Minrui WANG, Takashi Y. NAKAJIMA
Tokai University Research & Information Center
ESA-JAXA Pre-Launch EarthCARE Science and Validation Workshop @ Frascati, Italy

EarthCARE/MSI cloud product process
JAXA MSI L2 algorithm has beendeveloped by Tokai University (PI.
Prof. T. Y. Nakajima).

MSI/L1 data
Calculate the cloud flag andCloud phase using CLAUDIA

Day Water
Calculate COT, CDR, CTP using
the 3ch method of CAPCOM

COT, CTP, CDR

Day IceCalculate COT, (CDR), CTP, using
the 3ch method of CAPCOM

COT, CTP, (CDR)

Cloud Optical Thickness (COT), Cloud Droplet effective Radius (CDR), Cloud Top Pressure (CTP)

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Cloud detection algorithm (CLAUDIA):
Ishida and Nakajima (JGR 2010)

Concept of the Clear Confidence Level (CCL)• Quantitatively evaluate cloud existenceby the CCL (value of 0 to 1)• Two thresholds (Upper limit and Lowerlimit) for each individual test

Retrieval algorithm for the cloud properties (CAPCOM):
Nakajima and Nakajima (JAS 1995)
Kawamoto et al. (2001) etc.

• The CAPCOM uses LUT (Look up Table)Iteration Method to retrieve the cloud opticaland microphysical properties from satellitederived non-absorption and absorption band.
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MSI L2a standard productprocessing (ver0.4)

Outline of processing（Algorithm development in Tokai U.)

2023/7/31 released

Output Data

Input parameters from processing control unit

IF1

MSI L1cStandard Product[HDF]

ECMWF AUX
3D-MSI data(for advancedprocessing)
[HDF]

IF2

MSI L2aStandard productprocessing

OF1

OF2

OF3

Fixed files etc.

（called in algorithm）

Configuration
File [text]
Input Control[text]
Solar Irradiance
Data [text]
MSI Response Function
Data [text]
Land Ocean Data[text]
Use Channel
Definition [text]

I/O interface

L2a MSI One-sensor
Cloud Product[HDF]

Clear Confidence
Level[binary]
Data for algorithmvalidation[text]

Temporary files during processing （deleted after the process）

Look Up Table
Definition [text]

Dumped MSI L1c Data[binary]

Look Up Table[binary]

Dumped ECMWF Objective Data[binary]

Lowest Surface
Reflectivity
Data [binary]※Browse different files foreach month（Specify the storage location with anenvironment variable）

Cloud Flag/Phase[binary]
Dumped MSI L1c Data for water cloud[text]
Dumped ECMWF Objective Data for water cloud[text]
Additional Info[text]
Cloud Property Data [text]

15 Scenes of JAXA L1C data generated by NICAM/Joint Simulator
Scene No.

Time (UTC)

Scene No.

Time (UTC)

Scene 1（60001F）

00:05~00:16

Scene 9（60002F）

01:38~01:49

Scene 2（60001G）

00:17~00:27

Scene 10（60002G）

01:50~02:00

Scene 3（60001H）

00:28~00:39

Scene 11（60002H）

02:01~02:11

Scene 4（60002A）

00:40~00:51

Scene 12（60003A）

02:12~02:23

Scene 5（60002B）

00:52~01:02

Scene 13（60003B）

02:24~02:35

Scene 6（60002C）

01:03~01:13

Scene 14（60003C）

02:36~02:46

Scene 7（60002D）

01:14~01:25

Scene 15（60003D）

02:47~02:58

Scene 8（60002E）

01:26~01:37• Date: 2008/06/19• Local daytime scene in red(end with C, D, E, F)• Local nighttime scene in blue(end with G, H, A, B)

MSI L2A water cloud product
Cloud Optical Thickness

Cloud Effective Radius using band 3

SMILE in EarthCARE/MSI Band 1 and Band 3

Band1

Band2

Band3

Band4

Band # Wavelength (µm)

IFOV (km)

1

0.67

0.5

2

0.865

0.5

3

1.65

0.5

4

2.21

0.5

5

8.8

0.5

6

10.8

0.5

7

12.0

0.5

・ The MSI has Spectral MIsaLignmEnt (SMILE), which
is a center wavelength shift that appears as distortions ofspectrum images due to misalignment.

