In some of the domain-specific sectors, such as the climate domain, the provision of publicly available present-day high-resolution meteorological time series is often quite limited or completely lacking. This repeatedly leads to excessive deployment of synthetically generated (historical) meteorological time series (TMY) to support thermal performance assessments on both building and urban scale. These datasets are generally a misrepresentation of current weather variability, which may lead to erroneous inferences drawn from modelling results. In this regard, we outline the application potential of a visual analytics approach in the context of data quality assessment and validation of TMYs. For this purpose, we deployed a standalone visual analytics tool Visplore, enriched with interlinked dashboards, customizable visualizations, and intuitive workflows, to support continuous interaction and early visual feedback. Driven by such integrated visual representations and visual interactions to enhance the analytical reasoning process, we were able to detect critical multifaceted discrepancies, on different levels of granularity, between TMY and present-day meteorological time series and synthesize them into cohesive patterns and insights. These mainly entailed diverging temporal trends and event time lags, under- and overestimation of warming and cooling regimes, respectively, and seasonal discrepancies, in particular meteorological parameters, to name a few.
visual analytics; interactive visualization; visual computing; data quality; climate data