Optimal combination of Arctic sea ice extent measures: A dynamic factor modeling approach
International Journal of Forecasting, ISSN: 0169-2070, Vol: 37, Issue: 4, Page: 1509-1519
2021
- 11Citations
- 20Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
The diminishing extent of Arctic sea ice is a key indicator of climate change as well as being an accelerant for future global warming. Since 1978, Arctic sea ice has been measured using satellite-based microwave sensing; however, different measures of Arctic sea ice extent have been made available based on differing algorithmic transformations of raw satellite data. We propose and estimate a dynamic factor model that combines four of these measures in an optimal way and accounts for their differing volatility and cross-correlations. We then use the Kalman smoother to extract an optimal combined measure of Arctic sea ice extent. It turns out that almost all weight is put on the NSIDC Sea Ice Index, confirming and enhancing confidence in the Sea Ice Index and the NASA Team algorithm on which it is based.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0169207020301606; http://dx.doi.org/10.1016/j.ijforecast.2020.10.006; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85098177037&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0169207020301606; https://dx.doi.org/10.1016/j.ijforecast.2020.10.006
Elsevier BV
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