Hybrid forecasting: blending climate predictions with AI models
Hybrid forecasting: blending climate predictions with AI models
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混合预测：将气候预测与人工智能模型相结合
DOI：
10.5194/hess-27-1865-2023
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发表时间：
2023-05
影响因子：
6.3
通讯作者：
L. Slater;L. Arnal;M. Boucher;An Chang;S. Moulds;C. Murphy;G. Nearing;Guy Shalev;Chaopeng Shen;L. Speight;G. Villarini;R. Wilby;A. Wood;M. Zappa
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作者：
L. Slater;L. Arnal;M. Boucher;An Chang;S. Moulds;C. Murphy;G. Nearing;Guy Shalev;Chaopeng Shen;L. Speight;G. Villarini;R. Wilby;A. Wood;M. Zappa
抽象的。混合水文气候预测系统采用数据驱动（统计或机器学习）方法，利用并整合来自动态物理模型的各种预测-例如数值天气预测，气候，土地，水文和地球系统模型-成为最终的预测产品。它们被认为是提高气象和水文气候变量和事件，包括降雨、温度、径流、洪水、干旱、热带气旋或大气河流的预测技能的一种有前途的方法。由于天气和气候预测系统在亚季节到十年尺度上的进步，对人工智能优势的更好理解以及对计算资源和方法的扩展，混合预测方法现在越来越受到关注。这样的系统是有吸引力的，因为它们可以避免需要运行计算昂贵的离线土地模型，可以最大限度地减少动态输出中存在的偏差的影响，受益于机器学习的优势，并且可以从大型数据集学习，同时将不同的可预测性来源与不同的时间范围相结合。在这里，我们回顾了混合水文气候预测的最新进展，并概述了进一步研究的主要挑战和机遇。这些措施包括获得物理上可解释的结果，从新的数据源中吸收人类的影响，整合新的集成技术，以提高预测技能，创建无缝预测方案，合并短到长的前置时间，将初始陆地表面和海洋/冰条件，承认景观和大气强迫的空间变异性，并增加混合预测方案的业务吸收。
Abstract. Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land, hydrology, and Earth system models – into a final prediction product. They are recognized as a promising way of enhancing the prediction skill of meteorological and hydroclimatic variables and events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. Hybrid forecasting methods are now receiving growing attention due to advances in weather and climate prediction systems at subseasonal to decadal scales, a better appreciation of the strengths of AI, and expanding access to computational resources and methods. Such systems are attractive because they may avoid the need to run a computationally expensive offline land model, can minimize the effect of biases that exist within dynamical outputs, benefit from the strengths of machine learning, and can learn from large datasets, while combining different sources of predictability with varying time horizons. Here we review recent developments in hybrid hydroclimatic forecasting and outline key challenges and opportunities for further research. These include obtaining physically explainable results, assimilating human influences from novel data sources, integrating new ensemble techniques to improve predictive skill, creating seamless prediction schemes that merge short to long lead times, incorporating initial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes.