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Conclusions on 1D-VAR tests: - PowerPoint PPT Presentation

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Input data from BSRN (ISAC-CNR) and LGGE. Surface Temperature (°C). Surface Temperature (°C). Coupled simulation AROME ISBA_ES/Crocus. Stand-alone simulation ISBA_ES/Crocus Observation from emitted LW (BSRN). Observation from emitted LW. 2010 January 20th. to 31st.

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Input data from BSRN (ISAC-CNR) and LGGE

Surface Temperature (°C)

Surface Temperature (°C)

Coupled simulation AROME


Stand-alone simulation ISBA_ES/Crocus

Observation from emitted LW (BSRN)

Observation from emitted LW

2010 January 20th. to 31st

2010 January 20th. to 31st

The Concordiasi campaign

F. Rabier, E. Brun, A. Bouchard(*), V. Guidard, F. Karbou, O. Traulle, A. Doerenbecher, (Météo-France/CNRS; */CNES)‏

C. Genthon, D. Six, L. Arnaud (CNRS, UJF /LGGE)‏ -

Concordiasi (Rabier, 2010, BAMS):An international field experiment in Antarctica during Austral springs 2008-2009-2010

* To enhance the accuracy of weather prediction and climate records in Antarctica. The improvements in dynamical model analyses and forecasts will be utilized in chemical-transport models.  

* To improve our understanding of microphysical and dynamical processes controlling polar ozone, by providing the first quasi-Lagrangian observations of stratospheric ozone and particles, in addition to an improved characterization of the 3D polar vortex dynamics. Techniques for assimilating these Lagrangian observations are being developed.

  • To improve the assimilation of satellite observations over high latitudes by comparison with in-situ observations. In routine, poor spatial and temporal density observations over Antarctica : mainly over the coast (see figure 1)‏

All data are available on the website :

Fig. 1 : Radiosoundings assimilated in the system (black dots).

Concordia - DomeC station

Dumont d’Urville station

Observations (see figure 2 for the schedule of the Campaign):

Radiosoundings launched during: 15 September – 30 November 2008 and 20 November – 12 December 2009 at DomeC and Dumontd’Urville stations (see Fig. 1)‏

Stratospheric Balloons launched at Mc Murdo with in-situ measurements + dropsondes

Satellite data:

Infrared sensor : IASI (Infrared Atmospheric Sounding Interferometer), AIRS (Atmospheric Infrared Sounder)‏

Microwave sensor : AMSU-A/B (Advanced Microwave Sensor Unit)‏


Tuned version for Antarctica studies of the stretched model ARPEGE (Action de Recherche de Petites et Grandes Echelles) of Météo-France (developed in collaboration with ECMWF), with 60 levels in vertical (horizontal resolution less than 22 km over Antarctica and 16km over DomeC)‏

Fig. 2 : Schedule of the Concordiasi campaign

Results in preliminary studies : Rabier et al., 2010, BAMS; Guedj et al., IEEE, 2010; Bouchard et al., MWR,2010

1D-VAR retrieval studies

Aim : In global meteorological model ARPEGE, only 50 channels of IASI are assimilated.  Try to improve the assimilation of infrared channels


  • Observations launched :
    • 2008 : Radiosounding at DomeC (75°S ; 123°E) & Dumont d’Urville(66,40°S;140°E) stations in order to have 2 observations each day at each station, at OUTC and 12UTC, respectively. Complementary launch at the same time of IASI overpass.
    • 2009: As 2008 for DomeC station + Surface measurements (vertical profile of the ts from -10cm to -1cm) at the time of the sounding

Fig.3 : Profiles of temperature, and relative humidity from background (black line), analysis (red line), radiosounding (green line), the 12/12/2009

Retrieval of the skin temperature from IASI window channel (result on fig. 4)

Choice of the channel (943.25cm-1 with the highest transmittance ~0.99)

Fig. 4 : Temporal variation of the skin temperature (K) during the period : 20 November 2009 to 12 December 2009 at DomeC-0hTU

Statistics on meteorological conditions

Dome C:Dumont d’Urville

over 120 cases in 2008: 62% clear sky over 149 cases in 2008: 19 % clear sky

over 17 cases in 2009: 59% clear sky

Most of time Clear Cases at DomeC  Choice of DomeC for 1D-VAR study

1D-VAR system : 1D-VAR of the Met Office, part of the NWP SAF

Principle: From Observation and Background profiles (see fig. 3)  Retrieval of a profile by minimising a cost function

Inversion of temperature not seen in the model

Statistical results on cases of 2009 (DomeC station- 0hTU):

Fig.5: Statistics over 12 clear cases of bias and root mean square of the difference between radiosounding and output of 1D-VAR. 3 Tests are shown here.

