METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016
METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016

Algorithm Theoretical Basis Document（二行） for Cloud Type/Phase Product
Algorithm Theoretical Basis for Himawari-8 Cloud Mask Product（二行）

IMAIToshiharu**,
Takahito* andSUZUEYOSHIDA
Ryo**and YOSHIDA Ryo*
MOURI Kouki*, IZUMIHiroshi*（三行）

Abstract

This paper describes the algorithm theoreticalbasis for the Himawari-8 Cloud Mask Product
Abstract(CMP)
developedbytheMeteorological
Satellite
Center.
CMP is partofproduct,
the Fundamental
Cloud
The cloud type/phase product is an element of the fundamental
cloud
which incorporates
Product, which incorporates cloud phase, type and top altitude from Himawari-8/AHI and has beencloudmask, cloudand cloud top height. The Meteorological Satellite Center (MSC) of Japan
operational
sincetype/phase
7 July 2015.
Meteorological(JMA)
separated
type/phaseand cloudheight, which
were
The CMPAgency
algorithm
is basedon thecloud
cloudmask,
maskcloudtechniqueof NWC-SAF
fortopMSG/SEVIRIandNOAA/NESDIS
for GOES-R/ABI.
Most cloud
tests involve
based oncontained
in miscellaneouslevel 2 products
in detection
the Himawari-7
era, thresholdand put methods
them together
as aradiative
transfer
calculation
using
NWP
data
as
atmosphericprofiles.
The
thresholds
aremodifiedfundamental cloud product. The cloud type/phase product provides information on cloud typeusing offsets determined on the basis of comparison with the MODIS cloud mask product. Initial(opaque/semi-transparent/fractional) and the phase of cloud drop (water/ice/unknown or mix) at the cloud
results showed that the CMP hit ratio derived from such comparison was more than 85%. The CMP
top.algorithm
The analysis
based
the results
NWCSAF(NoWCasting
Facility)
was method
applied istomainly
SEVIRI
data,onwith
showing
hit ratios Satellitewith theApplication
MODIS product
cloud
type
algorithm
becausethe method is well established and relatively easy to understand. Cloud
werearound85% forall seasons.
phase comparison between the MSC product （三行）
and the Level 2 product of MODIS (MYD06L2) andCalipso (CAL_LID_L2_01kmCLay-ValStage1-V3-30) was carried out from 20 July to 2 August 2015.
began
operation.
1. Introduction
The ice phase capture ratio was greater than 80% for both
MODIS
and Calipso.
（一行）
This paper describes the algorithm theoretical basis for
Himawari-8 is the new geostationary satellite of the
CMP.
Japan Meteorological Agency (JMA). It was launched on（一行）
7 October 2014 and began operation on 7 July 2015. The
2. Algorithm
1. Introduction
Cloud type studies produce comparatively qualitativesatellite
carries the Advanced Himawari Imager (AHI),
information,
which is greatly improved over past imagers in terms of
2.1 Overviewenabling determination of clouds as opaque,
Cloud detection
the CMPinalgorithm
based typeon
its Himawari-8
number of bands
temporal/special
resolution
carriesand
theitsAdvanced
Himawari
Imagersemi-transparent
or infractional
the MSCiscloudcomparisonof observation
data difference
and clearis sky(Bessho which
et al.has2016).
Using AHI bands
data, for
JMA’s
(AHI),
16 observational
theproduct.
Brightness
temperature
the data
maincalculated
fromnumerical
weather
prediction(NWP)
Meteorological
Satellite
Center(MSC)
developed
theprovision of a wealth of information on cloud
metric utilized to determine cloud type. The algorithm
data. If observation data differ from clear sky data, theHimawari-8 Cloud Mask Product (CMP).
microphysics.
Meteorologicalsatellite
operatingtheoretical basis document for the cloud product ofpixel is cloudy. To discriminate between cloudy and clear
