global calibration of the geos-5 l-band microwave ... · 18/09/2012 · global calibration of the...
TRANSCRIPT
GMAO-18Sep12 – 1 / 28
Global Calibration of the GEOS-5 L-band
Microwave Radiative Transfer Model over
Land Using SMOS Observations
Gabrielle De Lannoy, Rolf Reichle, Valentijn PauwelsGlobal Modeling and Assimilation Office (Code 610.1), NASA/GSFC, Greenbelt, MD, USA
Laboratory of Hydrology and Water Management, Fac. Bioscience Engineering, Ghent University, Belgium
18 September 2012
Outline
Soil Moisture andMicrowaveRadiances
Calibration
BrightnessTemperatures
RTM Parameters
Remaining Biases
Conclusions
GMAO-18Sep12 – 2 / 28
Soil Moisture and Microwave Radiances
Calibration
Brightness Temperatures
RTM Parameters
Remaining Biases
Conclusions
Soil Moisture and Microwave
Radiances
Soil Moisture andMicrowaveRadiances
Tb
RTM
SMOS/SMAP
Calibration
BrightnessTemperatures
RTM Parameters
Remaining Biases
Conclusions
GMAO-18Sep12 – 3 / 28
Brightness Temperature (Tb)
Soil Moisture andMicrowaveRadiances
Tb
RTM
SMOS/SMAP
Calibration
BrightnessTemperatures
RTM Parameters
Remaining Biases
Conclusions
GMAO-18Sep12 – 4 / 28
� measured by passive microwave sensors� dry and warm land surface → high Tb [K]� wet and cold land surface → low Tb [K]
The relationship between brightness tem-perature (Tb) and soil moisture (SM) is verysensitive to local parameters
0.2 0.4 0.6
220
240
260
280
h = 00.3
0.6
0.9
1.2
1.51.8
Ts
Dry <−−−−−−−> Wet SM [−]
Tbh
[K]
0.2 0.4 0.6
220
240
260
280
h = 0 0.3 0.60.9
1.21.5
1.8
Ts
Dry <−−−−−−−> Wet SM [−]
Tbv
[K]
Tau-Omega Radiative Transfer Model (RTM)
Soil Moisture andMicrowaveRadiances
Tb
RTM
SMOS/SMAP
Calibration
BrightnessTemperatures
RTM Parameters
Remaining Biases
Conclusions
GMAO-18Sep12 – 5 / 28
Zero-order microwave radiative transfer model:
� soil contributions : moisture (dielectricconstant), roughness (h) → roughreflectivity (rp)
� vegetation contributions : opacity (τp)→ attentuation (Ap); scattering (ω)
� atmospheric contributions
TbTOA,p = Tbau,p + exp(−τatm,p)TbTOV,p
TbTOV,p = Ts(1− rp)Ap
+Tc(1− ωp)(1− Ap)(1 + rpAp)
+Tbad,prpAp2
GEOS-5 Catchment LSM (Fortuna 2.3; 5 cm surface layer):
� soil moisture, surface temperature (Ts = Tc), LAI,...
SMOS and SMAP
Soil Moisture andMicrowaveRadiances
Tb
RTM
SMOS/SMAP
Calibration
BrightnessTemperatures
RTM Parameters
Remaining Biases
Conclusions
GMAO-18Sep12 – 6 / 28
SMOS (ESA, Soil Moisture Ocean Salinity)
1q
2q
SMAP (NASA, Soil Moisture Active Passive)
� launched November 2009
� L-band radiometer
� sensing depth = 5 cm
� 40 km resolution
� launch 2014
� L-band radiometer/radar
� sensing depth = 5 cm
� 3-40 km resolution
→ Calibrate radiative transfer model using Tb data from SMOS toprepare for SMAP
SMOS and SMAP
Soil Moisture andMicrowaveRadiances
Tb
RTM
SMOS/SMAP
Calibration
BrightnessTemperatures
RTM Parameters
Remaining Biases
Conclusions
GMAO-18Sep12 – 7 / 28
SMOS (ESA, Soil Moisture Ocean Salinity)
1q
2q
SMAP (NASA, Soil Moisture Active Passive)
