Satellite-Derived Spatio-Temporal Distribution and Parameters of North Atlantic Polar Lows for 2015–2017

Golubkin, Pavel; Smirnova, Julia; Bobylev, Leonid

doi:10.3390/atmos12020224

Cited by 11 publications

(14 citation statements)

References 31 publications

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“…Both these aspects point toward that PLs in a weak shear being mainly in the decaying stage in accordance to Stoll et al (2021). Stoll et al, 2018;Zahn and von Storch, 2008a), the Nordic Seas features globally the highest density of PLs, in agreement with the observational dataset of Golubkin et al (2021).…”

Section: Differences In the Shear Categoriessupporting

confidence: 85%

“…In previous PL climatologies based on reanalysis datasets, the Irminger Sea features the highest density (Zahn et al, 2008;Stoll et al, 2018). However, Golubkin et al (2021) doubted that the Irminger Sea has a larger PL density than the Nordic Seas by investigation of their manually-derived PL list. Accordingly, this climatology has a higher density of PLs in the Nordic Seas than the Irminger Sea, with the latter being globally the area of second-highest density.…”

Section: Area Of Highest Polar-low Densitymentioning

confidence: 95%

“…The Golubkin list contains 131 PLs of the year 2015 -2017 for the whole North Atlantic, including the Labrador, Irminger, and Nordic Seas (Golubkin et al, 2021). It was derived by inspection of different satellite imagery combined with synoptic weather charts.…”

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confidence: 99%

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A global climatology of polar lows investigated for local differences and wind-shear environments

Stoll

2021

Preprint

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Abstract. Polar lows are intense mesoscale cyclones developing in marine polar air masses. This study presents a new global climatology of polar lows based on the ERA-5 reanalysis for the years 1979–2020. Criteria for the detection of polar lows are derived based on a comparison of six polar-low archives with cyclones derived by a mesoscale tracking algorithm. The characteristics associated with polar lows are considered by the criteria: (i) intense cyclone: large relative vorticity, (ii) mesoscale: small vortex diameter, and (iii) development in the marine polar air masses: combination of low dry-static stability and low potential temperature at the tropopause. Polar lows develop in all marine areas adjacent to sea ice or cold landmasses, mainly in the winter half-year. The length and intensity of the season are regionally dependent. The highest density appears in the Nordic Seas. For all ocean sub-basins, forward-shear polar lows are the most common, whereas weak shear and those propagating towards warmer environments are second and third most frequent, depending on the area. Reverse-shear polar lows and those propagating towards colder environments are rather seldom, especially in the Southern Ocean. Generally, PLs share many characteristics across ocean basins and wind-shear categories. The most remarkable difference is that forward-shear polar lows are often occurring in stronger vertical wind shear, whereas reverse-shear polar lows feature lower static stability. Hence, the contribution to a fast baroclinic growth rate is slightly different for the shear categories.

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Section: Differences In the Shear Categoriessupporting

confidence: 85%

Section: Area Of Highest Polar-low Densitymentioning

confidence: 95%

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A global climatology of polar lows investigated for local differences and wind-shear environments

Stoll

2021

Preprint

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“…Information regarding polar lows that was required to assess their roles in precipitation extrema was provided by the STARS data set image database (https: //projects.met.no/polarlow/stars-dat/, accessed on 1 February 2022) and PANGAEA (https://doi.org/10.1594/PANGAEA.903058, accessed on 1 February 2022). These data were analysed in previous studies [23][24][25][26].…”

Section: Datamentioning

confidence: 99%

Precipitation Extremes and Their Synoptic Models in the Northwest European Sector of the Arctic during the Cold Season

2022

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Precipitation extrema over the Barents Sea and the neighbouring locations in Europe were analysed using data obtained from station observations and a highly detailed ERA5 re-analysis dataset. These data did not always spatially coincide (on average, coincidence was ~50%). Daily amounts of precipitation were typically higher in the observation data, although there may be a reverse picture. The analysis revealed that at several stations and in many of the ERA5 grids, the set of precipitation extremes exists as a mixture of two different subsets. The cumulative distribution functions (CDF) of the largest population in the context of both the re-analysis and observational data are well described by Pareto’s law. However, very rare cases exist in which the values deviate and exceed this base distribution value in regions possessing large values. These super-large anomalies do not obey the statistical law common to all other extremes. However, this does not mean that the extremes can be arbitrarily large. They do not exceed the marginal values that are typical for this type of climate and season. The analysis confirms that extreme precipitation in the western sector of the Arctic is caused by the penetration of moist air masses from the Atlantic in the circulation systems of intense cyclones. At certain times, mesoscale convective systems are embedded in atmospheric fronts and can significantly contribute to the formation of precipitation. Intensification of such cyclones corresponding to global warming should lead to a transformation of typical CDF, as modern outliers will become regular components of the Pareto law. This change in the statistics of extreme events reflects the nonstationarity of the climate state. The influence of polar lows on the formation of large daily precipitation amounts is not felt.

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“…Deep learning has successfully been applied on several remote sensing applications to achieve state of the art results [17]- [20]. Cyclone type phenomena specifically, has been considered in assimilated data [21]- [23], passive microwave data [24], thermal infra-red (IR) data [25]- [29] and scatterometer data [30]. With the exception of [31], deep learning has however been largely overlooked for detecting mesocyclones in SAR data.…”

Section: Introductionmentioning

confidence: 99%

Recognition of polar lows in Sentinel-1 SAR images with deep learning

Grahn¹,

Bianchi²

2022

Preprint

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In this paper, we explore the possibility of detecting polar lows in C-band SAR images by means of deep learning. Specifically, we introduce a novel dataset consisting of Sentinel-1 images labeled as positive; representing a maritime mesocyclone, or negative; representing a normal sea state. The dataset is constructed using the ERA5 dataset as baseline and it consists of 2004 annotated images. To our knowledge, this is the first dataset of its kind to be publicly released. The dataset is used to train a deep learning model to classify the labeled images. Evaluated on an independent test set, the model yields an F-1 score of 0.95, indicating that polar lows can be consistently detected from SAR images. Interpretability techniques applied to the deep learning model reveal that atmospheric fronts and cyclonic eyes are key features in the classification. Moreover, experimental results show that the model is accurate even if: (i) such features are significantly cropped due to the limited swath width of the SAR, (ii) the features are partly covered by sea ice and (iii) land is covering significant parts of the images. By evaluating the model performance on multiple input image resolutions (pixel sizes of 500m, 1km and 2km), it is found that higher resolution yield the best performance. This emphasises the potential of using high resolution sensors like SAR for detecting polar lows, as compared to conventionally used sensors such as scatterometers.

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Satellite-Derived Spatio-Temporal Distribution and Parameters of North Atlantic Polar Lows for 2015–2017

Cited by 11 publications

References 31 publications

A global climatology of polar lows investigated for local differences and wind-shear environments

A global climatology of polar lows investigated for local differences and wind-shear environments

Precipitation Extremes and Their Synoptic Models in the Northwest European Sector of the Arctic during the Cold Season

Recognition of polar lows in Sentinel-1 SAR images with deep learning

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