Authors : Sahil Gupta, Sunil M. Wanjari, Brijesh Kanaujiya, Sanmesha Bhagat, Sahil Pancham,Tushar Magare
DOI : 10.46335/IJIES.2024.9.9.2
Abstract- Fluctuations in weather patterns pose numerous challenges for various sectors, including agriculture, aviation, and infrastructure. Given the unpredictable nature of climatic conditions, accurate weather forecasting has become increasingly crucial. Experts closely monitor technological advancements and evolving atmospheric trends to enhance forecasting accuracy. Weather forecasting plays a vital role in mitigating disasters, facilitating efficient aviation operations, supporting agricultural practices, and meeting diverse business needs. The integration of artificial intelligence (AI) and machine learning (ML) has expanded the scope of weather research by enabling the development of sophisticated models. However, before delving into analysis and model training, meticulous data extraction is paramount.
In our study, we aimed to identify crucial parameters within radar data sourced from multiple raw files, essential for making precise weather predictions. These parameters include the reflectivity factor, velocity factor, spectrum width, as well as latitude and longitude coordinates. Accurately extracting these radar elements is pivotal for selecting appropriate models, thereby ensuring a system of high accuracy and optimal efficiency.
