july 14, 2004alexander marshak 3d error assessment and cloud climatology from modis r.f. cahalan, a....

July 14, 2004 Alexander Marshak

3D Error Assessment and Cloud Climatology from MODIS

R.F. Cahalan, A. Marshak (GSFC)

K.F. Evans (University of Colorado)

L. Oreopoulos, T. Várnai, G. Wen (UMBC)

Extend 3D retrieval capabilities for both passive (Terra and Aqua) and active (THOR lidar) remote sensing

1. Multiple-instrument Cloud-Aerosol I3RC Cases and 3D Toolkit [I3RC = (International) Intercomparison of 3D Radiation Codes2. 3D Error Assessment and Cloud Climatology from MODIS3. 3D Cloud Retrieval from MISR4. Cloud Retrievals from THOR (Thickness from Offbeam Returns)


Task I: Multiple-instrument Cloud-Aerosol I3RC Cases and 3D Toolkit

Expected results

• Cloud cases from collocated MODIS, MISR and ASTER data• such cases are based directly on observed cloud fields;• all multi-instrument observed radiances are computable by “I3RC-certified” 3DRT codes;• the cases provide a basic for development of improved 3D retrievals.

• Open Source Toolkit (led by Robert Pincus)

• publicly documented MC Fortran code for 3DRT;• complements to widely used SHDOM.

• Educational pages on I3RC website (http://climate.gsfc.nasa.gov/I3RC/)

• case studies of different degrees of 3D complexity (from pp marine Sc to broken Cu) where students can learn about 3D RT and understand where and how pp approaches break down

http://climate.gsfc.nasa.gov/I3RC/




Multiple-instrument Cloud-Aerosol Casescase I: marine Sc (led by T. Varnai)

Images of the same marine Sc cloud from ASTER, MODIS and MISR taken on board of Terra on May 21, 2001 at 19:41 UTC over the Pacific Ocean

60 by 60 km ASTER image (nadir view, 15 m resolution)

60 by 60 km MODIS image (1 km and 250 m resolution)

60 by 60 km MISR image (275 m resolution, 26° and 60° viewing zenith angles)

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log2k

slope = -1.7

slope = -2.1

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ASTER

MODIS

MISR

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26° view

60° view

Scale (km)

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Wavenumber spectrum of variations in all five images


Multiple-instrument Cloud-Aerosol Casescase II: biomass burning (led by G. Wen)

Biomass burning region in Brazil, Aug. 9, 2001 centered at -17.10 Lat and -42.16 Lon


Multiple-instrument Cloud-Aerosol Casescase II: biomass burning (led by G. Wen)

ASTER (VNIR 15 m and SWIR 30 m)

MISR (0.67 m)0.275 km resolution (in nadir)

Cf (60o)

An (0o)

Ca (60o)

MODISRGB = 2.2, 0.86, 0.55 m

Biomass burning region in Brazil, Aug. 9, 2001 centered at -17.10 Lat and -42.16 Lon

60 km

1 km resolution

0.25 km resolution


Task II: 3D Error Assessment and Cloud Climatology from MODIS

Expected Results

• Error bounds that cloud horizontal variability introduces into retrievals

• Climatic distribution of 3D effects


Illustration of “illuminated” and “shadowy” pixels

(led by Tamas Varnai)

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Example with pixels’ temperature

front front

behindbehind

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Cold

Warm ILL SHAD


3D Error Assessment: Example

An example of 450x200 km2 area observed by MODIS with VIS and IR channels. The area has been divided into 36 areas of 50x50 km2 each.

0.86 µm reflectance

11 µm brightness temperature


Number of pixels

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# of illum. pxls # of shad. pxls

# of "pixels" per 50x50 km2 area

# of “illuminated” and “shadowed” pixels (total #: 107+) in 50x50 km2 areas

are statistically equal


Symmetry at 11 m

So is IR brightness temperature


Asymmetry at 0.86 and 2.1 m

Each dot corresponds to a 50x50 km2 area. Averaged reflectancies over “illuminated” pixels are plotted vs. “shadowed” ones.

The ill. slopes are much brighter than the shad. ones!


Effects on and reff

Comparison of mean optical depth, , and mean effective radius, reff, at the illuminated and shadowed portion of 50 by 50 km areas

3D effects may have a strong influence!


Example of climatic distribution of 3D effects

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Relative difference, D

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Histogram value

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Comparison of the histograms of the cloud asymmetry in optical depth retrieved from clouds over land and ocean. The inset shows the histograms of the asymmetry vs. differences between average optical depths of illuminated and shadowed pixels, TS and AS, respectively.


