Weather Parameter Analysis Using Interpolation Methods

Abstract

One of the key components in minimizing losses has been using rainfall forecasts at various temporal and spatial scales by businesses, governments, and farmers. Forecasts for other atmospheric characteristics are also important for agriculture. To maintain accurate weather forecasts, atmospheric procedures must be understood, measurements must be improved, and research and development must be continued. India’s Ministry of Earth Science has many programs and agencies, such as the India Meteorological Department (IMD), that strive to enhance forecasting. The efforts have culminated in fairly reliable predictions of rainfall patterns. This research aims to enhance prediction accuracy in different geographical areas ranging from weather subdivisions to agro-climate areas. We have used data that were obtained from the IMD, Pune. In the preprocessing technique session, we transformed data from the txt format to the csv format. We have used Matlab software for data analysis. Interpolation techniques have introduced detailed experimental position forecasts for the Marathwada region of Maharashtra, India. The linear interpolation method produces better results than a cubic, nearest neighbor, spline, shape-preserving, and PCHIP interpolation methods. The accuracy of linear interpolation methods is 90.92%. The accuracy of the cubic and PCHIP interpolation techniques is 90.76%, the accuracy of the shape-preserving and spline interpolation methods is 88.93%, and the accuracy of the nearest neighbor is 90.70%. Normal, heavy, and low rainfall are only 30–35%, 20–25%, and 35–40%, respectively. Marathwada region rainfall increased by 40 mm from 2021 to 2020. The annual temperature is 36–37°C in the Marathwada region, due to this Marathwada region falls under drought. So there is a need to change the crop pattern in the Marathwada region like temperature tolerant crops.