Surface Analysis (II)
description
Transcript of Surface Analysis (II)
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Surface Analysis (II)
M. Drusch
Room TT 063, Phone 2759
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Overview1. Motivation
2. Screen level analysis (2 m T and relative humidity)
3. Operational soil moisture analysis (‘local’ Optimum Interpolation)
- Motivation
- OI technique
- Evaluation of the analysis and the impact on the forecast
4. Satellite observations and future developments
- Remote sensing aspects
- Results from a Nudging experiment
- Design of the future surface analysis
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Screen-level analysis: 2D univariate statistical interpolation
N
i 1ii
aj XwX
1. Increments Xi are estimated at each observation location i from the observation and the interpolated background field (6 h or 12 h forecast).
2. Analysis increments Xia at each model grid point j are calculated from:
3. The optimum weights wi are given by: (B + O) w = b
b : error covariance between observation i and model grid point j (dimension of N observations)
B : error covariance matrix of the background field (N × N observations) B(i1,i2) = 2
b ×(i1,i2) with the horizontal correlation coefficients (i1,i2) and b = 1.5 K / 5 % rH the standard deviation of background errors.
O : covariance matrix of the observation error (N × N observations): O = 2
o × I with o = 2.0 K / 10 % rH the standard deviation of obs. errors
2
ii21 d
r
2
1expi,iμ 21
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Screen-level analysis: Quality controls and technical aspects
1. Number of observations N = 50, scanned radius r = 1000 km.
2. Gross quality checks as rH [2,100] and T > Tdewpoint
3. Observation points that differ more than 300 m from model orographie are rejected.
4. Observation is rejected if it satisfies: with = 3
5. Number of used observations varies from 4000 to 6000 (40% of the available observations) every 6 hours.
6. Increments are computed: q = (B + O)-1 X and bTq
2b
2oi σσγX
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Overview1. Motivation
2. Screen level analysis (2 m T and relative humidity)
3. Operational soil moisture analysis (‘local’ Optimum Interpolation)
- Motivation
- OI technique
- Evaluation of the analysis and the impact on the forecast
4. Satellite observations and future developments
- Remote sensing aspects
- Results from a Nudging experiment
- Design of the future surface analysis
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Evaporation and the Hydrological ‚Rosette‘
Rainfall starts
Rainfall ends
3: M
otiv
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n
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Motivation Climate
Simulated July surface temperature for
A) wet soil case (actual evapotranspiration is set to potential evapotranspiration)
B) dry soil case (no evapotranspiration)
GLAS atmospheric GCM , Shukla and Mintz [1982]
Mot
ivat
ion
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ECMWF long-term forecasts (from ENSEMBLES project)
3. M
otiv
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1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 200418
20
22
24
26
28
soil moisture
soil moisture 1&2
root zone soil moisture
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
-10
-5
0
5
10
15
20
25
30
OC
temperature
T2m
dew point temp
volumetric soil moisture 2 m temperatures
[%]
[º C
elsi
us]
Systematic errors in the land surface scheme result in a (dramatic) dry downwith summer values close to the permanent wilting point.The corresponding 2 m temperature forecasts show a strong warm bias exceeding 10 K during summer and 5 K during winter.
(monthly averages for North America)
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ECMWF long-term forecasts (from ENSEMBLES project)
3. M
otiv
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n
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
-120
-100
-80
-60
-40
-20
0
heat fluxes
latend heat
sensible heat
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
0.35
0.4
0.45
0.5
0.55
0.6
0.65
cloud cover
turbulent surface fluxes fractional cloud coverage
[W m
-2]
[%]
Latent heat flux is substantially reduced during summer, sensible heatflux is almost doubled. Due to less moisture in the atmosphere cloud coverage is also reduced. Surface pressure is reduced (not shown).The model has to be re-initialized with analysed soil moisture to preventfrom drifting into an unrealistic state.
(monthly averages for North America)
DA
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The analysis increments from the screen level analysis are used to produce increments for the water content in the first three soil layers (root zone):
and for the first soil temperature layer:
baι
baii rHrHbTTaΔΘ
ba T-TcT Superscripts a and b denote analysis and background ( = forecast), respectively, i denotes the soil layer.Coefficients ai and bi are defined as the product of optimum coefficients i and i minimizing the variance of analysis error and of empirical functions F1, F2, F3.
