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« Improvement of ensemble streamflow predictions over France of the
SAFRAN-ISBA-MODCOU model »
Guillaume Thirel (CNRM-GAME/GMME/MOSAYC)
PhD Director : Éric Martin
Jury :
President : Serge Chauzy (LA)
Reviewer : Vincent Fortin (Environment Canada)
Reviewer : Vazken Andréassian (CEMAGREF)
Examiner : Olivier Thual (CERFACS)
Examiner : Pierre Ribstein (UMR Sisyphe)
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Context
Floods = major environmental hazard
Damages on infrastructures, huge costs, human beings losses
Flood of the Garonne river at Toulouse in 1875
⇒ Need to better anticipate these events
Organisms (SCHAPI, Services de Prévision des Crues)
Hydrological models
Meteorological forecasts
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Context
Ensemble meteorological
forecasts
Post-treatment
Surface observations (snow, discharges, …)
Data assimilation
Hydrological model(s)
Forecasted discharges
Discharges calibration
(from Schaake et al., 2007)
Initial states
Meteorological forecasts
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ISBA
Physiographic data pour the soil and the vegetation
+
MODCOU
QrQi
E
H
G
Aquifer
Daily discharges
Surface scheme
Snow
SAFRANObservations + NWP outputs
Precipitation, température, humidity, wind, radiations
Hydrological model
Meteorological analysis
The SIM hydro-meteorological model
Distributed model
Coherent simulation of water and energy fluxes on :• Atmosphere• Surface/vegetation/surface soil• Surface and sub-surface hydrology
Grid mesh : 8x8 km
→ Co-operation Mines Paris Tech /SISYPHECo-operation Mines Paris Tech /SISYPHE
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Validations and valorisation of SIM
Validation of the simulations by meteorological and hydrological variables• Snow• River discharges and aquifer levels
Main applications :• Follow-up of soil hydric states, effective rainfall, snow conditions• Impact of climate change• Flood prediction (soil wetness, discharges)
Soil Water Index on 16/11/2009 Direction de la climatologie
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Application of SIM to ensemble streamflow predictions
Since 2004, everyday : ensemble discharge forecasts based on SIM (Fabienne Rousset-Regimbeau PhD, 2007). Based on the ECMWF EPS (precipitation+temperature) On the whole France, mid-term range (10 days)
Statistical analysis of precipitations and discharges Article Rousset, ECMWF newsletter spring 2007 Disaggregation of precipitations on a simple, but efficient way Discharges compared to a reference SIM simulation
Study case on a few recent floods
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Scheme of the ensemble discharge forecast system based on SIM
Observations + Meteorological
models
SIM ANALYSIS (daily)
SAFRAN10-year
Climatology Wind, Rad.,
Humidity
SOILAQUIFERS
RIVERS
ECMWF/PEARP EPSs 51/11 members, 10/2.5 days forecasts
ENSEMBLE PREDICTIONS
Spatial DISAGGREGATION
T + Precipitations
ISBA MODCOU
ENSEMBLE FORECASTS
SOIL AQUIFERS
RIVERS
ISBA MODCOU
SOIL AQUIFERS
RIVERS
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The Seine at Paris, March 2001 flood (decade flood)
Q90
Q50Q10
PhD Fabienne Rousset-Regimbeau
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Objectives
To improve the ensemble discharge forecast system
To explore the contribution of 2 EPSs To test an improvement of the model Qualify the chain in comparison with discharge observations
How : By comparing the impact of 2 EPSs on 2-day ensemble discharge
forecasts By improving the system with a past discharge assimilation system
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Plan
I Study : comparison of the impact of 2 EPSs in the SIM-based ensemble discharge forecast system
II Past discharges assimilation– 1) Justification
– 2) Choice of the method
– 3) Validation of the data assimilation system
III Impact of the past discharges assimilation system on the ensemble discharges forecasts
IV General conclusions and perspectives
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Plan
I Study : comparison of the impact of 2 EPSs in the SIM-based ensemble discharge forecast system
II Past discharges assimilation– 1) Justification
– 2) Choice of the method
– 3) Validation of the data assimilation system
III Impact of the past discharges assimilation system on the ensemble discharges forecasts
IV General conclusions and perspectives
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The 2 used EPSs
ECMWF EPS 51 members 10-day forecasts Singular vectors,
– Optimisation in 48H Resolution in our operational
database : 1.5º
PEARP EPS 11 members 2.5-day forecasts Singular vectors
– Optimisation in 12H Resolution in our operational
database : 0.25°
-> Objective : mid-term range -> Objectif : short-term range
The comparison is done on the first 48H common to both systems
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Precipitations disaggregation
Interpolation on the SAFRAN zones according to distance, then :
ECMWF EPS : altitudinal gradient PEARP EPS : correction of the mean bias point by point
SAFRAN ECMWF EPS (Day 1)
PEARP EPS (Day 1)
Precipitation amounts 11 March 2005 / 30 September 2006
All the statistical scores were better for the PEARP EPS
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Conclusions on the comparison
The ensemble discharges forecasts based on the PEARP EPS showed an improvement on small basins and for floods– Results confirmed by a set of statistical scores (RPSS, reliability
diagram, False Alarm Rate and Probability of Detection, seasonal study)– Low spread, reference used = SIM simulation– Interest for flood forecasting at a short-term range 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.
