Abstract: Bad weather, consisting of thunderstorms, normally causes the presence of strong winds and heavy rain that may develop into a storm over a certain area. Radar has been the most potential and powerful instrument used to detect and monitor the development of thunderstorms over a large area, however, it also has certain weaknesses. Weather radar can be affected by different sources of errors which have to be well considered and quantified for a proper interpretation of the collected data. We design a method that combines the Kalman filter with a multivariate analysis technique. The implementation of this technique is for the purpose of developing a formulation that may help to reduce error. These studies involved parameters such as temperature, humidity, point of gauge rainfall and weather radar reflectivity. The approach of using the Kalman filter combined with multivariate analysis is still a new way to improve radar rainfall estimates by prediction (time update) and correction (measurement update). This particular research was developed purposefully to reduce radar rainfall bias due to the uncertain sources of error seen in the weather radar and many studies have been developed but still did not achieve suitable values between radar readings with rain gauge returns.
R. Senthil Kumar and C. Ramesh, 2017. Improved Estimation of Radar Rainfall Bias over Tamil Nadu State of India Using a Kalman Filtering Approach. Journal of Engineering and Applied Sciences, 12: 5960-5967.