http://www.cnr.it/ontology/cnr/individuo/prodotto/ID44634
Rainfall estimation from X-band dual polarization radar using reflectivity and differential reflectivity (Articolo in rivista)
- Type
- Label
- Rainfall estimation from X-band dual polarization radar using reflectivity and differential reflectivity (Articolo in rivista) (literal)
- Anno
- 2006-01-01T00:00:00+01:00 (literal)
- Alternative label
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Eugenio Gorgucci, V. Chandrasekar, L. Baldini (literal)
- Pagina inizio
- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Rivista
- Note
- ISI Web of Science (WOS) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Istituto di Scienze dell'Atmosfera e del Clima - CNR
Colorado State University, Fort Collins, CO - USA (literal)
- Titolo
- Rainfall estimation from X-band dual polarization radar using reflectivity and differential reflectivity (literal)
- Abstract
- Radar observations of reflectivity and differential reflectivity from a measurement volume are biased due to attenuation and differential attenuation in the presence of rainfall along the path between the measurement volume and the radar. This attenuation is fairly small at S-band frequencies, but not negligible at C-band and higher frequencies such as X-band. The impact of attenuation and differential attenuation on the accuracy of rainfall estimation is studied. It is shown that the biases due to attenuation and differential attenuation nearly cancel each other when used in the rainfall algorithm that uses both reflectivity and differential reflectivity and result in small biases in the rainfall rate estimation. About 2000 X-band radar profiles were simulated based on S-band radar measurements of reflectivity and differential reflectivity, for validation purposes. Theoretical and simulation analyses show that rainfall rate computed at X-band using reflectivity and differential reflectivity without attenuation correction, results in very small bias for certain underlying mean raindrop shape models. (literal)
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