Strawman Plan for Inter-Calibration of Solar Channels Dave Doelling Tim Hewison

GSICS visible product Incoming solar irradiance spectrum CEOS uses Thuillier_2002 http://wgcv.ceos.org/docs/plenary/wgcv26/Solirrad.pdf Satellite visible channel spectral response and solar constants based on incoming solar Gain and offset to calibrate the visible channels Do we want to work in reflectance or radiance space? Apply correction as Rad = gain*(C-Co) or Ref = Refo*gain + offset ? Provide correction of calibration? Where Refo is the nominal gain, provided on GSICS page As day since launch function, or periodic gain updates Use operational space offset as offset Uncertainty of gain correction with respect to reference Based on a common absolute calibration MODIS-Terra for 2000 to present and historical?

Reference Sensor No current reference visible hyper-spectral sensor available, similar to IASI for IR CLARREO launch ~2018, VIS ~100km footprint, IR ~25km GOME & SCHIAMACHY absolute calibration uncertain to ~4% Reference sensor to have solar diffuser or well-established calibration MODIS (well characterized and known), ATSR, VIRS Reference to have as many common channels as possible Future NPOESS/METOP missions to continue with well-calibrated sensor, VIIRS similar to MODIS Need to establish the difference in absolute calibration when reference sensors are replaced

Methodology Use either the GEO-MODIS ray-match or invariant target as the primary approach and validate with the secondary GEO-MODIS is easy to implement Will not work for historical satellites Absolute calibration will change with reference Invariant Target Approach Will work for historical satellites and not tied to reference sensor, once absolute calibration has been established The reference satellite, which is common to all GEOs will monitor invariant target consistency Both methods still require spectral response corrections From model or hyper-spectral or multi-spectral observations

GEO-MODIS ray-match Approach Regress ray-matched coincident and collocated reflectances from GEO and reference Make sure the regression is linear Traceability to reference Monitor gain over time Apply correction factor to take out spectral differences Model or empirical regressions

Ray-matching to reference sensor Ray-match coincident GEO counts, radiances or reflectances and MODIS radiances or reflectances averaged over a 502 km ocean grid near the sub-satellite point (±15°lat by ±20°lon area) Use GEO provided space offset Perform monthly regressions to derive monthly gains Compute timeline trends from monthly gains

Apply spectral correction Approach: Model (MODTRAN) reflectance spectra is determined by surface reflectance and atmospheric absorption and is based on LUT for every individual ray-matched coincident reflectance pair Fokke et al. 2009 • Redo monthly regression to derive monthly gain with spectral correction

Invariant Target Approach Identify invariant targets consistently DCC, deserts, moon, clear oceans Determine scene identification thresholds For DCC, T<205K, sVIS<2% etc, limit auxiliary input Develop robust statistics Build bidirectional model From observations during stable time period Or Radiative Transfer Modeled Identify angular ranges that are predictable Indentify and build bidirectional model from reference sensor, using same approach as GEO Normalize the GEO to the reference bidirectional model Apply the spectral response difference from model or SCIAMACHY spectra

Normalize the GEO DCC with MODIS Indentify DCC between GEO and MODIS consistently Build an observed bidirectional reflectance model with DCC pixel reflectance Normalize the GEO DCC reflectances to MODIS MET9, 0.65µm, one month Terra-MODIS, 0.65µm Both approaches used T<205°K and sT<1°K and sVIS<2% Approach 1 Approach #1: Regress corresponding angular bins Approach #2: Develop bidirectional model with ray-matched GEO/MODIS calibration Approach: #3 Normalize theoretical model onto observed model MET9 MODIS Regress reflectances of corresponding bins and apply coefficients to all bins of MET9

( ) S S rmodi rmodj i k rVIRSi,k rj,k Normalize theoretical model onto observed model to develop complete reference model VIRS (observed, scans to 45° VZA) DISORT DISORT+VIRS Use all available angular bin ratios and observed reflectances to estimate unfilled bins ( ) _______ rmodi rmodj i k S S MVIRS bins N bin footprints rVIRSi,k rj,k = /MN Loeb et al, 2002 http://ams.allenpress.com/archive/1520-0450/42/2/pdf/i1520-0450-42-2-240.pdf

Correct for sensor spectral response differences • Use SCIAMACHY spectral response to estimate the spectral correction from MODIS to MET8 • Similar approach for a modeled spectra

Invariant target monthly gain approach Predicted MET9 Radiance, Wm-2sr-1um-1 DCC Deserts Clear Ocean Offset Again track over time

Desert Target Approach Deserts are a fixed land target, that are optimally Lambertian Have only one VZA in the GEO domain Have only a limited SZA with reference satellite in sun-synch orbit may need a common bidirectional model from VIRS, POLDER or MISR data to transfer absolute calibration between reference sensor MODIS bi-weekly surface bidirectional reflectance product? Deserts have unique spectral responses and should base results on several deserts for robustness May need to use a SCIAMACHY (30x120km footprint) or database spectral response Normalize to MODIS standard channels, or first with multi-channel satellites, if using bidirectional model from POLDER or MISR Desert spectral response can then be used to correct for spectral response differences between sensors Bidirectional spectral effects can be taken out with models

Monitor Stability over time Update calibration coefficients over time if necessary Both approaches need to evaluated for uncertainty in the gain derivation with respect to the reference satellite Reference absolute satellite calibration stated in GSICS Invariant target uncertainty: temporal target invariance, bidirectional models, and spectral correction, stratospheric aerosols GEO-MODIS regression uncertainty: standard error and spectral correction All Invariant targets and GEO-MODIS calibration can monitor relative calibration independently Desert site stability can be established through the reference satellite, two GEOs, or surface measurements DCC are well suited for GEO since their orbits do not degrade Moon looks are available on GEOs operational and can be used if characterized properly with phase angle, etc

Way Forward Agree this strategy Identify specialists working on each method Deserts, DCCs, Ocean, Moon, Ray-matching collocations... Coordinate presentations from GSICS members to cover each method at next meeting in Toulouse Agree to combine methods after period of research e.g. by Sept 2010 Decide a method of combining the methods to check consistency and identify most robust for each application Identify products for solar channel inter-calibration Develop prototype products e.g. by June 2011