1. © Imperial College LondonPage 1 Deriving clear-sky fluxes for GERB Jo Futyan Gist 21, CERES-GERB meeting 02/04/04 Boulder, Colorado

2. © Imperial College LondonPage 2 Why do we want clear sky flux data? Calculation of radiative forcing (e.g. clouds/ aerosol) – reference state Study of clear sky processes Model comparison – separate out uncertainties due to cloud representation Consistency with existing ERB datasets –ERBE/ CERES

3. © Imperial College LondonPage 3 GERB and SEVIRI data ARCH data (3x3 SEV pix) Instantaneous clear- sky for all times (days with at least 1 obs.) Monthly time-step mean (times with obs on at least 1 day) Daily mean clear-sky fluxes Instantaneous clear-sky ARCH Threshold choice BARG data Instantaneous clear-sky BARG (missing data) ARCH scale cloud mask Cloud cover data (MPEF, RMIB, other) BARG scale cloud mask Threshold choice Spatial Average and integrate Process clear pix Monthly mean clear-sky fluxes Monthly mean diurnal cycle interpolate Collect into time bins Average ? Interpolate through individual days average ?

4. © Imperial College LondonPage 4 Choice of cloud flag… RMIB – SW only flag, used for ADM selection MPEF - LW only flag, produced by EUMETSAT (CLM product) Need 24h cloud flag - ? Use MPEF? RMIBMPEF 11:45 3 rd Dec 2003

5. © Imperial College LondonPage 5 Cloud flags and fluxes

6. © Imperial College LondonPage 6 Cloud flag comparison RMIB: –Sun-glint – fixed for new data –Desert, due to vegetation changes - fix in progress MPEF : –misses some cloud –Problem over Sahara at night Compare overall performance with ISCCP –no concurrent SEVIRI/ ISCCP data

7. © Imperial College LondonPage 7 Comparison with ISCCP cloud fraction 12345 67 20 -40 060 40 0 sunglint desert RMIB finds more cloud than ISCCP vis MPEF – low fraction at 1200 UTC

8. © Imperial College LondonPage 8 ISCCP comparison continued… MPEF lies mostly within ISCCP range at 0000 & 0600 UTC Flags look reasonable –MPEF fraction only low in middle of day?

9. © Imperial College LondonPage 9 Impact on clear-sky flux/ albedo Look for biases due to cloud contamination Choose flag → clear sky fluxes/ albedo Form time-step mean at each pixel RMIB 0%MPEF 0% 24/12/03 – 13/01/04 (15 days with data)

10. © Imperial College LondonPage 10 Impact on clear-sky flux/ albedo RMIB/ combined flag for SW, MPEF for LW? –Differences in sampling between channels –Small amount of cloud contamination in LW-clear fluxes at all times Combined flag for day, MPEF only at night? –Best possible at all times –Introduces small diurnal bias in LW clear-sky fluxes significant bias in albedo using MPEF – not suitable in SW

11. © Imperial College LondonPage 11 Data Quantities Limited data if require 0% cloud esp for RMIB Relax threshold at BARG scale or use ARCH data How much data do we gain? how big are the errors? –Cloud contamination –Spatial variability

12. © Imperial College LondonPage 12 Example – MPEF flag Using ARCH – relax number of clear pixels required Relaxing % cloud threshold ARCH 25 clear pix

13. © Imperial College LondonPage 13 Using ARCH – relax number of clear pixels required Relaxing % cloud threshold

14. © Imperial College LondonPage 14 Using ARCH – relax number of clear pixels required Relaxing % cloud threshold

15. © Imperial College LondonPage 15 Using ARCH – relax number of clear pixels required Relaxing % cloud threshold

16. © Imperial College LondonPage 16 Using ARCH – relax number of clear pixels required Relaxing % cloud threshold

17. © Imperial College LondonPage 17 Errors in using ARCH data? Use average over clear ARCH pix to estimate GERB scale flux/ albedo Estimate error by sub- sampling clear footprints 1 GERB footprint 1 ARCH pixel Select n pixels at random + compare mean flux with the true value when all 25 pixels used Use albedo – convert to flux using true ISW Consider scene types separately

18. © Imperial College LondonPage 18 Errors in using ARCH data cont… Read off 95% (2 sd) spread at each footprint Find value including 95% of footprints of given surf type 1200 UTC – other times similar/ smaller errors

19. © Imperial College LondonPage 19 Errors… Errors in LW small - <10Wm -2 for 4 pix –Good - fewer alternatives in LW Large errors in SW for land (desert/coast) –10% error achieved at ~9 pix (except for coast) In average products these errors will cancel! Does benefit (more data) outweigh loss of accuracy? Temporally sub- sample Estimate error if only have n days of data –Problem – lack of clear data to sub-sample –Use 14 day period This is error in mean

20. © Imperial College LondonPage 20 Errors… Estimate error in time-step mean at each footprint with at least 1 day of data –Combine these estimates + knowledge of gain in number of days with data In LW always best to use few ARCH pix – for SW… oceandark vegdark des Errors small – doesn’t matter! For instantaneous data reduce npix until error ~ that from filling data –eg with time-step mean or via knowledge of diurnal cycle…

21. © Imperial College LondonPage 21 Effect on time-step mean fluxes – 1200 UTC

22. © Imperial College LondonPage 22 Effect on time-step mean fluxes 2. Estimate ‘error’ in monthly time-step mean See shift/ tail to lower LW (higher SW) flux –Expect symmetrical distrib n from spatial variability –Don’t expect to introduce clouds…

23. © Imperial College LondonPage 23 Effect on time-step mean fluxes 3. Larger differences often where have little data –Initial estimate may not represent mean well Regions with large error not same for SW/LW –SW - cloud contamination? –LW effect in part due to humidity? –Less of a dry bias?

24. © Imperial College LondonPage 24 To do… Investigate errors in using CERES ADMs to fill missing data in SW –Effects on average & instantaneous data Possible interpolation in LW - sinusoids? –Associated errors? Compare monthly mean clear sky flux and cloud forcing products with CERES –ERBE like & Geo-enhanced Study of radiative forcing of tropical convection over Africa!