A comparison of short range forecasts from two ensemble streamflow prediction systems
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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

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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

([email protected], +33 (0) 5 61 07 97 30)

A comparison of short-range forecasts from two Ensemble Streamflow Prediction Systems


Introduction
Introduction Noilhan (1) and F. Habets (3)

  • 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 Noilhan (1) and F. Habets (3)

Meteorological analysis

SAFRAN

Observations + NWP models

  • PPrecipitation, temperature,

    humidity, wind, radiations

E

Surface scheme

H

ISBA

+

G

Snow

Physiographic data for

soil and vegetation

Qr

Qi

Nash

Daily

Streamflow

Hydrological model

Poor

Weak to moderate

Good

MODCOU

Aquifer

Habets et al. (2008)


The sim based esps
The SIM based ESPS Noilhan (1) and F. Habets (3)

ENSEMBLE FORECASTS

ECMWF/PEARP Ensemble forecasts

51/11 members, 2-day forecasts

T+ Precip Spatial DESAGGREGATION

ENSEMBLE FORECAST SOIL

ISBA MODCOU

WAT. TABLES RIVERS FINAL STATES

ANALYSIS RUN (daily)

Observations

Meteor. models

10-year climatology Wind, Rad., Humidity

SAFRAN

SOIL WAT. TABLES RIVERS STATE

SOIL WAT. TABLES RIVERS FINAL STATE

ISBA MODCOU


The Seine in Paris, March 2001 flood Noilhan (1) and F. Habets (3)

Q90

Q50

Q10

  • Correct flood intensity and temporality prediction (rise, date of the flood peak, fall)

  • Correct spread

  • Flood prediction as early as 11-12 March : pre-alert, alert


Project outline Noilhan (1) and F. Habets (3)

  • 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


The two eps forecasts used as input

ECMWF Noilhan (1) and F. Habets (3)

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°

The two EPS forecasts used as input


Precipitation disaggregation
Precipitation disaggregation Noilhan (1) and F. Habets (3)

  • 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

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

Observations (5000 rain gauges)

ECMWF (Day 1)

PEARP (Day 1)

Statistical scores are always better for PEARP than for ECMWF rainfall


Bss low flows q10
BSS low flows (Q10) Noilhan (1) and F. Habets (3)

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

Red : PEARP better with 90% of certainty

ECMWF : 98 stations

PEARP : 184 stations

ECMWF : 33 stations

PEARP : 329 stations

Day 1

Day 2


Bss high flows q90
BSS High flows (Q90) Noilhan (1) and F. Habets (3)

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

Red : PEARP better with 90% of certainty

ECMWF : 49 stations

PEARP : 338 stations

ECMWF : 19 stations

PEARP : 486 stations

Day 2

Day 1


Distribution by basin size bss
Distribution by basin size (BSS) Noilhan (1) and F. Habets (3)

Q10 Day 1

Q90 Day 1

ECMWF

PEARP

Basins sizes

Basins sizes

Q10 Day 2

Q90 Day 2

Basins sizes

Basins sizes


Conclusions
Conclusions Noilhan (1) and F. Habets (3)

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
Perspectives Noilhan (1) and F. Habets (3)

  • 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)

Analysis

Observation

Perturbed simulation

Observation

Analysed simulation


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