Methodology to compare GERB-CERES filtered radiances

1. Methodology to compare GERB-CERES filtered radiances Grant Matthews CERES/GERB Science Team Meeting NCAR, Boulder, 1st April 2004

2. Introduction: GERB and CERES; the Differences • GERB Pixel level comparison • Comparison of instantaneous filtered radiance • Optimizing comparison results • Use of CalMon to ‘iron out’ inter-pixel variations • Conclusions

3. Introduction: CERES and GERB; the Differences

4. SW comparison considerations • CERES: Scanning radiometer with 20km footprint. One thermistor bolometer detector for each spectral channel views all lat/long regions on Earth. 2 Silver mirrors are used in its telescope. PSF “designed” to allow ‘stand alone’ measurements. • GERB: Non-Scanning radiometer with 50km footprint. Linear array of 256 thermopile detectors build up image of Earth disc ‘slice by slice’. Hence different Lat/long regions are sampled by different detectors at different times. 5 Silver mirrors are used in the telescope. Synergy with SEVIRI to resolution enhance and correct for PSF shape.

5. GERB Pixel level comparison • Previous comparison studies compared measurements by the two instruments after ‘binning’ into latitudinal zones. • Prior to full in-flight validation of pixel specific calibration coefficients, optimized geolocation and with SEVIRI data absent, binned GERB data is prone to significant random sampling errors. • Hence to reduce the first factor a comparison must be done specific to measurements made by each GERB pixel.

6. Comparison of Instantaneous Filtered Radiance • Raw measurement (Filtered Radiance) • Required product (Un-Filtered Radiance)

7. Two Compounding Problems with Unfiltered radiance comparison • Different number of mirrors gives different spectral responses (especially in region of 1000nm) • CERES and GERB unfiltering performed in different places by different groups Optimal Solution: Use RMIB filtering ratio, one group, one spectral database?

8. Plus calibration parameters are tied to filtered, not un-filtered radiance. • Hence the filtering ratio allows inflight construction of the GERB SW/Tot gain ratio for each GERB pixel:

9. Optimizing comparison results • ‘Stand alone’ GERB data (in comparison campaign) is subject to significant sampling errors because of PSF shape, smear and in the event of bad geolocation. • However, such error magnitudes can be quantified based on scene contrast obtained from surrounding CERES measurements. • Hence each CERES estimate ‘i’ of GERB gain ratio can be accompanied by an assoiciated error dependant on scene contrast, geolocation quality, smear amplitude and scene spectral content:

10. Optimizing comparison results (conti…) • Then when taking the average gain ratio value over the entire GERB-CERES campaign, these variances are used to weight the result (i.e low contrast bright scenes, away from the 1000nm silver dip, will be given the highest weighting)

11. Use of CalMon to ‘iron out’ inter-pixel variations • Likely that in some regions of the 256 pixel array there will be CERES estimates of the gain ratio with high random errors due to lack of low contrast bright scenes. • To GERB’s advantage, on each rotation every detector views a very bright calibration source, the CalMon.

12. CalMon is an integrating sphere, hence spatial variation in its output should be VERY SMOOTH! • Therefore interpixel high frequency variation in the CalMon output is not real. If the CERES estimates of gain ratio is used, much of the variation will be down to the random sampling errors. • Filter out interpixel variations in Fourier domain to recover CERES estimate of CalMon radiance

13. When counting up Fourier coefficients, use the derived sampling errors and relevant RMS noise as weighting (i.e. don’t use DFT, this will account for noisy or dead pixels). • Simultaneously use ground determined gain ratio ‘B’ to determine CalMon radiance, filter in the same way.

14. Finally, to take advantage of high signal in CalMon output, perform a SNR weighted average over all rotations ‘m’ during daylight hours of the campaign:

15. Conclusions • Relative inter-pixel distribution across array should match • Off axis telescope effect on SR given by: • CERES/GERB systematic SW radiometric difference:

16. Conclusions • Knowledge of Scene contrast used to weight the CERES-GERB comparison • Use the spatial uniformity of CalMon to ‘iron out’ noise and systematic inter-pixel variations from sampling noise and SR uncertainty • Use vast quantity of GERB data to lesson impact of ‘CalMon smear’ and instrument noise • Similar consideration of the IBB as a uniform source would allow better SNR in gain determination and a future CERES-GERB night time comparison for LW channel?