by nan feng department of atmospheric sciences the university of alabama in huntsville huntsville,...

ByNan Feng

Department of Atmospheric SciencesThe University of Alabama in Huntsville

Huntsville, AL

Satellite Remote Sensing II - ATS 770 Presentation3October 26th, 2011

Earth Radiation Budget

OutlineI. IntroductionII. UncertaintiesIII. Angular Distribution Models (ADM)IV. ValidationsV. Conclusion

Introduction to Earth’s Radiation Budget

• Absorption of Insolation and Emission of terrestrial radiation drive the General Circulation of the Atmosphere

Introduction to Earth’s Radiation Budget

Largely responsible for Earth’s weather and climate

Medium-Low LevelUnderstanding

IPCC report 2007

Direct and indirect effects of tropospheric

aerosols

Surface

Increased planetary albedo:Scattering solarCOOLING!Decreased planetary albedo:Absorbing solarWARMING!

Impact clouds and precipitation processesCOMPLICATED!

Sun

Aerosol climate impactDirect effects •Scattering solar energy •Absorbing solar/terrestrial energyIndirect effects (Modify cloud properties)•More droplets----clouds are brighter (Twomey, 1977 )•More droplets----longer cloud life time (Albrecht,1989)Semi-direct effect•Absorbing aerosols heat airs and evaporate clouds (Hansen et al., 1997)ARF

EstimationAerosol Radiative Forcing = FAEROSOL FCLEAR-SKY –

•Global Climate Models

•Global Observations

How can we study earth radiation

budget ?

•Satellite

measurements of

radiative quantities.

Spectral Categories Instruments for Radiation budget :

• Narrowband Sensors• Broadband Sensors

Field of View Categories :• Wide field-of-view (WFOV) Nonscanner

- Often called FLAT-PLATE sensors - Measure radiation horizon to horizon - 120o angular resolution - Example : Nimbus7 ERB, ERBE, CERES - Longer lifetime due to less wear

• Narrow field-of-view (NFOV) Scanner - AVHRR, Nimbus 6,7 ERB, ERBE,

CERES

The Earth Radiation Budget Experiment (ERBE) Mission

The Goddard Space Flight Center built the Earth Radiation Budget Satellite (ERBS) on which the first ERBE instruments were launched by the Space Shuttle Challenger in 1984.

ERBE instruments were also launched on two National Oceanic and Atmospheric Administration weather monitoring satellites, NOAA 9 and NOAA 10 in 1984 and 1986.

Both had two instruments : Scanner & Non-Scanner

http://eosweb.larc.nasa.gov/PRODOCS/erbe/table_erbe.html

ERBE Observed Global Longwave

Radiation

The Clouds and Earth Radiant Energy System (CERES)

CERES SCAN MODES

Rotating Azimuth Plane

Cross-Track Scan mode

Unique feature!

CERES Spatial Coverage

TRMM-PFM Terra-FM1/FM2 Aqua-FM3/FM4

Cross-Track (FAPS)

CLAMS-Scan (RAPS & PAPS)

Special-Scan (PAPS)

CERES Scan Modes

Design SpecificationsOrbits: 705 km altitude, 10:30 a.m. descending node (Terra) or 1:30 p.m. ascending node (Aqua),

sun-synchronous, near-polar;

350 km altitude, 35° inclination (TRMM)

Spectral Channels:

Shortwave : 0.3 - 5.0 µmWindow: 8 - 12 µmTotal: 0.3 to 200 µm

Swath Dimensions: Limb to limb

Angular Sampling: Cross-track scan and 360° azimuth biaxial scan

Spatial Resolution: 20 km at nadir (10 km for TRMM)

CERES has four main objectives:

• Provide a continuation of the ERBE record of radiative fluxes at the top of the atmosphere (TOA) , analyzed using the same algorithms that produced the ERBE data

• Double the accuracy of estimates of radiative fluxes at TOA and the Earth's surface

• Provide the first long-term global estimates of the radiative fluxes within the Earth's atmosphere

