Predictive Meteorology with PySpark: Analyzing and Forecasting Weather Data

The spread of huge information across numerous realms, including social networks and financial markets, has led to the emergence of big data analytics as a means to gain useful understandings. This study explores using big data analytics in climate forecasting using instruments like Hadoop and Apache Spark. The challenges of predicting complex meteorological situations are talked about, emphasizing the likely benefits of advanced analytics. The paper stresses the importance of Hadoop’s distributed data system(HDFS)and the MapReduce algorithm for productive data handling. Two separate implementations, MapReduce and Spark, are used to analyze data, showing their strengths in managing enormous datasets. Additionally, sequence analysis is exhibited using an Autoregressive Integrated Moving Averages (ARIMA) model to predict future temperatures. The experimental results reveal valuable discoveries, like ordinary temperature variations, extreme temperature stations, and the application of sequence analysis for temperature prediction. The paper ends by stressing the relevance of big data analytics, Hadoop, and Spark in improving weather prediction. This has wide implications for sectors reliant on precise meteorological insights.