Home About HyMeX
Motivations
Science questions
Observation strategy
Modelling strategy
Target areas
Key documents
Organisation
International coordination
Working groups
Task teams
National contributions
Endorsements
Resources
Database
Data policy
Publications
Education and summer schools
Drifting balloons (BAMED)
SOP web page
Google maps data visualisation
Workshops Projects
ASICS-MED
MOBICLIMEX
MUSIC
IODA-MED
REMEMBER
FLOODSCALE
EXAEDRE
Offers Links Contacts
Science & Task teams
Science teams
Task teams
Implementation plan
Coordination
International Scientific Steering Committee (ISSC)
Executive Committee for Implementation and Science Coordination (EC-ISC)
Executive Committee - France (EC-Fr)
HyMeX France
HyMeX Italy
HyMeX Spain
Archive
by Grazioli, J., Tuia, D. and Berne, A.
Abstract:
A data-driven approach to the classification of hydrometeors from measurements collected with polarimetric weather radars is proposed. In a first step, the optimal number of hydrometeor classes (nopt) that can be reliably identified from a large set of polarimetric data is determined. This is done by means of an unsupervised clustering technique guided by criteria related both to data similarity and to spatial smoothness of the classified images. In a second step, the nopt clusters are assigned to the appropriate hydrometeor class by means of human interpretation and comparisons with the output of other classification techniques. The main innovation in the proposed method is the unsupervised part: the hydrometeor classes are not defined a priori, but they are learned from data. The approach is applied to data collected by an X-band polarimetric weather radar during two field campaigns (from which about 50 precipitation events are used in the present study). Seven hydrometeor classes (nopt = 7) have been found in the data set, and they have been identified as light rain (LR), rain (RN), heavy rain (HR), melting snow (MS), ice crystals/small aggregates (CR), aggregates (AG), and rimed-ice particles (RI).
Reference:
Grazioli, J., Tuia, D. and Berne, A., 2015: Hydrometeor classification from polarimetric radar measurements: a clustering approachAtmospheric Measurement Techniques, 8, 149-170.
Bibtex Entry:
@Article{Grazioli2015,
  Title                    = {Hydrometeor classification from polarimetric radar measurements: a clustering approach},
  Author                   = {Grazioli, J. and Tuia, D. and Berne, A.},
  Journal                  = {Atmospheric Measurement Techniques},
  Year                     = {2015},

  Month                    = {January},
  Number                   = {1},
  Pages                    = {149-170},
  Volume                   = {8},

  Abstract                 = {A data-driven approach to the classification of hydrometeors from measurements collected with polarimetric weather radars is proposed. In a first step, the optimal number of hydrometeor classes (nopt) that can be reliably identified from a large set of polarimetric data is determined. This is done by means of an unsupervised clustering technique guided by criteria related both to data similarity and to spatial smoothness of the classified images. In a second step, the nopt clusters are assigned to the appropriate hydrometeor class by means of human interpretation and comparisons with the output of other classification techniques. The main innovation in the proposed method is the unsupervised part: the hydrometeor classes are not defined a priori, but they are learned from data. The approach is applied to data collected by an X-band polarimetric weather radar during two field campaigns (from which about 50 precipitation events are used in the present study). 
Seven hydrometeor classes (nopt = 7) have been found in the data set, and they have been identified as light rain (LR), rain (RN), heavy rain (HR), melting snow (MS), ice crystals/small aggregates (CR), aggregates (AG), and rimed-ice particles (RI).},
  Copublication            = {3: 3 Sw},
  Doi                      = {10.5194/amt-8-149-2015},
  Owner                    = {hymexw},
  Timestamp                = {2016.01.07},
  Url                      = {http://www.atmos-meas-tech.net/8/149/2015/}
}