• Provide cloud property estimates that are consistent with the radiative fluxes from surface to TOA

Limitations:

• Inadequate diurnal variation, only twice daily observations (diurnal problem)

• Satellite sensors do not measure exactly the wavelength integrated radiation budget quantities (spectral correction problem or unfiltering problem)

• Radiance-to-flux conversion (angular dependence problem)

The uncertainties of ERB studies

• Radiance calibration

• Filtered to Unfiltered Radiances

• Cloud contamination

• Clear Sky Estimation

• Radiances to flux Conversion - ADM

Radiance to Flux Conversion

•Satellite measures radiance (I(o,,)) at a given sun-satellite geometry during overpass

•This radiance must be converted to flux

•If surface is Lambertian, then for isotropic scattering, flux F(o) = π * I(o,,)

•However, for non-lambertian surfaces, the scattering is not isotropic

2

0

2/

000 sincos),,()( IddF

Isotropic Scattering

AnisotropicScattering

BackwardForward

Radiance to Flux Conversion

• Angular measurements can be integrated to obtain non-Lambertian flux (F(o))

• Anisotropic factor or angular distribution model = The Ratio of the Lambertian flux to non-Lambertian flux

• ADM = Ratio of equivalent Lambertian flux to actual flux

• R(o,,) = π*I(o,,)/ F(o)

)(

),,(),,(

oij

lkoijlkoij F

IR

where : is the average radiance (corrected for Earth-sun distance in the SW) in an angular bin , is the upwelling flux in a solar zenith angle bin, which is determined by directly integrating over all angles (Loeb et al., 2003). The set of angles oi, k, and .

ADMs (Sorting-into-Angular-Bins, SABs)

Large ensemble of radiance measurements are first sorted into discrete angular bins and parameters that define an ADM scene type and ADM anisotropic factors for a given scene type(j) are given by

jI),,( lkoi )( oijF

jI

Examples ADMs as the function of 0.55

0.55: 0.0-0.1

0.55: 0.2-0.4

> 0.6

0.55:0.1-0.2

0.55: 0.2-0.4

< 0.6

Glint

ADM Scene Identification

The main reason for defining ADMs by scene type is to reduce the error in the albedo estimate.

Earth scenes have distinct anisotropic characteristics which depend on their physical and optical properties. (e.g. thin vs thick clouds; cloud-free, broken, overcast, etc.)

Scene identification must be self-consistent. Biases in cloud property retrievals (e.g. due to 3D cloud effects) should not introduce biases in flux/albedo estimates.

CERES Single Scanner Footprint (SSF) ProductCoincident CERES radiances and imager-based cloud

and aerosol properties

Use VIRS (TRMM) or MODIS (Terra or Aqua) to determine following in up to 2 cloud layers over every CERES FOV:

Macrophysical: Factional coverage, Height, Radiating Temperature, Pressure

Microphysical: Phase, Optical Depth, Particle Size, Water Path

Clear Area: Albedo, Skin Temperature, Aerosol optical depth

Scene Types for CERES/TRMM SW ADMsADM Category Scene Type Stratification Actual

Total 

Clear

Ocean - 4 Wind Speed Intervals 4Land - 2 IGBP Type Groupings 2Desert - Bright and Dark 2Snow - Theoretical 1

    Cloud

Ocean - Liquid and Ice- 12 Cloud Fraction Intervals- 14 Optical Depth Intervals

62 (L)53 (I)

Land - 2 IGBP Type Groupings- Liquid and Ice- 5 Cloud Fraction Intervals- 6 Optical Depth Intervals

45Desert - Bright and Dark Deserts

- Liquid and Ice- 5 Cloud Fraction Intervals- 6 Optical Depth Intervals

33Snow - Theoretical 1

Total   203

Scene Types for CERES/TRMM LW and WN ADMs

ADM Category Parameter Stratification Total

Clear

Ocean3 Precipitable Water 155 Vertical Temperature Change

Land 3 Precipitable Water

155 Vertical Temperature Change

Desert 3 Precipitable Water

155 Vertical Temperature Change

Broken Cloud Field

(4 intervals)

Ocean/Land/Desert

3 Precipitable Water

288 (O)288 (L)288 (D)

6 DT (Sfc-Cloud)4 IR Emissivity

Overcast Ocean+ Land+Desert

3 Precipitable Water

1267 DT (Sfc-Cloud)6 IR Emissivity

TRMM ADMsBetter scene identification and Increased ADM sensitivity to anisotropy

• using collocated VIRS and CERES data.

