Hybrid weather forecasting models based on deep learning and mode decomposition methods

Abstract

The weather is an incessant, data-intensive, multifaceted, chaotic and newlinedynamic process. These properties make weather forecasting an impressive newlinechallenge. Weather forecasts, especially forecasting rainfall is a most newlineimportant and difficult task due its dependence on various climatic and newlineweather parameters. The risks of severe weather events including droughts newlineand floods due to climate changes require accurate and timely forecasting of newlinerainfall. Hence, the main objective of this research work is to develop hybrid newlinemodels to improve the accuracy of rainfall forecasts. newlineRainfall in the monsoon season (June to September) of India varies newlinedaily, time to time, month to month and it also varies from place to place. newlineThis spatiotemporal variation of the Indian Summer Monsoon Rainfall newline(ISMR) at different scales increases the complexity of its prediction. As India newlineis an agricultural country, the livelihoods of the people depend on crop newlineproduction. The inter-annual variability of ISMR affects agricultural newlineproduction and water resources which in turn affects the overall economy of newlineIndia. In order to alleviate problems caused by excessive and insufficient newlinemonsoon rainfall, it is important to predict ISMR. Therefore, models need to newlinebe developed to improve the forecast of Indian monsoon rainfall. newline