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| Titel |
Spatiotemporal monthly rainfall reconstruction via artificial neural network – case study: south of Brazil |
| VerfasserIn |
P. S. Lucio, F. C. Conde, I. F. A. Cavalcanti, A. I. Serrano, A. M. Ramos, A. O. Cardoso |
| 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.67-76 |
| Datensatznummer |
250007855
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| Publikation (Nr.) |
 copernicus.org/adgeo-10-67-2007.pdf |
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| Zusammenfassung |
| Climatological records users, frequently, request time
series for geographical locations where there is no observed meteorological
attributes. Climatological conditions of the areas or points of interest
have to be calculated interpolating observations in the time of neighboring
stations and climate proxy. The aim of the present work is the application
of reliable and robust procedures for monthly reconstruction of
precipitation time series. Time series is a special case of symbolic
regression and we can use Artificial Neural Network (ANN) to explore the
spatiotemporal dependence of meteorological attributes. The ANN seems to be
an important tool for the propagation of the related weather information to
provide practical solution of uncertainties associated with interpolation,
capturing the spatiotemporal structure of the data. In practice, one
determines the embedding dimension of the time series attractor (delay time
that determine how data are processed) and uses these numbers to define the
network's architecture. Meteorological attributes can be accurately
predicted by the ANN model architecture: designing, training, validation and
testing; the best generalization of new data is obtained when the mapping
represents the systematic aspects of the data, rather capturing the specific
details of the particular training set. As illustration one takes monthly
total rainfall series recorded in the period 1961–2005 in the Rio Grande do
Sul – Brazil. This reliable and robust reconstruction method has good
performance and in particular, they were able to capture the intrinsic
dynamic of atmospheric activities. The regional rainfall has been related to
high-frequency atmospheric phenomena, such as El Niño and La Niña
events, and low frequency phenomena, such as the Pacific Decadal
Oscillation. |
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