1. 1 Deriving cloud parameters for O 3 profile retrieval Zhaonan Cai 1, Xiong Liu 1, Kai Yang 2, Kelly Chance 1 1 SAO 2 UMD 4 th TEMPO Science Team Meeting, Washington DC, June 2, 2016

2. 2 Outline Motivation and introduction Cloud retrieval using optimal estimation Implementation in SAO OMI ozone profile algorithm Retrieval sensitivities of cloud parameters for TEMPO Summary and Future Outlook

3. 3 Motivation Cloud top pressure (P c ) is essentially a source of systematic error in ozone profile retrieval The cloud treatment in ozone profile retrieval VS the cloud retrieval algorithm Similarities: MLER, IPA Differences: surface pressure, ground albedo, cloud albedo assumption Different “soft” calibration are applied (OMI: Liu et al., 2010, Joiner 2006) Ozone profile retrieval usually need to combine bands/pixels, CTP are co-added weighted by cloud fraction. The effective cloud top pressures are wavelength dependent in UV/VIS Retrieve cloud parameters from co-added radiance using optimal estimation approach, perform retrieval error analysis Several studies show that retrieving more cloud parameters (P c, τ c, f c ) will improve ozone profile retrieval

4. 4 Introduction SAO Ozone profile retrieval algorithm Optimal estimation based algorithm TB climatology (Bak J. 2012) Retrievals of O 3 profile including tropospheric O 3 from UV by GOME, GOME-2, OMI (e.g., Liu et al., 2005, 2010; Cai et al., 2012). OMI: UV-1 (13*48 km 2, 270-308 nm) + UV-2 (13*24 km 2, 312-330 nm), co-added CTP from OMCLDRR Wavelength and cross-track dependent “Soft” calibration Cloud retrieval algorithm operates in UV/VIS: OMCLDRR (RRS, 345-355 nm, direct fitting) (Joiner 2006, Vasilkov 2009) OMCLDO2 (O4, 460-490 nm, DOAS) (J. R. Acarreta 2004) CTP is sensitive to radiometric calibration Liu et al., 2010, updated Joiner et., 2006 Different “Soft”calibration

5. 5 Equivalent difference in pressure estimated from the penetrate depth of photons Photons penetrate into somewhere in the middle of cloud. Using 361 nm will result in lower cloud top pressure than using 477 nm Large variabilities depends on geometries, COD, cloud fraction. Introduction

6. 6 Ratio= co-adding diff. / retrieval error Error pattern changes in co-added radiance Improved S/N and fitting residual Pixel inhomogeneity induced λ-shift Total ozone error ~4%, tropospheric ozone error ~10% 1σ Difference between P c retrieved from co-added UV2 radiance and from co- added UV2 P c (using RRS 345-355 nm)

7. 7 Optimal estimation algorithm Optimal estimation approach for cloud retrieval (Poulsen 2012; P. D. Watts 2011; Wang 2016) Retrieve cloud fraction and P c near the ozone fitting window Forward model based on RRS look up table (LIDORT-RRS) (Kai Yang et al., 2013) Perform direct retrieval sensitivity and retrieval error analysis Retrieve cloud parameters from co-added spectra, instead of using co-added cloud product

8. 8 Jacobians for cloud top pressure Ring Refl 60% of the abs. at 477 nm

9. 9 SNR=500 SNR=2000 Retrieval sensitivity for cloud top pressure

10. 10 Implementation in SAO OMI ozone profile algorithm Averaged OMI solar spectrum Empirical bias correction (Liu et al., 2010) Retrieve Gaussian slit function from cross-correlation of reference and OMI solar spectrum Mixed Lambert-Equivalent Reflectivity (MLER) Cloud albedo is set to 0.8, for fully cloudy scene, cloud albedo is allowed to vary Surface albedo is taken from OMILER climatology (Kleipool et al., 2008) using co-added UV-2 radiance (two or more). Fitting Variablesa prioria priori error 1Cloud top pressureOMCLDRR Climatology30% orSurface pressureNCEP-fnl + OMI altitude adj.30 hPa 3Surface albedo 0 th OMILER + 347 nm fitting0.05 4Surface albedo 1 st 0.00.01 orEffective cloud fraction 0 th 347 nm fitting0.05 orEffective cloud fraction 1 st 0.00.01 7I/F wavelength shift0.0 nm0.01 nm RRS (345-355 nm), O4(350-365 nm) * 2 nd order cloud fraction and scale factor of Ring for O4

