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Data assimilation experiments for AMMA, using radiosonde observations and satellite

observations over land

F. Rabier, C. Faccani, N. Fourrié, F. Karbou, J-P Lafore,

P. Moll, M. Nuret, J-L Redelsperger

Météo-France and CNRS, Toulouse, France

A. Agusti-Panareda

ECMWF, Reading, g

F. Hdidou

Direction de la Météorologie Nationale, Morocco

O. Bock

IGN, France

AMMA: The African Monsoon Multidisciplinary

AnalysisAnalysis

Better understand the mechanisms of the African monsoon and prevent dramatic situations

(Redelsperger et al, 2006)

Enhanced observations over West Africa in 2006

In particular, major effort to enhance the radiosonde network

(Parker et al, 2008)

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Impact of using the AMMA radiosonde dataset

New radiosonde stations

Enhanced time samplingp g

AMMA database: additional data which were not received in real time + enhanced vertical resolution

Bias correction for RHdeveloped at ECMWF (Agusti-Panareda et al)

Data impact studies With various datasets,With and without RH bias correction

Number of soundings provided on GTS in 2006 and 2005

Period: 15 July- 15 September, 0 and 12 UTC

Validation of Total Column Water Vapour analyses: Comparison with GPS data at Tombouctou

CNTR: data from GTS

AMMA: from the AMMA database

NO AMMA

AMMABC

AMMABC: AMMA + bias correction

PreAMMA: with a 2005 network

NOAMMA: No Radiosonde data

GPS: Observations

Very poor performance of NO AMMA

Best performance of AMMABC

Observations

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Impact on monthly mean precipitation over Africa

AMMABC: AMMA + bias correction

PreAMMA: with a 2005 network

NOAMMA: No Radiosonde data

CPC: Observations

Similar results obtained at ECMWF

Very poor performance of NO AMMA

Best performance of AMMABC

Monthly averaged RR better with bias

correction

Faccani et al, 2009

Impact on quantitative prediction of precipitation over Africa

CNTR: data from GTS

AMMA: from the AMMA database

Higher scores for AMMABC

AMMA: from the AMMA database

AMMABC: AMMA + bias correction

PreAMMA: with a 2005 network

NOAMMA: No Radiosonde data

Lowest scores for NO AMMA

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Downstream impact

Impact on geopotential at 500hPa, averaged over 45 days

48hr forecasts: AMMABC vs PREAMMA

Improvements wrt European radiosondesaveraged over 45 days, day 3 range

AMMABC in greyPREAMMA in black

Faccani et al, 2009, W and F

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Assimilating low-level humidity observations over land

Microwave observations over land

High emissivity (~1.0)

Top of AtmosphereEnergy source

Only channels that are the least sensitive to the surface are currently assimilated

Remaining large uncertainties on land emissivity and skin temperature

Surface (emissivity, temperature)

Signal attenuated by theatmosphere

Assimilation of MW observations over land

New methods for estimating the land surface emissivity (Karbou et al. 2006) operational at Météo-France since July 2008.

Karbou et al, 2009

Impact of emissivity on simulations

Simulations from CTL

Time series of global correlations between observations and RTTOV simulations over land :

AMSU-B ch2 (150 GHz), August 2006Simulations from EXP

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Impact of assimilating low-level humidity observations over land on the African Monsoon during AMMA

Improved emissivity parametrisation

•Better simulation by the Radiative Transfer Model of the low-level peaking channels

Density of Density of ControlControl ExperimentExperiment

•Possibility to assimilate more channels

•Experiments performed during AMMA in 2006

yyassimilated AMSUassimilated AMSU--B B Ch5 during August Ch5 during August

20062006

Assimilation of humidity observations over landAssimilation of AMSUAssimilation of AMSU--B Ch2 (150 GHz) & Ch 5 (183B Ch2 (150 GHz) & Ch 5 (183±±7 GHz) over land, 45 days7 GHz) over land, 45 days

TCWVTCWV (EXP)(EXP) -- TCWVTCWV (CTL)(CTL)

TCWVTCWV (CTL)(CTL)

Karbou et al, 2010 a and b, W and F

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Humidity bias correction (from ECMWF) over the AMMA region is beneficial

Summary of AMMA results

Significant positive impact of additional AMMA radiosonde data on the humidity analysis and on precipitation over Africa

Positive downstream impact over Europe

Using more satellite data over land also has a large positive impact in the Tropics

AMMA special issue Weather and Forecasting: papers by Faccani et al, 2009 and Kabou et al, 2010a and 2010b.

HUMIDITY BIAS CORRECTION FOR AMMA_2006 RADIOSOUNDING(f THORPEX DAOS ti )(for THORPEX-DAOS meeting)

M.Nuret, O. Bock, J.P. LaforeMETEO-FRANCE and LAREG/IGN

(mathieu.nuret@meteo.fr)

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HUMIDITY BIAS DETECTION

From O. Bock. and M. Nuret, 2009, Weather and Forecasting

HUMIDITY BIAS CORRECTION

• Methodology: CDF matching between sondes to be corrected(Vaisala RS92, RS80-A and MODEM) vs “reference sonde” (see Nuret et al JAOT 2008)(see Nuret et al., JAOT, 2008)

• reference sonde = RS92 at night (unbiased) – 1st set of correctionStaggered sampling at Niamey (RS92 and RS80)

• AMMA_2008 intercomparison campaign with a reference sonde(SnowWhite sonde) => new set of correction (for RS92, RS80-Aand MODEM) to be delivered (Autumn 2010)

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EXAMPLE OF BIAS CORRECTION TABLES

MODEMtoo

moist

RS80-A

too

dry

Soundings corrected inthe AMMA database

CORRECTION EVALUATION

S-G

PS

) in

mm

DAY NIGHT

IWV

(R

S

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•2nd set of correction, will applied to all sonde-types, for DAY (2 solar elevations) and NIGHT: RS80-A, RS92, MODEM M2K2

R f d S i S Whi d

WORK PLAN

•Reference sonde = Swiss SnowWhite sonde