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– 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

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

EOF Analysis - PATMOS/AVHRR

• Total Cloud Fraction - 1st EOF


• Low & Mid Cloud Fraction - 1st EOF


• High Cloud Fraction - 1st EOF

EOF Analysis - ISCCP



• Low & Mid Cloud Fraction - 1st EOF


• High Cloud Fraction

EOF Analysis - MODIS (2005 Terra: collection 5)


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

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?

Time Series Analysis - Tropics Over Land

• 15 S to 15 N• High Cloud Fractions

– MODIS collection 4 daytime only!


• High clouds• Not taking into account diurnal effects


• High clouds• Strong diurnal cycle as seen by the satellites


• Differences in satellite observation times


• High clouds• Not taking into account diurnal effects


• High clouds• Strong diurnal cycle as seen by the satellites


• High clouds• Accounting for diurnal effects


• High clouds• Accounting for diurnal effects


• High cloud• Accounting for diurnal effects & standardizing

Time Series Analysis - Tropics Over Water

• High cloud

• Not Corrected • Corrected

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/


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


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

Extra slides


• Low clouds• Not taking into account diurnal effects


• Low clouds• Strong diurnal cycle as seen by the satellites


• Differences in satellite observation times


• Effects of satellite drift




• Low clouds• Accounting for diurnal effects




• Low clouds• Accounting for diurnal effects & standardizing

Time Series Analysis - Stratus Regions




• Low clouds• diurnal cycle as seen by the satellites – approx. sine wave






• Low cloud• Accounting for diurnal effects & standardizing




• Low cloud• Accounting for diurnal effects & standardizing

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


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


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

Documents

Exploring the similarities and differences between MODIS, PATMOS and ISCCP