earth radiation budget from nistar
DESCRIPTION
Earth Radiation Budget from NISTAR. Patrick Minnis NASA Langley Research Center May 11, 2007. - With help from Dave Doelling & Rabi Palikonda, SSAI. Why measure ERB from L1?. • NISTAR is an active cavity sensor - absolute calibrations - PowerPoint PPT PresentationTRANSCRIPT
DSCOVR Workshop
NASA Langley Research Center / Atmospheric Sciences
Earth Radiation Budget from NISTAR
Patrick Minnis
NASA Langley Research Center
May 11, 2007
- With help from Dave Doelling & Rabi Palikonda, SSAI
DSCOVR Workshop
NASA Langley Research Center / Atmospheric Sciences
Why measure ERB from L1?
• NISTAR is an active cavity sensor - absolute calibrations
• New approach to an old problem that requires more stitching of data and interpolation, etc.– Minimizes correction for missing hours
• DSCOVR albedo and daytime OLR can serve as constraints on CERES & serve as a calibration source -
a complementary approach
– CERES is out of balance by 6 Wm-2
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How do we measure ERB with DSCOVR?
• NISTAR measures TOT & SW radiances of entire disk, LW = SW-TOT
- Viewing & illumination geometry varies slowly over time within a narrow range of angles near the backscatter position
- Can only determine global albedo & daytime OLR
- Nocturnal OLR is an educated guess
• Radiance observations must be converted to irradiance (flux)
- Apply anisotropic directional models (ADMs)
- Need cloud information
- Other considerations
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March 21, 1986, 15° east from L1 April 15, 1986, 15°E of L1
Changing View of Earth With Season
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CORRECTION MODELS
SW albedo
• Small (2% to 8%) but variable sliver of sunlight is always out of view, depends on offset from L1
- Missing light correction
• To determine albedo, a set of SW ADM correction models needed
- Bidirectional reflectance correction
OLR
• Most of darkside Earth is never seen
- Nightside correction
• To determine OLR from LW radiance, a set of LW ADM correction models needed
- Limb-darkening correction
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Development of Correction Models & Analysis Approach
• Initial study used ERBE scanner data - monthly averages
• Most recent (2002) used ISCCP & ERBE combined - 3 hr => 1 hr
- developed simulated NISTAR radiances
- constructed correction factors for range of DSCOVR views
- determined variability & estimated error in albedo
- developed correction models: seasonal & L1 dependence
=> set of algorithms that can utilize cloud information to compute correction factors for any time & L1
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• TRIANA GLOBAL BIDIRECTIONAL FACTOR triana (utc) = REF triana (utc) / triana(utc) • TRIANA GLOBAL MISSING LIGHT ALBEDO CORRECTION
FACTOR
FACTOR (utc) = [earth
∑ erbe ( i) i cos (lat i) / earth
∑ i cos( lat i) ] / [ triana (utc ) ]
• TRIANA GLOBAL LIMB DARKENING FACTOR triana (utc ) = RADtriana (utc ) / OLRtriana (utc ) • TRIANA GLOBAL NIGHTSIDE OLR CORRECTION FACTOR
OLRFACTOR (utc) = [earth
∑ OLRerbe (tT) cos( lat i) / earth
∑ cos( lat i) ] / [OLRtriana (utc )]
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DSCOVR Simulator• Construct hourly global radiation and cloud field
- Convert 3-hr ISCCP GEO radiances to BB albedo (< 60° lat)
- monthly NB-BB conversion ERBS/GEO
- use ISCCP clouds to select ERBE ADM
- normalize to ERBE (CERES TISA GEO method)
- Use NOAA-9/10 ERBE data > 60° latitude
- Interpolate to hourly using CERES TISA interpolations
• Compute global ERB every hour
• Use ISCCP clouds & regional albedos w/ ERBE ADMs to compute NISTAR radiances at specified UTC & L1 position, 1985-88
• Calculate correction factors for monthly mean radiance conversion
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Narrowband-broadband conversion
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Testing Interpolation
• normalize to ERBS, compare to NOAA-9 ERBE fluxes
• RMS error least for all categories, but CS
DSCOVR Workshop
NASA Langley Research Center / Atmospheric Sciences
March 21, 1986, 15° east from L1
Simulated albedo/reflectance field
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DSCOVR Workshop
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Mean SW parameters as function of L1 orbit position, March 1986
Missing light factorBDR Factor
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Daily variability in missing light correction is small < 0.5%
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Variation of monthly mean missing light factor as function of L1, 00 UTC
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Mean LW parameters as function of L1 orbit position, March 1986LD variability < 0.2%
OLR (nightside) correction factor
var < 1.1%
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Variation of monthly mean nightside OLR factor as function of L1, 00 UTC
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DSCOVR Workshop
