Steve Platnick1, Gala Wind2,1, Zhibo Zhang3, Hyoun-Myoung Cho3,
G. T. Arnold2,1, Michael D. King4 , Steve Ackerman5, Brent Maddux5
11NASA Goddard Space Flight Center,
232SSAI, 3U. of Maryland Baltimore County,
454U. Colorado/LASP, 5U. Wisconsin, Madison
AGU Fall Meeting
A44B
6 Dec 2012
San Francisco, CA
Sensitivity of Marine Warm Cloud RetrievalStatistics to Algorithm Choices: Examples fromMODIS Collection 6 Development Code
What is a Cloud: The Pixel-Level Choices AlgorithmDeveloper’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
Outline
Cloud
Cloud
Clear
Clear
What Do We Mean by a Cloud Mask?
Ideal pixel
Clear
Clear
What Do We Mean by a Cloud Mask?
Cloud
Cloud
Clear
Clear
Clear
Clear
Overcast
Clear Sky
Partly Cloudy
Cloud
Cloud
Clear
Clear
Clear
Clear
Satellite
Cloud Mask
(likelihood of
“Not Clear”)
What Do We Mean by a Cloud Mask?
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 & re
No τ or re possible
τ only (ignore respectral information)?
Implicit filtering
Cloud Pixel Filtering/QA Choices: C5 Granule Example
1 April 2005, MODIS Aqua
MODIS 250/500 m composite
Cloud Pixel Filtering/QA Choices: C5 Granule Example
1 April 2005, MODIS Aqua
Clear Sky Restoral Flags
cloud
edges
250m
partly
cloudy
pixels
spatial/spectraltests (glint,dust, smoke)
MODIS 250m Heterogeneity
global analysis, low maritime water clouds
Pixel Counts
1.0
0.01
0.1
1km cloud
edges
250m
partly cloudy
1km cloud edge &
250m partly cloud
removed
3D artifacts
more likely
Pixel Filtering: Retrieval Outcome
Terra MODIS April 2005, maritime water clouds
CTP ≥ 680mb, ±30° latitude
Successful COT & re
COT
re (2.1 µm)
Successful COT & re
COT
re2.1 – re3.7
Pixel Filtering: Retrieval Outcome
Terra MODIS April 2005, maritime water clouds
CTP ≥ 680mb, ±30° latitude
Retrievals consistent w/breakdown of 1D forward model
•44% of cloudy pixels are associated w/edges or designated as partlycloudy by the 250m cloud mask
•40% of edge/partly cloudy pixel retrievals fail (simultaneous COTand re solution fall outside LUT space)
Successful COT & re
Failure (minor)
Failure (major)
Pixel Filtering: Sampling Statistics
Terra MODIS April 2005, maritime water clouds
CTP ≥ 680mb, ±30° latitude
Pixel Filtering: Retrieval Outcome
SEVIRI, 15 min imagery, 11 August 2009, maritime water clouds
CTP ≥ 680mb, ±30° latitude, ±55° VZA
Successful COT & re
COT
re (1.6 µm)
Successful COT & re
Failure
Fraction of Population (%)
20% of cloudy pixels are associated w/edges, 68% of those retrievals fail
Pixel Filtering/QA Choices: Global Mean Sensitivity
Cloud Retrieval Difference: with edge/250m filtering – w/out
τ
re,2.1
∆τ=±4
∆re,2.1=±2 µm
April 2005, MODIS Terra
Summary (1)
•Tropical/subtropical marine warm cloud partly-cloudy retrievals(edge pixels and those identified by 250m observations) arebiased w.r.t. the filtered pixel population.
Biases are consistent w/breakdown of 1D cloud model.
-Retrievals will not correctly describe interaction of the cloudwith the radiation field, microphysics, or derived water path.
Frequency of these pixels depends on the spatial scales of thesatellite observations and the clouds.
•MODIS Cloud Product
Collection 5: These pixels were removed/filtered (“Clear SkyRestoral” algorithm).
Collection 6: Will attempt retrievals on these pixels. Allow usersto explore the consequences of the partly cloudy categories.Regardless, a significant fraction of such retrievals “fail” for thelatitude zone studied.
•All algorithms do consider the suitability of a pixel/FOV for use withthe forward model – either explicitly or implicitly.
•Spatial heterogeneity and related sampling issues ARE NOTunique to the MODIS product.
Other satellite sensors have similar issues and consequentlyinherent 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 achallenge.
Summary (2)