A canonical model for seasonal climate prediction using Big Data

Abstract

Abstract This article addresses the elaboration of a canonical model, involving methods, techniques, metrics, tools, and Big Data, applied to the knowledge of seasonal climate prediction, aiming at greater dynamics, speed, conciseness, and scalability. The proposed model was hosted in an environment capable of integrating different types of meteorological data and centralizing data stores. The seasonal climate prediction method called M-PRECLIS was designed and developed for practical application. The usability and efficiency of the proposed model was tested through a case study that made use of operational data generated by an atmospheric numerical model of the climate area found in the supercomputing environment of the Center for Weather Forecasting and Climate Studies linked to the Brazilian Institute for Space Research. The seasonal climate prediction uses ensemble members method to work and the main Big Data technologies used for data processing were: Python language, Apache Hadoop, Apache Hive, and the Optimized Row Columnar (ORC) file format. The main contributions of this research are the canonical model, its modules and internal components, the proposed method M-PRECLIS, and its use in a case study. After applying the model to a practical and real experiment, it was possible to analyze the results obtained and verify: the consistency of the model by the output images, the code complexity, the performance, and also to perform the comparison with related works. Thus, it was found that the proposed canonical model, based on the best practices of Big Data, is a viable alternative that can guide new paths to be followed.

Keywords

• Big Data
• Hadoop
• Hive
• MapReduce
• Seasonal climate prediction
• Atmospheric numerical model