concordiasi satellite data assimilation at high latitudes
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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
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