A novel algorithm for detection of precipitation in tropical regions using PMW radiometers (Articolo in rivista)

Type
Label
  • A novel algorithm for detection of precipitation in tropical regions using PMW radiometers (Articolo in rivista) (literal)
Anno
  • 2015-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.5194/amt-8-1217-2015 (literal)
Alternative label
  • Casella, D., Panegrossi, G., Sanò, P., Milani, L., Petracca, M., and Dietrich, S. (2015)
    A novel algorithm for detection of precipitation in tropical regions using PMW radiometers
    in Atmospheric measurement techniques (Internet)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Casella, D., Panegrossi, G., Sanò, P., Milani, L., Petracca, M., and Dietrich, S. (literal)
Pagina inizio
  • 1217 (literal)
Pagina fine
  • 1232 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.atmos-meas-tech.net/8/1217/2015/amt-8-1217-2015.html (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 8 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Consiglio Nazionale delle Ricerche - Istituto di Scienze dell'Atmosfera e del Clima (CNR-ISAC), Rome, Italy (literal)
Titolo
  • A novel algorithm for detection of precipitation in tropical regions using PMW radiometers (literal)
Abstract
  • A novel algorithm for the detection of precipitation is described and tested. The algorithm is applicable to any modern passive microwave radiometer on board polar orbiting satellites independent of the observation geometry and channel frequency assortment. The algorithm is based on the application of canonical correlation analysis and on the definition of a threshold to be applied to the resulting linear combination of the brightness temperatures in all available channels. The algorithm has been developed using a 2-year data set of co-located Special Sensor Microwave Imager/Sounder (SSMIS) and Tropical Rainfall Measuring Mission precipitation radar (TRMM-PR) measurements and Advanced Microwave Sounding Unit (AMSU) Microwave Humidity Sounder and TRMM-PR measurements. This data set was partitioned into four classes depending on the background surface emissivity (vegetated land, arid land, ocean, and coast) with the same procedure applied for each surface class. In this paper we describe the procedure and evaluate the results in comparison with many well-known algorithms for the detection of precipitation. (literal)
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