# Presentation: 185927

## The MODIS Aerosol Retrieval Algorithms

- Developed by
- Yoram Kaufman, Didier Tanre,  Shana Mattoo
-  Lorraine Remer, Rob Levy, Allen Chu, Vanderlei Martins
- and many others

## The Importance of Aerosols (a brief list)

- Environmental
- Radiation budget
- Cloud formation and rainfall
- Visibility
- Human Health
- Respiratory ailments
- Heart disease

## What do we want to know about aerosols to determine their impacts?

- Total Amount
- Size distribution
- Geographic location and transport
- Spatial distribution in the horizontal and vertical
- Shape
- Optical properties
- Chemical properties

## MODIS on its own can significantly help us  to determine:

- Total Amount
- Size  distribution - over ocean
- Geographic location and transport
- Spatial distribution in the horizontal
- Optical properties - light scattering

-     MODIS on its own tells us little or nothing about:
-    Size distribution - over land
-    Spatial distribution in the vertical
-    Shape
-    Optical properties - absorption
-    Chemical properties

## MODIS makes no direct measurements of 
the physical properties of aerosols.

MODIS measures only reflected radiation and this signal is used to derive the physical properties of the aerosols! 

- the physical properties of aerosols.
- MODIS measures only reflected radiation and this signal is used to derive the physical properties of the aerosols!

## Two MODIS Algorithms

- Ocean and Land algorithms are totally separate but use similar techniques to derive aerosol properties.
- To increase the signal to noise ratio in the data we use a statistical approach that groups many pixels.
- Both algorithms are applied to individual boxes
- of 20 x 20 pixels at 500 Meter resolution
- Both algorithms produce 10 Km products.

## Land or Ocean

- If all pixels in the 10 x 10 kilometer box are ocean the Ocean Algorithm is used.
- If any land pixels are observed in the 10 Km box the Land Algorithm is used.
- The MOD35 cloud mask product is used to determine if each pixel is land or ocean.

## MODIS retrieval algorithms make use of a carefully calibrated

- and properly geo-located signal.

## We can think of any remote sensing retrieval algorithm as having three phases:
 Removing distortion from the signal
 Separating signal from noise
 Correctly interpreting the signal

-  Removing distortion from the signal
-  Separating signal from noise
-  Correctly interpreting the signal

## Removing Distortion

- GAS Correction
- Most earth science remote sensors have to take into account changes in the signal due to atmospheric gases - water vapor, ozone and CO2.

## Removing DistortionGas Correction

- To correct the top of atmosphere (TOA) reflectance signal for the effects of atmospheric gasses MODIS uses:
-  Ancillary (outside) Data
-  NCEP, GDAS      - meteorological data for water vapor
-  TOVS or TOAST - ozone analysis.
- MODIS level two product
- MOD07 -  atmospheric profile to determine water vapor
- If these are not available climatological data is used as a first guess assumption for these values.

## Ocean Algorithm

- Separating signal from noise:
- Cloud Masking
- Sediment Masking
- Statistical treatment of pixels
- Glint Masking

## Cloud Masking

- Spatial Variability Tests
- Visible channel brightness
- Cirrus Cloud Removal
-       -- Near IR Tests
-         MOD35 Cloud Product Tests
-         --  Infrared Tests

## Spatial Variability

- Areas of aerosol are usually quite uniform and look “smooth” to our eyes.
- Clouds are generally much less uniform and
- look “bumpy”.
-  3 x 3 boxes of pixels within the 10 Km Box
- are evaluated in the 0.55 channel to see if they are
- uniform or variable.

## Spatial Variability

- Any group of 9 pixels with :
- Standard deviation > 0.0025 is identified as cloud
- The upper left pixel of this group is discarded.
- Information is used from neighboring pixels to the right
- and below the 10 Km box to evaluate the status of pixels
- near the edge.
- The 10 Km boxes at the right hand edge of the swath
- and at the end of the granule are discarded.

## Dust Plume

- Dust Aerosol

- Cloud

- Cloud

- Cirrus Cloud

- Cloud

- Cloud Shadow

## Dust Plume

- Results of 3 x 3 Variability  cloud  screening

- Center of large “smooth”
-  clouds not detected

## This is a spectral test using the

- ratio of  0.47 /  0.66  < 0.75

- We see this naturally since
- the absorption in the blue
- makes dust look brown to our
- eyes

## Slide 19

## There are 3 tests

- we apply to identify
- Cirrus clouds

- If any one of these tests
- indicate cirrus is present
- we label the pixels as
- cloudy and mask them

- In this case we label the
- pixel as “non-cloudy”
- but reduce the quality
- of the retrieval for the whole
- 10 x 10 Km box

## we will attempt to make an aerosol retrieval

- After screening for clouds we look for and eliminate:
-  Sediments
-  Glint to within 40 degrees of the specular refelection

- We further eliminate the brightest 25%
- and darkest 25% of the remaining pixels
- in the 0.86 channel.
- This is done to eliminate any remaining
- outliers so that we can arrive at the “true”
- mean reflectance values for the box.