Each locationseen with multipleincidence anglesper overpassEach snapshot:σ ∼ 4 K
Each locationseen once peroverpass at fixed40o incidenceangle (conicalscan)σ ∼ 1.3 K
Tb[K]
30 6040 50
TBv
TBh
20
Incidence Angle [o]
SMAPSMOS
Calibration
Soil Moisture andMicrowaveRadiances
Calibration
Problem
θ-pol
Parameters
J
BrightnessTemperatures
RTM Parameters
Remaining Biases
Conclusions
GMAO-18Sep12 – 8 / 28
Before Calibration
GMAO-18Sep12 – 9 / 28
Large time-mean bias
200 220 240 260 280 300
1 Jan 2011 - 1 Jan 2012, 36 km⇐ SMOS observed Tb, H-pol, 42.5o
⇓ Model-minus-Observations , with themodel using prescribed RTM parameters(SMAP, L-MEB literature, ECMWF-SMOS)
Lit1 (SMAP) Lit2 (L-MEB) Lit3 (EC-SMOS)
−50 −40 −30 −20 −10 0 10 20 30 40 50 [K]
Before Calibration
GMAO-18Sep12 – 10 / 28
Large differences in temporal variability
5 10 15 20
1 Jan 2011 - 1 Jan 2012, 36 km⇐ SMOS observed Tb, H-pol, 42.5o
⇓ Model-minus-Observations , with themodel using prescribed RTM parameters(SMAP, L-MEB literature, ECMWF-SMOS)
Lit1 (SMAP) Lit2 (L-MEB) Lit3 (EC-SMOS)
−10 −8 −6 −4 −2 0 2 4 6 8 10 [K]
SMOS versus CLSM/RTM
Soil Moisture andMicrowaveRadiances
Calibration
Problem
θ-pol
Parameters
J
BrightnessTemperatures
RTM Parameters
Remaining Biases
Conclusions
GMAO-18Sep12 – 11 / 28
Ascending multi-angular Tb, annual (2011) global average
32.5 37.5 42.5 47.5 52.5 57.5
200
220
240
260
Inc Angle [o]
Tb H
[K]
a)
32.5 37.5 42.5 47.5 52.5 57.5240
250
260
270
280
Inc Angle [o]
Tb V
[K]
b)
SMOSLit1Lit2Lit3CalD2
� bias is angle- and polarization-dependent� after calibration with 2010 data, CLSM/RTM Tb will be unbiased vs.
SMOS in 2011
RTM-Parameters
Soil Moisture andMicrowaveRadiances
Calibration
Problem
θ-pol
Parameters
J
BrightnessTemperatures
RTM Parameters
Remaining Biases
Conclusions
GMAO-18Sep12 – 12 / 28
Different calibration scenarios:1) Selected parameters α(Nα), control vector:
Parameter [min, max] A B C D
hmin [0, 2.0] X X X X∆h ≡ hmax − hmin [0, 1.0] X X X Xω [0, 0.3] X XbH [0, 0.7] X X∆b ≡ bV − bH [-0.15, 0.15] X X
� microwave roughness h
� scattering albedo ω
� vegetation opacity τ= bp LEWT LAI
2) Prior information α0(Nα):bounded Gaussian, centralized around Lit1, Lit2, Lit3
⇒ total of 12 calibration scenarios (e.g. CalD2)
Multi-Variate Objective Function J
Soil Moisture andMicrowaveRadiances
Calibration
Problem
θ-pol
Parameters
J
BrightnessTemperatures
RTM Parameters
Remaining Biases
Conclusions
GMAO-18Sep12 – 13 / 28
For 6 angles, 2 polarizations, 2 orbit directions (∑
θ
∑
p
∑
d), penalize
� differences in long-term mean� differences in long-term standard deviation� deviations from prior (literature) parameter values
Search algorithm: particle swarm optimization; 1 year (2010)
J = Wm
∑
θ
H,V∑
p
A,D∑
d
Nθ,p,d
N
(< Tbo > − < Tb(α) >)2θ,p,dσ2m
}
J<.>,o
+Ws
∑
θ
H,V∑
p
A,D∑
d
Nθ,p,d
N
(s[Tbo]− s[Tb(α)])2θ,p,dσ2s
}
Js[.],o
+Wα
1
Nα
Nα∑
i=1
(α0,i − αi)2
σ2α0,i
}
JαNα parameters simul-taneously optimized ateach gridcell individually
Globally Averaged J
Soil Moisture andMicrowaveRadiances