• VIRS is a narrowband imager – 2km spatial

resolution

• CERES has footprint of 10km (TRMM) at nadir

• 200 shortwave and 100 longwave scene types

http://asd-www.larc.nasa.gov/InversionLoeb et al., 2003; JAM, 42, 240-265Loeb et al., 2003; JAM, 42, 1748-1769

Comparisons between TRMM and Terra CERESTRMM

• Only 9 months of data (Jan-Aug, 1998 + March

2000)

• Spatial coverage limited to ±38o only

• 350 km precessing orbit with 35o inclination 46

days for full range of SZA

• land cover types = only 4 categories based on

IGBP

TERRA

• global coverage

• increased sampling

• Data available since 2000

• need for new ADMs because spatial resolution

and geographic coverage different

Terra CERES ADMs

CERES Terra SW ADMs – (a) Ocean – (1) Clear

Conditions: MODIS pixel-level cloud cover fraction less or equal than 0.1%

Instantaneous TOA fluxes are determined using combination of empirical and theoretical ADMs as follows:

�̂�=𝜋 𝐼 (𝜃0 ,𝜃 ,𝜙)

𝑅 (𝑤𝑘 ,𝜃0 ,𝜃 ,𝜙 ) [𝑅 h𝑡 (𝑤𝑘 , 𝐼 )

𝑅 h𝑡 (𝑤𝑘 , 𝐼 )]

is determined from wind speed-dependent empirical ADMs that are derived from CERES data

are theoretical radiative transfer model anisotropic factors evaluated at the measured CERES radiance and mean CERESradiance in a given ADM angular bin, respectively.

SW ADMs – (a) Ocean – (2) Clouds

Continuous ADMs using analytical functions that relate CERES radiances and imager parameters (e.g. cloud fraction and cloud optical depth.)

Where, is the retrievedCloud optical depth of the ith

Pixel within the CERES FOV

Try to combine f and into a single parameter Third order polynomial Five paras sigmoidal fit

SW ADMs – (a) Ocean – (2) Clouds

Continuous ADMs using analytical functions that relate CERES radiances and imager parameters (e.g. cloud fraction and cloud optical depth.)

The sigmoidal fit relative errorremains less than 1% in every cloudfraction interval

Similar results are obtained when other angular bins are considered or when separate fits are derived for mixed-phased and ice clouds.

The polynomial fit relative errorreaches -3% at intermediate cloud fractions The close relationship btw SW radiance and occurs in spite of the rather large range of cloud properties associated with a given range In general, the rms error in predicting instaneous SW radiances using the sigmoidal fit is btw 5% and 10%.

SW ADMs – (a) Ocean – (2) Clouds

Continuous ADMs using analytical functions that relate CERES radiances and imager parameters (e.g. cloud fraction and cloud optical depth.)

SW ADMs – (a) Ocean – (2) Clouds

In each solar zenith angle interval, the liquid water clouds show well-defined peaks in anisotropy for = - 30 to -60 and close to nadir due to the cloud glory and rainbow features, while peaks in anisotropy occur for ice clouds between = 30 to 60 in the specular reflection direction, also observed by Chefer et al. (1999) in POLDER measurements. Likely due to horizontally oriented ice crystals.

