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| Titel |
Increasing spatial resolution of CHIRPS rainfall datasets for Cyprus with
Artificial Neural Networks (ANN) |
| VerfasserIn |
Filippos Tymvios, Silas Michaelides, Adrianos Retalis, Dimitrios Katsanos, Jos Lelieveld  |
| Konferenz |
EGU General Assembly 2016
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 18 (2016) |
| Datensatznummer |
250123956
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| Publikation (Nr.) |
 EGU/EGU2016-3301.pdf |
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| Zusammenfassung |
| The use of high resolution rainfall datasets is an alternative way of studying climatological
patterns in regions where conventional rain measurements are sparse or not available. Starting
in 1981 to near-present, CHIRPS (Climate Hazards Group InfraRed Precipitation with
Station data ) dataset incorporates a 5x5km2 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 this rainfall dataset for Cyprus to 1x1km2 by correlating the CHIRPS
dataset with altitute information, NDVI vegetation index from satellite images at
1x1km2and precipitation measurements from the official raingauge network of the
Cyprus Department of Meteorology, utilizing Artificial Neural Network models. |
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