land surface: modelling & data assimilation · kalman filter(dwd) off-line land surface (nldas,...
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![Page 1: Land Surface: Modelling & Data Assimilation · Kalman Filter(DWD) OFF-LINE Land surface (NLDAS, GLDAS, UK MetO Meteo-France) Who, When and Why ? •Coiffier et al. 1987 (Use of 2m](https://reader034.vdocuments.us/reader034/viewer/2022042109/5e89e76a0e3f63111b0c980e/html5/thumbnails/1.jpg)
Soil schemes: modeling&assimilation - G. Balsamo
Slide 1
Slide 1
Land Surface:Modelling & Data Assimilation
Gianpaolo Balsamo
European Centre for Medium-Range Weather Forecasts (ECMWF)
ARPA-SIM, Bologna, 18 February 2008
![Page 2: Land Surface: Modelling & Data Assimilation · Kalman Filter(DWD) OFF-LINE Land surface (NLDAS, GLDAS, UK MetO Meteo-France) Who, When and Why ? •Coiffier et al. 1987 (Use of 2m](https://reader034.vdocuments.us/reader034/viewer/2022042109/5e89e76a0e3f63111b0c980e/html5/thumbnails/2.jpg)
Soil schemes: modeling&assimilation - G. Balsamo
Slide 2
Slide 2
OutlineIntroduction
- The Earth Integrated Forecast System
- The role of Land Surface (LS)
- The role of data assimilation
- LS observational network
Modelling the land surface - Motivations
- Simplification vs. Realism in LS parameterizations
- TESSEL scheme
Analysing the land surface- Motivations
- Current practice in NWP (OI, EKF)
- New methods (simplified 2D-VAR, EnKF)
Modelling & data assimilation synergy- The example of soil hydrology (HTESSEL)
- The Cal/Val benchmark/strategy (field-site to global simulations + Data Assimilation)
Conclusions and Perspectives
![Page 3: Land Surface: Modelling & Data Assimilation · Kalman Filter(DWD) OFF-LINE Land surface (NLDAS, GLDAS, UK MetO Meteo-France) Who, When and Why ? •Coiffier et al. 1987 (Use of 2m](https://reader034.vdocuments.us/reader034/viewer/2022042109/5e89e76a0e3f63111b0c980e/html5/thumbnails/3.jpg)
Soil schemes: modeling&assimilation - G. Balsamo
Slide 3
Slide 3
Earth energy cascadeThe sun emits 4 x 1026 W
the Earth intercepts 1.37 kW/m2
This energy is distributed between- Direct reflection (~30%)
- Conversion to heat, mostly by surface absorption (~43%), re-radiated in the infrared
- Evaporation, Precipitation, Runoff (~22%)
- Rest of the processes (~5%, Winds, Waves, Convection, Currents, Photosynthesis, Organic decay, tides, … )
Robinson & HendersonSellers, 1999
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 4
Slide 4
Earth water cycle
Atmosphere recycling time scales associated with land reservoir
-Precipitation 4.5/107 = 15 days
-Evaporation 4.5/71 = 23 daysEvaporation
71
TerrestrialAtmosphere
Land
4.5
Rain
107
[•] = 1015 kg = teratons
[•] = 1015 kg yr-1
Runoff
36
Chahine, 1992
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 5
Slide 5
Role of land surfaceAtmospheric general circulation models need boundary
conditions for the enthalpy, moisture (and momentum) equations: Fluxes of energy, water at the surface.
Water budget
E
P
Y
0.9 mmd-12.21.4
H
LE RT
RS
27 40 65 134 Wm-2
Energy budget
ERA40 land-averaged values 1958-2001
Carbon budget (natural)
NEE
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Soil schemes: modeling&assimilation - G. Balsamo
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Slide 6
Role of land surface (2)
Numerical Weather Prediction models need to provide near surface weather parameters (temperature, dew point, wind, low level cloudiness) to their customers.
