EarthCARE Level 2 Aerosol
26 May 2021
2nd ESA EarthCARE Validation Workshop
25-28 May 2021 (online)
ESA UNCLASSIFIED – For ESA Official Use Only

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Goal/Topic of this discussion•

What has been learned from previous aerosol validation campaigns?

• Imager validation is straight forward → AERONET → but what goes beyond thesimple AERONET comparison?
• Common approaches / Strategies:•

E.g. statistics of certain regimes /aerosol types compared to EarthCARE statistics vs.
direct overpass comparisons•In-situ vs. remote sensing• What are the key regions for certain aerosol types (lidar ratio, depolarization) andwill we have Cal/Val sites available in each region (regional networking)?
• Are there gaps in this current Cal-Val group (could be regional but also topical)?
→ this can be used as input for campaign planning• Will we have access to unpredictable aerosol types: like stratospheric smoke,
volcanic ash, polar stratospheric aerosols?
• Is there model support for Aerosol Cal/Val, e.g. use of aerosols transport models
such as CAMS?
• Satellite support for Aerosol Cal/Val, which other satellites could support our
Cal-Val?
2EarthCARE L2 Aerosol Retrievals•L2 processor description (presentations are available here)•L2 processor developers’ needs (responses are available here)•Notation: A- ATLID only, M- MSI only, AM- ATLID+MSI, ACM- ATLID+CPR+MSI
Vertically integrated and layerwise(M-AOT, A-LAY, AM-COL)
Quantity

At nadirAcross-track

Vertical profiles(A-PRO, ACM-CAP)
Quantity

At nadir

Macrophysics Aerosol layer height/depth

A-ALD, A-TC

AM-ACD

Aerosol fraction

A-TC, ACM-COM

Aerosol layer classification

A-ALD, A-TC

AM-ACD

Aerosol species

A-TC, ACM-COM

Aerosol optical thickness

M-AOT, A-ALD, A-AER,
A-EBD, ACM-CAP

M-AOT, AM-ACD

Aerosol extinction

A-AER, A-EBD, ACM-COM,
ACM-CAP

Aerosol(per species)

Layer-mean extinction-tobackscatter ratio
Layer-mean particle linear
depolarisation ratioÅngström exponent

Uncertainty

Extinction-to-backscatter ratio A-AER, A-EBD, ACM-CAP*

A-ALD

Particle linear depolarisation
ratio

M-AOT (670/865nm),
AM-ACD (355/670nm)

A-AER, A-EBD

M-AOT (670/865nm),
AM-ACD (355/670nm)

Locations/scenes/regimes

Measurements needed

Aerosol layer detection• Multiple aerosol layers• Aerosol layers with strong internal structure• Attributing aerosol plumes at nadir to features in theacross-track imagery• Ground-based (& scanning?) and airborne
lidars

A-ALD,
AM-ACD, A-TC

Aerosol/cloud discrimination• Cloud embedded in aerosol layers• Ground-based lidars

A-ALD,
AM-ACD, A-TC

Macrophysics

Aerosol

A-ALD

Large AOT uncertainties over
• Range of different land classes (biomes) & ocean
land; sensitivity to aerosol• Range of different aerosol classes
classification• Ground-based, e.g. AERONET(-OC)• Ship-based sun photometers, e.g. MAN• Satellite imagers, e.g. MODIS, VIIRS, 3MI

Products

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M-AOT

Hybrid End-to-End Aerosol Classification(HETEAC)
Aerosol model assumptions within the retrieval algorithms

4Hybrid End-to-End Aerosol Classification
Calculation

AerosolModel

Microphysical
Properties

AerosolTypereff, mR, mI

Measurement

BBR

Radiative
Properties

OpticalPropertiesδ, LR, Å

EarthCARE Validation Workshop, 25 – 28 May 2021

ATLID
MSI

Experimental Basis for Aerosol Classification
Smoke
Dust and Smoke

Dust
Ash
Pollution
Mixtures with dust
MarineIllingworth et al., BAMS 2015
EarthCARE Validation Workshop, 25 – 28 May 2021

