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

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

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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

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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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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