![]() ![]() They then suggested that the analysis of the rain DSDs observed on the ground are superimposed DSDs. They also showed that some modes of rain DSDs have a persistence larger than several minutes. They found these multimodal shapes in the disdrometric data acquired in two climatic regions: one tropical (Boyélé in Congo), and the other temperate (Brest in France). ![]() Sauvageot and Koffi indicated that the rain DSDs observed over a short period generally has an erratic shape, with several relative maximums. obtained around 28% of multimodal spectra, a proportion similar to that of Radhakrisna and Narayana Rao. In contrast with the disdrometric data from Graz in Austria, Ekerete et al. ![]() With the disdrometric data from Chilbolton in England, Ekerete et al. merged into a spectrum, five neighbouring spectra of 1 min duration, to smooth the data. Before applying this method, Ekerete et al. Thus, a diameter D i has a hollow if only N( D i−1) > N( D i) < N( D i+1) < N( D i+2). A multimodal spectrum being made up of several sub-spectra separated by hollow, each sub-spectrum having a peak (or a mode), they propose to identify the number of peaks from the number of hollow. suggested that this method is not sufficient to properly identify multimodal spectra. Radhakrisna and Narayana Rao applied this method and obtained around 30% of multimodal spectra in their rain DSDs data, with integration time T = 5 min, observed on the ground, at Gadanki in India. Radhakrisna and Narayana Rao and Sauvageot and Koffi simplified this principle as follows: a diameter D i ( i is the index of a diameter class in a rain DSD spectrum) has a peak if only N( D i−1) N( D i+1). To identify the peaks in the rain DSDs, measured on the ground, Steiner and Waldvogel used the following principle: if the concentration N( D) of a given diameter is significantly higher than those of neighbouring diameters, then a peak exists at this diameter. Several studies are carried out on the spectra of the rain drop size distributions (DSDs), which are not well-fitted by unimodal DSD models, in particular, those which have several peaks (multimodal spectra). For applications using the relationships deduced from the rain DSDs, results suggested that the measurement time scale must be taken into account when choosing appropriate relationships. Furthermore, parameters of the shape functions (gamma and lognormal) increase or decrease markedly according to the measurement duration of the rain DSD spectra. The optimal measurement time is found to be 10 min. This population decreases according to the measurement duration of the spectra. Analysis of the occurrence statistics of the structuring of the spectra reveals that the Spectra ill adjusted by a unimodal DSD model represents 5 to 15% of the population of 1 min rain DSDs. Results show that there is an improvement in the structuring of the rain DSDs according to their measurement duration. Superimposed rain DSDs were then parameterized with the rainfall rate, using the scaling law formalism. These criteria are used to assess the level of fitting of rain DSD spectra. The efficiency of these models was characterized by statistical criteria, mainly Nash and KGE. Rain DSD data considered are those collected from 2005 to 2007 near Djougou city in the north-western region of Benin Republic. This paper focused on modelling rain drop size distributions (DSDs) of various integration time steps using unimodal DSD models (gamma and lognormal). ![]()
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