A comparison of short-range forecasts from two Ensemble

Streamflow Prediction Systems

G. Thirel (1), F. Rousset-Regimbeau (2), E. Martin (1), J. Noilhan (1) and F. Habets (3)

(1) CNRM-GAME, Météo-France, CNRS, GMME/MC2, France,

(2) Direction de la climatologie, Météo-France, France,

(3) UMR SISYPHE, UPMC, ENSMP, CNRS, Paris, France

(guillaume.thirel@meteo.fr, +33 (0) 5 61 07 97 30)

Introduction

Every day since 2004 : an Ensemble Streamflow Prediction System (ESPS) based on SIM (hydro-meteorological model) (Rousset, 2007). over all of France forced by ECMWF Ensemble Prediction System (EPS) forecasts medium-range (10 days), validated (more details on a poster, on Thursday, Hall A,

13:30 –15:00)

⇒ Increasing needs for short-range forecasting (Mediterranean region) A short-range ESPS based on the Météo-France EPS (PEARP)

Short-range (2 days)

OBJECTIVES : To compare the impacts of two 2-day EPS forecasts on this ESPS.

– ECMWF EPS forecasts – Météo-France PEARP EPS forecasts

The SIM hydro-meteorological model

ISBA

Physiographic data for soil and vegetation

+

MODCOU

QrQi

E

H

G

Aquifer

DailyStreamflow

Surface scheme

Snow

SAFRANObservations + NWP modelsPPrecipitation, temperature, humidity, wind, radiations

Hydrological modelPoor

Weak to moderate

Good

Nash

Habets et al. (2008)

Meteorological analysis

The SIM based ESPS

ObservationsMeteor. models

ANALYSIS RUN (daily)

SAFRAN10-year

climatology Wind, Rad.,

Humidity

SOIL WAT. TABLES

RIVERS FINAL STATE

ECMWF/PEARP Ensemble forecasts51/11 members, 2-day forecasts

ENSEMBLE FORECASTS

T+ Precip Spatial

DESAGGREGATION

ISBA MODCOU

ENSEMBLE FORECAST

SOIL WAT. TABLES

RIVERS FINAL STATES

ISBA MODCOU

SOIL WAT. TABLES

RIVERS STATE

The Seine in Paris, March 2001 flood

Q90

Q50Q10

• Correct flood intensity and temporality prediction (rise, date of the flood peak, fall)• Correct spreadFlood prediction as early as 11-12 March : pre-alert, alert

Comparison on the first two days of simulation 569 days of simulation (10 March 2005 – 30 September 2006) 881 gauge stations compared

Project outline :

The two EPS forecasts used as input Precipitation disaggregation Streamflow prediction scores Conclusions, perspectives

Project outline

The two EPS forecasts used as input

ECMWF• 51 members• Homogeneous resolution• 10 days (+5)• Singular vector,

– leading time 2 days• Resolution in operational database 1.5°

PEARP• 11 members• Zoomed version • 60 H forecast• Singular vectors

– leading time 12 hours– over Europe

• Resolution in operational database : 0.25°

Precipitation disaggregation

ECMWF : altitudinal gradient (climatological)• 2 mm/m/year where altitude < 800m • 0.7 mm/m/year where altitude > 800m

PEARP : bias removal calibrated over one year

Observations (5000 rain gauges)

ECMWF (Day 1)

PEARP (Day 1)

Results over the test period : 10 March 2005 – 30 September 2006

Statistical scores are always better for PEARP than for ECMWF rainfall

BSS low flows (Q10)

Blue : ECMWF better with 90% of certainty according to the resampling testRed : PEARP better with 90% of certainty

Day 1 Day 2

ECMWF : 98 stations

PEARP : 184 stations

ECMWF : 33 stations

PEARP : 329 stations

BSS High flows (Q90)

Day 1 Day 2

Blue : ECMWF better with 90% of certainty according to the resampling testRed : PEARP better with 90% of certainty

ECMWF : 49 stations

PEARP : 338 stations

ECMWF : 19 stations

PEARP : 486 stations

Distribution by basin size (BSS)

Basins sizes

Q10 Day 1

Q10 Day 2

Q90 Day 2

Q90 Day 1

ECMWF

PEARPBasins sizesBasins sizes

Basins sizes

Conclusions

PEARP disaggregated rainfall was better than ECMWF.– The disaggregation method was different for the two EPS (must be

adapted to the meteorological model).

The PEARP ESPS showed improvements on floods and small scale basins at short-range scale– Results confirmed by various statistical scores (RPSS, reliability

diagrams, False Alarm and Hit Rates, and a seasonal study.)– Interest for flood forecasting in France (SCHAPI)

Details of the study in On the impacts of short-range meteorological forecasts for ensemble streamflow predictions, G. Thirel, F. Rousset-Regimbeau, E. Martin, F. Habets, Journal of Hydrometeorology, 2008, Accepted.

Perspectives

Improvement of rainfall disaggregation

Forecast « real » streamflows :

• To be compared with observations, not a reference run of the model

• Build a realistic initial state for the model : Assimilation of observed streamflows to retrieve a correct soil moisture (description of the method on a poster on Thursday, Hall A, 17:30 - 19:00)

Observation Perturbed simulation

Analysed simulation

ObservationAnalysis