Generative adversarial networks (GANs) have been recently adopted for
super-resolution, an application closely related to what is referred to as
"downscaling" in the atmospheric sciences: improving the spatial resolution of
low-resolution images. The ability of conditional GANs to generate an ensemble
of solutions for a given input lends itself naturally to stochastic
downscaling, but the stochastic nature of GANs is not usually considered in
super-resolution applications. Here, we introduce a recurrent, stochastic
super-resolution GAN that can generate ensembles of time-evolving
high-resolution atmospheric fields for an input consisting of a low-resolution
sequence of images of the same field. We test the GAN using two datasets, one
consisting of radar-measured precipitation from Switzerland, the other of cloud
optical thickness derived from the Geostationary Earth Observing Satellite 16
(GOES-16). We find that the GAN can generate realistic, temporally consistent
super-resolution sequences for both datasets. The statistical properties of the
generated ensemble are analyzed using rank statistics, a method adapted from
ensemble weather forecasting; these analyses indicate that the GAN produces
close to the correct amount of variability in its outputs. As the GAN generator
is fully convolutional, it can be applied after training to input images larger
than the images used to train it. It is also able to generate time series much
longer than the training sequences, as demonstrated by applying the generator
to a three-month dataset of the precipitation radar data. The source code to
our GAN is available at this https URL

介绍了一种基于生成对抗网络的重复随机超分辨率 GAN，该 GAN 可以应用于低分辨率气象数据，生成时间演化的高分辨率气象数据。

利用生成对抗网络进行随机超分辨率，在时间演化大气场上进行下采样

Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric  Fields with a Generative Adversarial Network

This paper has been written to mark 25 years of operational medium-range
ensemble forecasting. The origins of the ECMWF Ensemble Prediction System are
outlined, including the development of the precursor real-time Met Office
monthly ensemble forecast system. In particular, the reasons for the
development of singular vectors and stochastic physics - particular features of
the ECMWF Ensemble Prediction System - are discussed. The author speculates
about the development and use of ensemble prediction in the next 25 years.

本文介绍了欧洲中期天气预报中心 (ECMWF) 的多模式集合预报系统的发展历程，特别是它独特的奇异向量和随机物理特征，并对未来 25 年的集合预测发展和应用进行了推测。