Recent updates

New datasets

Additional:
Collection of datasets at 532 nm
for transformation 355-532 nm

EarthCARE Validation Workshop, 25 – 28 May 2021

Continuously updated(Athena Floutsi)

Aerosol Components

Smoke• 4 (pure) aerosol components
to calculate mixing states• Define microphysical properties foreach component• Calculate effective radius & refractive
index of the mixtureà Input for radiation calculation

Dust
Pollution
Volume contribution

MarineDust
Coarse mode

Sea salt
Coarse mode

Pollution
Fine mode

Smoke
Fine mode

reff, µm

1.94

1.94

0.14

0.14

mR (355 nm)

1.54

1.37

1.45

1.50

mI (355 nm)

0.006

4.e-8

1.e-3

0.043

Shape

Spheroid

Spherical

Spherical

Spherical

EarthCARE Validation Workshop, 25 – 28 May 2021

Effective radius
Refractive index

Validation Needs for HETEAC
Good example: A-LIFE, Cyprus 2017
Validation by aircraft + lidar campaigns
Ideal:

Number size distribution1 – 2 km
HETEAC - Lidar

Effect. diam.

Airborne
in situ

Airborne aerosol in situ (small aircraft / UAV)
within the aerosol layer (e.g., effective diameter,refractive index) combined with:– Ground-based / airborne HSR lidar (2nd aircraft)providing lidar ratio & depolarization ratio– Bonus: Radiation measurements
Strong aerosol features (dust, pollution, smoke, marine) and mixtures
EarthCARE Validation Workshop, 25 – 28 May 2021

Validation approaches and strategies proposed
Ideas with respect to the Co-Location (Matching of observations in place and time):• An analysis of representativeness of measurements will be done after the measurements have beentaken, using model and satellite datasets• Coincident and validated geostationary observations will be used as a comparison for the imager
products• Direct underpasses beneath the EarthCARE track will be performed during the airborne campaigns• Coordinate with models (e.g. DREAM) for special runs during field campaigns• The ground-based sites will follow the validation strategies developed by EARLINET and Cloudnet forprevious missions (CALIPSO, Aeolus).
• Synergy is sought with EUMETNET E-Profile network of nearby (~10 km) profiling stations (AO proposal
submission planned)• CERES PSF size is bigger than BBR swath (maximum 18 km nadir) : Need to use MSI to improve matching• Of more concern will be the biases introduced by conditional sampling arising from the difference inviewing geometry…
• Estimation of the effective impact of urban environment• Focus on specific aerosol and cloud types
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Validation approaches and strategies proposed
Ideas with respect to the use of statistical approaches:• Long-term data sets from ground (networks like LALINET or single station like Lampedusa)• MPL-NET: Monthly, Annual, and Decadal diurnal averages of all product variables:àcustomized L3 products for EarthCARE validation, but altered for ideal sampling and targeted validation• Collocated measurements from the 3 experiments at different sites, analyze statistically and in relation to
day/night differences and different aerosol types and cloud systems.
• EarthCARE mean aerosol profiles against LIVAS-CALIPSO and Aeolus climatological profiles• Special attention to certain conditions, e.g. wave-clouds.
• Deriving conclusions from ensembles of collocations• Statistical anaylsis conducted from the aerosols measurements obtained during the long-duration
balloon flights Strateole in the equatorial region around the tropopause• Multiple scattering difference: modify the Monte Carlo simulation of CALIPSO so that it can provide
accurate simulations for ATLID. Simulating depolarization ratio measurements of ATLID 355 nm
water cloud backscatter and computing water cloud lidar ratios for various effective droplet sizes
and variances. The analysis will be statistical and global scale in nature.
• Check of L2 products internally, i.e. with respect to viewing angle (MSI-AOD should statistically nothave a swath dependence à MSI-J)

