Steve PlatnickSteve Platnick11

&

M. D. KingM. D. King11, J. Riedi, J. Riedi22, G. T. Arnold, G. T. Arnold1,31,3, P. Hubanks, P. Hubanks1,31,3, G. Wind, G. Wind1,31,3 R. Pincus 4, , L. Oreopoulos 1,5

2 Laboratoire d’Optique Atmosphérique, USTL, Lille, France, 3 SSAI, Inc., Seabrook MD, 4 NOAA/ESRL, Boulder CO,

5 UMBC/JCET, Baltimore MD

Yoram J. Kaufman Symposium on Aerosols, Clouds, and Climate

NASA GSFC, Greenbelt, MD

30 May 2007

Spectral Signatures for the Remote Sensing of Clouds

• Satellite Cloud Spectral Signature Animation

• MODIS Cloud Optical & Microphysical Retrieval Capability and Issues

– Fundamental algorithm issues: cloud detection and phase

– Level-3 (1° aggregation) sensitivity studies

• MODIS Level-3 Cloud Retrieval Applications: Cloud Susceptibility and Grid-scale Inhomogeneity Biases

Outline

MODIS Data Granule Spectral Animation Example

Canadian Fires, MODIS Terra, 7 July 2002, 1630 UTC

watercloud

sea ice

icecloud smoke

MODIS Data Granule Spectral Animation Example, cont.in spectral order, excluding ocean bands (thanks to L. Gonzales, R. Simmons)

QuickTime™ and aMPEG-4 Video decompressor

are needed to see this picture.

sea icesea ice

smokesmokeice cloudice cloud

true colorSWIR composite

(RGB = VIS, 1.6, 2.1 µm)

MODIS Data Granule Spectral Animation Example, cont.

Kaufman, Y. J. and Holben, B. N. , “Calibration of the AVHRR visible and near-IR bands by atmospheric scattering, ocean glint and desert reflection”, Int. J. Remote Sens., 1993.

Fraser and Kaufman, “Calibration of satellite sensors after launch”, Appl. Opt., 1986.

Kaufman and Mekler , “Possible causes of calibration degradation of the AVHRR visible and near-IR channels”, Appl. Opt., 1995.

Vermonte and Kaufman, “Absolute calibration of AVHRR visible and near-IR channels using ocean and cloud views”, Int. J. Remote Sens., 1995.

MODIS Aqua Solar Bands Response Trending (3.5 yrs)(J. Xiong et al., MODIS Characterization & Support Team)

VIS

SWIR

NIR

Some MOD06 Optical/Microphysical Collection 5 Changes (King, Platnick, Riedi, Wind, Arnold, Hubanks, Pincus;

modis-atmos.gsfc.nasa.gov/products_C005update.html)

• To retrieve or not to retrieve? New “Clear Sky Restoral” algorithm implemented after cloud mask to further discriminate cloudy pixels and remove cloud edges.

• Cloud thermodynamic phase: liquid water or ice libraries? Updated cloud retrieval phase algorithm. A difficult problem (incomplete spectral coverage in SWIR)!

• Multilayer/multiphase scenes: detectable? New “research-level” multilayer cloud flag. Level-3 code separately aggregates single layer and multilayer cloud fraction, as well as single layer retrievals.

• Ice cloud models. New ice cloud models (Baum et al. 2005).

• Surface spectral albedo, including ancillary information regarding snow/ice extent. New MODIS-derived global snow-free land surface spectral albedo maps; snow/ice spectral albedo maps for Antarctica, Greenland; hemispheric average ecosystem-based snow/ice albedo over land and sea ice; new IGBP ecosystem map. Surface albedo mitigation w/new 1.6-2.1 µm retrievals over ocean and snow/ice surfaces.

• Retrieval uncertainty. New pixel-level , re, WP retrieval uncertainties from 3 fundamental error sources (baseline) & estimates for uncertainty in L3 means.

SWIR composite Cloud Mask overall conf. “Clear Sky Restoral”

cloudy

probably cloudy

spatial/spectral tests

edge detection

250m cloud mask

probably clear

clear

SWIR composite Cloud Mask overall conf.Retrieval process. phase

cloudy

probably cloudy

liquid water

ice

undetermined

probably clear

clear

Cloud_Phase_Optical_Properties

Optical Thickness, Effective Radius Retrievals

optical thickness effective radius (µm)

100 100101101

ice water

40 2552010 30 10 15 20

ice water

Cloud_Optical_Thickness Cloud_Effective_Radius

Partial retrievals not aggregated to Level-3

Collection 5 (C5) Algorithm Sensitivity Studies

• C5-C4 global mean comparisons are ambiguous. C5-C4 global mean comparisons are ambiguous.

