EarthCARE L2 validation
ALGORITHM DEVELOPER NEEDS
25 May 2021
2nd EarthCARE calibration/validation
workshop
ESA UNCLASSIFIED – For ESA Official Use Only

1

OUTLINE• Mapping L2 products to physical quantities• Survey of L2 processor developers’ needs (responses are available here)
1. Critical physical assumptions in processors
2. Major retrieval uncertainties
3. Validation needs•

Examples across validation paradigms: observation vs retrieval space•

Summary of validation needs: common themes•

What can L2 algorithms provide?

2Cloud-top, vertically integrated and layerwise
Quantity

Rain

Aerosol(per species)

At nadirCloud/precipitation fraction

A-TC, C-TC, AC-TC

M-CM, A-TC, C-TC, AC-TC

M-CM, AM-CTH

A-TC, C-TC, AC-TC

Aerosol layer height/depth
Aerosol layer classification

A-ALD, A-TC
A-ALD, A-TC

AM-ACD
AM-ACD

Cloud/precipitation
classification
Aerosol fraction
Aerosol species

Optical thickness

M-COP, A-EBD, ACM-CAP

M-COP

Extinction

Effective radius

M-COP

Effective radius

M-COP

Water content

A-ICE, ACM-COM, ACM-CAP

Surface snow rate*

M-COP, A-ICE, ACM-CAP
M-COP, A-ICE, C-CLD,ACM-CAP
C-CLD, ACM-CAP

A-TC, ACM-COM
A-TC, ACM-COM
A-EBD, ACM-COM,ACM-CAP
A-ICE, ACM-COM, ACM-CAP

Optical thickness

M-COP, A-EBD, ACM-CAP

M-COP

Effective radius

M-COP, ACM-CAP

M-COP

Water path

M-COP, ACM-CAP

M-COP

Surface rain rate*Rain water path

C-CLD, ACM-CAP
C-CLD, ACM-CAP

Aerosol optical thickness

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

Water path

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

Radiation

Quantity

M-COP, AM-CTH

Macrophysics Cloud-top phase & type

Liquid cloud

Across-track

M-COP, A-CTH,A-TC, C-TC, AC-TC

Cloud-top height

Ice cloud& snow

At nadir

Vertical profiles

Broadband radiances @TOA
Radiative fluxes @TOA

M-AOT, AM-ACD

C-CLD, ACM-CAP
Snow rate
C-CLD, ACM-CAP
Snow median diameter
Extinction-to-backscatter ratio A-EBD, ACM-CAP
A-EBD, ACM-COM,Extinction
ACM-CAP
ACM-COM, ACM-CAP
Effective radius
C-CLD, ACM-COM,Water content
ACM-CAP
C-CLD, ACM-CAP
Rain rate
C-CLD, ACM-CAP
Rain water content
C-CLD, ACM-CAP
Median drop size
A-AER, A-EBD, ACM-COM,
Aerosol extinction
ACM-CAP

A-ALD

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

A-ALD

Particle linear depolarisation
ratio

A-AER, A-EBD

Broadband radiances
Radiative fluxes
Heating rates

ACM-RTACM-RTACM-RT

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

M-AOT (670/865nm),
AM-ACD (355/670nm)
BM-RAD, ACM-RT
BMA-FLX, ACM-RT

3

Uncertainty

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 airbornelidars

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

Macrophysics Cloud layer detection andstructure

Liquid clouds not fully
resolved by radar/lidar
synergy
CPR surface clutter removal

Ice cloud& snow

Liquid cloud
Rain
AerosolRadiation

Snow microphysics (e.g.
PSDs, microwave scattering,density & fallspeed)

Multiple cloud layers•
Cirrus over liquid cloud
Non-precipitating liquid clouds
Physical depth of liquid clouds (i.e. cloud base height) •Mixed-phase layers embedded in ice•
Liquid (& liquid-topped mixed-phase) clouds below
optically thick ice clouds•• Warm liquid clouds within cold rain
•
••
•
•
•
•

• Range of surface types••

Ice microphysics (e.g. PSDs, •ice optics, mass-size rel’n)Surface snow rate•
How to account for•radiatively-important liquid•
clouds not detected by ATLID •Relation between rain rate•and drop size distribution••Melting layer structure &•radar attenuation

Ground-based (& scanning?) lidars &radiometers
Ground-based ceilometers/lidars
Multiple-frequency radars to constrain Wband attenuation
Microwave radiometers for LWP
Aircraft profiles of LWC• Ground-based radars