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Validation approaches and strategies proposed
Ideas with respect to the Wavelength dilemma:• Using same wavelength:• ACTRIS/ LALINET : Operate lidars at multiple wavelengths, including 355 nm and depolarisation à who willmeasures 355 nm depolarisation?
• The Hyytiälä station will be equipped with Vaisala CL61 ceilometer (includes depolarization @ 910.55 nm)• Specific non-network lidars at EarthCARE wavelength: UK, EVE polarization (linear and circular) lidar…

• Ideas for converting to EarthCARE wavelength• Aerosols profiles at 532 nm, when AERONET AOD are measured, can be extrapolated to 355 nm, using the
AERONET co-located Angström exponent.
• Expected wavelength dependence λ1 – λ0, will be tested with model simulations• Extinctions are calculated from concentrations and size of the particles, considering typology & mean
refractive index. Conducted (in the visible domain) at the wavelength of EarthCARE measurementsà
type validation• Multiple scattering difference: Modify Monte Carlo simulation algorithms developed for CALIPSO toprovide accurate simulations for ATLID. This includes simulating depolarization ratio of ATLID 355 nm
water cloud backscatter and computing water cloud lidar ratios for various effective droplet sizes andvariances.

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Validation approaches and strategies proposed
Ideas with respect to synergistic products:• Synergistic products at ACTRIS are already similar. Will be enhanced throughcombination of cloud and aerosol profiling at combined ACTRIS stations.
• Running our own retrievals combining more instruments than available on EarthCARE
or using different wavelengths.
• GARRLIC/Grasp is intended to be used often• the inversion provides vertical distribution of aerosol concentration, fine and coarse mode
refractive index (assumed constant vertically) and the fine and coarse mode size distributions.
These inverted parameters are then used to compute aerosol extinction, backscatter anddepolarisation profiles at any wavelength.
• Development of GRASP retrieval for applying the same scheme in EarthCARE and in groundmeasurements
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Validation approaches and strategies proposed
Ideas with respect to novel approaches:• Fu-Liou-Gu model used to solve cirrus cloud radiative properties of single-layered cirrus clouds àResults from EarthCARE will be compared to similar TOA cloud-radiative effect (CRE) from MPLNET
and historical CALIPSO dataset• Use of Ocean surface return for AOD. Changes between ATLID and CALIPSO are straightforward(off-nadir angle, Fresnel reflectance coefficient of the ocean surface between 355 and 532 nm).
Correction based on the polarization channel. Subsurface scattering should increase in the UV but
this is not fully understood yet.
• We want to explore the possibility to use dual-field of view configuration on the EVE lidar system toestimate the multiple scattering factor from ground measurements.
• Involvement of other aerial platforms that incorporate simple instrumentation.
• Involvement of solar airplanes with lidar and radiometers (research task) – see
https://skydweller.aero• Influence of multiple scatter will be evaluated with model simulations; will then be included in lidar
equation as a factor• Automated precipitation detection has been incorporated into MPLNET processing(developmental-level) à Will be used to identify potential case studies for drizzle from CPR
products

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Selected parameters in need of validation– Aerosol-cloud discrimination– Layering (layer boundary detection)– Extinction/AOD– Lidar ratio– Aerosol Depolarization– Ångström parameter– Aerosol Typing
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Aerosol – Cloud discrimination
Needs: Aerosol – cloud – clear sky discrimination
Vertical (ATLID & ATLID+MSI)•••

Vertical feature detection (boundary detection), any lidar
wavelength will do.
Lidar + radar combination for detecting cloud boundaries(in case of aerosol above clouds), i.e. CloudNET, ACTRIS
like.
Close to the track (<10 km)

Horizontal context (MSI):
Validation of M-CM cloud mask flag “confident clear”•••

Cloud edge detection vs aerosol (e.g. high spatial
resolution imager cloud mask like Sentinel 2 (20m) )Thick aerosol load vs thin cloud detection (e.g. higher
spectral resolved imager cloud masks like VIIRS, MODIS
and FCI (as MSI has limited channels for cloud detection)
MSI’s viewing zenith angle dependency should be taken
into account in validation (smile).

