# Presentation: 380457

## Aqua/MODIS and Parasol/POLDER3 Observations of Clouds and Aerosols PropertiesJérôme Riédi, Didier Tanré, Frédéric Parol, Jean-Luc Deuzéand the PARASOL science team.Laboratoire d’Optique Atmosphérique, USTL, Lille, France

***OUTLINE***ContextContribution from POLDER/ParasolInstruments synergyData and products availability

**Notes:**
Talk is about current activities undergoing at LOA about merging observations from POLDER and MODIS instruments.

Thanks to people at GSFC for expertise on MODIS (S. Platnick et al)

All results here are preliminary : don't look for the perfect product but for the opportunities to make better products.

## Context

**Notes:**
Activities are obviously related to the A-Train mission with a special interest on POLDER3/Parasol  and MODIS/Aqua

## Instrumental Background : POLDER

- CNES/LOA instrument,  Parasol launched  Dec. 2004
- ~ 705 km polar orbits, ascending (13:30 a.m.)
- Sensor Characteristics
- 10 spectral bands ranging from 0.443 to 1.020 μm
- 3 polarised channels
- Wide FOV CCD Camera with 1800 km swath width
- +/- 43 degrees cross track
- +/- 51degrees along track
- Multidirectionnal observations (up to 16 directions)
- Spatial resolution : 6x7 km
- No onboard calibration system - Inflight vicarious calibration :
- 2-3% absolute calibration accuracy
- 1% interband – 0.1% interpixel over clouds

**Notes:**
POLDER is a wide field of view camera with CCD array and a rotating wheel for spectral and polarization measurements.

Parasol mission carries a POLDER like instrument, however :
- CCD array has been rotated 90 degrees to allow more directional observation (up to 16 instead of 14 originally)
- swath is reduced and isn't centered on MODIS swath
- polarization are at 490, 670 and 865 nm
- a 1020 nm channel has been added to match the Calipso lidar channel

## Contribution of POLDER/PARASOL to the A-Train

**1.5 year of POLDER3/Parasol data already available**

## Contribution of POLDER/Parasol to the A-TrainBenefits from multiangle observations : Application for clouds

-  Cloud spherical albedo is retrieved under up to 16 directions
- Directional product provided at  = 670nm (land) and 865 nm (ocean)

## Testing cloud models from multiangle observation

## Testing cloud models from multiangle observation

- Sphere used for liquid clouds

- Sphere used for ice clouds

## Testing cloud models from multiangle observation

## But ... We have many other potential culprit for deviation from Plane Parallel ModelMODIS can help in removing or detecting effects from particle size, subpixel variability, etc... 

**Notes:**
A quick illustration of the interest of getting high resolution from MODIS :

1 km radiances from MODIS are used to compute a metric for POLDER subpixel variability. (green channel in the right false color composite).

This piece of knowledge is of primary importance to build clouds ADM for different classes of cloud types based on altitude/structure. 

Also this is extremely helpful to flag subpixel broken cloud and correctly identified cloud contaminated pixels for aerosols retrievals.

Pay particular attention to clouds edges, cloud ridges and low broken clouds.

## Detection of small broken clouds / pixel heterogeneity within Parasol FOV

- R : 2.1 μm

- G : 0.86 SDTV

- B : 0.86 μm

- True color RGB
- Mid Atlantique

- MODIS Obs.

**Notes:**
A quick illustration of the interest of getting high resolution from MODIS :

1 km radiances from MODIS are used to compute a metric for POLDER subpixel variability. (green channel in the right false color composite).

This piece of knowledge is of primary importance to build clouds ADM for different classes of cloud types based on altitude/structure. 

Also this is extremely helpful to flag subpixel broken cloud and correctly identified cloud contaminated pixels for aerosols retrievals.

Pay particular attention to clouds edges, cloud ridges and low broken clouds.

