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Neural networks for rainfall forecasting by atmospheric downscaling
SMHI, Research Department, Hydrology.ORCID iD: 0000-0002-1986-8374
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2004 (English)In: Journal of hydrologic engineering, ISSN 1084-0699, Vol. 9, no 1, 1-12 p.Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
2004. Vol. 9, no 1, 1-12 p.
Keyword [en]
neural networks, rainfall, forecasting, Japan
National Category
Oceanography, Hydrology, Water Resources
Research subject
Hydrology
Identifiers
URN: urn:nbn:se:smhi:diva-1330DOI: 10.1061/(ASCE)1084-0699(2004)9:1(1)ISI: 000187787100001OAI: oai:DiVA.org:smhi-1330DiVA: diva2:814112
Available from: 2015-05-26 Created: 2015-05-26 Last updated: 2015-05-26Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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  • de-DE
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