On the removal of random variables in data sets of meteorological observations     

P. Termonia 《Meteorology and Atmospheric Physics》2001,78(3-4):143-156

Summary For some purposes it is not necessary to use a full data set of strongly spatially correlated parameters. In some situations, too much information may even be unwanted. We propose a procedure for finding a non-redundant choice of synoptic stations that is sufficient to capture the relevant physical patterns based on rule N. The technique makes use of principle component analysis and cluster analysis. The above-mentioned procedure can be applied to generic data sets. As an illustration, we apply it here to the strongly correlated two-meter temperature observed in the Belgian synoptic network during the winter period. We find that about three or four stations from this network are necessary. The method also suggests an intelligent choice of stations that are most suitable to be used. Received April 5, 2001/Revised August 1, 2001  相似文献   

Consistent scale-dependency of future increases in hourly extreme precipitation in two convection-permitting climate models     

Helsen  Samuel  van Lipzig  Nicole P. M.  Demuzere  Matthias  Vanden Broucke  Sam  Caluwaerts  Steven  De Cruz  Lesley  De Troch  Rozemien  Hamdi  Rafiq  Termonia  Piet  Van Schaeybroeck  Bert  Wouters  Hendrik 《Climate Dynamics》2020,54(3):1267-1280

Climate Dynamics - Convection-permitting models (CPMs) have been proven successful in simulating extreme precipitation statistics. However, when such models are used to study climate change,...  相似文献