ESA-JAXA Pre-Launch EarthCARE
Science and Validation Workshop
13 – 17 November 2023 | ESA-ESRIN, Frascati (Rome), Italy

Combined use of passive and active remote sensing tocharacterize the vertical stratification of the cloudthermodynamic phase
Takashi M. Nagao 1 and Kentaroh Suzuki 1
1 Atmosphere and Ocean Research Institute, The University of Tokyo, Japan

Three techniques for cloud phase determinations independent of
the relationship between temperature and ice-phase fraction
Active Lidar

Passive SWIR Meas.

Passive Polarimetry(depolarization ratio)

Ice

LiquidPolarized reflectance: 𝐿𝑝,865(R,G,B) = (0.44, 1.6, 2.2 µm)

Scattering Angle(Lidar attenuation)(Hirakata et al., 2014)(Riedi et al., 2010)

EarthCARE, A-Train

GCOM-C/SGLI⚫ These techniques have different penetration depths into clouds: Lidar & POL are limited to
optically shallow layers, whereas SWIR can penetrate deeper into clouds (One sensor is not enough)⚫ We combine two cloud phase data from CALIPSO lidar & MODIS SWIRs to characterize thevertical stratification of the cloud phase; Mention the possible combination of SGLI SWIR & POL1

Data 1: Cloud particle type derived from CALIPSO lidar (CALIOP)⚫ Utilized the CALIOP cloud particle type product developed by Prof. Okamoto's group (Yoshidaet al., 2010; Hirakata et al., 2014), available from the JAXA A-Train Product Monitor⚫ This product offers cloud particle types, but they were binarized into liquid (0) or ice (1) for ouranalysis, and the ice phase fraction (0. – 1.) was then calculated based on cloud bins detecteda) CloudSat/CPR radar reflectivityc) Principle of CALIOP-based
cloud particle type determinationb) CALIOP cloudparticle type

Warm
Water

Supercooled
Water

Liquid3D-Ice2D-Plate

3D + 2D(Hirakata et al., 2014)

Ice

JAXA EarthCARE Research A-Train Producthttps://www.eorc.jaxa.jp/EARTHCARE/research_product/ecare_monitor.html

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Data 2: Cloud phase retrieval from SWIR channels
COTLIQ←→ COTICE⚫ A SWIR-based cloud phase retrieval algorithm utilizing the 1.6 &2.1 µm channels was implemented for consistent application toMODIS & SGLI (Nagao & Suzuki, 2021).
⚫ This algorithm retrieves total COT, CER, & ice COT fraction(ICOTF) to total COT, using two SWIRs & one VNIRRTOA (1.6µm) - RTOA (2.2µm)

Principle of ICOTF retrieval(RGB) = (0.44, 1.6, 2.2 µm)

CERLIQ = CERICE(Ice COT Fraction)

𝑰𝑪𝑶𝑻𝑭 =𝑪𝑶𝑻𝑰𝑪𝑬𝑪𝑶𝑻𝑳𝑰𝑸 + 𝑪𝑶𝑻𝑰𝑪𝑬Retrieved ice COT fraction

CER

ICE

LIQ
Ice COT frac.
(ICOTF)

RTOA(2.2µm)
Nagao & Suzuki, 2021 ESS (https://doi.org/10.1029/2021EA001912)

LIQ

ICOTF

ICE 3

Comparing the global characteristics of the ice phase/COT fractions⚫ Difference: The CALIOP-derived ice phase fraction exhibits more values close to either 0 or 1
(dark blue & dark red)⚫ This study interpret this difference in context of the distinct penetration depths between lidar andSWIR, seeking insight into the vertical stratification of the cloud phase through their combined
and complementary usea) CALIOP-derived ice phase fraction*LIQb) MODIS SWIR-derived ice COT frac.