- If the pixels that are eliminated in this final
- step include those that reduce the quality
- of the retrieval, the high quality flag is restored.

- If there are
- at least 10 remaining pixels in
-    the 0.86m channel
-  and 30 remaining pixels in
-    all of the other channels

## Assumptions

- We need to make several assumptions to
- correctly infer the aerosol characteristics
- from the remaining signal.
- Assumptions include:
- Surface reflectance values
- A bi-modal aerosol distribution
- A set of aerosol properties (models)
-     for each of the aerosol modes.

## Separation of signal and noise

- The spectral reflectances measured by the satellite from the remaining pixels contains elements of both the ocean surface and atmospheric aerosols.
- We still need to remove surface effects.

## Ocean Surface 

- Contributions to the total signal due to
- the ocean surface include:
- Sun glint reflection from surface waves
- Reflection from whitecaps
- Lambertian reflectance from underwater scattering.

## After removing as much of the noise as possible due to the surface signal we are left with an optical signal representing aerosols that must be correctly interpreted.

We are still very limited in our ability to correctly infer the aerosol properties from this signal.

We use our knowledge and experience to construct
models of aerosols which represent real world conditions.

With the help of these models we can correctly
Interpret the measured signal.

- We are still very limited in our ability to correctly infer the aerosol properties from this signal.
- We use our knowledge and experience to construct
- models of aerosols which represent real world conditions.
- With the help of these models we can correctly
- Interpret the measured signal.

## Aerosol “models” over oceanAre represented by theoretical “modes”

| Fine (Small) Mode | Refractive Index
=0.47-->0.86m | rg |  | reff | Comments |
| --- | --- | --- | --- | --- | --- |
| 1 | 1.45-0.0035i | 0.07 | 0.40 | 0.10 | Water  Soluble |
| 2 | 1.45-0.0035i | 0.06 | 0.60 | 0.15 | Water  Soluble |
| 3 | 1.40-0.0020i | 0.08 | 0.60 | 0.20 | Water  Soluble with humidity |
| 4 | 1.40-0.0020i | 0.10 | 0.60 | 0.25 | Water  Soluble with humidity |

| Coarse (Big) Mode | Refractive Index
=0.47-->0.86m | rg |  | reff | Comments |
| --- | --- | --- | --- | --- | --- |
| 5 | 1.35-0.001i | 0.40 | 0.60 | 0.98 | Wet sea salt type |
| 6 | 1.35-0.001i | 0.60 | 0.60 | 1.48 | Wet sea salt type |
| 7 | 1.35-0.001i | 0.80 | 0.60 | 1.98 | Wet sea salt type |
| 8 | 1.53-0.003i (0.47)
1.53-0.001i (0.55)
1.53-0.000i (0.66)
1.53-0.000i (0.86) | 0.60 | 0.60 | 1.48 | Dust-like type
 |
| 9 | 1.53-0.003i (0.47)
1.53-0.001i (0.55)
1.53-0.000i (0.66)
1.53-0.000i (0.86) | 0.50 | 0.80 | 2.50 | Dust-like type
 |

- ATBD

## Radius (μm)

- MODIS aerosol retrieval over ocean

- Find one coarse mode and
-   one fine mode
-     that combine to match the
-       observed spectral reflectances

## Look Up Tables -  LUT

- The radiative transfer code is computationally time
- consuming.

- To make the radiative transfer code run more efficiently
- we make use of a set of pre-computed tables
- for the various sets of possible angles and amounts of
- aerosol.

- We interpolate from the values in the table for angles
- and aot values not in the LUT

## Creating the LUT

- We must consider which atmospheric scenarios (combination of aerosol + Rayleigh + surface + other) are representative of what MODIS observes (including appropriate geometry, MODIS bandwidth information, etc).
- LUT utilizes vector radiative transfer (vRT) code to simulate:

-  a,T,s:  (path radiance, transmission, backscattering)
- 	Combination of Rayleigh + Aerosol
-  s  (surface reflectance)
- Combination of  foam / whitecaps (assuming V=6 m/s) + water leaving radiance” (nonzero at 0.55 m only)