arXiv subjects

Mikhail Krinitskiy

Publications and source records attributed to Mikhail Krinitskiy.

3 recordsLinked to original sources

arXiv2026

Borey: A High-Resolution Regional Atmosphere-Ocean-Sea Ice-Wave Forecasting System and Hindcast Dataset for the Barents and Kara Seas

Borey is a high-resolution regional modeling and operational forecasting system for the Barents and Kara Seas. It combines WRF for the atmosphere, NEMO-SI3 for the ocean and sea ice, and WW3 for waves on approximately 3--6\,km grids, and generates daily forecasts to 72 hours. We describe the model chain and production workflow and present an accompanying hourly hindcast of surface conditions from August 2015 to August 2023. The archive provides aligned atmosphere, ocean, sea ice, and wave fields for regional marine studies and a baseline for evaluating the operational system. Comparisons with observations and observation-based products show that Borey captures much of the variability in near-surface atmospheric conditions and ocean temperature. Skill in the evaluated WRF, NEMO, and SI3 forecasts changes only modestly across the three-day window. The main limitations are persistent rather than rapidly growing errors: sea surface temperature is generally too cold, sea ice concentration and occurrence are overestimated during seasonal retreat, and significant wave height is underestimated. Borey should therefore complement observation-constrained products. The planned public release will provide hourly surface fields, native grids, provenance information, and validation outputs for regional analysis, model development, and carefully evaluated data-driven forecasting and data-assimilation research.

physics.ao-ph↗

arXiv2022

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

Global warming made the Arctic available for marine operations and created demand for reliable operational sea ice forecasts to make them safe. While ocean-ice numerical models are highly computationally intensive, relatively lightweight ML-based methods may be more efficient in this task. Many works 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 they need for operation. In this work, 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 improve its quality by using additional weather data and training on multiple regions, ensuring 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.

cs.LG↗

arXiv2020

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

Polar mesocyclones (PMCs) and their intense subclass polar lows (PLs) are relatively small atmospheric vortices that form mostly over the ocean in high latitudes. PLs can strongly influence deep ocean water formation since they are associated with strong surface winds and heat fluxes. Detection and tracking of PLs are crucial for understanding the climatological dynamics of PLs and for the analysis of their impacts on other components of the climatic system. At the same time, visual tracking of PLs is a highly time-consuming procedure that requires expert knowledge and extensive examination of source data. There are known procedures involving deep convolutional neural networks (DCNNs) for the detection of large-scale atmospheric phenomena in reanalysis data that demonstrate a high quality of detection. However, one cannot apply these procedures to satellite data directly since, unlike reanalyses, satellite products register all the scales of atmospheric vortices. It is also known that DCNNs were originally designed to be scale-invariant. This leads to the problem of filtering the scale of detected phenomena. There are other problems to be solved, such as a low signal-to-noise ratio of satellite data and an unbalanced number of negative (without PLs) and positive (where a PL is presented) classes in a satellite dataset. In our study, we propose a deep learning approach for the detection of PLs and PMCs in remote sensing data, which addresses class imbalance and scale filtering problems. We also outline potential solutions for other problems, along with promising improvements to the presented approach.

physics.ao-ph↗