Evaluating SMILE on EarthCARE/MSI cloud product• A paper describing an evaluationof the Spectral MIsaLignmEnt(SMILE) on the MSI cloudproduct (Wang et al. 2023) waspublished in Jan. 2023.
• Wang, M., Nakajima, T. Y., Roh,W., Satoh, M., Suzuki, K., Kubota,T., and Yoshida, M.: Evaluation of
the spectral misalignment on theEarth Clouds, Aerosols andRadiation Explorer/multi-spectral
imager cloud product, Atmos.
Meas. Tech., 16, 603–623,
https://doi.org/10.5194/amt-16603-2023, 2023.
• This paper used the Japanesesynthetic MSI L1 data (Roh et al.
2023)

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Nakajima-King Diagram• To evaluate the error caused by the SMILE in
the radiative transfer, we used Nakajima-Kingdiagrams.
• Nakajima-King diagrams were developed forestimating COT and CDR using twowavelength observations in the visible light(e.g., band 1 of MSI) and near-infrared light(e.g., band 3 of MSI) regions (Nakajima andKing, 1990; Nakajima et al., 1991).
• These diagrams are used as the basis ofremote sensing of cloud characteristics fromvisible and near-infrared light observations.

From Nakajima and King (1990)

Nakajima-King Diagram: Case PixMS of Band1

Voronoi particles

Spherical particles

Δτ=0.89〜2.7%
Δre=0.08µm〜1.0%Δτ=0.19〜2.3%
Δre= 0.13µm〜1.6%
Shallow water cloudΔτ=1.12〜3.5%
Δre= 0.148µm〜0.4%

Δτ=0.176〜2.2%
Δre= 0.113µm〜0.3%

Deep convective cloudSummary for the SMILE error (Shallow Water Cloud)Summary for the SMILE error (Deep Convective Cloud)Spatial Error distribution evaluation using NICAM/Joint-Simulator data
• The synthetic MSI L1 data for MSI cloud product algorithm were created by
the Joint-Simulator (Hashino et al., 2013; Satoh et al., 2016), and input 3.5km-mesh global atmospheric simulation data.
• The data were calculated by a global storm-resolving atmospheric model,
Nonhydrostatic Icosahedral Atmospheric Model (NICAM) (Tomita andSatoh, 2004; Satoh et al., 2008, 2014).
• The SMILE was considered using response functions depending on the pixel
number from ESA.
• MSI data were generated using the fixed response function at the nadir
location as the control data.
• By applying the MSI algorithm to the nadir and SMILE sets of simulated
radiance data, we compared the two sets of cloud retrieval product andevaluated the error caused by SMILE.

• Panel (a) shows the radiance at
0.659 µm (band 1 of MSI) ,which indicates the approximate
location of target clouds bymarking areas with relatively
high radiance (red-orange zone).
• For shallow warm clouds, 71,870of the 344,064 pixels were
defined as water clouds by theMSI cloud profiling algorithm.
• The average error of COT was0.89 %, whereas the averageerror of CDR was 3.13 %.

• Panel (a) shows brightness
temperature at 10.8 µm (band 6 ofMSI) , which indicates theapproximate location of target
clouds by marking areas withelatively low brightness temperature(blue zone).
• For deep convective clouds, 29,501
of the 344,064 pixels were defined
as ice cloud by the MSI cloudprofiling algorithm.
• The average error of COT was
1.38 %, whereas the average errorof CDR was 3.60 %.

Summary• We developed the EarthCARE/MSI L2 cloud product algorithm forshallow water cloud (normal product).
• To support the further multi-sensors researches, implementation of deep convective
cloud (ice cloud) product for researchers is on going, estimated to be finished by
the end of 2023.

• Based on Nakajima-King diagram and CAPCOM, the influence of SMILE
to the cloud retrieval is not so significant for oceanic cases.
• Typical case of shallow water clouds τ=8, re=8µm, Δτ=0.19〜2.3%,
Δre=0.50µm〜6.2% when solar zenith angle is 60 degrees.
• Typical case of deep convective clouds τ=8, re=40µm, Δτ=0.18〜2.2%,
Δre=1.4µm〜3.4% when solar zenith angle is 60 degrees.

