DESERT DUST SATELLITE RETRIEVAL INTERCOMPARISON

1. DESERT DUST SATELLITE RETRIEVAL INTERCOMPARISON Elisa Carboni1, G.Thomas1, A.Sayer1, C.Poulsen2, D.Grainger1, R.Siddans2, C.Ahn3, D.Antoine4, S.Bevan5, R.Braak6, H.Brindley7, S.DeSouza-Machado8, J.Deuze9, D.Diner10, F.Ducos9, W.Grey5, C.Hsu11, O.V.Kalashnikova10, R.Kahn10, C.Salustro11, D.Tanre‘9, O.Torres11, B.Veihelmann6 (1) University of Oxford, Oxford, UK. (2) Rutherford Appleton Laboratory, Didcot, UK. (3) Science Systems and Applications, Maryland, USA (4) Laboratoire d'Océanographie de Villefranche (LOV), FRANCE (5) Swansea University, UK (6) KMNI, NL (7) Imperial College, UK (8) University of Maryland Baltimore County, USA (9) LOA, UST de lille, FRANCE (10) JPL, Pasadina, USA (11) GSFC NASA, USA Imperial College - 19 Feb. 2008

2. Desert Dust satellite Retrieval Intercomparison (DRI) • OUTLINE: • Introduction • Scope • Dataset included • Aeronet comparison • Results of individual datasets • Dataset vs dataset • land • ocean • Means of all dataset • Conclusion

3. Desert dust retrieval intercomparison • Main purpose/tasks: • Look at desert plume from satellite over bright surface • Identify the differences in the different desert dust aerosol retrievals with the aim of helping understand the retrieval problem, not to find the 'best' one and identify a winner • Help the algorithm developer to identify strengths and the weaknesses Further algorithm and aerosol characterisation improvements

4. Desert dust retrieval intercomparison - Datasets SEVIRI: ORAC (E.Carboni, C.Poulsen, G.Thomas, D.Grainger, R.Siddans, A.Sayer) Globaerosol (C.Poulsen, G.Thomas,D.Grainger, R.Siddans, E.Carboni, A.Sayer) Imperial VIS (H.Brindley) Imperial IR (H.Brindley) AATSR: ORAC (A.Sayer, G. Thomas, E.Carboni, D.Grainger, C.Poulsen, R.Siddans) Globaerosol (G.Thomas, C.Poulsen,D.Grainger, R.Siddans, E.Carboni, A.Sayer) Swansea (W.Grey, S.Bevan)AIRS: JCET (S.DeSouza-Machado) OMI: NASA-GSFC (O.Torres, C.Ahn) KNMI (B.Veihelmann, R.Braak)MISR: JPL (D.Diner, R.Kahn, O.V.Kalashnikova)MERIS: LOV (D.Antoine) SEAWIFS: LOV (D.Antoine) MODIS: NASA-GSFC (C. Hsu, C.Salustro) POLDER: Ocean (D.Tanre', J.Deuze, F.Ducos) Land (D.Tanre', J.Deuze, F.Ducos)

5. Desert dust retrieval intercomparison TECHNICAL DISCUSSION region of comparison:lat: 0:45(N) deg lon: -50(W):50(E) deg Period: March 2006 Strategy: first retrieval run algorithm as they are Data provided: AOD 550nm a)Daily image (average in regular common grid 0.5 lat. lon. box)‏ comparison and discussion All satellite dataset vs. all second retrieval run modified algorithm b) Average in a radius of 50Km from Aeronet sites to compare with average in a 30min on Aeronet data second comparison and identification of the problems All satellite dataset vs. AERONET

6. Desert dust retrieval intercomparison - Datasets Rerieval over: Ocean, x x x x x x x x x x x x x Land x x x x x x x x x x x x Aeronet x x x x x x x x x x x x x x Time UTC: 12:12 10: 13: 16: 12:12 12:12 Orbit local time 10:30 10:30 10:30 x x x x x x x x x SEVIRI: ORAC Globaerosol Imperial VIS Imperial IR AATSR: ORAC Globaerosol Swansea AIRS: JCET OMI: NASA-GSFC KNMI MISR: JPL MERIS: LOV SEAWIFS: LOV MODIS: NASA-GSFC POLDER: Ocean Land

7. SEVIRI ORAC vs AERONET

8. SATELLITE vs AERONET AATSR GLOB AATSR ORAC AATSR SWA MERIS LOV OMI NASA MISR MODIS OMI KNMI POLDER OCEAN SEAWIF LOV SEVIRI IMPERIAL IR SEVIRI ORAC

9. Satellite AOD of dataset (8 March 2006) – different coverage AATSR GLOB AATSRGLOB AATSR ORAC AATSR SWA AIRS MERIS LOV MISR MODIS OMI KNMI POLDER LAND POLDER OCEAN OMI NASA SEAWIF LOV SEVIRI IMPERIAL IR SEVIRI IMPERIAL VIS SEVIRI GLOB SEVIRI ORAC

10. Satellite datasets monthly means AATSR GLOB AATSRGLOB AATSR ORAC AATSR SWA AIRS MERIS LOV MISR MODIS OMI KNMI POLDER LAND POLDER OCEAN OMI NASA SEAWIF LOV SEVIRI IMPERIAL VIS SEVIRI GLOB SEVIRI ORAC SEVIRI IMPERIAL IR

11. COMPARISON satellite vs satellite INSERT SCATTER-DENSITY PLOTS!!! WHICH ONE??? in the following slide I used (like exemple) AATSR ORAC (first in alphabetic order), which one I can use?

12. satellite vs satellite – AATSRGLOB - LAND here one example for AATSRORAC (first in alphabetic order), which one I can use?

13. satellite vs satellite - AATSRGLOB - LAND

14. satellite vs satellite – AATSRGLOB - OCEAN

15. satellite vs satellite - AATSRGLOB - OCEAN

16. Correlation coefficient (CC) LAND OCEAN we like

17. Root mean square differences (RMSD) LAND OCEAN we like

18. Monthly average of all dataset – March 2006 INSERT AOD MOVIE!!!

19. Monthly average of all dataset – March 2006 AOD Nday STD STD/AOD

20. DRI - Conclusion - All dataset show a reasonably good agreement with Aeronet - the discrepancy increase significantly when compare satellite vs satellite dataset. Possibly due to the fact that Aeronet itself make a good datacut (=> comparison satellite-Aeronet are done only in good conditions). - the satellite dataset itself could be affect by cloud contaminations and other errors...?? - The monthly mean of the satellite dataset differ, mainly due to different satellite coverage (overpass, swap...) and cut of data. - Cut of data is one of the more affecting point. e.g. observing the monthly means over ocean (where all the retrieval are more confident, and the general comparisons with aeronet are better) between samesatellite there are still discrepancy, possible due to aerosol model and retrieval algorithm but also due to datacut. - some dataset make a restrictive data cut and cut mainly the higher part of the plume. - A way to follow AOD for march 2006 is the average of all dataset and it present incredibly good continuity also in the passage ocean-land and between area with different number of datasets. Anyway STD between dataset are sometime comparable with the value of average AOD itself, and is higher in correspondence with the desert dust plume.STD/AOD could be >1 especially over land bright surface... (unlukly aeronet station are mainly outside this region)