M. Krinitskiy

发表

Clustering of polar vortex states using convolutional autoencoders

S. Gulev, M. Krinitskiy, Y. Zyulyaeva, 2019 .

Detection Uncertainty Matters for Understanding Atmospheric Rivers

John P. O’Brien, W. Cleveland, K. Kashinath, 2019, Bulletin of the American Meteorological Society.

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

A. Shpilman, S. Gulev, M. Krinitskiy, 2022, Remote. Sens..

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

S. Gulev, M. Krinitskiy, Vadim Yuryevich Rezvov, 2022, Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022).

Artificial Neural Networks for the Identification of Partial Differential Equations of LandSurface Schemes in Climate Models

M. Krinitskiy, V. Stepanenko, R. Chernyshev, 2021, Proceedings of The 5th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2021).

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)

S. Gulev, O. Zolina, N. Tilinina, 2019, Journal of Applied Meteorology and Climatology.

Visual clustering of marine sediment particles using a combination of unsupervised machine learning methods

M. Krinitskiy, V. Golikov, D. Borisov, 2022, Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022).

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

M. A. Krinitskiy, M. Krinitskiy, 2017, Oceanology.

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

S. Gulev, N. Tilinina, M. Lazzara, 2018, Atmosphere.

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

Mikhail Krinitskiy, Marina Aleksandrova, Alexander Gavrikov, 2020, Remote. Sens..

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

S. Gulev, M. Krinitskiy, N. Anikin, 2023, Remote Sensing.

Data-driven approximation of downward solar radiation flux based on all-sky optical imagery using machine learning models trained on DASIO dataset

S. Gulev, M. Krinitskiy, N. Anikin, 2022, Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022).

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

A. V. Sinitsyn, M. A. Krinitskiy, M. Krinitskiy, 2016, Oceanology.

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

Mikhail Krinitskiy, M. Krinitskiy, 2017 .