Description: Ground-based and spaceborne weather radars provide a wealth of remotely sensed precipitation measurements. A key issue for the quantitative use of these data in various applications is the space-time variability of the microstructure of precipitation, described by the drop size distribution (DSD hereafter). Although the variability of rainfall intensity at scales smaller than the radar resolution (radar-subgrid scales hereafter) has been studied extensively, there is no large-enough data set on the spatial variability of the DSD at radar-subgrid scales to perform robust spatial analyses. The aim of this proposal is to set up an experiment addressing this lack by deploying a network of 14 disdrometers covering the typical radar pixel (i.e. about 1x1ca.km2 for ground-based radars, and about 5x5 km2 for spaceborne radars). From the data collected, the space-time variability of the DSD will be quantified and characterized. Its influence on the accuracy of the estimation of precipitation using radar will be investigated. In particular, a stochastic simulator of space-time fields of DSD will be developed to provide a useful tool for the evaluation of the different precipitation remote sensing techniques and the associated retrieval algorithms.
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Tags: Regen ? Messgerät ? Niederschlagshöhe ? Radar ? Niederschlagsintensität ? Regenwasser ? Wetterdaten ? Berechnungsverfahren ? Fernerkundungsdaten ? Messdaten ? Quantitative Analyse ? Geoinformation ? Messung ? Daten ? Fernerkundung ? Niederschlag ? Wetter ? Auflösungsvermögen ? Distrometer ? Stochastik ? Zeitverlauf ? drop size distribution ? radar-subgrid scale ?
License: Creative Commons Namensnennung-keine Bearbeitung-Nichtkommerziell 4.0
Language: Englisch/English
Time ranges: 2008-02-01 - 2011-01-31
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