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CAMELOT Observation Techniques and Mission Concepts for Atmospheric Chemistry Task 4:

CAMELOT Observation Techniques and Mission Concepts for Atmospheric Chemistry Task 4: Assessment of Cloud Contamination Caroline Poulsen & Richard Siddans PM5, RAL, 28 th -29 th January 2009. Task 4. Generate a reference set of basic cloud statistics

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CAMELOT Observation Techniques and Mission Concepts for Atmospheric Chemistry Task 4:

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  1. CAMELOT Observation Techniques and Mission Concepts for Atmospheric Chemistry Task 4: Assessment of Cloud Contamination Caroline Poulsen & Richard Siddans PM5, RAL, 28th-29th January 2009

  2. Task 4 • Generate a reference set of basic cloud statistics • Generate statistics on cloud as a function of • geographical region • season and time of day, • pixel size and observation geometry, • A key objectives are to trade of the relative benefits of • GEO vs LEO • LEO at different local times or non sun-synch

  3. Data • We have Selected SEVIRI data from Climate Monitoring SAF • The data was uploaded to RAL on a daily basis • To date, we are storing data from May 2007-April 2008 and have requested 1 year of data

  4. Locations

  5. Generation of simulated polar orbit information from SEVIRI • Statistics have been generated for a selection of polar orbit times resolutions and inclinations • SEVIRI • 98 degree inclinations • anxt=9.30,13.30,17.30 • anxt=9.30 • anxt=10.30 (MODIS) • anxt=9.30,13.30,15.30 • 57 degree inclination • anxt=0.30,06.30,12.30,18.30 • Resolutions • 10, 20, 50km

  6. FOR REGION P1, likelihood of at least one “cloud-unaffected” observation within a “field-of-regard”, within a time-window.

  7. FOR REGION When Pixels are larger than the FOR they are counted once

  8. Results for varying pixel sizeKampala

  9. Results for varying pixel sizeLondon

  10. With optical depth threshold

  11. Results for varying location Bold lines show 24 hour Dashed lines show daylight

  12. Results for CTH threshold cut

  13. Different threshold scenarios applied • Optical depth • 0, 1, 5, 10 • Cloud fraction • 0, 5%, 10%, 20% • Cloud height • 3km threshold

  14. Results for varying cloud thresholdsKampala July

  15. Variation with cloud threshold London July

  16. Polar cloud mask 1145 1245 1345 1445 polar_20070703_1p5_10_98

  17. Results for varying orbit scenario

  18. Variation of pixel size with across track distance Complete coverage each day Complete coverage every 3 days

  19. Seasonal variation for different orbits Bold lines show 24 hour probabilities Dashed lines show day light probabilities

  20. Primary conclusions • Geostationary orbit generally provides more individual, cloud-unaffected hourly samples during the day • By combining 3 or 4 polar orbiters, the probability of obtaining at least 1 cloud-unaffected sample during the day approaches that available from geostationary orbit • though differences remain significant for small FOR • Pixel size strongly affects sampling. Geostationary sampling is only advantageous compared to polar orbit if the geostationary pixel size is comparable over the region of interest.

  21. 15/12/2007 10:45 Clear benefit of smaller pixel size!

  22. Other Factors • Increasing the cloud optical depth threshold increases significantly the number of valid pixels • Increasing the allowed cloud fraction increases the number of valid pixels but not by much (for the range considered). • Accepting cloud below 3km increases the cloud free probability over London in particular. • Location and meteorology significantly affect cloud-free sampling • Differences in daylight sampling between GEO/polar more significant during winter, when daylight hours are short.

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