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地球系统预测的未来:模型-数据融合的进展

The future of Earth system prediction: Advances in model-data fusion.

作者信息

Gettelman Andrew, Geer Alan J, Forbes Richard M, Carmichael Greg R, Feingold Graham, Posselt Derek J, Stephens Graeme L, van den Heever Susan C, Varble Adam C, Zuidema Paquita

机构信息

National Center for Atmospheric Research, Boulder, CO, USA.

European Centre for Medium-Range Weather Forecasts, Reading, UK.

出版信息

Sci Adv. 2022 Apr 8;8(14):eabn3488. doi: 10.1126/sciadv.abn3488. Epub 2022 Apr 6.

Abstract

Predictions of the Earth system, such as weather forecasts and climate projections, require models informed by observations at many levels. Some methods for integrating models and observations are very systematic and comprehensive (e.g., data assimilation), and some are single purpose and customized (e.g., for model validation). We review current methods and best practices for integrating models and observations. We highlight how future developments can enable advanced heterogeneous observation networks and models to improve predictions of the Earth system (including atmosphere, land surface, oceans, cryosphere, and chemistry) across scales from weather to climate. As the community pushes to develop the next generation of models and data systems, there is a need to take a more holistic, integrated, and coordinated approach to models, observations, and their uncertainties to maximize the benefit for Earth system prediction and impacts on society.

摘要

对地球系统的预测,如天气预报和气候预测,需要基于多层次观测的模型。一些整合模型与观测的方法非常系统且全面(例如数据同化),而一些则是针对特定目的且定制化的(例如用于模型验证)。我们回顾了当前整合模型与观测的方法及最佳实践。我们强调未来的发展如何能够实现先进的异构观测网络和模型,以改进从天气到气候等不同尺度下地球系统(包括大气、陆地表面、海洋、冰冻圈和化学)的预测。随着该领域推动开发下一代模型和数据系统,有必要对模型、观测及其不确定性采取更全面、综合和协调的方法,以最大限度地提升地球系统预测的效益及其对社会的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5197/8985915/15e4ac96015a/sciadv.abn3488-f1.jpg

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