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 Raupach, T.H. and Berne, A.
Abstract:
We present a new approach for spatial interpolation of experimental raindrop size distribution (DSD) spectra. The DSD is fundamental to the study and understanding of precipitation and its monitoring and modelling. It is measured insitu using disdrometers at point locations. Disdrometers provide a (non-parametric) DSD spectrum in which drop concentrations are provided per class of drop diameter. Our approach uses geostatistics to estimate the same non-parametric DSD at unmeasured locations. Non-stationarity due to intermittency is taken into account through estimation of the dry drift of drop concentrations, using a rain occurrence field. Principal component analysis is used to express the DSD spectra in terms of uncorrelated components that can be interpolated independently at a requested point. These interpolated components can then be recombined into the full DSD. Estimation uncertainty for the interpolated DSD spectra is provided. Because all bulk rainfall variables can be calculated from the DSD and the entire DSD is estimated, the technique effectively interpolates all bulk variables at once. Leave-one-out testing shows that the technique estimates the DSD with minimal bias, and it is shown that the technique can be easily adapted to perform stochastic simulation of the non-parametric DSD.
Reference:
Raupach, T.H. and Berne, A., 2016: Spatial interpolation of experimental raindrop size distribution spectraQuaterly Journal of the Royal Meteorological Society, 142, 125-137.
Bibtex Entry:
@Article{Raupach2016b,
  Title                    = {Spatial interpolation of experimental raindrop size distribution spectra},
  Author                   = {Raupach, T.H. and Berne, A.},
  Journal                  = {Quaterly Journal of the Royal Meteorological Society},
  Year                     = {2016},

  Month                    = {August},
  Number                   = {S1},
  Pages                    = {125-137},
  Volume                   = {142},

  __markedentry            = {[hymexw:]},
  Abstract                 = {We present a new approach for spatial interpolation of experimental raindrop size distribution (DSD) spectra. The DSD is fundamental to the study and understanding of precipitation and its monitoring and modelling. It is measured insitu using disdrometers at point locations. Disdrometers provide a (non-parametric) DSD spectrum in which drop concentrations are provided per class of drop diameter. Our approach uses geostatistics to estimate the same non-parametric DSD at unmeasured locations. Non-stationarity due to intermittency is taken into account through estimation of the dry drift of drop concentrations, using a rain occurrence field. Principal component analysis is used to express the DSD spectra in terms of uncorrelated components that can be interpolated independently at a requested point. These interpolated components can then be recombined into the full DSD. Estimation uncertainty for the interpolated DSD spectra is provided. Because all bulk rainfall variables can be calculated from the DSD and the entire DSD is estimated, the technique effectively interpolates all bulk variables at once. Leave-one-out testing shows that the technique estimates the DSD with minimal bias, and it is shown that the technique can be easily adapted to perform stochastic simulation of the non-parametric DSD.},
  Copublication            = {2: 2 Sw},
  Doi                      = {10.1002/qj.2801},
  Keywords                 = {precipitation; drop size distribution; geostatistics; stochastic simulation; interpolation;},
  Owner                    = {hymexw},
  Timestamp                = {2018.11.29},
  Url                      = {http://onlinelibrary.wiley.com/doi/10.1002/qj.2801/full}
}