Concordiasi Satellite data assimilation

at high latitudes

F. Rabier, A. Bouchard, F. Karbou, V. Guidard, S. Guedj,

A. Doerenbecher, E. Brun, D. Puech

+ other participants to Concordiasi

Overview

Data Assimilation over Antarctica– 1. Infrared sensor assimilation

– 2. Microwave sensor assimilation

– 3. Assimilation and forecast Results

Field campaign: Additional in situ data

Rationale:

Analyses over Antarctica important for weather, climate and ozone chemistry.

Try to optimize the use of satellite data to compensate for the lack of conventional observations.

< 16km

Data Assimilation over Antarctica

1. Assimilation of infrared sensors

Assimilation of IASI and AIRS over polar areas (sea ice and land)

Example of the increase of data over polar areas

IASI channels 167 (100hPa) and 306 (300hPa)Black dots: pixels assimilated in operations

Color dots (Tb) : assimilation of IASI over land and sea ice for high peaking channels

2. Assimilation of microwave sensors

Improved representation of surface emissivity

•Old emissivity operational scheme : Grody (1998) or Weng(2001) depending on frequency, used until July 2008

•Dynamical approach for the estimation of the emissivity from Satellite observations over land (Karbou 2006)

Emissivity derived from AMSU/A ch3 and AMSU/B-ch1 are assigned to the temperature & humidity soundings channels respectively

•The estimation of emissivity has been adapted to Antarctica : snow and sea ice surfaces

2. Assimilation of microwave sensors

Comparison of the new emissivity calculation with the old one, over sea ice

Fg-departure (K) (obs- first guess) histograms for AMSU-A, ch4 (July 2007)

Fg-departure (K) (obs- first guess) histograms for AMSU-B, ch2 (July 2007)

Old

New

Use of additional microwave data

AMSUB- Ch3 AMSUA- Ch5

CONTROL

EXP

Density

of data

2. Assimilation of microwave sensors

Overall number of data over area

3. Assimilation and forecast results

Fit of short-range forecasts to Antarctic radiosondes

Data South of 65 S

TemperatureZonal wind

3. Assimilation and forecast results

1000hPa

800hPa

600hPa

400hPa

200hPa

0hPa

NobsRMS

Impact of the data assimilation on forecast over high latitudes

Comparison of RMSE for forecasts at 48h and 72h Error (experiment with additional data (AMSUA/B, AIRS, IASI)) – Error (Control)

Average over latitude, over 20 days (20/07/07--> 8/08/07), Geopotential data

72h

EQ EQ50°S 40°S

48h

Blue:

Positive

impact of

additional

data

3. Assimilation and forecast results

Field campaignAdditional in situ data

150 radiosoundings from Concordia, 75 from Dumont d’Urville Were provided on GTS High resolution profiles available on demand In situ measurements at Concordia

18 Stratospheric balloons– Meteorological sensors, ozone sensors– Particle counter to study stratospheric clouds– GPS radio-occultations

12 driftsondes with 50 dropsondes in each

ACAR-like data and dropsonde data will be provided on GTS

http://www.cnrm.meteo.fr/concordiasi/

2008

2010

Overview of the field experiment

Concordia and Dumont d’Urville soundings

Statistics

Concordiasi Website: http://www.cnrm.meteo.fr/concordiasi-dataset/

Dumont d’Urville (66,40°S;140°E) Concordia on DomeC (75°S;123°E)

- Usual hour of RS launch : 0hTU

- Addiational RS for Concordiasi : 12hTU

- Statistics of meteorological conditions over 149 cases:

- 35% cirrus

- 39% Ac/As

- 48% Stratocumulus

- 19% clear

- Usual hour of RS launch : 12hTU

- Additional RS for Concordiasi : 0hTU

- Stat meteo over 120 cases:

- 62% clear

- 29% almost cloudy

- 10% cloudy

Concordiasi

2008: – Preparatory data assimilation studies– In situ radiosonde data

2009:– 1D-Var studies with radiosonde data as validation– Test campaign for stratospheric balloons (elsewhere)

2010:– Stratospheric balloons over Antarctica– Data impact studies

Balloon data

NWP users encouraged to use the data,

available on the GTS

Trajectories for late winter/ early spring (Austral)

Vorcore 2005

Sept-Oct 2005

Nov 2005

Dec 2005-Feb 2006