streamflow assimilation for improving ensemble streamflow forecasts g. thirel (1), e. martin (1),...
TRANSCRIPT
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
([email protected], +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!