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DA 22.-31.3. 2006 Surface Analysis (II) M. Drusch Room TT 063, Phone 2759.

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DA 22.-31.3. 2006 Surface Analysis (II) M. Drusch Room TT 063, Phone 2759
Transcript

DA

22.

-31.

3. 2

006

Surface Analysis (II)

M. Drusch

Room TT 063, Phone 2759

DA

22.

-31.

3. 2

006

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

DA

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-31.

3. 2

006

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

DA

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006

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

DA

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-31.

3. 2

006

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

DA

22.

-31.

3. 2

006

Evaporation and the Hydrological ‚Rosette‘

Rainfall starts

Rainfall ends

3: M

otiv

atio

n

DA

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006

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

DA

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3. 2

006

ECMWF long-term forecasts (from ENSEMBLES project)

3. M

otiv

atio

n

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)

DA

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006

ECMWF long-term forecasts (from ENSEMBLES project)

3. M

otiv

atio

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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3. 2

006Operational OI soil moisture analysis

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

chn

iqu

e

DA

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3. 2

006Operational OI soil moisture analysis:

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

chn

iqu

e

DA

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3. 2

006Operational OI soil moisture analysis:

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

chn

iqu

e

DA

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3. 2

006Operational OI soil moisture analysis:

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

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

I te

chn

iqu

e

DA

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3. 2

006Operational OI soil moisture analysis:

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.

DA

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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]

DA

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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 [%]

DA

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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

DA

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3. 2

006Validation against OK Mesonet

observations 3.

Eva

luat

ion

DA

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006

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

DA

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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.

DA

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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

DA

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-31.

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006

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

DA

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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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006

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],

DA

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006

Oklahoma data sets 20024.

Rem

ote

sen

sin

g as

pec

ts

DA

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3. 2

006Bias correction / CDF matching

x x’

CDFM(x’) = CDFS(x)

Cumulative DistributionFunction

TMIECMWF

4. R

emot

e se

nsi

ng

asp

ects

DA

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3. 2

006TMI soil moisture transformation

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 %

4. R

emot

e se

nsi

ng

asp

ects

DA

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006

Corrected TMI soil moisturevolumetric surfacesoil moisture [%]

for 06/06/2004

the modelled first guess

TMI Pathfinder data

corrected TMI data set

4. R

emot

e se

nsi

ng

asp

ects

DA

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006

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

DA

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006

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.

DA

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006

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.

DA

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006

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)

DA

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3. 2

006

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

DA

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3. 2

006

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


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