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Title: Downscaling CHIRPS precipitation data: an artificial neural network modelling approach
Authors: Retalis, Adrianos 
Tymvios, Filippos S. 
Katsanos, Dimitrios K. 
Michaelides, Silas 
Keywords: Climate models;Deep neural networks;Gages;Geostationary satellites;Neural networks;Precipitation (meteorology);Rain gages;Satellite imagery
Category: Civil Engineering
Field: Engineering and Technology
Issue Date: 3-Jul-2017
Publisher: Taylor and Francis Ltd.
Source: International Journal of Remote Sensing, 2017, Volume 38, Issue 13, Pages 3943-3959
metadata.dc.doi: http:/
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: 01431161
Rights: © 2017 Informa UK Limited, trading as Taylor & Francis Group
Type: Article
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