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Mauro del Longo, Andrea Montani, Tiziana Paccagnella, Silvano Pecora and Giuseppe Ricciardi

Use of the COSMO-LEPS ensemble for hydrologic forecasts in the Warning Operational Center of Emilia Romagna (Italy). Mauro del Longo, Andrea Montani, Tiziana Paccagnella, Silvano Pecora and Giuseppe Ricciardi ARPA Emilia Romagna - Italy. www.arpa.emr.it. Aim of this work.

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Mauro del Longo, Andrea Montani, Tiziana Paccagnella, Silvano Pecora and Giuseppe Ricciardi

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  1. Use of the COSMO-LEPS ensemble for hydrologic forecasts in the Warning Operational Center of Emilia Romagna (Italy) Mauro del Longo, Andrea Montani, Tiziana Paccagnella, Silvano Pecora and Giuseppe Ricciardi ARPA Emilia Romagna - Italy www.arpa.emr.it

  2. Aim of this work A combination of probabilistic meteorological inputs and scheduling of hydrologic/hydraulic models within the system give us a lot of information needing a more complex OPERATIONAL interpretation.It can be useful to fulfill the recent ITALIAN CIVIL PROTECTION law linking forecast activity and probable risk scenarios.A conservative approach can consist in choosing the heaviest rain, the highest peak or the earliest one, or the maximum discharge volumes forecast; however, i.e. due, in some cases, to underdispersivity of probabilistic meteorological forcasts, it can not eliminate all missed alarms while it can generate too many false alarms.An alternative approachcan consist in combining all informations in real time, to discern among different forecast scenarios and to give each scenario a “Subjective weight” (subjective forecast), expecially by mean of COSMO LEPS.

  3. Introduction Meteorological products (Cosmo suites - Leps, I7, I2 N2-RUC) Hydrological and hydraulic products (Mike-NAM/HD, HEC-HMS/RAS, Topkapi/Sobek) Scheduling and operational use Operational System Activities Methodology Case studies Conclusions

  4. COSMO-LEPS (run at ECMWF)COSMO Consortium 16 Representative Members driving the 16 COSMO-model integrations (weighted according to the cluster populations) Using either Tiedtke or Kain-Fristch convection scheme (members 1-8 T, members 9-16 KF) + Perturbations in turbulence scheme and in physical parameterisations 3 levels 500 700 850 hPa 4 variables Z U V Q d+3 d+4 d d+5 d+1 d+2 d-1 Cluster Analysis and RM identification Cluster Analysis and RM identification older EPS 00 2 time steps younger EPS 12 European area clustering period Complete Linkage Montani Andrea NWP4 Room 102 Friday, 13 Sep • suite runs twice a day (00 and 12UTC) as a “time-critical application” managed by ARPA-SIMC on behalf of COSMO consortium; • Δx ~ 7 km; 40 ML; fc+132h; • COSM0 v4.26 since January 2013; • computer time(30 million BUs for 2013)provided by the ECMWF member states in COSMO. COSMO-LEPS Integration Domain COSMO-LEPS clustering area

  5. Elements of modeling system FEWS Observation network Discharge field measurements Monitoring network : water levelgauges (bluetriangle) raingauges (green dots) thermometers (green dots) dams (violet) Modeling tools and suites

  6. Activities • From: • time of forecast • comparison among models • consistency • scales • season • meteorological phenomena • We define the variability and reliability of inputs to hydrological-hydraulic chains • The reliability judgment on models and the use of ensembles give informations about the uncertainty Meteorological Probabilistic Bulletin Hydrological • From: • reliability of hydrological-hydraulic chains • comparison among outputs • catchment, river network and hydraulic devices conditions • hydrological phenomena • post correction, manual forecast • judgment on outputs • warning level We define the variability and reliability of hydrologic flood forecast

  7. Methodology We present the activity of the Flood Warning Center of Emilia Romagna on some events occurred in the last year and we propose a methodology for a deeper analysis. • Present activity • We look at 5-day forecast to identify: • the probability of exceedance of a hydrometric threshold • the number of members exceeding the threshold • the forecast consistency in the prediction at the ranges +5, +4, …. +1day Time at threshold exceeding / Peak time Threshold exceeding duration All members median of maximum levels Maximum level range Members exceeding thresholds

  8. Case studies CASE1 Date 14-20/05/2013 Catchment: 41.000 km2 Lead time 144 h CASE2 Date 10-11/11/2012 624 and 694 km2 Lead time 120 h CASE3 Date 10-11/03/2013 Catchment: 694 km2 Lead time 120 h

