Advanced

Neural Networks for rainfall forecasting by atmospheric downscaling

Olsson, J.; Bertacchi Uvo, Cintia LU ; Jinno, K.; Kawamura, A.; Nishiyama, K.; Koreeda, N.; Nakashima, T. and Morita, O. (2004) In Journal of Hydrologic Engineering 9(1). p.1-12
Abstract
Several studies have used artificial neural networks (NNs) to estimate local or regional recipitation/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... (More)
Several studies have used artificial neural networks (NNs) to estimate local or regional recipitation/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. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Journal of Hydrologic Engineering
volume
9
issue
1
pages
1 - 12
publisher
American Society of Civil Engineers (ASCE)
external identifiers
  • scopus:1642578865
ISSN
1084-0699
DOI
10.1061/(ASCE)1084-0699(2004)9:1(1)
language
English
LU publication?
yes
id
84b809e2-dae6-4c1f-a5ac-cea77a7aaa94 (old id 915491)
date added to LUP
2008-01-17 12:50:46
date last changed
2017-08-20 04:22:21
@article{84b809e2-dae6-4c1f-a5ac-cea77a7aaa94,
  abstract     = {Several studies have used artificial neural networks (NNs) to estimate local or regional recipitation/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.},
  author       = {Olsson, J. and Bertacchi Uvo, Cintia and Jinno, K. and Kawamura, A. and Nishiyama, K. and Koreeda, N. and Nakashima, T. and Morita, O.},
  issn         = {1084-0699},
  language     = {eng},
  number       = {1},
  pages        = {1--12},
  publisher    = {American Society of Civil Engineers (ASCE)},
  series       = {Journal of Hydrologic Engineering},
  title        = {Neural Networks for rainfall forecasting by atmospheric downscaling},
  url          = {http://dx.doi.org/10.1061/(ASCE)1084-0699(2004)9:1(1)},
  volume       = {9},
  year         = {2004},
}