ICE

LIQ

* The ice phase fraction was calculated based on all the cloud bins detected by CALIOP, regardless of whether they were single or multi-layercloudsICE4

Combined use of the two cloud phases from CALIOP and MODIS SWIR
⚫ First, the CALIOP-derived ice phase fraction and MODIS SWIR-derived ICOTF were binarizedwith a threshold value of 0.5 to obtain cloud phase classes for ‘liquid (LIQ)’ and ‘ice (ICE)’. Thesecloud phase classes were then combined to define the four categories:
CALIOP

LIQ

ICE

LIQ

ICE

MODIS SWIR

LIQ

ICE

ICE

LIQ

BT(8.6µm) - BT(11µm)

Zonal distributions of cloud phase fractional occurrence(Vertically inhomogeneity)

BTD-based cloud phase↓ determination

ICE
LIQ✓ LIQ/LIQ and ICE/ICE can mainly increase vertically homogeneous liquid and ice clouds✓ LIQ/ICE is thought to include liquid-top mixed-phase clouds, while ICE/LIQ would include multi-layer
clouds, this conjecture is supported by BTD-based cloud phase identification

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Interpretation in terms of droplet vertical distribution using CloudSat/CPRCALIOP

LIQ

LIQ

ICE

ICE

MODIS SWIR

LIQ

ICE

LIQ

ICEa) 41.7 %b) 4.9 %c) 19.1 %d) 34.3 %multi layers

discontinuity⚫ The four-categories of cloud phase were associated with the distinct droplet vertical profile⚫ When the SWIR-based cloud phase exhibits ICE (b, d), a similarity in Ze profiles are found⚫ This results suggest that the combined use of lidar & SWIR better characterizes verticalstratification of the cloud thermodynamic phase

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GCOM-C / SGLI, the successor to ADEOS-II / GLI
SGLI channels

GCOM-CCH

WL
[nm]

VN1

IFOV
[m]

CH

WL
[µm]

IFOV
[m]

380

SW1

1.05

VN2412

SW21.38

VN3443SW31.63250†

VN4490SW42.211000

VN5530

TI1

10.8

TI212.01000

VIS/NIR

VN6565

VN7673.5

VN8673.5

250†

250†

SWIRTIR† 250 m resolution over land and coastal
area, 1 km over offshore

SWIR & POL

Launch Data

Dec. 24, 2017 (in operation)

VN9763Orbit

Sun-synchronous(Descending local time: 10:30)

VN10868.5

Instrument

Second generation GLobal Imager (SGLI)

VN11868.5

O2 A-band & TIR

Wavelength

380 nm – 12 µm, 19 chs.

P1673.5→ CGT (→ Nc w/ COT, CER)

Resolution

250 m - 1 km

P2868.5

Swath> 1000 km

Obs. Freq.

2 - 3 dayPOL

→ two cloud phases

1000→ CBH → downward LW Flux

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Comparing the SWIR- & POL-based cloud phases (preliminarily)
RGB (0.44, 1.6, 2.2 µm)

SWIR-based (ICOTF)

POL-based (Ice Prob.)< weighting function >RTOA @ SWIR

1st Oct. 2021
Japan

SWIR-based

POL-based
ICE

ICE

Rp, TOA @ PL

LIQ
LIQ

8
Fractional occurrence of ice phase

SGLI-based cloud and radiation product: global（Jan. – Nov. 2021）

Cloud properties

COT

CER

SW @ TOA - Upward

Cloud phase (ICOTF)

SW @ SFC Downward

CTH

LW @ TOA - Upward

CBH

LW @ SFC - Downward

Radiative flux

⚫ Retrieved cloud property using the SGLI multi-channels with our implemented algorithm and thenestimated the shortwave/longwave (SW/LW) radiative fluxes at TOA/SFC9SGLI-estimated of cloud radiative effect (preliminarily)
2B-FLXHR-LIDAR (Matus & L’Ecuyer 2017 JGR)

SGLI

SW

LWSW

-43.6

19.4(→24.1*)

-48.3

24.0

LWTOABOA⚫ The SGLI-based CRE estimates were consistent with the CRE based on the A-train multi-sensor
observations. However, there remains some negative bias in the upward LW due to anunderestimation of ~ 1 km in the TIR-based CTH retrieval for ice clouds.
* if the ~ 1 km CTH bias is corrected

