A 20-Year Climatology of Sea Ice Leads Detected in Infrared Satellite Imagery Using a Convolutional Neural Network

Hoffman, Jonathan; Ackerman, S. A.; Liu, Yinghui; Key, Jeffrey R.

doi:10.3390/rs14225763

Cited by 10 publications

(6 citation statements)

References 30 publications

(84 reference statements)

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“…This is similar to Hoffman et al. (2022) who observed a small, but significant increase in pan‐Arctic leads from satellite data over the same period, despite large uncertainty due to the increasing cloud cover in the Arctic.…”

Section: Discussionsupporting

confidence: 90%

“…Meanwhile, recent observational data based on MODIS imagery (Willmes et al, 2023) show a significant positive trend of 2% per decade in lead frequencies in the Beaufort Sea over the period from 2002 to 2021 (during April only). This is similar to Hoffman et al (2022) who observed a small, but significant increase in pan-Arctic leads from satellite data over the same period, despite large uncertainty due to the increasing cloud cover in the Arctic.…”

Section: Discussionsupporting

confidence: 89%

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Breaking the Ice: Exploring the Changing Dynamics of Winter Breakup Events in the Beaufort Sea

Rheinlænder,

Regan,

Rampal

et al. 2024

JGR Oceans

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The Beaufort Sea has experienced a significant decline in sea ice, with thinner first‐year ice replacing thicker multi‐year ice. This transition makes the ice cover weaker and more mobile, making it more vulnerable to breakup during winter. Using a coupled ocean‐sea‐ice model, we investigated the impact of these changes on sea‐ice breakup events and lead formation from 2000 to 2018. The simulation shows an increasing trend in the Beaufort Sea lead area fraction during winter, with a pronounced transition around 2007. A high lead area fraction in winter leads to greater growth of new, thin ice within the Beaufort region while also leading to enhanced sea ice transport out of the area. Despite the large export, consisting primarily of thinner first‐year ice, we find little evidence that winter breakup amplifies the advection of multi‐year ice from the central Arctic into the Beaufort Sea. Overall, the export offsets ice growth, resulting in negative volume anomalies and preconditioning a thinner and weaker ice pack at the end of the cool season. Our results indicate that large breakup events may become more frequent as the sea‐ice cover thins and that such events only became common after 2007. This result highlights the need to represent these processes in global‐scale climate models to improve projections of the Arctic.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionsupporting

confidence: 89%

Breaking the Ice: Exploring the Changing Dynamics of Winter Breakup Events in the Beaufort Sea

Rheinlænder,

Regan,

Rampal

et al. 2024

JGR Oceans

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“…Finally, as validation, a photograph is shown looking northward from southern Wisconsin, taken at 4:05 P.M. local time (2105 UTC). The primary advantage of the detection method is that it uses techniques previously demonstrated to be effective in satellite remote sensing applications [12,13], and that it can be applied to this new application without changing the model architecture. The technique also is also relatively simple to run in an operational sense because the detections are based on a single brightness temperature difference image rather than more complex multi-spectral time series of images [18].…”

Section: Discussionmentioning

confidence: 99%

“…The first step in the formation of a contrail detection method is to utilize a dataset of contrail images that can be used to train, test, and validate a detection method [18]. The detection architecture is based on the same AI architecture that was used to detect quasi-linear sea ice lead features as described by Hoffman et al in 2021 [12] and updated in 2022 [13]. Both the contrail and sea ice lead detection methods uses a particular kind of convolutional neural network, U-Net, to perform image segmentation, which was first described by Ronneberger et al in 2015 [11].…”

Section: Methodsmentioning

confidence: 99%

The Application of a Convolutional Neural Network for the Detection of Contrails in Satellite Imagery

et al. 2023

Self Cite

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This study presents a novel approach for the detection of contrails in satellite imagery using a convolutional neural network (CNN). Contrails are important to monitor because their contribution to climate change is uncertain and complex. Contrails are found to have a net warming effect because the clouds prevent terrestrial (longwave) radiation from escaping the atmosphere. Globally, this warming effect is greater than the cooling effect the clouds have in the reduction of solar (shortwave) radiation reaching the surface during the daytime. The detection of contrails in satellite imagery is challenging due to their similarity to natural clouds. In this study, a certain type of CNN, U-Net, is used to perform image segmentation in satellite imagery to detect contrails. U-Net can accurately detect contrails with an overall probability of detection of 0.51, a false alarm ratio of 0.46 and a F1 score of 0.52. These results demonstrate the effectiveness of using a U-Net for the detection of contrails in satellite imagery and could be applied to large-scale monitoring of contrail formation to measure their impact on climate change.

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“…Climate change in the Arctic has been the focus of scientific research in recent years [1][2][3][4], but the polar climate in this area of the planet is extremely harsh, with low temperatures and ice for many years [5]. The Arctic region has long winters, extremely low temperatures, strong air-sea exchanges, and high humidity, which is manifested as fog.…”

Section: Introductionmentioning

confidence: 99%

Temperature and Relative Humidity Profile Retrieval from Fengyun-3D/VASS in the Arctic Region Using Neural Networks

Petropoulos

et al. 2023

Remote Sensing

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In this study, a new technique is proposed to retrieve temperature and relative humidity profiles under clear sky conditions in the Arctic region based on the artificial neural network (ANN) algorithm using Fengyun-3D (FY-3D) vertical atmospheric sounder suit (VASS: HIRAS, MWTS-II, and MWHS-II) observations. This technology combines infrared (IR) and microwave (MW) observations to improve retrieval accuracy in the middle and low troposphere by reducing the sensitivity of the neural networks (NNs) to cloud coverage. The approach was compared against other methods available in the literature on retrieving profiles only from FY-3D/HIRAS data. Furthermore, its retrieval performance was tested by comparing the NNs’ prediction accuracy versus the corresponding FY-3D/VASS and Aqua/AIRS L2 products. The results showed that: (1) NNs retrieval accuracy is higher during the warm season and over the ocean; (2) the retrieval accuracy of NNs has been significantly improved compared with satellite L2 products; (3) referring to radiosonde observations, the retrieval accuracy of NNs below 600 hPa is effectively improved by adding the information of the MW channel, especially on land where cloud clearing is more difficult. The root mean square error (RMSE) of temperature and relative humidity in the cold season were reduced by 0.3 K and 2%, respectively. The advanced NNs proposed herein offer a more stable retrieval performance compared with NNs built only by FY-3D/HIRAS data. The study results indicated the potential value in time and space domain of the NN algorithm in retrieving temperature and relative humidity profiles of the Arctic region from FY-3D/VASS observations under clear-sky conditions. All in all, this work enhances our knowledge towards improving operational use of FY-3D satellite data in the Arctic region.

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A 20-Year Climatology of Sea Ice Leads Detected in Infrared Satellite Imagery Using a Convolutional Neural Network

Cited by 10 publications

References 30 publications

Breaking the Ice: Exploring the Changing Dynamics of Winter Breakup Events in the Beaufort Sea

Breaking the Ice: Exploring the Changing Dynamics of Winter Breakup Events in the Beaufort Sea

The Application of a Convolutional Neural Network for the Detection of Contrails in Satellite Imagery

Temperature and Relative Humidity Profile Retrieval from Fengyun-3D/VASS in the Arctic Region Using Neural Networks

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