Barbuda

Antigua

MISR 250 m

The Climatology of Small Tropical Oceanic CumuliNew Findings to Old Problems(Analysis of EOS-Terra data)

Larry Di Girolamo, Guangyu Zhao, Bill Chapman and Iliana GenkovaDepartment of Atmospheric Sciences, University of Illinois at Urbana-Champaign

Barbuda

Antigua

MISR 250 m

Satellite remotely sensedsmall cloud properties

carries large errors

Properties may include cloud fraction, height, optical depth, effective radius, LWP…

Known ProblemsMeasured cloud fraction = fraction of pixels detected as cloudy

If we have “perfect” cloud detection (i.e., if pixel contains any amount of cloud, however defined, then label it cloudy), then measured cloud fraction will be an overestimate of the “true” cloud fraction:

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rtri

⎛ ⎝ ⎜

⎞ ⎠ ⎟2

Ae (ri ) ≤ At ≤ Ae (ri )

Based on 684 stochastic cloud fields for ri/rt = 32 (Di Girolamo and Davies 1997)

Perfect cloud detection is bad for estimating the true cloud fraction (but good as a cloud mask for retrieving clear sky properties)

Known Problems

“Perfect” cloud detection does not exist.

Two competing effects in estimating cloud fraction:

(1) overestimation caused by partially-filled cloud pixels that were classified as cloud

(2) underestimation by optically thinner, partially-filled cloud pixels that were classified as clear

“… spatial resolution errors in cloud fraction using an ISCCP-type algorithm with MODIS data would be less than 0.02.” Wielicki and Parker (1992)

“For broken clouds, the average ISCCP cloud amounts are about 5%”… smaller/larger than that estimated by surface observer/Landsat. Rossow et al. (1993)

ISC

CP

(D2)

MO

DIS

(MO

D35

)M

ISR

(Nad

ir R

CC

M)

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DJF 2004/05

D2 Daytime

Many spectral tests

Clear + Probably Clear = Clear

1 spectral test1 spatial testNo angular test

ClearHC + ClearLC = Clear

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40 50 60 70 80 90 100-60

-40

-20

0

20

40

60MODIS

MISR AN

ISCCP

Cloud Fraction [%]

DJF 2004/05

MISR AN BRF MISR RCCM MODIS MOD35

Orbit 26396, Block 107-111, South Pacific, December 3, 2004

Ae = 4%

Ae = 11% Ae = 1%

Zhao and Di Girolamo (submitted to GRL)

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ASTER RGB 15m

ASTER on EOS-Terra

• 15-m Visible bands; 90-m Thermal IR bands

• Tasked for RICO between September 2004 and March 2005

• Analysis between September and December:

448 scenes (~60 km x 60 km) over 38 separate days

• Manually eliminated scenes containing any amount of cirrus:

124 scenes from 28 separate days

• Cloud masks derived manually for each scene

0

0.2

0.4

0.6

0.8

1

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

frccm

f1100

f1000

fmod

ASTER Cloud fraction

0

0.2

0.4

0.6

0.8

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0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4ASTER Cloud fraction

No Sunglint(32 scenes)

Sunglint(92 scenes)

Zhao and Di Girolamo (submitted to GRL)

f15 = 9%f1000 =81%fRCCM=72%fMOD=12%

f15 = 8%f1000 =30%fRCCM =21%fMOD =8%

ASTER 15-m Cloud Masks

Cloud Mask Comparisons between ASTER, RCCM, and MOD35 for the 124 ASTER scenes

Number of scenes

ASTER[%]

MISR[%]

MODIS[%]

True CF at 1 km

[%]

Sunglint 92 7 47 30 50

Non-sunglint 32 10 34 12 49

Zhao and Di Girolamo (submitted to JGR)

0.0001

0.001

0.01

0.1

0 5 10 15 20 25 30

Cloud FractionCumulative Cloud Fraction

Cloud Equivalent Diamter [km]

Trade Wind Cumuli Statistics from ASTER - RICO(fraction, size distribution, area vs. perimeter, clustering, height)

0.001 0.01 0.10

2000

4000

6000

8000

10000

0-0.5

0-1.0

0-2.0

0-3.0

0-4.0

all

Normalized Frequency

Cloud Diameter [km]

Zhao and Di Girolamo (submitted to JGR)

Trade Wind Cumuli Statistics from ASTER - RICO

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Genkova et al. (Submitted to RSE)

ASTER 90m, MISR 1100m, and MODIS 5000m Cloud Top AltitudeOver 41 ASTER scenes

0 m > 3450 m or ocean

Stereo Height (m) Cloud Top Pressure (mb)> 1000mb or ocean

830mb

ASTER MISR MODIS

Summary for Small Clouds• MISR-RCCM does a great job at identifying pixels that contain some clouds… this

leads to large overestimates of the “true” cloud fraction over regions dominated by broken clouds.

• Outside of sunglint, uncertainties in cloud fraction estimates using MODIS-MOD35 are as predicted from earlier studies when looking at the mean. However, there is a bias that increases with increasing true cloud fraction, reaching an overestimate in cloud fraction of ~ 0.1 when true cloud fractions are ~ 0.25 - 0.35.

• Over sunglint, cloud fraction estimates using MODIS-MOD35 are of questionable value. We need to worry about such issues in regional trend analysis.

• Estimates of cumulus cloud fraction from MISR and MODIS (… and others) strongly depend on the spatial distribution of the underlying cloud field.

• For the trade cumuli observed over the RICO domain, MISR cloud top heights provide distributions that are consistant with ASTER and in situ observations, and provides excellent coverage of the cloud field. MODIS cloud top height distributions are skewed low, and provides only marginal coverage.

• Robust statistics on the macrophysical properties of small clouds can be had by tasking ASTER at “no cost” (next: 10 weeks over Gulf of Mexico as part of GoMACCS; 6 months over Indian Ocean)

ASTER 15m