Increasing spatial resolution of CHIRPS rainfall datasets for Cyprus with 
artificial neural networks

Tymvios , Filippos; Michaelides , Silas; Retalis , Adrianos; Katsanos , Dimitrios; Lelieveld, J.

doi:10.1117/12.2242820

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Increasing spatial resolution of CHIRPS rainfall datasets for Cyprus with artificial neural networks

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Lelieveld, J.
Atmospheric Chemistry, Max Planck Institute for Chemistry, Max Planck Society;

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Citation

Tymvios, F., Michaelides, S., Retalis, A., Katsanos, D., & Lelieveld, J. (2016). Increasing spatial resolution of CHIRPS rainfall datasets for Cyprus with artificial neural networks. In K. Themistocleous, D. G. Hadjimitsis, & S. Michaelides (Eds.), Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016). doi:10.1117/12.2242820.

Cite as: https://hdl.handle.net/11858/00-001M-0000-002C-92C2-5

Abstract

The use of high resolution rainfall datasets is an alternative way of studying climatological regions where conventional rain measurements are sparse or not available. Starting in 1981 to near-present, the CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) dataset incorporates a 5kmx5km resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis, severe events and seasonal drought monitoring. The aim of this work is to further increase the resolution of the rainfall dataset for Cyprus to 1kmx1km, by correlating the CHIRPS dataset with elevation information, the NDVI index (Normalized Difference Vegetation Index) from satellite images at 1kmx1km and precipitation measurements from the official raingauge network of the Cyprus' Department of Meteorology, utilizing Artificial Neural Networks. The Artificial Neural Networks' architecture that was implemented is the Multi-Layer Perceptron (MLP) trained with the back propagation method, which is widely used in environmental studies. Seven different network architectures were tested, all with two hidden layers. The number of neurons ranged from 3 to10 in the first hidden layer and from 5 to 25 in the second hidden layer. The dataset was separated into a randomly selected training set, a validation set and a testing set; the latter is independently used for the final assessment of the models' performance. Using the Artificial Neural Network approach, a new map of the spatial analysis of rainfall is constructed which exhibits a considerable increase in its spatial resolution. A statistical assessment of the new spatial analysis was made using the rainfall ground measurements from the raingauge network. The assessment indicates that the methodology is promising for several applications.