# Presentation: 085267

## A Study of the Effects of Two-Layer Clouds and Absorbing Aerosol on 

**A Study of the Effects of Two-Layer Clouds and Absorbing Aerosol on **
**OMI Cloud Pressures Derived from Rotational Raman Scattering**
- Alexander Vasilkov1, Joanna Joiner2, Pawan K. Bhartia2, Robert Spurr3, Matt McGill2, and Edward Browell4
- 1. Science Systems and Applications, Inc., Lanham, MD
- 2. NASA Goddard Space Flight Center, Greenbelt, MD
- 3. RT Solutions Inc., Cambridge, MA
- 4. NASA Langley Research Center, Hampton, VA

**Abstract**
- Noticeable differences between cloud-top pressures derived from the OMI rotational Raman scattering (RRS) algorithm and values from Aqua/MODIS and Cloud Physics Lidar (CPL) have been observed in certain geophysical situations such as two-layer cloud scenarios and absorbing aerosol tropospheres. Absorbing aerosol can cause the OMI algorithm to retrieve cloud pressures lower than those from MODIS. In the presence of multiple cloud decks, the OMI algorithm retrieves higher pressures than MODIS values for the top of the upper deck (to which MODIS is sensitive). Radiative transfer calculations with RRS were performed to simulate the effects of these geophysical conditions on the OMI cloud pressure retrievals. Additional simulations were done in order to quantify possible errors related to the use of the Mixed Lambert-Equivalent Reflectivity (MLER) concept as currently used in the OMI cloud pressure algorithm. The results show that (1) MLER is adequate for optically thick clouds; (2) For a model of optically thin cloud overlapping optically thick cloud, the algorithm retrieves cloud pressure that is up to 200 hPa higher than for lower layer cloud-top pressure; (3) Absorbing aerosol placed above the cloud reduces the retrieved cloud pressure by 100-200 hPa depending on cloud optical depth.

**OMI rotational Raman scattering (RRS) cloud pressure algorithm**
- The OMI cloud pressure algorithm is based on RRS in the atmosphere that results in filling-in of solar Fraunhofer lines (one of two OMI cloud pressure algorithms - other is from O2-O2 absorption band). Clouds shield the atmosphere below from satellite observations thus reducing the filling-in. Effective (”scattering”) cloud pressure is retrieved by a minimum-variance technique that fits the pre-computed reflectance to the observed reflectance in the spectral window of 392 to 398 nm The ”scattering” cloud pressure refers to a pressure level reached by back-scattered photons averaged over a weighting function.The algoritthm makes use of the Mixed Lambert-Equivalent Reflectivity (MLER) concept. According to MLER, the total top-of-the-atmosphere (TOA) radiance is considered as a weighted sum of clear sky sub-pixel radiance and overcast sky sub-pixel radiance  *I**t* = *I**g* (1- *f*) + *I**c* *f*,    where *f* is the cloud fraction. The radiances *I**g* and *I**c* * *are computed with the assumption of Lambertian reflecting surfaces that have pre-defined reflectivities *R**g* and *R**c*. The cloud fraction is determined from the above equation.
- The TOA reflectance is taken as a sum of the spectrally linear component accounting for Raleigh and aerosol scattering and the high-frequency component accounting for RRS: *R*TOA =* A* +* B* + *r*(*p*, , *R**s*, *geom*). The cloud pressure,* p*, is derived from spectral fitting the observed reflectance to the calculated reflectance. The latter is calculated using a lookup table approach.

**Comparisons of OMI cloud pressures with Aqua/MODIS and lidar data: identification of problems**

**Acknowledgments.**** **The first** **author acknowledges support through NASA contract NAS5-00220. The authors are thankful to the OMI and CPL processing teams for providing data. Special thanks are to  Dr. C. Ahn of SSAI for providing software for collocation.

**Key Reference**
- -Joiner, J. and  A. Vasilkov,  “First results from the OMI rotational Raman scattering cloud  pressure algorithm,” *IEEE Transactions on Geoscience and Remote Sensing*, *44*, No. 5, 1272-1282, 2006.

**Optically thick lower layer with ****=50**
- For low optical depths of the upper layer, there is an interesting effect of enhanced filling-in at TOA. The effect is explained by light that penetrates through optically thin upper layer and undergoes multiple scattering between the cloud layers. The multiple scattering between the cloud layers increases filling-in. The range of optical depths of the upper layer at which the effect takes place is typical for cirrus clouds.
**Implication:**
- The RRS cloud pressure algorithm retrieves cloud pressure that is up to 200 hPa higher than the lower layer cloud-top pressure (left y-axis).
- For high optical depths of the upper layer (>~30), light slightly penetrates through optically thick upper layer and there is small influence of the lower cloud layer on filling-in at TOA.
**Implication:**
- The RRS cloud pressure algorithm retrieves cloud pressure that is closer to but somewhat higher than upper layer cloud-top pressure (right y-axis).
- As  , the retrieved pressure tends toward the upper cloud-top pressure.

