Identification of characteristic model-observation deviations for
coupled data assimilation
Gernot Geppert and Felix Ament Meteorological Institute, Universität Hamburg
Workshop on Data Assimilation in Terrestrial Systems, Bonn 19. September 2016
Introduction
Goal: Strongly-coupled data assimilation
⇒ exploit links between model components
• Instantaneous deviations are not appropriate due to small-scale fluctuations.
• Instead use characteristic patterns in differences between forecast and observation.
t T
2 mnight day
dry soil wet soil
t T
2 mnight day
low thermal cond.
high thermal
cond.
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Characteristic deviations
Combine atmospheric state vector elements and observations into pseudo-observations which can be shown to be related to soil and surface states:
• gradients, biases, phase shifts
• estimate correlations with desired state or paramter (eg. soil moisture, thermal conductivity)
t T
2 mnight day
dry soil wet soil
t T
2 mnight day
low thermal cond.
high thermal
cond.
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Virtual reality framework
To assess the impact in data assimilation experiments at a later stage we use a virutal reality framework:
• multi-year simulations with TerrSysMP (B. Schalge's talk, Wed)
• COSMO coupled to CLM
• 1 km horizontal resolution
• simulation results stored with 15 min resolution
longitude / degrees east
latitude/degreesnorth
STG KIT
7.63 7.57
8.52 8.50
9.41 9.43
10.30 10.36
47.54 47.54
48.14 48.14
48.74 48.74
49.34 49.34
Bare soil Needleleaf evergreen Broadleaf deciduous Grass Crop
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Setting up ICON-LES in the virtual reality
We use ICON in a large-eddy-simulation configuration to capture the coupled system as realistically as possible.
• limited-area simulations ⇒ small domain inside the virtual reality
• ≈ 150 m and 300 m spatial resolution
longitude / degrees east
latitude/degreesnorth
9.81
9.85 9.88
9.92 9.96
10.00 10.03
10.08 10.11
10.16
48.34 48.34
48.39 48.39
48.44 48.44
48.49 48.49
cropland broadleaf forest needleleaf forest grassland bare soil
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Setting up ICON-LES in the virtual reality
• remap quadrilateral COSMO and CLM data to triangular ICON grid
• initial data for ICON-LES simulations
• boundary data every 15 min
• convert CLM variables to ICON-compatible variables
• matric potential to soil moisture index
• CLM soil state to q
surf• adapt variable names and units
...
96000 96400 96800 97200 97600 98000 98400 98800 P
96000 96400 96800 97200 97600 98000 98400 98800 W
−0.18
−0.12
−0.06 0.00 0.06 0.12 0.18 0.24 0.30V
−3.6
−3.3
−3.0
−2.7
−2.4
−2.1
−1.8
−1.5 U
2.8 3.2 3.6 4.0 4.4 4.8 5.2 5.6 T
284.5 284.8 285.1 285.4 285.7 286.0 286.3 286.6
...
96000 96400 96800 97200 97600 98000 98400 98800 P
96000 96400 96800 97200 97600 98000 98400 98800 W
−0.18
−0.12
−0.06 0.00 0.06 0.12 0.18 0.24 0.30V
−3.6
−3.3
−3.0
−2.7
−2.4
−2.1
−1.8
−1.5 U
2.8 3.2 3.6 4.0 4.4 4.8 5.2 5.6
T
284.5 284.8 285.1 285.4 285.7 286.0 286.3 286.6
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ICON-LES simulations
Currently three simulations:
• ensemble with perturbed initial soil moisture (10 days)
• ensemble with perturbed initial soil moisture and perturbed soil thermal conductivity (3 days)
• single realisation of a 1-year continuous simulation
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ICON-LES simulations
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ICON-LES simulations
• ensemble with perturbed initial soil moisture (10 days)
Jun012010 Jun022010
Jun032010 Jun042010
Jun052010 Jun062010
Jun072010 Jun082010
Jun092010 0.10
0.15 0.20 0.25 0.30 0.35 0.40
soilmoisture/m3m−3
deciduous broadleaf trees, clay/loam
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Morning temperature gradient
• ensemble with perturbed initial soil moisture (10 days)
−0.10 −0.05 0.00 0.05 0.10
soil moisture anomaly θ
0/ m
3m
−3−0.20
−0.15
−0.10
−0.05 0.00 0.05 0.10
anomaly in mo rning temp erature increase
d T d t
0/ K h