Cloud mask is used to discriminate cloudy pixels
organizations around the world have made out cloud type
NWCSAF (Meteo France 2012) describes the cloud type
pixels, it is desirable for observation variables in clear
from clear ones in satellite data. Numerous satellite
andcloudrequire
phase inrecognitionof their importance,
determinationthat MSC to
usesdiffer
for reference.
GOES-Rand cloudy conditions
and for The
the physicalproducts
cloudmask information.
By way andoftheMeteorologicalCenter(SST)(MSC)
of Japancloud
theoretical
document
reason typebehindalgorithm
the difference
to bebasis
explicit(e.g.,
example,Sea SurfaceSatellite
Temperature(Yasudaandtemperature,2010)reflectanceand withemissivity
the but
top theseand
Shirakawa 1999),
Clear (JMA)Sky Radiance(CSR)(Uesawa
Meteorological
Agencyalso began
to provide
the(Pavolonis
also deals
cloud oftypes,
transmittance
relating
theInatmosphere).
2009) and Aerosol
Detection(Okawara
et al. 2003)
fundamental
cloud product
when
Himawari-8
enteredareits
arecloud phasessuch toas clouds
ice or andwater.
cloud phase
Ratherthanencompassing
retrievalofthese offive
derivedinclear
pixels.
Conversely,
Cloud
Grid
operative period. MSC simultaneously separates cloudstudies, the difference in the imaginary partthevariables, most of the tests in the CMP algorithm involve
Information (Tokuno 2002) is processed in cloudy pixels.
mask, cloud type/phase and cloud top height from othercomplex refractive index between two bands is used tothe use of brightness temperature or reflectance data with
There were no MSC cloud mask products before thelevel 2 satellite products and puts them together into a
determine the ice or water particle phase for IR bands.
similar tendencies. Cloud mask tests are based on thestart of Himawari-8’s operation; each satellite productsinglecalledmask
the fundamental
canStrabala
et al.algorithm(1994) proposed
cloud particle(NWC)
phase
cloud maskof the NoWCasting
had itsoutput
own cloudprocess. Ascloud
cloudproduct.
mask isItalso
beused forfor most
otherHimawari-8
level 2 products
those fordiscrimination
using 8-,Facility
11- and(SAF)
12-micronbrightness
Satellite Application(Meteo-France,
requiredproducts,such
MSCasdecided
to2012) and the
Oceanic
develop CMP
as a resource
availablefor Himawari-8
aerosols,
sea surface
temperatureand volcanicash, andtemperatures
and National
their difference
fromand
dataAtmosphericcollected by
Administration
Environmental
products
common.
CMP
a part of their
the Fundamental
users
noinlongerneed
to iscomputeown cloudthehigh-resolution(NOAA)/National
infrared sounder (HIRS)
on board
Satellite,
Data,
and
Information
Service(NESDIS)Cloud
Product(FCP),
which
contains
cloudphase,
type
information.
NOAA’s polar orbiters. For near-infrared bands, Baum et(Heidinger and Straka III, 2012).
and top height for Himawari-8/AHI pixels (Mouri et al.
Radiative transfer calculation using NWP data for
2016a, 2016b). FCP has been produced since Himawari-8* System Engineering Division, Data Processing Department, Meteorological Satellite Center** Meteorological Satellite and Observation System Research Department, Meteorological Research Institute* Office of Observation Systems Operation, Observation Department, Japan Meteorological Agency
August 31, Division,
2015, Accepted
NovemberDepartment,
20, 2015) Meteorological Satellite Center**(Received
System Engineering
Data Processing(Received September 1, 2015, Accepted 20 November 2015)
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METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016
METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016

