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Using SCIAMACHY to calibrate GEO imagers

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Presentation on theme: "Using SCIAMACHY to calibrate GEO imagers"— Presentation transcript:

1 Using SCIAMACHY to calibrate GEO imagers
D. Doelling NASA LaRC Benjamin Scarino, Dan Morstad, Arun Gopalan SSAI GSICS/GWRG Session Beijing, China, March 5-8, 2012

2 Background In a perfect world, GSICS would use a well-calibrated visible hyper-spectral sensor to calibrate the GEO imagers The CLARREO SW instrument will be absolutely calibrated to 0.3% at hyper-spectral wavelengths with a 100x100km footprint Earliest CLARREO instrument will be 2022 The sensor that most resembles CLARREO visible hyper-spectral sensor would be SCIAMACHY 0.3µm to 2.1µm range to derive trace gas concentrations 30x240km footprint Calibrated using a solar diffuser Absolute calibration uncertainty between 2% to 6% depending on wavelength Can the current operational SCIAMACHY be used as a calibration reference sensor?

3 Outline Use Aqua-MODIS as the visible reference for all comparisons
Aqua-MODIS more stable than Terra-MODIS Not based on if Aqua or Terra-MODIS is brighter Inter-calibrate SCIAMACHY and Aqua-MODIS 0.65µm channel using NSNOs Determine SCIAMACHY stability compared against Aqua-MODIS Determine relative calibration difference Inter-calibrate GEO with SCIAMACHY pseudo (GEO SRF) radiances using ray-matching Validate with ray-matched Aqua-MODIS and GEO calibration

4 SCIAMACHY Aqua-MODIS NSNO (top view)
ENVISAT ENVISAT ground track Aqua ground track SCIAMACHY footprint centers SCIAMACHY NSNO footprint NSNO footprint SCIAMACHY earth view SCIAMACHY limb view Aqua • NSNO local time is 11:45AM, near local noon, assuring azimuthal match • The Envisat and Aqua ground intersect is at 71°N and occurs 14 x daily • Perform monthly regressions of SCIAMACHY and Aqua radiances between April and September

5 SCIAMACHY Aqua-MODIS 0.65µm, July 2010
N Force Stderr 2.8% SCIAMACHY pseudo Aqua-MODIS radiance Aqua-MODIS radiance • 15 minute coincident, <70° SZA • ~ km sub-sampled MODIS pixels are averaged into a 30x240km SCIAMACHY footprint

6 SCIAMACHY Aqua-MODIS 0.65µm, 2004-2010
• Monthly standard deviation of force fits = 0.44% • Relative temporal trend of -0.2%/decade • SCIAMACHY is temporally calibrated as well as Aqua-MODIS • both use solar diffusers for on orbit calibration • both are within their uncertainty of 2% at 0.65µm

7 GEO SCIAMACHY precise ray-matching (top view)
GEO sub-satellite point Envisat ground track 22.5° GEO VZA 240 km 30 km 7.5° VZA 22.5° VZA 40 km radii ray-match tolerance

8 GEO SCIAMACHY precise ray-matching (side view)
240 km 80 km diameter ray-match tolerance GEO satellite Envisat satellite SCIAMACHY footprints Earth surface 2° GEO view angle range 15° SCIAMACHY view angle range 22.5° VZA Enivisat ground-track

9 GEO SCIAMACHY precise ray-matching
Precise ray-matching yields ~40 matches per year ENVISAT has a 35 day repeat cycle SCIAMACHY has a 50% Earth view duty cycle Each SCIAMACHY footprint location is matched once a month Expand tolerance radii Precise ray-match => 40-km radii Approximate ray-match => 160-km radii Does off nadir ray-matching introduce angular view biases? Test using GEO radiance calibrated against Aqua-MODIS Can’t verify if sensor over its lifetime if it is degrading

10 Aqua-MODIS tp Meteosat-9 spectral band adjustment factor (SBAF)
Slope N Stderr 0.28% • Bands are similar • Most of the slope is from solar constant difference • Uncertainty due to SBAF is 0.28% • Regress SCIAMACHY Aqua-MODIS and Meteosat-9 pseudo radiances over GEO/LEO equatorial ocean matching domain • Takes into account both spectral response function and solar constant differences

