1. Validation of OMPS-LP Radiances P. K. Bhartia, Leslie Moy, Zhong Chen, Steve Taylor NASA Goddard Space Flight Center Greenbelt, Maryland, USA

2. Motivation Find causes of large radiance residuals from the L2 algorithm Improve altitude registration methods Isolate systematic errors in measured and calculated radiances Evaluate accuracy of MLS and NCEP data Better understand information content of measurement

3. Methodology Radiance Simulation –Bass & Paur cross-sections –Atlas/SUSIM solar irradiance –MLS O 3, temp and GPH profiles –OMPS-NP reflectivity limb RTM is scalar but nadir is vector –NO 2 climatology, No aerosols Measured data –Solar irradiances –Ungridded UV (290-350 nm) radiances from two “high gain” images

4. MLS GPH uncertainties 64 km 48 km 32 km 16 km Z*  500 m error in GPH will produce ~8% error in calculated radiances  Error in T that causes GPH errors will produce additional error in radiances

5. Scalar radiance error at TH = 40 km, R = 0.3 Error is shown for λ = 325, 345, 385, 400, 449, 521 nm (solid lines) and 602, 676, 756, 869, 1020 nm (dashed line) Same scalar radiance error pattern prevails for all wavelengths Amplitude at 345 nm is reduced (- 3.5% to +5.5%), due to larger R

6. % change in 350 nm radiance due to aerosols % change shown for TH = 20, 25, 30, 35, 40 km Surface reflectivity = 0 λ = 350 nm

7. LP Focal Plan Schematic Designed for sequencing HG Long/LG long/HG Short/LG short: 1: 4.5: 7: 4.5 Total dynamic range gain: x140 Two interleaved exposures in 1:31 ratio Ratio: 1:4.5 Low gain High gain

8. High Gain Image in UV No HG data HG long There are systematic differences between HG & LG images so our plan is not to use LG image in the UV. This will free up some bytes for other use. HG short No data

9. Optical distortions in HG Image Variation of wavelength with TH variation is smaller than instrument bandpass, but still needs to be corrected. variation is 4 times worse for LG image. Fixed column no

10. Optical distortions in HG Image Variation of TH with wavelength Fixed row no

11. Wavelength Under-sampling Without under-sampling corrn interpolation error can be as large as 3% Radiances convolved with OMPS bandpass

12. Solar Irradiance (SI) Comparison SUSIM smoothed with OMPS bandpass OMPS in FWHM/10 steps No adj +0.6 nm shift error decreases

13. SI comparison results We have ~0.6 nm error in in the 290-320 nm band Error decreases with increase in so it is not pixel shift type error Bias remaining after +0.6 nm shift is partly due to error and partly due to radiometric calibration errors

14. Explanation of large radiance residuals in L2 Partly due to  error Partly due to error in SI assumed in calculating the radiances in L2 – L2 SI doesn’t agree with Atlas/SUSIM

15. Radiance Comparison Example (49.5 km, 70S) Black: Measured Red: Calculated Radiance bias Irradiance bias There may be spectral bias between radiance and irradiance.

16. Alt Registration using 305 nm No O 3 abs strong O 3 abs Strong sens to TH weak sens to TH 305 nm not affected by reflectivity

17. An example: 70S April 2, 2012 600 m error No TH error Variation in est. TH error with alt is probably due to error in MLS GPH

18. Conclusions Release 1 data has 0.6 nm error in the UV. – This amounts to 10% error in O 3 x-section. – Limb UV ozone profiles are therefore not reliable. – Error in VIS profile is TBD. Release 1 altitude registration seems quite accurate (±300 m) – To improve accuracy we will need to rely on multiple approaches. MLS temperature probably has larger bias in the mesosphere than MLS has estimated

19. Conclusions (cont’d) Given uncertainties and low vert resolution of of NCEP temperature data it may not be possible to produce accurate/high precision MR profiles from LP above ~40 km – Density profiles are not affected by this problem – It may be possible to improve NCEP temp profiles above 40 km using LP, but this needs further investigation.