al. (2012) investigated cirrus clouds using MODIS data. （二行） tropopause level. Although effective emissivity differs
This document outlines the cloud type/phase product andfrom the product of actual cloud emissivity and actual

Algorithm Theoretical Basis for Himawari-8
Cloud Mask Productthe related algorithm, which is based on the NWCSAF
cloud amount, it has similar characteristics in that（二行）

algorithm (Meteo France 2012). The product includes
observed radiance is related to the ratio of contribution
IMAI
Takahito* and YOSHIDA
Ryo**top radiation and surface radiation. This
information on cloud type and cloudphase.
The
from cloud（三行）

document also describes the required input data, output

means that thick cloud has high effective emissivity

data and evaluation.

and thin cloud has low effective emissivity.
EUMETSAT:
AbstractAnintergovernmental
Europeanfor the Himawari-8
Maskthat
Product
1.1. Content This paper describes the algorithm theoretical basisorganization
founded Cloudin 1986
operates the(CMP) developed by the Meteorological Satellite Center.
CMP
is
part
of
the
Fundamental
Cloud
Meteosatgeostationary
satellite
andthe
Product, which incorporates cloud phase, type and top altitude from Himawari-8/AHI and has beenThis is theoperational
Algorithmsince
Theoretical
Basis Document for
low-earth-orbiting Metop and Jason satellites. The
7 July 2015.
the cloud type/phaseproduct.
It describesorganization
runs a satellite
application facility(SAF)
The CMPalgorithm
is basedtheon algorithms
the cloud mask technique
of NWC-SAF
for MSG/SEVIRIandNOAA/NESDIS
for GOES-R/ABI.
tests involvethresholdmethods based
used and covers
related practicalconsiderations.Most cloud detection
to support
research,development
and onoperationalradiative transfer calculation using NWP data as atmosphericprofiles.
The
thresholds
aremodifiedactivities not carried out at EUMETSAT headquarters.
using offsets determined on the basis of comparison with the MODIS cloud mask product. Initial
1.2. Definitions, acronyms and abbreviations
𝜑𝜑𝜑𝜑{𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠−𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠} : Angle between sun and satellite.
results showed that the CMP hit ratio derived from suchcomparison was more than 85%. The CMP
JMA:Japan hit
Meteorological
Agency.
algorithm was applied to SEVIRI data, with results
showingratios with the
MODIS product
LRC: Local radiative center as incorporated in GOES-R
AHI: Advanced
Himawari(mounted onwere around
85% for Imager
all seasons.
（三行） Cloud Type and Cloud Height Algorithm Theoretical
Himawari-8 and -9 for observation with 16 bands;

central wavelengths: 0.47, 0.51, 0.64, 0.86, 1.6, 2.3, 3.9,
1. Introduction
6.2, 6.9, 7.3, 8.6, 9.6, 10.4, 11.2, 12.4, 13.3 microns) .
（一行）
Aqua: NASA’s low-earth-orbit
earth science satellite.
Himawari-8 is the new geostationary satellite of the
𝐴𝐴𝐴𝐴𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 : Surface albedo depending on surface type, sunJapan
Meteorological Agency (JMA). It was launched on
zenithangle,andatmosphericconditions
7 October 2014
began operationon 7 July and2015. otherThevariables.
satellite
carries the Advanced Himawari Imager (AHI),BRDF:isBi-directionalreflectance
distribution
which
greatly improved
over past
imagers function.
in terms of
its
numberNASA’sof bandssatellite
and its for
temporal/special
resolution
Calipso:analyzing clouds
and(Besshoal. 2016).
UsingandAHI
data,
aerosolsetin relation
to weather
climate.
TheJMA’sunit isMeteorological
Satellite
Center(MSC)
developed
equipped with cloud-aerosol lidar for observationtheof
Himawari-8 Cloud Mask Product (CMP).
aerosols and cloud microphysics.
Cloud mask is used to discriminate cloudy pixels
Cloud mask: A cloud mask product providing informationfrom clear ones in satellite data. Numerous satellite
on pixel
clear/cloudy
statusinformation.
and aerosol
products
requirecloud mask
Bydetection.
way of
Cloud
mask
data
are
createdfrom
Himawari-8’s
example, Sea Surface Temperature (SST) (Yasudaoutput,
andShirakawaSky ofRadiance(CSR) (Uesawa
and are 1999),
also anClear
element
the fundamental
cloud
2009)
and Aerosol
Detectional. 2003)
product.
Cloud maskdata(Okawara
contain et
only
high- areandderivedinclear
pixels.
Conversely,
Cloud
Grid
low-quality flags, while the cloud type product
Information (Tokuno 2002) is processed in cloudy pixels.
provides a much wider range of data on quality.
There were no MSC cloud mask products before theDaytime: The period during which the sun zenith angle isstart of Himawari-8’s operation; each satellite productthan
80 degrees.
hadsmaller
its owncloudmask process. As cloud mask is also
Effective
emissivity
at 11.2products,
micronsMSC(ε(11.2)):
required for most Himawari-8
decidedThetodevelopCMP
as a resource
available
effective
emissivityof 11.2
micronsforatHimawari-8
the center
productsin common.
CMP is ofa partthe Fundamental
wavelength
is the productcloudofamountand cloud
Cloud
Product(FCP),
which
contains
cloudphase,
type
emissivity as estimated in consideration of cloud at
theand top height for Himawari-8/AHI pixels (Mouri et al.
2016a, 2016b). FCP has been produced since Himawari-8

