A comparison between single site modeling and multiple site modeling approaches using Kalman filtering
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...
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| Format: | conferenceObject |
| Language: | eng |
| Published: |
2015
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| Online Access: | http://hdl.handle.net/10773/13620 |
| Country: | Portugal |
| Oai: | oai:ria.ua.pt:10773/13620 |
| Summary: | 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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