Post on 27-Mar-2015
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
RE (
hP
a)
PR
ES
SU
RE (
hP
a)
PR
ES
SU
RE (
hP
a)
PR
ES
SU
RE (
hP
a)
PR
ES
SU
RE (
hP
a)
PR
ES
SU
RE (
hP
a)
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