– What is the relative effect of the Clear Sky Restoral algorithm (removal What is the relative effect of the Clear Sky Restoral algorithm (removal of pixels)? What is the effect of changes in the phase algorithm of pixels)? What is the effect of changes in the phase algorithm (redistribution of water and ice PDFs)? Effect of changes in surface (redistribution of water and ice PDFs)? Effect of changes in surface albedo maps, reflectance look up tables, …?albedo maps, reflectance look up tables, …?

• Submitted two research runs to the MODIS Atmosphere Team production Submitted two research runs to the MODIS Atmosphere Team production system (MODAPS)system (MODAPS)

– C5 operational code with the Clear Sky Restoral algorithm bypassedC5 operational code with the Clear Sky Restoral algorithm bypassed

– C5 operational code with a “C4-like” phase algorithmC5 operational code with a “C4-like” phase algorithm

• Run on April 2005 MODIS Terra and Aqua dataRun on April 2005 MODIS Terra and Aqua data

• Resulting changes in cloud properties from changes in fundamental Resulting changes in cloud properties from changes in fundamental detection and phase discrimination provide:detection and phase discrimination provide:

– quantitative assessment of “improvements” or measure of the quantitative assessment of “improvements” or measure of the inherent “noise” in retrieval algorithms?inherent “noise” in retrieval algorithms?

Difference in Cloud Fraction: C5 – C5 run w/out Clear Sky Restoral

6 Apr 20056 Apr 2005

Difference in Cloud Fraction: C5 – C5 run w/C4 Phase Algorithm

Monthly Mean Cloud Effective Radiuswater cloud “standard” re retrieval & cloud fraction

Cloud_Fraction_Liquid_FMean

Cloud_Effective_Radius_Liquid_QA_Mean_Mean

April 2005Aqua C5 (QA mean)

Liquid cloud fraction

1.0

0

0.5

Difference in <re> : C5 – C5 run w/out Clear Sky Restoral Liquid clouds, April 2005Liquid clouds, April 2005

Clear Sky Restoral has little effect on re !

Difference in <> : C5 – C5 run w/out Clear Sky Restoral Liquid clouds, April 2005Liquid clouds, April 2005

Clear Sky Restoral increases the mean as expected (e.g., eliminates broken cloud or aerosol

portion of PDF)

Difference: C5 run w/out Clear Sky Restoral vs. C5Water cloudWater cloud , Terra 8-Day Aggregation, S. Atlantic, 30 March - 6 April 2005, Terra 8-Day Aggregation, S. Atlantic, 30 March - 6 April 2005

reduction of counts for “broken” cloud fields “Means” don’t mean a thing!?

<oper> = 8.6

<noCSR> = 7.3 (-15%)

Differences Between Modes and Means?Optical Thickness, MODIS Aqua, April 2005Optical Thickness, MODIS Aqua, April 2005

Uncertainty in Mean : Daily & Monthly ExampleBaseline/minimum expected uncertainty for water clouds, MODIS Aqua C5Baseline/minimum expected uncertainty for water clouds, MODIS Aqua C5

Assumption: pixel-level error

sources correlated

Assumption: daily errors

uncorrelated (optimistic)

MODIS – ISCCP (D2) Optical Thickness Differences July 2002 to May 2004, linearly-weighted means July 2002 to May 2004, linearly-weighted means ((Pincus, Batstone, PlatnickPincus, Batstone, Platnick))

Data set differences include sampling (spatial and temporal) in addition to instruments and algorithms.

Temporal re-sampling of ISCCP to match MODIS sun synchronous observations doesn’t improve agreement substantially (not shown).

MODIS and ISCCP-like vs. pc Joint Histograms Global Oceans 50N-50S, August 2001, MODIS TerraGlobal Oceans 50N-50S, August 2001, MODIS Terra

An observation-only, instantaneous approach to assessing the radiative cloud sensitivity to microphysics (i.e., cloud susceptibility):– Uses operational Collection 5 MODIS global daily Level-3 joint histograms

of liquid water cloud optical thickness (c) and effective radius (re).

– Individual c, re combinations from joint histograms and <Tc>, T(z), q(z), Asfc, input to broadband code for each grid.

– Ancillary data sets same as used in MODIS cloud algorithm (NCEP GDAS, surface spectral albedo maps from Moody et al., 2005)

– Unperturbed and perturbed (due to re changes) TOA albedo differences gives susceptibility.

