1. Orbit Characteristics and View Angle Effects on the Global Cloud Field
Brent C. Maddux1,2
Steven A. Ackerman1
Steve Platnick3
Paul Hubanks4
1Cooperative Institute for Meteorological Satellite Studies
2Department of Atmospheric and Oceanic Sciences-U of Wisconsin
3NASA Goddard Space Flight Center, Greenbelt, Maryland
4Wyle Information Systems, McLean, VA 22102

2. Comparison to other cloud datasets (apples-to-apples)
Motivation
Comparison to other cloud datasets (apples-to-apples)
Place ‘error bars’ on global mean statistics
Quantify cloud variability globally

3. Daytime Cloud Fraction Coast lines evident
Aqua 7 Year Mean
Global Means
Daytime Cloud Fraction
Coast lines evident
Differences where expected: maritime stratocumulus decks, SH land, etc.
Terra 9 Year Mean
Spatial differences aren’t very pronounced, some differences over stratocumulus and land areas.

4. MODIS Cloud Fraction Terra (Red) and Aqua (Black)
Global Cloud Amount Anomaly
CA Anomaly (%) 3 2 1 -1 -2 -3
Time series is very stable and Aqua and Terra MODIS agree very well

5. Cloud Fraction vs Viewing Angle
7 years of Aqua and Terra
16% increase from near nadir to edge of scan
View angle effect not constant for all cloud types
Cloud Fraction vs Sensor Zenith Angle
Sensor Zenith Angle
Cloud Fraction (%)

6. Single Day in the MODIS Orbit
Geostationary-like view
16 day orbit procession
U-Shape at edges of convection and cloudy regions
Increase in pixel size with view angle
Terra Daytime Cloud Fraction

7. Daytime Cloud Fraction Mean
Nadir to 10°
60° to edge of scan°
Nadir to 10° minus 60 and greater
Differences not uniform
Largest differences not where thin high clouds exist

8. Nadir vs Edge of Scan Changes are not uniform
Near Nadir
Near Edge of Scan
Nadir vs Edge of Scan
Cloud Fraction (%)
Changes are not uniform
Largest changes in CF aren’t the same as largest changes in optical properties
Near Nadir ≤10°
Near Edge of Scan ≥50°
Cloud Top Pressure (hPa)
Cloud Effective Radius Ice (μm)
Cloud Effective Radius Liquid (μm)
Cloud Optical Depth Ice
Cloud Optical Depth Liquid

9. L3 Statistical Uncertainty Study: Zonal Retrieval Statistics vs
L3 Statistical Uncertainty Study: Zonal Retrieval Statistics vs. VZA Effective Radius, water clouds
Similar biases for all categories, and reasonably symmetric => partly cloudy pixel but not shadowing impact OR vertical microphysical structure
Significance of these biases?
What about regional biases?

10. L3 Statistical Uncertainty Study: Global Retrieval Statistics vs
L3 Statistical Uncertainty Study: Global Retrieval Statistics vs. Zenith Angle Effective Radius, water clouds, MODIS Aqua, April 2005
operational (all bins)
backward oblique bin (-53° to -65° VZA)
forward oblique bin (53° to 65° VZA)
fwd. nadir bin 12
≥24

11. Mean Cloud Fraction Difference (MOD35-MOD06)
This is the 7 year mean difference of the cloud mask and the retrieval fraction. You can see that the differences are greatest over regions where broken cumulus, aerosols and highly reflective surfaces.
(%)
MOD06 cloud fraction is a quality assured subset of MOD35 to retrieve better cloud optical properties

12. Mean Cloud Fraction Difference in Percent (MOD35-MOD06)/MOD35
This is the 7 year mean difference of the cloud mask and cloud retrieval fraction divided by that cloud mask fraction. By looking at the difference in percent it is a little more obvious where clouds are being missed relative to the cloud fraction, but these regions tend to have few clouds anyway, e.g. the Sahara or mountain ranges.
(%)
Differences are due to the QA stuff(clear sky restoral and cloud edges, thin clouds, and surfaces influences).

