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There are many challenges associated with obtaining high-fidelity sea ice concentration (SIC) information, and products that rely solely on passive microwave measurements often struggle to represent conditions at low concentration, especially within the marginal ice zone and during periods of active melt. Here, we present a newly gridded SIC product for the Alaskan Arctic, generated with data from the National Weather Service Alaska Sea Ice Program (hereafter referred to as ASIP), that synthesizes a variety of satellite SIC and in situ observations from 2007–present. These SIC fields have been primarily used for operational purposes and have not yet been gridded or independently validated. In this study, we first grid the ASIP product into 0.05° resolution in both latitude and longitude (hereafter referred to as gridded ASIP, or grASIP). We then perform extensive intercomparison with an international database of ship-based in situ SIC observations, supplemented with observations from saildrones. Additionally, an intercomparison between three ice products is performed: (i) grASIP, (ii) a high-resolution passive microwave product (AMSR2), and (iii) a product available from the National Snow and Ice Data Center (MASIE) that originates from the US National Ice Center (USNIC) operational IMS product. This intercomparison demonstrates that all products perform similarly when compared to in situ observations generally, but grASIP outperforms the other products during periods of active melt and in low-SIC regions. Furthermore, we show that the similarity in performance among products is partly due to the deficiencies in the in situ observations' geographical distribution, as most in situ observations are far from the ice edge in locations where all products agree. We find that the grASIP ice edge is generally farther south than both the AMSR2 and MASIE ice edges by an average of approximately 50 km in winter and 175 km in summer for grASIP vs. AMSR2 and 10 km in winter and 40 km in summer for grASIP vs. MASIE.
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Coastal Sea Ice Concentration Derived from Marine Radar Images: A Case Study from Utqiaġvik, Alaska
We apply the Canny edge algorithm to imagery from the Utqiaġvik coastal sea ice radar system (CSIRS) to identify regions of open water and sea ice and quantify ice concentration. The radar-derived sea ice concentration (SIC) is compared against the (closest to the radar field of view) 25 km resolution NSIDC Climate Data Record (CDR) and the 1 km merged MODIS-AMSR2 sea ice concentrations within the ∼11 km field of view for the year 2022–2023, when improved image contrast was first implemented. The algorithm was first optimized using sea ice concentration from 14 different images and 10 ice analysts (140 analyses in total) covering a range of ice conditions with landfast ice, drifting ice, and open water. The algorithm is also validated quantitatively against high-resolution MODIS-Terra in the visible range. Results show a correlation coefficient and mean bias error between the optimized algorithm, the CDR and MODIS-AMSR2 daily SIC of 0.18 and 0.54, and ∼−1.0 and 0.7%, respectively, with an averaged inter-analyst error of ±3%. In general, the CDR captures the melt period correctly and overestimates the SIC during the winter and freeze-up period, while the merged MODIS-AMSR2 better captures the punctual break-out events in winter, including those during the freeze-up events (reduction in SIC). Remnant issues with the detection algorithm include the false detection of sea ice in the presence of fog or precipitation (up to 20%), quantified from the summer reconstruction with known open water conditions. The proposed technique allows for the derivation of the SIC from CSIRS data at spatial and temporal scales that coincide with those at which coastal communities members interact with sea ice. Moreover, by measuring the SIC in nearshore waters adjacent to the shoreline, we can quantify the effect of land contamination that detracts from the usefulness of satellite-derived SIC for coastal communities.
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- Award ID(s):
- 1928126
- PAR ID:
- 10654429
- Publisher / Repository:
- MDPI (Multidisciplinary Digital Publishing Institute)
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 16
- Issue:
- 18
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 3357
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/rs16183357
