1. Steve Platnick 1, Gala Wind 2,1, Zhibo Zhang 3, Hyoun-Myoung Cho 3, G. T. Arnold 2,1, Michael D. King 4, Steve Ackerman 5, Brent Maddux 5 1 1 NASA Goddard Space Flight Center, 23 2 SSAI, 3 U. of Maryland Baltimore County, 45 4 U. Colorado/LASP, 5 U. Wisconsin, Madison AGU Fall Meeting A44B 6 Dec 2012 San Francisco, CA Sensitivity of Marine Warm Cloud Retrieval Statistics to Algorithm Choices: Examples from MODIS Collection 6 Development Code

2.  What is a Cloud: The Pixel-Level Choices Algorithm Developer’s Make -Explicit (partly cloudy pixel filtering by the developer) -Implicit (filtering invoked by retrieval failures)  Sensitivity of Cloud Optical Property Retrievals to Choices  Sampling fraction, τ, r e Outline

3. Cloud Clear What Do We Mean by a Cloud Mask? Ideal pixel Clear

4. What Do We Mean by a Cloud Mask? Cloud Clear Overcast Clear Sky Partly Cloudy

5. Cloud Clear Satellite Cloud Mask (likelihood of “Not Clear”) What Do We Mean by a Cloud Mask?

6. MODIS Cloud Pixel Filtering Choices: Explicit & Implicit Masked as Clear & Not Clear Total Number of Pixels (1 km) = Developer Choices  Retrieve edge/250m partly cloudy pixels?  Provide a τ- only retrieval when multispectral retrievals fail? Not Clear Categories:  Overcast (?)  Cloud Edge  250m “hole”  Possibly heavy smoke/dust, glint? Explicit filtering Retrieval Outcomes:  Successful τ & r e  No τ or r e possible  τ only (ignore r e spectral information)? Implicit filtering

7. Cloud Pixel Filtering/QA Choices: C5 Granule Example 1 April 2005, MODIS Aqua MODIS 250/500 m composite

8. Cloud Pixel Filtering/QA Choices: C5 Granule Example 1 April 2005, MODIS Aqua Clear Sky Restoral Flags cloud edges 250m partly cloudy pixels spatial/spectral tests (glint, dust, smoke)

9. MODIS 250m Heterogeneity global analysis, low maritime water clouds Pixel Counts 1.00.010.1 1km cloud edges 250m partly cloudy 1km cloud edge & 250m partly cloud removed 3D artifacts more likely

10. Pixel Filtering: Retrieval Outcome Terra MODIS April 2005, maritime water clouds CTP ≥ 680mb, ±30° latitude Successful COT & r e COT r e (2.1 µm)

11. Successful COT & r e COT r e 2.1 – r e 3.7 Pixel Filtering: Retrieval Outcome Terra MODIS April 2005, maritime water clouds CTP ≥ 680mb, ±30° latitude Retrievals consistent w/breakdown of 1D forward model

12. 44% of cloudy pixels are associated w/edges or designated as partly cloudy by the 250m cloud mask 40% of edge/partly cloudy pixel retrievals fail (simultaneous COT and r e solution fall outside LUT space) Successful COT & r e Failure (minor) Failure (major) Pixel Filtering: Sampling Statistics Terra MODIS April 2005, maritime water clouds CTP ≥ 680mb, ±30° latitude

13. Pixel Filtering: Retrieval Outcome SEVIRI, 15 min imagery, 11 August 2009, maritime water clouds CTP ≥ 680mb, ±30° latitude, ±55° VZA Successful COT & r e COT r e (1.6 µm) Successful COT & r e Failure Fraction of Population (%) 20% of cloudy pixels are associated w/edges, 68% of those retrievals fail

14. Pixel Filtering/QA Choices: Global Mean Sensitivity Cloud Retrieval Difference: with edge/250m filtering – w/out τ r e,2.1 ∆ τ =±4 ∆ r e,2.1 =±2 µm April 2005, MODIS Terra

15. Summary (1) Tropical/subtropical marine warm cloud partly-cloudy retrievals (edge pixels and those identified by 250m observations) are biased w.r.t. the filtered pixel population.  Biases are consistent w/breakdown of 1D cloud model. - Retrievals will not correctly describe interaction of the cloud with the radiation field, microphysics, or derived water path.  Frequency of these pixels depends on the spatial scales of the satellite observations and the clouds. MODIS Cloud Product  Collection 5: These pixels were removed/filtered (“Clear Sky Restoral” algorithm).  Collection 6: Will attempt retrievals on these pixels. Allow users to explore the consequences of the partly cloudy categories. Regardless, a significant fraction of such retrievals “fail” for the latitude zone studied.

16. All algorithms do consider the suitability of a pixel/FOV for use with the forward model – either explicitly or implicitly. Spatial heterogeneity and related sampling issues ARE NOT unique to the MODIS product.  Other satellite sensors have similar issues and consequently inherent sampling biases for low marine clouds, e.g., CloudSat [Zhang et al., A33G], microwave imagers, etc. - How to communicate to this to the variety of users is a challenge. Summary (2)