## Contribution of POLDER/Parasol to the A-TrainBenefits from multiangle observations : Application for aerosols

- Over Ocean « Cases 1-2-3 »

- Acc 

- AccrAcc, mAcc, 

- AccrAcc, mAcc, mcoarse, 
- NSIns/cs+cns)

- GLINT

- West

- East

- 13 views of the same target

**(Herman et al., J.G.R. 2005)**

## Contribution of POLDER/Parasol to the A-TrainBenefits from polarization observations : Application for clouds

**Contribution of POLDER/Parasol to the A-Train****Benefits from polarization observations : Application for clouds**

*Riedi et al*

*Bréon, Chepfer et al*

*Cloud Phase*

*Cloud microphysics*

## Contribution of POLDER/Parasol to the A-TrainBenefits from polarization observations : Application for aerosols

**Clear atmosphere (AOT=0.03)  :  the reflectance at TAO  is close to the surface values**

- AEROSOL

**Illustration for Biomass Burning Aerosols (from POLDER2)**

**Hazy atmosphere (AOT=0.31)  :  large aerosol contribution**

## Contribution of POLDER/Parasol to the A-TrainBenefits from polarization observations : Application for aerosols

**Clear atmosphere (AOT=0.08)  :  the reflectance at TAO  is again close to the surface values**

**Illustration for Desert Aerosols (POLDER2)**

**Hazy atmosphere (AOT=0.71)  : no aerosol contribution**

## Discrimination between spherical and non-spherical aerosols

*Deuzé, Herman et al*

**Contribution of POLDER/Parasol to the A-Train****Benefits from polarization observations : Application for aerosols**

## Potential Synergy for Clouds

- Cloud detectionCloud detection can be tricky under many circumstances (heavy aerosol loading, glint, bright surfaces : desert, snow/ice)  Cloud layers heightDeriving multiple cloud top pressure (O2, Rayleigh, CO2 slicing, H20) to detect multilayer clouds and better describe vertical structure Cloud thermodynamic phaseCombination of information on particle shape and absorption properties help Improved cloud retrievalsex : Using Size retrieval from MODIS to improve multidirectionnal OT retrievals from POLDERCloud HeterogeneitiesUsing MODIS 250m information to understand angular behavior in POLDER measurements  and separate 3D effect from subpixel heterogeneities

**Notes:**
Questions :

What are the possible synergies between the different instruments of the Atrain and particularly between POLDER / MODIS ?
How do we combine these measurements to allow combined retrievals ?
When is 1 + 1 >  1 ?  i.e., how do we make sure the interference of information from Polder and Modis are constructive  ?

We illustrate here the first 3 points (+ point 5 really quickly)

Point 4 is undergoing through different approach :
- classical Look-Up-Table going : Tau/Re with MODIS -> input Re for directional Tau retrieval from POLDER
- Optimal estimation technique (L. Labonnote)

## Cloud thermodynamic phaseCombination of information on particle shape and absorption properties 

- BasisPolarization (Riedi et al)mostly single scattering sensitive to particle shapeTop of cloud but see through it if very thinSWIR  (Platnick et al)Differential Water/Ice Absorption sensitive to particle sizeSome depth in the cloudThermal IR (Baum et al)Diff. Water/Ice, also sensitive to surf. emissivity, H2OSome depth in the cloud except thin cirrus

- Cirrus ? Thin ?

- Water ? Mixed ?

- H2O ?

- Surface spectral albedo ?

**Notes:**
Kind of the same consideration as for cloud top pressure : all 3 methods have different sensitivity to different situation and we ought to be able to learn from their different answer to any particular situation.

Validation and also interpretation will have to be made using radar/lidar observation from Calipso/Cloudsat.

## Cloud thermodynamic phase 

- LIQUID

- ICE

- Scattering Angle

- Typhoon Nabi
- 2 Sept. 2005

**Notes:**
Cloud phase using polarization relies on analysis of multiangle signature of spherical vs non-spherical particles.

Spherical particles (liquid droplets) exhibit the remarkable cloud bow and supernumerary bow features whereas ice clouds will usually have a fairly flat decreasing polar as a function of scat. angle.

Look for papers by Goloub et al and Riedi et al for more information on polarization technique. Send email to riedi@loa.univ-lille1.fr if you wish an electronic copy of those or of my PhD thesis on the subject. (available in French only)

## Cloud thermodynamic phase 

- Cloud thermodynamic phase

- LIQUID

- ICE

- Scattering Angle

- Typhoon Nabi
- 2 Sept. 2005

**Notes:**
Animation of successive sequences really demonstrates the different signatures of liquid and ice clouds in polarization,

Look for the eye of the typhoon that shows up very bright when observed under bow scattering conditions (again ~ 140 degrees scat. angle).