ECMWF model(s) and resolutions
Length Horizontal Vertical Remarks
resolution levels
- Deterministic 10 d T799 (25 km) L91 00+12 UTC
- Ensemble prediction 15 d T399 (50 km) L62 2x(50+1)
- Monthly forecast 1 m T159 (125 km) L62 (Ocean coupled)
- Seasonal forecast 6 m T95 (200 km) L40 (Ocean coupled)
- Assimilation physics 12 h T255(80 km)/ L91 T95(200 km)
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Soil schemes: modeling&assimilation - G. Balsamo
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Slide 7
How to initialize the Land Surface: Current Practice in NWP centers
Use of 2m observations
with OI Analysis
(ECMWF, Météo-France,
HIRLAM, MSC)
or Simplified
Kalman Filter(DWD)
OFF-LINE Land surface
(NLDAS, GLDAS, UK MetO
Meteo-France)
Who, When and Why ?Who, When and Why ?
•Coiffier et al. 1987 (Use of 2m for land surface)•Mahfouf 1991 (OI / Variational formulation of the land surface analysis)•the operational application comes few years later (94-95 at ECMWF, 99 Météo-France.)
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Soil schemes: modeling&assimilation - G. Balsamo
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Slide 8
L-band TbL-band Tb C-band TbC-band Tb C-band scat. IR TsC-band scat. IR Ts
EVOLUTION OF LAND SURFACE DATA ASSIMILATION SYSTEMS
T/H 2mT/H 2m
hourly 6-hourly
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 9
Slide 9
L-band TbL-band Tb C-band TbC-band Tb C-band scat. IR TsC-band scat. IR Ts
OBSERVATIONS FOR SOIL MOISTURE ANALYSIS
T/H 2mT/H 2m
INFORMATIVITY on SOIL MOISTURE
2008/2012 AVAILABILITY now
+ Large Information content
+ Global Coverage
+ Reduced Atmospheric Contrib.
-Not Available ‘till 2009
+ Global coverage
+ Relatively reduced Atmospheric contrib.
- RFI
- Vegetation masking VCW>1kg/m2
+ Large coverage
- Cloud Masking
- Model Bias
+ Wide validation
-Coverage
-Variable Information Content
![Page 10: Land Surface: Modelling & Data Assimilation · Kalman Filter(DWD) OFF-LINE Land surface (NLDAS, GLDAS, UK MetO Meteo-France) Who, When and Why ? •Coiffier et al. 1987 (Use of 2m](https://reader034.vdocuments.us/reader034/viewer/2022042109/5e89e76a0e3f63111b0c980e/html5/thumbnails/10.jpg)
Soil schemes: modeling&assimilation - G. Balsamo
Slide 10
Slide 10
OutlineIntroduction
- The Earth Integrated Forecast System
- The role of Land Surface (LS)
- The role of data assimilation
- LS observational network
Modelling the land surface - Motivations
- Simplification vs. Realism in LS parameterizations
- TESSEL scheme
Analysing the land surface- Motivations
- Current practice in NWP (OI, EKF)
- New methods (simplified 2D-VAR, EnKF)
Modelling & data assimilation synergy- The example of soil hydrology (HTESSEL)
- The Cal/Val benchmark/strategy (field-site to global simulations + Data Assimilation)
Conclusions and Perspectives
![Page 11: Land Surface: Modelling & Data Assimilation · Kalman Filter(DWD) OFF-LINE Land surface (NLDAS, GLDAS, UK MetO Meteo-France) Who, When and Why ? •Coiffier et al. 1987 (Use of 2m](https://reader034.vdocuments.us/reader034/viewer/2022042109/5e89e76a0e3f63111b0c980e/html5/thumbnails/11.jpg)
Soil schemes: modeling&assimilation - G. Balsamo
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Slide 11
History of ECMWF 2m T errors
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Soil schemes: modeling&assimilation - G. Balsamo
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Slide 12
The challenges for Land Surface Modeling
Capture natural diversity of land surfaces (heterogeneity) via a simple set of equations
Focus on elements which affects more directly weather and climate (i.e. soil moisture, snow cover).