Basis (Pavolonis 2010).
began Look-up
operation.table containing radiance values to support
LUT:
This
paper describes
the algorithm
basiswaterfordiscrimination
betweenice phasetheoretical
cloud and
CMP.
phase cloud. The radiances were calculated using
（一行）
RSTAR
(see
“RSTAR”
in this section) under various
2. Algorithm
conditions for ice phase and water phase.
MODIS:
Moderate resolution imaging spectroradiometer
2.1
Overview
Cloud
CMP
algorithm is based ononboarddetection
the AquainandtheTerrasatellites.
comparisonof observationand clear sky data
MSC: Meteorological
Satellitedata
Center.
calculated
fromnumerical
weather
prediction(NWP)isNighttime: Period during which the sun
zenith angle
data. If observation data differ from clear sky data, thelarger than 93 degrees.
pixel is cloudy. To discriminate between cloudy and clear
NWCSAF: Nowcasting satellite application facility.
pixels, it is desirable for observation variables in clear
λ: Centerwavelengthof theandcloudyconditions
to AHI
differbands
and informicrons.
the physical:
Effective
radiusofcloudparticles.
𝑟𝑟𝑟𝑟reason
behind the difference to be explicit (e.g.,
𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒
temperature,reflectanceand emissivity of the top andreflectance.
𝑅𝑅𝑅𝑅𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 : Bi-directionaltransmittance
relating
to clouds
the wavelength
atmosphere).of
R (λ): Observedreflectivity
at theandcentralRatherthanencompassing
retrievalofthese five
λ microns, where λ is 0.64 or 1.6.
variables, most of the tests in the CMP algorithm involve
RSTAR: A radiative transfer code developed by thethe use of brightness temperature or reflectance data withCenter for Climate System Research at the University
similar tendencies. Cloud mask tests are based on theof Tokyo1986).
cloudmask(Nakajima
algorithmandofTanaka
the NoWCasting(NWC)
RTTOV:A
radiative
transfercode
developedbySatellite Application Facility (SAF) (Meteo-France,
2012)
and the(Eyre
National
EUMETSAT
1991). Oceanic and Atmospheric
Administration
Environmental
Surface category: (NOAA)/National
MSC uses the Global
Land CoverSatellite,
Data,
and
Information
Service
Characteristics Data Base Ver 2.01 of USGS(NESDIS)
to identify(Heidinger and Straka III, 2012).
Radiative transfer calculation using NWP data for* Office of Observation Systems Operation, Observation Department, Japan Meteorological Agency** System Engineering Division, Data Processing Department, Meteorological Satellite Center(Received September 1, 2015, Accepted 20 November 2015)
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METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016
METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016

earth surface attributes. The cloud type product
Cloudy pixels in Cloud Mask (Imai and Yoshida 2016)（二行）
features four main categories: 1. water area; 2. lowland,

are processed in the cloud type product.

Algorithm Theoretical Basis for Himawari-8 Cloud Mask Producturban or crop area; 3. mountain, desert or rock area;and 4. forest area.
τ: Optical thickness.

（二行）

2. Outline of cloud type and phase product
IMAI Takahito* and YOSHIDA Ryo**（三行）

𝜃𝜃𝜃𝜃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 : Sun zenith angle (the angle from the sun to thezenith at an arbitrary point on the earth’s surface).

