On the use of Ray-Matching to transfer calibration D. Doelling, P. Minnis NASA LaRC Raj Bhatt, Dan Morstad SSAI GSICS/GWRG Session Daejon, Korea, March 29-April 1, 2011

GEO to MODIS Cross-Calibration Method Ray-match coincident GEO counts (proportional to radiance) and MODIS radiances use a 0.5°x0.5° lat by lon grid to mitigate navigation and time matching errors Use MODIS as reference since GEOs have no onboard calibration Normalize solar constants and SZA, obtain MODIS equivalent radiance Perform monthly GEO/MODIS regressions of the gridded radiances, and derive monthly gains Compute timeline trends from the monthly gains

GOES-12/Terra-MODIS GOES-12 gain GOES-12/Terra-MODIS July 2003 GOES-12 gain based on Terra-MODIS Gain = 0.68 29 published count offset

Remove SRF difference between GEO and MODIS using pseudo SCIAMACHY radiances SCIAMACHY is a imaging spectrometer designed to measure trace gas absorption 3200 wavelengths between 0.25 and 1.75µm in 6 bands, which are normalized to each other Use nadir footprints, size= 32 (along track) x215km ENVISAT, SS@10AM local time, launched March 2002 Compute SRF adjustment factors Regression from all SCIAMACHY footprint pseudo radiances over the GEO/MODIS calibration domain Use spectral response function from GEO and MODIS Validate Cross calibrate GEO and MODIS over land and ocean Derive SCIAMACHY adjustment factors for land and ocean separately The gains for ocean and land should be similar

GOES-12/Terra-MODIS, Sept. 2009 no spectral correction Ocean Land Gain = 0.8431 Offset = 31.4 Gain = 0.8880 Offset = 47.0 • GEO gain dependent on instrument spectral response and scene type • Note surface type effects mainly the offset under clear-sky conditions • The gain difference is 5.3%, and the offset should be 29.0

SCIAMACHY* spectra Ocean Forest G-12 39.9 Terra 24.6 G-12 19.4 Terra 17.4 • Clear-sky SCIAMACHY mean & sigma spectral response over ocean and forest • Compute the pseudo G12 and MODIS radiance from SCIAMACHY *Courtesy of SCIAMACHY team

SCIAMACHY* spectra Low Cloud High Cloud Terra 329.1 Low Cloud High Cloud G-12 199.3 Terra 200.8 • Bright cold high clouds have radiance ratios near one • Bright low clouds have more absorption in the near IR

SCIAMACHY spectral corrections, July 2003 Ocean (All months) Land (Oct) • Use all SCIAMACHY footprints that fall into the GEO equatorial domain during • Compute pseudo GOES and MODIS radiances using SCIA spectra • Derive spectral correction using a cubic fit for ocean and water

GOES-12/Terra-MODIS, July 2003 with spectral correction Ocean Land Gain = 0.8313 Offset = 30.7 Gain = 0.8336 Offset = 29.8 • The gain difference before spectral correction = 5.3%, offset=31.4, 47.0 • With spectral correction the gain difference = 0.3%, offset close to 29

Which MODIS for reference? Use MODIS as an absolute reference for calibration transfer using ray-matching Use desert and DCC to determine, which MODIS is more stable Libya, Egypt, Arabian, Dome C, Greenland, Sonora used Also Terra/Aqua NSNOs indicates that there is relative trending between the two MODIS instrument team indicates that Aqua is better characterized MODIS absolute calibration error is ~2%

10-year Clear-sky CERES SW BB monthly sigma Terra-CERES (%) (10:30AM) Aqua-CERES (%) (1:30PM) • ISCCP uses the entire clear-sky earth as a calibration target for AVHRR • Use the CERES clear-sky SW (BB) deseasonalized monthly radiance to derive a seasonal climatology, then take the standard deviation of each of 120 months subtracted from climatology. • Sahara, especially Egypt has the lowest variability, Arabia, Greenland and Antarctica are stable targets

LaRC MODIS desert methodology, Arabia 10%- 5%- 0%- BRDF sigma, vz=25° RAZ bins SZA bins Daily spatial sigma radiance Daily TOA radiance • Use a spatial sigma threshold to identify clear-sky, not necessarily pristine • Deseasonalize monthly mean radiances, by using 12 month running means, or derive trends for each month and average trends for overall trend • Or use BRDF using a 3 year stable time period

