. Gulev, Margarita Markina, Natalia Tilinina, Polina Verezemskaya, Bernard Barnier, Ambroise Dufour, Olga Zolina, Yulia Zyulyaeva, Mikhail Krinitskiy, Ivan Okhlopkov, and Alexey Sokov
DOI: 10.1175/JAMC-D-19-0190.1
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Detection Uncertainty Matters for Understanding Atmospheric Rivers
BAMS
Travis A. O’Brien, Ashley E. Payne, Christine A. Shields, Jonathan Rutz, Swen Brands, Christopher Castellano, Jiayi Chen, William Cleveland, Michael J. DeFlorio, Naomi Goldenson, Irina Gorodetskaya, H ́ector Inda D ́ıaz, Karthik Kashinath, Brian Kawzenuk, Sol Kim, Mikhail Krinitskiy, Juan M. Lora, Beth McClenny, Allison Michaelis, John O’Brien, Christina M. Patricola, Alexandre M. Ramos, Eric J. Shearer, Wen-Wen Tung, Paul Ullrich, Michael F. Wehner, Kevin Yang, Rudong Zhang, Zhenhai Zhang, Yang Zhou
DOI: 10.1175/BAMS-D-19-0348.1
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2019
Clustering of Polar Vortex States Using Convolutional Autoencoders
Proceedings of the Information Technologies and High-Performance Computing
Mikhail Krinitskiy, Yulia Zyulyaeva, Sergey Gulev
2018
Deep Convolutional Neural Networks Capabilities for Binary Classification of Polar Mesocyclones in Satellite Mosaics
Atmosphere 2018, 9(11), 426
Krinitskiy, M., P. Verezemskaya, K. Grashchenkov, N. Tilinina, S. Gulev, M. Lazzara
DOI: 10.3390/atmos9110426
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Tropical cyclogenesis in warm climates simulated by a cloud-system resolving model
Climate Dynamics, 2018, 50
Fedorov, A., L. Muir, W. Boos, and J. Studholme
DOI: 10.1007/s00382-018-4134-2
2016
Modification of Globwave satellite altimetry database for sea wave field diagnostics
Oceanology 56(2):301-306
Alexander Gavrikov, Mikhail Krinitskiy, Vika Grigorieva
DOI: 10.1134/S0001437016020065
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Модификация базы данных спутниковой альтиметрии GLobWave для решения задач диагностики поля морского волнения
Океанология 56(2):322-327
А. В. Гавриков, М. А. Криницкий, В. Г. Григорьева
DOI: 10.7868/S0030157416020064
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AI-based estimation of significant wave height from X-band shipbourne navigation radar using convolutional neural networks
Moscow University Physics Bulletin, 2023, Vol. 78, Suppl.1, pp. S128–S137. © Allerton Press, Inc., 2023
M. A. Krinitskiy, V. A. Golikov, N. N. Anikin, A. I. Suslov, A. V. Gavrikov & N. D. Tilinina
DOI: 10.3103/S0027134923070159
PDF download
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