000195723 001__ 195723
000195723 005__ 20181203023413.0
000195723 0247_ $$2doi$$a10.1016/j.atmosres.2013.11.008
000195723 022__ $$a0169-8095
000195723 02470 $$2ISI$$a000333504300010
000195723 037__ $$aARTICLE
000195723 245__ $$aInfluence of small scale rainfall variability on standard comparison tools between radar and rain gauge data
000195723 260__ $$aNew York$$bElsevier$$c2014
000195723 269__ $$a2014
000195723 300__ $$a14
000195723 336__ $$aJournal Articles
000195723 520__ $$aRain gauges and weather radars do not measure rainfall at the same scale; roughly 20 cm for the former and 1 km for the latter. This significant scale gap is not taken into account by standard comparison tools (e.g. cumulative depth curves, normalized bias, RMSE) despite the fact that rainfall is recognized to exhibit extreme variability at all scales. In this paper we suggest to revisit the debate of the representativeness of point measurement by explicitly modelling small scale rainfall variability with the help of Universal Multifractals. First the downscaling process is validated with the help of a dense networks of 16 disdrometers (in Lausanne, Switzerland), and one of 16 rain gauges (Bradford, United Kingdom) both located within a 1 km2 area. Second this downscaling process is used to evaluate the impact of small scale (i.e.: sub - radar pixel) rainfall variability on the standard indicators. This is done with rainfall data from the Seine-Saint-Denis County (France). Although not explaining all the observed differences, it appears that this impact is significant which suggests changing some usual practice.
000195723 6531_ $$aradar
000195723 6531_ $$arainfall
000195723 6531_ $$arain gauge data
000195723 700__ $$aGires, A.
000195723 700__ $$aTchiguirinskaia, I.
000195723 700__ $$aSchertzer, D.
000195723 700__ $$aSchellart, A.
000195723 700__ $$0240615$$aBerne, A.$$g176402
000195723 700__ $$aLovejoy, S.
000195723 773__ $$k138$$q125-138$$tAtmospheric Research
000195723 909C0 $$0252154$$pLTE$$xU11534
000195723 909CO $$ooai:infoscience.tind.io:195723$$particle$$pENAC
000195723 917Z8 $$x106743
000195723 937__ $$aEPFL-ARTICLE-195723
000195723 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000195723 980__ $$aARTICLE