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Soil schemes: modeling&assimilation - G. Balsamo
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Slide 13
TESSEL scheme
High and lowvegetationtreated separately
Variable root depth
Revised canopyresistances,including airhumidity stress onforest
No rootextraction ordeep percolationin frozen soils
New treatmentof snow underhigh vegetation
+ 2 tiles (ocean & sea-ice)
Tiled ECMWF Scheme for Surface Exchanges over Land
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Soil schemes: modeling&assimilation - G. Balsamo
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Slide 14
Vegetation Type (H and L) at T799GLCC(1998)
6 dominant high veg. type (TVH)
9 dominant low veg. type (TVL)
Used to assign:
rootdistributionLAI and Rs_minroughness lengths
by a lookup table
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Soil schemes: modeling&assimilation - G. Balsamo
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Slide 15
Vegetation Cover (H and L) at T799GLCC(1998)
Note: the cover CVH and CVL are fraction of land use by TVH and TVL and their sum is equal the unity
Used to calculate:
bare ground fraction as
Bare_frac=1ΣCV(TVi)*RCOV(TVi)
with RCOV provided by a lookup table
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 16
Slide 16
OutlineIntroduction
- The Earth Integrated Forecast System
- The role of Land Surface (LS)
- The role of data assimilation
- LS observational network
Modelling the land surface - Motivations
- Simplification vs. Realism in LS parameterizations
- TESSEL scheme
Analysing the land surface- Motivations
- Current practice in NWP (OI, EKF)
- New methods (simplified 2D-VAR, EnKF)
Modelling & data assimilation synergy- The example of soil hydrology (HTESSEL)
- The Cal/Val benchmark/strategy (field-site to glo
Conclusions and Perspectives
![Page 17: Land Surface: Modelling & Data Assimilation · Kalman Filter(DWD) OFF-LINE Land surface (NLDAS, GLDAS, UK MetO Meteo-France) Who, When and Why ? •Coiffier et al. 1987 (Use of 2m](https://reader034.vdocuments.us/reader034/viewer/2022042109/5e89e76a0e3f63111b0c980e/html5/thumbnails/17.jpg)
Soil schemes: modeling&assimilation - G. Balsamo
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Slide 17
Case study: Europe, May-June1994 (1)
ECMWF
German (DWD)
Day 2 forecasts
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 18
Slide 18
Case study: Europe, May-June1994 (2)
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 19
Slide 19
Case study: Europe, May-June1994 (3)
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Soil schemes: modeling&assimilation - G. Balsamo
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Slide 20
Near surface atmospheric errors
In the French forecast model (~10km) local soil moisture patterns anomalies at time t0 are shown to correlate well with large 2m temperature forecast errors (2-days later)
Balsamo, 2003dry soil
wet soil
G nR H LE
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 21
Slide 21
Link between soil moisture and atmosphere
The main interaction of soil moisture and atmosphere is due to evaporation and vegetation transpiration processes.
Ws bare ground
Wp vegetation
Eg Etr
E
0 <SWI< 1
Ws
Wp
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 22
Slide 22
Optimum Interpolation land surface analysis(oper. surface analysis at Météo-France/MSC/ECMWF…) Mahfouf 1991, Bouttier 1993, Giard and Bazile 2000, Mahfouf et al. 2003, Belair et al 2003
Sequential analysis (every 6h)Correction of surface parameters (Ts, Tp, Ws, Wp) using 2m increments between
analysed and forecasted values
Optimum Interpolation of T2m and RH2m using SYNOP observations interpolated
at the model grid-point (by a 2m analysis)
T2m
t
Wp
t
RH2m
t
6-h 12-h 18-h 0-h
Tuning of the OI statistics and regressions and accuracy of 2m analyses are key components
∆ T2m = T2ma - T2m
f∆ RH2m = RH2ma - RH2m
f
Tsa - Ts
f = ∆ T2m
Tpa - Tp
f = ∆ T2m / 2πWs
a - Wsf = αWsT ∆ T2m + αWsRH ∆ RH2m
Wpa - Wp
f = αWpT ∆ T2m + αWpRH ∆ RH2m
αWp/sT/RH = f (t, veg, LAI/Rsmin, texture, atm.cds.)
