Validation of Climate Research Unit High Resolution Time-Series Rainfall Data over Three Source Region: Results of 52 Years

2013 ◽  
Vol 726-731 ◽  
pp. 3542-3546 ◽  
Author(s):  
Jonathan Arthur Quaye-Ballard ◽  
Ru An ◽  
Richard Ruan ◽  
Kwaku Amaning Adjei ◽  
Samuel Akorful-Andam

The purpose of this paper was to validate the rainfall data of Climate Research Unit high resolution Time-Series version 3.1 (CRU TS 3.1) with meteorological ground-based Rain Gauge (RG) measurements and determine the possibility of its integration with ground-measured rainfall. The research primarily advocates on the need for complementing ground-based datasets with CRU TS 3.1global datasets for sustainable studies in protecting the environment. The Source Region of the Yellow, Yangtse and Lancang Rivers (SRYYLR), China was taken as the study area. The data was validated by using the data from seventeen meteorological RG stations at SRYYLR. Statistical technique based on Linear Regression (LR), Cumulative Residual Series Analysis (CRSA) and Geo-Spatial techniques based on batch processing, cell statistics, map algebra, re-sampling, extraction by mask, geo-statistical interpolation and profiling along transects by interpolation of a line were used. The study revealed that although CRU TS 3.1 datasets are underestimated compared to the RG datasets, they can be efficiently and effectively be used for rainfall trend analysis with 90% level of confidence because of the analyses by different techniques revealed similar profile trends.

2015 ◽  
Vol 16 (2) ◽  
pp. 548-562 ◽  
Author(s):  
Auguste Gires ◽  
Ioulia Tchiguirinskaia ◽  
Daniel Schertzer ◽  
Alexis Berne

Abstract Data collected during four heavy rainfall events that occurred in Ardèche (France) with the help of a 2D video disdrometer (2DVD) are used to investigate the structure of the raindrop distribution in both space and time. A first type of analysis is based on the reconstruction of 36-m-height vertical rainfall columns above the measuring device. This reconstruction is obtained with the help of a ballistic hypothesis applied to 1-ms time step series. The corresponding snapshots are analyzed with the help of universal multifractals. For comparison, a similar analysis is performed on the time series with 1-ms time steps, as well as on time series of accumulation maps of N consecutive recorded drops (therefore with variable time steps). It turns out that the drop distribution exhibits a good scaling behavior in the range 0.5–36 m during the heaviest portion of the events, confirming the lack of empirical evidence of the widely used homogenous assumption for drop distribution. For smaller scales, drop positions seem to be homogeneously distributed. The notion of multifractal singularity is well illustrated by the very high-resolution time series.


2001 ◽  
Vol 5 (4) ◽  
pp. 645-652 ◽  
Author(s):  
B. Sivakumar

Abstract. This study of the behaviour of rainfall dynamics at different temporal scales identifies the type of approach most suitable for transformation of rainfall data from one scale to another. Rainfall data of four different temporal scales, i.e. daily, 2-day, 4-day and 8-day, observed over a period of about 25 years at the Leaf River basin, Mississippi, USA, are analysed. The correlation dimension method is employed to identify the behaviour of rainfall dynamics. The finite correlation dimensions obtained for the four rainfall series (4.82, 5.26, 6.42 and 8.87, respectively) indicate the possible existence of chaotic behaviour in the rainfall observed at the four scales. A possible implication of this might be that the rainfall processes at these scales are related through a chaotic (scale-invariant) behaviour. However, a comparison of the correlation dimension and coefficient of variation of each of the time series reveals an inverse relationship between the two (higher dimension for lower coefficient of variation and vice versa). The presence of a large number of zeros in the higher resolution time series (that could result in an underestimation of the dimension) and the possible presence of a higher level of noise in the lower resolution time series (that could result in an overestimation of the dimension) might account for such results. In view of these problems, it is concluded that the results must be verified using other chaos identification methods and the existence of chaos must be substantiated with additional evidence. Keywords: rainfall, chaos, scaling, correlation dimension, number of variables, coefficient of variation, data size, noise, zeros


2002 ◽  
Vol 2002 (0) ◽  
pp. 121-122
Author(s):  
Mamoru TANAHASHI ◽  
Yuichi FUKUCHI ◽  
Katsuhiko FUKUZATO ◽  
Toshio MIYAUCHI

2010 ◽  
Vol 516 ◽  
pp. A91 ◽  
Author(s):  
S. Vargas Domínguez ◽  
A. de Vicente ◽  
J. A. Bonet ◽  
V. Martínez Pillet

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