结合基于物理的建模和机器学习的大气-海洋模型的中程预测

IF 4.4 2区 地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES
Dhruvit Patel, Troy Arcomano, Brian Hunt, Istvan Szunyogh, Edward Ott
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引用次数: 0

摘要

本文探讨了一种混合建模方法的潜力,该方法将机器学习(ML)与传统的基于物理的建模相结合,用于中尺度以上的天气预报。它扩展了Arcomano et al. (2022, https://doi.org/10.1029/2021ms002712)和Arcomano et al. (2023, https://doi.org/10.1029/2022gl102649)的工作,前者测试了该方法在中短期天气预报中的应用,后者研究了该方法在气候模拟中的潜力。本文预报试验采用的混合模式是基于低分辨率、简化的参数化大气环流模式SPEEDY。除了SPEEDY的混合预测变量外,该模式还具有三个纯粹基于ml的预测变量:6小时累积降水、海面温度和海洋顶部300 m深层的热含量(与Arcomano等人,2023,https://doi.org/10.1029/2022gl102649中使用的模式相比,这是一个新添加的变量)。该模式能够根据季节预测El Niño周期及其与3-7个月降水的全球遥相关。该模式捕获了与开尔文波和罗斯比波以及MJO有关的降水的赤道变异性。赤道地区降水预测在东太平洋为15天,在西太平洋为11.5天。尽管该模型的空间分辨率较低,但对于这些任务,它的预测能力可与已发表的高分辨率、纯物理基础、传统的业务预测模型相媲美。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Prediction Beyond the Medium Range With an Atmosphere-Ocean Model That Combines Physics-Based Modeling and Machine Learning

Prediction Beyond the Medium Range With an Atmosphere-Ocean Model That Combines Physics-Based Modeling and Machine Learning

This paper explores the potential of a hybrid modeling approach that combines machine learning (ML) with conventional physics-based modeling for weather prediction beyond the medium range. It extends the work of Arcomano et al. (2022, https://doi.org/10.1029/2021ms002712), which tested the approach for short- and medium-range weather prediction, and the work of Arcomano et al. (2023, https://doi.org/10.1029/2022gl102649), which investigated its potential for climate modeling. The hybrid model used for the forecast experiments of the paper is based on the low-resolution, simplified parameterization atmospheric general circulation model SPEEDY. In addition to the hybridized prognostic variables of SPEEDY, the model has three purely ML-based prognostic variables: the 6 hr cumulative precipitation, the sea surface temperature, and the heat content of the top 300 m deep layer of the ocean (a new addition compared to the model used in Arcomano et al., 2023, https://doi.org/10.1029/2022gl102649). The model has skill in predicting the El Niño cycle and its global teleconnections with precipitation for 3–7 months depending on the season. The model captures equatorial variability of the precipitation associated with Kelvin and Rossby waves and MJO. Predictions of the precipitation in the equatorial region have skill for 15 days in the East Pacific and 11.5 days in the West Pacific. Though the model has low spatial resolution, for these tasks it has prediction skill comparable to what has been published for high-resolution, purely physics-based, conventional, operational forecast models.

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来源期刊
Journal of Advances in Modeling Earth Systems
Journal of Advances in Modeling Earth Systems METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
11.40
自引率
11.80%
发文量
241
审稿时长
>12 weeks
期刊介绍: The Journal of Advances in Modeling Earth Systems (JAMES) is committed to advancing the science of Earth systems modeling by offering high-quality scientific research through online availability and open access licensing. JAMES invites authors and readers from the international Earth systems modeling community. Open access. Articles are available free of charge for everyone with Internet access to view and download. Formal peer review. Supplemental material, such as code samples, images, and visualizations, is published at no additional charge. No additional charge for color figures. Modest page charges to cover production costs. Articles published in high-quality full text PDF, HTML, and XML. Internal and external reference linking, DOI registration, and forward linking via CrossRef.
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