High Resolution Time Series Measurements of Bio-optical and Physical Variability in the Coastal Ocean as Part of HyCODE

2001 ◽  
Author(s):  
T. Dickey
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.


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

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.


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