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

TL;DR

VARENN is a novel graphical method that converts complex spatiotemporal climate data into 2D images, enabling CNNs to classify climate trends effectively and quantify variable importance.

Contribution

This paper introduces VARENN, a new visualization technique that enhances climate data analysis by integrating multiple variables into images for neural network modeling.

Findings

CNN models accurately classified temperature and precipitation trends.

Variable importance and seasonal/interannual variations significantly affect model accuracy.

VARENN effectively summarizes complex spatiotemporal data for climate studies.

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.