摘要翻译：
北极海冰面积的减少是气候变化的一个关键指标，也是未来全球变暖的助燃剂。自1978年以来，北极海冰一直使用基于卫星的微波传感进行测量；然而，根据对原始卫星数据的不同算法转换，可以获得对北极海冰范围的不同测量。我们提出并估计了一个动态因子模型，该模型以一种最优的方式结合了四个度量指标，并考虑了它们不同的波动性和相互相关性。然后利用Kalman平滑器提取北极海冰范围的最优组合测度。事实证明，几乎所有的权重都放在NSIDC海冰指数上，证实并增强了对海冰指数和它所基于的NASA团队算法的信心。
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英文标题：
《Optimal Combination of Arctic Sea Ice Extent Measures: A Dynamic Factor
  Modeling Approach》
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作者：
Francis X. Diebold, Maximilian G\"obel, Philippe Goulet Coulombe,
  Glenn D. Rudebusch, Boyuan Zhang
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最新提交年份：
2020
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分类信息：

一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  The diminishing extent of Arctic sea ice is a key indicator of climate change as well as 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 the raw satellite data. We propose and estimate a dynamic factor model that combines four of these measures in an optimal way that 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.
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PDF链接：
https://arxiv.org/pdf/2003.14276