−1r = −0.74 deciduous broadleaf trees
t T
2 mnight day
dry soil wet soil
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Lapse rate
• ensemble with perturbed initial soil moisture (10 days)
−0.10 −0.05 0.00 0.05 0.10
soil moisture anomalyθ0/m3m−3
−0.015
−0.010
−0.005 0.000 0.005 0.010 0.015 0.020
anomalyinverticaltemperaturegradient
dT dz 0 /Km−1
r= 0.83 deciduous broadleaf trees
-15 -10 -5 04 Jun 05 Jun 06 Jun
temperature difference to lowest level /K 0
500 1000 1500 2000 2500 3000
height/m
dry, bare soil dry, forest wet, bare soil wet, forest
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Morning temperature gradient vs soil moisture - 1 year
0.10 0.15 0.20 0.25 0.30 0.35 0.40
0.10 0.15 0.20 0.25 0.30 0.35 0.40
volumetric soil moisture
−0.50.00.51.01.52.0 0.10
0.15 0.20 0.25 0.30 0.35 0.40
−0.50.00.51.01.52.0
temperature gradient / K h
−1temperature gradient / K h
−1temperature gradient / K h
−1temperature gradient / K h
−1temperature gradient / K h
−1temperature gradient / K h
−1temperature gradient / K h
−1temperature gradient / K h
−1temperature gradient / K h
−1temperature gradient / K h
−1temperature gradient / K h
−1temperature gradient / K h
−1−0.50.00.51.01.52.0−0.50.00.51.01.52.0
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Morning temperature gradient vs soil moisture - 1 year
0.10 0.15 0.20 0.25 0.30 0.35 0.40
0.10 0.15 0.20 0.25 0.30 0.35 0.40
volumetric soil moisture
−0.50.00.51.01.52.0 0.10
0.15 0.20 0.25 0.30 0.35 0.40
−0.50.00.51.01.52.0
temperature gradient / K h
−1temperature gradient / K h
−1temperature gradient / K h
−1temperature gradient / K h
−1temperature gradient / K h
−1temperature gradient / K h
−1temperature gradient / K h
−1temperature gradient / K h
−1temperature gradient / K h
−1temperature gradient / K h
−1temperature gradient / K h
−1temperature gradient / K h
−1−0.50.00.51.01.52.0−0.50.00.51.01.52.0 longitude / degrees east
latitude/degreesnorth
9.81
9.85 9.88
9.92 9.96
10.00 10.03
10.08 10.11
10.16
48.34 48.34
48.39 48.39
48.44 48.44
48.49 48.49
cropland broadleaf forest needleleaf forest grassland bare soil
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Morning temperature gradient vs soil moisture - 1 year
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
−1.0
−0.5 0.0 0.5 1.0
co rrelation
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Lapse rate vs soil moisture - 1 year
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
−1.0
−0.5 0.0 0.5 1.0
co rrelation
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Morning temperature gradient - May--July
crop broadleaf grass bare
−1.0
−0.5 0.0 0.5 1.0
co rrelation
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Lapse rate vs soil moisture - May--July
crop broadleaf grass bare
−1.0
−0.5 0.0 0.5 1.0
co rrelation
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Thermal conductivity
• ensemble with perturbed initial soil moisture and perturbed soil thermal conductivity (3 days)
00:00:00 12:00:00 00:00:00 12:00:00 00:00:00 12:00:00 00:00:00 286
288 290 292 294 296 298 300
temperature/K
t T
2 mnight day
low thermal cond.
high thermal
cond.
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Thermal conductivity
• ensemble with perturbed initial soil moisture and perturbed soil thermal conductivity (3 days)
00:00:00 12:00:00 00:00:00 12:00:00 00:00:00 12:00:00 00:00:00
284 286 288 290 292 294 296 298 300 302
temp erature / K
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Conclusions - Work in progress
• strongly coupled data assimilation should exploit (known) links between model components
• this requires combining states and observations across space or time
• use independent, high-resolution model to identify characteristic deviations
• avoid spurious relationships
• capture system as realistically as possible
• for soil moisture, there is no universal applicability, we are limited to favorable meteorological conditions (ie. water limited ET)
• outlook: conditional sampling for irregular parameter updates
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