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 23
Slide 23
Variational surface analysisMahfouf (1991), Callies et al. (1998), Rhodin et al. (1999),
Bouyssel et al. (2000), Hess (2001), Seuffert et al. (2004), Balsamo et al. (2004)
Formalism:
x is the control variables vectory is the observation vectorH is the observation operator
Continuous analysis
T2m
t
Wp
t
RH2m
t
6-h 12-h 18-h 0-h
The analysis is obtained by the minimization of the cost function J(x)
B is the background error covariance matrix
R is the observation error covariance matrix
= ½ (x – xb) T B1 (x – xb) + ½(y – H(x))T R1 (y – H(x))
J(x) = J b(x) + J o(x)
Advantages: Easier assim. asynop. obs.Extension on longer assim. Window (24h)
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 24
Slide 24
(Bouyssel et al. 2000)
The shape of the cost function J for full 2D-VAR
J=f(Ws,Wp,Ts,Tp)
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 25
Slide 25
How the simplified 2D-VAR method worksWp W’p=Wp + δWp
=
p
pp
p
p
p
T
WYWY
WYWY
δδ
δδ
δδ
δδ
)(
-1)(
)2(
)1(
...H
( ))( -1)()1(2)1( ,,...,,)( pPb YYY YxHy ∆∆∆∆=−
From a perturbation of the initial total soil moisture δ Wp applied on each model land grid-point.
δY (i) = YG (i) - YG’ (i) Guess G
Guess G’
Y
t
t=0 1 2 … p
∆Y (i) = YG (i) - YO (i)
δWp
Y=(T2m ,Tb,Ts )
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 26
Slide 26
The 2D hypothesis is validated with simulated observations on a real situation
From a prescribed initial error From a prescribed initial error WpWp
The 6-h forecast errors The 6-h forecast errors on on TT2m2m and and RHRH2m2m
Analysis error Analysis error
2D hypothesis
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 27
Slide 27
Convergence of 2D-VAR analysisSimulated observations (consistent to SWI=0.5) are assimilated over
a 10-day period
A 24-h 2D-VAR analysis with optimised settings
Real observations experiments are then considered
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 28
Slide 28
Soil Moisture produced by the ELDAS project
Habets et al. (2003)
The same comparison is produced for the ELDAS soil moisture obtained with the ARPEGE model. An improved match of soil moisture patterns and gradients is obtained on the SAFRAN-ISBA-MODCOU validation area.
ELDAS cycle
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 29
Slide 29
June July
(CNES, 2003) Images SPOT/VEGETATIONVariation of NDVI 2003 within respect to 2002
+positive Index 2003/2002 +negative
Variation of SWI at 30 June 2003 compared to 30 June 2000 (ELDAS)
30 June 2003 (exp. 2D-Var + Ecoclimap (Masson et al. 2003) after 2-month cycle)
Drought of summer 2003: Comparison of soil moisture and NDVI anomaly
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Soil schemes: modeling&assimilation - G. Balsamo
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Slide 30
C-band TbC-band Tb
IR TIR Tskinskin
How Microwave and Infra-red Radiances may be informative on soil water content?
Important – clear sky only10.8 μm(27.7 THz)superficial
Low (except rainy area)5 cm6.9 GHz~1cm
Negligible21 cm1.4 GHz~5 cm
Atmospheric absorptionWavelengthFrequencySounding soil depth
L-band TbL-band Tb
Ws
Wp
Ws
Wp
C-band TbC-band TbL-band TbL-band Tb
Soil moisture modifies soil dielectric const. emissivity ε
IR TsIR Ts
Tb = ε Ts
Soil moisture affects Skin temperature and heating rate
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 31
Slide 31
G G’ Obs.
Tb, H
t
Wp
t
Tb, V
t
0-h 1-h 2-h 3-h … ………… 23-h 0-h 0-h 1-h 2-h 3-h … …… 23-h 0-h
L-bandL-band & C-BandC-Band TB
Every hour (except RFI in C-band)
IR TskinIR Tskin (or HR)
TsIR
Wp
t
t
Morning
(except Clouds)
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Soil schemes: modeling&assimilation - G. Balsamo
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Slide 32
PHASE I
THE OFF-LINE LSS ISBA is driven by near surface
atmospheric forcing to obtain the LAND SURFACE STATE
SMOS (&SMAP) L-BAND simulated TBH,V
PHASE II
THE MICROWAVE RT Model LSMEM is used to compute the brightness temperature at 1.4GHz
TB,h from microwave RT model (Drusch et al. 1999, 2001)
PHASE III
SPATIAL and TEMPORAL location of the simulated TB t-1/2-h
t+1/2-h
t
Superficial soil moisture from ISBA
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 33
Slide 33
OSSE: Assimilation of Simulated Brightness Temperature
The assimilation of HYDROS simulated H and V polarization L-band brightness temperatureis investigated in a 10-day DA experiment using GSWP-II forcing to create a reference landsurface state (from 1-y ISBA model run).