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Plan
I Study : comparison of the impact of 2 EPSs in the SIM-based ensemble discharge forecast system
II Past discharges assimilation– 1) Justification
– 2) Choice of the method
– 3) Validation of the data assimilation system
III Impact of the past discharges assimilation system on the ensemble discharges forecasts
IV General conclusions and perspectives
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Justification
Choice of the observations : – Snow : concerns only a limited part of the territory and discharges are
influenced
– Aquifer layers : many data but only few aquifers simulated into SIM
– River discharges : many data over all of France available daily
Choice of the variable to modify : – River water content : efficient for the short-term range, less for the mid-
term range
– Soil water content : concerns the whole territory, impact until the mid-term
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Strategy
186 stations assimilated over France– Low human influence
– Good quality of observations (Banque Hydro)
– Good quality of SIM simulations
Principle : to use observed discharges to improve the discharges simulations, by adjusting the ISBA soil moisture
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Plan
I Study : comparison of the impact of 2 EPSs in the SIM-based ensemble discharge forecast system
II Past discharges assimilation– 1) Justification
– 2) Choice of the method
– 3) Validation of the data assimilation system
III Impact of the past discharges assimilation system on the ensemble discharges forecasts
IV General conclusions and perspectives
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The BLUE (Best Linear Unbiased Estimator)
Analysed state
Background state Innovation
vector
Observed discharges
Choice of the BLUE because :
Low dimensions of the problem
Possibility to compute the solution in its matricial form
Hypothesis : unbiased errors and linear model
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Determination of the K matrix components
To estimate the observations (R) and background covariance errors (B) matrices and calibrate these two matrices between them
To define the state variable : the ISBA soil moisture, but which one?
To estimate the Jacobian matrix H
x
yH
Discharges
Soil moisture
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ISBA physics
Runoff : Dunne Subgrid depending on the fraction
of the mesh saturated
Drainage : gravitational subgrid
Improvement of the hydrological transfers in the soil (Decharme et al., 2006; Quintana Seguí et al., 2009)
Discharges : coming from ISBA runoff and drainage
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State variable
3 possible choices :
Soil water content : w2+w3(runoff + drainage)
Root zone water content : w2(runoff)
2 soil layers water contents separately : (w2,w3)(runoff and drainage)
Spatial aggregation (sum of the soil water contents over each sub-basin)
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Sensitivity of the Jacobian
Perturbation of 1% : the Jacobian varies according to the sign
Perturbation of 0.1% : low modification according to the sign
Thus, we chose to apply a perturbation of +0.1%
-> respect of the linearity
Clear temporal evolution : the Jacobian will be re-calculated for each assimilation
Jacobiennes 0.1%
0
1
2
3
4
5
6
7
sept
-05
oct-0
5
nov-
05
janv-
06
mars
-06
avr-0
6
mai-0
6
juin-
06
juil-0
6
Jaco
bie
nn
e
H J1 +0,1%
H J1 -0.1%
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Filling of the Jacobian matrix
3 gauging stations y1, y2 and y3.
