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

EOF Analysis - PATMOS/AVHRR

• Low & Mid Cloud Fraction - 1st EOF

EOF Analysis - PATMOS/AVHRR

• High Cloud Fraction - 1st EOF

EOF Analysis - ISCCP

• Total Cloud Fraction - 1st EOF

EOF Analysis - ISCCP

• Low & Mid Cloud Fraction - 1st EOF

EOF Analysis - ISCCP

• High Cloud Fraction

EOF Analysis - MODIS (2005 Terra: collection 5)

• Total Cloud Fraction - 1st EOF

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!

Time Series Analysis - Tropics Over Land

• High clouds• Not taking into account diurnal effects

Time Series Analysis - Tropics Over Land

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

Time Series Analysis - Tropics Over Land

• Differences in satellite observation times

Time Series Analysis - Tropics Over Land

• High clouds• Not taking into account diurnal effects

Time Series Analysis - Tropics Over Land

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

Time Series Analysis - Tropics Over Land

• High clouds• Accounting for diurnal effects

Time Series Analysis - Tropics Over Land

• High clouds• Accounting for diurnal effects

Time Series Analysis - Tropics Over Land

• 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/

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

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

Extra slides

Time Series Analysis - Tropics Over Land

• Low clouds• Not taking into account diurnal effects

Time Series Analysis - Tropics Over Land

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

Time Series Analysis - Tropics Over Land

• Differences in satellite observation times

Time Series Analysis - Tropics Over Land

• Effects of satellite drift

Time Series Analysis - Tropics Over Land

• Low clouds• Not taking into account diurnal effects

Time Series Analysis - Tropics Over Land

• Low clouds• Accounting for diurnal effects

Time Series Analysis - Tropics Over Land

• Low clouds• Accounting for diurnal effects

Time Series Analysis - Tropics Over Land

• Low clouds• Accounting for diurnal effects & standardizing

Time Series Analysis - Stratus Regions

Time Series Analysis - Stratus Regions

• Low clouds• Not taking into account diurnal effects

Time Series Analysis - Stratus Regions

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

Time Series Analysis - Stratus Regions

• Low clouds• Not taking into account diurnal effects

Time Series Analysis - Stratus Regions

• Low clouds• Accounting for diurnal effects

Time Series Analysis - Stratus Regions

• Low cloud• Accounting for diurnal effects & standardizing

Time Series Analysis - Stratus Regions

• Low clouds• Accounting for diurnal effects

Time Series Analysis - Stratus Regions

• 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

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

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