# Presentation: 880083

## OMI operational pollution SO2 data and improvements with new spectral fit combo SO2/O3 algorithm

- Goddard Earth Sciences and Technology Center (GEST), UMBC, Baltimore, MD
- Laboratory for Atmospheres, NASA Goddard Flight Center, MD
- Michigan Technological University , Houghton, MI
- Joint Center for Earth Systems Technology (JCET), UMBC, Baltimore, MD
- Department of Atmospheric and Oceanic  Physics, University of Maryland College Park, MD

- N. A. Krotkov 1,  K. Yang 1, P.K. Bhartia 2,  S. A. Carn 3, A. J. Krueger 4, R.R. Dickerson 5,  C. Li 5

## OMI operational PBL SO2 data are produced with provisional  BRD algorithm using 4 discrete UV2 wavelengths combined in 3 pairs 

- OMSO2  algorithm [Krotkov et al  2006; Yang et al 2007]  uses calibrated residuals at SO2 absorption band centers produced by the NASA operational ozone algorithm (OMTO3 V8.5 [P.K. Bhartia] ) and radiative cloud pressures from Rotational Raman algorithm [J. Joiner and A. Vasilkov]

**Operational PBL SO****2**** retrieval uses constant Air Mass Factor AMF =0.36 **
**AMF is based on a-priori SO****2**** vertical profile in Planetary Boundary Layer with **_**Center of Mass Altitude (CMA) ~0.9 km **_** **
**PBL SO2 data should be used only for optimal viewing conditions:**
**SZA<50deg**
**OMI view angle < 40deg**
**Cloud fraction < 0.2**
**Terrain height < 1.5km**

**Notes:**
Currently, operational SO2 data are produced with an intermediate algorithm using soft calibrated residuals from a TOMS-like total ozone algorithm at discrete UV wavelengths. 

We currently use linked ozone, cloud pressure and SO2 algorithms for Aura/OMI SO2 retrievals

## Operational PBL SO2 data: high measurement noise 

*<m(z)>**    -profile average OMI   **	** sensitivity to SO**2** at height z*

- Operational PBL SO2 data: high measurement noise

- Operational *<m(z)>=* 0.36

- Center of mass altitude (CMA)~1km

- OMI SO2 sensitivity:

- Aircraft measured in-situ PBL SO2 profiles: USA [Taubman et al 2006];
- China ‘EastAure05’ [Dickenson et al 2007]

- SO2 noise ~ 4 *d(lnI)* [%]       	~ 0.8 DU

**Notes:**
Quantification of anthropogenic SO2 emissions requires off-line corrections of the average photon path, characterized by the air-mass factor (AMF). 

First-order correction to the operational OMI SO2 product, three factors to be considered:

The AMF corrections in turn require a-priori information about SO2 plume center of mass altitude, total ozone and surface albedo. 

## Systematic errors in operational PBL SO2 data:(PBL SO2 = [Slant Column Density] /0.36)

- SAA

**Notes:**
Time averaging reduces noise, but emphasizes small systematic errors:
1) Ozone profile artifacts: +/-30lat, +60N=> need better ozone profile climatology [McPeters, Labo, Logan]
2) Negative SO2  N and S of real So2 sources:  sliding median residual correction ?
3) North Atlantic positive SO2:  low signal artifact?
4) Antarctic belt:  similar artifact to N Atlantic?

3) Negative correlation between biomass burning (smoke) aerosols and SO2 ?

## New combo O3 and SO2 ISF algorithm uses OMI hyperspectral measurements

**Improved profile shapes: O****3**** (****Ω****) uses V8 climatology, SO****2  ****(****Ξ****) uses explicitly specified profile shape (from models)**
**Initial guess: latitude based O****3 ****guess or an adjacent pixel O****3**** value, no SO****2****, derive MLER cloud fraction (*****F******c*****) at λ=333 nm, using OMCLDRR cloud pressure.**
**Iterative Spectral Fitting (ISF) Method [Kai Yang et al submitted GRL’08]:**

- New combo O3 and SO2 ISF algorithm uses OMI hyperspectral measurements

**Notes:**
Profile shapes: O3 (Ω) uses V8 climatology, SO2  (Ξ) uses Gaussian specified by a central height and a width.
Initial guess: latitude based O3 guess or an adjacent pixel O3 value, no SO2, derive MLER cloud fraction (Fc) at λ=333 nm, using OMCLDRR cloud pressure.
Iterative Spectral Fitting (ISF) Method:

## Background noise is reduced with new ISF algorithm

## Future work

- Operational implementation of  combo Ozone SO2 Iterative Spectral Fit algorithm
- Better stray light and Ring corrections
- Using SO2 profile shapes from CTM model

- Better aerosol and cloud correction using A-train data and improvements based on comparison with models and measurements.

