1. RGP geolocation analysis

2. The geolocation problem We don’t have all the necessary information: –Optical model needs tuning Can prob. do this now but not sufficient because….. –Require spin axis misalignment details –Start of line accuracy out of spec. Have per image correction derived from SEVIRI by column to column jitter remains

3. The geolocation problem What does geolocation accuracy mean for the data? EXAMPLE 1: clear sky coast –Land point contaminated with ocean and ocean with land (ignoring unfiltering error which exacerbates the problem) Ocean SW radiance 20Wm -2 sr -1 and land 70Wm -2 sr -1 0.5 pixel error implies: Ocean and land 45Wm -2 sr -1 If this occurs 25% or time average radiances become: Ocean: 26.25 (31% bias) Land: 85Wm -2 sr -1 (21% bias) 0.1 pixel error 25% of time reduces this to 6% and 4% biases resp.

4. The geolocation problem What does geolocation accuracy mean for the data? EXAMPLE 2: Clear ocean and cloud –Cloud point contaminated with ocean and ocean with cloud (ignoring unfiltering error which exacerbates the problem) Ocean SW radiance 20Wm -2 sr -1 and cloud 150Wm -2 sr -1 0.5 pixel error implies: Ocean and land 85Wm -2 sr -1 If this occurs 5% or time average radiances become: Ocean: 23.25 (16% bias) Land: 147Wm -2 sr -1 (2% bias) 0.1 pixel error 25% of time reduces this to 3.2% and 0.4% biases resp. NOTE cloud forcing calculations: errors compound

5. How good is the reprocessing geo? Reprocessing Compared to optimal

6. How does this compare to NRT geo Reprocessed compare to NRT

7. One pixel geolocation difference Dark blue 5% Light blue 10% Cyan 20% Green 30% Yellow 40% Reed 50% White 100%

8. Dark blue 5% Light blue 10% Cyan 20% Green 30% Yellow 40% Reed 50% White 100%

9. Dark blue 5Wm -2 Light blue 10Wm -2 Cyan 20Wm -2 Green 30Wm -2 Yellow 40Wm -2 Reed 50Wm -2 White 100Wm -2

10. DERIVING the BANANA Pixel azimuth and elevation derived from lon-lat determined by RMIB reprocessed geolocation matching and NANRG satellite position

11. DERIVING the BANANA Pixel azimuth and elevation derived from lon-lat determined by RMIB reprocessed geolocation matching and NANRG satellite position Probability distribution of pixel azimuth and elevation built up from the full dataset

12. Distribution of pixel position We can then look at the proportion of the time the pixel is a given distance from the most probable position 1% < Purple < 5% 5% < Blue < 10% 10% < Cyan < 25% 25% < Green < 50% 50% < Orange < 60% 60% < Red

13. Distribution of pixel position We can then look at the proportion of the time the pixel is a given distance from the most probable position 1% < Purple < 5% 5% < Blue < 10% 10% < Cyan < 25% 25% < Green < 50% 50% < Orange < 60% 60% < Red

14. Summary Reprocessing geo very close to optimal –Within 0.25 pixel except towards disk edge –Not possible in NRT Cost 32,000€, or slower than real time archive or more work solution (still non-ideal as level 1.5 and level 2 geo disconected) NRT geo often more than 0.5 pixel different from reprocessing Updated optical model in level 1.5 NANRG + current paramters with per column azimuth and elevation correction better than 0.2 pixel to reprocessing 90% of time With per image azimuth and elevation correction better than 0.3 pixel to reprocessing 90% of time Need to asses what final decision means on products