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by Tiesi, A., Miglietta, M. M., Conte, D., Drofa, O., Davolio, S., Malguzzi, P. and Buzzi, A.
Abstract:
Results of the assimilation of high-density data to initialize the high-resolution meteorological model MOLOCH (CNR-ISAC) are described. The local analysis and prediction system (LAPS), a mesoscale data assimilation system developed at NOAA, is applied to modeling a case study of heavy precipitation that occurred over Liguria, north-western Italy, on November 4, 2011, causing severe flood in the city of Genoa. This case is representative of some episodes that affected the region in the last few years, where the coastal orography, besides enhancing the convective uplift, contributed to the formation of convergence lines over the sea, responsible for the onset of convective cells. The present work aims at the implementation of a model-based operational short-range prediction system, with particular focus on quantitative precipitation forecasting in a time range up to 12–24 h. The use of LAPS analysis as initial condition for the MOLOCH model shows a positive impact on the intensity and distribution of the simulated precipitation with respect to the simulations where only large-scale analyses are employed as initial conditions. Effects on the models simulations are due to the assimilation of surface network data, radio-sounding profiles, radar and satellite (SEVIRI/MSG) data.
Reference:
Tiesi, A., Miglietta, M. M., Conte, D., Drofa, O., Davolio, S., Malguzzi, P. and Buzzi, A., 2016: Heavy rain forecasting by model initialization with LAPS: a case studyIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9, 2619-2627.
Bibtex Entry:
@Article{Tiesi2016,
  Title                    = {Heavy rain forecasting by model initialization with LAPS: a case study},
  Author                   = {Tiesi, A. and Miglietta, M. M. and Conte, D. and Drofa, O. and Davolio, S. and Malguzzi, P. and Buzzi, A.},
  Journal                  = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  Year                     = {2016},

  Month                    = {June},
  Number                   = {6},
  Pages                    = {2619-2627},
  Volume                   = {9},

  Abstract                 = {Results of the assimilation of high-density data to initialize the high-resolution meteorological model MOLOCH (CNR-ISAC) are described. The local analysis and prediction system (LAPS), a mesoscale data assimilation system developed at NOAA, is applied to modeling a case study of heavy precipitation that occurred over Liguria, north-western Italy, on November 4, 2011, causing severe flood in the city of Genoa. This case is representative of some episodes that affected the region in the last few years, where the coastal orography, besides enhancing the convective uplift, contributed to the formation of convergence lines over the sea, responsible for the onset of convective cells. The present work aims at the implementation of a model-based operational short-range prediction system, with particular focus on quantitative precipitation forecasting in a time range up to 12–24 h. The use of LAPS analysis as initial condition for the MOLOCH model shows a positive impact on the intensity and distribution of the simulated precipitation with respect to the simulations where only large-scale analyses are employed as initial conditions. Effects on the models simulations are due to the assimilation of surface network data, radio-sounding profiles, radar and satellite (SEVIRI/MSG) data.},
  Copublication            = {7: 7 It},
  Doi                      = {10.1109/JSTARS.2016.2520018},
  ISSN                     = {1939-1404},
  Keywords                 = {Analytical models; Atmospheric modeling; Data models; Numerical models; Predictive models; Remote sensing; Surface topography; Data analysis; high-resolution weather forecast; model initialization;},
  Owner                    = {hymexw},
  Timestamp                = {2018.11.27},
  Url                      = {http://dx.doi.org/10.1109/JSTARS.2016.2520018}
}