Neural Networks for rainfall forecasting by atmospheric downscaling
(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:
https://lup.lub.lu.se/record/915491
- author
- Olsson, J. ; Bertacchi Uvo, Cintia LU ; Jinno, K. ; Kawamura, A. ; Nishiyama, K. ; Koreeda, N. ; Nakashima, T. and Morita, O.
- organization
- publishing date
- 2004
- 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
- 2016-04-01 16:08:08
- date last changed
- 2022-04-07 03:11:06
@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)}}, doi = {{10.1061/(ASCE)1084-0699(2004)9:1(1)}}, volume = {{9}}, year = {{2004}}, }