The cloud type/phase product provides information on

cloud type (opaque, semi-transparent or fractional) and𝜃𝜃𝜃𝜃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 : Satellite zenith angle (the angle from the satellite
the phase of cloud drop (water, ice or unknown/mixed).
Abstract
Thisat paper
describespoint
the algorithm
theoretical The
basismethod
for theofHimawari-8
Mask
Product techniqueto the zenith
an arbitraryon the earth’s
analysis is Cloudbased ona threshold(CMP)
developedbytheMeteorological
Satellite
Center.
CMP
is
part
of
the
Fundamental
surface).
largely modeled on the NWCSAF cloud Cloud
type algorithm.
Product, which incorporates cloud phase, type and top altitude from Himawari-8/AHI and has been
𝑇𝑇𝑇𝑇(𝜆𝜆𝜆𝜆) : Observedbrightness
Cloudy pixels in Cloud Mask (Imai and Yoshida 2016)
operational
since 7temperature
July 2015. at the center
wavelength ofwhere isisbased
3.9, on
6.2,the7.3,
8.6,maskareprocessedin the cloud for
typeMSG/SEVIRI
product.
The microns,
CMP algorithm
cloud
techniqueof NWC-SAF
andfor GOES-R/ABI. Most cloud detection tests involve threshold methods based on
11.2, 12.4NOAA/NESDISor 13.3.
radiative brightnesstransfer calculation
using
data as atmospheric
The thresholds are modified
temperature
at NWP
the center
𝑇𝑇𝑇𝑇𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 (𝜆𝜆𝜆𝜆): Estimated
2.1. Input profiles.
data
using offsets determined on the basis of comparison with the MODIS cloud mask product. Initialwavelength of λ microns as calculated under the
results showed that the CMP hit ratio derived from such comparison was more than 85%. The CMP
assumptionof cloudpresencewith data,a height
i. AHI
observation
algorithm
was applied
to SEVIRI
with results
showing
hit ratiosdata
with the MODIS productcorresponding
to the 85%
surface.
corresponds to thewere aroundfor allλseasons.
AHI observation wavelength. By way of example, （三行）
it
Reflectances at 0.64 and 1.6 microns and brightness

may be 6.2, 7.3, 8.6 and so on. Plev is a constant value
1. Introduction
for pressure between the surface and the tropopause.
By way of example,（一行）it may be surface, 850 hPa, 500
Himawari-8 is the new geostationary satellite of thehPa and so on. A surface value indicates that there is noJapan Meteorological Agency (JMA). It was launched on
cloud. RTTOV
is usedestimation
outlined
7 October2014 andbeganforoperation
on as
7 July2015.later.
The
EstimatedbrightnessHimawari
temperature
at the(AHI),
center
𝑇𝑇𝑇𝑇𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 (𝜆𝜆𝜆𝜆):satellite
carriesthe Advanced
Imagerwavelength
λ microns
which
is greatlyofimproved
over aspastcalculated
imagers inunderterms theof
its assumption
number of bands
and its
temporal/specialof opaquecloud.
In regard toresolution
radiative(Bessho2016).
AHI 𝑇𝑇𝑇𝑇𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝data,
JMA’stransfer,ettheal.cloud
top isUsingat height
𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 (𝜆𝜆𝜆𝜆), which
Meteorological
Satellite
Center(MSC)
developed
theaccords with 𝑇𝑇𝑇𝑇(𝜆𝜆𝜆𝜆). λ corresponds to the observation
Himawari-8 Cloud Mask Product (CMP).
wavelength. By way of example, it may be 6.2, 7.3, 8.6
Cloud mask is used to discriminate cloudy pixels
and so on.
from clear ones in satellite data. Numerous satellite
Terra: NASA’s
low-earth-orbit
science satellite.
products
requirecloud mask earth
information.
By way of*
http://edc2.usgs.gov/glcc/globdoc2_0.php
example, Sea Surface Temperature (SST) (Yasuda andShirakawa
1999),
Clear
Skywhich
Radiance(CSR)
Twilight time:
Periodduringthe sunzenith(Uesawa
angle is2009)
and thanAerosol
Detection2003) aresmaller
93 degrees
and (Okawara
larger thanet80al.degrees.
derived in clear pixels. Conversely, Cloud Grid
Information (Tokuno 2002) is processed in cloudy pixels.
2. Outline of cloud type and phase productThere were no MSC cloud mask products before thestart of Himawari-8’s operation; each satellite product
Theitscloudon
had
own type/phase
cloud maskproductprocess.provides
As cloudinformation
mask is also
cloud
type
semi-transparent
fractional)
requiredfor (opaque,most Himawari-8
products,orMSC
decidedandtodevelopas a drop
resource
availablefor Himawari-8
thephaseCMPof cloud(water,ice or unknown/mixed).
productsin common.
CMP
is a partthe FundamentalThe
methodof analysis
is basedon aofthresholdtechnique
Cloud
Product(FCP),
which
contains
cloudphase,
typelargely modeled on the NWCSAF cloud type algorithm.
and top height for Himawari-8/AHI pixels (Mouri et al.
2016a, 2016b). FCP has been produced since Himawari-8

