# Presentation: 472502

## Evaluation of OMI Cloud Pressure from Rotational Raman Scattering Using Aircraft and Satellite Data

**Joanna Joiner****1****, Alexander Vasilkov****2****, Kai Yang****3****, **
**Gordon Labow****2****, ****P. K. Bhartia****1****,  Robert Spurr****4****, **
**Matt McGill (and CPL team)****1****, Gerald Heymsfield****1****, **
**Lihua Li****1****, Lin Tian****1****, Edward Browell****5****, **
**1****NASA/Goddard Space Flight Center**
**2****Science Systems and Applications, Inc., **
**3****University of Maryland, Baltimore County/GEST**
**4****RT Solutions Inc., ****5****NASA Langley Research Center**** **

## Outline

- Background and summary of validation campaign results
- Radiative transfer simulations with Rotational Raman Scattering (RRS) for a model of Plane-Parallel Cloud (PPC)
- Comparisons of OMCLDRR cloud pressures with OMTO3 and MODIS climatology
- Effect of cloud pressures on OMTO3 total ozone retrievals
- Comparisons of OMI cloud pressures with Cloud Physics Lidar (CPL) cloud-top pressures and Cloud Radar System (CRS)
- Conclusions

- Joanna Joiner et al. OMI meeting

## Background

- Estimates of cloud pressure are needed to accurately derive trace gas information from UV/VIS spectrometers and are important in their own right for climate
- There are two methods to estimate cloud pressure with OMI:
- Rotational-Raman scattering (RRS) in the UV 2) O2-O2 absorption at 477 nm
- Both differ fundamentally with IR emission (e.g. MODIS) in that cloud shield atmosphere below from Rayleigh/Raman scattering or O2-O2 absorption
- Both methods are new; have not been used in an operational setting
- Both methods were tested pre-launch with GOME (near nadir; morning orbit); RRS at 360 nm agreed well with O2-A band from GOME, improved O3
- Both methods use the Mixed Lambertian-Equavalent Reflectivity model
- Cloud reflectivity adjusted empirically to account for light penetration in thin/broken clouds based on limited modeling
- OMI trace-gas algorithms use this model
- V1 OMCLDRR algorithm uses large filling-in of Ca Fraunhofer lines between 392-398 nm
- large signal mitigates striping errors
- Soft-calibration  based on Antarctica data (assume minimal cloud effect)

- Joanna Joiner et al. OMI meeting

## Summary of results from INTEX-B, AVE and MODIS comparisons (see Vasilkov poster for more details)

- There are significant differences between OMI cloud algorithms (analysis on-going)
- Cloud pressures too low in presence of absorbing aerosol; Aerosol index is not always a good indicator of aerosol in/above clouds.
- V1 Cloud pressures too low (sometimes negative) in thin/patchy clouds (problems with MLER model)
- Comparisons with MODIS show that differences in the presence of multiple cloud decks depend on the optical thickness of the upper cloud deck (as indicated by MODIS cirrus reflectance).
- Filling-in of solar lines due to Raman scattering is extremely sensitive to non-Lambertian behavior such as sea glint and non-Lambertian surfaces
- Direct beam is not Rayleigh/Raman scattered; appears as a very high cloud
- Effect on absorbers (O2-O2) is opposite – biases results toward surface pressure.

- Joanna Joiner et al. OMI meeting

## Plane parallel cloud radiative transfer simulation, retrieval of MLER cloud pressure: Can we simulate what we see in the OMI data?

- LIDORT-RRS (Spurr) code calculates radiance and RRS filling-in
- Simulations of plane-parallel cloud (PPC)  cloud-top heights of 2-10 km (1 km thick), cloud optical depths 5-50, and surface albedo 0.05, 392 nm (V1 wavelengths)
- Absorbing aerosol (=1.0 and 0=0.9) above/beneath cloud
- Multiple cloud decks (vary optical depth of both decks)
- Cloud pressure retrieved with MLER model

- Joanna Joiner et al. OMI meeting

## Errors in MLER Cloud Pressures

- - **Deep convective**** ****clouds and optically thick fontal clouds:**
-  MLER produces small errors, slightly overestimates cloud pressure
**Cirrus (high thin clouds): **MLER cloud pressures are erroneously low. This effect is seen in OMCLDRR retrievals (e.g. some negative cloud pressures in V1).

- Reflec     0.30   0.47              0.65                                               0.83 Cl frac    0.20   0.45              0.74                                               1.0

- Joanna Joiner et al. OMI meeting

## Cloud pressure error from absorbing aerosol

- Below the cloud (negligible effect)

- Above the cloud (erroneously low cloud pressure) – suggested in INTEX data

- Joanna Joiner et al. OMI meeting

## Simulation of 2 layer cloud

- Joanna Joiner et al. OMI meeting

## Monthly mean cloud pressures (Jan 2005)

**MODIS IR (different scale)**

**Clouds too high**

- R>40%

- f>60%

**OMTO3 clim.**
**R>60%**

**OMCLDRR Rc=80%**

**OMCLDRR Rc=80% “glint” filtered**

**More low clouds center of swath removed**

- Joanna Joiner et al. OMI meeting

- Obvious OMTO3 O3 errors due to incorrect climatological cloud pressures

- Some improvement using OMCLDRR cloud pressures, but at cloud band edges, too low pressures produce O3 errors

- Joanna Joiner et al. OMI meeting

- Same as in previous slide (V1 from 392-398  nm

- Using shorter wavelengths for cloud retrieval improves pressures at cloud band edges; reduces dependence on surface, (non-Lambertian effects)

- Joanna Joiner et al. OMI meeting

## Clear scene diagnostic (Pcld-Psurf); 350-395 nm differences

- 395 nm: Significant negative bias and swath dependence, produces too low pressures in thin/broken clouds

- 350 nm: Bias and swath dependence much reduced, aerosol effect now obvious

- In cloudy regions, largest differences between 350 and 395 occur in thin/broken clouds, center of swath (fixes problems with V1)

- Joanna Joiner et al. OMI meeting

## Comparisons with CPL and CRS (06’ CALIPSO/CloudSat)

- Joanna Joiner et al. OMI meeting

## Comparisons with CPL & CRS (V2)(06’ CALIPSO/CloudSat validation)

-  OMI cloud pressures closer to CPL lower cloud deck when upper deck absent;
- OMI has higher pressure than CPL with upper deck

- Joanna Joiner et al. OMI meeting

## Summary and Future Work

- Non-Lambertian behavior produces erroneously low cloud pressures
- Cloud shadowing
- Sea glint
- Thin cloud phase function
- Non-Lambertian surface (low reflectivities; broken, thin clouds)
- Errors in chlorophyll climatology (produces errors in thin/broken clouds only)
- MLER in thin/broken clouds can produce too low cloud pressures
- Absorbing aerosol (above cloud) produces too low cloud pressures (suggests sensitivity to aerosol height as simulated)
- Multiple cloud decks produce difference wrt IR, pressures closer to lower cloud deck.
- Problems mitigated by using shorter wavelengths with more Rayleigh scattering, no ocean Raman scattering (V2 will be released soon!)
- Plan more validation with Cloudsat and Calipso

- Joanna Joiner et al. OMI meeting