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CloudCast: A Satellite-Based Dataset and Baseline for Forecasting Clouds
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( IF 4.7 ) Pub Date : 2021-03-02 , DOI: 10.1109/jstars.2021.3062936
Andreas Holm Nielsen 1 , Alexandros Iosifidis 1 , Henrik Karstoft 1
Affiliation  

Forecasting the formation and development of clouds is a central element of modern weather forecasting systems. Incorrect cloud forecasts can lead to major uncertainty in the overall accuracy of weather forecasts due to their intrinsic role in the Earth's climate system. Few studies have tackled this challenging problem from a machine learning point-of-view due to a shortage of high-resolution datasets with many historical observations globally. In this article, we present a novel satellite-based dataset called “CloudCast.” It consists of 70 080 images with 10 different cloud types for multiple layers of the atmosphere annotated on a pixel level. The spatial resolution of the dataset is 928 × 1530 pixels (3 × 3 km per pixel) with 15-min intervals between frames for the period January 1, 2017 to December 31, 2018. All frames are centered and projected over Europe. To supplement the dataset, we conduct an evaluation study with current state-of-the-art video prediction methods such as convolutional long short-term memory networks, generative adversarial networks, and optical flow-based extrapolation methods. As the evaluation of video prediction is difficult in practice, we aim for a thorough evaluation in the spatial and temporal domain. Our benchmark models show promising results but with ample room for improvement. This is the first publicly available global-scale dataset with high-resolution cloud types on a high temporal granularity to the authors’ best knowledge.

中文翻译:

CloudCast:基于卫星的数据集和用于预测云的基准

预测云的形成和发展是现代天气预报系统的核心要素。错误的云预报由于其在地球气候系统中的固有作用,可能导致天气预报的总体准确性出现重大不确定性。由于缺乏全球范围内许多历史观测数据的高分辨率数据集,因此很少有研究从机器学习的角度解决这一具有挑战性的问题。在本文中,我们介绍了一个名为“ CloudCast”的新颖的基于卫星的数据集。它由70 080张具有10种不同云类型的图像组成,用于在像素级别上标注的多层大气。在2017年1月1日至2018年12月31日期间,数据集的空间分辨率为928×1530像素(每像素3×3 km),每帧之间的间隔为15分钟。所有框架都居中并投影在整个欧洲。为了补充数据集,我们使用当前最先进的视频预测方法(例如卷积长短期记忆网络,生成对抗网络和基于光流的外推方法)进行评估研究。由于视频预测的评估在实践中很困难,因此我们旨在在时空领域进行全面评估。我们的基准模型显示出令人鼓舞的结果,但仍有足够的改进空间。这是第一个公开发布的全球规模数据集,具有高分辨率的云类型,且具有作者所掌握的最高时间粒度。生成对抗网络,以及基于光流的外推方法。由于视频预测的评估在实践中很困难,因此我们旨在在时空领域进行全面评估。我们的基准模型显示出令人鼓舞的结果,但仍有足够的改进空间。这是第一个公开发布的全球规模数据集,具有高分辨率的云类型,且作者的知识水平高,时间粒度高。生成对抗网络,以及基于光流的外推方法。由于视频预测的评估在实践中很困难,因此我们旨在在时空领域进行全面评估。我们的基准模型显示出令人鼓舞的结果,但仍有足够的改进空间。这是第一个公开发布的全球规模数据集,具有高分辨率的云类型,且具有作者所掌握的最高时间粒度。
更新日期:2021-04-09
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