Abstract
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.
Highlights
C LOUD forecasting remains one of the major unsolved challenges in meteorology, where cloud errors have widereaching impacts on the overall accuracy of weather forecasts [1], [2]
Similar insights can be discovered from data-driven methods for cloud dynamics, but obtaining adequate observations of clouds globally has been a substantial obstacle for developing data-driven cloud forecasting methods to date. To tackle this problem and spark further research into datadriven atmospheric forecasting, we introduce a novel satellitebased dataset called “CloudCast” that facilitates the evaluation of cloud forecasting methods with a global perspective
Due to numerous available metrics, we select the ones with the highest ranking score in the referenced paper. As several of these are common in the computer vision and machine learning literature, we only go through the nonstandard metrics
Summary
C LOUD forecasting remains one of the major unsolved challenges in meteorology, where cloud errors have widereaching impacts on the overall accuracy of weather forecasts [1], [2]. Current datasets for global cloud forecasting exhibit coarse spatial resolution (9 × 9 to 31 × 31 km) and low temporal granularity (one to multiple hours between images) [5], [12]–[15] We overcome both these issues by using geostationary satellite images, arguably the most consistent and regularly sampled global data source for clouds [1]. Since these satellites can obtain images every 5–15 min with a relatively high spatial resolution (1 × 1 to 3 × 3 km), they provide an essential ingredient for developing data-driven weather systems, which is an abundance of historical observations.
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