– Daily susceptibilities aggregated to provide monthly means.– Calculations assume no change in water amount with microphysical changes

Does not address:– Current day vs. pre-industrial changes (not a sensitivity but a climate change

question that includes feedbacks)– Cloud amount and precipitation sensitivities (requires statistical and/or

modeling studies to eliminate dynamic/thermodynamic sensitivities)

MODIS Level-3 Application: Cloud Susceptibility (Oreopoulos, Platnick)

January 2005 April 2005

July 2005 October 2005

-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180

90

60

30

0

-30

-60

-90

longitude

0 2 4 6 8 10susceptibility x 1000

-180 -120 -60 0 60 120 180

90

60

30

0

-30

-60

-90

longitude

0 2 4 6 8 10susceptibility x 1000

-180 -120 -60 0 60 120 180

90

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-30

-60

-90

longitude

0 2 4 6 8 10susceptibility x 1000

-180 -120 -60 0 60 120 180

90

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longitude

0 2 4 6 8 10susceptibility x 1000

-180 -120 -60 0 60 120 180

90

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-30

-60

-90

longitude

0 2 4 6 8 10susceptibility x 1000

Susceptibility (S), N=1 cm-3, LWC=0.3 gm-3, MODIS Terra

October2005

S has ≈ re3 dependence

-180 -120 -60 0 60 120 180

90

60

30

0

-30

-60

-90

longitude

4 8 12 16 20 24 28effective radius (µm)

smaller Sover bright surfaces

-180 -120 -60 0 60 120 180

90

60

30

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-30

-60

-90

longitude

0 2 4 6 8relative susceptibility x 1000

-180 -120 -60 0 60 120 180

90

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longitude

0 2 4 6 8relative susceptibility x 1000

-180 -120 -60 0 60 120 180

90

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longitude

0 2 4 6 8relative susceptibility x 1000

-180 -120 -60 0 60 120 180

90

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longitude

0 2 4 6 8relative susceptibility x 1000

-180 -120 -60 0 60 120 180

90

60

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-60

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longitude

0 2 4 6 8relative susceptibility x 1000

Relative Susceptibility (Srel), N/N=10%, MODIS Terra

April 2005

July 2005 October 2005

January 2005

smaller Srel

over bright surfaces

Srel correlates with Acloud

-180 -120 -60 0 60 120 180

90

60

30

0

-30

-60

-90

longitude

0.0 0.2 0.4 0.6 0.8cloud albedo

October2005

Susceptibility-cloud fraction relations are important!

Global Susceptibility Forcing Examples

0

0.2

0.4

0.6

0.8

1

1.2

January April July October

global IAE for =1N cm-3TerraAqua

Month

0

0.5

1

1.5

2

January April July October

global IAE for =10%NTerraAqua

Month

Global TOA flux change for N=1cm-3, LWC=0.3 gm-3

Global TOA flux change for N/N=10%

0.4

0.6

0.8

1.0

1.2

0.2

0

0.5

1.0

1.5

2.0

0

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90

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longitude (°)

0 5 10 15 20 25 30 35CRF bias (Wm-2)

-180 -120 -60 0 60 120 180

90

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longitude (°)

0 5 10 15 20 25 30 35CRF bias (Wm-2)

-180 -120 -60 0 60 120 180

90

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-60

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longitude (°)

0 5 10 15 20 25 30 35CRF bias (Wm-2)

-180 -120 -60 0 60 120 180

90

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30

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-30

-60

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longitude (°)

0 5 10 15 20 25 30 35CRF bias (Wm-2)

Sensitivity to Grid Horizontal Inhomogeneity for MODIS Terra Water Clouds: Differences in Monthly Cloud Radiative Forcing from Daily L3

CRF(<>grid, <re>grid) – CRF( vs. re histograms for the grid)

April 2005

Oct 2005

smaller biasesover bright surfaces

Jan 2005

July 2005

-180 -120 -60 0 60 120 180

90

60

30

0

-30

-60

-90

longitude (°)

0 5 10 15 20 25 30 35CRF bias (Wm-2)

CRF= 10.0 Wm-2CRF= 9.7 Wm-2

CRF= 9.0 Wm-2CRF= 10.2 Wm-2

• Collection 5 enhancements to the MOD06/MYD06 optical and microphysical Collection 5 enhancements to the MOD06/MYD06 optical and microphysical product include: Clear Sky Restoral, updated phase algorithm, new ice product include: Clear Sky Restoral, updated phase algorithm, new ice models, L2 and L3 uncertainty estimates (for a subset of error sources), models, L2 and L3 uncertainty estimates (for a subset of error sources), multilayer flag research product.multilayer flag research product.

