Earth Radiation BudgetObservations

Hai-Tien Lee

Arnold GruberUniversity of Maryland College Park, CICS/ESSIC-NOAA

Robert G. EllingsonFlorida State University, Dept. of Meteorology

Istvan LaszloNOAA/NESDIS

NOAA/NESDIS Cooperative Research Program 3rd Annual Science Symposium Fort Collins, CO, August 15-16, 2006

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Earth Radiation Budget

Kiehl and Trenberth, 1997. Bull. Amer. Meteor. Soc., 78, 197-208.

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History• First measurements of OLR as early as 1959 from Explorer-7

• Experiments - ERB, ERBE, ScaRaB, CERES, GERB

• First routine measurements of OLR from operational satellites began in 1974

– NOAA scanning radiometer ( SR)- window channel ( 10-12 microns)

– Linear algorithm between window radiances and total OLR – based on radiative calculations with model atmospheres

– Evolved a few years later to a non linear algorithm which is still in use today - adjusted for different spectral interval

• SR data 1974-1978, AVHRR 1979-onward

AVHRR and HIRSOLR Algorithms and Products

Tropical AVHRR OLR Anomaly Time/Longitude plot

1984-2003[5S-5N]

Contour level 10Wm-2

Pastel yellow are within ±10Wm-2

El Nino

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ERBE-AVHRR Daytime OLR July 1985

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Clear-sky OLR Anomaly (Jan 1998)

AVHRR OLR lacks sensitivity to water vapor variation, especially the upper tropo. humidity (UTH).

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Multi-spectral HIRS OLR Algorithm

ai=regression coefficients=local zenith angle

Ellingson et al. (1989)

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OLR = a0(θ) + ai(θ)⋅N ii

∑ (θ)

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OLR = Iν↑ (zt;μ,φ)μdμdφdν

0

1

∫0

2π

∫0

∞

∫

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Iν↑ (zt ;μ ,φ) =εν

∗Bν∗(0)Tν (zt ,0;μ,φ) + Bν ( ′ z )

∂Tν (zt , ′ z ;μ ,φ)

∂ ′ z d ′ z

0

zt∫

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N i(μ) = Iν↑ (zt ,μ) f i(ν )dν

Δν i∫

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μ =cos(θ)

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Regression Model• Channels and spectral intervals – stepwise regression based

on 1600 Phillips soundings and radiation transfer model

HIRS Channel Wavelength (μm) Atmos Sensitivity

H7 13.1-13.6 Near Sfc temp

H10 7.8 – 8.5 Lower trop water vapor

H12 6.6-6.9 Upper trop water vapor

H3 14.3-14.7 Air temp- at 100mb

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Validation of Multi-spectral OLR Algorithms

Ellingson et al., 1994: Validation of a technique for estimating outgoing longwave radiation from HIRS radiance observations J. Atmos. Ocean. Technol., 11, 357-365.

Ba et al., 2003: Validation of atechnique for estimating OLR with the GOES sounder. J. Atmos. Ocean. Technol., 20, 79–89.

HIRS OLR is Operational since 1998.

HIRS OLR Climate Data Record

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Equator Crossing Times for NOAA Polar Orbiters

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Pingping Xie, 2006

AVHRR OLR PC3 and Satellite Observation Time

PC seems related to changes in satellite observations time

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Inter-satellite CalibrationSatellites Bias (Wm-2)

TN 0.15

N06 1.80

N07 2.13

N08 2.03

N09 Reference

N10 0.53

N11 -5.36

N12 -2.42

N14 -5.14

N15 -3.65

N16 -3.25

Collocation:• 1°x1° lat/lon• ±30 minutes

Homogeneity filter:• Std error of mean OLR < 1 Wm-2

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OLR Climatological Diurnal Model

Tsaidam Basin Western Pacific

25 Years of Monthly Mean OLR Local Time Composite

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OLR = a0 + a1COSπ ( t − t0)

12+ a2COS

2π (t − t0)12

HIRS product is as stable as ERBS-NS.

ERBS NS vs. HIRSBest-fit line slope = 0.998STD = 0.97 Wm-2

r = 0.86

Tropical 20 NS

ERBS Non-scanner and HIRS 1985-1999

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Tropical Mean OLR 1984-2003

Relative to HIRSERBE SC (ERBS)

-2.9±0.1

n=60

CERES TRMM

-0.1±0.2

n=8

CERES Terra X

-0.5±0.1

n=56

CERES Aqua X

-1.0±0.2

n=25

ERBE NS -4.4±0.1

n=170Tropical 20 NS

HIRS OLR is a reliable and traceable Transfer Standard

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HIRS-CERES 2000-2004STD diff

Global= 2.8 Wm-2

* CERES ES4 from Terra-Xtrack Ed.2

Mean diffGlobal avg= 1.5 Wm-2

RMS diffGlobal= 4.0 Wm-2

Global RMS Diff = 4.0 Wm-2

Outlook

Synergy between Operational Polar-Orbiting and Geostationary Satellite OLR Products

NOAA/MetOp/NPOESS - HIRS, IASI, CrIS, ERBS/CERES

GOES-EGERB

Met-8/9

Geostationary

GMSFY-2C

MTSATGOES-W Met-5

The End

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Florida

Amazon

NevadaStorm track

Andes

Sierra Madre

Occidental

Yucatan

Gulf of Mexico

Subtropical Oceans

Blue: HIRSRed: GOES

GOES and GERB OLR data provide detailed diurnal variation information that we can use it to construct and examine the diurnal models. This figure shows the phase information of the OLR diurnal variation for the GOES-E full disk domain with some typical patterns at selected sites. The HIRS-based diurnal model was compared against that of the GOES, which acts as a reference for error analysis.

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12618

Nevada

Validate HIRS OLR Diurnal Model using GOES Observations