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Neural networks for rainfall forecasting by atmospheric downscaling
SMHI, Forskningsavdelningen, Hydrologi.ORCID-id: 0000-0002-1986-8374
Vise andre og tillknytning
2004 (engelsk)Inngår i: Journal of hydrologic engineering, ISSN 1084-0699, E-ISSN 1943-5584, Vol. 9, nr 1, s. 1-12Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Several studies have used artificial neural networks (NNs) to estimate local or regional precipitation/rainfall on the basis of relationships with coarse-resolution atmospheric variables. None of these experiments satisfactorily reproduced temporal intermittency and variability in rainfall. We attempt to improve performance by using two approaches: (1) couple two NNs in series, the first to determine rainfall occurrence, and the second to determine rainfall intensity during rainy periods; and (2) categorize rainfall into intensity categories and train the NN to reproduce these rather than the actual intensities. The experiments focused on estimating 12-h mean rainfall in the Chikugo River basin, Kyushu Island, southern Japan, from large-scale values of wind speeds at 850 hPa and precipitable water. The results indicated that (1) two NNs in series may greatly improve the reproduction of intermittency; (2) longer data series are required to reproduce variability; (3) intensity categorization may be useful for probabilistic forecasting; and (4) overall performance in this region is better during winter and spring than during summer and autumn.

sted, utgiver, år, opplag, sider
2004. Vol. 9, nr 1, s. 1-12
Emneord [en]
neural networks, rainfall, forecasting, Japan
HSV kategori
Forskningsprogram
Hydrologi
Identifikatorer
URN: urn:nbn:se:smhi:diva-1330DOI: 10.1061/(ASCE)1084-0699(2004)9:1(1)ISI: 000187787100001OAI: oai:DiVA.org:smhi-1330DiVA, id: diva2:814112
Tilgjengelig fra: 2015-05-26 Laget: 2015-05-26 Sist oppdatert: 2018-01-11bibliografisk kontrollert

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