• Completed initial Level-3 test runs of C5 with C4 algorithm modules to assist in Completed initial Level-3 test runs of C5 with C4 algorithm modules to assist in understanding algorithm changes. understanding algorithm changes.

• Histograms (1D and 2D) necessary to help understand/use retrieval statistics. Histograms (1D and 2D) necessary to help understand/use retrieval statistics. Comparing “means” between different algorithms/instruments sensitive to Comparing “means” between different algorithms/instruments sensitive to different parts of the PDF (by design or observations) is apples and oranges. different parts of the PDF (by design or observations) is apples and oranges.

• MODIS vs. ISCCP comparison tools completed and initial analysis begun.MODIS vs. ISCCP comparison tools completed and initial analysis begun.

• Cloud susceptibility tools/analysis (using 2D Cloud susceptibility tools/analysis (using 2D vs. vs. rree histograms) begun. histograms) begun.

• Begun initial efforts to compare phase and multilayer detection with CALIPSO Begun initial efforts to compare phase and multilayer detection with CALIPSO (w/Bob Holz, Steve Ackerman), and tools for L3 geometric sensitivities (w/Bob Holz, Steve Ackerman), and tools for L3 geometric sensitivities (w/Robert Pincus, Paul Hubanks, Steve Ackerman, Brent Maddux).(w/Robert Pincus, Paul Hubanks, Steve Ackerman, Brent Maddux).

• MODIS Atmosphere Team Collection 5 reprocessing completed in early April MODIS Atmosphere Team Collection 5 reprocessing completed in early April 2006 for Aqua, in Feb 2007 for Terra. All products now archived and 2006 for Aqua, in Feb 2007 for Terra. All products now archived and distributed via the MODIS LAADS system (disk storage archive w/ftp access, distributed via the MODIS LAADS system (disk storage archive w/ftp access, search capability, subsetting, etc.). Distribution from Goddard DAAC search capability, subsetting, etc.). Distribution from Goddard DAAC discontinued.discontinued.

Summary

Extras

Some Solar Reflectance Optical/Microphysical Retrieval Issues

Critical issues (esp. for global processing):

• To retrieve or not to retrieve?

• Cloud thermodynamic phase: liquid water or ice libraries?

• Multilayer/multiphase scenes: detectable?

• Ice cloud models

• Surface spectral albedo, including ancillary information regarding snow/ice extent

• Other ancillary information: Atmospheric corrections require moisture & temperature profiles, pc; 3.7 µm retrievals require Tc, Tsfc (band contains solar and emissive radiance)

• 3-D cloud effects

• Retrieval uncertainty (pixel-level and aggregated)

Retrieval Uncertainty Estimates

opt. thickness:

effective radius:

500 25 500 25

250 250ice water

c/c (%) re/re (%)

Error sources: cal./fwd. model (5%), sfc. albedo(15%), atmo. correction (20% PWError sources: cal./fwd. model (5%), sfc. albedo(15%), atmo. correction (20% PW cc))

Cloud_Optical_Thickness_Uncertainty Cloud_Effective_Radius_Uncertainty

Multilayer Flag• Multilayers w/different phases: disagreement between IR-phase retrieval and phase derived for Multilayers w/different phases: disagreement between IR-phase retrieval and phase derived for

optical/microphysical retrieval (SWIR bands, cloud mask tests, …).optical/microphysical retrieval (SWIR bands, cloud mask tests, …).• General multilayer: 0.94 µm water vapor absorption band.General multilayer: 0.94 µm water vapor absorption band.

liquid water

ice

undetermined

ML, retr’d as water

ML, retr’d as ice

ML undetermined

SWIR composite

from Quality_Assurance_1km

MODIS Aqua Granule Example20 Aug 2006, Central Am./NW SA, true color composite

Error sources: calibration/forward model,Error sources: calibration/forward model,surface albedo, atmospheric correctionsurface albedo, atmospheric correction

WP (gm-2) WP/WP (%)

103 1031021010210

ice water ice water

50250 50250

ice water

MODIS Aqua Granule Example, cont. IWP, LWP, and Baseline Uncertainty Estimate

Cloud_Water_Path Cloud_Water_Path_Uncertainty

MODIS Aqua Granule Example, cont. Uncertainty vs. IWP: Ocean Pixels Only

MODIS Aqua Granule Example, cont. Uncertainty vs. IWP: Ocean Pixels Only

MODIS Aqua Granule Example, cont. Uncertainty vs. IWP: Ocean Pixels Only

Differences Between Modes and Means?Optical Thickness, Aqua, April 2005Optical Thickness, Aqua, April 2005