Products
A-ALD,AM-ACD, A-TC
A-ALD,AM-ACD, A-TC
M-CLD, AM-CTH,A-TC, C-TC,AC-TC
A-TC, C-TC,
AC-TC
C-TC, C-FMR• Bulk precipitation and particle imaging
Stratiform vs convective
C-CLD,
measurements at surface
Rimed snow
• Profiles of in-situ particle properties in ice
ACM-CAPclouds
Cloud-tops across range of temperatures, locations & • In situ aircraft measurements
A-ICE,cloud types• Visible & IR radiances
ACM-CAP
Range of meteorological conditions• Ground-based radars and in-situ at surface C-CLD, ACM-CAP
Deep ice clouds: embedded mixed-phase layers• Microwave radiometer for LWP
Layered cloud scenes• Profiles of liquid water content
ACM-CAP
Warm liquid clouds within cold rain
Warm rain (maritime)• Ground-based multiple-frequency radars
C-CLD,
Convective/stratiform rain
• Dual-pol weather radars over ocean
ACM-CAP
Dependence on snow properties aloft• Ground-based & airborne multiple-frequency C-CLD,
Continuity of mass flux & size distributions acrossradar
ACM-CAPmelting layer• Ground-based, e.g. AERONET(-OC)
Range of different land classes (biomes) & ocean
M-AOT• Ship-based sun photometers, e.g. MAN
Range of different aerosol classes• Satellite imagers, e.g. MODIS, VIIRS, 3MI

Large AOT uncertainties over•land; sensitivity to aerosol•classification
Detection of and/orrepresentation of fluxes over • Range of snow-covered surfaces
snow-covered surfaces

4• High-latitude ground stations

BM-RAD,BMA-FLX

Observation space

Retrieval space

ATLID, CPR, MSI, BBR

L2 algorithms

Remotely
sensedIn-situ(aircraft &groundbased)

RETRIEVAL(satellite,aircraft &groundbased)

Directly validate
retrieved quantities

Validation

FORWARD
MODEL

Evaluate different
physical assumptions inthe retrievals

FORWARD
MODEL

EarthCAREINSRUMENTSIMULATOR

Validation

DATA
ASSIMIL-ATIONModels

Evaluation of ice microphysics assumptions
using aircraft measurements
Hogan et al. (2012) used ground-based and airborne radars collocated within-situ aircraft measurements to show that the mass-size relationship ofBrown & Francis (1995) needs to be applied using the average particle
diameter rather than the maximum diameter; the latter implicitly assumes
ice particles are spheres.
-

Used in-situ particle imaging measurements to characterize average
ice particle shape (oblate spheroids with aspect ratio 0.6)

-Used aircraft measurements and mass-size relation toaccurately model differential radar reflectivity from the Chilbolton radar-Used in-situ PSD and mass-size relations to model radar reflectivity at
X- and W-bands measured by ER-2 during the TC4 campaign-Showed substantial reduction in an +5 dBZ over-estimate of radarreflectivity in the Rayleigh scattering regime at X- and W-bands.

6

Observation space

Retrieval space

ATLID, CPR, MSI, BBR

L2 algorithms

EarthCAREFORWARD
MODEL(aircraft &groundbased)
Compare two retrievals;
ideally the validation retrieval:•RETRIEVAL(satellite,aircraft &groundbased)

EarthCARE+ instruments:- multiple-frequency radar- microwave radiometer- polarimetric radar

INSRUMENTSIMULATOR

Remotelysensed

DATA
ASSIMIL-ATION

Validation

In-situ

FORWARD
MODELValidation•Has more information thanEarthCARE
Has been validated in-situ

ModelsEvaluation of snow retrievals
Direct validation:••

Ground-based Doppler radar retrievals of rimed snow at Hyytiälä(BAECC 2014) are validated against in-situ estimates of snowdensity
Retrieved density factor
is well-correlated withliquid water pathestimated from amicrowave radiometer

Mason et al. (2018),
JGR-Atmospheres

aggregation

Statistical validation:•LWP used as a proxy togenerate CFADs ofobserved and retrieved
quantities in unrimed
and rimed snow regimes(low vs high LWP)

riming8

Observation space

Retrieval space

ATLID, CPR, MSI, BBR

L2 algorithms

EarthCAREFORWARD
MODEL

Maximize collocation byscanning along EarthCARE
tracks or intersecting throughdistributed networks

