Maximising the continuity of synergistic
cloud-aerosol-precipitation retrievals
between the A-Train and EarthCare
Shannon Mason (ECMWF & NCEO),Robin Hogan (ECMWF & University of Reading)& Ben Courtier (NCEO)
shannon.mason@ecmwf.int© ECMWF December 6, 2023

A-Train

CloudSat
Cloud Profiling
Radar (CPR)

Aqua
MODIS imaging
radiometer
CERES broadband
radiometer

CALIPSOCALIOP lidar

ICE
PRECIP-COLUMN
RAIN-PROFILE
SNOW-PROFILE
CloudSat-only retrievals

CWC-RVODCALIPSO

EarthCARE

MODIS

Ice cloud and aerosol
profile retrievals

Passive cloud and aerosolretrievals

DARDAR

CCCM

CloudSat-CALIPSOMODIS retrievalof ice cloud & snow

CERES-CloudSat-CALIPSOMODIS composite retrievalson CERES footprint

CloudSat-MODIS retrievalof ice & liquid clouds

Cloud Profiling Radar (CPR)
Atmospheric Lidar (ATLID)
Multispectral Imager (MSI)
Broadband radiometer (BBR)

ATLID+CPR+MSI

CAPTIVATECCM-CAP
Unified & synergistic
retrieval of cloudaerosol-precipitation

Clouds, Aerosol and Precipitationfrom mulTiple Instruments using a
VAriational TEchnique

BBR

ACM-CAP
Unified & synergistic
retrieval of cloudaerosol-precipitation

CCM-RADCCM-3DACM-3D

ACMB-DF

Radiative fluxes,
heating rates andradiative closure
at nadir

3D scene reconstruction

3D scene reconstruction

CCM-RTACM-RT

Radiative transfer

Radiative transfer

Top-of-atmosphereradiative closure
assessment

input products
CALIPSOCALIOP
attenuatedbackscatter

βECMWF

Meteorologicalparameters &surface properties
on A-Train track

Synergistic
targetClassification

xj = f(T)state vector x
First iteration: a-priori state vector xa

ice andsnow

α, N0’, sRadar reflectivity& path-integrated
attenuation (PIA)

meltingliquid
aerosolrain
layer
cloud R, NNsX
L, N

1-way on first (backward) passand 2-way on second (forward) pass

CloudSatT, q, O3
Ts , ε, A

DARDAR

CAPTIVATE
mforward model H(x)Z, PIAsurface properties

Inc. multiple scattering & hydrometeor fallspeed

radar
Z, VD ,PIA

MODIS

Solar and thermal
infrared radiances+ error estimatesand detection status
for each measurement

I, TB

observation vector y

Cloud-AerosolPrecipitation fromCloudSat-CALIPSO-MODIS

outputproduct

radiometerβ

I, TB

cost function JCompare y – H(x) & x – xa
Check for convergence

if converged

CCM-CAPlidar

minimizer
Update thestate vector

if notconverged

calculate retrieval error
Error covariances & averaging
kernels for retrieved and derived
variables

Kalman smoothernext
profilefor selectedstate variables
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CAPTIVATE: key features• Advantages of synergistic retrievals are wellestablished:– radar-lidar synergy is greatest for ice cloud;
most other components rely on one or theother active sensor– But some measurements act as integrated
constraints with contributions from different
components of the atmosphere (radar PIA
from liquid cloud and rain; solar radiancesfrom ice and liquid cloud)• Advantages of a unified retrieval in complex
and layered cloud scenes– Mass flux conserved across the melting layer– Mixed-phase/supercooled liquid layers– Ice clouds over warm liquid cloud– Drizzle/rain colocated with liquid cloud
EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS

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CCM-CAP: Ice and snow
• Retrievals of optically thin ice at cloud-top are underconstrained (lidar-only)– Assimilating the radar noise floor provides an upper limit on
the forward-modelled radar reflectivity of ice clouds retrieved
from lidar-only measurements– Results in an overall reduction in effective radius compared
to previous retrievals (e.g. DARDAR), consistent with very
high-sensitivity airborne radar-lidar retrievals, but notpreviously• Ice water path consistent with CloudSat retrievals;
higher snow rates may be informed by different
microphysical assumptions

