streamflow assimilation for improving ensemble streamflow forecasts g. thirel (1), e. martin (1),...

Streamflow assimilation for improving ensemble streamflow

forecasts

G. Thirel (1), E. Martin (1), J.-F. Mahfouf (1), S. Massart (2), S. Ricci (2), F. Regimbeau (3), F. Habets (4).

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

(2) CERFACS, France,

(3) Direction de la Climatologie, Météo-France, France,

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

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

Introduction

2 ensemble streamflow prediction systems (ESPS) at a short- and mid-term range at Météo-France– Based on the distributed hydrometeorological model SIM

– ECMWF-based ESPS (10-day range, 1.5°, 51 members)

– PEARP-based ESPS (60-h range, 0.25°, 11 members)

Need to improve the initial states by an assimilation system

First validation of the ESPSs against streamflows observations

ISBA

Physiographic data for soil and vegetation

+

MODCOU

QrQi

E

H

G

Aquifer

DailyStreamflow

Surface scheme

Snow

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

Hydrological modelPoor

Weak to moderate

Good

Nash

Habets et al. (2008)

Meteorological analysis

The SIM hydro-meteorological model

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, 11/2-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

Initial states of ESPS : need for improvement

Adjusted by BLUE

Strategy

186 stations assimilated over France– Low human influence

– Good quality of observations

– Not too bad results given by SIM

Aim : to use observed streamflow in

order to improve streamflow simulation,

by adjusting the ISBA soil moisture

The BLUE equations

Analysed state

Background state

Innovation vector

Jacobian H :

H determines the sensitivity of streamflows to soil moisture variations

Hypothesis : linearity of the model

-> H is computed with SIM runs initialized by perturbed soil moisture states (perturbation around 0.1%)

Observed streamflows

streamflows

x : control variable

Experiments (10 March 2005 / 30 September 2006, 186 stations)

6 experiments : 3 variable states * 2 physics of the model

Daily assimilation, daily observations

Jacobian matrix filling

3 gauging stations Q1, Q2 et Q3.

w1, w2 et w3 moderated sums of soil moistures on the basins

Jacobian matrix :

0

0 0

0

basins

stations

186 stations

Principle of the assimilation system

IS2 will be retained

IS2 combines the best Nash and rmse scores, and the lowest increments

The Doubs at Besançon

Scores for a selection of 148 stations

The Garonne at Portet-sur-Garonne

An exemple of the effect on ensemble forecasts

PEARP ECMWF

Some statistical scores

spread

0

2

4

6

8

10

12

1 2 3 4 5 6 7 8 9 10

Days

Spread w ithout assimil

Spread w ith assimil

Scores for a selection of 148 assimilated stations for the 10-day ECMWF-SIM

RMSE

RMSE

0

5

10

15

20

25

30

35

1 2 3 4 5 6 7 8 9 10

Days

rmse w ithout assimil

rmse w ith assimil

Scores are presented against streamflow observations

Brier Skill Score day 1

BSS

-2

-1,5

-1

-0,5

0

0,5

1

99 98 95 90 80 70 60 50 40 30 20 10 5 2 1

Quantiles

D1 without assimil

D1 with assimil

Brier Skill Score day 10

BSS

-2

-1,5

-1

-0,5

0

0,5

1

99 98 95 90 80 70 60 50 40 30 20 10 5 2 1

Quantiles

D10 without assimil

D10 with assimil

Ranked Probability Skill Score

RPSS

0

0,050,1

0,150,2

0,25

0,30,35

0,40,45

0,5

1 2 3 4 5 6 7 8 9 10

Days

RPSS w ithoutassimil

RPSS w ithassimil

Decomposition of Brier

Decomposition Brier Day 1

0

0,05

0,1

0,15

0,2

0,25

0,3

99 98 95 90 80 70 60 50 40 30 20 10 5 2 1

Quantiles

Resolution without assimil

Reliability without assimil

Uncertainty without assimil

Resolution with assimil

Reliability with assimil

Uncertainty with assimil

BSS for PEARP-SIM and ECMWF-SIM

Day 1

-2

-1

0

1

99 98 95 90 80 70 60 50 40 30 20 10 5 2 1

Quantiles

ECMWF w ithout assimil

ECMWF w ith assimil

PEARP w ithout assimil

PEARP w ith assimil

BSSs are unbiased with the Weigel et al. (2007) method because of the impact of the number of members

PEARP is slightly better, but without the unbiasing, ECMWF wins!

Conclusions and perspectives

A streamflow assimilation system has been implemented and validated for the SIM suite

– Better simulation of flows and initial states for the ESPSs (Thirel et al., submitted to the Journal of Hydrology)

Significative improvement of ensemble streamflow forecasts when initialized by the assimilated SIM suite

– Lower RMSE, better BSS and RPSS– Few differences between SIM-PEARP and SIM-ECMWF– It is the first time that the ensemblist SIM is compared to observations, not a

reference run

Perspectives : – Optimizing computing costs and the quality of the assimilation system– Using another operator (EnKF?)– Implementing the assimilation system into the SIM-ECMWF operational suite

(2012?)

Thank you for your attention!