Reaching out across the Web .. ...分享 Zuojun Yu, physical oceanographer, freelance English editor


科技英语写作基础(系列):阅读AI helps missing climate data

已有 586 次阅读 2020-6-13 10:03 |个人分类:Scientific Writing|系统分类:论文交流| 科技英语写作基础

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Artificial intelligence reconstructs missing climate information



Historical temperature measurements are the basis of global climate datasets like HadCRUT4. 

This dataset contains many missing values, particularly for periods before the mid-twentieth century, although recent years are also incomplete.


Here we demonstrate that artificial intelligence can skilfully fill these observational gaps when combined with numerical climate model data

评论:US English uses “skillfully.” I am losing this battle, but as long as I can manage, I will use “model outputs.”

We show that recently developed image inpainting techniques perform accurate monthly reconstructions via transfer learning using either 20CR (Twentieth-Century Reanalysis) or the CMIP5 (Coupled Model Intercomparison Project Phase 5) experiments. 

评论:People or animals perform, techniques do NOT.

The resulting global annual mean temperature time series exhibit high Pearson correlation coefficients (≥0.9941) and low root mean squared errors (≤0.0547 °C) as compared with the original data. 

These techniques also provide advantages relative to state-of-the-art kriging interpolation and principal component analysis-based infilling. 

When applied to HadCRUT4, our method restores a missing spatial pattern of the documented El Niño from July 1877. 

With respect to the global mean temperature time series, a HadCRUT4 reconstruction by our method points to a cooler nineteenth century, a less apparent hiatus in the twenty-first century, an even warmer 2016 being the warmest year on record and a stronger global trend between 1850 and 2018 relative to previous estimates. 

评论:Too long a sentence.

We propose image inpainting as an approach to reconstruct missing climate information and thereby reduce uncertainties and biases in climate records.

评论:Use “to reduce.”

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