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CCM-CAP: Drizzle & rain
• Rain drop size distribution (DSD) scales as a function of rain rate (Abel & Boutle, QJRMS, 2012),
but can also retrieve deviations from this relation when constrained by observations– PIA can provide a strong constraint on retrieving DSDs with high concentrationsof small drops in warm rain (Mason et al., ACP, 2017)• Rain rate and liquid cloud both contribute to PIA• Compared with CloudSat rain products– PRECIP-COLUMN doesn’t resolve features within the profile(e.g. evaporation or collision-coalescence), but is well-constrained by PIA– RAIN-PROFILE has distinct attenuating and non-attenuating regimes

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CCM-CAP: liquid cloudObservations• Retrievals of non-precipitating liquid cloud
closely resemble MODIS cloud retrievals• But it’s likely that a majority of liquid cloudgoes undetected by spaceborne radar-lidar– radar is sensitive to precipitatinghydrometeors, while lidar is quickly
extinguished near cloud-topRetrievals– Missing liquid clouds have a strong
shortwave/solar radiance signal, so can’t beignored from a cloud-radiation perspective– Have demonstrated some success bycoarsely represent missing liquid cloud withthe assumption that liquid cloud is colocatedwith rain.

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CCM-RAD: radiative transfer at nadir

CCM-CAPECMWF• CCM-CAP retrieved cloud and aerosol properties are inputs
to radiative transfer model• Radiative transfer model is the offline version of the ECMWF
forecast model’s fast and accurate radiation scheme, ecRad(Hogan and Bozzo, 2018)

Meteorological andsurface parameters
along A-Train track

Ice cloudWater content andeffective radius

T, q, O3
Ts , ε, A

IWC, reffAerosol

Mass mixing ratioq• Unlike the EarthCARE radiative product (ACM-RT), theradiative transfer is currently run just at nadir, not across theMODIS swath• We also use CCM-CAP as the inputs for a more robustradiative closure assessment across the MODIS swath using
the method of Barker et al. (2011), which underpins theACM-3D & ACM-RT products for EarthCARE

Liquid cloud
Water content andeffective radius

LWC, reffecRad

Gas optics
Aerosol optics
Cloud optics

CCM-RAD
Radiative fluxes andheating rates fromCCM-CAPoutputproductclearcloudySolvercloudfraction

CCM-RAD: radiative fluxes & heating rates• ecRad computes LW & SW radiative fluxes
through the atmosphere• Radiative heating rates calculated from net
radiative flux over each layer• Top-of-atmosphere (TOA) fluxes vs CERES
measurements• LW radiative closure well-constrained;
determined by cloud-top height/temperature• SW radiative closure more sensitive to cloudphase, retrieved properties and surface albedo• Assumption of liquid cloud in rain improvesshortwave fluxes at TOA, but drastically changes
heating rates in the lower atmosphere– Cloud base height assumption from spaceborne
radar-lidar could be evaluated using surface
fluxesEUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS

CCM-RAD: radiative closure• With coarse distinctions between cloud regimes; more
careful selection could be made to target specific processes• Radiative closure assessment provides a built-in evaluationof biases in the radiative properties of clouds retrieved by
CCM-CAP. This can be used to quantify improvements,
e.g. in microphysical properties of ice clouds• Increased noise attributed to inherent mismatch betweenthe ~1km scale of CloudSat-CALIPSO measurements andthe ~20km CERES footprint.
– Should be substantially reduced by carrying out across-track
radiative closure assessment (Barker et al. 2011 & ACM-RT)• SW closure more challenging than LWEUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS

Summary• CCM-CAP: unified and synergistic retrieval of clouds,
aerosols and precipitation from the A-Train– Advantages over a patchwork of single-instrument andsynergistic retrievals, especially in complex and layeredscenes– Continuity with EarthCARE synergistic retrievals– Current applications:•Testing and development of EarthCARE synergistic retrievals•

Evaluation of ECMWF integrated forecast system•

Forward-modelling of novel microwave/radar sensors• Paper and initial dataset to follow– Starting with 1 year of A-Train data (2008)– To expand to first 5 years of A-Train (2006 to2011); period of optimal CloudSat-CALIPSO
performance– Looking for users!
Contact shannon.mason@ecmwf.int• Downstream products: radiative fluxes and heating rates– CCM-RAD at nadir using ECMWF radiation scheme;
Radiative closure vs CERES provides a built-in evaluationof CCM-CAP– Also providing CCM-CAP as input to 3D radiative transfer
methodology (Barker et al.
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