Developing a reliable weather forecasting model is a complicated task, as it\nrequires heavy IT resources as well as heavy investments beyond the financial\ncapabilities of most countries. In Lebanon, the prediction model used by the\ncivil aviation weather service at Rafic Hariri International Airport in Beirut\n(BRHIA) is the ARPEGE model, (0.5) developed by the weather service in\nFrance. Unfortunately, forecasts provided by ARPEGE have been erroneous\nand biased by several factors such as the chaotic character of the physical\nmodeling equations of some atmospheric phenomena (advection, convection,\netc.) and the nature of the Lebanese topography. In this paper, we proposed\nthe time series method ARIMA (Auto Regressive Integrated Moving Average)\nto forecast the minimum daily temperature and compared its result with\nARPEGE. As a result, ARIMA method shows better mean accuracy (91%)\nover the numerical model ARPEGE (68%), for the prediction of five days in\nJanuary 2017. Moreover, back to five months ago, in order to validate the accuracy\nof the proposed model, a simulation has been applied on the first five\ndays of August 2016. Results have shown that the time series ARIMA method\nhas offered better mean accuracy (98%) over the numerical model ARPEGE\n(89%) for the prediction of five days of August 2016. This paper discusses a\nmultiprocessing approach applied to ARIMA in order to enhance the efficiency\nof ARIMA in terms of complexity and resources.
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