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Cloud algorithms and applications for TEMPO Joanna Joiner, Alexander Vasilkov, Nick Krotkov, Sergey Marchenko, Eun-Su Yang, Sunny Choi (NASA GSFC)
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TEMPO Clouds: cloud optical centroid pressure and effective cloud fraction Default baseline algorithm: OMI rotational-Raman algorithm (CLDRR) – Fitting window currently 346-354 nm – Mixed-Lambertian cloud model – Validated with CloudSat, O2-O2 intercomparisons – shown to improve O 3 and SO 2 retrievals – Requires lookup table to be generated using a solar irradiance spectrum – Soft calibration improves retrievals (striping) for OMI/OMPS, use data over Antarctica (will not have this luxury for TEMPO!) – Sensitive to spectral errors (e.g., OMPS solar diffusor features and undersampling are issues; for OMI straylight an issue; solar variations a possible issue, currently not accounted for)) – Applied to OMPS (Vasilkov et al., 2014, AMT); required changes to OMI code (spline interpolation; use of synthetic solar spectrum to generate tables) – Some difference seen between OMI and OMPS that are currently not understood – Added simple error estimates, errors go as 1/f r
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TEMPO clouds: other options O2-O2 (~477 nm) – Implemented by KNMI for OMI, – ~P 2 dependence, weak band (~1% signal) – Validated with CloudSat, OMCLDRR intercomparisons – New visible fitting at GSFC also fits this band, minor differences with KNMI fitting – Potential backup cloud algorithm for TEMPO – Currently implementing, testing F90 version, very fast, currently uses same surface reflectivity as KNMI, uses climatological temperature profiles (important!)
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Tall poles for CLDRR applied to TEMPO Soft-calibration – Significant striping seen in OMI (could be an issue for any cloud algorithm from similar instruments) – How do we apply soft calibration for TEMPO? Using data over land did not work well for OMI (surface BRDF effects an issue?) use cloud climatology from OMI; identify areas of low cloud pressure variability (e.g., low marine clouds)? posterior correction to cloud OCPs?
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Backups
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First Global Free Tropospheric NO 2 Concentrations Derived Using a Cloud Slicing Technique Applied to Satellite Observations from the Aura Ozone Monitoring Instrument (OMI) S. Choi 1,2, J. Joiner 2, Y. Choi 3, B. N. Duncan 2, E. Bucsela 4 (Currently in AMTD) 1 Science Systems and Applications, Inc. (SSAI), 2 NASA Goddard Space Flight Center, 3 University of Houston, 4 SRI Interntaional NASA GSFC Laboratory for Atmospheric Chemistry and Dynamics These global maps show 3-month seasonal averages of free tropospheric NO 2 mixing ratio (gridded at 6 o latitude x 8 o longitude resolution) for Dec-Feb (top panel) and Jun-Aug (bottom panel) 2005- 2007. These maps show clear signatures of anthropogenic contributions near densely populated regions as well as lightning contributions over tropical oceans.
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Comparison of OMPS and OMI OMPS OMI Cloud pressure retrievals of Jan 07, 2013 (ECF>0.05) Most cloud OCP patterns are same (northern Pacific, Mexico, northern Atlantic, northern China) OMI OCP retrievals are somewhat lower than OMPS particularly in the tropics 8
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Comparison of PDFs of cloud pressure, OMI O2-O2 added 9 Southern mid-latitudes Tropics Northern mid-latitudes Differences between OMI RRS and OMPS cloud pressures appear to be similar to differences between OMI RRS and OMI O2-O2 except for the differences in the tropics.
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