Published on Tue Jul 23 2019

VARENN: Graphical representation of spatiotemporal data and application to climate studies

Takeshi Ise, Yurika Oba

Analyzing and utilizing spatiotemporal big data are essential for studies about climate change. Such data are not fully integrated into climate models owing to limitations in statistical frameworks. VARENN (visually augmented representation of environment for neural networks) was used to efficiently summarize monthly observations of climate data.

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Abstract

Analyzing and utilizing spatiotemporal big data are essential for studies concerning climate change. However, such data are not fully integrated into climate models owing to limitations in statistical frameworks. Herein, we employ VARENN (visually augmented representation of environment for neural networks) to efficiently summarize monthly observations of climate data for 1901-2016 into 2-dimensional graphical images. Using red, green, and blue channels of color images, three different variables are simultaneously represented in a single image. For global datasets, models were trained via convolutional neural networks. These models successfully classified rises and falls in temperature and precipitation. Moreover, similarities between the input and target variables were observed to have a significant effect on model accuracy. The input variables had both seasonal and interannual variations, whose importance was quantified for model efficacy. VARENN is thus an effective method to summarize spatiotemporal data objectively and accurately.