LaRC MODIS desert methodology, Arabia BRDF Monthly Normalized trend Deseasonalized Normalized trend BRDF Daily TOA Radiance Stderr=1.3% Gain=0.2%/decade Stderr=1.0% Gain=0.15%/decade • Both methods reveal that Arabian desert site is observed with Aqua is stable • The methods are very consistent

Desert calibration using site specific TOA BDRF Terra-CERES 0.65µm Aqua-CERES 0.65µm Range -1.1 to -1.9, -1.5 mean %/decade Range -0.9 to +0.4, -0.3 mean %/decade Terra DCC Aqua DCC • Desert and DCC indicate Aqua more stable than Terra • Use Aqua as reference

Radiometrically scale Terra to Aqua MODIS Remove calibration differences between Terra and Aqua-MODIS, use Aqua-MODIS as reference Use Terra and Aqua NSNO (within 15 minutes) to cross-calibrate Terra and Aqua Assume Terra and Aqua have same SRF Correct Terra radiances to match Aqua Use simple gain factor Both GEO/Terra and GEO/Aqua ray-matching should provide independent reference If ray-matching is robust, both Terra and Aqua based calibration transfer should be identical

Terra/Aqua MODIS 0.65 µm NP, July 1, 2004 Similar for Terra/Aqua at 71°N 0.65 µm NP, July 1, 2004 • nadir • off-nadir • Increase dynamic range by using off-nadir 50km regions • Use 50km regions to mitigate time mismatch • Off nadir regions along the longitude of constant VZA are at solar noon and have similar RAZ • Off nadir slopes are nearly identical to nadir slopes

Terra/Aqua Collection 5 Band1 (0.65µm) Band1 (0.65µm) Nov 2003 Apr 2009 Terra to Aqua adjustments applied to collection 5 1.011 1.019 1.032 • Terra/Aqua relative calibration, shows two major discontinuities • Use 3 adjustment factors • Off-nadir is less noisy over time

Terra-MODIS Band1 Collection 5 to 6 adjustment factors Mirror side 0 Mirror side 1

Terra/Aqua Collection 6 Band1 (0.65µm) Band1 (0.65µm) corrected adjustments applied For collection 6 1.026 7.804e-6*DSL+1.008 2.8%/decade

Compare Aqua-MODIS/GEO, Terra-MODIS/GEO, desert and DCC calibrations First, determine if stability is consistent between all methods Second, determine the absolute gain difference SRF correction between MODIS and historical GEO sensors can be on order of 5% Ray-matching relies on bright cloud targets Offset is from space count Bright clouds are spectrally flat at surface However atmospheric correction can vary with cloud height and season, DCC~1%, but stratus ~5% Need to develop scene dependent SRF adjustments

GOES-12/MODIS Collection 5 Before Terra Adjustment After Terra Adjustments • DCC and deserts normalized to Terra • Note GEO/Terra diverging after 2009 from DCC and desert before adjustment

Met-9/MODIS collection 5 Before Terra Adjustment After Terra Adjustment

MET-9/MODIS Collection 5 relative Before TA adjustment After TA adjustment • Normalized gains

MET-7 Libya desert Daily TOA radiance Daily BRDF TOA radiance Monthly BRDF TOA radiance Monthly BRDF TOA radiance after SCIAMACHY correction 10% variation due to atmosphere

Comparison of MET-7 with MODIS, DCC and Desert • SCIAMACHY correction for Desert and DCC • Absolute calibration of DCC and desert based on Aqua • GEO/MODIS not spectrally corrected

GEO/MODIS variability MET-7/MODIS 0.65µm pseudo radiances from SCIAMACHY Met-8/MODIS0.86µm pseudo radiances from SCIAMACHY Ice water ~5% ~10% • Seasonal cycle due to looking at various scenes • Assume bright cloud surface is spectrally flat • Need to take out atmosphere, depending on scene type • Use scene dependent SCIAMACHY models

Conclusions Ray-matching calibration transfer accuracy depends Stability of reference sensor (systematic) SRF correction (systematic) Matching errors due navigation, time difference, and angular thresholds (random noise) Ray-matching improvements Use Aqua Collection 6 as reference Radiometrically scale Terra to Aqua, two independent references Use of SCIAMACHY for SRF correction Ray-matching needed improvements Scene dependent SRF corrections Verify using other methods, such as DCC and deserts