[Douville et al. (2000), Mahfouf (1991)]
3. O
I te
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Optimum coefficientsCoefficients a, b and c can be written as: a = Cv × × F1F2F3
b = Cv × × F1F2F3
c = (1 - F2)F3
with: Cv vegetation fraction (clow +chigh),
ΘrH,rHT,ΘT,
2
brH
arH
bT
bΘ ρρρ
σ
σ1
Φσ
σα
ΘT,rHT,ΘrH,
2
bT
aT
brH
bΘ ρρρ
σ
σ1
Φσ
σβ
2rHT,
2
brH
arH
2
bT
aT ρ-
σ
σ1
σ
σ1
F1, F2, F3 empirical functions
From univariate statistical interpolation theory (Daley, 1991). errors, correlationof background errorsbetween variables x and y.
3. O
I te
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e
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Statistics of background errorsbθσ Based on forecast differences between day 1 and 2 of the net surface
water budget.33b
θ mm 01.0σ
aσ 222 oba σ
1
σ
1
σ
1Standard deviation of analysis error:
K 1.2σaT
% 4.47σarH
Statistics of background errors for soil moisture derived from theMonte Carlo Experiments
coefficient
value -0.82 -0.92 -0.90 0.83 0.93 0.91 -0.99
1Tθρ3Tθρ
2Tθρ3rHθρ
2rHθρ1rHθρ rHTρ
3. O
I te
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Empirical functions
1. Winter / night time correction: M : cos mean solar zenith angle
2. Weak radiative forcing correction: r : atmospheric transmittance rmin: 0.2 rmax: 0.9
S0 : solar constantM : cos mean solar zenith angle : mean dw surface solar radiation forecast
gR
3. Mountain correction: Z : model orographie Zmin : 500 m Zmax: 3000 m
0.5μλtanh12
1F M1 = 7
Μμ
M0
g
r μS
Rτ
rminrmax
rminr
ττ
ττ
F2 =
0 r < rmin
1 r > rmax
rmin < r < rmax
2
maxmin
max
ZZ
ZZF3 =
0 Z > Zmax
1 Z < Zmin
Zmin < Z < Zmax
3. O
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Further limitations
Soil moisture increments are set to 0 if:
1. The last 6 h precipitation exceeds 0.6 mm.
2. The instantaneous wind speed exceeds 10 m s-1.
3. The air temperature is below freezing.
4. There is snow on the ground.
3. O
I te
chn
iqu
e
Analysed screen level parameters are used as proxy ‘observations’ for the root zone soil moisture analysis. The relationship between 2 m temperature andrelative humidity and soil moisture is often rather weak and intermittent.
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Impact study: Soil moisture increments3.
Eva
luat
ion
80°S80°S
70°S 70°S
60°S60°S
50°S 50°S
40°S40°S
30°S 30°S
20°S20°S
10°S 10°S
0°0°
10°N 10°N
20°N20°N
30°N 30°N
40°N40°N
50°N 50°N
60°N60°N
70°N 70°N
80°N80°N
160°W
160°W 140°W
140°W 120°W
120°W 100°W
100°W 80°W
80°W 60°W
60°W 40°W
40°W 20°W
20°W 0°
0° 20°E
20°E 40°E
40°E 60°E
60°E 80°E
80°E 100°E
100°E 120°E
120°E 140°E
140°E 160°E
160°E
-250
-200
-150
-100
-50
-10
10
50
100
150
200
250
experiment 1: Optimal Interpolation, atmospheric 4DVarvs
experiment 2: Open Loop (no analysis), atmospheric 4DVar
OI
[mm]
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Humidity increments3.
