Ships as a sensor network to observe ice field properties

Kotovirta, Ville; Karvonen, Juha; Polach, Franz von Bock und; Berglund, Robin; Kujala, Pentti

doi:10.1016/j.coldregions.2010.11.005

Cited by 7 publications

(2 citation statements)

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“…The main reason for this choice is that it is easier to understand and more straightforward to implement than, e.g., the optical flow techniques proposed by Rohith et al () and Karvonen (). Others have used FFT‐based phase correlation instead of cross correlation, which is less sensitive to intensity variations between image pairs (Karvonen, ; Kotovirta et al, ). This study addresses this issue by normalizing the radar images, which results in evenly distributed backscatter intensities independent of range (section 3.1).…”

Section: Discussionmentioning

confidence: 99%

“…O'Connell () explores the detectability of ice hazards (icebergs, bergy bits, and growlers) by shipboard MR, Lu et al () employ MR to estimate the ice berg distribution during a circumpolar Antarctic cruise, and Parsa () investigate the suitability of shipborne dual‐polarized MR data for ice age classification. More interestingly, in this context, Kotovirta et al () propose a prototype shipboard sensor network that includes MR for sea ice imaging and drift retrieval. The shipboard MR sea ice velocities they present, however, are limited to periods during which the ship was drifting with the ice and neglect ship motion (i.e., their velocities are relative to the ship).…”

Section: Introductionmentioning

confidence: 99%

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Arctic Sea Ice Drift Measured by Shipboard Marine Radar

et al. 2018

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This study presents Arctic sea ice drift fields measured by shipboard marine X‐band radar (MR). The measurements are based on the maximum cross correlation between two sequential MR backscatter images separated ∼1 min in time, a method that is commonly used to estimate sea ice drift from satellite products. The advantage of MR is that images in close temporal proximity are readily available. A typical MR antenna rotation period is ∼1–2 s, whereas satellite revisit times can be on the order of days. The technique is applied to ∼4 weeks of measurements taken from R/V Sikuliaq in the Beaufort Sea in the fall of 2015. The resulting sea ice velocity fields have ∼500 m and up to ∼5 min resolution, covering a maximum range of ∼4 km. The MR velocity fields are validated using the GPS‐tracked motion of Surface Wave Instrument Float with Tracking (SWIFT) drifters, wave buoys, and R/V Sikuliaq during ice stations. The comparison between MR and reference sea ice drift measurements yields root‐mean‐square errors from 0.8 to 5.6 cm s−1. The MR sea ice velocity fields near the ice edge reveal strong horizontal gradients and peak speeds > 1 m s−1. The observed submesoscale sea ice drift processes include an eddy with ∼6 km diameter and vorticities <–2 (normalized by the Coriolis frequency) as well as converging and diverging flow with normalized divergences <–2 and >1, respectively. The sea ice drift speed correlates only weakly with the wind speed (r2 = 0.34), which presents a challenge to conventional wisdom.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%