Soil moisture analysis at DWD - Cosmo Public areaSMA Ecoclimap (T2m, Rh2m) depending on experim. TERRA (2 layers) LM rotated (0.2 x 0.2 ) GME / LM Europe Physiografic data (LAI, plcov,

LM(E) user meeting, Langen 2006

Soil moisture analysis at DWD

M. Lange, R. Hess, W. Wergen

DWD

Experiences from operations and ELDAS

Outline• Motivation for soil moisture analysis• The method• Experiences with LM(E)• Results from Eldas• Conclusions

LM(E) user meeting, Langen 2006

LM(E) user meeting, Langen 2006

(E-TESSEL) – TESSEL: JJA

Increased

Latent heat flux(Wm-2)

increased

Low cloudcover

Small impact onHigh cloud coverMedium cloudcover

Due to:

Lower canopy resistance

LM(E) user meeting, Langen 2006

x

2d var (z,t) soil moisture analysis

LM(E) user meeting, Langen 2006

)()()()( 221

221 obs

mmTobs

mmbT

b TTOTTBJ −−+−−=−− ηηηη

))(()(

0

221

211

21

2 bmobs

mT

mTmTT

mTbana TTOBO

J

ηηη −Γ+ΓΓ+=

=∇−−−−

QMAMB

BOJATt

mTT

mT

+=

+ΓΓ=∇=+

−−−

1

112

12

2 )(

Cost function penalizes deviations from observations and initial soil moisture content

Calculate minimum of cost function analytically

Analysis error and update of Background error covariance matrix:

)00:0(/)00:15,00:12(),()( 22222 dwbdTTT mmTbmTbmm =Γ−Γ+= ηηη

Assumption: Linearity between variation of T2m and wb

• LME: SMA against No SMASix week Test Period June/July 2004

• LME vs. LMParallelsuite April-November 2005

• Climatology of soil moisture increments

LM user meeting, Langen 2006

Experiences from operations

SMA (T2m) reduces effectively Bias and Rmse of T2m in LME

LM user meeting, Langen 2006

No SMA vs. SMA-T2m, Bias, Rmse T2m(12:00, 15:00)

SMA-T2m: LME beats LM

LM user meeting, Langen 2006

LM user meeting, Langen 2006

Hydrological annual budget of SMA far from being closed

LM user meeting, Langen 2006

Large positive bias for T2m in 2004, 2005

in spite of soil moisture analysis

2001 2006

Eldas

• Combine european expertise in soil moisture assimilation to buildand implement a soil moisture assimilation system at a number ofeuropean weather centers.

• Build a demonstration database covering at least one seasonalcycle.

• Validate the soil moisture fields against independent observations.

• Assess the added value of soil moisture assimilation to predict thehydrological cycle on the continental scale with focus on predictionof droughts and the risk of floodings.

Development of a European Land Data Assimilation System to predict droughts and floods

LM user meeting, Langen 2006

Eldas model experiments covering the periodApril 15 – December 31 2000

3 hourlyPrecipitation

data

Externalforcing

EcoclimapSMA(T2m, Rh2m)depending on

experim.

TERRA(2 layers)

LM rotated(0.2° x 0.2°)

EuropeGME / LM

Physiograficdata (LAI, plcov,

zgeo…)

Soil moistureassimilation

system

Land surfacescheme

GridEldasDomain

Model base

LM user meeting, Langen 2006

Experimental setup

Model experiments

++Precipìtationforcing

++Rh2m++++T2m

No SMA(Soil moisture fromprevious forecast, Initialdata from SMA-T2m)

SMA-T2m+Rh2m+Precip.

SMA-T2m+Precip.

SMA-T2m+Rh2m

SMA–T2mUsed

Observations

LM user meeting, Langen 2006

LM user meeting, Langen 2006

Significant impact on T2m with analysed precipitation, small effect with Rh2m in SMA

LM user meeting, Langen 2006

Positive Impact on Rh2m even more pronounced.Again higher impact from analysed precipitation data.

LM user meeting, Langen 2006

Improvement of 24 hr precipitation forecast with precipitation forcing

LM user meeting, Langen 2006

Eldas domain average daily background errorvariance top layer (mm^2)

Eldas domain average daily background errorvariance bottom layer (mm^2)

Confidence in soil moisture Analysis best during vegetation period for thebottom layer in top layer highest before and behind the vegetation period.

Bg

erro

r var

ianc

e to

p la

yer

(mm

2 )

Bg

erro

r var

ianc

e bo

ttom

laye

r (m

m2 )

Gridpoint validation of soil moisture at LindenbergUsing analysed precipitation in the LM forecast

LM user meeting, Langen 2006

LM user meeting, Langen 2006

Soil moisture is climate monitor

Summer 2003

Conclusions• SMA improves the forecast of screen level parameter and

precipitation. Using analysed precipitation has strongest positiveimpact. A high qualityprecipitation product at analysis time thereforewould be very much appreciated.

• SMA compensates for deficits in the flux parameterisation. Thisprevents from recovering errors in the land surface scheme duringoperations and leads to drifts in the soil moisture content.

• The interdisciplinarity between modellers, validators, hydrologistsand agronoms should be enforced to come to more realistic soilmoisture values applicable for flood and drought prediction.

• Future satellite informations on soil moisture content and heatingrates available from microwave and thermal infrared radiation datashould be used in the analysis. Resources are necessary to realisethis!

LM user meeting, Langen 2006

Thanks to …

• Dr. Gerd Vogel who provided validationdata from Lindenberg.

• Thomas Hanisch who provided assistancewith model experiments.

• Ulrich Damrath for verification results.

LM user meeting, Langen 2006