Eva
luat
ion
80°S80°S
70°S 70°S
60°S60°S
50°S 50°S
40°S40°S
30°S 30°S
20°S20°S
10°S 10°S
0°0°
10°N 10°N
20°N20°N
30°N 30°N
40°N40°N
50°N 50°N
60°N60°N
70°N 70°N
80°N80°N
160°W
160°W 140°W
140°W 120°W
120°W 100°W
100°W 80°W
80°W 60°W
60°W 40°W
40°W 20°W
20°W 0°
0° 20°E
20°E 40°E
40°E 60°E
60°E 80°E
80°E 100°E
100°E 120°E
120°E 140°E
140°E 160°E
160°E
-15
-10
-7.5
-5
-2.5
-0.5
0.5
2.5
5
7.5
10
15
80°S80°S
70°S 70°S
60°S60°S
50°S 50°S
40°S40°S
30°S 30°S
20°S20°S
10°S 10°S
0°0°
10°N 10°N
20°N20°N
30°N 30°N
40°N40°N
50°N 50°N
60°N60°N
70°N 70°N
80°N80°N
160°W
160°W 140°W
140°W 120°W
120°W 100°W
100°W 80°W
80°W 60°W
60°W 40°W
40°W 20°W
20°W 0°
0° 20°E
20°E 40°E
40°E 60°E
60°E 80°E
80°E 100°E
100°E 120°E
120°E 140°E
140°E 160°E
160°E
-5
-2
-1.5
-1
-0.5
-0.1
0.1
0.5
1
1.5
2
5
OI mean humidity increments [%]
[%]
OL – OI difference of mean humidity increments [%]
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Forecast skills3.
Eva
luat
ion
Temperature at 1000 hPa
grey: OIblack: OLsolid: North Americadotted: Europedashed: East Asia
Root-mean-square error E
area height 24 h 72 h 120 h 168 h 216 h
NorthernHemisphere
1000 0.1 0.1 0.5 10.0 1.0
850 0.1 0.1 5.0 - 5.0
700 5.0 1.0 - - 10.0
Europe 1000 0.1 0.1 0.1 - -
850 0.1 0.1 5.0 - -
700 - 10.0 - - -
East Asia 1000 0.1 0.1 5.0 5.0 0.5
850 0.1 0.1 - - 0.2
700 - - - - 5.0
North America
1000 0.1 0.1 - - -
850 0.1 0.1 - - -
700 5.0 - - - -
Significance levels for the Sign test
The proxy ‘observations’ are efficient in improving the turbulent surface fluxesand consequently the weather forecast on large geographical domains.
2afE jj
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observations 3.
Eva
luat
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Validation of forcing data3.
Eva
luat
ion
area averages for Oklahoma
daily precipitationdaily precipitationmodel forecast (OI)observations
total amount of rainfall:June 87.3 mm model on 19 days
87.8 mm observations on 9 daysJuly 110. mm model on 26 days 79. mm observations on 20 days
daily downward shortwave radiationmodel forecast (OI)observations
Correlation : 0.85Bias : - 0.7 Wm-2
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Validation of soil moisture3.
Eva
luat
ion
area averages for Oklahoma
surface soil moisture
model forecast (OI)
observationsmodel forecast (OL)
• Too quick dry downs (model problem).• Too much precip in July (model problem).• Too little water added in wet conditions (analysis problem).• NO water removed in dry conditions (analysis problem).
root zone soil moisture
model forecast (OI)
observationsmodel forecast (OL)
• Precipitation errors propagate to the root zone.• Analysis constantly adds water.• The monthly trend is underestimated.
The current analysis fails to produce more accurate soil moisture estimates.
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Overview1. Motivation
2. Screen level analysis (2 m T and relative humidity)
3. Operational soil moisture analysis (‘local’ Optimum Interpolation)
- Motivation
- OI technique
- Evaluation of the analysis and the impact on the forecast
4. Satellite observations and future developments
- Remote sensing aspects
- Results from a Nudging experiment
- Design of the future surface analysis
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Wavelengths and soil moisture4.
Rem
ote
sen
sin
g as
pec
ts Wavelength pros Cons
IR • good temporal resolution• good spatial resolution
• cloud free situations only• model is needed to infer the energy balance at the surface (indirect information)
Microwave(scatterometer)
• acceptable temporal resolution• acceptable spatial resolution• all weather tool
• strong dependency on incidence angle• effects of surface roughness and vegetation• radiative transfer complex
Microwave(radiometer)
• acceptable temporal resolution• all weather tool• most direct signal• radiative transfer established
• coarse spatial resolution
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ERS-1/2 scatterometer derived soil moisture
4. R
emot
e se
nsi
ng
asp
ects
Data set produced by:Institute of Photogrammetryand Remote Sensing, Vienna University of Technology
Basis:ERS scatterometer backscattermeasurements
Method:change detection method forextrapolated backscatter at40º reference incidence angle
Output:topsoil moisture content in relativeunits (0 [dry] to 1 [wet])
http://ipf.tuwien.ac.at/radar/ers-scat/home.htm
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AMSR-E derived soil moisture4.
