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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)
2
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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9
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
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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