ISCCP/GISS ISCCP/UMD UMD/MODIS July 2001 Zonal Mean all-sky surface SW down W/m 2 ) Figure 1.
Exploring the similarities and differences between MODIS, PATMOS and ISCCP
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Transcript of 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