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Surface feature height, spacing and form drag coefficients over Arctic sea ice from Operation IceBridge, 2009-2015

<p>Data used to estimate the neutral form drag coefficient over Arctic sea ice using high-resolution IceBridge laser (ATM) data, from Petty et al., (2017). The data here include the raw 1D linear profiling (along the edge of the ATM swath) data of surface feature data: e.g.&nbsp;1km_xyres2m_20cm/1D/[year]/&nbsp;and also 10 km mean along-track surface feature and form drag estimates:&nbsp;1km_xyres2m_20cm/1D/ATMO/.</p> <p>The code to generate these data can be found here:&nbsp;https://github.com/akpetty/ibdrag2017. The Python pickle files have been converted to CSV here to aid data ingestion. 2D surface feature data using the same input data can be found here:&nbsp;https://zenodo.org/record/6617715</p> <p>The following raw IceBridge datasets are used to create these data:</p> <ul> <li>L1B ATM data:&nbsp;<a href="https://nsidc.org/data/docs/daac/icebridge/ilatm1b/">https://nsidc.org/data/docs/daac/icebridge/ilatm1b/</a>.</li> <li>L1B DMS imagery:&nbsp;<a href="http://nsidc.org/data/iodms1b%7D">http://nsidc.org/data/iodms1b}</a>.</li> <li>IceBridge IDCSI4 and quick-look sea ice thickness&nbsp;data:&nbsp;<a href="http://nsidcorg/data/docs/daac/icebridge/evaluation_products/sea">http://nsidcorg/data/docs/daac/icebridge/evaluation_products/sea</a>-ice-freeboard-snowdepth-thickness-quicklook-index.html and&nbsp;<a href="http://nsidc.org/data/idcsi4.html">http://nsidc.org/data/idcsi4.html</a>.</li> </ul> <p><strong>References</strong></p> <p>Petty, A. A., M. C. Tsamados, N. T. Kurtz, S. L. Farrell, T. Newman, J. P. Harbeck, D. L. Feltham, and J. A. Richter-Menge (2016), Characterizing Arctic sea ice topography using high-resolution IceBridge data, The Cryosphere, 10(3), 1161&ndash;1179, doi:10.5194/tc-10-1161-2016.</p> <p>Petty, A. A., M. C. Tsamados, N. T. Kurtz (2017), Atmospheric form drag over Arctic sea ice using remotely sensed ice topography observations, J. Geophys. Res. Earth Surf., 122, doi:10.1002/2017JF004209.</p>

ShareScore

36/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
4
Harmonization
4
Access
16
Reuse readiness
8
Engagement
4