13th ems annual meeting & 11th european conference on applications of meteorology (ecam) | 09...

13th EMS Annual Meeting & 11th European Conference on Applications of Meteorology (ECAM) | 09 – 13 Sep.2013 | Reading, United Kingdom

ASI – AS19 Interfacing hydrological and meteorological models in forecasting systems (Monday, 9 September 2013)

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

ARPA Emilia Romagna - Italy

Use of the COSMO-LEPS ensemble for hydrologic forecasts in the Warning Operational Center of Emilia

Romagna (Italy)

www.arpa.emr.it



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 approach can 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.

Aim of this work



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

Spatial Resolution

Temporal Resolution

Runs per day Lead time

Cosmo -LEPS/LM-

DET7 km 3 h 2 120 h

CosmoI7 7 km 1 h 2 72 h

CosmoI2 2.8 km 1 h 2 48 h

RUC 2.8 km 1 h 8 18 h

Hydrological model Hydraulic model

Mike11 HD-NAM

Lumped modelAverage basin area about

100km2

11.000 sections100- 500 m

1D, “Quasi” 2D

HEC HMS-RASLumped model

Average basin area about 100km2

11.000 sections100- 500 m

1D, “Quasi” 2D

Topkapi-Sobek Distributed modelGrid size 250m or 500m

11.000 sections100- 500 m

1D, “Quasi” 2D

Mike11 NAM-

HDHEC HMS-

RASTopkapi-Sobek

Cosmo -LEPS/LM-DET

Every 6 hours17 ensemble

members runs in parallel





CosmoI7 Every 3 hours Every 3 hours Every 3 hours

CosmoI2 Every hourNot yet

implementedNot yet

implemented

RUC Every hourNot yet

implementedNot yet

implemented



COSMO-LEPS (run at ECMWF) COSMO Consortium

d-1d-1 dd d+5d+5d+1d+1 d+2d+2 d+4d+4d+3d+3

older EPSolder EPS

younger EPSyounger EPS

clustering clustering periodperiod

0000

1212

Cluster Analysis and RM identificationCluster Analysis and RM identification

4 variables4 variables

Z U V QZ U V Q

3 levels3 levels

500 700 850 hPa500 700 850 hPa

2 2 time time stepssteps

Cluster Analysis and RM identificationCluster Analysis and RM identification

European European areaarea

Complete Complete LinkageLinkage

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

COSMO-LEPS

clustering area

• 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

Montani AndreaNWP4 Room 102Friday, 13 Sep



Hydrological/hydraulic modellingMike NAM-HD11 Hec HMS-RAS Topkapi - Sobek

Elements of modeling system FEWS Observation network

Modeling tools and suites

Discharge field measurements

Monitoring network :water level gauges (blue triangle)

raingauges (green dots)thermometers (green dots)

dams (violet)



From:• time of forecast• comparison among models• consistency• scales• season • meteorological phenomenaWe 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

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

Probabilistic Bulletin

Activities

We define the variability and reliability of hydrologic flood forecast



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.

Methodology

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Hydrological Ensamble Prediction

Members exceeding thresholds

Time at threshold exceeding / Peak time Threshold exceeding duration

All members median of maximum levels

Maximum level range

Present activityWe 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 (hours) 6 12 18 24 30 36 42 48 54 60 66 72 78 84 90 96 102 108 114 120 126 132 138 144 150 156

Large Catchment

Small Catchment Ensemble informations (Leps)

Coupled Forecast (Leps/I7)Observed simulation

forecast/I2/I7Observed Simulation/Upstream discharges/RUC

Ensemble Forecast (Leps)

Observed Simulation/RUC

Coupled Forecast

(Leps/I2)Coupled followup (Leps/I7)

Date 10-11/11/2012624 and 694 km2 Lead time 120 h

Date 10-11/03/2013Catchment: 694 km2 Lead time 120 h

Date 14-20/05/2013Catchment: 41.000 km2 Lead time 144 h

LARGE Lead time: 48 - 120 hSpatial scale: 30.000 - 5.000 km2

REGIONAL Lead time: 24 - 48 hSpatial scale: 5.000 -1.000 km2

MULTICATCHMENT Lead time: 12 - 24 hSpatial scale: 1.000 - 500 km2

SINGLE CATCHMENT Lead time: 6 - 12 hSpatial scale: 500 - 200 km2

Case studies

CASE3

CASE2

CASE1



Case 1: Po at Piacenza

Case 2/3: Parma at Ponte Verdi and Secchia at Lugo

GEOGRAPHICAL LOCALIZATIONS OF CASE STUDIES



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Observed level since 16th to 22th at Piacenza