The 2D-VAR analysis is initialized with a background model error of 10% (SWI) and the observations error is set to 3 K.
The analysis plays for about 50% in the convergence towards the ISBA-GSWP-II reference (starting from a medium soil moisture Wp=0.5(Wfc-Wwl)
97.5
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 34
Slide 34
Soil moisture Error (% vol.)Soil moisture Error (% vol.)Analysis – ReferenceAnalysis – Reference
OSSE: Assimilation of HYDROS Simulated Brightness Temperature
The assimilation of HYDROS simulated H and V polarization L-band brightness temperature is investigated in a 10-day DA experiment using GSWP-II forcing to create a reference land surface state (from 1-y ISBA model run).
The 2D-VAR analysis is initialized with a background model error of 10% (SWI) and the observations error is set to 3 K.
The analysis plays for about 50% in the convergence towards the ISBA-GSWP-II reference (starting from a medium soil moisture Wp=0.5(Wfc-Wwl)
97.5
0
10
20
30
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 35
Slide 35
Extended / Ensemble Kalman Filter
Simplified VAR/EKF Methods
Variational
Optimal Estimation Theory
Data Assimilation Techniques applied for Land Surface
( ) 1111 ---- += RHHRHBK TT
0)( ää
( ) ( ) xHxxx dd +=+ HH
( ))( bba H xyKxx -+=
Optimum Interpolation
(...)f==
αα
QMMAB +=+
H(x)
H(x)
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 36
Slide 36
Off-line vs. Atmospheric Coupled LDAS Balsamo et al. 2007
Within CaLDAS an Off-line version of GEM-15km is available, MEC-15km Same dynamical/physical core GEM-15km
In the Off-line version the forcing is applied at ~50 m (28th level of GEM)
The comparison is proper (same innovations, same atmospheric model trajectory).
A SBL (Delage 1997) is implemented and allows to maintain and interactive layer
A multi-observation OSSE using the simplified 2D-VAR scheme is run.
Diagnostics from Jacobians and the information content theory confirm a good approximation over North America (GEM-core domain) with a reduction of noisy signal which seems beneficial (i.e. no convection). Results are still preliminary (1 day considered) and further tests are in progress.
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 37
Slide 37
OutlineIntroduction
- The Earth Integrated Forecast System
- The role of Land Surface (LS)
- The role of data assimilation
- LS observational network
Modelling the land surface - Motivations
- Simplification vs. Realism in LS parameterizations
- TESSEL scheme
Analysing the land surface- Motivations
- Current practice in NWP (OI, EKF)
- New methods (simplified 2D-VAR, EnKF)
Modelling & data assimilation synergy- The example of soil hydrology (HTESSEL)
- The Cal/Val benchmark/strategy (field-site to Global simulation + Data Assimilation)
Conclusions and Perspectives
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 38
Slide 38
TESSEL land surface scheme: + and -
High and lowvegetationtreated separately
Variable root depth
Revised canopyresistances,including airhumidity stress onforest
Inhibited root extraction,or drainagein frozen soils
New treatmentof snow underhigh vegetation
+ 2 tiles (ocean & sea-ice)
Tiled ECMWF Scheme for Surface Exchanges over Land
A single soil textureglobally, excessive drainage
Too little surfacerunoff
Too early snowmelting
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 39
Slide 39
Surface Water reservoirs (ERA-40)
DA increments redistribute water and constraint near-surface errors
snow Soilmoisture
Early snowmelting
moisture deficit
anticipate moisture supply
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 40
Slide 40
Cold processes I: Snow DA increments
ERA-40 ERA-Interim1992, daily SWE increments
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 41
Slide 41
HTESSEL scheme
R1 > R2
D1 < D2
P1 = P2
_1 > _ 2
R2
Fine texture Coarse texture
Hydrology-TESSEL
- Global Soil Map (FAO)
- New formulation of Hydraulic properties