- Lower layer:
- Optically thick,
- liquid water cloud

- Upper layer:
- cirrus cloud

- Altitude, km

- Sun

- 5

- 9

- 18

- OMI UV backscatter measurements

- MODIS
- Thermal IR measurements

**Two layer cloud model**

**Conclusions**

- One layer cloud: ( upper cloud 0): – green arrow
- Two layer cloud:
- Optically thin upper layer – blue arrow
- Optically thick upper layer – orange arrow

**Radiative transfer simulations: errors due to MLER**

-  LIDORT-RRS code calculates radiance and RRS filling-in
-  Simulations were carried out for a model of plane-parallel cloud (PPC) with geometric thickness of 1 km. The Henyey-Greenstein phase function was assumed with *g*=0.85.
-  Cloud pressure is retrieved from simulated data using interpolation of lookup tables generated for
- Clear sky and cloudy radiances, *I*g & *I*c, are calculated for two opaque Lambertian surfaces with pre-specified reflectivities of *R*g=0.15 and *R*c=0.80.
-  Filling-in that is calculated for a Lambertian surface with different albedo located at different pressures in the atmosphere.

**Multi layer clouds: ****INTEX-B campaign, 03/16/06**

**Radiative transfer simulations: absorbing aerosol**

**Possible MLER error: ****04’ AVE CPL data**

- OMI reflectivity over the retrieval area is about 70%, the effective cloud fraction is about 80%.

**Results **
- 	(1) Errors due to the MLER assumption are relatively small (within  50 hPa) and slightly depend on cloud height for optically thick cloud with optical depth > 10. For that cloud, the contribution from the effective clear sky sub-pixel approximately compensates the contribution of light coming from below the homogeneous cloud.
- 	(2) For optically thinner clouds, errors significantly increase and depend on cloud height

- OMI (MODIS) retrieves slightly lower (higher) cloud heights than CPL does (only OMI pixels with cloud fraction > 20% were used for the comparison). This is expected based on our simulations. Only the top-most CPL detected layer is shown (multiple cloud decks were present for a few pixels near 18.2 hr).

- Absorbing aerosol with absorption optical depth of 0.1 was placed above/below the cloud.

**Results **
- 	(1) Absorbing aerosol placed above the cloud reduces the retrieved cloud pressure by approx. 100 hPa for optically thick clouds and up to 200 hPa for clouds with optical depth lower than 10 (left figure).
- 	(2) Absorbing aerosol below the cloud affects the retrieved cloud pressure very weakly (right figure).

- MODIS multi-layer flag (Collection 5 data) and lidar indicate presence of multilayered clouds in regions circled. OMI RRS retrieves higher cloud pressures ( 300 hPa) than MODIS, close to lidar lower layer..

- OMI Aerosol Index indicates the presence of absorbing aerosols in the vicinity of the clouds. OMI RRS retrieves lower cloud pressures (up to 100 hPa) than MODIS. We are checking INTEX data further to confirm presence of aerosol.

-      Differences between OMI RRS, MODIS, and CPL cloud pressures  are not only due to the OMI MLER algorithm but also depend on geophysical situations such as two-layer clouds and the presence of absorbing aerosol. Our simulations agree qualitatively with the observations.
-      Results of radiative transfer simulations with RRS show that (1) MLER is adequate for optically thick clouds with error within  50 hPa; (2) For a model of optically thin cloud overlapping optically thick cloud, the algorithm retrieves cloud pressure that is up to 200 hPa higher than for lower layer cloud-top pressure. For optically thick upper layer cloud, the algorithm retrieves cloud pressure that is close to upper layer cloud-top pressure; (3) Absorbing aerosol placed above the cloud reduces the retrieved cloud pressure by 100-200 hPa depending on cloud optical depth. Absorbing aerosol below the cloud does not significantly affect the retrieved cloud pressure. RRS is sensitive to aerosol height and we plan to exploit this effect in the future.

**Absorbing aerosol: ****INTEX-B campaign, 03/12/06**

**OMCLDRR from UV-2 **

- The current version of OMCLDRR makes use of the fitting window from the Ca line region (392-398 nm). Using the results of extensive validation efforts, we identified several problems with the current version. We have implemented a new version that fixes most of the problems.The main change is moving the fitting window to the UV-2 channel (346-356 nm). Increased Rayleigh scattering mitigates problems with sea glint, cloud showing, non-Lambertian of surface, MLER, and clouds, thin cloud phase function effects.
- Results shown on the left panels are from the new version. Below are the same comparisons with MODIS but from the current version. The new version of OMCLDRR improves an agreement with MODIS in cases of the retrieval of too low cloud pressures.