Total precipitation 15 may 00:00 - 20 may 00.00: 135 mm

Partial precipitation 15 may 00:00 17 may 12.00 80 mm18 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 12:00Observed

Discharge 5.300 m3/s Peak time 18th may at 10.00


Meteosat image on European-Atlantic area on 17th May 2013 at 08:00 LTC



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Hydrological Ensamble Prediction at Piacenza 2013 05 13

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2013 05 13 PiacenzaMaximum level frequency analysis

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2013 05 14 PiacenzaMaximum level frequency analysis


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

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2013 05 15 Piacenza Maximum level frequency analysis

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2013 05 16 Piacenza Maximum level frequency analysis

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Case 2: Parma at Ponte Verdi and Secchia at Lugo

Total site precipitation from 10th to 11th Nov 2012Parma basin: Bosco di Corniglio station 180 mmSecchia basin: Ospitaletto station 183 mm

Reflectivity maps from operational ARPA-SIMC radars on 11th Nov 2012 05.30 UTC (up) 06.00 UTC (down)



Case 2: Parma at Ponte Verdi and Secchia at Lugo

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Hydrological Ensamble Prediction at Lugo 2012 11 08

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Hydrological Ensamble Prediction at Ponte Verdi 2012 11 08

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Deterministic forecast driven by Cosmo I7 0.00

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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

Observed flood peak on 11th Nov 11:00 LTCParma h max = 3.18 m Q max = 500 mc/sLugo h max = 2.40 m Q max = 535 mc/s



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2012 11 08 Ponte VerdiMaximum level frequency analysis

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2012 11 08 Lugo Maximum level frequency analysis

From 8th to 10th Nov shift and slight reduction of thickness of the band. The number of threshold exceedings after a first increase falls down0.1

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2012 11 09 Ponte VerdiMaximum level frequency analysis

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2012 11 09 Lugo - Maximum level frequency analysis

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2012 11 10 Lugo - Maximum level frequency analysis

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2012 11 10 Ponte Verdi Maximum level frequency analysis

Case 2: Parma at Ponte Verdi and Secchia at Lugo - FREQUENCY ANALYSIS

10th Nov: Ponte Verdi 0 members exceed L3

10th Nov: Lugo 1 members exceed L2

Deterministic and probabilistic meteorological analisys are not sufficient.What if we add a probabilistic hydrological analisys?



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Sobek -Hydrological Ensamble Prediction at Ponte Verdi 2012 11 10

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2012 11 10 Ponte Verdi Sobek Maximum level frequency analysis

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Sobek Hydrological Ensamble Prediction at Lugo 2012 11 10

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2012 11 10 Lugo Sobek Maximum level frequency analysis

10th Nov: Ponte Verdi 9 members exceed L3

Cosmo I7 (green line) driven run still underestimates

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Sobek -Hydrological Ensamble Prediction at Ponte Verdi 2012 11 10

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Sobek Hydrological Ensamble Prediction at Lugo 2012 11 10

Parma at Ponte Verdi and Secchia at Lugo - SOBEK ANALYSIS at + 48 hours

10th Nov: Lugo 7 members exceed L2



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HEP at Ponte Verdi 2013 03 13

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HEP at Ponte Verdi 2013 03 12

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HEP at Ponte Verdi 2013 03 11F

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+72

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11th March

12th March

13th March

Case 3: Parma at Ponte Verdi – FALSE ALARM

13th March abrupt dump of the signal without threshold exceedings

11th March forecast gives threshold exceedings between 14th and 15th March

12th March forecast consistency with fair fading



CASE1Deterministic

Ensemble m1,m2….

Observed

CASE2: Deterministic

Ensemble m1,m2….

Observed

CASE3

Deterministic

Ensemble m1,m2…. Observed

Conclusions1_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.

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.



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

Future step

Thank you for attention

100th Anniversary of Italian Hydrographic Istitute

www.arpa.emr.it

13th ems annual meeting & 11th european conference on applications of meteorology (ecam) | 09...

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