论文标题

使用深层生成模型提高降水预测的准确性和分辨率

Increasing the accuracy and resolution of precipitation forecasts using deep generative models

论文作者

Price, Ilan, Rasp, Stephan

论文摘要

众所周知,准确地预测极端降雨是困难的,但随着气候变化增加了这种极端的频率,对社会而言也更为重要。全球数字天气预测模型通常无法捕获极端,并且以太低的分辨率而无法采取可行的方式产生,而区域高分辨率模型在计算和人工方面都非常昂贵。在本文中,我们探讨了深层生成模型的使用来同时纠正和低尺度(超级解决)全球合奏对美国大陆的预测。具体而言,使用细粒度的雷达观测作为我们的地面真理,我们通过定制的训练程序和增强损失函数来培训有条件的生成对抗网络(造成的校正),以产生基于粗糙的,偏见的预测的高分辨率,基于粗糙的,全球降水的预测的高分辨率的合奏,此外还基于其他相关的气象学领域。我们的模型的表现优于插值基线,以及基于超分辨率的单变量方法,并在一系列已建立的概率指标上接近操作区域高分辨率模型的性能。至关重要的是,一旦受过训练,校正者就会在一台机器上以几秒钟的速度产生预测。这些结果引发了有关区域模型必要性的令人兴奋的问题,以及数据驱动的降尺度和校正方法是否可以转移到迄今无法获得高分辨率预测的数据贫困地区。

Accurately forecasting extreme rainfall is notoriously difficult, but is also ever more crucial for society as climate change increases the frequency of such extremes. Global numerical weather prediction models often fail to capture extremes, and are produced at too low a resolution to be actionable, while regional, high-resolution models are hugely expensive both in computation and labour. In this paper we explore the use of deep generative models to simultaneously correct and downscale (super-resolve) global ensemble forecasts over the Continental US. Specifically, using fine-grained radar observations as our ground truth, we train a conditional Generative Adversarial Network -- coined CorrectorGAN -- via a custom training procedure and augmented loss function, to produce ensembles of high-resolution, bias-corrected forecasts based on coarse, global precipitation forecasts in addition to other relevant meteorological fields. Our model outperforms an interpolation baseline, as well as super-resolution-only and CNN-based univariate methods, and approaches the performance of an operational regional high-resolution model across an array of established probabilistic metrics. Crucially, CorrectorGAN, once trained, produces predictions in seconds on a single machine. These results raise exciting questions about the necessity of regional models, and whether data-driven downscaling and correction methods can be transferred to data-poor regions that so far have had no access to high-resolution forecasts.

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