- VIC surface runoff
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 42
Slide 42
Soil Type at T799FAO(2003)
6 dominant soil type
Used to assign:hydraulic properties (drainage and surf. runoff)field capacity & wilting point for SM analysis
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 43
Slide 43
A revised hydrology scheme (H-TESSEL)
A spatially variable hydrology scheme is being tested following Van den Hurk and Viterbo 2003
Use of a the Digital Soil Map of World (DSMW) 2003
Infiltration based on Van Genuchten 1980 and Surface runoff generation based on Dümenil and Todini 1992
Van den Hurk and Viterbo 2003
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 44
Slide 44
Field Capacity and Permanent Wilting Point
Soil DiffusivitySoil ConductivityTESSELTESSEL
0.6620.267Organic6
0.5410.335Very fine 5
0.4480.279Fine4
0.3820.133Medium-fine3
0.3460.151Medium2
0.2420.059Coarse1
FC [m³/m³] PWP [m³/m³] SoilHTESSEL
0.3230.171Loamy1
FC [m³/m³] PWP [m³/m³] SoilTESSEL
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 45
Slide 45
The soil texture classification database
Dominant soil type from FAO2003 (at native resolution of ~ 10 km)
█coarse █medium █med-fine █fine █very-fine █organic
Soil texture percentage occupation as a function of resolution
0%
10%
20%
30%
40%
50%
60%
70%
coarse medium medium-fine fine very-fine organic
input (10 km) T21 T42 T159 T799
The interpolation to model grid is donewithin the IFS by the prepdata (interporoutine) preserving the dominant texture type at various resolution (T21-T799). Important for “upscalability”
FAO 2003 from Freddy.Nachtergaele, after a survey of the available datasets.
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 46
Slide 46
The orography runoff generation
Also the standard deviation of orography is scaling with resolution (especially T159-799).
fraction runoff s of the grid-point area S.
b
S
S
w
w
S
säää
ääää
ä−−=
Runoff as a function of orography (b is based on standard deviation of orography)
10mm/hUp to~30% Surfacerunoff in complex orography
minmax
min
ss
ssb
−−=;
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 47
Slide 47
Verification Strategy for the new Hydrology
Field sites
(Offline)Catchment (Offline)
Global (Offline)
Coupled GCM
Coupled GCM + DA
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 48
Slide 48
Field site verification of HTESSEL
observed atmospheric forcing
observed SM/LE/H
observed Tb
Ancillary data as in operational (no local readjustment)
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 49
Slide 49
SEBEX (sandy soil)
HTESSEL show a consistent improvement of soil moisture and evaporation with respect to TESSEL
Savannah, desert climate
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 50
Slide 50
BERMS (Boreal Forest)
HTESSEL show a consistent improvement of Top 1m soil moisture with respect to TESSEL and a better represented interannual variability
Forest, snow dominated site
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 51
Slide 51
Insitu Network
Agrometeorologic Network
-Russia: 63 stations
-Ukraine: 96 stations
Soil Property Measurements
-volumetric density, total water holding capacity, field capacity, wilting level
-sampled at several cross-sections in 3 fields for each station
-evaluated periodically
By Klaus Scipal
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 52
Slide 52
Soil Properties - Russia
TESSEL
HTESSEL
By Klaus Scipal
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 53
Slide 53
Soil Properties - Ukraine
TESSEL
HTESSEL
By Klaus Scipal
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 54
Slide 54
Basin-scale verification of HTESSEL
observed atmospheric forcing (GSWP2)
observed total Runoff (GRDC)
ERA-40 atmospheric TCWV variations combined with GRDC runoff to obtain Terrestrial Water Storage change
dS/dt = (P - E) - R
Ancillary assigned by closest IFS grid resolution (T255 for 1x1 regular lat lon grid).
