Towards replacing precipitation ensemble predictions systems using machine learning

IF 2 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES Atmospheric Science Letters Pub Date : 2024-08-16 DOI:10.1002/asl.1262
Rüdiger Brecht, Alex Bihlo
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Abstract

Forecasting precipitation accurately poses significant challenges due to various factors affecting its distribution and intensity, including but not limited to subgrid variability. Although higher resolution simulations are often considered to improve precipitation forecasts, it is crucial to note that simply increasing resolution may not suffice without appropriate adjustments to parameterization schemes or tuning. Traditionally, ensembles of simulations are used to generate uncertainty predictions associated with precipitation forecasts, but this approach can be computationally intensive. As an alternative, there is a growing trend towards leveraging neural networks for precipitation prediction, which offers potential computational advantages. We propose a new approach to generating ensemble weather predictions for high-resolution precipitation without requiring high-resolution training data. The method uses generative adversarial networks to learn the complex patterns of precipitation and produce diverse and realistic precipitation fields, allowing to generate realistic precipitation ensemble members using only the available control forecast. We demonstrate the feasibility of generating realistic precipitation ensemble members on unseen higher resolutions. We use evaluation metrics such as RMSE, CRPS, rank histogram and ROC curves to demonstrate that our generated ensemble is almost identical to the ECMWF IFS ensemble, on which our model was trained on.

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利用机器学习取代降水集合预测系统
由于影响降水分布和强度的各种因素(包括但不限于子网格变异性),准确预报降水是一项重大挑战。尽管人们经常考虑通过提高分辨率模拟来改善降水预报,但必须注意的是,如果不对参数化方案或调整进行适当调整,仅仅提高分辨率可能是不够的。传统上,模拟集合用于生成与降水预报相关的不确定性预测,但这种方法计算量大。作为一种替代方法,利用神经网络进行降水预测的趋势日益明显,这种方法具有潜在的计算优势。我们提出了一种无需高分辨率训练数据即可生成高分辨率降水集合天气预报的新方法。该方法使用生成式对抗网络来学习降水的复杂模式,并生成多样化和逼真的降水场,从而只需使用可用的控制预报即可生成逼真的降水集合成员。我们证明了在未见过的更高分辨率上生成真实降水集合的可行性。我们使用 RMSE、CRPS、等级直方图和 ROC 曲线等评估指标来证明,我们生成的降水集合与 ECMWF IFS 集合几乎完全相同,而我们的模型就是在 ECMWF IFS 集合上训练的。
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来源期刊
Atmospheric Science Letters
Atmospheric Science Letters METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
4.90
自引率
3.30%
发文量
73
审稿时长
>12 weeks
期刊介绍: Atmospheric Science Letters (ASL) is a wholly Open Access electronic journal. Its aim is to provide a fully peer reviewed publication route for new shorter contributions in the field of atmospheric and closely related sciences. Through its ability to publish shorter contributions more rapidly than conventional journals, ASL offers a framework that promotes new understanding and creates scientific debate - providing a platform for discussing scientific issues and techniques. We encourage the presentation of multi-disciplinary work and contributions that utilise ideas and techniques from parallel areas. We particularly welcome contributions that maximise the visualisation capabilities offered by a purely on-line journal. ASL welcomes papers in the fields of: Dynamical meteorology; Ocean-atmosphere systems; Climate change, variability and impacts; New or improved observations from instrumentation; Hydrometeorology; Numerical weather prediction; Data assimilation and ensemble forecasting; Physical processes of the atmosphere; Land surface-atmosphere systems.
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