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Steve Platnick 1 , Gala Wind 2 ,1 , Zhibo Zhang 3 , Hyoun-Myoung Cho 3 ,

Sensitivity of Marine Warm Cloud Retrieval Statistics to Algorithm Choices: Examples from MODIS Collection 6 Development Code. 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

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Steve Platnick 1 , Gala Wind 2 ,1 , Zhibo Zhang 3 , Hyoun-Myoung Cho 3 ,

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  1. Sensitivity of Marine Warm Cloud Retrieval Statistics to Algorithm Choices: Examples from MODIS Collection 6 Development Code Steve Platnick1, Gala Wind2,1, ZhiboZhang3, Hyoun-Myoung Cho3, G. T. Arnold2,1, Michael D. King4 , Steve Ackerman5, Brent Maddux5 1NASA Goddard Space Flight Center, 2SSAI, 3U. of Maryland Baltimore County, 4U. Colorado/LASP, 5U. Wisconsin, Madison AGU Fall Meeting A44B 6 Dec 2012 San Francisco, CA

  2. Outline • 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, re

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

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

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

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

  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 cloud edges 250m partly cloudy pixels spatial/spectral tests (glint, dust, smoke) Clear Sky Restoral Flags

  9. MODIS 250m Heterogeneity global analysis, low maritime water clouds 3D artifacts more likely 1km cloud edge & 250mpartly cloud removed Pixel Counts 1km cloud edges 250m partly cloudy 0.01 0.1 1.0

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

  11. Pixel Filtering: Retrieval Outcome Terra MODIS April 2005, maritime water clouds CTP ≥ 680mb, ±30° latitude Successful COT& re COT re2.1 – re3.7 Retrievals consistent w/breakdown of 1D forward model

  12. Pixel Filtering: Sampling Statistics Terra MODIS April 2005, maritime water clouds CTP ≥ 680mb, ±30° latitude Successful COT & re Failure (minor) Failure (major) • 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 re solution fall outside LUT space)

  13. Pixel Filtering: Retrieval Outcome SEVIRI, 15 min imagery, 11 August 2009, maritime water clouds CTP ≥ 680mb, ±30° latitude, ±55° VZA Successful COT& re Fraction of Population (%) COT re (1.6 µm) Successful COT & re Failure 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 ∆τ=±4 re,2.1 ∆re,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. Summary(2) • 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.

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