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 55
Slide 55
Quantitative estimate of Global Water budget:Dataset for Mid-latitude River BasinsSeneviratne et al. 2004, J. Climate, 17 (11), 2039-2057
Hirschi et al. 2006, J. Hydrometeorology, 7(1), 39-60
“BSWB”
http://iacweb.ethz.ch/data/water_balance/
Courtesy of Sonia Seneviratne
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 56
Slide 56
Case Study: Mississippi & Illinois
Water-balance Estimates
Observations(soil moisture+groundwater+snow)
Seneviratne et al. 2004, J. Climate, 17 (11), 2039-2057
corr=0.8, r2=0.71
Courtesy of Sonia Seneviratne
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 57
Slide 57
European catchments: Validation using ERA-40 derived BSWB (Basin Scale Water Budgets)
HTESSEL increases the storage w.r.t. TESSEL, closer to Annual variations estimated by the BSWB dataset
TESSEL is better offline than in ERA-40 due to P6h bias over Europe
DA works efficiently to correct soil moisture by adding water and preserving evaporation
∆SM
ET
dS
P
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 58
Slide 58
Total Runoff per basin
1. tura2. ob3. volga4. don5. dnepr6. neva7. baltic8. elbe9. odra10. wisla11. danube12. northeast_europe13. po14. rhine15. weser16. ebro17. garonne18. rhone19. loire20. seine21. france22. central_europe
1. yukon2. mackenzie3. columbia4. arkansas5. mississippi6. missouri7. ohio8. murray_darling9. changjiang10. syrdarya11. selenga12. irtish13. amudarya14. amur15. lena16. yenisei17. podkamennaya_tunguska18. vitim19. Tom
BIAS
RMSE
Soil Moisture dominant
Snow dominant
TESSELHTESSEL
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 59
Slide 59
Interaction of early snow
melt and runoff is
particularly evident
In Siberian basins
Snow melt and runoff
HTESSEL better reproducethe runoff peak
Early snow melt displacethe peak by 1 month
RMSE in runoff increases due to double penalty
R
P
E
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 60
Slide 60
Rhone Agg experiment: Daily runoff8 × 8 km and 1° runs for a 4-year period (1 August 1985 – 1 August 1989)
By Bart van den Hurk (KNMI)
TESSEL showlow-pass-filterbehavior
HTESSEL morealike obs
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 61
Slide 61
Global-scale verification of HTESSEL
At global scale land surface can be compared to climatological datasets
Result obtained offline should be also verified in climate run experiments where there is a feedback from the atmosphere.
a better dS/dt and R --> improved (P - E), dW/dt, ..
Data assimilation experiments although expensive offer an excellent verification looking at land surface data assimilation increments.
dS/dt = (P - E) - R + dA
a better model --> reduced dA increments
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 62
Slide 62
Total runoff (Qualitative) GSWP2 model output vs.GRDC-composite estimate 1986-1995)
H-TESSEL
TESSEL
GRDC
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 63
Slide 63
Climate runs (1-year run coupled): surface T2m compared with analysis
H-TESSELTESSEL
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Soil schemes: modeling&assimilation - G. Balsamo
Slide 64
Slide 64
Reduction [%] of RMSE against all the climatological datasets used in CLIMPLOT for the 4ens 2000/092001/08 period
Climate runs (extensive validation): Scores of HTESSELHow to quantify climate improvements?
Relying on datasets
- need to be aware we are not scoring against observations
Assuming RMS can be reduced “asymptotically”
- (RMS_new < RMS_old)
If we normalized RMS reduction (with RMS_old)
- HTESSEL vs. TESSELin 32R3
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Soil schemes: modeling&assimilation - G. Balsamo
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Long DA cycle with HTESSEL
A long DA experiment at T159L91 is done with TESSEL and HTESSEL (01/04-01/11/2906)
Differences in soil moisture analysis increments can be interpret as improvements of the slow model component
- |ΔSM(HTESSEL)| > |ΔSM(TESSEL)|
- |ΔSM(HTESSEL)| < |ΔSM(TESSEL)|
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Soil schemes: modeling&assimilation - G. Balsamo
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OutlineIntroduction
- The Earth Integrated Forecast System
- The role of Land Surface (LS)
- The role of data assimilation
- LS observational network
Modelling the land surface - Motivations
- Simplification vs. Realism in LS parameterizations
- TESSEL scheme
Analysing the land surface- Motivations
- Current practice in NWP (OI, EKF)
- New methods (simplified 2D-VAR, EnKF)
Modelling & data assimilation synergy- The example of soil hydrology (HTESSEL)
- The Cal/Val benchmark/strategy (field-site to glo
Conclusions and Perspectives
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Summary and conclusionLand Surface Modelling and Data Assimilation are closely related
A simplified variational method (Balsamo et al. 2004) which make use the “model” as observation operator to establish the link between soil moisture and the observation
The good news is that LDAS can work offline (Balsamo et al. 2007)
Land surface analysis should not compensate for model bias
An improved hydrology (HTESSEL) shows a better match to field site observations of soil moisture while preserving good performance of TESSEL in LE/H.