CONTROL : B-matrix of Met Office, R-matrix : diagonal as ARPEGE, KCARTA coef.

1- red line: EXP with analysis from CONTROL (Skin temperature of the background)‏

2- green line : EXP with analysis from CONTROL + skin temperature retrieved from IASI window channel ( 943.25 cm-1 with the highest transmittance-see fig. 9-blue dots)‏

3 -blue line : EXP with analysis from CONTROL + skin temperature retrieved from IASI window channel and background error covariance matrix (B-matrix) modified, values have been multiplied by 5 for Relative Humidity and by 2 for Temperature part.

Studies the following impacts:

Impact of the B-matrix (Error covariance matrix of the background (fig. 5) see the impact of decrease of the weight of the information from model

Impact of the skin temperature using one retrieved from IASI windows channel instead one from background (fig .4 & 5)

Conclusions on 1D-VAR tests:

Retrieval done at DomeC station with data from the 2 first part of Concordiasi campaign

Comparison of skin temperature: best agreement between one retrieved from IASI window channel (943.25cm-1) than on from background model comparing with manual measurement and synop information.

Best improvement of the analysis (fig.5) from a statistical point of view using the skin temperature retrieved from IASI window channel and a B-matrix with value increased on the relative humidity part.

Best result with the 3rd case of fig. 10 : use of the skin temperature retrieved from IASI (impact on T profile) and B-matrix modified (impact on the RH profile)‏

Towards an improvement of snow surface temperature simulation over the Antarctic Plateau in NWP models

Above results highlight the importance of the model background of surface temperature when assimilating observations from IASI or other infrared sounders. Because of its unique radiative and thermal properties, snow surface experiences much more rapid changes than any other common continental or marine surfaces. Even at Dome C, diurnal variation of surface temperature is frequently larger than 15°C. in January (see Fig 6)

Like most operational NWP models, ARPEGE uses a rather simple parameterization of snow cover which captures only about half of the diurnal cycle of surface temperature.

Concordia station at Dome C offers a unique framework to investigate snow/atmosphere interactions over the Antarctic Plateau and improve model simulations:

  • Flat, horizontal and quasi-infinite terrain
  • No vegetation
  • Relatively homogeneous snow surface (scale dependant)
  • Moderate wind velocity
  • Sophisticated instrumentation at Concordia base:
    • BSRN radiation observations (ISAC-CNR)
    • LGGE/IPEV and PNRA boundary layer and surface observations
    • LGGE continuous snow temperature profiles
    • LGGE detailed density, temperature and SSA profiles in Summer (G. Picard, LGGE)
    • Radio-soundings by PNRA

Stand-alone simulations of snow surface temperature:

Local observations (radiation, temperature, humidity, wind)‏ have been used to run and evaluate a detailed snow model (ISBA_ES/Crocus) from January 2010 the 20 th. to the 31st.

Fig 6 shows that snow surface temperature is realistically simulated with the snow model when using observations as forcing data

Coupled simulations of snow surface temperature:

A similar experiment has been conducted by implementing the detailed snow model into the AROME regional NWP model.

Fig 7 shows that the simulated surface temperature is less realistic but still reasonable. Further work will study the impact of an improved snow parameterization on the IASI assimilation process.



Rabier et al., The Concordiasi project in Antarctica, BAMS, 2010, 69-86

Bouchard et al., Enhancements of Satellite Data Assimilation over Antarctica, MWR, 2010

Genthon et al. Meteorological atmospheric boundary layer measurements and ECMWF analyses during summer at Dome C, Antarctica, 2010, J. Geophys. Res., 115

Projet Calva