NASA Langley Research Center / Atmospheric Sciences
DSCOVR Workshop
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SUMMARY
• Both diurnal and seasonal variability are significant for all DSCOVR correction parameters
• Varibilities also sensitive to DSCOVR offset phase angle (from L1)
• One year correction factors computed:
– BRF 1.1216 -- 1.1616 SD 0.0018 -- 0.0176 (1.5%)
– MLCF 1.0063 -- 1.0259 SD 0.0008 -- 0.0048 (0.5%)
– LDC 1.0354 -- 1.0519 SD 0.0002 -- 0.0012 (0.2%)
– NSCF 0.9448 -- 1.0083 SD 0.0021 -- 0.0106 (1.1%)
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APPLICATIONS
• Correction factors can be used independently to correct NISTAR measurements
- will not account for dramatic scene changes
- Need updating with CERES data and models
• Same analysis approach can be used if cloud data and narrowband radiances available (CERES TISA approach)
- should yield same radiance as CERES, on average, bias would indicate differences in calibration
- Cloud data from EPIC insufficient
- Cloud data from GEO + MODIS best option
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Use Canned Correction Models
• Perform fits to simulated data as function of month/day, L1 position, UTC, Fourier or EO fits
• Apply to NISTAR observations
• Unless drastic changes in scene distributions occur,
monthly means should be extremely accurate
– Night side?
– ADM uncertainties?
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Global Albedo Correction Model
This model is designed to predict the corrected global mean albedo for given GMT and day:
global (tg,td) triana (L1L1tg,td)·F(L1L1tg,td)
where tg is the GMT and td is the day of the year.
F(L1L1tg,td) is the missing light albedo correction function.
F(L1L1tg,td)=mnTmn(tg,td)·nL1m
L1
where m, n=0, 1, 2, 3 and
Tmn(tg,td)= ijCij tjg ti
d
where i, j=0, 1, 2, 3.
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DSCOVR Workshop
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Global OLR Correction Model
This model is designed to predict the corrected global mean albedo for given GMT and day:
OLRglobal
(tg,td) OLRtriana(L1L1tg,td)·FS(L1L1tg,td)
where tg is the GMT and td is the day of the year.
Folr (L1L1tg,td) is the OLR correction function.
Folr(L1L1tg,td)=mnTmn(tg,td)·nL1m
L1
where m, n=0, 1, 2, 3 and
Tmn(tg,td)= ijCij tjg ti
d
where i, j=0, 1, 2, 3.
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DSCOVR Workshop
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Modeled & observed night side correction
factors1986
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Use External Cloud Data
• CERES already performs a similar analysis, not real time- TISA algorithms the basis for simulation
• CERES algorithms applied in real time GEO data subsets
- global application only computer/manpower limited
• Accounts for changes in climate, yields better daily values
– Parallax problems?
– ADM uncertainties?
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Using GEO-LEO Data
• Intercalibrate all GEOs and LEO imagers to a single source
- done & ongoing
• Apply common cloud retrieval algorithm
- ongoing for subsets
• Fusion: GEO-LEO can provide cloud clearing for aerosol & surface property retrievals
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Example: real-time cloud retrievals at 4 km resolution, 18 UTC, 7 Nov 2006Cloud properties are derived every 30 minutes from GOES-11 & 12 over CONUS and merged - these include all of the same properties derived from MODIS for CERES
http://www-angler.larc.nasa.gov/satimage/products.html
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Example: real-time cloud retrievals from MTSAT, 02 UTC, 7 Nov 2006
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Example: full-disk cloud retrievals from Meteosat, 12 UTC, 26 April 2007
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Example: full-disk cloud retrievals from GOES, 8 May 2007
GOES-11 (W) GOES-12 (E)
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Parallax Problems: What size regions with VZA?
Would we need to worry about this in making corrections? Will be a factor at all high VZA/SZA.
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Summary
• Capability to monitor global albedo/ERB globally from NISTAR- Estimate night side OLR, not true climate monitor
- Cannot get regional scale ERB
• Corrections do not appear to be highly variable interannually - need more data + new CERES models
• Explicit corrections can be made using GEO-LEO data- Is this approach too redundant with CERES?
- Or is it the means to constrain/compare CERES?
• Basic algorithms developed for either approach
- Need to be refined, streamlined, & documented
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EPIC Notes
• EPIC could be a good calibration reference
- always has another imager with proper geometry
• EPIC view angles, when matched with LEO/GEO imagers provide capability to estimate cloud particle habit/shape, aerosol & surface properties