Abstract
Climatological data are mostly based on data captured by the local climatological stations. It is necessary to interpolate stations’ data to get the information about conditions for entire country. Types of interpolation method and their visualization do affect the perception of final results. Moreover, the data sources and models have uncertainties associated with them. It is generally important to be able to visualize those uncertainties, and especially to be able to quickly focus on areas where there is considerable disagreement. The follow-up interpretation of visualized uncertainty can be very helpful in some cases. Czech Hydrometeorological Institute provides a lot of datasets from more than 250 stations. The article is focused on potential effective ways of the interpolation and visualization of uncertainty of the temperature time-series datasets which are also one of the most traditionally measured data. A final comparing of the interpolation and interpretation are based on the uncertainty visualization which are relatively new approaches used in the geographical research but their potential has been already proven by wide usage in GIS analysis. The evaluation of interpolation methods as well as geographic data orderliness according to the uncertainty visualization is accomplished and discussed in the paper. Subsequent visualization of analysed phenomenon using this approach brings augmented and more accurate (geo) information to the user, which helps to better decision.
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Brus, J., Voženílek, V., Popelka, S. (2013). An Assessment of Quantitative Uncertainty Visualization Methods for Interpolated Meteorological Data. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2013. ICCSA 2013. Lecture Notes in Computer Science, vol 7974. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39649-6_12
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DOI: https://doi.org/10.1007/978-3-642-39649-6_12
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