1 / 45

Exploring the similarities and differences between MODIS, PATMOS and ISCCP

Exploring the similarities and differences between MODIS, PATMOS and ISCCP. Amato Evan, Andrew Heidinger & Michael Pavolonis Collaborators: Brent Maddux, Richard Frey, Chris O’Dell & Steven Ackerman http://cimss.ssec.wisc.edu/clavr/amato/. Outline. EOF analysis

awen
Download Presentation

Exploring the similarities and differences between MODIS, PATMOS and ISCCP

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Exploring the similarities and differences between MODIS, PATMOS and ISCCP Amato Evan, Andrew Heidinger & Michael Pavolonis Collaborators: Brent Maddux, Richard Frey, Chris O’Dell & Steven Ackerman http://cimss.ssec.wisc.edu/clavr/amato/

  2. Outline • EOF analysis • PATMOS & ISCCP d2: total, high, mid & low cloud fractions • MODIS collection 5 total cloud fractions • NON-PHYSICAL time series comparisons • Discuss the effects of diurnal cycles • “Diurnally correct” time series • Conclusions • Significance of the findings • Future work

  3. EOF Analysis • Remove the seasonality & ENSO then standardize every pixel • Reveal patterns in the data sets that may or may not be physical that explain the most variance • EOF maps are not rotated – variance in the EOF maps can be removed from the data set

  4. EOF Analysis - PATMOS/AVHRR • Total Cloud Fraction - 1st EOF

  5. EOF Analysis - PATMOS/AVHRR • Low & Mid Cloud Fraction - 1st EOF

  6. EOF Analysis - PATMOS/AVHRR • High Cloud Fraction - 1st EOF

  7. EOF Analysis - ISCCP • Total Cloud Fraction - 1st EOF

  8. EOF Analysis - ISCCP • Low & Mid Cloud Fraction - 1st EOF

  9. EOF Analysis - ISCCP • High Cloud Fraction

  10. EOF Analysis - MODIS (2005 Terra: collection 5) • Total Cloud Fraction - 1st EOF

  11. EOF Analysis - Summary • Each data set is probably influenced non-physical artifacts • may be driven by geometry or the algorithms • PATMOS & MODIS – artifacts may be more regional (poles) • PATMOS also has a ‘striping’ effect • ISCCP – Artifacts introduced by satellite geometry are pervasive

  12. Time Series Analysis • Every data set contains measurements made at different times • AVHRR: changing sampling rates and over pass time (drift) • ISCCP: IR data is 3-hourly, VIS + IR is daytime only • MODIS: changing sampling rates • Since clouds have a diurnal cycle, maybe a comparison of the data sets should take observation times into account?

  13. Time Series Analysis - Tropics Over Land • 15 S to 15 N • High Cloud Fractions • MODIS collection 4 daytime only!

  14. Time Series Analysis - Tropics Over Land • High clouds • Not taking into account diurnal effects

  15. Time Series Analysis - Tropics Over Land • High clouds • Strong diurnal cycle as seen by the satellites

  16. Time Series Analysis - Tropics Over Land • Differences in satellite observation times

  17. Time Series Analysis - Tropics Over Land • High clouds • Not taking into account diurnal effects

  18. Time Series Analysis - Tropics Over Land • High clouds • Strong diurnal cycle as seen by the satellites

  19. Time Series Analysis - Tropics Over Land • High clouds • Accounting for diurnal effects

  20. Time Series Analysis - Tropics Over Land • High clouds • Accounting for diurnal effects

  21. Time Series Analysis - Tropics Over Land • High cloud • Accounting for diurnal effects & standardizing

  22. Not Corrected • Corrected Time Series Analysis - Tropics Over Water • High cloud

  23. Comparison Time Series - Summary • Accounting for diurnal affects can greatly influence the results of a time series analysis (and probably a correlation analysis) • This effect is greatest in the presence of a strong diurnal cycle, generally over land • May be especially important when comparing two different polar orbiting satellites • These diurnal corrections for more regions and cloud types can be viewed at • http://cimss.ssec.wisc.edu/clavr/amato/

  24. Exploring the similarities and differences between MODIS, PATMOS and ISCCP Conclusions • Each data set contains some artifacts that probably are not physical • However, when the diurnal cycle is considered, despite those artifacts, there is excellent agreement between all three data sets • Even when absolute cloud amounts are not in agreement, the data sets are still very well correlated

  25. Exploring the similarities and differences between MODIS, PATMOS and ISCCP Future Work • Quantifying how the artifacts in the EOF analysis are affecting the long-term cloud signals • Possible to create a “best fit” cloud climatology that utilizes more that one data set • Similar to the work being done by Chris O’Dell • Use the diurnal information of ISCCP to “correct” the drift signal in the PATMOS data

  26. Extra slides

  27. Time Series Analysis - Tropics Over Land • Low clouds • Not taking into account diurnal effects

  28. Time Series Analysis - Tropics Over Land • Low clouds • Strong diurnal cycle as seen by the satellites

  29. Time Series Analysis - Tropics Over Land • Differences in satellite observation times

  30. Time Series Analysis - Tropics Over Land • Effects of satellite drift

  31. Time Series Analysis - Tropics Over Land • Low clouds • Not taking into account diurnal effects

  32. Time Series Analysis - Tropics Over Land • Low clouds • Accounting for diurnal effects

  33. Time Series Analysis - Tropics Over Land • Low clouds • Accounting for diurnal effects

  34. Time Series Analysis - Tropics Over Land • Low clouds • Accounting for diurnal effects & standardizing

  35. Time Series Analysis - Stratus Regions

  36. Time Series Analysis - Stratus Regions • Low clouds • Not taking into account diurnal effects

  37. Time Series Analysis - Stratus Regions • Low clouds • diurnal cycle as seen by the satellites – approx. sine wave

  38. Time Series Analysis - Stratus Regions • Low clouds • Not taking into account diurnal effects

  39. Time Series Analysis - Stratus Regions • Low clouds • Accounting for diurnal effects

  40. Time Series Analysis - Stratus Regions • Low cloud • Accounting for diurnal effects & standardizing

  41. Time Series Analysis - Stratus Regions • Low clouds • Accounting for diurnal effects

  42. Time Series Analysis - Stratus Regions • Low cloud • Accounting for diurnal effects & standardizing

  43. EOF Analysis - Summary • Each data set is probably influenced non-physical artifacts • These artifacts may be driven by geometry or the algorithms • PATMOS & MODIS – artifacts may be more regional (poles) • PATMOS – Also has a ‘striping’ effect • ISCCP – Artifacts introduced by satellite geometry are pervasive

  44. Exploring the similarities and differences between MODIS, PATMOS and ISCCP Conclusions • Each data set contains some artifacts that probably are not physical • When the diurnal cycle is considered, despite those artifacts, there is excellent agreement between all three data sets • Even when absolute cloud amounts are not in agreement, the data sets are still very well correlated

  45. Exploring the similarities and differences between MODIS, PATMOS and ISCCP Future Work • Understand how the artifacts in the EOF analysis are affecting the long-term cloud signals • Possible to create a “best fit” cloud climatology that utilizes more that one data set • Similar to the work being done by Chris O’Dell

More Related