10Summary⚫ The two pieces of cloud phase information obtained from active lidar and passive SWIR,
each binarized into liquid or ice, were then combined to define the four categories ofcloud phases⚫ Then investigated through comparisons with CloudSat/CPR radar profile statistics toillustrate how cloud vertical structures vary systematically with the four categories ofcloud phase⚫ The results suggest that the combined use of complementary information from threesensors (lidar, SWIR, and radar) can better characterize the vertical structures of thecloud thermodynamic phase⚫ While combination between lidar and SWIR are limited along the spacecraft track, thecombination of SGLI SWIR and POL is another possible candidate with an alternative tolidar that can provide wider horizontal coverage• Also introduced the SGLI-derived cloud properties and radiation products that are worth comparing toEarthCARE observations to understand the vertical and horizontal structure of clouds. (we believe)

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Example of SGLI-derived cloud property retrievals
RGB (0.44, 1.6, 2.2 µm)

Cloud optical thickness

Effective radius

Cloud phase (ICOTF)

SWIRs←
1st Oct. 2021
Japan

VIR & SWIRO2 A-band & TIR ↓Top temperature(Top height)

Geometric thickness（Base height）

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Comparing the temperature dependences of the ice-phase fractions⚫ Difference: The lidar-based ice-phase fraction was mostly either 0 or 1 (pure liquid or ice),whereas the SWIR-derived ICOTF continuously varied between 0 and 1 along with BT→ This study aims to interpret these differences in context of the distinct penetration depths
between lidar and SWIR, seeking insight into the vertical stratification of the cloud phase throughtheir combined and complementary useIcea) CALIOP(upper cloud layer*)b) MODIS SWIR-derived

Liquid* The ice phase fraction was calculated based on all CALIOP-detected cloud bins for single layer clouds, and only the first upper cloud for multilayer clouds

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Consistency with cloud phase identified by MODIS BT difference
Fig. 7 The joint distributions of brightness temperature (BT) at MODIS 11 µm bandand brightness temperature difference (BTD) between MODIS 8.6 µm and 11 µm bands.
CALIOP

LiquidLiquidIceIceMODIS

LiquidIce

LiquidIcea)b)c)d)
Based onBTDIce↑↓

LiquidThe negative BTD suggests liquid water clouds,
consistent with the CALIPO-derived cloud phase.

The positive BTD suggests ice-phase clouds,
consistent with the CALIPO-derived cloud phase.
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Principle of the retrieval algorithm••••

Based on the combination three retrieval techniques:(1) COT & CER from VNR & SWIR
(2) CER & Cloud phase (Ice COT fraction) from SWIRs(3) CTH & CGT from TIR & O2 A-band
Assumes a plane-parallel layer with mixed LIQ & ICE
Retrieves 5 variables with at least 5 channels of SGLI
Searching a solution w/ LM iteration + OEM
1) COT & CER separation①②

COTLI←→COTIC
Q

CTH③(CTT)
④
CB

E

H=⑤
CERLIQ = CERICE
CER

2) CER & cloud phase separation

3) CTH & CGT separation

CER
CTH

CGT
CER
COT

Ice COT frac.
(cloud phase)16

Validations with ground-based measurements
Cloud Base Heightvs. EUMETNET E-PROFILE (Ceilometer)Bias: -70 m
RMSE: 770 m

Cloud Base Heightvs. Ship-borne Ceilometer

Bias: 50 m
RMSE: 480 m†Target accuracy- CGT of water clouds ：300 m (Scene)- CTH：1 km (Scene)17

Validation with ground-based measurements
Downward SW flux (all-sky)vs. BSRN

Downward LW flux (all-sky

Bias: -4 W/m2
RMSE: 99 W/m2

Bias: +8 W/m2
RMSE: 22 W/m2

vs. BSRN†Target accuracy- Downward SFC LW： 10 W/m2 (0.1 deg., monthly)- Downward SRC SW：13 W/m2 (0.1 deg., monthly)18

Comparison of satellite-based cloud phase< Fractions of Ice-Containing Clouds >a) Our Product(using SWIRs)b) CALIPSO(JAXA A-train Product)c) MODIS Standard Product(MOD06)

ICELIQ✓ Difference in ice cloud fraction change with respect to CTT→ Need to investigate if the difference is due to algorithms or sensors✓ MOD06 is likely to misidentify ice clouds as liquid water clouds.
• The CERs in b) c) and d) are obtained from MOD06. The absence of CER > 30 µｍ is probably is due to the maximum value of the liquid cloud
CER of 30 µm in MOD06.

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