globalchange  > 影响、适应和脆弱性
DOI: 10.1016/j.atmosres.2017.12.011
Scopus记录号: 2-s2.0-85044360104
论文题名:
Pseudo-radar algorithms with two extremely wet months of disdrometer data in the Paris area
作者: Gires A.; Tchiguirinskaia I.; Schertzer D.
刊名: Atmospheric Research
ISSN: 1698095
出版年: 2018
卷: 203
起始页码: 216
结束页码: 230
语种: 英语
英文关键词: Disdrometer ; Multifractal ; Radar algorithm ; Rainfall
Scopus关键词: Fractals ; Rain ; Disdrometer data ; Disdrometers ; Drop size distribution ; Innovative methodologies ; Multi fractals ; Radar relation ; Scale invariant ; Specific differential phase ; Radar ; algorithm ; homogeneity ; hydrometeorology ; hypothesis testing ; instrumentation ; parameterization ; quantitative analysis ; size distribution ; time series ; France ; Ile de France ; Paris ; Ville de Paris
英文摘要: Disdrometer data collected during the two extremely wet months of May and June 2016 at the Ecole des Ponts ParisTech are used to get insights on radar algorithms. The rain rate and pseudo-radar quantities (horizontal and vertical reflectivity, specific differential phase shift) are all estimated over several durations with the help of drop size distributions (DSD) collected at 30 s time steps. The pseudo-radar quantities are defined with simplifying hypotheses, in particular on the DSD homogeneity. First it appears that the parameters of the standard radar relations Zh − R, R − Kdp and R − Zh − Zdr for these pseudo-radar quantities exhibit strong variability between events and even within an event. Second an innovative methodology that relies on checking the ability of a given algorithm to reproduce the good scale invariant multifractal behaviour (on scales 30 s – few h) observed on rainfall time series is implemented. In this framework, the classical hybrid model (Zh − R for low rain rates and R − Kdp for great ones) performs best, as well as the local estimates of the radar relations’ parameters. However, we emphasise that due to the hypotheses on which they rely these observations cannot be straightforwardly extended to real radar quantities. © 2017 Elsevier B.V.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/108955
Appears in Collections:影响、适应和脆弱性
气候变化事实与影响

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作者单位: HMCo, Ecole des Ponts, UPE, Champs-sur-Marne, France

Recommended Citation:
Gires A.,Tchiguirinskaia I.,Schertzer D.. Pseudo-radar algorithms with two extremely wet months of disdrometer data in the Paris area[J]. Atmospheric Research,2018-01-01,203
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