## Cloud thermodynamic phase 

- SWIR + VIS
- RGB Composite
- (MODIS bands 1, 2 and 7)

- BTD   8 – 11 μm

- - 6

- + 6

- Typhoon Nabi
- 2 Sept. 2005

**Notes:**
Illustration of the two additional pieces of information provided by MODIS.

Look for Baum et al paper for the IR bispectral technique description and also the MOD06 cloud top properties ATBD from Menzel et al.

## Cloud thermodynamic phaseCombination of information on particle shape and absorption properties help 

- ICE

- LIQUID

- UNKOWN

- POLARIZATION

- SWIR/VIS Ratio

- IR Bispectral

**Notes:**
Usually, aka in a normal processing for phase retrieval, we would try to get an answer from one technique only and get an answer as often as possible.

The implementation of each individual technique here is quite different from their respective implementation within operational MODIS or POLDER standalone products.

The philosophy here with the combination of POLDER and MODIS is to get information from each technique only when we think the technique provides meaningful information within safe limits. This is why we get lots of unknowns in polarization and SWIR individual test.

Dark red (blue) and light red (blue) stand for high confidence and low confidence liquid (ice) phase.

Haven't figured out exactly yet the safe limits of IR phase so we decide almost everywhere but set the decision to lower confidence everywhere. 

## Cloud thermodynamic phase

- ICE

- LIQUID

- MIXED

- Results from the combined POLDER/MODIS phase algorithm

**Notes:**
The final product is built by merging  the 3 individual previous tests, taking into account confidence in individual retrievals and additional information from the multi cloud top pressure retrievals. 

Decision is taken using a logical decision tree designed from expert knowledge of the three individual products. (No !! it's not just magical !)

The final index is no more a binary liquid/ice mask but a more continuous index ranging from 0 (high confidence liquid) to 200 (high confidence ice) passing through mixed phase levels when we think the situation indicates multilayer clouds or mixed phase clouds (which I agree can be defined as almost anything). We still have a few undetermined cases when information is insufficient. 

We anticipate that a continuous index from liquid to ice will be more suitable for use in model comparison or data assimilation if that occurs one day.

## Cloud layers heightDeriving multiple cloud top pressure (O2, Rayleigh, CO2 slicing) to detect multilayer clouds and better describe vertical structure 

- Basis We do expect differences in pressure due to resp. sensitivities and we also expect increasing differences in case of multilayer situations

- CO2

- O2

- Rayleigh

- Single Layer

- Two Layers

- CO2

- Rayleigh

- O2

- O2 : Oxygen band differential absorption

- Rayleigh : Polarization Rayleigh Scattering absorption

- CO2 : CO2 Slicing (IR)

**Notes:**
A few words on the different pressure :

Rayleigh : based on above cloud Rayleigh optical depth determination using 490 nm and 865 nm polarization signal. Tau Rayleigh is then related to pressure level. Mostly single scattering regime so provide information related to cloud top ( if cloud OT is < 1.0 may be sensitive to signal coming from below the cloud)

Oxygen A band differential absorption : 765 and 763 nm
Provides information more correlated with the middle of the cloud. Not very efficient at picking high thin clouds

CO2 slicing : better suited for higher than lower clouds and also mostly correlated with cloud top pressure. 

In case of thin high cloud overlaying lower cloud we expect CO2 pressure to be smaller or roughly equal to Rayleigh and both significantly smaller than O2.

## Cloud layers heightDeriving multiple cloud top pressure (O2, Rayleigh, CO2 slicing) to detect multilayer clouds and better describe vertical structure 

- Example of retrieved Cloud Top Pressure Histograms for Ice clouds

**Notes:**
Histograms were build from 3 days statistics of operationnal POLDER3/Parasol and MODIS/Aqua (collection 5) cloud product.

Only ice clouds as seen by POLDER are considered here.

Something seems odd with the high pressure small peak in the CO2 ice clouds. Maybe a phase issue or CO2 problem. Haven't looked at this yet.

