Localized Precipitation Forecasts from a Numerical Weather Prediction Model Using Artificial Neural Networks

Kuligowski, Robert J.; Barros, A. P.

doi:10.1175/1520-0434(1998)013<1194:lpffan>2.0.co;2

Cited by 138 publications

(53 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The accurate prediction of precipitation, which is one of the most important meteorological variables, is very important for the planning and management of water resources (Kulligowski & Barros, 1998;Freiwan & Cigizoglu, 2005). Numerical atmospheric models are widely employed to estimate meteorological events (Ramirez et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of daily precipitation using wavelet—neural networks

Partal

Ciğizoğlu

2009

Hydrological Sciences Journal

View full text Add to dashboard Cite

This study aims to predict the daily precipitation from meteorological data from Turkey using the wavelet-neural network method, which combines two methods: discrete wavelet transform (DWT) and artificial neural networks (ANN). The wavelet-ANN model provides a good fit with the observed data, in particular for zero precipitation in the summer months, and for the peaks in the testing period. The results indicate that wavelet-ANN model estimations are significantly superior to those obtained by either a conventional ANN model or a multi linear regression model. In particular, the improvement provided by the new approach in estimating the peak values had a noticeably high positive effect on the performance evaluation criteria. Inclusion of the summed sub-series in the ANN input layer brings a new perspective to the discussions related to the physics involved in the ANN structure.Key words wavelet transforms; artificial neural networks; precipitation; Turkey; estimation Prévision de précipitation journalière à l'aide de réseaux neuronaux-ondelettes Résumé Cette étude cherche à prévoir les précipitations journalières à partir de données météorologiques Turques, à l'aide de la méthode de réseau neuronal-ondelettes qui combine deux méthodes: la transformée en ondelettes discrète (TOD) et les réseaux de neurones artificiels (RNA). Le modèle RNA-ondelettes produit un bon accord avec les données observées, en particulier pour l'absence de précipitation pendant les mois d'été, et pour les pics durant la période de test. Les résultats indiquent que les estimations du modèle RNA-ondelettes sont significativement supérieures à celles que l'on obtient avec un modèle RNA conventionnel ou avec un modèle de régression multilinéaire. En particulier, l'amélioration permise par la nouvelle approche pour l'estimation des valeurs de pic a un effet positif notable sur les critères de performance. L'inclusion des sous-séries sommées dans la couche d'entrées du RNA ouvre de nouvelles perspectives en termes de prise en compte de la physique dans la structure d'un RNA.

show abstract

Section: Introductionmentioning

confidence: 99%

“…The ANN models are frequently employed for rainfall forecasting (Hsu et al, 1997;Kulligowski & Barros., 1998;Hall, 1999;Silverman & Dracup, 2000;Applequist et al, 2002;Ramirez et al, 2005;Freiwan & Cigizoglu, 2005).…”

Section: Introductionmentioning

confidence: 99%

Prediction of daily precipitation using wavelet—neural networks

Partal

Ciğizoğlu

2009

Hydrological Sciences Journal

View full text Add to dashboard Cite

show abstract

“…No wonder that advantages of NNs were convincingly demonstrated only by researchers who used properly sampled data. Good examples are the results achieved by Gardner and Dorling (1999) who used hourly data to predict nitrogen oxides pollution, and Kuligowski and Barros (1998a) who used six-hourly data for precipitation forecasts.…”

Section: Discussionmentioning

confidence: 99%

The impact of time‐averaging on the detectability of nonlinear empirical relations

Yuval

Hsieh

2002

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

SUMMARYThis paper studies how time-averaging of observations can affect the detectable nonlinear empirical relations in the data. The example used is the simulation of the precipitation rate as a daily, weekly and monthly variable. The feedforward neural network (NN) model is employed to simulate the precipitation rate. A measure of the nonlinearity of the NN relation is introduced and is used to calculate the nonlinearity of the NNs. It is found that the use of data averaged over periods longer than the inherent time-scale of the involved variables can result in a dramatic weakening of the detected nonlinearity. A suggested theoretical explanation asserts that averaging of independent samples of the data records yields distributions approaching the multi-variate normal, in which case the relations among the variables are closer to linear.

show abstract

“…The ANN is one of the Model Output Statistics (MOS) tools for correcting regional biases embedded in RCM or GCM [23,24]. The predictor variables for input to the ANN are precipitable water, relative humidity and temperature (average, minimum and maximum).…”

Section: Statistical Downscaling: Overviewmentioning

confidence: 99%

Terrestrial Sediment Yield Projection under the Bias-Corrected Nonstationary Scenarios with Hydrologic Extremes

Moon¹,

Kang

2016

Water

View full text Add to dashboard Cite

For reliable prediction of sediment yield in a watershed, fine-scale projections for hydro-climate components were first obtained using the statistical bias correction and downscaling scheme based on the combination of an Artificial Neural Network (ANN), Nonstationary Quantile Mapping (NSQM) and Stochastic Typhoon Synthesis (STS) sub-modules. Successively, the hydrologic runoff and sediment yield from the land surfaces were predicted through the long-term continuous watershed model, Soil and Water Assessment Tool (SWAT), using the bias-corrected and downscaled Regional Climate Model (RCM) output under the Intergovernmental Panel on Climate Change's (IPCC's) A1B climate change scenario. The incremental improvement of the combined downscaling process was evaluated successfully during the baseline period, which provides projected confidence for the simulated future scenario. The realistic simulation of sediment yield is closely related to the rainfall event with high intensity and frequency. During the long-term future period, the Coefficient of River Regime (CORR) reaches 353.9 (27.2% increase with respect to baseline). The projection for annual precipitation by 2040 and 2100 is a 25.7% and a 57.2% increase with respect to the baseline period, respectively. In particular, the increasing CORR rate (33.4% and 72.5%) during the flood season is much higher than that for the annual total amount. However, the sediment yield is expected to increase by 27.4% and 121.2% during the same periods, which exhibits steeper trends than the hydrologic runoff. The June, July, August (JJA) season occupies 83.0% annual total sediment yield during the baseline period, which is similar during the projection period. The relative change of sediment yield is 1.9-times higher than that of dam inflows.

show abstract

Localized Precipitation Forecasts from a Numerical Weather Prediction Model Using Artificial Neural Networks

Cited by 138 publications

References 16 publications

Prediction of daily precipitation using wavelet—neural networks

Prediction of daily precipitation using wavelet—neural networks

The impact of time‐averaging on the detectability of nonlinear empirical relations

Terrestrial Sediment Yield Projection under the Bias-Corrected Nonstationary Scenarios with Hydrologic Extremes

Contact Info

Product

Resources

About