“Forward” vs. “Backward” scattering

from Loeb and Coakley (1998)

Based on AVHRR data

from Buriez et al. (2001)

Based on Polder data

Earlier studies on 3D effect: For oblique sun, clouds appear too thick & forward reflection is too low


“Forward” vs. “Backward” scatteringMODIS geometry

MODIS granule Gro

und

track

of s

atel

lite

satellite

Incoming sunlight

Incoming sunlight

MODIS observesforward scattering

MODIS observesback scattering

At 40o latitude, clouds are not viewed from the exact forward and backward directions but rather 50o off the plane of solar azimuth


“Forward” vs. “Backward” scatteringMODIS data

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Solar zenith angle (°)

Nov. 1, 8, 15, 22, 29 in 2000, 2001, 2002, 2003.10 MODIS granules from Terra in 2000 and 2001 and from both Terra and Aqua in 2002 and 2003. Total: 300 granules. Form a ring around the Earth at roughly 40o North. Liquid clouds only with > 2.


“Forward” vs. “Backward” scatteringMODIS data

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Viewing zenith angle (°)

Solar zenith angle (°)

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Results after the influence of various SZA is equalized across the track

Viewing zenith angle (°)Back scatter Forward scatter

Mean optical depth as a function of SZA and VZA

Mean optical depth (normalized by SZA) as a function of VZA


“Forward” vs. “Backward” scatteringMODIS data: saturated pixels

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(°)Viewing zenith angle Back scattering Forward scattering

Fraction of “saturated” pixels as a function of VZA


Climatic distribution of 3D effects(led by Lazaros Oreopoulos)

• Latitudinal variation (-70° to 70°) of inhomogeneity parameter of Cahalan (1994) and optical depth for water clouds from MODIS data.

• Variations of optical depth are possibly exaggerated due to biases in optical depth retrievals under oblique illumination.

• 3D retrievals are needed to remove such biases.

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from the histogram of optical depth for the entire month

the average for an entire year of monthly values

=exp(lnτ )

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Task II: Conclusion

• Statistical asymmetry is a direct signature of cloud 3D structure that cannot be taken into account in 1D retrievals:

• Estimate the errors that horizontal cloud variability introduces into retrievals of cloud properties;

• Study the climatology of 3D effects by analyzing how cloud 3D structure varies with geographical region, season and climatic conditions.


Task III: 3D Cloud Retrievals from MISR(led by Frank Evans)

Expected results

• 3D algorithm for cloud optical depth and top height retrievals

• Importance of textural and angular parameters for optical depth and height

• Estimates of improvements


3D Cloud Retrievals from MISR3D cloud retrieval algorithm

The liquid water path (LWP) from LES cloud fields is shown in the upper left.

The middle left has the LWP fields for one of the stochastic fields generated with statistics of the 8 LES fields.

The stochastic field with a grid spacing of 67 m is averaged 4x4 columns to obtain the MISR nadir resolution optical depth and cloud top height shown in the lower left.

Reflectances at the nine MISR angles are computed with the SHDOM 3D radiative transfer code.

The reflectances at MISR resolution for the five angles used in the retrieval simulation are shown in the right column.


Task IV: THOR Lidar Retrievals(led by Bob Cahalan and Tamas Varnai)

•Objectives - Measure geometrical thickness of optically thick clouds

•Accomplishments - Measured cloud geometric thicknesses: 500–1000 m ± 30 m, > 25

•Exp. results - algorithms for cloud geometrical thickness and extinction retrievals


THOR Color Composite (R,G,B) = (1,7,8)

NASA P-3B at 8.53 km

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Thin Cirrus Cloud Layer

Thick Lower Stratus Deck

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THOR + ARM estimate (m)

Flight altitude:


3D Error Assessment and Cloud Climatology from MODIS

A. Marshak, R.F. Cahalan (GSFC)

K.F. Evans (University of Colorado)

L. Oreopoulos, T. Várnai, G. Wen (UMBC)

Extend 3D retrieval capabilities for both passive (Terra and Aqua) and active (THOR lidar) remote sensing

1. Multiple-instrument Cloud-Aerosol I3RC Cases and 3D Toolkit [I3RC = (International) Intercomparison of 3D Radiation Codes2. 3D Error Assessment and Cloud Climatology from MODIS3. 3D Cloud Retrieval from MISR4. Cloud Retrievals from THOR (Thickness from Offbeam Returns)

july 14, 2004alexander marshak 3d error assessment and cloud climatology from modis r.f. cahalan, a....

Documents

d cloud retrieval

cloud climatology

cloud retrievals

marine sc cloud

d complexity

d toolkit i3rc

observed cloud fields

d retrieval capabilities