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by Raupach, T. and Berne, A.
Abstract:
The drop size distribution (DSD) describes the microstructure of liquid precipitation. The high variability of the DSD reflects the variety of microphysical processes controlling raindrop properties and affects the retrieval of rainfall. An analysis of the effects of DSD subgrid variability on areal estimation of precipitation is presented. Data used were recorded with a network of disdrometers in Ardèche, France. DSD variability was studied over two typical scales: 5 km × 5 km, similar to the ground footprint size of the Global Precipitation Measurement (GPM) spaceborne weather radar, and 2.8 km × 2.8 km, an operational pixel size of the Consortium for Small-Scale Modeling (COSMO) numerical weather model. Stochastic simulation was used to generate high-resolution grids of DSD estimates over the regions of interest, constrained by experimental DSDs measured by disdrometers. From these grids, areal DSD estimates were derived. The error introduced by assuming a point measurement to be representative of the areal DSD was quantitatively characterized and was shown to increase with the size of the considered area and with drop size and to decrease with the integration time. The controlled framework allowed for the accuracy of retrieval algorithms to be investigated. Rainfall variables derived by idealized simulations of GPM- and COSMO-style algorithms were compared to subgrid distributions of the same variables. While rain rate and radar reflectivity were well represented, the estimated drop concentration and mass-weighted mean drop diameter were often less representative of subgrid values.
Reference:
Raupach, T. and Berne, A., 2016: Small-scale variability of the raindrop size distribution and its effect on areal rainfall retrievalJournal of Hydrometeorology, 17, 2077-2104.
Bibtex Entry:
@Article{Raupach2016a,
  Title                    = {Small-scale variability of the raindrop size distribution and its effect on areal rainfall retrieval},
  Author                   = {Raupach, T. and Berne, A.},
  Journal                  = {Journal of Hydrometeorology},
  Year                     = {2016},

  Month                    = {July},
  Number                   = {7},
  Pages                    = {2077-2104},
  Volume                   = {17},

  Abstract                 = {The drop size distribution (DSD) describes the microstructure of liquid precipitation. The high variability of the DSD reflects the variety of microphysical processes controlling raindrop properties and affects the retrieval of rainfall. An analysis of the effects of DSD subgrid variability on areal estimation of precipitation is presented. Data used were recorded with a network of disdrometers in Ardèche, France. DSD variability was studied over two typical scales: 5 km × 5 km, similar to the ground footprint size of the Global Precipitation Measurement (GPM) spaceborne weather radar, and 2.8 km × 2.8 km, an operational pixel size of the Consortium for Small-Scale Modeling (COSMO) numerical weather model. Stochastic simulation was used to generate high-resolution grids of DSD estimates over the regions of interest, constrained by experimental DSDs measured by disdrometers. From these grids, areal DSD estimates were derived. The error introduced by assuming a point measurement to be representative of the areal DSD was quantitatively characterized and was shown to increase with the size of the considered area and with drop size and to decrease with the integration time. The controlled framework allowed for the accuracy of retrieval algorithms to be investigated. Rainfall variables derived by idealized simulations of GPM- and COSMO-style algorithms were compared to subgrid distributions of the same variables. While rain rate and radar reflectivity were well represented, the estimated drop concentration and mass-weighted mean drop diameter were often less representative of subgrid values.},
  Copublication            = {2: 2 Sw},
  Doi                      = {10.1175/JHM-D-15-0214.1},
  Eprint                   = { https://doi.org/10.1175/JHM-D-15-0214.1},
  Keywords                 = {Atm/Ocean Structure/ Phenomena; Microscale processes/variability; Precipitation; Observational techniques and algorithms; Radars/Radar observations; Remote sensing; Forecasting; Numerical weather prediction/forecasting; Models and modeling; Stochastic models;},
  Owner                    = {hymexw},
  Timestamp                = {2018.11.29},
  Url                      = {https://doi.org/10.1175/JHM-D-15-0214.1}
}