Summary• Our spatial error distribution results showed that Δτ and Δre were 0 at the102nd pixel (nadir), and the error tended to increase gradually from thenadir toward both sides, which was especially significant for deep
convective cloud.
• Be similar with the averaged error from CAPCOM radiative transfer
simulation, Δre was larger than Δτ in the spatial distribution.
• In most oceanic cases, both Δτ and Δre were less than 10 %.
• Generally, the results of the NICAM/Joint-Simulator data matched thoseof the CAPCOM radiative transfer simulation well, suggesting that theerror in COT and CDR caused by the SMILE was not significant, andcould be regarded as negligible in most oceanic cases.

Summary• However, our simulations are based on observations over oceanic areas,
which is much less strongly influenced by surface albedo than land areas.
• The surface albedo values used in the NICAM/Joint-Simulator data were0.04–0.05, which did not change substantially throughout the scene. Butthings could be much more complicated in observations over land areas.
• For the VIS channel (band 1 of MSI), which is close to the red edge of green
vegetation, small shifts in the central wavelength can lead to uncertainties
due to the rapid change in surface reflectance.
• Therefore, for the aerosol product algorithm development (by Dr. MayumiYoshida), adjustment is required to deal with the influence of SMILE.

CLAUDIA-2（Cloud and Aerosol Unbiased Decision Intellectual Algorithm-2• MSIではGroup1を4種類、Group2を1種類それぞれテストを行い、最終的に両者をマージしてCCLの実数値を求める。
Ishida and Nakajima, 2009より

CLAUDIA-2（Cloud and Aerosol Unbiased Decision Intellectual Algorithm-2• 晴天信頼度（Clear Confidence Level, CCL）によって曇と晴、あるいはその間の状態を表現する（0.0が完全に曇、1.0は完全に晴）CAPCOM

Three steps of cloud remote sensing•••

Modeling of atmospheric scattering.
Generating LUT by using an “forward program” such asradiative transfer.
Retrieve target physical parameters by use of an “inversionprogram”.

Retrieval algorithm for the cloud properties (CAPCOM)
Response Function
Inversion tools

Forward tools

Three steps of cloud remote sensing

Executables

Source codes
Look Up Tables•••

Modeling of atmospheric scattering.
Generating LUT by using an “forwardprogram” such as radiative transfer.
Retrieve target physical parameters byuse of an “inversion program”.

Setup of CAPCOM Forward Simulation• For shallow water clouds：• COT(τ)：1, 2, 4, 8, 16, 32, 48, 64• CDR (Re)（μm）：2, 4, 8, 16, 32• For deep convective clouds：• COT(τ) ：0.1, 0.5, 1, 2, 4, 8, 16, 32, 48, 64, 80 ,90 ,100
• CDR (Re) （μm）：5, 10, 20, 40, 60, 80, 100, 110, 120, 130, 140, 150• (Number in red: typical case of each type of cloud)Quantitative evaluation of SMILE• To evaluate the error of COT and CDR quantitatively, we obtained the first derivation of radiance, ΔL,with respect to COT or CDR from the Nakajima-King diagram, as

dL/dτ, dL/dReand then we obtained the reciprocals as
dτ/dL, dRe/dLand multiplied them by the radiance deviation ΔL between the two response functions (at one of four
selected pixels, and on the nadir location) to get the COT and CDR estimation errors, Δτ and Δre,respectively.
• As our evaluation criteria for the SMILE-based error, we performed the following analytical estimationof shortwave radiation (Fsw) due to CDR change under the assumed constant cloud water content, W.
𝐹𝑆𝑊 = 343 × 0.6 × 0.81 × 0.51 × 1 − 0.51 ×

Δ𝑟𝑒
Δ𝑟= 42 × 𝑒𝑟𝑒𝑟𝑒under the global mean distribution, if CDR decreased by 10 %, then Fsw would decrease by about 4.2
W/m2. An error of this size or larger would be non-negligible in the cloud profiling algorithm ofEarthCARE/MSI.