1. © Imperial College LondonPage 1 Estimating the Saharan dust loading over a west African surface site GIST 26: May 2007

2. © Imperial College LondonPage 2 Outline Cloud and dust detection tools over land Dust loading estimation Existing methodologies Accounting for meteorology Potential for direct radiative effect estimation Caveats

3. © Imperial College LondonPage 3 Cloud, dust or clear? For starters: Cloud Combination of NWCSAF and RMIB cloud flags Dust NWCSAF dust flag

4. © Imperial College LondonPage 4 Performance: 1. Assessed visually ‘by eye’ : subjective 2. Assessed through comparison with AERONET sites. If an AERONET retrieval has been made within 15 minutes of SEVIRI observation, site is assumed clear or ‘dusty’

5. © Imperial College LondonPage 5 Original NWC tests Blue: NWC cloud 8 th March 2006: 1200 UTC Red: RMIB cloud Yellow: NWC dust

6. © Imperial College LondonPage 6 8 th March 2006: 1200 UTC 10.8  m – 3.9  m test removed (cloud) 10.8  m – 12.0  m threshold made dependent on total column water vapour (dust) RAE DK AG BZ IER DMN DJ

7. © Imperial College LondonPage 7 SITELat/LonOriginal (%)New (%) Agoufou15.3 N, 1.5 W77.2 Banizoumbou13.5 N, 2.7 E75.876.8 DMN Maine Soroa 13.2 N, 12.0 E81.8 IER Cinzana13.3 N, 5.9 W74.487.8 Ras El Ain31.7 N, 7.6 W32.577.5 Dakar14.4 N,17.0 W71.087.0 D’jougou 9.8 N, 1.6 E43.773.2 Successful classification AERONET comparisons: Total of 577 observations (4 months of data). 1200 UTC only

8. © Imperial College LondonPage 8 Dust Quantification Using visible info: problematic over desert Using IR info: better contrast (note time dep.) but dust properties poorly known BUT, previous attempts made with Meteosat IR channel: IDDI (Legrand et al.) Determines a clear-sky reference image and relates (cloud-free) deviations from this to dust amount Main assumption - unchanging atmospheric conditions from reference state over time-window

9. © Imperial College LondonPage 9 Can we use a similar approach but take changing meteorology into account? Focus on one AERONET site (Banizoumbou, Niger), and one time-slot, 1200 UTC Meteorology from ECMWF analyses interpolated to site location Points only retained if identified as not cloudy or dusty (NWC and RMIB flags) Analysis performed through March-June 2006

10. © Imperial College LondonPage 10 Can we use a similar approach but take changing meteorology into account? ‘Clear-sky’ points identified using maximum T B108 value through a rolling time-window of set length T B108max T sfc and TCWV values from ECMWF analyses retained for each point analysed Clear-sky values ‘corrected’ to the conditions on any given day using:  T B108clr = c 1  T sfc + c 2  TCWV Only requires relative variation in T sfc and TCWV to be correct, not absolute values

11. © Imperial College LondonPage 11 Optimal window length?

12. © Imperial College LondonPage 12 Optimal window length? Suggests site is never ‘clear’ and that a window of > 14 days is required 14 day window

13. © Imperial College LondonPage 13 Optimal window length? 28 day window

14. © Imperial College LondonPage 14 Fitting  T B108clr Perfect knowledge of T sfc, TCWV 1 K (5 %)  random error distribution applied to T sfc (TCWV) values Nadir view

15. © Imperial College LondonPage 15 ECMWF reliability? ECMWF extracted Tsfc, and AMF auxiliary site air surface temperature values Correlation = 0.85 ECMWF extracted TCWV, and retrieved values from MWR at main AMF site Correlation = 0.98

16. © Imperial College LondonPage 16 Results Dust signal,  d = (T B108max +  T B108clr ) – T B108 Original: correlation = 0.67, rms = 0.36 Corrected: correlation = 0.88, rms = 0.23 28 day rolling window period

17. © Imperial College LondonPage 17 Estimating corresponding radiative effect… Could be done using identical approach? Use T B108max as a guide to identify ‘clear-sky’ OLR from GERB Include vertical information on temperature and water vapour content through deep layer relative humidities ‘Clear-sky’ OLR values ‘corrected’ to the conditions on any given day using:  OLR clr = c 1  T sfc + c 2  ln(UTH/UTH max ) + c 3 ln(LTH/LTH max ) Direct radiative effect of dust given by: DRE = (OLR max +  OLR clr ) – OLR

18. © Imperial College LondonPage 18 For Banizoumbou ‘Correcting’ the ‘clear-sky’ OLR to account for changes in surface temperature and relative humidity isolates the dust effect on the OLR. Essentially same idea as calculating clear sky OLR explicitly and subtracting from ‘dusty’ observation (see Vincent’s talk), but removes need for absolute accuracy in profiles etc. assuming relative variation is correct. Direct radiative effect: 17 ± 5 W m -2 per unit  067

19. © Imperial College LondonPage 19 Conclusions/Caveats Encouraging results over the sample site – what happens at other locations? Preliminary work suggests that if the surface type is similar (i.e. arid/semi-arid), the correlation between  T B108 and aerosol optical depth is always improved with a correction applied. However this requires further detailed corroboration. Issues: Assessing the quality of ECMWF (or alternative) analyses in data sparse desert regions (or over other AERONET sites) How to extend in time – quality of cloud/dust detection schemes/ availability of meteorological data/size of signal Variations in dust layer height, surface emissivity, and …? Quality of AERONET retrievals themselves?

20. © Imperial College LondonPage 20 Other sites? - preliminary SITENo of points  range OriginalCorrected Agoufou340.15 – 4.08 0.87 0.91 Dakar540.18 – 1.03 0.62 0.67 D’jougou160.22 – 1.10-0.33-0.14 DMN Maine Soroa 290.22 – 4.05 0.79 0.83 IER Cinzana 400.20 – 1.85 0.46 0.56 Ras El Ain270.06 – 0.69 0.22 0.20