By adapting a flow-following physical framework to the statistical modeling of large spatio–temporal datasets, KAUST researchers have developed a more robust and realistic general method for dealing with wind-driven phenomena. The approach promises to greatly improve the accuracy of pollutant dispersion prediction by incorporating more physically realistic processes into geostatistical modeling.
from General Physics News - Science News, Physics News, Physics, Material Sciences, Science https://ift.tt/N5aIXBm
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