## PARASOL and CALIOP Cloud Top Pressure Preliminary Comparison

- 15 June 2006
- POLDER Cloud Cover > 95%
- CALIOP Alt. Stdev < 0.5 km

**Notes:**
Histograms were build from 3 days statistics of operationnal POLDER3/Parasol and MODIS/Aqua (collection 5) cloud product.

Only ice clouds as seen by POLDER are considered here.

Something seems odd with the high pressure small peak in the CO2 ice clouds. Maybe a phase issue or CO2 problem. Haven't looked at this yet.

## PARASOL and CALIOP Cloud Top Pressure Preliminary Comparison

- PARASOL and CALIOP Cloud Top Pressure Preliminary Comparison

- 15 June 2006
- POLDER Cloud Cover > 95%
- CALIOP Alt. Stdev < 0.5 km

**Notes:**
Histograms were build from 3 days statistics of operationnal POLDER3/Parasol and MODIS/Aqua (collection 5) cloud product.

Only ice clouds as seen by POLDER are considered here.

Something seems odd with the high pressure small peak in the CO2 ice clouds. Maybe a phase issue or CO2 problem. Haven't looked at this yet.

## Example : aerosols over cloud

- 24 Septembre 2005

**Notes:**
Let's focus on a region with stratocumulus cloud field and where we have biomass burning smoke all around the place.

## Example : aerosols over cloud

- O2 Pressure

- Rayleigh P.

- CO2 Pressure

- Usually with single layer :  O2 > Rayleigh > CO2 with small differences
- And here we have :   O2 > CO2 >> Rayleigh    due to presence of aerosol in the upper layer

- 100 hPa

- 1000 hPa

**Notes:**
Images are zoom on the center orbit of previous slide off coast of Namibia.

Cloud pressure retrievals are stretched equally so colors are comparable.

Looking at CO2 and O2 there's a clear evidence of low clouds in the image center. However the Rayleigh gives a very different pictures with apparently high clouds at the same place.

If we had high clouds above lower cloud we would expect O2 > Rayleigh > or = CO2
But here we have O2 > CO2 >> Rayleigh  !!!

This is due to presence of an aerosol layer above the stratocumulus deck.
Aerosols produce high polarized reflectances even at 865 nm cancelling out part of the spectral dependency of Rayleigh scattering and thus biasing the cloud top pressure retrieved from that method.  

This has been observed many times in that region and we'd better be able to flag these cases to prevent cloud retrievals being aggregated to level 3.

## POLDER3/Parasol Products availability

- Data available since March 2005. Level1 : calibrated georeferenced dataLevel2 : daily products – one file per orbit swathLevel3 : monthly products Joint Atmosphere product (selected daily and monthly products)Data processed with collection 2 algorithms (_heritage from POLDER1 and POLDER2 mission_)L1 data and products (also including MODIS/Aqua, Caliop, ...) available online from the ICARE data center

**http://www.icare.univ-lille1.fr/**

**Notes:**
Questions :

What are the possible synergies between the different instruments of the Atrain and particularly between POLDER / MODIS ?
How do we combine these measurements to allow combined retrievals ?
When is 1 + 1 >  1 ?  i.e., how do we make sure the interference of information from Polder and Modis are constructive  ?

We illustrate here the first 3 points (+ point 5 really quickly)

Point 4 is undergoing through different approach :
- classical Look-Up-Table going : Tau/Re with MODIS -> input Re for directional Tau retrieval from POLDER
- Optimal estimation technique (L. Labonnote)

## Summary

- PARASOL/MODIS combination is a real opportunity to improve many existing parameters and can help design a next generation sensorPARASOL/MODIS open perspectives to extend the active sensors observation to the full swath, increasing statisticsPARASOL/MODIS offer a test suite for definition of future cloud/aerosol missions1.5 year of POLDER3/Parasol data are available through the ICARE data center : **http://www.icare.univ-lille1.fr/**Get involved : we provide users with POLDER/MODIS joint dataset and software to merge data and try ideas.

