Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations

• Published in: The cryosphere 2015-09, Vol.9 (5), p.1797-1817
• Main Authors: Ivanova, N, Pedersen, L. T, Tonboe, R. T, Kern, S, Heygster, G, Lavergne, T, Sørensen, A, Saldo, R, Dybkjær, G, Brucker, L, Shokr, M
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Analysis; Annual variations; Atmospheric correction; Atmospheric water; Atmospheric water vapor; Brightness temperature; Calibration; Climate; Climate change; Climate monitoring; Climate trends; Dielectric properties; Evaluation; High seas; Ice; Interannual variability; Microwave imagery; Microwave radiometers; Monitoring; Polar environments; Polar regions; Radiometers; Roughening; Sea ice; Sea ice concentrations; Sensitivity; Standard deviation; Summer; Temperature; Trends; Water area; Water vapor; Water vapour; Winter
• ISSN: 1994-0424; 1994-0416; 1994-0424; 1994-0416
• DOI: 10.5194/tc-9-1797-2015

Sea ice concentration has been retrieved in polar regions with satellite microwave radiometers for over 30 years. However, the question remains as to what is an optimal sea ice concentration retrieval method for climate monitoring. This paper presents some of the key results of an extensive algorithm inter-comparison and evaluation experiment. The skills of 30 sea ice algorithms were evaluated systematically over low and high sea ice concentrations. Evaluation criteria included standard deviation relative to independent validation data, performance in the presence of thin ice and melt ponds, and sensitivity to error sources with seasonal to inter-annual variations and potential climatic trends, such as atmospheric water vapour and water-surface roughening by wind. A selection of 13 algorithms is shown in the article to demonstrate the results. Based on the findings, a hybrid approach is suggested to retrieve sea ice concentration globally for climate monitoring purposes. This approach consists of a combination of two algorithms plus dynamic tie points implementation and atmospheric correction of input brightness temperatures. The method minimizes inter-sensor calibration discrepancies and sensitivity to the mentioned error sources.