1. By Nan Feng Department of Atmospheric Sciences The University of Alabama in Huntsville Huntsville, AL Satellite Remote Sensing II - ATS 770 Presentation3 October 26 th, 2011 Earth Radiation Budget

2. Outline I.Introduction II.Uncertainties III.Angular Distribution Models (ADM) IV.Validations V.Conclusion

3. Introduction to Earth’s Radiation Budget Absorption of Insolation and Emission of terrestrial radiation drive the General Circulation of the Atmosphere

4. Introduction to Earth’s Radiation Budget Largely responsible for Earth’s weather and climate

5. Medium-Low Level Understanding IPCC report 2007

6. Direct and indirect effects of tropospheric aerosols Surface  Increased planetary albedo: Scattering solarCOOLING!  Decreased planetary albedo: Absorbing solarWARMING!  Impact clouds and precipitation processesCOMPLICATED! Sun

7. Aerosol climate impact Direct effects Scattering solar energy Absorbing solar/terrestrial energy Indirect 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 Estimation Aerosol Radiative Forcing = F AEROSOL F CLEAR-SKY –

8. Global Climate ModelsGlobal Climate Models Global ObservationsGlobal Observations How can we study earth radiation budget ? Satellite measurements of radiative quantities.

9. 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 - 120 o 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

10. The Earth Radiation Budget Experiment (ERBE) Mission

11. 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

12. ERBE Observed Global Longwave Radiation

14. The Clouds and Earth Radiant Energy System (CERES)

15. CERES SCAN MODES Rotating Azimuth Plane Cross-Track Scan mode Unique feature!

16. CERES Spatial Coverage TRMM-PFM Terra-FM1/FM2 Aqua-FM3/FM4 Cross-Track (FAPS) CLAMS-Scan (RAPS & PAPS) Special-Scan (PAPS) CERES Scan Modes

17. Design Specifications Orbits: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 µm Window: 8 - 12 µm Total: 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)

18. 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

19. 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)

20. The uncertainties of ERB studies Radiance calibration Filtered to Unfiltered Radiances Cloud contamination Clear Sky Estimation Radiances to flux Conversion - ADM

21. 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 Isotropic Scattering Anisotropic Scattering Backward Forward

22. 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 )

23. 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

25. 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

26. 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.

27. CERES Single Scanner Footprint (SSF) Product  Coincident 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

28. Scene Types for CERES/TRMM SW ADMs ADM CategoryScene Type StratificationActual Total Clear Ocean- 4 Wind Speed Intervals4 Land- 2 IGBP Type Groupings2 Desert- Bright and Dark2 Snow- Theoretical1 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 45 Desert- Bright and Dark Deserts - Liquid and Ice - 5 Cloud Fraction Intervals - 6 Optical Depth Intervals 33 Snow- Theoretical1 Total 203

29. Scene Types for CERES/TRMM LW and WN ADMs ADM Category Parameter StratificationTotal Clear Ocean 3 Precipitable Water15 5 Vertical Temperature Change Land3 Precipitable Water 15 5 Vertical Temperature Change Desert3 Precipitable Water 15 5 Vertical Temperature Change Broken Cloud Field (4 intervals) Ocean/Land/De sert 3 Precipitable Water 288 (O) 288 (L) 288 (D) 6 DT (Sfc-Cloud) 4 IR Emissivity Overcast Ocean+ Land+Desert 3 Precipitable Water 126 7 DT (Sfc-Cloud) 6 IR Emissivity

30. TRMM ADMs Better 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/Inversion Loeb et al., 2003; JAM, 42, 240-265 Loeb et al., 2003; JAM, 42, 1748-1769

31. Comparisons between TRMM and Terra CERES TRMM Only 9 months of data (Jan-Aug, 1998 + March 2000) Spatial coverage limited to ±38 o only 350 km precessing orbit with 35 o 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

32. Terra CERES ADMs

33. 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:

34. 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.)

35. 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 error remains less than 1% in every cloud fraction 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 error reaches -3% at intermediate cloud fractions  In general, the rms error in predicting instaneous SW radiances using the sigmoidal fit is btw 5% and 10%.

36. 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.)

37. 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.

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

39. Terra ADMs Improvements : 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 2 o in shortwave wind-speed resolution (over ocean) increase to 2 m/s over land, ADMs built for 1 o x1 o lat-lon regions at 1 month temporal resolution NDVI used to separate sub-regions within 1 o x1 o regions

40. Terra CERES ADMs: Validation A 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

41. 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.

42. 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 (69.5  N) Multi-angle TOA Flux Consistency Tests (Merged dataset of MISR-MODIS-CERES) Instantaneous TOA Flux Uncertainties Test

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

44. 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.

45. 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)

46. 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

47. Questions

48. Backup slides

49. 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.

50. Filtered To Unfiltered Radiance Unfiltered radiance Filtered radiance Conversion

51. Anisotropy in Satellite Observations L1L2 F1 = πL1 F1 ≠ F2 L1 ≠ L2 F2 = πL2 MISR L1B IMAGE Therefore, Lambertian assumption will not work ! MISR

52. 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.

53. ERBE ADMs The Model The parameters were calculated as a function of 12 scene types. Scene type AcronymCloud 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 pco5 - 50 Partly cloudy over land or desert pcl5 - 50 Partly cloudy over land-ocean mix pcm5 - 50 Mostly cloudy over ocean mco50 - 95 Mostly cloudy over land or desert mcl50 - 95 Mostly cloudy over land-ocean mix mcm50 - 95 Overcast ovr95 - 100 Day-night LW flux difference divides overcast into overcast over ocean (ovo) and overcast over land (ovl).

54. ERBE SW ADMs Solar zenith angleViewing zenith angle Relative azimuth angle 0 - 25.84 deg. 0 - 15 deg. 0 - 9 deg. 25.84 - 36.8715 - 279 - 30 36.87 - 45.5727 - 3930 - 60 45.57 - 53.1339 - 5160 - 90 53.13 - 60.0051 - 6390 - 120 60.00 - 66.4263 - 75120 - 150 66.42 - 72.5475 - 90150 - 171 72.54 - 78.46 171 - 180 78.46 - 84.26 84.26 - 90.00

55. 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

56. 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)

57. 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