Rem
ote
sen
sin
g as
pec
ts
Typical day with coverage of 28 half orbits.(http://nsidc.org/data/docs/daac/ae_land_l2b_soil_moisture.gd.html)
Data set produced by:National Snow and Ice Data Center(NSIDC), Boulder, Colorado
Basis:brightness temperatures at 10.7 and 18.7 GHz horizontal and vertical polarization
Method:change detection method fornormalized polarization ratios
Output:surface soil moisture [g cm-3],vegetation water content [kg m-3]
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TMI Pathfinder Data Set
0 5 10 15 20 25 30 35 40 45(%)
July 2nd, 1999
(Gao et al. 2006)
4. R
emot
e se
nsi
ng
asp
ects
Data set produced by:Dept. Civil and Environmental Engineering,Princeton University, NJ
Basis:brightness temperatures at 10.65 GHz horizontalpolarization
Method:physical retrieval based onland surface microwave emission model andauxiliary data sets from theNorth American Land Data Assimilation Study project
Output:surface soil moisture [cm3 cm-3],
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Oklahoma data sets 20024.
Rem
ote
sen
sin
g as
pec
ts
DA
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x x’
CDFM(x’) = CDFS(x)
Cumulative DistributionFunction
TMIECMWF
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ects
DA
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r2 = 0.66r2 = 0.69
r2 = 0.01r2 = 0.18
• CDF matching reduces systematic errors: The bias has been removed and the dynamic range has been adjusted.• The random error may increase.
transfer funcion03/2002-10/2002
x‘-x
x
Bias: -11.67 %
Bias: -0.35 %
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ects
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Corrected TMI soil moisturevolumetric surfacesoil moisture [%]
for 06/06/2004
the modelled first guess
TMI Pathfinder data
corrected TMI data set
4. R
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asp
ects
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Nudging set up4.
TM
I N
ud
gin
g ex
per
imen
t
00 03 06 09 12 15 18 21 00 03 06 09 12 15 18 21 00
Delayed cut-off
4D-Var (12 h)
AN AN
FC FC
AN
FC
TMI sampling
period (daily)soil
moistureanalysis
1/4 2/4 1/4 2/4
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4. T
MI
Nu
dgi
ng
exp
erim
ent
Validation of soil moisture
area averages for Oklahoma
surface soil moisture
• Nudging / satellite data remove water effectively and produce a realistic dry down.• Nudging the satellite results in the most accurate surface soil moisture estimate.
root zone soil moisture
• The information introduced at the surface propagates to the root zone.• The monthly trend is well reproduced using the nudging scheme.
Satellite derived soil moisture improve the soil moisture analysis and results in the most accurate estimate.
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Forecast skill4.
TM
I N
ud
gin
g ex
per
imen
t
correlation (observation / fc) bias
OI
OLNudging
rH
T
rH
TThe impact of the satellite data on the forecast quality (of screen level variables) is neutral (correlation). The biases obtained from the nudging experiment are slightly higher when compared against the OI and lowerwhen compared against the OL.
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Soil moisture increments4.
TM
I N
ud
gin
g ex
per
imen
t
[mm]accumulated increments over June and July 2002
OptimalInterpolation(2 m T and RH)
Nudging(TMI soil moisture)
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The future Surface Data Assimilation System
4. F
utu
re s
urf
ace
anal
ysis
00 03 06 09 12 15 18 21 00 03 06 09 12 15 18 21 00
Delayed cut-off
4D-Var (12 h)
ANAN
FCFC
AN ANEarly Delivery Analysis4D-Var (6 h)
00 UTC FC
12 UTC FC
SDAS
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Land Data Assimilation Systems LDAS
Development of advanced systems for the assimilation of satellite observationsto improve the analysis of the state of the land surface (and consequently the numerical weather forecasts).
North America : NLDAS, Globe : GLDAS (NASA GSFC, see http://ldas.gsfc.nasa.gov)
Canada: CLDAS(Meteorological Service of Canada)
Europe: ELDAS(KNMI, see http://www.knmi.nl/samenw/eldas)
4. F
utu
re s
urf
ace
anal
ysis