Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/10093
Title: Downscaling CHIRPS precipitation data: an artificial neural network modelling approach
Authors: Retalis, Adrianos 
Tymvios, Filippos S. 
Katsanos, Dimitrios K. 
Michaelides, Silas 
Major Field of Science: Engineering and Technology
Field Category: Civil Engineering
Keywords: Climate models;Deep neural networks;Gages;Geostationary satellites;Neural networks;Precipitation (meteorology);Rain gages;Satellite imagery
Issue Date: 3-Jul-2017
Source: International Journal of Remote Sensing, 2017, vol. 38, no. 13, pp. 3943-3959
Volume: 38
Issue: 13
Start page: 3943
End page: 3959
Journal: International Journal of Remote Sensing 
Abstract: The Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) is a high-resolution climatic database of precipitation embracing monthly precipitation climatology, quasi-global geostationary thermal infrared satellite observations from the Tropical Rainfall Measuring Mission (TRMM) 3B42 product, atmospheric model rainfall fields from National Oceanic and Atmospheric Administration–Climate Forecast System (NOAA CFS), and precipitation observations from various sources. The key difference with all other existing precipitation databases is the high-resolution of the available data, since the inherent 0.05° resolution is a rather unique threshold. Monthly data for the period from January 1999 to December 2012 were processed in the present research. The main aim of this article is to propose a novel downscaling method in order to attain high resolution (1 km × 1 km) precipitation datasets, by correlating the CHIRPS dataset with altitude information and the normalized difference vegetation index from satellite images at 1 km × 1 km, utilizing artificial neural network models. The final result was validated with precipitation measurements from the rain gauge network of the Cyprus Department of Meteorology.
ISSN: 1366-5901
DOI: 10.1080/01431161.2017.1312031
Rights: © Taylor & Francis
Type: Article
Affiliation : National Observatory of Athens 
Cyprus Department of Meteorology 
The Cyprus Institute 
Cyprus University of Technology 
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