x1, x2 and x3 soil water contents summed on the sub-basins
0
0 0
0
sub-basins
stations
Jacobian H :
discharges
Soil moisture
Finite differences
j
iij x
yH
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Principle of the assimilation system
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Implementation of the assimilation system
PALM coupler (CERFACS) : dynamical coupler dynamique of parallel calculation codes, many applications (data assimilation, coupling)
Friendly interface, modular software Intuitive gestion of data exchanges, buffer storage -> few
modifications of the ISBA and MODCOU codes Simple cluster coupling Use of the Météo-France super-computer
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Plan
I Study : comparison of the impact of 2 EPSs in the SIM-based ensemble discharge forecast system
II Past discharges assimilation– 1) Justification
– 2) Choice of the method
– 3) Validation of the data assimilation system
III Impact of the past discharges assimilation system on the ensemble discharges forecasts
IV General conclusions and perspectives
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Assimilation of real observations
6 experiments : 3 state variables * 2 physics of the model Daily assimilation, daily observations
Period : 10 March 2005 / 30 September 2006
186 assimilated stations
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The Doubs river at Besançon
-> experiment (modification of the layers 2 and 3 soil moistures + improved physics)
2EI
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combines the best Nash and RMSE scores, as well as the lowest increments
(soil moisture + improved physics) will be kept
Scores for 148 assimilated stations
Scores for 49 independent stations
2EI
2EI
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Conclusion on the discharges assimilation system
Observed discharges assimilated for the first time in SIM– Positive impact of the use of PALM : CPU time save (parallel computation on
the Météo-France super-computer), modularity
Validation of the assimilation system– System validated on SIM-analysis– Assimilation of real observations : several configurations tested, significative
improvement of the scores, low increments
Article in preparation
For initializing the ensemble discharges forecasts, we will keep :
State variable : mean of the soil moisture into the 2 ISBA layers
The assimilated states (assimilation + improved physics) daily2EI
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Perspectives of improvement of the assimilation system
Improvement of the background and observations errors
Reduction of the number of sub-basins in a sub-basin– Less simulations needed for computing H
Tests of other assimilation methods– External loop? (i.e. re-calculating the Jacobian around the analysed state
until it converges) -> tests showed low improvements
– EnKF?
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Plan
I Study : comparison of the impact of 2 EPSs in the SIM-based ensemble discharge forecast system
II Past discharges assimilation– 1) Justification
– 2) Choice of the method
– 3) Validation of the data assimilation system
III Impact of the past discharges assimilation system on the ensemble discharges forecasts
IV General conclusions and perspectives
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Conditions of the study
Studied period : 11 March 2005 – 30 September 2006 Scores on 148 assimilated stations Use of the 10-day ECMWF EPS
3 systems of ensemble discharges forecasts were compared : – The real-time system
– A re-forecast initialized by the initial states (modification of the soil moisture of both layers, without the improved physics)
– A re-forecast initialized by the initial states (modification of the soil moisture of both layers, with the improved physics)
2EI
1EI
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Some statistical scores
Spread
Ratio-dispersion
0
0,1
0,2
0,3
0,4
0,5
1 2 3 4 5 6 7 8 9 10
Jours
Ratio-dispersionsans assimilation
Ratio-dispersion EI1
Ratio-dispersion EI2
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RMSE
Scores computed in comparison with observations
Ratio-RMSE
0
0,2
0,4
0,6
0,8
1
1,2
1,4
1 2 3 4 5 6 7 8 9 10
Jours
Ratio-RMSE sansassimilation
Ratio-RMSE EI1
Ratio-RMSE EI2
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Brier Skill Score day 1
Perfect model
Clima-tology
-1
0
1
Q99 Q98 Q95 Q90 Q80 Q70 Q60 Q50 Q40 Q30 Q20 Q10 Q5 Q2 Q1
Quantiles
BSS J1 sans assimilation
BSS J1 EI1
BSS J1 EI2
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Brier Skill Score day 10
Perfect model
Clima-tology
-1
0
1
Q99 Q98 Q95 Q90 Q80 Q70 Q60 Q50 Q40 Q30 Q20 Q10 Q5 Q2 Q1
Quantiles
BSS J10 sansassimilationBSS J10 EI1
BSS J10 EI2
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Conclusion on the impact of the assimilation
