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| Titel |
Statistical and neural classifiers in estimating rain rate from weather radar measurements |
| VerfasserIn |
C. I. Christodoulou, S. C. Michaelides |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7340
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| Digitales Dokument |
URL |
| Erschienen |
In: Observation, Prediction and Verification of Precipitation (EGU Session 2006) ; Nr. 10 (2007-04-26), S.111-115 |
| Datensatznummer |
250007861
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| Publikation (Nr.) |
 copernicus.org/adgeo-10-111-2007.pdf |
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| Zusammenfassung |
| Weather radars are used to measure the
electromagnetic radiation backscattered by cloud raindrops. Clouds that
backscatter more electromagnetic radiation consist of larger droplets of
rain and therefore they produce more rain. The idea is to estimate rain rate
by using weather radar as an alternative to rain-gauges measuring rainfall
on the ground. In an experiment during two days in June and August 1997 over
the Italian-Swiss Alps, data from weather radar and surrounding rain-gauges
were collected at the same time. The statistical KNN and the neural SOM
classifiers were implemented for the classification task using the radar
data as input and the rain-gauge measurements as output. The proposed system
managed to identify matching pattern waveforms and the rainfall rate on the
ground was estimated based on the radar reflectivities with a satisfactory
error rate, outperforming the traditional Z/R relationship. It is
anticipated that more data, representing a variety of possible
meteorological conditions, will lead to improved results. The results in
this work show that an estimation of rain rate based on weather radar
measurements treated with statistical and neural classifiers is possible. |
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