Calibration
Problem
θ-pol
Parameters
J
BrightnessTemperatures
RTM Parameters
Remaining Biases
Conclusions
GMAO-18Sep12 – 14 / 28
4Lit1 Lit2 Lit3
Lit CalA CalB CalC CalD10
1
102
103
104
J [−
]
Scenario
a)
� ⇑ J is reduced after calibration� ⇑ CalA (only calibrating h) is
clearly not optimal� ⇒ mean bias (J<.>,o) is the
largest component, and mostreduced through calibration
� ⇒ standard deviation difference(Js[.],o) optimized, but stronglyconstrained by LSM variations
Lit CalA CalB CalC CalD10
1
102
103
104
J <.>
,o [−
]
Scenario
b)
Lit CalA CalB CalC CalD10
1
102
J s[.],
o [−]
Scenario
c)
Lit CalA CalB CalC CalD10
0
101
J α [−]
Scenario
d)
Brightness Temperatures
Soil Moisture andMicrowaveRadiances
Calibration
BrightnessTemperatures
Global
Sensitivity
RTM Parameters
Remaining Biases
Conclusions
GMAO-18Sep12 – 15 / 28
CLSM/RTM minus SMOS Tb - Mean
Soil Moisture andMicrowaveRadiances
Calibration
BrightnessTemperatures
Global
Sensitivity
RTM Parameters
Remaining Biases
Conclusions
GMAO-18Sep12 – 16 / 28
H-pol, 42.5 o, ascending, 1/1/2011-1/1/2012 (validation period)Lit1 (SMAP) Lit2 (L-MEB)
Lit3 (EC-SMOS) Calibrated
−50 −40 −30 −20 −10 0 10 20 30 40 50 [K]
� mostly unbiased long-term mean in the period after calibration� bias independent of incidence angle and pol (not shown)
CLSM/RTM minus SMOS Tb - Standard Deviation
Soil Moisture andMicrowaveRadiances
Calibration
BrightnessTemperatures
Global
Sensitivity
RTM Parameters
Remaining Biases
Conclusions
GMAO-18Sep12 – 17 / 28
H-pol, 42.5 o, ascending, 1/1/2011-1/1/2012 (validation period)Lit1 (SMAP) Lit2 (L-MEB)
Lit3 (EC-SMOS) Calibrated
−10 −8 −6 −4 −2 0 2 4 6 8 10 [K]
� preserved temporal variability, while reducing the bias
Sensitivity of Tb H(42.5o) to Soil Moisture
Soil Moisture andMicrowaveRadiances
Calibration
BrightnessTemperatures
Global
Sensitivity
RTM Parameters
Remaining Biases
Conclusions
GMAO-18Sep12 – 18 / 28
Lit CalA CalB CalC CalD
−2
−1.5
−1
−0.5
0
dTb H
/dS
M [K
/ 0.
01 m
3 .m−
3 ]
Scenario
Lit1 Lit2 Lit3
� rule of thumb for bare soil:dSM ∼ 0.01 m3.m−3 corresponds to dTbH (40o) ∼ 2-3 K
� Lit3 clearly lacks sensitivity, because of a too high h-parameter� after calibration, the sensitivity is reasonable and closest to Lit2,
regardless of prior values
→ Important for assimilation: a difference between observed andsimulated Tb will be mapped to a change in soil moisture
RTM Parameters
Soil Moisture andMicrowaveRadiances
Calibration
BrightnessTemperatures
RTM Parameters
Global
N America
Veget. class
τ
Remaining Biases
Conclusions
GMAO-18Sep12 – 19 / 28
Global Averages
Soil Moisture andMicrowaveRadiances
Calibration
BrightnessTemperatures
RTM Parameters
Global
N America
Veget. class
τ
Remaining Biases
Conclusions
GMAO-18Sep12 – 20 / 28
4Lit1 Lit2 Lit3
Lit CalA CalB CalC CalD0
1
<h>
[−]
Scenario
a)
Lit CalA CalB CalC CalD0
0.5
1
<τ>
[−]
Scenario
b)
Lit CalA CalB CalC CalD0
0.2
ω [−
]
Scenario
c)
Parameter estimates are moreconsistent for the mostcomplex calibration scenario.