ADMs for Terra CERES:1. Shortwave:- Clear Land: Stratify by IGBP type + vegetation index + taer

1×1 latitude and longitude equal area regions with a temporal resolution of 1 month

- Clouds over Land: Continuous scene type using sigmoidal functional fits to data.- Clear Snow/ice: Stratify by NDSI (permanent snow, fresh snow, or sea ice. Further stratified into ‘bright’ and dark subclasses)- Clouds over Snow: greater dependence on vza than cloud free scence.2. Longwave and Window: - Cloud-free conditions: more surface types and high angular bins resolutions (Stratified by precipitable water, imager-based surface skin temperature and etc.)- Cloudy conditions: a function of precipitable water, surface and cloud top temperature, surface and cloud top emissivity and cloud fraction.

Terra ADMsImprovements :

• using collocated MODIS and CERES data.

• MODIS is a multispectral (36) imager with

250m, 500m, 1km spatial resolution

• CERES has footprint of 20 km (Terra, Aqua) at nadir

• scene type information from MODIS

• angular bin resolution sharpened to 2o in shortwave

• wind-speed resolution (over ocean) increase to 2 m/s

• over land, ADMs built for 1ox1o lat-lon regions at 1 month

temporal resolution

• NDVI used to separate sub-regions within 1ox1o regions

Terra CERES ADMs: ValidationA series of consistency tests are performed to

evaluate uncertainties in TOA fluxes derived with the CERES SW and LW ADMs: • Regional Mean TOA Flux Error Test (SW, LW

and WN)

• Instantaneous TOA Flux Uncertainties Test

• Comparisons with ERBE-Like TOA Fluxes

• Comparison with radiative transfer model

Regional Mean TOA Flux Error (Direct Integration)• Regionally averaged ADM-derived TOA fluxes are

compared with regional mean fluxes obtained by direct integration of observed mean radiances (DI fluxes).

• regions of 10×10 latitude and longitude, over several months.

• The regional all-sky ADM is constructed by sorting the radiances in a region by viewing geometry (, 0 ,) and evaluating the ratio of the mean radiance in an angular bin to the DI flux, obtained by integrating radiances in all angular bins.

• Compare ADM-derived TOA fluxes over 1 regions from different viewing geometries.

• Comparing CERES Terra ADMs and surface observations (Programmable Azimuth Plane Scans Over ARM-SGP TEST)

• Terra-Aqua Instantaneous TOA Flux Comparison over Greenland (69.5N)

• Multi-angle TOA Flux Consistency Tests (Merged dataset of MISR-MODIS-CERES)

Instantaneous TOA Flux Uncertainties Test

Instantaneous TOA Flux Uncertainties Test

Clear-sky multiangle SW TOA flux consistency: (a) Relative difference [F(=50-60) –F(Nadir)]/F(Nadir); (b) Relative RMS difference

Validation results:

• Based on all results and a theoretically derived conversion btw TOA flux consistency and TOA flux error, the best estimate of the error in CERES TOA flux due to the radiance-to-flux conversion is 3% (10Wm-2) in the SW and 1.8% (3 to 5 Wm-2 ） in the LW.

• Monthly mean TOA fluxes based on ERBE ADMs are larger than monthly mean TOA fluxes based on CERES Terra ADMs by 1.8 Wm-2 and 1.3 Wm-2 in the SW and LW, respectively.

To summary

The Angular Characteristics of TOA Radiance depends on • Viewing Geometry [Loeb et al., 2002; Suttles

et al., 1988]

• Surface characteristics (snow is brighter than vegetation) [Loeb et al., 2002; Suttles et al., 1988]

• Atmospheric Characteristics (clouds, aerosols) [Loeb et al., 2002; Li et al., 2000; Zhang et al., 2005; Falguni et al., 2011]

• Current CERES ADMs = f(geometry, surface, clouds)

References• Leob, N.G., N.M. Smith, S. Kato, W.F. Miller, S.K.Gupta,

P.Minnis, and B.A. Wielicki, 2003: Angular distribution models for top-of-atmosphere radiative flux estimation from the Clouds and the Earth’s Radiant Energy System instrument on the Tropical Rainfall Measuring Satellite. Part I: Methodology. J. Appl. Meteor., 42, 240-265.