  9. GEOGRAPHICAL LOCALIZATIONS OF CASE STUDIES Case 1: Po at Piacenza Case 2/3: Parma at Ponte Verdi and Secchia at Lugo

  10. Case 1: Po at Piacenza Meteosat image on European-Atlantic area on 17th May 2013 at 08:00 LTC Total precipitation 15 may 00:00 - 20 may 00.00: 135 mm Partial precipitation 15 may 00:00 17 may 12.00 80 mm 18 may 12:00 19 may 12:00 45 mm Forecast Discharge 5.500- 6.000 m3/s Peak time 18th may from 00:00 to 12:00 Observed Discharge 5.300 m3/s Peak time 18th may at 10.00

  11. 2013 05 15 Piacenza Maximum level frequency analysis 1- P 0.95 0.9 0.8 0.5 0.1 0,0 2,0 4,0 6,0 8,0 10,0 h [m] Case 1: Po at Piacenza • Consistency of the ensemble forecast (red dashed spaghetti plot) from 13th to 16th May show: • the consolidation of the meteorological dynamic system • the rotation and reduction of thickness of the band of threshold exceedings (see left diagrams) • Observed flood (continuous blue line) peak on 18th May h max = 5.96 m • Second observed peak on 21st May h max = 6.50 m

  12. Case 2: Parma at Ponte Verdi and Secchia at Lugo Reflectivity maps from operational ARPA-SIMC radars on 11th Nov 2012 05.30 UTC (up) 06.00 UTC (down) Total site precipitation from 10th to 11th Nov 2012 Parma basin: Bosco di Corniglio station 180 mm Secchia basin: Ospitaletto station 183 mm

  13. Case 2: Parma at Ponte Verdi and Secchia at Lugo Observed flood peak on 11th Nov 11:00 LTC Parma h max = 3.18 m Q max = 500 mc/s Lugo h max = 2.40 m Q max = 535 mc/s Deterministic forecast driven by Cosmo I7 Deterministic forecast driven by Cosmo I7 • From 8th to 10th Nov: • the variability of the signal decreases • the peak values first increase and than decrease • peak timing is consistent

  14. Case 2: Parma at Ponte Verdi and Secchia at Lugo - FREQUENCY ANALYSIS 10th Nov: Ponte Verdi 0 members exceed L3 From 8th to 10th Nov shift and slight reduction of thickness of the band. The number of threshold exceedings after a first increase falls down 10th Nov: Lugo 1 members exceed L2 Deterministic and probabilistic meteorological analisys are not sufficient. What if we add a probabilistic hydrological analisys?

  15. Parma at Ponte Verdi and Secchia at Lugo - SOBEK ANALYSIS at + 48 hours 10th Nov: Ponte Verdi 9 members exceed L3 Cosmo I7 (green line) driven run still underestimates 10th Nov: Lugo 7 members exceed L2

  16. Case 3: Parma at Ponte Verdi – FALSE ALARM 11th March forecast gives threshold exceedings between 14th and 15th March 11th March Forecast consistency between +72 and +96 hours 12th March forecast consistency with fair fading 12th March 13th March abrupt dump of the signal without threshold exceedings 13th March

  17. Conclusions 1_Joint use of deterministic and ensemble forecast helps to reduce MISSED ALARM compared to the deterministic only case (e.g. Case 2). 2_Ensemble predictions providing thresholds exceeding several days in advance, even for small basins (e.g. Case 2 and 3), gives forecasters MORE TIME TO UNDERTAKE ANALYSIS, useful for false alarm case (e.g. Case 3). 3_Coupling the meteorological ensemble with a hydrological/hydraulic multi model gives forecasters more information too. Only the PROFESSIONAL SKILL can give a “subjective weight” to the forecast. CASE1 Deterministic Ensemble m1,m2…. Observed CASE2: Deterministic Observed Deterministic Ensemble m1,m2…. 4_It is therefore necessary to know the limits of forecasting systems. Verification through statistical analysis and comparisons between forecast from skilled operator and observations are essential to test and improve the quality of forecasts, allowing a “DIAGNOSTIC” on hydrometeorological models and a self assessment on forecast products. CASE3 Ensemble m1,m2…. Observed

  18. Future step • To test and improve forecast quality we are going to extend the presented work: • building the forcasted hydrogram setting a set of constraints and separately analizing each variable (e.g. peak time, peak discharge …) • using other events • implementing statistical analysis • analyzing QPF, discharges, time at threshold exceeding and duration • comparing subjective forecasts and observations • considering performance indicators Thankyouforattention 100th Anniversary of Italian Hydrographic Istitute www.arpa.emr.it

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