Param A B C D
hmin X X X X∆h X X X Xω X XbH X X∆b X X
� < h > = time-averaged(soil moisture dependent)
� < τ > = time-averaged(LAI dependent),polarization-averaged
� ω = constant
Spatial Patterns: < τ >, < h >, ω
GMAO-18Sep12 – 21 / 28
BE
FO
RE
(Lit2
)A
FT
ER
(Cal
D2)
0 0.06 0.12 0.18 0.24 0.3
0 0.3 0.6 0.9 1.2 1.5
0 0.04 0.08 0.12 0.16 0.2
SM
OS
(ret
rieva
l)
� from homogeneous to locally optimized parameters
� vegetation/soil/climate patterns
(need RTM recalibration with new CLSM parameters)
� calibrated and SMOS τ have a similar magnitude
Spatial R: < τ >, < h >, ω
Soil Moisture andMicrowaveRadiances
Calibration
BrightnessTemperatures
RTM Parameters
Global
N America
Veget. class
τ
Remaining Biases
Conclusions
GMAO-18Sep12 – 22 / 28
L1 L2 L3 A1 A2 A3 B1 B2 B3 C1C2C3D1D2D3L1L2L3A1A2A3B1B2B3C1C2C3D1D2D3
Scenario
Sce
nario
a)
<h> [−]
L1 L2 L3 A1 A2 A3 B1 B2 B3 C1C2C3D1D2D3L1L2L3A1A2A3B1B2B3C1C2C3D1D2D3
Scenario
Sce
nario
c)
−0.2
0
0.2
0.4
0.6
0.8
1
ω [−]
L1 L2 L3 A1 A2 A3 B1 B2 B3 C1C2C3D1D2D3L1L2L3A1A2A3B1B2B3C1C2C3D1D2D3
Scenario
Sce
nario
b)
<τ> [−]
� spatial parameter patterns arereasonable consistent acrossmost calibration scenarios
� big differences with prior patterns
Optimal < τ >, < h >, ω
GMAO-18Sep12 – 23 / 28
Lit1 Lit2 Lit3 CalD2
1 2 3 4 5 6 7 8 9 10 12 14 160
1
<h>
[−]
IGBP Vegetation Class
a)
1 2 3 4 5 6 7 8 9 10 12 14 160
1
2
<τ>
[−]
IGBP Vegetation Class
b)
1 2 3 4 5 6 7 8 9 10 12 14 160
0.1
0.2
ω [−
]
IGBP Vegetation Class
c)
IGBP land cover
1 Evergreen Needleleaf Forest2 Evergreen Broadleaf Forest3 Deciduous Needleleaf Forest4 Deciduous Broadleaf Forest5 Mixed Forest6 Closed Shrublands7 Open Shrublands8 Woody Savannas9 Savannas10 Grasslands12 Croplands14 Crop and Natural Vegetation16 Barren or Sparsely Vegetated
� CalD2 = Lit2 as prior,
calibrate hmin, hmax, bH , bV , ω
� reasonable optimal parameters;
large within-class variability
� when using class-averaged (as
opposed to local) calibrated
parameters, the RTM still performs
better than with Lit1, Lit2 or Lit3→ use aggregate CalD2 pa-
rameters in unobserved regions
Vegetation Opacity τ
Soil Moisture andMicrowaveRadiances
Calibration
BrightnessTemperatures
RTM Parameters
Global
N America
Veget. class
τ
Remaining Biases
Conclusions
GMAO-18Sep12 – 24 / 28
01/01/2010 01/07/2010 01/01/2011 01/07/2011 31/12/2011
0.10.20.30.40.5
τ [−
]
- Little River, Georgia (• SMOS retrievals; – CLSM/RTM)- Walnut Gulch, Arizona (� SMOS retrievals; – CLSM/RTM)
⇒ Vegetation opacity values from the calibrated CLSM/RTM distiguishwell between more and less vegetated areas, and are consistent withSMOS retrievals
Remaining Biases
Soil Moisture andMicrowaveRadiances
Calibration
BrightnessTemperatures
RTM Parameters
Remaining Biases
Conclusions
GMAO-18Sep12 – 25 / 28
Hovm oller Plots: CLSM/RTM minus SMOS Tb
GMAO-18Sep12 – 26 / 28
Ascending H-pol Ascending V-pol
−10 −5 0 5 10 [K]
� 6-angle average (32.5, . . . , 57.5)o
� using full-pol SMOS data only (early 2010: switch dual-full pol)
Clear seasonal biases remaining,
partly due to CLSM/RTM Tb biases, partly due to SMOS Tb biases
For example: ascending V-pol in North America:
� simulated TbV cannot exceed CLSM Ts, irrespective of the parameters
� ascending SMOS sees military radar, remaining RFI contamination may be present
Hovm oller Plots: CLSM/RTM minus SMOS Tb
GMAO-18Sep12 – 27 / 28
Ascending H-pol Ascending V-pol
Descending H-pol Descending V-pol
−10 −5 0 5 10 [K]→ Warm/cold bias in asc/desc
SMOS instrument calibration
Conclusions
Soil Moisture andMicrowaveRadiances
Calibration
BrightnessTemperatures
RTM Parameters
Remaining Biases
Conclusions
GMAO-18Sep12 – 28 / 28
� Biases between SMOS and CLSM/RTM Tb, when using literature
parameters
� RTM calibration/validation (split sample 2010 / 2011)
� multi-angular; multi-polarization; multi-orbit; local
� objective function: minimize
� differences in long-term mean;
� differences in long-term standard deviation;
� deviations from prior (literature) parameter values.
� long-term unbiased Tb, seasonal to diurnal biases remaining
� realistic effective RTM-parameter patterns
� Future
� new CLSM climatology will need new calibration
� Tb assimilation, SMAP L4 SM product
Questions? In review, Journal of Hydrometeorology; [email protected]