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

F. Hdidou

Direction de la Météorologie Nationale, Morocco

O. Bock

IGN, France

AMMA: The African Monsoon Multidisciplinary

Analysis

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)

Impact of using the AMMA radiosonde dataset

New radiosonde stations

Enhanced time sampling

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

Bias correction for RH developed 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

Impact on mean TCWVCNTR: data from GTS AMMA: from the AMMA database AMMABC: AMMA + bias correction

PreAMMA: with a 2005 network NOAMMA: No Radiosonde data

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

CNTR: data from GTS

AMMA: from the AMMA database

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

NO AMMA

AMMABC

Observations

Impact on quantitative prediction of precipitation over Africa

Higher scores for AMMABC

Lowest scores for NO AMMA

CNTR: data from GTS

AMMA: from the AMMA database

AMMABC: AMMA + bias correction

PreAMMA: with a 2005 network

NOAMMA: No Radiosonde data

Downstream impact

Impact on geopotential at 500hPa, averaged over 45 days 48hr forecasts: AMMABC vs PREAMMA

3 day range: AMMABC vs PREAMMA

Fit to European RadiosondesScores at the 3 day range, PREAMMA versus AMMABC

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Assimilating low-level humidity observations over land

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

Microwave observations over land

High emissivity (~1.0)

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

Remaining large uncertainties on land emissivity and skin temperature

Top of AtmosphereEnergy source

Surface (emissivity, temperature)

(1) Upwelling radiation

(2) Dow

nw

ellin

g rad

iation (3

) Surf

ace

em

issi

on

Signal attenuated

by the atmosphere

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

Density of Density of assimilated assimilated AMSU-B Ch5 AMSU-B Ch5

during August during August 20062006

ControControll

ExperimentExperiment

Improved emissivity parametrisation

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

•Possibility to assimilate more channels

•Experiments performed during AMMA in 2006

Assimilation of humidity observations over land

Assimilation of AMSU-B Ch2 (150 GHz) & Ch 5 (183±7 GHz) over land, 45 Assimilation of AMSU-B Ch2 (150 GHz) & Ch 5 (183±7 GHz) over land, 45 daysdays

TCWV (EXP) - TCWV (CTL)TCWV (EXP) - TCWV (CTL)

TCWV (CTL)TCWV (CTL)

Karbou et al, 2009

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

Significant positive impact of additional AMMA RS 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

Results in a AMMA special issue Weather and Forecasting

Summary of AMMA results

Radiosonde RH Bias correction Well-documented dry bias for Vaisala sonde types (e.g. Wang et al., 2002, Nuret et al., 2008).

Motivation: In West Africa many radiosondes are located within a region of strong low-level moisture gradient and there is lack of ppn in the short-range forecast over Sahel.

Can be used in data impact studies of enhanced AMMA radiosonde network, AMMA reanalysis experiment and water budget studies within the AMMA project.

Based on the ECMWF operational RS bias correction implemented in CY32r3.

Main differences between AMMA and OPER. RS RH bias correction:

Takes into account the dependence of bias on the observed RH values, which is very important in the Sahel because of its pronounced seasonal cycle.

Agusti-Panareda et al

Radiosonde (RS) RH Bias correction: RESULTSComparison with GPS TCWV

RS-GPS: BIAS

Olivier Bock

UNCORRECTED RS

CORRECTED RS

Agusti-Panareda et al

Impact of radiosonde bias correction: RESULTS

Mean total daily PPN FC (T+42-T+18) [mm/day] 1 to 31 Aug 2006, 12 UTC

RSBIAS CORRECTION

RSBIAS CORRECTION – CNTRL

CNTRL

OBS: RFE 2.0 (NOAA CPC)

Agusti-Panareda et al

The ECMWF AMMA reanalysisAnna Agustí-Panareda, Carla Cardinali, Jean-Philippe Lafore, …

Period: 1 May – 30 September 2006

Resolution: T511 (~40 km), L91

Extra data used: sonde profiles of wind, temperature and humidity extracted from the AMMA database

IFS cycle with improved physics: CY32r3 (Bechtold et al., ECMWF Newsletter No. 114, Winter 2007/08, pp. 29-38)

AMMA radiosonde humidity bias correction (Agustí-Panareda et al. 2008, submitted to Q.J.R.Meteorol.Soc)

Agusti-Panareda et al

DFS= degrees of freedom for signalDFS =Tr (δH(xa)/ δy)

Calculated for each station, averaged 1-15 August 2006

Large impact of additional AMMA data

Faccani et al

More influence

Assimilation of humidity observations over land

Objective scores %radiosondes geopotential, 24-hr fcst, 34 cases,

CTL CTL --- BIAS/TEMP--- BIAS/TEMP__ __

EQM/TEMPEQM/TEMP

EXP EXP --- BIAS/TEMP--- BIAS/TEMP__ __

EQM/TEMPEQM/TEMP

PR

ES

SU

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hP

a)

PR

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hP

a)

PR

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SU

RE (

hP

a)

PR

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OPER EXPERIMENT

Correlations between observations and RTTOV simulations AMSU-A ch4, August 2006Correlations between observations and RTTOV simulations AMSU-A ch4, August 2006

First step towards the assimilation of surface sensitive observations over landFirst step towards the assimilation of surface sensitive observations over land

The effect of land surface emissivity

The effect of land surface emissivity

Number of assimilated ch7 AMSU-A data (Temperature 10 km) , August 2006Number of assimilated ch7 AMSU-A data (Temperature 10 km) , August 2006

OPER EXPERIMENTChange of land emissivity

A land emissivity parameterisation at Météo-FranceA land emissivity parameterisation at Météo-France

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

AMSU-B ch2 (150 GHz), August 2006

Impact of emissivity on simulations

Simulations from CTL

Simulations from EXP

Similar results obtained at ECMWF

Monthly averaged RR better with bias

correction

Faccani et al, 2009

Impact on monthly mean precipitation over Africa

Very poor performance of NO AMMA

Best performance of AMMABC

AMMABC: AMMA + bias correction

PreAMMA: with a 2005 network

NOAMMA: No Radiosonde data

CPC: Observations