Work done uses “pre-15 july meeting” datasets for year 2004 Two stages:

1. GlobAEROSOL-related work at ECMWF: September 2009 C. Peubey, A. Benedetti, J.-J. Morcrette, L. Jones • Work done uses “pre-15 july meeting” datasets for year 2004 • Two stages: • validation of the GlobAEROSOL data (vs Aeronet, MODIS data and analysis) • selection of a dataset to be assimilate in the GEMS-ECMWF system. Currently, assimilation experiments have been performed for the SEVIRI dataset.

2. 1. Validation of GlobAEROSOL products:550 nm Aerosol Optical Depths March October GEMS (model) • Large spread within the GlobAEROSOL product, • MERIS presents the lowest values for the Saharian dust aerosols • Large values over Brazil for SEVIRI (and MERIS) in March • AATSR agrees the most of MODIS MODIS MERIS AATSR SEVIRI

3. 1. Validation of GlobAEROSOL products: 550 nm Aerosol Optical Depths Djougou x x x Ascension Island Campo Grande Comparison against AERONET stations for March 2004 (Saharian Dust event) Djougou (Aeronet 675 nm) Ascension Island (Aeronet at 550 nm) Campo Grande (Aeronet at 550 nm) (Modis analysis)  systematic errors: too large for sea salt, too small for dust, too large for anthropogenic aerosols.

4. 1. Validation of GlobAEROSOL products: 550-865 nm Angstrom exponents October March GEMS • Land/sea discontinuity in regions where continuity is known to exist (off coast west-Sahara and central Africa) • Negative Angstrom exponent for MERIS MERIS • Good for assimilation? AATSR • Can we relay on quality control and on the use of obs. errors fiom the retrievals to “clean” the data? • Choice of SEVIRI to start with SEVIRI

5. 2. Assimilation of SEVIRI optical depths • Aerosol model: • Aerosol assimilation system. The ECMWF 4D-Var system (12 hour window) adapted to assimilate AODs (Benedetti et al., JGR, 114, D13025 2009): • Seviri assimilation experiments: • GEMS-ECMWF forecast, T159 (~120km) horizontal resolution, 60 vertical layers ( top level at 0.1 hPa). • Aerosol module (Morcrette et al., 2009) treats sea-salt, desert dust, black carbon, organic matter and sulphate aerosols. Three size bins are considered for sea-salt and desert dust aerosols. • All species are subject to dynamical processes (vertical diffusion, convection and advection by the semi-Lagrangian scheme) and physical processes (dry and wet deposition, sedimentation and sources). • The background information is the AOD first-guess from the model which is calculated from the mixing ratio of the different aerosol species using tabulated optical properties. • The minimization of the 4D-Var cost function is performed with respect to the total aerosol mixing ratio. Increments in this quantity are then redistributed to the different species using the forward model background information. • Assimilation experiments of SEVIRI optical depth at 550 nm for the 1-15 March 2004 period (covers strong dust storm event over West-Africa). • Data = hourly optical depths at 10 UT, 13 UT and 16 UT. • Prior to assimilation, data have been thinned down to the 1°x1° resolution. • Observation errors from the retrievals + 1 test with error fixed at 30% of the AOD

6. 2. Assimilation of SEVIRI optical depths: Issues with the data • “noise” over land, especially over Brazil • cloud contamination MODIS composite image 02/03/2004 SEVIRI daily AODs 02/03/2004 MODIS daily AODs 02/03/2004 • “saturation” at AOD=2 SEVIRI daily AODs 06/03/2004

7. 2. Assimilation of SEVIRI optical depths: Importance of the observation errors and quality control Obs. Error from retrieval (%) 02/03/2004 Prior to assimilation, data are quality controlled: Low obs error and large deviation  rejected In the present case, retrieval observation errors are low, a lot of data are rejected but data passing the QC have a strong impact. First-guess departure (O-B) 02/03/2004 using retrieval errors using errors fixed 30 % Large dot = data rejected by quality control

8. 2. Assimilation of SEVIRI optical depths: Importance of the observation errors and quality control • observation errors fixed at 30% (red) of the AOD vs errors provided with retrieved AOD (black) • more desirable first-guess and analysis departure distributions with the retrieval errors: smaller bias and more Gaussian shape compared to using the errors of 30 %. • the number of assimilated data in the retrieval error case is half that of the 30% error case. Obs minus first-guess Obs minus analysis

9. 2. Assimilation of SEVIRI optical depths: assimilation diagnostics over the 1-15 March period Number of used obs (using retrieval errors) used data, obs-fg all data, obs-fg used data, obs-ana MEAN

10. 2. Assimilation of SEVIRI optical depths: 6hour-forecasts 3 Mar 8 Mar 4 Mar 5 Mar 6 Mar 7 Mar “free run” Seviri retrieval errors Seviri errors=30% Modis

11. 2. Assimilation of SEVIRI optical depths: Comparison short-forecast/AERONET Banizoumbou (675 nm) Djougou (675 nm) Agoufou (675 nm) errors=30% retrieval errors MODIS Aeronet Sao Paulo (500 nm) Ascension Island (550 nm) Capo Verde (675 nm)

12. 2. Assimilation of SEVIRI optical depths: Conclusion . Results of the feasibility test of SEVIRI AOD assimilation: • the benefit of assimilating SEVIRI (compared to the realistic “free run” forecast ) is seen mainly in the western part of the dust plume, where SEVIRI data help maintaining high values of AOD. • differences between SEVIRI and MODIS AODs also reflected in the analysis. SEVIRI experiment has lower values in the plume probably due to the fact that SEVIRI AODs < 2 . • Compared to the “free run” forecast, short-forecasts from SEVIRI and MODIS analysis have both slightly higher AODs of sea-salt aerosol. • The choice of observation errors is crucial for both the weight given to the data and the quality control (low error  strict QC). Using the retrieval errors helped to remove cloud contaminated data and spurious data over Brazil. It also reduces the bias between first-guess and observations. On the other hand, the rate of rejection is quite high. • Future: to test the assimilation of AATSR, and newly reprocessed SEVIRI