Layering
Needs: Layer boundary detection
Vertical (ATLID & ATLID+MSI)• Vertical layering (boundary detection), any lidar wavelength will do.
-Separation of layers by Lidar-ratio & Depolarization would be a bonus.
• Close to the track (<10 km)• Planetary boundary layer height
Horizontal context (ATLID+MSI & MSI):• Combination of 3D scanning lidar together with vertical layer products might enable a
3D description of any aerosol layers.
• flight patterns during airborne campaigns- Will there be observations planned by teams to detect unpredictable aerosol types:
stratospheric smoke, volcanic ash, polar stratospheric aerosols ( & PSC)

Aerosol Extinction/Backscatter & AOD
Needs• Extinction and Backscatter profiles at 355nm (ATLID) (or 532 with Ångström exponent)• Ground-based or airborne lidar with Raman / HSRL capability• Best in combination with sun photometer• Distance to track < 100 km•

AOT at• 355 nm (ATLID-MSI synergy)• 670 nm (MSI, ATLID-MSI synergy)• 865 nm (MSI, ATLID-MSI synergy)•

Evaluation of Extinction/AOD error estimates provided by L2 processors

MSI Specific:• Separation of AOTs over ocean and land (MSI)• If possible, separation of AOT validation over land for different surface type (MSI)• MSI’s viewing zenith angle dependency should be taken into account in validation (smile)• Ground-based measurements (e.g. AERONET) as well as collocated satellite based imager
AOTs (e.g. MODIS, VIIRS, 3MI) are essential in validation due to the narrow swath of MSI

Aerosol Lidar Ratio
Needs• Profile at 355m (ATLID)• Ground-based or airborne lidar with Raman / HSRL capability• Different aerosol types (dust, marine, smoke, pollution, …)• Distance to track < 100 km- Will there be observations planned by teams to detect unpredictable aerosol types:
stratospheric smoke, volcanic ash, polar stratospheric aerosols

Depolarization
Needs• Well calibrated lidar Depolarization profiles at 355nm (ATLID)• Depol. in free air• Depol. in Aerosol layers• Depol. in Cirrus clouds• Depol. of ocean surface return (AOD from surface backscatter)• Different aerosol types (dust, marine, smoke, pollution, …)• Distance to track < 100 km- Will there be observations planned by teams to detect unpredictable aerosol types:
stratospheric smoke, volcanic ash, polar stratospheric aerosols ?

We need depolarization at 355 nm

355 nm 532 nm

1064 nm•Spectral slope 355 – 532 nm depends on
aerosol type•

Good aerosol typing anddata base is necessary to transfer the
depolarization ratio from 532 to 355 nm
à additional uncertainties•

Dp355 is sensitive input in aerosol
classification
Recommendation:
If possible, upgrade your lidar with 355 nm
depolarization channel!
Ideal: Depolarization ratio at 355 and 532 nm!

Haarig et al., ACP, 2017a,b, 2018, see also Burton et al., 2015, Hu et al., 2019, Hofer et al., 2020
2nd EarthCARE Validation Workshop, 25 – 28 May 2021

Ångström parameter• Should be considered as diagnostic quantity. Estimated from retrieved AOTs
• Ångström parameter (355 nm, 670 nm ATLID+MSI) and (670 nm, 865 nm MSI over ocean only)• Ground and ship-based measurements (e.g. AERONET-OC, MAN) as well as collocated satellite-based
imager Ångström parameters ( e.g. MODIS, VIIRS, 3MI) are essential in validation due to the narrowswath of MSI

Aerosol Typing
Needs• Aerosol typing following the HETEAC description• Mixtures of specific aerosol main-types (when retrieved)•••

Typing can originate from• Combination of lidar ratio and depolarization ratio at 355 or 532 nm• HSRL lidar measurements from a multi-wavelength system +depol. channel(s))• Airborne in situ / UAV in the aerosol layer (e.g., effective diameter, refractive index)(combined with lidar profiles for layer information if available)
Different aerosol types (dust, marine, smoke, pollution) and mixtures
Distance to track < 100 km

