Abstract
Abstract 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, crowdsourcing 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 crowdsourcing 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 suggest promising research questions. Further, this study illustrates that crowdsourcing and deep learning complement each other well for the exploration of image datasets.
Highlights
Humans excel at detecting interesting patterns in images, for example, those taken from satellites
Machine learning techniques, deep learning, have demonstrated their ability to mimic the human capacity for identifying patterns, from satellite cloud imagery (e.g., Wood and Hartmann 2006)
Stevens et al (2020) described a collective cloud classification activity by a team of 13 scientists supported by the International Space Science Institute (ISSI)
Summary
Flower, Fish, and Gravel Mesoscale patterning of shallow cumulus is a common feature in satellite imagery Organization on these scales is largely ignored in modeling studies of clouds and climate. The choice of new and evocative names was motivated by the judgement that the patterns were different than those that have been previously described, for instance in studies of stratocumulus or cold-air outbreaks Support for this judgement is provided by an application of the neural network from Wood and Hartmann (2006) and Muhlbauer et al (2014), which was trained to distinguish between “No mesoscale cellular convection (MCC),” “Closed MCC,” “Open MCC,” and “Cellular, but disorganized.”. Their automatically detected classes appear less striking to the human eye
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