# Presentation: 408873

## OMI aerosol products and validation

**OMI aerosol products and validation**
**Remco Braak, **_**Mark Kroon**_**, Pepijn Veefkind, Omar Torres, Pieternel Levelt**
**Aura Science Team Meeting, Boulder, 12 September 2006**

**Notes:**
This picture is taken on a particularly smoggy day in Beijing. It looks like it’s taken early in the morning or late at night, but by the elevation of the sun, you can see that that’s not the case.

What I usually tell with this picture is that aerosols locally represent a health hazard (case in point: the Beijing picture), but that on a larger scale, aerosols affect the radiative balance of the Earth’s atmosphere. Global monitoring of aerosols is therefore important in assessing these effects. I then make a bridge to the presentation proper by saying something like “...and that’s where OMI comes in.”

## OMI aerosol products

**	****Near-UV method****	****Multi-wavelength method**
- Maintainer:	Omar Torres (GSFC)	Pepijn Veefkind (KNMI)
- Heritage:	TOMS	none (new)
- Status:	Provisionally released	Ready for provisional release
- ’s employed:	342.5 and 388 nm	~8 ’s between 340 and 500 nm
- Main	Absorption optical thickness,	Extinction optical thickness,	retrievals: 	UV aerosol absorbing index	Aerosol type
- Principle:	1) Determine *aerosol index*	1) Fit spectrum to find AOT for		~50 aerosol models 	2) Determine aerosol type	from aerosol index and	2) Choose best aerosol model	aerosol climatology	based on RMS error (2)	3) Determine AOT and SSA	3) Spread in AOTs for different	from lookup tables for the	models gives indication for	aerosol type	accuracy

**Notes:**
The main differences between the models are
The Near-UV algorithm is ‘old’ (TOMS heritage), the multi-wavelength algorithm is ‘new’;
Omar seems to stress nowadays that the near-UV algorithm is particularly good in retrieving the absorption optical thickness, whereas the muliwavelength algorithm retrieves extinction optical thicknesses.
The ATBD says that the near-UV algorithm is especially suited for land retrievals, the multi-wavelength algorithms for ocean retrievals (because of uncertainties in the surface albedo at the longer wavelengths). This has never been stressed by Pepijn or Omar, so I would refrain from mentioning it.

## Advantages of Near-UV

- Aerosol types can be distinguished from surface spectral absorption characteristics
- Low surface albedo over land
- Source: GOME-1

- NUV

- VIS

- NIR

**Notes:**
Pepijn advised me to include this slide for the AT-2 workshop, in order to illustrate the advantage of the near-UV. In the near-UV, all surfaces (both land and water) are dark, which is advantageous for aerosol retrieval. Most other satellite instruments that retrieve aerosols observe at visible or longer wavelengths.

## Multi-wavelength aerosol product example

**Multi-wavelength aerosol product example**
- Case study: Sahara, 12 June 2006

- MODIS image                                      OMI multi-wavelength AOT

## Multi-wavelength aerosol product example

**Multi-wavelength aerosol product example**
- Case study: Sahara, 28 June 2006

- MODIS image                                      OMI multi-wavelength AOT

- OMI multi-wavelength AI

## Validation: AERONET

**Validation: AERONET**
- _Ae_rosol _Ro_botic _Ne_twork
- Sun photometers (CIMEL CE-318)
- Measure the AOT every 15 minutes(****** =** 340 - 1020 nm)
- ~120 ground stations currently active

- CIMEL ****340, 380, 412, 440, 443, 490, 500, 532, 551, 555, 667, 675, 870, 1020, 1640 nm

**Notes:**
The world plot shows the AERONET stations currently active.

The picture is the AERONET CIMEL at TNO, The Hague. The detector is nadir-pointing here (idle), but every fifteen minutes, it will erect itself to point upwards, towards the Sun, and scanning the Almucantar and the principal plane. In the blue box on the right are the electronics; the bucket-like thing on the left is the transmitter that sends the data to – in this case – METEOSAT. This CIMEL determines AOTs at 440, 675, 870, and 1020 nm.

## Near-UV product validation example: TOMS data

- Aerosol optical thickness (AERONET)

- Single-scattering albedo (AERONET)

- SSA (TOMS)

- AOT (TOMS)

- 82% of points are within expected accuracy limits (0.1 or 30%)

- 63% within +/- 0.0387% within +/- 0.05

- From Torres et al. JGR 110 (2005)

- Performance for OMI may be expected to be similar or better.

