Research Activities at CIMA Foundation and contributions to task teams activities

Transcription

1 4 HyMeX Workshop Bologna, 8-10 June 2010 Research Activities at CIMA Foundation and contributions to task teams activities G. Boni(1), F. Castelli(2,1), L. Ferraris(1), S. Gabellani(1), A. Parodi(1), N. Rebora(1), R. Rudari(1), F. Siccardi(1), F. Silvestro(1) (1) CIMA Research Foundation, Via Magliotto,2, 17100, Savona, Italy. (2) DICEA University of Florence, Via di Santa Marta, 3, Florence, Italy

2 Spatial DATA: Point OBSERVATIONS: Observation: Meteosat Second Generation Radar National Network Model outputs: Global Circulation Model (IFS) Italian Local Area Models: COSMO LAMI 7 COSMO LAMI 2.8 Observation: Monitoring Sensors Network Quantity: 2502 Raingauges 218 Barometers 1366 Hydrometers 1986 Thermometers 419 Radiometers 581 Hygrometers 835 Anemometers 225 Snowgauges CIMA Foundation Data Base Italian Civil Protection Department Back-Up Data Base DEWETRA REAL TIME DATA Sensors Map Italian Radar Network COSMO LAMI I7 Satellite MSG CIMA Foundation

3 An Algorithm for Real-Time Rainfall Rate Estimation by Using Polarimetric Radar: RIME (Silvestro et al. 2009) RIME Accumulated Precipitation Radar [mm] Raingauge [mm] Contribution to total rainfall RIME Radar/Raingauge (db)

4 Towards optimal multi-sensor rainfall estimation 24 hours accumulated rainfall 24 hours accumulated rainfall Italian Radar Network Italian raingauge Network 24 hours accumulated rainfall from Italian Radar Network corrected with Raingauge measurements (Koistinen & Puhakka algorithm )

5 Monitoring energy fluxes: ACHAB model June 8th, 2010 Energy balance model with assimilation of Satellite estimated LST Daily EF and energy fluxes estimations from 2005

6 Monitoring of snow cover dynamics: S3M model June 8th, 2010 January 8th, 2010 Energy/mass balance model corrected with snow cover estimation from MODIS Daily snow cover and depth since nov. 2009

7 Flood-PROOFS: Flood - PRObabilistic Operational Forecasting System Droegemeier et al., 2000 Ferraris et al., 2002 Siccardi et al., 2005 Rebora et al., 2006

8 Probabilistic discharge forecast (meto. input: COSMO 7)

9 Severe rainfall events classification and predictability Type I d=18 hrs Type II d= 7 hrs Type I events: Long-lived (lasting more than 12 hours) Spatially distributed (more than 50x50 km 2 ) Equilibrium convective conditions: higher predictability Type II events: Brief and localized (lasting less than 12 hours) Spatially concentrated (less than 50x50 km 2 ) Non-equilibrium convective conditions: lower predictability POSTER HPF32

10 Contribuions to Hymex 1.Large DB of HM data for italian territory I. optimal estimation of distributed physical variables via data fusion/assimilation II. building of case studies/well-monitored testsites III. monitoring of mass/energy fluxes 2. Hydro-meteorological modelling capabilities I. uncertainty propagation in the hydrometeorological modelling chain II. analysis of past events, hindcast III. model uncertainty evaluation IV. effects of data assimilation in hydrometeorological models V. predictability of extreme events activities related mainly with WG2 and WG3

11 Contribution to Implementation plan Testcase. Liguria region Long term validation of probabilistic flash flood forecasting chain at regional scale (floodproofs) I. use of high quality long term hydrological series for validation (ground observations, meteo-radar, satellite) II. identification of sources of uncertainty III. propagation of uncertainty from meteorology to hydrology evaluation of the hydrologic cycle dynamics for some well monitored small basins

12 Flood forecasting (NWP Stochastic) Multi Basin EPS LEPS Observations Datasets Data Assimilation Dimensionless peak discharge CDF Parameter Set Predicted Hydrographs Rainfallrunoff model Downscaling