Hypothesis Generation in Climate Research with Interactive Visual Data Exploration

Abstract

One of the most prominent topics in climate research is the investigation, detection, and allocation of climate change. In this paper, we aim at identifying regions in the atmosphere (e.g., certain height layers) which can act as sensitive and robust indicators for climate change. We demonstrate how interactive visual data exploration of large amounts of multi-variate and time-dependent climate data enables the steered generation of promising hypotheses for subsequent statistical evaluation. The use of new visualization and interaction technology--in the context of a coordinated multiple views framework--allows not only to identify these promising hypotheses, but also to efficiently narrow down parameters that are required in the process of computational data analysis. Two datasets, namely an ECHAM5 climate model run and the ERA-40 reanalysis incorporating observational data, are investigated. Higher-order information such as linear trends or signal-to-noise ratio is derived and interactively explored in order to detect and explore those regions which react most sensitively to climate change. As one conclusion from this study, we identify an excellent potential for usefully generalizing our approach to other, similar application cases, as well.

Where to see climate change best in radio occultation variables – study using GCMs and ECMWF reanalyses

“…ERA-40, with known deficiencies at high latitudes as shown by Santer et al (2004) and visualized by Kehrer et al (2008), and ERA-INT trends differ considerably in the ARC in Fig. 6a, where the ERA-INT period is about 10 years more recent than the ERA-40 period.…”

“…Employing both, ERA-40 and ERA-INT, provides on the one hand an insight in trends of the two reanalyses and on the other hand an insight in the development of trends in two different periods, which are separated by about 10 years. Since reanalyses are more reliable after 1979 when satellite data were included, we only use ERA-40 data of the satellite era 1980 to 2001 (Uppala et al, 2005;Kehrer et al, 2008). ERA-INT is used over 1989 to 2005, the end chosen so that trends do not span 2006 and beyond.…”

“…Besides the tropics, Antarctica provides eye-catching negative LS indicators (also for pressure fields). These indicators must be seen with reservation, however, as the quality of ERA-40 is deficient at high southern latitudes according to Santer et al (2004) and visualized by Kehrer et al (2008) and Ladstädter et al (2010). The seasonal dependence of ERA-40 indicators is also reflected in pressure.…”

Interactive selection of multivariate features in large spatiotemporal data

“…Due to the complexity, it has become prevalent to use multiple linked views to simultaneously show, explore, and analyze different aspects of multivariate data. Examples include SimVis [2] and follow-up research works like [3,11] that demonstrates the ability of multiple linked views to enable iterative feature specification and hypotheses generation. Our work also follows the same practice.…”

Hypothesis Generation by Interactive Visual Exploration of Heterogeneous Medical Data

“…At this stage, the field of visual analytics can offer solutions to analysts to overcome these limitations [3] [4]. The visual analysis methods enable analysts to quickly build new hypotheses through interaction with the data.…”