BACKUP Slides

24Common practices applied to QC and calibration•

Many networks (ACTRIS, MPL-NET, AERONET, BSRN) have their own, well established QC andcalibration approaches•

Some non-network stations follow ACTRIS QC's•

Only a few have not thought of any QA/QC yet, e.g.:••"Currently there is no convergence yet on common QA or (inter)calibration procedures for our
instrument type"•This is something that we would work on with specific funding. Such actions are on hold at the
momentNon-network instruments may follow different approaches, e.g.:•

HALO Airborne instrumentation undergoes specific QC agreed for HALO campaigns•

Occasional comparison of PSC measurements with neighbouring lidar (Esrange,
30km ENE)•

Comparison with in-situ balloon measurements of cirrus
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Aerosol Profile Retrievals
A-PRO
A-LAY
ACM-CAP26

Processor

A-PRO (ATLID Profile processor)

Input Products

1. FeatureMask
2. ATLID L1
3. Auxiliary Met data

Output Products(reported on Joint
standard Grid grid)

1. Aerosol and cloud opticalproperties.
2. Target Classification
3. Aerosol type

Brief Description•

Two Step approach to deriving optical properties:
1. “Conventional” HSRL techniques applied to smoothed fields.
2. High resolution “Optimal Klett-like” retrievals aimed at clouds.

•

Cloud/aerosol discrimination and aerosol typing based on using Backscatter thresholds as well as S
and linear depolarization ratio.

••

Cloud phase determination via layer integrated backscatter-vs-integrated depolarization ratio.
Aerosol type based on depol.-vs-lidar-ratio using the Hybrid End-to-End Aerosol Classification(HETEAC)Total Extinction

Retrieved Extinction

Lidar-Ratio

Retrieved Lidar-Ratio

Linear Depolarization Ratio

6/9/21Retrieved Classification28

Processor

A-LAY (ATLID Layer processor)

Input Products

1. A-NOM
2. A-EBD, A-TC
3. X-MET, X-JSG

Output Products
1. Cloud top height and classification (A-CTH)(reported on JSG grid) 2. Aerosol layer descriptor (A-ALD)• Aerosol layer boundaries• Aerosol layer mean optical properties• Column, tropospheric and stratospheric AOT• Column aerosol classification probabilities

Brief Description•

Wavelet Covariance Transform technique combined with a threshold approach is applied to the Mieco-polar signal for cloud/aerosol discrimination and retrieval of
1. Cloud top height and cloud top classification/layering information
2. Aerosol layer boundaries•

Input from A-PRO is used to calculate layer-mean aerosol optical properties (extinction, backscatter,lidar ratio, depolarization ratio) and column/tropospheric/stratospheric aerosol optical thickness.

•

The products are specifically designed to generate input for the synergistic ATLID-MSI Column
processor.

Simulated Particulate (Mie) Attenuated Backscatter

CloudsRetrieved Cloud Top Height and Class
Cloud Top ClassThin
Thick over thick
Validationneeds:
Thick
Thin over thickThin over thin
• Cloud top height• Aerosol layer boundaries• Layer mean opticalproperties @ 355 nm• extinction• backscatter
Comparison to A-TC
depolarization ratio
Target•Classificationwater ice aerosol• lidar ratio• AOT @ 355 nm

Modified
Aerosol

Retrieved Aerosol Layer Boundaries
Lower boundary
Upper boundary

Retrieved Aerosol Optical Thickness (355 nm)

Retrieved Layer-Mean Optical Properties (Layer 1)

Processor

ACM-CAP Synergistic cloud, aerosol andprecipitation retrieval from ATLID, CPR and MSI
onboard EarthCARE

Input Products

A-EBD
C-FMR
M-RGR

Output Products(reported on Joint
standard Grid grid)

The algorithm retrieves the properties of clouds,
aerosols and precipitation from the combination ofnadir-pointing cloud radar, lidar and radiometer.