**Notes:**
NOTE: These are not OMI measurements! The data were obtained with TOMS, but are similar to what will be produced by OMI (similar wavelengths and similar algorithm). The results shown here therefore serve as an example of what the OMI near-UV aerosol algorithm should be capable of.

## Multi-wavelength product validation with AERONET

- MW aerosol extinction optical thickness over European and African stations, May-July 2005
- Left: best-fitting aerosol model
- Right: average of all aerosol models with 2 < threshold.

**Notes:**
These are OMI measurements!

On the left side, ALL results for ALL AERONET stations are shown (i.e. all OMI-AERONET overpasses during May-July 2005 in a band of 159 over Africa and Western Europe). Strict cloud screening has been applied, but no threshold has been set on the goodness-of-fit parameter chi2. The OMI AOT shown is the AOT as derived for the BEST-fitting aerosol model (even though chi2 may be larger than the threshold).

The same applies to the right side, but here the threshold has been set, and the AOT shown is the average of the AOTs for all models with chi2 < threshold. Any error bar represents the standard deviation of the AOTs averaged this way. chi2 < threshold chosen such that on average at least 5 of the 50 aerosols models report a value below this number. In some cases none of the models may achieve this hence no data point. In other cases the average value of OMI AOT for the models below threshold is reported.

## A-Train opportunities

- Other instruments on the A-Train that observe aerosols:
- on EOS-Aqua: MODIS and AIRS
- on PARASOL: POLDER
- on CALIPSO: CALIOP

**Notes:**
CALIPSO and CloudSat have only recently been launched.

## Comparison between OMI (MW) and MODIS

-                                                       MODIS
- OMI multi-wavelength

**Notes:**
This slide requires user interaction.

When the slide opens, it shows a MODIS picture of aerosol optical thickness at 550 nm.

Then, when you click the mouse, the MODIS picture is overlain by basically the same picture, but regridded to the OMI grid.

Finally, after an other click, the picture moves to the upper left and is joined by an OMI picture in the lower right, showing the OMI aerosol optical tickness at 500 nm. Several features coincide, such as the low values in Normandy, and higher values towards the south of France. Also the west-east gradient in the Po valley is visible in both pictures.

Regridding makes it possible to do pixel-by-pixel analyses

## Results of comparison with MODIS

- Averages over May-July 2006                                           MODIS collection 4

-   OMI AOT at 471 nm  MODIS AOT at 470 nm           OMI - MODIS

**Notes:**
Here, global (well, as global as the limited dataset permits) averages of the OMI and MODIS AOTs at similar wavelengths. Qualitatively, features match between the two, such as the increased values over the Sahel and Atlantic to the west (desert dust), and further south (biomass burning). Over Europe, qualitative differences seem to be bigger, but this may be due to the color scale.

Since MODIS does not retrieve AOTs over the Sahara, this area shows up in white in all three figures.

The figure to the right shows the difference OMI – MODIS.

Note: MODIS collection 4 has been used here. Recently, collection 5 has been released, but we haven’t had the time yet to look into those data.

## Results of Comparison

- Ocean only*r* = 0.862,  = 0.650,  = 0.032, *N* ~ 350,000

**Results of Comparison**
*r*  = 0.833,  = 0.477,  = 0.053, *N* ~ 620,000
*r correlation, **** slope, **** intercept, N # obs *

**Notes:**
Here, the same data as in the previous slide are represented in the form of a scatter plot (on the left), showing a clear correlation between OMI and MODIS AOT. However, the scatter cloud deviates considerably from the 1-1 line, hinting at an overestimation by OMI at large AOT, and an underestimation at lower AOTs.

The plots on the right are similar, but for land and ocean pixels separately. This indicates that retrievals over ocean are better than retrievals over land, which is understandable because the surface albedo of the ocean is much better defined than that for land.

The values of N don’t add up because there are also combined land-and-ocean pixels.

## Conclusions

**Conclusions**
- Use of near-UV holds unique information on aerosols.
- Comparisons for small subset are promising, both with AERONET and with MODIS.
- Tools are in place for more thorough validation and verification studies.
- Near-UV product will become available publicly this fall this year.
- Multi-wavelength product follows shortly afterwards.

- Pepijn Veefkind	    Remco Braak