13. Difference in high and low cloud fraction
CTP histogram from CTP (red) and CTPvsOD (blue) histogram
Pressure Level (hPa)
This is the mean CTP histogram (red) SDS for 1 year of data and the cloud top pressure histogram from the mean CTP vs OD (water and ice) for global data. These are relative numbers, i.e. all red dots add to 100 and all blue dots add to This shows that after the optical properties are excluding significantly more low cloud. Differences are due to the QA (clear sky restoral and cloud edges, thin clouds, and surfaces influences).
Cloud Fraction
Difference in high and low cloud fraction

14. Cloud Fraction Day Difference for 7 yrs (Aqua minus Terra)
OCT
JUL
APR
JAN
NOV
AUG
MAY
FEB
DEC
SEP
JUN
MAR

15. Grid Cell Size and Swath Overlap
Cloud Top Pressure vs Latitude
Swath overlap causes more cloud to be averaged into the middle cloud bin 50 40 30
Cloud Fraction 20
At 1 degree resolution when the mean gridcell cloud fraction is between 700 and 400 hPa the clouds in that gridcell are only mid clouds about 21 % of the time. Compared to ¼ degree resolution where the number of mid clouds is 56. 10 EQ
Latitudes Included

16. Grid Cell Size and Swath Overlap
Cloud Top Pressure Histogram
High Cloud <400 hPa
Mid Cloud >400 and <700 hPa
Low Cloud >700 hPa
At 1 degree resolution when the mean gridcell cloud fraction is between 700 and 400 hPa the clouds in that gridcell are only mid clouds about 21 % of the time. Compared to ¼ degree resolution where the number of mid clouds is 56.

17. Cloud fractions need to characterize the global cloud trend within ±1%
Summary
Cloud fractions need to characterize the global cloud trend within ±1%
View angle dependencies are large across swath
16% cloud fraction (>60 locally)
30hPa for cloud top pressure (>200 hPa locally)
2μm for effective radii (10μm locally)
2 for optical depth (20 locally)

18. Pixel vs Area Weighting
Not a uniform offset
Doesn’t change long term mean (.2%)
Polar regions oscillate opposite mid-latitudes
MODIS Terra Cloud Fraction Area (Red) and Pixel (Blue)
If data is weighted by pixel counts, such that each pixel is included equally then the long term mean is virtually unchanged BUT the timeseries is not only different is is out of phase.
This is not simply an aggregation issue, but an issue of what data to include, the red line is equivalent to a single overpass data set, the other is a multiple over pass data set.

19. L3 Statistical Uncertainty Study: Zonal Retrieval Statistics vs
L3 Statistical Uncertainty Study: Zonal Retrieval Statistics vs. VZA Optical Thickness, water clouds
Bias is within the range of the mean pixel-level uncertainties for nadir views. Not that bias should be directly equated with these mean uncertainties, but helpful – or at least practically useful – to know that the mean uncertainties appear to provide a bias limit.

20. Summary of VZA Results For water clouds:
Zonal mean statistics: at all latitudes (land and/or ocean) VZA dependencies for t and re are within product’s mean instantaneous uncertainties
Optical Thickness zonal means:
ocean clouds show some asymmetry (slight indication of shadowing on sun view side of scan?)
land clouds symmetric.
Effective Radius:
no obvious shadowing effect (symmetric), both zonal and some regional analysis

21. This figure would argue for not using the edge of scan, especially at higher latitudes.

22. Cloud Retrieval Fraction for one month (MOD06 optical properties cloud fraction)
This is the cloud retrieval fraction for a single July. It is simply the fraction (successful retrievals)/(all possible retrievals). So this cloud fraction will be smaller over regions of small/broken clouds, or over surfaces with hard to retrieve clouds, i.e. sunglint, desert, ice…

23. Cloud Fraction for one month (MOD35)
This is the MODIS cloud mask cloud fraction for a single july. This is the actual cloud fraction regardless of whether a microphysical cloud retrieval can be run.

24. Mean Cloud Fraction Difference for on month (MOD35 minus MOD06)
This is the difference cloud mask minus the cloud retrieval fraction for the first two slides.

25. Mass Concentration for 1 orbit day

26. Cloud Fraction
MODIS cloud mask (MOD35, described in Ackerman et. al 1999)
Up to 19 channels in vis and IR
1km to 250m resolution
For Level-3 statistics there are clear and cloudy pixels

27. Two types of variability Natural: Artificial:
Introduction
Two types of variability
Natural:
Time and spatial scales
Artificial:
Instrument (Aqua-Terra Differences)
Orbit (polar vs geostationary)
Algorithm- channels choices

28. Find distribution of re and od vs solar angles Pdfs from poster
2.1 and 37 differences
TTD:
CTPDH vs angle for Aqua 2004
Find distribution of re and od vs solar angles
Pdfs from poster
Make a error bar plot on dataset
Include figures on angle attribution for two main influence, pixel size and thin clouds at oblique angles