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- Ch3AMSUA- 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 Temperature Zonal wind 3. Assimilation and forecast results 1000hPa 800hPa 600hPa 400hPa 200hPa 0hPa Nobs RMS

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 50°S 40°S 48h Blue: Positive impact of additional data 3. Assimilation and forecast results

Field campaign Additional 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 Overview of the field experiment

Concordia and Dumont d’Urville soundings Statistics Concordiasi Website: 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 Feb 2006