A vector autoregressive methodology for short-term weather forecasting: tests for Lebanon
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A vector autoregressive methodology for short‑term weather forecasting: tests for Lebanon Wissam Abdallah1 · Nassib Abdallah1 · Jean‑Marie Marion1 · Mohamad Oueidat1 · Pierre Chauvet1 Received: 19 May 2020 / Accepted: 3 August 2020 © Springer Nature Switzerland AG 2020
Abstract Weather forecasting has been a major challenge due to the uncertain nature of the weather. Numerical models, such as the “Action de Recherche Petite Echelle Grande Echelle” (ARPEGE), the “Global Forecasting System”, the “European Center for Medium-Range Weather Forecasts”, are widely adopted by many meteorological services to forecast weather parameters. Under certain conditions, numerical models may have lower forecast accuracy, which is due to several factors such as the chaotic nature of the partial differential equations that simulate the evolution of the atmosphere and the difficulty of forecasting over countries with a fairly small surface area but with a very varied relief. This paper proposes a time series analysis approach based on the vector autoregression model (VAR) as an alternative and robust solution. The results are very promising (average of about 96.67% of precision between real values and three predicted parameters: temperature minimum, maximum humidity and precipitation) in the field of short-term weather parameter forecasting. In addition, the use of VAR models has solved the major problem posed by the chaotic equations of the ARPEGE model with greater accuracy on the one hand, and the execution time, forecasting accuracy and robustness of the SARIMA univariate models on the other. Keywords Time series analysis TSA · Vector autoregression VAR · Autoregressive moving average ARMA · Weather prediction model
1 Introduction The World Meteorological Organization (WMO) has called for the integration of efforts needed to improve the accuracy of weather forecasting[1]. Indeed, the development of effective weather prediction models has always been particularly difficult and demanding for meteorological services [2]. For example, in Lebanon, which was the study area for this work, the forecast model used by the Civil Aviation Meteorological Service at Beirut Rafik Hariri International Airport (BRHIA) is the ARPEGE model (0.5). Unfortunately, the forecasts provided by ARPEGE have often been erroneous and biased. In the case of Lebanon, the numerical model ARPEGE (0.5) covers certain regions with different climatic
characteristics, such as the Bekaa and Mount Lebanon. In fact, the forecast made by ARPEGE for the Bekaa region is the same as that for the Mount Lebanon region since they are in the same grid; however, the characteristics of the two regions are very different and the results of the ARPEGE forecast are therefore considered to be erroneous. Temperature forecasting is used in agriculture and hydrology. Precipitation is of interest in Lebanon because the Bekaa plain and the northern part of the country are affected by heavy rainfall. In 2018–2019, floods caused by heavy rainfall led the government to establish a committe
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