INSRUMENTSIMULATOR(satellite,aircraft &groundbased)

RETRIEVAL

Remotelysensed

Use retrieval to forward-model
independent measurements &correlations

DATAASSIMIL-ATION

Validation

In-situ

FORWARD
MODELValidation(aircraft &groundbased)
Correlations between retrieved
quantities provide insights intoprocesses

Mature & well-calibrated
retrievals e.g. rain rate fromweather radar network

ModelsValidation of rain retrievals
Direct validation:••

Maximizing correlative data:•EarthCARE tracks intersecting weather
radar network for validating rain
retrievals over oceans•Ground-based remote sensing: scansalong EarthCARE tracks rather thanvertically-pointing

Forward-modelling independentX-band radar from ER-2 aircraft during TC4
campaign•Mason et al. (2017), ACP

Statistical validation:•Retrievals from94-GHz
Doppler radar

joint histograms (correlations betweenquantities); can map precipitation regimes
between observation and retrieval space

Forward-modelled& observed
10-GHz Doppler
radar variables
10

Observation spaceRetrieval space

ATLID, CPR, MSI, BBR

L2 algorithms

EarthCARE

Validation

In-situ

FORWARD
MODEL

Validation

FORWARD
MODEL(aircraft &groundbased)

Remotely
sensedSimulated EarthCARE scenes
Assimilation of EarthCARE:- Improved forecasts- Real-time monitoring ofinstruments

INSRUMENTSIMULATORRETRIEVAL(satellite,aircraft &groundbased)DATA
ASSIMIL-ATION

Model “truth”
Typically use retrievals formodel evaluation, not the otherway aroundModels

Target classification evaluation
using simulated EarthCARE scenes

HawaiiATLID (A-TC):•

Detects about 25% of ice cloud by volume•

Around 5% by mass

CPR (C-TC):• Around 50% by volume (missing cloud-tops & cirrus)• +90% by mass (sensitive to snow)• Up to 97% including where CPR is extinguishedSynergy (AC-TC):• ATLID provides +10% by volume not detected byCPR• ATLID-only detections makes negligible contribution
by mass of ice• But cloud-tops & cirrus are critical to radiation
12

Target classification evaluation
using simulated EarthCARE scenes

Halifax

ATLID (A-TC):•

Detects about 25% of liquid cloud by volume•

Around 5% by mass•

Scattered shallow liquid clouds are well-represented•

Missing 95% of liquid in deeper layers, convective
cores, and collocated with rain

CPR (C-TC):• Liquid cloud detection is very rareSynergy (AC-TC):• Same as ATLID• Is it possible to make a sensible assumption?
• Assuming liquid cloud in cold rain resolves
almost 80% of liquid by mass
• Improves ACM-CAP assimilation of MSI solar
radiances; downstream improvement in ACM-RT

13

SUMMARY OF DEVELOPER NEEDS•

An incomplete survey of developer needs: detailed discussions in ATLID/Aerosols, CPR/Clouds andRadiation sessions•Identified two major kinds of retrieval uncertainties:••

Dependence on upstream target classification products: are the features we’re trying to retrieveadequately resolved?
•Complex & layered cloud and aerosol scenes•

Identifying and characterizing surface types for passive and active instruments•

Ground-based validation: ”bottom-up” remote-sensed view of aerosol & cloud layers alongside insitu measurements at surface, to complement EarthCARE’s blind zones

Universality: how well do our physical assumptions hold in other locations/regimes?
•Physical assumptions may be mature, but often based on specific/limited studies•

Opportunity to identify gaps, and target locations/regimes with campaigns•

For statistical validation we need to be able to isolate processes: selecting/sub-setting data bylocation, correlated measurements, weather regime, cloud/precipitation type, etc.
14

HOW CAN L2 ALGORITHMS
BE LEVERAGED?
•EarthCARE L2 algorithms run with inputs from airborne and ground-based instruments•

Benefit of validating in “retrieval space” without introducing a second retrieval•

But requires a high level of pre-processing:• Calibration & correction• Common grid for synergistic measurements• Contextual information (instrument status, surface characterization, atmospheric profiles from re-analysis)• Description and metadata•

Can EarthCARE algorithms be used to generate validation data in “observation space”?
•

Forward-model non-EarthCARE instruments (e.g. X-band Doppler radar)•

Forward-model EarthCARE-like instruments from ground-based configurations?

15