A comparison between single site modeling and multiple site modeling approaches using Kalman filtering | Zendy

Magda Monteiro | Zendy; Marco Costa | Zendy

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A comparison between single site modeling and multiple site modeling approaches using Kalman filtering

Author(s) -

Magda Monteiro,

Marco Costa

Publication year - 2015

Publication title -

aip conference proceedings

Language(s) - English

Resource type - Conference proceedings

SCImago Journal Rank - 0.177

H-Index - 75

eISSN - 1551-7616

pISSN - 0094-243X

DOI - 10.1063/1.4912410

Subject(s) - kalman filter , radar , representation (politics) , computer science , state space representation , calibration , state space , extended kalman filter , set (abstract data type) , radar tracker , remote sensing , algorithm , artificial intelligence , mathematics , geography , statistics , telecommunications , politics , political science , law , programming language

This work presents a comparative study between two approaches to calibrate radar rainfall in real time. The weather radar provides continuous measurements in real-time which have errors of either meteorological or instrumental nature. Locally, gauge measurements have a greater performance than radar measurements that can be used to improve radar estimates. One way of doing that is via a state space representation associated to the Kalman filter algorithm. In the single- site modeling approach we use the linear calibration model applied in [1] and [3] while the multivariate state-space model proposed in [6] is used in the multiple site approach. This work aims to discuss and compare these two different state space formulations based on the same data set

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