• Leob, N.G., S. Kato, K. Loukachine, and N.M. Smith (2005), Angular distribution models for top-of-atmosphere radiative flux estimation from the Clouds and the Earth's Radiant Energy System instrument on the Terra satellite. Part I: Methodology, J.Atmos. Oceanic. Technol., 22, 338-351

• Loeb, N. G., Kato, S. et al., Angular Distribution Models for Top-of-Atmosphere Radiative Flux Estimation from the Clouds and the Earth’s Radiant Energy System Instrument on the Terra Satellite. Part II: Validation, American Meteorological Society DOI: 10.1175/JTECH1983.1, 2007

Questions

Backup slides

Filtered to unfiltered radiance

• Radiometric count conversion algorithms convert the detector digital count into filtered radiances.

• For use in science applications, radiances from earth scenes should be independent of the optical path in the instrument.

Filtered To Unfiltered Radiance

Unfiltered radiance

Filtered radiance

Conversion

Anisotropy in Satellite Observations

L1 L2F1 = πL1

F1 ≠ F2 L1 ≠ L2

F2 = πL2

MISR L1B IMAGE

Therefore, Lambertian assumption will not work !

MISR

Sampling issues

CERES provides two overpasses over a given scene per day. How cloud the limited observations represent the diurnal variation of solar reflected and earth emitted radiation? (temporal sapling problem)

Solution: Using CERES observations from multiply satellites (EOS-AM, EOS-PM, and TRMM), reduce time sampling error by 78%.

CERES has a larger footprint on the order of 10-20 km at nadir. In aerosol forcing studies, part of samples are discard due to cloud contamination. This, however, induce a spatial sampling issue.

ERBE ADMsThe ModelThe parameters were calculated as a function of 12 scene types.

Scene type Acronym Cloud coverage (%)•Clear over ocean clo 0 - 5•Clear over land cll 0 - 5•Clear over snow cls 0 - 5•Clear over desert cld 0 - 5•Clear over land-ocean mix clm 0 - 5•Partly cloudy over ocean pco 5 - 50•Partly cloudy over land or desert pcl 5 - 50•Partly cloudy over land-ocean mix pcm 5 - 50•Mostly cloudy over ocean mco 50 - 95•Mostly cloudy over land or desert mcl 50 - 95•Mostly cloudy over land-ocean mix mcm 50 - 95•Overcast ovr 95 - 100

Day-night LW flux difference divides overcast into overcast over ocean (ovo) and overcast over land (ovl).

ERBE SW ADMs

Solar zenith angle Viewing zenith angle Relative azimuth angle

0 - 25.84 deg. 0 - 15 deg. 0 - 9 deg.

25.84 - 36.87 15 - 27 9 - 30

36.87 - 45.57 27 - 39 30 - 60

45.57 - 53.13 39 - 51 60 - 90

53.13 - 60.00 51 - 63 90 - 120

60.00 - 66.42 63 - 75 120 - 150

66.42 - 72.54 75 - 90 150 - 171

72.54 - 78.46 171 - 180

78.46 - 84.26 84.26 - 90.00

ERBE LW ADMs

For each of the twelve scene types, the LW anisotropic factor and LW Standard deviation were derived as a function of:

· four seasons · winter northern hemisphere (Dec., Jan., Feb.) · spring northern hemisphere (Mar., Apr., May.) · summer northern hemisphere (Jun., Jul., Aug.) · fall northern hemisphere (Sep., Oct., Nov.)

· 10 colatitude regions · 7 viewing zenith angles

Scanner - A set of three co-planar detectors (longwave, shortwave and total energy), all of which scan from one limb of the Earth to the other, across the satellite track (in it's normal operational mode).

The ERBE Scanning Detectors :

1). One Total wavelength (0.2 – 50 μm)

2). One Long wavelength (5 – 50 μm)

3). One Short wavelength (0.2 – 5 μm)

Nonscanner - A set of five detectors

• one which measures the total energy from the Sun (0.2 – 50 μm)

• two of which measure the shortwave and total energy

from the entire Earth disk (0.2 – 5 μm)• two of which measure the shortwave and total

energy

from a medium resolution area beneath the satellite