forecasting Article Visual Analytics for Climate Change Detection in Meteorological Time-Series Milena Vuckovic * and Johanna Schmidt   Citation: Vuckovic, M.; Schmidt, J. Visual Analytics for Climate Change Detection in Meteorological Time-Series. Forecasting 2021, 3, 276–289. https://doi.org/10.3390/ forecast3020018 Academic Editor: Umberto Triacca Received: 19 March 2021 Accepted: 16 April 2021 Published: 19 April 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH, 1220 Vienna, Austria; johanna.schmidt@vrvis.at * Correspondence: milena.vuckovic@vrvis.at Abstract: The importance of high-resolution meteorological time-series data for detection of trans- formative changes in the climate system is unparalleled. These data sequences allow for a com- prehensive study of natural and forced evolution of warming and cooling tendencies, recognition of distinct structural changes, and periodic behaviors, among other things. Such inquiries call for applications of cutting-edge analytical tools with powerful computational capabilities. In this regard, we documented the application potential of visual analytics (VA) for climate change detection in meteorological time-series data. We focused our study on long- and short-term past-to-current meteorological data of three Central European cities (i.e., Vienna, Munich, and Zürich), delivered in different temporal intervals (i.e., monthly, hourly). Our aim was not only to identify the related transformative changes, but also to assert the degree of climate change signal that can be derived given the varying granularity of the underlying data. As such, coarse data granularity mostly offered insights on general trends and distributions, whereby a finer granularity provided insights on the frequency of occurrence, respective duration, and positioning of certain events in time. However, by harnessing the power of VA, one could easily overcome these limitations and go beyond the basic observations. Keywords: climate change; meteorological time-series; global warming; visual analytics; visual computing 1. Introduction 1.1. Background The unprecedented global increase in the frequency and magnitude of extreme weather events and related consequences (e.g., heat waves, flooding and drought, severe storms, wildfires) is being recognized as one of the most pressing environmental issues and a worldwide health and lifestyle concern. The synthesis reports published by the Intergov- ernmental Panel on Climate Change (IPCC) confirmed that these observed transformative changes in climate system are closely tied to the anthropogenic processes and related elevated emissions of greenhouse gases (GHG) in the Earth’s atmosphere [1,2]. It is a well-documented fact that GHG such as water vapor, carbon dioxide (CO 2 ), methane (CH 4 ), nitrous oxide, which occur naturally in the atmosphere, along with the synthetic fluorinated gases, which originate from a variety of industrial processes, have the tendency to absorb, store and reradiate long-wave radiation emitted from Earth’s surface back to Earth’s surface [3,4]. The effect is generally known as the ‘greenhouse effect’ and has a significant impact on energy budget of the Earth system, resulting in global atmospheric warming and chaotic weather patterns worldwide [3]. The global character of this phe- nomenon is mainly driven by the fact that Earth’s atmosphere intermixes globally, meaning that this phenomenon is of no geographical or spatial specificity. However, the degree to which this drives site-specific environmental issues, such as the air, water and soil pollution, alongside the occurrence of landslides, fluvial flooding, wildfires in forest landscapes, to Forecasting 2021, 3, 276–289. https://doi.org/10.3390/forecast3020018 https://www.mdpi.com/journal/forecasting