temperatures at 3.9, 7.3, 8.6, 11.2 and 12.4 microns arebeganinoperation.
used
the product.
This paper describes the algorithm theoretical basis for
CMP.
ii. Radiative transfer calculation data based on JMA’s（一行）
globalspectralmodel
2. Algorithm

Wavelengths
2.1
Overview correspond to those of the AHI observation
detection
dataClouddescribed
above.in the CMP algorithm is based oncomparison of observation data and clear sky data
calculated
from data
numerical weather prediction (NWP)
iii.
Cloud mask
data. If observation data differ from clear sky data, theiv. Satellite navigation data
pixel is cloudy. To discriminate between cloudy and clear
Valuesit represent
longitude,variables
satellitein zenith
pixels,is desirablelatitude,
for observationclear
angle,
satellite
azimuthtoangle,
sunand cloudy
conditions
differ sun
and zenith
for theangle,
physicalazimuthangle andthe
scanning
time forreason behinddifference
to eachbe pixel.
explicit (e.g.,temperature, reflectance and emissivity of the top andtransmittance
relating to clouds and the atmosphere).
v.
Ancillary data
Rather than encompassing retrieval of these fivevariables, most of the tests in the CMP algorithm involve
Values represent elevation on land, surface category andthe use of brightness temperature or reflectance data withwhite sky albedo. The surface category is described insimilar tendencies. Cloud mask tests are based on the
Subsection
The whiteof the (NWC)
MODIS
cloud mask1.2.
algorithm
of skythe albedo
NoWCastingalbedo(Moodyat al. (SAF)
2005) is(Meteo-France,
used for theSatelliteproductApplication
Facility2012) andproduct.
the National Oceanic and Atmospheric
type/phase
Administration(NOAA)/National
Environmental
Satellite,
Data,
and
Information
Service(NESDIS)2.2. Process flow of the cloud type/phase product(Heidinger and Straka III, 2012).
Radiative transfer calculation using NWP data for* Office of Observation Systems Operation, Observation Department, Japan Meteorological Agency** System Engineering Division, Data Processing Department, Meteorological Satellite Center(Received September 1, 2015, Accepted 20 November 2015)
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METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016
METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016

– Level 4 (Relatively low cloud in the middle cloud)（二行）

As the cloud phase product is associated with the cloud

0.5 × 𝑇𝑇𝑇𝑇400ℎ𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 (11.2) + 0.5 × 𝑇𝑇𝑇𝑇600ℎ𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 (11.2) ≤ 𝑇𝑇𝑇𝑇(11.2) < 𝑇𝑇𝑇𝑇600ℎ𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 (11.2)

Algorithm Theoretical Basis for Himawari-8 Cloud Mask Producttype product, the cloud type procedure is run first (Fig.

（二行）– Level 5 (Low cloud)
IMAI Takahito* and YOSHIDA**
𝑇𝑇𝑇𝑇600ℎ𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 (11.2)Ryo≤ 𝑇𝑇𝑇𝑇(11.2)< 0.5 × 𝑇𝑇𝑇𝑇600ℎ𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 (11.2) + 0.5 × 𝑇𝑇𝑇𝑇𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 (11.2)
（三行）

1).

