New developments in Soil Moisture Analysis at DWD filemoisture through stomata resistance....

New developments in Soil Moisture Analysis at DWD

M. Lange, R. Hess, W. WergenDeutscher Wetterdienst,

Offenbach, Germany

LM user meeting, Langen 2007


Outline

• Motivation for SMA in GME• Parameterisation of the variational SMA

scheme.• Experiences from operations

– Impact on screen level temperature– Accumulation of soil ice by the SMA


Almo exceeds LME Precipitation due to soil moisture initialisation

Running monthly mean precipitation and evaporation Daily soil moisture top and bottom layers

Top layer LME

Top layer ALMO

Bottom layer LME

Bottom layer ALMO

Positive feedback of dry/wet soil on precipitation forecast

Dry soil

Less cloudsand rain, strong

radiation input

EnhancedDry out

Desiccationof plants,

Weakevaporation

High vegetationcover, Strongevaporation

Refillingof soil

Wet soil

More cloudsand

precipitation,


Long term dry out in GME leads to positive Bias in T2m during summer

Development of a soil moisture analysisfor GME

• Soil moisture measurements are not available on a dense network, Radar observations even not on theglobal scale.

• Explore forecast errors in screen level parameter to correct initial soil moisture.

• A variational soil moisture assimilation scheme runsdaily for the LME model. This requires at least twoadditional model forecast runs. This iscomputationally too expensive for GME.



2d var (z,t) soil moisture analysis applied in LME

)()()()( 221

221 obs

mmTobs

mmbT

b TTOTTwwBwwJ −−+−−= −−

44 344 21errorfcmT

bmobs

mT

mTmTT

mTbana wTTOBOww2

221

211

21

2 ))(()( −Γ+ΓΓ+= −−−−

Cost function penalizes deviations from observations and initial soil moisture content

Analysed soil moisture depends on T2m forecast error and sensitivity ∂T2m/∂w

0=∇J

)00:0()00:15,00:12(2

wT m

∂∂

Parameterisation of ∂T2m(12:00,15:00)/∂w(0:00) with ∂Lhfl/∂w from penman eqn. at obs time

ShflLhfl Δ−≅Δ

a

msp r

TTcShfl )( 2Δ−Δ=Δ ρ

GShflLhflRn −−−=0Surface energy balance equation

Soil moisture variation:

ma

p Trc

Lhfl 21

Δ⎟⎠⎞

⎜⎝⎛ −

−≅Δααρ


ΔLhfl (W/m2)

ΔT2m

(10*

K)

)00:12(~)00:12( 2mTLhfl ΔΔ

LAI

slaf f

rrr

+=

wathumtemradssss FFFFrrrr ***)( 1max,

1min,

1max,

1 −−−− −+=

fa

apotsnowipl rr

rEfffLhfl+

−−= )1)(1(


tlppwpfcap

pwprootwat fww

wwF

)( −

−=

Evaporation from vegetation depends on soilmoisture through stomata resistance

Sensitivity of 2m temperature on soil moisture

root

rootk

pwproot

s

s

s

LAIfak zdz

wwr

rr

frrLhfl

wLhfl ,

max,

)1(1)( −

−+

=∂∂


root

rootk

tlppwpfcapk

wat

zdz

fwwwF ,

)(1

−=

∂∂ Root density constant with

soil depth!

root

rootk

pwproot

s

s

s

LAIfap

a

k

m

zdz

wwr

rr

frrLhfl

cr

wT ,

max,

2 )1(1)(1 −

−+

⎟⎠⎞

⎜⎝⎛−

=∂∂

αα

ρ


Validation against model forecast

• Variaton of initial soil moisture at selectedgridpoints in the whole range betweenplant wilting point and field capacity.

• Compare Sensitivity of T2m on soilmoisture with parameterisation.


Horizontal decoupling hypothesis is valid


All gridpointsLhfl(11:00-12:00) > 200 W/m2

Parameterisation can be applied during radiation conditions in the whole range between plant wilting point and field capacity

No further need for additional model runs !dT2m(12:00)/dwb(0:00) (model)

dT2m/dw

b(12:00) (theorie)

dT2m/dw

b(theorie)

dT2m/dwb (model)

Date: 20050525


Status and further plan

• Development phase almost finished.• First validation experiments with the LM

model are planned to start soon.• Implementation in GME should be ready

end of summer 2007.


Experiences from operations

• Impact of SMA in LME on bias in 2m temperature.

• SMA affects soil ice content.


Routine No SMA

Bias T2m on LM domain, avg 12:00, 15:00 Rmse T2m on LM domain, avg 12:00, 15:00

SMA reduces effectively Bias and Rmse of T2m in LME


Accumulation of soil ice by adding water in SMA

Water and ice conten layer 5 (27-81cm), LME, gridpoint (323,354)

H2O

(mm

)

Soil water

Soil ice


Modification of SMA

1.) Frozen soil at initial time of the next forecast: Tso(vv=24hr)<T0Apply only negative SMA increments:Wso(ana)=Min( Wso(mod)+ δwa, Wso(mod) )

2.) Soil unfreezed at begin of next forecast but soil frozenat time of analysis: Tso(vv=24hr)>T0, Tso(t=0)<T0

Positive soil moisture increments are possible if maximum liquid water content is notexceeded:

Wso(ana)=Min(Wso(mod)+δwa, Wlmax)


Modification of SMA

If soil is frozen at begin of the next forecast:Apply only negative SMA increments:Wso(ana)=Min( Wso(mod)+ δwa, Wso(mod) )

If soil is unfreezed at begin of next forecast but soiltemperature below freezing point at time of analysis:

Positive soil moisture increments are possible if maximum liquid water content is notexceeded and soil ice occurs.

Wso(ana)=Min(Wso(mod)+δwa, Wlmax)


Soil ice content layer 5 (27-81cm)

Routine No SMA

New SMA

Fast reduction of soil ice when T2m is sensitive on soil moisture


Conclusions• Soil moisture analysis is appropriate to prevent from permanent

drifts and to reduce forecast errors in screen level parameter. • A parameterisation of the SMA scheme is developed for GME to

save computational costs for additional model runs required in thevariational method.

• The parameterisation can be applied successfully under conditionswhen transpiration from vegetation dominates.

• Side effects should be considered when model changes areintroduced.

• Accumulation of soil ice through the SMA is fixed by suppressingpositive increments in frozen soil.

Acknowledgement• Thanks to Emanuele Zala from meteo

swiss who provided the Almo data for2006.



Almo exceeds LME Precipitation due to soil moisture initialisation

Running monthly mean precipitation and evaporation

New developments in Soil Moisture Analysis at DWD filemoisture through stomata resistance....

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