Global warming has made the Arctic increasingly available for marine operations and created a demand for reliable operational sea ice forecasts to increase safety. Because ocean-ice numerical models are highly computationally intensive, relatively lightweight ML-based methods may be more efficient for sea ice forecasting. Many studies 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 needed for marine operations. In this article, 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 we can improve the model’s quality by using additional weather data and training on multiple regions to ensure 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.

The eddy‐resolving (1/12°) global ocean reanalysis GLORYS12 is assessed against 14 years (2002–2015) of independent hydrographic observations collected at 59.5°N in the North Atlantic. Two multi‐observations statistical analyses, ISAS‐15 and ARMOR‐3D, and an eddy‐permitting (1/4°) reanalysis, GLORYS025, also contribute to this comparison. The mean thermohaline structure along the 59.5°N section revealed by the observations is well‐reproduced by GLORYS12, except for the overflow waters. A good agreement with observations is found for linear trends over the whole period, exhibiting a dipole‐like pattern with a cooling/freshening in the main thermocline and a warming/salinization below. However, localized discrepancies with observations suggest the need for improvement in the reanalysis system (especially in the overflows representation and the consistency between the forcing and data assimilation system) and the deep observational array. The reanalysis reliably represents the ocean heat content in the upper 700‐m layer but shows significant differences with observations between 700 and 2,000 m. The meridional volume and heat transports across the 59.5°N section are compared for years when ADCP observations were available. The reanalysis does not reproduce the variability observed in the western boundary current but agrees well with observed transports in the other parts of the section. The reanalysis reproduces the major mesoscale eddy features that contribute to the meridional transport and provides the large‐scale context of their location. The analysis of time correlation at all measurement points demonstrated that GLORYS12 is the most accurate among the analyzed datasets used in this study to represent and explain the observed ocean characteristics and variability along that section.

Total Cloud Cover (TCC) retrieval from ground-based optical imagery is a problem that has been tackled by several generations of researchers. The number of human-designed algorithms for the estimation of TCC grows every year. However, there has been no considerable progress in terms of quality, mostly due to the lack of systematic approach to the design of the algorithms, to the assessment of their generalization ability, and to the assessment of the TCC retrieval quality. In this study, we discuss the optimization nature of data-driven schemes for TCC retrieval. In order to compare the algorithms, we propose a framework for the assessment of the algorithms’ characteristics. We present several new algorithms that are based on deep learning techniques: A model for outliers filtering, and a few models for TCC retrieval from all-sky imagery. For training and assessment of data-driven algorithms of this study, we present the Dataset of All-Sky Imagery over the Ocean (DASIO) containing over one million all-sky optical images of the visible sky dome taken in various regions of the world ocean. The research campaigns that contributed to the DASIO collection took place in the Atlantic ocean, the Indian ocean, the Red and Mediterranean seas, and the Arctic ocean. Optical imagery collected during these missions are accompanied by standard meteorological observations of cloudiness characteristics made by experienced observers. We assess the generalization ability of the presented models in several scenarios that differ in terms of the regions selected for the train and test subsets. As a result, we demonstrate that our models based on convolutional neural networks deliver a superior quality compared to all previously published approaches. As a key result, we demonstrate a considerable drop in the ability to generalize the training data in the case of a strong covariate shift between the training and test subsets of imagery which may occur in the case of region-aware subsampling.

An anomalously strong Novorossiysk bora (strong coastal wind) observed in January–February 2012 with synthetic aperture radar (SAR) images from the and satellites is studied using the high resolution Weather Research and Forecasting Model (WRF). The bora was reliably reproduced not only in the narrow coastal zone, but also far in the open part of the Black Sea. As a result of modeling we demonstrated that an optimally configured WRF—ARW model with nested grids and horizontal resolution equal to 9/3/1 km quite well qualitatively and quantitatively reproduces the bora events. The details and structure of the bora (for example, wind strips and other peculiarities) seen on the SAR images were clearly reproduced by numerical modeling. Coupled analysis allowed us to conclude that these two alternative methods of investigating this dangerous meteorological phenomenon are highly effective.

We present multi-sensor measurements from satellites, unmanned aerial vehicle, marine radar, thermal profilers, and repeated conductivity–temperature–depth casts made in the Kara Gates strait connecting the Barents and the Kara Seas during spring tide in August 2021. Analysis of the field data during an 18-h period from four stations provides evidence that a complex sill in the Kara Gates is the site of regular production of intense large-amplitude nonlinear internal waves. Satellite data show a presence of a relatively warm northeastward surface current from the Barents Sea toward the Kara Sea attaining 0.8–0.9 m/s. Triangle-shaped measurements using three thermal profilers revealed pronounced vertical thermocline oscillations up to 40 m associated with propagation of short-period nonlinear internal waves of depression generated by stratified flow passing a system of shallow sills in the strait. The most intense waves were recorded during the ebb tide slackening and reversal when the background flow was predominantly supercritical. Observed internal waves had wavelengths of ~100 m and traveled northeastward with phase speeds of 0.8–0.9 m/s. The total internal wave energy per unit crest length for the largest waves was estimated to be equal to 1.0–1.8 MJ/m.