## Additional Material for Discussion

- Cloud detection

## Cloud detectionCloud detection can be tricky under many circumstances (heavy aerosol loading, glint, bright surfaces : desert, snow/ice)

- Basis

- MODIS multispectral total reflectance measurements are not always sufficient to perform perfectly under all conditions- hard time under glint condition, heavy smoke/dust, snow/ice surface, ...POLDER can also get into troubles due to lower resolution and limited spectral range- hard time with thin cirrus, low broken clouds, snow/ice surface, ...Taken advantages of the combined high resolution, multispectral, multiangle and polarisation measurement increases greatly the chance to get a correct cloud detection (though you're still stuck in the mud when you need to settle on the definition of a cloud)

## Cloud detectionCloud detection can be tricky under many circumstances (heavy aerosol loading, glint, bright surfaces : desert, snow/ice). Even worse when mixture of those ...

- Example : Glint/Dust

- Dust

- A2005.249.1330

**Notes:**
This MODIS/Aqua granule is featuring Spain and part of Western/ North Africa.

The scene is interesting because of the presence of dust off coast of Morocco in a part of the granule where observation geometries fall in the sunglint region as we'll see on next slide. 

## Cloud detectionCloud detection can be tricky under many circumstances (heavy aerosol loading, glint, bright surfaces : desert, snow/ice). Even worse when mixture of those ...

- Example : Glint/Dust

- Glint

- Conf. Clear

- Prob. Clear

- Prob.. Cloud

- Conf.. Cloud

- MODIS MYD35 Col. 5

- A2005.249.1330

**Notes:**
Glint contaminated part of the granule is overlayed with violet on the left image.

Image on the right is a blow up of the dust/glint region on the left.

MODIS cloud mask is having hard time with this kind of scene because I guess the glint bright surface sort of  cancel part of the spectral signature of mineral dust.

## Cloud detectionCloud detection can be tricky under many circumstances (heavy aerosol loading, glint, bright surfaces : desert, snow/ice). Even worse when mixture of those ...

- Example : Glint/Dust :

- Aerosol detection is easier when looking off glint which is always possible with POLDER

- heavy aerosol

- clear

- more or less conf. cloudy

**Notes:**
Spectral signature of mineral dust is much more obvious looking off glint. Combined with spatial homogeneity indicator provided by MODIS 1km data, dust detection is much more easy. 

Note however that the MODIS cloud properties product has implemented a different set of test to complement the original MOD35 cloud mask and is able to remove this kind of event quite effectively too. (the set of tests needed is a little more complex though)

## Example : Smoke over land mixed with clouds

- Cloud detectionCloud detection can be tricky under many circumstances (heavy aerosol loading, glint, bright surfaces : desert, snow/ice). Even worse when mixture of those ...

**Notes:**
Another difficult case on the edge of cloud definition : heavy loads of biomass burning smoke.

Spectral behavior is somehow helpful but in case of extreme events the problem is still delicate to deal with. MODIS cloud mask performs well under most smoke conditions and addition of spatial variability test can also improve the initial decision. 

However when smoke is that thick AND  over land AND with clouds underneath there we start to be in real trouble ...

## Polarisation RGB

- Total Reflect. RGB

**Notes:**
Now let's take a look a polarization : Images above are colored composite of 3 individual POLDER sequence. A sequence correspond to one « shot » of the wide field camera in all spectral and polarization channel. It's really like taking pictures successively at different wavelength.

Top images are composited from total radiance observation (close to true color image composite) and images below are the corresponding composite of the 3 polarized channel (B:490, G:670 and R:865 nm)

White lines indicates iso angle of scattering (diffusion angle) by step of 10 degrees, and the straight line correspond to the principal plane. Backscatter direction is at the center of the small « circle » bottom right corner of each sequence.

Smoke on the top right corner of sequence is clearly discernible in total radiance but appears even more clearly in polarization : bright and with with a pinkish coloration at higher scattering angle. Clouds in this range of scattering angle (80 to 120 degree)  exhibit much different polarization (lower).

## Example : Smoke over land mixed with clouds

- Cloud detectionCloud detection can be tricky under many circumstances (heavy aerosol loading, glint, bright surfaces : desert, snow/ice). Even worse when mixture of those ...

- heavy aerosol

- clear

- more or less conf. cloudy

**Notes:**
not much comment here. 

Same fundamentals as for the glint/dust issue :
- cloud mask has hard time in extreme events
- can use additional spectral/spatial variability test to get rid of this smoke within MODIS cloud properties product
- but multiangle polarization measurements makes our life a lot easier in those cases.