Quantitative precipitation forecast correction using neural network
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Abstract
In this paper, a model of neural networks is proposed as an effective technique for the correction of the quantitative precipitation forecast provided by the WRF model. For this, a Multi-Layer Perceptron is used, with the aim of using the output (observations provided by the surface stations) to establish a relationship with the input elements (WRF outputs). Model training is carried out with real rainfall accumulation data corresponding to 2017; and the evaluation is carried out with the period between November 4, 2018 and February 28, 2019. The correction of the quantitative precipitation forecast in the analyzed stations was achieved, the improvement for the mountain station was more significant and, in cases where the WRF overestimates the accumulated rainfall.
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FuentesA., SierraM., & MorfaY. (2020). Quantitative precipitation forecast correction using neural network. Revista Cubana De Meteorología, 26(3). Retrieved from http://rcm.insmet.cu/index.php/rcm/article/view/515
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Cofiño, A. S., Gutirrez, J. M. and Ivanissevich, M. L. 2004. Evolving modular networks with genetic algorithms. Application to nonlinear time series. 2004.
Cybenko, G. 1989. Aproximation by supperpositions of a sigmoidal function. 1989. pp. 203-314.
Grell, G.A. and Freitas, S. R. 2013. A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling. 2013. pp. 23845-23893.
Gutierrez, J. M., et al. 2004. Redes Probabilísticas y Neuronales en las Ciencias Atmosféricas. Instituto Nacional de Meteorología, Universidad de Cantabria: s.n., 2004.
Haykin, S. 1998. Neural Networks: A Comprehensive Foundation. 2, 1998.
Hidalgo, H. G., Dettinger, M. D. and Cayan, D. R. 2008. Downscaling with constructed analogues: Daily precipitation and temperature fields over the United States. Report, 2007-027, California Climate Change Center. Program California Energy Commission : Public Interest Energy Research (PIER), 2008, p. 48.
Hsieh, W. W. and Tang, B. 1998. Applying neural network models to prediction and data analysis in meteorology and oceanography. 1998. pp. 1855-1870.
Manual del WRF. 2004. Mesoscale & Microscale Meteorology Division, ARW Version 3 Modeling System User’s Guide. Complementary to the ARW Tech Note. Colorado, USA: NCAR: Boulder, 2004, p. 411.
Sanaz, M. 2015. Bias Correction of Global Circulation Model Outputs Using Artificial Neural Networks. Instituto de Tecnología de Georgia: s.n., 2015.
Schizas, C. N., Pattichis, C. S. and Michaelides, S.C. 1994. Artificial neural networks in weather forecasting. 1994. pp. 219-230.
Sierra, L. M., et al. 2014. Sistema de Predicción a muy corto plazo basado en el Acoplamiento de Modelos de Alta Resolución y Asimilación de Datos. 2014.