11. 11 Surface pressure The “soft” calibration shifts P c towards lower pressures. Much better fitting residual ~0.06% Remove the cross-track dependent pattern in res. OE-RRS (345-355 nm)

12. 12 Retrieval error Random error OE-RRS (345-355 nm) OE-O4 (350-365 nm) Jul 1, 2008 Cloud top pressure

13. 13 Cloud fraction > 0.1 OE-RRS (345-355 nm)OE-O4 (350-365 nm) OMCLDRR OMCLDO2 (477 nm) Jul 1, 2008

14. 14 OE-RRS retrieves higher cloud than OMCLDRR (> 100 mb) Mainly due to the use of RRS-LUT and “soft” calibration Similar pattern, less outliers OE-O4 (361 nm) retrievals agree well with OMCLDO2 (477 nm) Expected lower pressures due to the wavelength dependent penetrate depth OE-RRS vs OMCLDRR OE-O4 vs OMCLDO2 OE-RRSOMCLDRR-v003 OE-O4OMCLDO2-v003

15. 15 Histogram of OMCLDO2, OMCLDRR, OE (O4-361) and OE (RRS) retrievals Jul 1, 2008

16. 16 Pc, fc, τ c can be retrieved from the combination of O 2 -O 2 and O 2 -A bands (Daniel et al. 2003, Diedenhoven et al. 2007, Deelen et al. 2008). The retrieval of cloud optical depth can further improve ozone profile retrievals (Diedenhoven et al. 2007) Pc, fc from 760 nm Retrieve Pc, fc, τ c from UV(360-390 nm) + NIR (760 nm) Diedenhoven, B. v., et al. (2008). Retrieval sensitivities of cloud parameters for TEMPO

17. 17 O4 O2-B O2-γRRS 345-355 nm RRS 360-390 nm O 2 -O 2 460-490 nm O 2 -O 2 628 nm O 2 - γ band 688 nm O 2 -B band

18. 18 The state vector comprise following: - cloud top pressure P c - cloud optical depth τ c - cloud fraction f c - surface albedo A s - Scale factor for H 2 O Cloud model: a single, plane-parallel, layer of liquid water or ice particles. The layer is assumed to be geometrically infinitely thin (Poulsen, Siddans et al. 2012) Forward model: VLIDORT (GEOCAPE Tool), IPA Retrieval model: optimal estimation, sensitivity analysis

19. 19 O2-B Weighting function w.r.t P c O4 O2-γ

20. 20 Retrieval sensitivity O2-B band contains significant information of Pc for all cloud fractions, and less about optical depth and cloud fraction, for optical thin cloud. For O2-O2 bands, the sensitivity to Pc is higher for lower-cloud, due to the quadratic dependent of the O2-O2 absorption. UV/VIS provide more information for τ c f c No single band has the best capability PcPc τcτc fcfc AsAs

21. 21 PcPc τcτc fcfc AsAs Multi-bands improve retrieval of cloud parameters. UV+O2-B can capture most of the improvements

22. 22 Retrieval Error

23. 23 CTP retrieval error COD retrieval error hPa Surface albedo COD

24. 24 Summary and Future Outlook The SAO ozone profile retrieval algorithm is updated to retrieve wavelength- dependent cloud fraction and cloud top pressure near the ozone fitting window. Cloud top pressures are retrieved from OMI using RRS and O4 (361 nm) using optimal estimation approach, as well as the error estimations. RRS retrieve higher cloud compared to OMCLDRR and O4 (361 nm) shows good agreement with OMCLDO2. Sensitivity study for TEMPO show that cloud top pressure, cloud optical depth and cloud fraction can be retrieved from the combination of UV and O2-B. Next-term work will include: - Correct the effects of ignoring polarization and temperature-dependent absorption of O4 - Validate total ozone and ozone profile retrievals using new cloud top.

25. 25 Back up

26. 26 Cloud fraction

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30. 30 Vasilkov, A., et al. (2008).