AC-TC
C-CD
X-MET

Brief Description
This product uses all the information to try to obtain the best possible estimate of cloud, aerosol andprecipitation properties in any situation.
• The directly retrieved Aerosol variable: Aerosol total number concentration• Derived from this are: aerosol extinction, aerosol mass content and aerosol median volume diameter(incl. their 1 sigma random error)• Target classification comes from AC-TC and in case of aerosols come from A-PRO
6/9/2131

6/9/2132

Aerosol Imager Retrievals
M-AOT

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Processor

M-AOT (MSI Aerosol Optical thickness processor)

Input Products

1. M-RGR (MSI regridded L1c product)
2. M-CM (MSI L2a cloud mask)
3. X-MET (ECMWF meteorological parameters on
EarthCARE swath)

Output Products(reported on native
MSI grid)

1. Aerosol optical thickness over land at 670 nm
and over ocean at 670 nm and 865 nm;Ångström parameter (670nm, 865 nm) overocean

Brief Description•

Usage of TOA radiances that are corrected for gaseous absorption ofwater vapour and ozone, carbon dioxide and methane•

Optimal estimation based retrieval using Gauss-Newton approach•

Two separate forward operators for land and ocean surfaces both relying on LUT interpolation- Ocean algorithm: surface contribution simulated to be following Cox and Munk (1954)- Land algorithm: Lambertian reflector assumed; relying on prior information about the surface- HETEAC aerosol typing (Wandinger et al. 2016) used in order to ensure consistency within
EarthCARE retrieval chain

Model AOT(670nm) field

AOT(670nm)

AOT(865nm)

Ångström (670,865nm)

Model AOT(670nm) field

AOT(670nm)

AOT(865nm)

Ångström (670,865nm)

Underlying assumptions and priors• De-coupling of weakly gases• Climatological amounts of CO2 and CH4• Ocean surface parameterization following Cox and
Munk (1954) – wind speed from X-MET• Lambertian surfaces over land using black sky albedo
of MODIS MCD43 climatology as prior for SWIR-2• Internal, 25 pre-defined mixings of HETEAC
components in LUTs including the four pure HETEAC
types• Fixed vertical distribution of aerosol types according to
Aerosol cci (Holzer-Popp 2013)• Aerosol climatology (MAC v1 Kinne et al. 2013) used asprior and over land for fixed mixing of types
Known uncertainties• Higher AOT uncertainties expected over land than over ocean due to the stronger TOA
signal contribution of the surface than the aerosol• Aerosol type assumption can lead to additional large uncertainties over landProcessor

AM-COL (ATLID-MSI Column processor)

Input Products

1. A-CTH, A-ALD
2. M-RGR, M-CM, M-COP, M-AOT
3. X-MET, X-JSG

Output Products
1. Synergistic cloud top height difference (AM-CTH)(reported on JSG grid) 2. Synergistic aerosol column descriptor (AM-ACD)• Spectral aerosol optical thickness• Ångström exponent• Aerosol type

Brief Description•

Height-resolved information from ATLID is combined with MSI column products toretrieve
1. Synergistic cloud top height information along and across track
2. Synergistic aerosol column information along and across track•

Cloud classification from MSI is used to extrapolate CTH differences from ATLID
track to MSI swath.

•

ATLID AOT@355 nm is combined with MSI AOT@670 nm (over land and ocean)and AOT@865 nm (over ocean) to retrieve Ångström exponent and estimate
aerosol type.

•

Aerosol classification and homogeneity information from MSI is used to
extrapolate AOT@355 nm from ATLID track to MSI swath.

Simulated Particulate (Mie) Attenuated Backscatter

CloudsRetrieved Cloud Top Height Difference Along Track

Modified
Aerosol
Retrieved Spectral Aerosol Optical Thickness Along Track

Validation needs:• AOT @ 355, 670, 865 nm• Aerosol type
Retrieved Cloud Top Height Difference Across Track

Retrieved Spectral Aerosol Optical Thickness Across Track