The cryosphere · 2018 · 49 citations · 22 references
Natural SciencesEarth SciencesOceanographyPhysical OceanographySea Ice OceanographyTime Series AnalysisRemote Sensing Of IceSea Ice ConcentrationSea Ice DriftSea Ice PhysicsSea Ice Remote SensingSea Ice StudiesDaily Sea Ice Concentration ProductsLagrangian TrackingSatellite-based Sea Ice ConcentrationSea Ice Age DistributionSea Ice Age MappingSea Ice Concentration AlgorithmsSea Ice Concentration Time SeriesSea Ice Drift Tracking
Abstract. A new algorithm for estimating sea ice age (SIA) distribution based on the Eulerian advection scheme is presented. The advection scheme accounts for the observed divergence or convergence and freezing or melting of sea ice and predicts consequent generation or loss of new ice. The algorithm uses daily gridded sea ice drift and sea ice concentration products from the Ocean and Sea Ice Satellite Application Facility. The major advantage of the new algorithm is the ability to generate individual ice age fractions in each pixel of the output product or, in other words, to provide a frequency distribution of the ice age allowing to apply mean, median, weighted average or other statistical measures. Comparison with the National Snow and Ice Data Center SIA product revealed several improvements of the new SIA maps and time series. First, the application of the Eulerian scheme provides smooth distribution of the ice age parameters and prevents product undersampling which may occur when a Lagrangian tracking approach is used. Second, utilization of the new sea ice drift product void of artifacts from EUMETSAT OSI SAF resulted in more accurate and reliable spatial distribution of ice age fractions. Third, constraining SIA computations by the observed sea ice concentration expectedly led to considerable reduction of multi-year ice (MYI) fractions. MYI concentration is computed as a sum of all MYI fractions and compares well to the MYI products based on passive and active microwave and SAR products.
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A tutorial on support vector regression
Alex Smola, Bernhard Schölkopf · Statistics and Computing · 2004 · 13K citations
The Optimality of Naive Bayes.
Harry Zhang · 2004 · 1.4K citations