Voltar
Artigo Produção Nacional Revisado por pares
BIBTEX RIS

Nowcasting model of low wind profile based on neural network using SODAR data at Guarulhos Airport, Brazil

2018; Taylor & Francis; Volume: 39; Issue: 8 Linguagem: Inglês

10.1080/01431161.2018.1425562

ISSN

1366-5901

Autores

Gutemberg Borges França, Manoel Valdonel de Almeida, Suzanna Maria Bonnet, Francisco Leite Albuquerque Neto,

Tópico(s)

Solar Radiation and Photovoltaics

Resumo

A generalized regression neural network model was tested – as a nowcasting tool – to forecast the low wind profiles up to 45 min (i.e. at heights of 10, 100, 200, and 300 m) at the Guarulhos International Airport, São Paulo, Brazil. A data set representing over 4 years was generated from sonic detection and ranging and surface meteorological station, which registered vertical wind profiles with intervals from 10 m to approximately 500 m in height every 15 min, and surface meteorological variables were collected each minute, respectively. These data were simultaneously used to train, validate, and test the proposed model. The u and v forecasts generated at 300, 200, and 100 m were better than at 10 m, which could certainly be attributable to the surface roughness. In addition, the results also revealed that the performance of the model is time-dependent – decreasing over time – and that this may be correlated with the fact that the neural network is a statistical rather than physical model. The forecasts of wind components u and v are slightly biased (or closely matched to observations) at all heights, and forecast intervals with maximum values have median and average errors equal to 0.070 and −0.017 ms−1, respectively. The forecast model's results were evaluated using the values of four categorical statistics: probability of detection; probability for non-events; bias; and false-alarm ratio, with respectable minimum and maximum values for u wind principal components equal to 0.841, 0.833, 0.159, 0.981 at 10 m for 45-min forecasts and 0.989, 0.987, 0.011, 0.999 at 300 m for 15-min forecasts, respectively.

Referência(s)