CloudCast: A Satellite-Based Dataset and Baseline for Forecasting Clouds
This provides a high-resolution dataset for weather forecasting, addressing a shortage of such data for machine learning applications, but it is incremental as it focuses on data creation and baseline evaluation.
The authors tackled the problem of forecasting cloud formation and development by introducing CloudCast, a novel satellite-based dataset with 70,080 high-resolution images annotated for 10 cloud types, and evaluated state-of-the-art video prediction methods, showing promising results with room for improvement.
Forecasting the formation and development of clouds is a central element of modern weather forecasting systems. Incorrect clouds 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 paper, 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 x 1530 pixels (3x3 km per pixel) with 15-min intervals between frames for the period 2017-01-01 to 2018-12-31. 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.