A machine learning model of Arctic sea ice motions

Zhai, Jun; Bitz, Cecilia M.

doi:10.1002/essoar.10504769.1

Cited by 7 publications

(11 citation statements)

References 17 publications

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“…Sea ice concentration (SIC) and sea ice thickness (SIT) play important roles in sea ice motion estimation or prediction [1][2][3][13][14][15][16]. Here, we also attempted to train the RF model by including SIC and SIT as input variables, to estimate SIM.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to the normal measures, including bias, mean absolute error (MAE), root mean squared error (RMSE), and correlation (r), for the evaluation of SIM data, we also took skill [15,16,25] into consideration, which was calculated as follows:…”

Section: Methods Of Evaluationmentioning

confidence: 99%

“…However, such SIM data cannot accurately reflect the dynamics of sea ice under the impact of internal friction, ocean currents, and coastal boundaries [3,8]. In recent years, machine learning (ML) methods have been shown to have great potential for sea ice motion estimation and prediction [13][14][15][16]. For example, Palerme and Müller (2021) [14] reproduced the result that the wind field is the most important factor affecting the Arctic ice speed, and the influence of parameters related to sea ice and shore boundaries cannot be ignored since they accounted for 20-30% of the variable importances (for the concept of importance in RF models, see [17]).…”

Section: Introductionmentioning

confidence: 99%

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Estimating Winter Arctic Sea Ice Motion Based on Random Forest Models

Zhang,

Shi,

Leppäranta

et al. 2024

Remote Sensing

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Sea ice motion (SIM) plays a crucial role in setting the distribution of the ice cover in the Arctic. Limited by images’ spatial resolution and tracking algorithms, challenges exist in obtaining coastal sea ice motion (SIM) based on passive microwave satellite sensors. In this study, we developed a method based on random forest (RF) models to obtain Arctic SIM in winter by incorporating wind field and coastal geographic location information. These random forest models were trained using Synthetic Aperture Radar (SAR) SIM data. Our results show good consistency with SIM data retrieved from satellite imagery and buoy observations. With respect to the SAR data, compared with SIM estimated with RF model training using reanalysis surface wind, the results by additional coastal information input had a lower root mean square error (RMSE) and a higher correlation coefficient by 31% and 14% relative improvement, respectively. The latter SIM result also showed a better performance for magnitude, especially within 100 km of the coastline in the north of the Canadian Arctic Archipelago. In addition, the influence of coastline on SIM is quantified through variable importance calculation, at 22% and 28% importance of all RF variables for east and north SIM components, respectively. These results indicate the great potential of RF models for estimating SIM over the whole Arctic Ocean in winter.

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Section: Discussionmentioning

confidence: 99%

Section: Methods Of Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimating Winter Arctic Sea Ice Motion Based on Random Forest Models

Zhang,

Shi,

Leppäranta

et al. 2024

Remote Sensing

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“…Traditional [4] 2017 MCC tracker with hybrid example-based super-resolution model [128] 2017 A faster cross-correlation based tracking with several updates [129] 2018 A optical-flow based tracking with super-resolution enhancement [130] 2019 A multi-step tracker for ice motion tracking [131] 2020 Rotation-invariant ice floe tracking [132] 2021 Integrating the cross-correlation with feature tracking [133] 2022 Integrating locally consistent flow field filtering with cross-correlation DL-based [134] 2019 An encoder-decoder network with LSTM to predict ice motion trajectory [135] 2021 A CNN model to predict the arctic sea ice motions [136] 2021 A multi-step machine learning approach to track icebergs…”

Section: Ice Motionmentioning

confidence: 99%

“…Similarly, Ref. [135] established a CNN model and introduced previous day ice velocity, concentration, and present-day surface wind to track and predict the arctic sea ice motions. Results reveal that the designed CNN model computes the sea ice response with a correlation of 0.82 on average with respect to reality, which surpasses a set of local pointwise predictions and a leading thermodynamic-dynamical model.…”

Section: Dl-based Trackingmentioning

confidence: 99%

Object Tracking Based on Satellite Videos: A Literature Review

et al. 2022

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Video satellites have recently become an attractive method of Earth observation, providing consecutive images of the Earth’s surface for continuous monitoring of specific events. The development of on-board optical and communication systems has enabled the various applications of satellite image sequences. However, satellite video-based target tracking is a challenging research topic in remote sensing due to its relatively low spatial and temporal resolution. Thus, this survey systematically investigates current satellite video-based tracking approaches and benchmark datasets, focusing on five typical tracking applications: traffic target tracking, ship tracking, typhoon tracking, fire tracking, and ice motion tracking. The essential aspects of each tracking target are summarized, such as the tracking architecture, the fundamental characteristics, primary motivations, and contributions. Furthermore, popular visual tracking benchmarks and their respective properties are discussed. Finally, a revised multi-level dataset based on wpafb videos is generated and quantitatively evaluated for future development in the satellite video-based tracking area. In addition, 54.3% of the tracklets with lower ds are selected and renamed as the Easy group, while 27.2% and 18.5% of the tracklets are grouped into the Medium-ds group and the Hard-ds group, respectively.

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Bridging observations, theory and numerical simulation of the ocean using machine learning

Sonnewald

Lguensat

Jones

et al. 2021

Environ. Res. Lett.

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Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study. The incorporation of machine learning (ML) techniques offers exciting possibilities for advancing the capacity and speed of established methods and for making substantial and serendipitous discoveries. Beyond vast amounts of complex data ubiquitous in many modern scientific fields, the study of the ocean poses a combination of unique challenges that ML can help address. The observational data available is largely spatially sparse, limited to the surface, and with few time series spanning more than a handful of decades. Important timescales span seconds to millennia, with strong scale interactions and numerical modelling efforts complicated by details such as coastlines. This review covers the current scientific insight offered by applying ML and points to where there is imminent potential. We cover the main three branches of the field: observations, theory, and numerical modelling. Highlighting both challenges and opportunities, we discuss both the historical context and salient ML tools. We focus on the use of ML in situ sampling and satellite observations, and the extent to which ML applications can advance theoretical oceanographic exploration, as well as aid numerical simulations. Applications that are also covered include model error and bias correction and current and potential use within data assimilation. While not without risk, there is great interest in the potential benefits of oceanographic ML applications; this review caters to this interest within the research community.

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A machine learning model of Arctic sea ice motions

Cited by 7 publications

References 17 publications

Estimating Winter Arctic Sea Ice Motion Based on Random Forest Models

Estimating Winter Arctic Sea Ice Motion Based on Random Forest Models

Object Tracking Based on Satellite Videos: A Literature Review

Bridging observations, theory and numerical simulation of the ocean using machine learning

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