Intrinsic characteristics of the ensemble discharges few modified (spread)
Significative impact of the assimilation for the first days, less important then
Then, the physics improvement improves the forecast quality
Use of the forecasts by the forecasts eased (False Alarm Rates, POD)
Article in preparation
SIM-PEARP less impacted than SIM-ECMWF, scores very close
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Plan
I Study : comparison of the impact of 2 EPSs in the SIM-based ensemble discharge forecast system
II Past discharges assimilation– 1) Justification
– 2) Choice of the method
– 3) Validation of the data assimilation system
III Impact of the past discharges assimilation system on the ensemble discharges forecasts
IV General conclusions and perspectives
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General conclusions and perspectives
Two ensemble discharges forecasts systems based on SIM– Impact of the PEARP EPS at a short-range, on small basins and for floods
A past discharge assimilation system implemented in SIM– Validation : significative impact on SIM-analysis
– Low non-linearities
Impact on the ensemble discharges– Strong impact of the assimilation system at a short-range, then low impact
– But the improvement of the physics allows better forecasts at a mid-term range
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Perspectives
Implementation of the assimilation system for initializing the operational SIM-ECMWF chain in real-time
Adding aquifer layers in SIM, and then assimilation of aquifer levels (PhD UMR SISYPHE Alexandra Stouls)
Improvement of the meteorological uncertainty taking into account (EPS disaggregation)
Taking into account of uncertainties linked to hydrology : into the initialization and via a stochastic physics or a multi-model forecast
Seasonal forecasts with SIM (PhD CNRM Stéphanie Singla)
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My work here on EFAS
Use of satellital snow data for improving the proxy– Particule filter and EnKF
Study of its impact on the EFAS forecasts– Probabilistic statistical scores
2nd step : to see how to use other sources of rainfall data in order to improve the proxy
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Thank you for your attention!
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Visualisation des sorties en temps réel
Site intramet : http://intra.cnrm.meteo.fr/pedeb/
Sélection d’environ 100 stations
- prévision de débits
- tableau d’alerte
=> Visualisation du risque + de la persistance (ou non) de la prévision
Probabilité de dépassement du seuil d’alerte
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BSS hauts débits (Q90)
Jour 1 Jour 2
CEPMMT : 49 stations
PEARP : 338 stations
CEPMMT : 19 stations
PEARP : 486 stations
Bleu : CEPMMT meilleur (90% de certitude selon un test de ré-échantillonnage)Rouge : PEARP meilleur (90% de certitude)
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Distribution par taille de bassin (BSS)
Q10 Jour 1
Q10 Jour 2
Q90 Jour 2
Q90 Jour 1
CEPMMT
PEARPTailles des bassins Tailles des bassins
Tailles des bassinsTailles des bassins
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Variance d’erreur d’observations
Erreurs des mesures des stations indépendantes : matrice diagonale
Tests sur des cas synthétiques : 2e méthode meilleure (Nash) et donc retenue
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Répartition spatiale de la variance d’erreur d’ébauche
Moyenne pondérée des 2 couches
Couche 3 uniquementCouche 2 uniquement
B et R diagonales
B estimée en perturbant l’analyse météorologique SAFRAN, puis comparaison de l’humidité obtenue avec l’humidité de référence
R estimée selon les débits observés
R et B calibrées grâce à un unique coefficient
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Expériences jumelles
Variable d’état = moyenne pondérée des humidités des 2 couchesAssimilation sur une période de 3 mois, tous les 5 jours, fenêtre d’assimilation de 5 joursEtat initial modifié, obs = simulation de référence
Convergence assez rapide malgré les non-linéarités
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Scores avec le BLUE itéré
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Un exemple de l’impact sur les prévisions d’ensemble des débits
IS2
IS1
Sans assimilation
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Ranked Probability Skill Score
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RMSE par taille de bassin
Jour 1 Jour 10
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Résolution
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Fiabilité
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Incertitude
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Taux de fausses alarmes
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Taux de réussite
Jour 1
Jour 10
Sans assimilationAvec EI1Avec EI2