气候变化领域集成服务门户(CCPortal): Combining crowd-sourcing and deep learning to explore the meso-scale organization of shallow convection

CCPortal

DOI	10.1175/BAMS-D-19-0324.1
	Combining crowd-sourcing and deep learning to explore the meso-scale organization of shallow convection
	Rasp S.; Schulz H.; Bony S.; Stevens B.
发表日期	2020
ISSN	00030007
期号	2020
英文摘要	Humans excel at detecting interesting patterns in images, for example those taken from satellites. This kind of anecdotal evidence can lead to the discovery of new phenomena. However, it is often difficult to gather enough data of subjective features for significant analysis. This paper presents an example of how two tools that have recently become accessible to a wide range of researchers, crowd-sourcing and deep learning, can be combined to explore satellite imagery at scale. In particular, the focus is on the organization of shallow cumulus convection in the trade wind regions. Shallow clouds play a large role in the Earth’s radiation balance yet are poorly represented in climate models. For this project four subjective patterns of organization were defined: Sugar, Flower, Fish and Gravel. On cloud labeling days at two institutes, 67 scientists screened 10,000 satellite images on a crowd-sourcing platform and classified almost 50,000 mesoscale cloud clusters. This dataset is then used as a training dataset for deep learning algorithms that make it possible to automate the pattern detection and create global climatologies of the four patterns. Analysis of the geographical distribution and large-scale environmental conditions indicates that the four patterns have some overlap with established modes of organization, such as open and closed cellular convection, but also differ in important ways. The results and dataset from this project suggests promising research questions. Further, this study illustrates that crowd-sourcing and deep learning complement each other well for the exploration of image datasets. (Capsule Summary) Crowd-sourcing and deep learning are combined to explore the meso-scale organization of shallow clouds in the subtropics. © 2020 American Meteorological Society.
语种	英语
scopus关键词	Climate models; Crowdsourcing; Earth (planet); Geographical distribution; Learning algorithms; Pattern recognition; Satellite imagery; Anecdotal evidences; Environmental conditions; Global climatology; Pattern detection; Radiation balance; Research questions; Shallow convection; Shallow cumulus convection; Deep learning
来源期刊	Bulletin of the American Meteorological Society
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/177869
作者单位	Technical University of Munich, Germany; Max Planck Institute for Meteorology, Hamburg, Germany; Sorbonne Université, LMD/IPSL, CNRS, Paris, France
推荐引用方式 GB/T 7714	Rasp S.,Schulz H.,Bony S.,et al. Combining crowd-sourcing and deep learning to explore the meso-scale organization of shallow convection[J],2020(2020).
APA	Rasp S.,Schulz H.,Bony S.,&Stevens B..(2020).Combining crowd-sourcing and deep learning to explore the meso-scale organization of shallow convection.Bulletin of the American Meteorological Society(2020).
MLA	Rasp S.,et al."Combining crowd-sourcing and deep learning to explore the meso-scale organization of shallow convection".Bulletin of the American Meteorological Society .2020(2020).