Combining signal processing and machine learning techniques for real time measurement of raindrops

IEEE Transactions on Instrumentation and Measurement

Volumn 50, Issue 6, 2001, Pages 1717-1724

  Denby, Bruce ^a,b,c   Prévotet, Jean Christophe ^a,d   Garda, Patrick ^a,d   Granado, Bertrand ^d   Barthes, Laurent ^c   Golé, Peter ^c   Lavergnat, Jacques ^c   Delahaye, Jean Yves ^c  

^a IEEE   (France)

^b LABORATOIRE DES SCIENCES DU CLIMAT ET DE L ENVIRONNEMENT   (France)

^c SERVICE D AÉRONOMIE DU CNRS   (France)

^d SORBONNE UNIVERSITÉ   (France)

Author keywords

Machine learning; Meteorology; Neural networks; Optical disdrometer; Real time instrumentation; Telecommunications

Indexed keywords

OPTICAL DISDROMETER; RAINDROP REAL TIME MEASUREMENT; REAL TIME INSTRUMENTATION;

ALGORITHMS; LEARNING SYSTEMS; METEOROLOGY; MICROWAVE MEASUREMENT; OPTICAL COMMUNICATION; OPTICAL DEVICES; RAIN; SIGNAL FILTERING AND PREDICTION; TELECOMMUNICATION LINKS;

SIGNAL NOISE MEASUREMENT;

EID: 0035719284     PISSN: 00189456     EISSN: None     Source Type: Journal    
DOI: 10.1109/19.982973     Document Type: Article

References (12)

1. A spectrograph for the automatic analysis of raindrops
2. Instrumental uncertainties in Z-R relations
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4. 1984: A new optical instrument for simultaneous measurement of raindrop diameter and fall speed distribution
5. An optical disdrometer for use in high wind speeds
6. The generalized correlation method for estimation of time delay
7. Real time readout data acquisition techniques in meteorological applications
8. Structured neural network approach for measuring raindrop sizes and velocities
9. TDNN approach to measuring raindrop sizes and velocities
10. Path- and area-integrated rainfall measurements by microwave attenuation in 1-3 cm band