11 Aqua-MODIS/Meteosat-9 raymatching
September 2009 Met-9/Aqua Force Fit 0.547 N Stderr % Trend 1.4%/decade Stderr 0.53% • temporal uncertainty of ray-matched calibration is 0.53%

12 SCIAMACHY Meteosat-9 0.65µm ray-match
precise approximate Force fit 0.996 N Stderr % Force fit 0.991 N Stderr % Meteosat-9 radiances based on ray-matching SCIAMACHY pseudo Meteosat-9 radiances All matches between • The force fits are within 0.5%, well within the uncertainty of 4% • 13 times more points with approximate method

13 SCIAMACHY clusters SCIAMACHY has 56 Earth view clusters, each with its own integration time, footprint resolution, and spectral range Cluster for trace gas retrievals have highest spectral resolution Meteosat-9 spectral response function (SRF) is contained nearly in cluster #24 spectral range Cluster #24 has a footprint size of 30x60km, ¼ of SCIAMACHY Spectral range of 0.613μm to 0.726μm  at  20nm resolution Compute lost energy adjustment factor (LEAF) Regress SCIAMACHY Met-9 SRF convolved pseudo radiances with Met-9 SRF limited by clustert #24 spectral range Use all the approximate case footprints The single LEAF value was 0.96

14 Meteosat-9 0.65µm spectral response function
The out of band energy ~4% based on LEAF

15 SCIAMACHY Meteosat-9 0.65µm ray-match
precise approximate Force fit 0.996 N Stderr % Force fit 0.991 N Stderr % Meteosat-9 radiances based on ray-matching SCIAMACHY pseudo Meteosat-9 radiances All matches between Approximate cluster Force fit 0.991 N Stderr % With LEAF • All force fits are within 0.5%, well within the uncertainty of 4% • LEAF is a single value and probably adds noise to the regression

16 GEO and Aqua-MODIS spectral response functions
• Now test the ray-matching with several GEOs and bands

17 Comparison of precise and approximate SCIAMACHY/Met-9 force fit 2007-2010
• All force fits are within 0.5%, well within the uncertainty of ~4% • All approximate ray-matching is not adding any angular biases

18 Perform calibration validation
• If Aqua-MODIS/SCIAMACHY and SCIAMACHY/GEO, where GEO was calibrated against Aqua-MODIS, ray-matches were all performed perfectly then the following ratio would equal 1.0 • If the ratio is within the uncertainty than the methodology is validated • The total uncertainty is the square root of the sum of the squares of the uncertainties of the individual terms • All terms are temporal regression standard errors, • For Met µm/SCIA it is 0.35% Met-9/SCIA 0.65µm Approximate footprint annual means Mean force fit 0.991 Trend 0.19%/year Stderr 0.35%

19 Ray-matching calibration validation
Left column total uncertainty without SBAF, right column with SBAF • Most ratios within 1% and all within the total uncertainty • Validating that all three ray-matches are consistent • Need to revisit the Met µm Aqua/Met-8 calibration, however this band has a large SBAF uncertainty since it is in the near IR absorptions bands

20 Operational SCIAMACHY Meteosat-9 0.65µm calibration
Perform 3-monthly (monthly) approximate SCIAMACHY-radiance/Met µm count footprint (cluster) regression force fits to compute the gain Adjust the SCIAMACHY calibration to match Aqua-MODIS Compare the calibration gains with other methods All other methods use Aqua-MODIS as calibration reference Aqua-MODIS/Met-9 ray-match (shown earlier) Terra-MODIS adjusted first to Aqua-MODIS Met-9 ray-match using NSNOs over polar region Libyan Desert DCC

21 Meteosat-9 0.65µm gain comparison
• SCIAMACHY/Meteosat µm calibration is within 1% of all other calibration coefficients • Has one of the lowest monthly uncertainty at ~0.55% • SCIAMACHY based GEO calibration as good as other methods and has the advantage of not need any spectral corrections • The calibration can be used to verify spectral corrections of other methods

22 Conclusions SCIAMACHY can either validate an existing GEO calibration or if enough sampling is available operationally calibrate GEO visible sensors SCIAMACHY/Meteosat µm calibration is within 1% of all other calibration coefficients Has one of the lowest monthly uncertainty at ~0.55% This calibration technique can be applied to future CLARREO instrument if visible pointing is not possible SCIAMACHY is temporally calibrated as well as Aqua-MODIS Monthly uncertainty of 0.44% A relative trend of 0.2%/decade


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