GSWP2 runs 1986-1995 show that HTESSEL addresses the issue of storage.
Runoff bias is reduced and Runoff timing is improved (when not combined with snow early melting)
Long DA runs: reduction of sm increments at mid-latitudes.
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Near Future model&DA developmentsImprove treatment of land surface water
- Continuous assessment of LSM hydrology (e.g. GLACE-2)
- 2008 SMOS/ASCAT year (lakes)
Vegetation seasonal cycle- Introduction of monthly LAI
- Prepare for CTESSEL (GEOLAND-2)
Snow “patches”- SnowMIP2 results now available allow to tune the snow scheme
(adding simple parameterizations as water refreeze.
Offline suite- GSWP2 experimental framework can be extended to E4 and OD
- Link with land surface data assimilation
Data Assimilation via simplified 2D-VAR/EKF- ASCAT soil moisture assimilation
- L-band brightness temperature (SMOS 2009)
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Soil schemes: modeling&assimilation - G. Balsamo
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Slide 69
Acknowledgements
Thanks to:
- Anton Beljaars , Pedro Viterbo, Martin Hirschi (ETH), Bart van den Hurk (KNMI), Alan Betts for HTESSEL
- Matthias Drusch and Patricia Derosnay for the L-band feedback and DA discussions
- Klaus Scipal for Soil moisture verification (from Global Soil Moisture Data Bank)
- Seasonal/monthly forecast teams (Tim Stockdale, Paco Doblas-Reyes, Frederic Vitart, Laura Ferranti, …)
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Compatibility and information to users
Web-page inECMWF site (Paul, Umberto)
Soil moisturerescale scriptin IFS(Jan, Joerg, Nils)
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Ecoclimap ECOCLIMAP merge climatic zone and Land Use datasets making
use of AVHRRNDVI to identify relevant biomes.
The NDVI seasonality is used to obtain derived properties (eg. LAI)
January
June
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Influence MATRIX (HK)Cardinali et al. 2003 ; Chapnik et al. 2004
This diagnostic tool allows to separate the information content of each observation in the analysis
xa=xb+K(y-Hxb) analysis equation
with A=(I-KH)B analysis error covariance Matrix
Hxa=(I-HK)Hxb+HKy Analysis in observation space
if we calculate the derivative ∂ … / ∂y 0 we have :
Tr ( ∂ Hxa / ∂y0) = Tr (HK) Amount of information extracted from observation y0
if H is linear = Tr ((B-A) B-1) Analysis Error Reduction
H
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L-band TbL-band TbC-band TbC-band Tb
IR TsIR Ts
T/H 2mT/H 2m
Contribution of each observation source in the Soil Moisture Analysis (N. America): 2D-VAR on 24-h time window using hourly distributed observations (date 01/07/1995)
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Observation Source
N. of observations
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
50.0
% of contribution [ 100 (B-A) B-1 ]
N. Analysis Influence (%)
N. 7396 7392 7904 7874 3301 8757 8757
A nalysis Inf luence (%) 20.8 15.1 11.0 7.4 9.3 6.1 7.5
sigma Obs. 3.0 3.0 3.0 3.0 3.0 2.0 0.002
Tb (L-Band) H
Tb (L-Band) V
Tb (C-Band) H
Tb (C-Band) V
T skin (IR) AM+PM
T2m (6-h) Q2m (6-h)
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Improvements in the 3 latest IFS cycles
32R1 vs. 31R2 32R2 vs. 32R1 32R3 vs. 32R2
McRad: RRTM SW, No Physics change New convection Modis Alb, McICA V.Diff, HTESSEL