@article{MAKHILLJEAS2017122215030,
    title = {Improved Estimation of Radar Rainfall Bias over Tamil Nadu State of
India Using a Kalman Filtering Approach},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {12},
    number = {22},
    pages = {5960-5967},
    year = {2017},
    issn = {1816-949x},
    doi = {jeasci.2017.5960.5967},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2017.5960.5967},
    author = {R. Senthil and},
    keywords = {Bias,radar rainfall,Kalman filter,time update,measurement,weaknesses},
    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.}
    }