- algorithm improvements to account for changes in instrument performance
- Transition to future instruments (GOME-2, NPP)

## Pixel noise in operational PBL SO2 data

- Using AMF =0.36  results in  pixel noise StDev~1DU over background areas and near –nadir view angles
- Both positive and negative SO2 data within +/-2DU in 95% cases
- Near zero area average <SO2> and reduced noise: 0.3 DU (1 )  in 100km averages
- Negative artifacts due to clouds

- Pollution SO2

- Volcanic SO2:  use volcanic trop. or stratospheric data!

**Notes:**
Only use scan positions: 10-50
No clouds (negative bias)

Loaded orbit /omi/live/col3/OMSO2/2005/04/05/OMI-Aura_L2-OMSO2_2005m0405t0358-o03843_v003-2008m0214t215459.he5

Min lon: 119.181; Max lon: 147.667 at ascending node

Creating PostScript file : orbit03843_so2pbl.ps

Enter latitude min and max (-90,90): 10,45
Enter longitude min and max (-180,180) or (0,360): 110,145
Select xTrack position range for plotting? (y/n) n
Filter data using reflectivity/cloud fraction? (y/n) n
Filter data by Solar Zenith Angle? (y/n) n
Filter data by Cloud Top Pressure? (y/n) n
Filter SO2 data using SO2 Quality Flag? (y/n) n

Region statistics:

Mean SO2 for region (PBL): 0.022
SO2 Std Dev (PBL): 1.426
Mean SO2 for region (3 km): 0.005
SO2 Std Dev (3 km): 0.553
Mean SO2 for region (5 km): 0.007
SO2 Std Dev (5 km): 0.252
Mean SO2 for region (15 km): 0.005
SO2 Std Dev (15 km): 0.190

Mean SOI_P1 for region: 0.040
SOI_P1 Std Dev: 0.128
Mean SOI_P2 for region: 0.135
SOI_P2 Std Dev: 0.130
Mean SOI_P3 for region: 0.066
SOI_P3 Std Dev: 0.101

## 3 year average OMI SO2 burdens in different regions of the world 

- 3 year average OMI SO2 burdens in different regions of the world

- 25.5 million tons of SO2 was emitted by Chinese factories in 2005 up 27% from 2000

- Popocatepetl volcano

- Tula Industrial complex

**Notes:**
IN the US Ohio River valley was identified as major SO2 pollutatnt. 

In Mexico:  Tula industrial complex near Mexico City rivals Popocatepetl volcano.  

Previously unknown major emitters have been identified.  

Long-term averages can be used to determine the global distribution of anthropogenic SO2 burdens, and the efficacy of abatement strategies. 

Spatial smoothing and/or time averaging allow significant signal to noise enhancements. Applying these techniques, power plant emissions in Greece, Bulgaria, Turkey, and the US Ohio river valley can be seen in OMI data as well emissions from Persian Gulf refineries, and pollution plumes in a Mexico city area industrial complex. Unknown sources are also being identified. Using long-term averages anthropogenic SO2 burdens can be compared directly in different parts of the world.

OMI average operational PBL SO2 retrievals showing stationary anthropogenic sources over:
USA, Ohio valley
Popocatepetl volcano with Tula industrial complex, North of Mexco city
 Eastern China industrial region 
Emission inventories indicate that:
 Largest increases in tropospheric SO2 emissions have occurred in Asia during the last 20 years (35% per decade over China between 1979-2000 [Massie et al 2004] )
2) This result in positive trends (17% per decade) in sulfate aerosol loading over China  in winter between 1979 and 2000.