Weather Data Analysis and Visualization Using PySpark and PowerBI

Weather analysis data comprises a lot of information to understand atmospheric conditions. The following document provides a 10-year summary of local metric weather data analysis. The main activities focused on discovering different patterns of weather data related to temperature, rainfall, wind velocity, and humidity apart from extreme heat, air quality and effects on health issues due to diseases transmitted by vectors for protection. Several data analysis techniques were put into application on the part of the research team, broadly categorized into summary statistics, time series analyses, and classification algorithms. Results are as follows: A clear trend of increasing average temperature has emerged over the course of the study. Seasonal fluctuations have therefore caused varying levels of precipitation, resulting in rainfall occurring more frequently during some periods. Lastly, unique weather clusters have been identified in the isolated corridors around the regional weather. This information impacts other areas such as agriculture, road construction, and adaptation to climate. Results of microclimate assessment can also be used in public awareness programs and inform at-risk youth in early warning systems for weather-related health risks. The thunderstorm that produces the wind is driven by changed precipitation, air stability, and radiant energy, though winds are important. We then discussed the location and number of wind energy plants to be placed.