Transcript The SCM Experiments at ECMWF Gisela Seuffert and Pedro Viterbo

The SCM Experiments at ECMWF
Gisela Seuffert and Pedro Viterbo
European Centre for Medium Range Weather Forecasts
ELDAS Progress Meeting
12./13.12.2002
The Goals at ECMWF in ELDAS
•
Build a system that complements the use of 2T/2RH information to get
an optimal estimate of soil water assimilating:
- thermal IR heating rates
- MW brightness temperature
- precipitation and radiation
•
Test, validate amd intercompare that system
(Single-Column Experiments, comparison with measurements)
•
Annual soil moisture data base for Europe (1.10.1999 – 31.12.2000)
•
ECMWF expects to have a system that can go into pre-production by
the end of ELDAS (2004)
Experiment Design
Atm. initial conditions +
dynamics forcing from
ECMWF reanalysis (ERA40)
Single-column model of the
ECMWF NWP model
+ microwave emissivity model
Increments (daily)
Observation of precipitation
+ radiation
First guess: T2m,RH2m,Tb
Soil moisture analysis scheme
OI or Extended Kalman Filter
Soil moisture
Background error
Observations: T2m,RH2m,Tb
Soil moisture analysis systems
Optimal Interpolation:
Extended Kalman Filter:
•
Used in the operational ECMWFforecast since 1999 (Douville et al.,
2000)
•
•
Fixed statistically derived forecast
errors
•
Updated forecast errors
•
Criteria for the applicability of the
method
- atmospheric and soil exceptions
- corrections when T and RH error
are negatively correlated
•
Criteria for the applicability of the
method
- no ‘direct’ atmospheric exceptions
- soil exceptions still to be tested
Used in the operational DWDforecast since 2000 (Hess, 2001) *
* Changes:
Assimilation of 2m- T and RH, mw-Tb
-
Model forecast operator accounts for water
transfer between soil layers
Test adaptive EKF
Extended Kalman Filter
Forecast
(first guess)
Analysed forecast for
new soil moisture at t+24h
Comparison with observations
T2m,RH2m,Tb
Opt. Soil moisture
t0
Minimization
3 perturbed forecasts
for each state variable
t+9h
t+12h
Simulated
T2m,RH2m,Tb
t+15h
t+24h
Time
Changes to the original algorithm
• Model forecast operator M accounts for water transfer between soil
layers:
 ijt   pt ,ij
M ij  0
 i   p0,i
forecast
t
j
t+24
Perturbed forecast
layer i
p,j
time
• Q-Problem:
1) Q constant:
- defined by innovation error and size of soil moisture increments:
H ( xa  xa ,opt )( y  Hxb )T  R  ( y  Hx a )( y  Hxb )T
2) Adpative Kalman Filter (Mayer and Tapley’s estimator, 1976):



k
T


1
 N 1



(
i
)


(
k
)


(
i
)


(
k
)

MA
(
i

1
)
M

A
(
i
)



 
N  1 i k  N 1 
 N 

Q(k )  0.8  Qˆ (k  1)  0.2  Qˆ (k )
Qˆ (k ) 
Observations
Murex:
•
•
•
•
1.6 – 9.10.1997 (1995- 1998)
Forcing:
SW , (unbiased) LW , precipitation
Validation:
Soil Moisture, Rnet, H, G, LE=Rnet-H-G, Ts
Assimilation/Validation:
T2m, RH2m, synthetic mw-Tb
SGP 97:
-
15.6 – 19.7.1997
Little Washita site (2) (Central Facility site(3))
Forcing: SW , LW , precipitation
Validation: Soil Moisture, Rnet, H, G, LE, Ts
Assimilation/Validation: T2m, RH2m, mw-Tb
Correction of downward longwave radiation
Procedure to correct downward longwave radiation:
1.
2.
3.
Bias
Height difference between model and observation
Model error using measurements at Carpentras
Comparison of OI-Weights and EKF-Gain matrix
Temperature:
blue - OI weights
green/black – EKF gain matrix
Relative Humidity:
blue - OI weights
green/black – EKF gain matrix
• OI weights and KF gain matrix
adapt similarly to atmospheric
conditions
• OI puts more weight on
RH-observations
Soil moisture increments
Murex Experiment (1.6- 9.10.1997)
Soil
Sensible
Moisture
Heat
Flux
Latent
Heat Flux
T2m
error
RH2m
error
Soil moisture, Ts, Tg (5cm), mw-Tb at 6 LT
Soil moisture, Ts, Tg, mw-Tb at 6 LT (Tb every 3rd day)
SGP97 (15.6 – 20.7. 1997)
Soil
moisture
Latent
Heat Flux
Soil moisture, Ts, Tg, mw-Tb at 12 LT
Gisela Seuffert:
Conclusions
• EKF and OI give nearly similar results
• Assimilation of mw-Tb improves the soil moisture simulation
• Assimilation of screen level T, RH and mw-Tb gives best results
- especially when mw-Tb data are not available every day
• Assimilation of T, RH and mw-Tb improves either soil moisture
or latent heat flux
Plans
Assimilation aspects:
• Minimize the combined errors in prediction of soil moisture, latent heat flux and
screen level observations
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Further mw-Tb assimilation experiments (viewing angle, times)
•
Assimilation of heating rates
Technical aspects:
• Paper(s) focusing on the
- new features of assimilation method
- assimilation of mw-Tb
- assimilation of heating rates
•
Summer 2003: Build production system for the annual data base
•
End of 2003: Start production