– Level 6 (Very low cloud)
0.5 × 𝑇𝑇𝑇𝑇600ℎ𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 (11.2) + 0.5 × 𝑇𝑇𝑇𝑇𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 (11.2) ≤ 𝑇𝑇𝑇𝑇(11.2) < 𝑇𝑇𝑇𝑇𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 (11.2)
Abstract

This paper describes the algorithm theoretical basis for the Himawari-8 Cloud Mask Product(CMP) developed by the Meteorological Satellite Center.
CMP is part of the Fundamental Cloud– Level 7 (Extremely low cloud)
Product, which incorporates cloud phase, type and top altitude from Himawari-8/AHI and has been
𝑇𝑇𝑇𝑇𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 (11.2) ≤ 𝑇𝑇𝑇𝑇(11.2)
operational since 7 July 2015.
The CMP algorithm is based on the cloud mask technique of NWC-SAF for MSG/SEVIRI andNOAA/NESDIS for GOES-R/ABI. Most cloud detection
tests
involvethreshold methods based on
ii. Cloudphase
information
radiative transfer calculation using NWP data as atmospheric profiles. The thresholds are modified
using offsets determined on the basis of comparison with the MODIS cloud mask product. Initial
Cloud phase is identified as ice, water
results showed that the CMP hit ratio derived from such comparison was more than 85%. The CMP
unknown/mixed.
algorithm was applied to SEVIRI data, with results
showing hit ratios with the MODIS productwere around 85% for all seasons.
Fig. 1. Process flow of the cloud type/phase （三行）
3. Cloud type/phase product algorithm
product
1. Introduction（一行）
Himawari-8 is the new geostationary satellite of the
2.3. Output data
Japan Meteorological Agency (JMA). It was launched on
7 October 2014 and began operation on 7 July 2015. Thei.
Cloud carries
type information
satellite
the Advanced Himawari Imager (AHI),
which is greatly improved over past imagers in terms of
itsClouds
numberareof identified
bands andasitsopaque,temporal/special
resolutionor
semi-transparent(Bessho etTheal.opaque2016).andUsing
AHI data,categories
JMA’s
fractional.
semi-transparent
Meteorological
Satellite
Center(MSC)
developed
have seven levels based on brightness temperaturetheas
Himawari-8 Cloud Mask Product (CMP).
observed at 11.2 microns, and the fractional category has
Cloud mask is used to discriminate cloudy pixels
only one level. A total of 15 types of information arefrom clear ones in satellite data. Numerous satellite
provided.
The sevenare classifiedBybasedonproducts require
cloud levels
mask information.
way ofcomparison
betweenT(11.2)and Tplev(11.2).
example, Sea
SurfaceTemperature(SST) (Yasuda andShirakawa 1999), Clear Sky Radiance (CSR) (Uesawa
Aerosolhigh
Detection–2009)
Level and1 (Extremely
cloud) (Okawara et al. 2003) are
derivedinclear
pixels.
Cloud Grid
𝑇𝑇𝑇𝑇(11.2) < 0.5 × 𝑇𝑇𝑇𝑇400ℎ𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 (11.2) + Conversely,
0.5 × 𝑇𝑇𝑇𝑇200ℎ𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 (11.2)
Information (Tokuno 2002) is processed in cloudy pixels.
There were no MSC cloud mask products before the– Level 2 (High cloud)start of Himawari-8’s operation; each satellite product(11.2) + 0.5 × 𝑇𝑇𝑇𝑇200ℎ𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 (11.2) ≤ 𝑇𝑇𝑇𝑇(11.2) < 𝑇𝑇𝑇𝑇400ℎ𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 (11.2)
0.5
𝑇𝑇𝑇𝑇400ℎ𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃
had× its
own
cloud mask process. As cloud mask is also
required for most Himawari-8 products, MSC decided to
CMP as higha resource
availablefor Himawari-8–develop
Level 3 (Relatively
cloud in the
middle cloud)productsin common. CMP is a part of the Fundamental
𝑇𝑇𝑇𝑇
400ℎ𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 ≤ 𝑇𝑇𝑇𝑇(11.2) < 0.5 × 𝑇𝑇𝑇𝑇400ℎ𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 (11.2) + 0.5 × 𝑇𝑇𝑇𝑇600ℎ𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 (11.2)
Cloud Product (FCP), which contains cloud phase, type
and top height for Himawari-8/AHI pixels (Mouri et al.
2016a, 2016b). FCP has been produced since Himawari-8

or

began
operation.
3.1.
Cloud
type productThis paper describes the algorithm theoretical basis for
CMP.
Cloud type determination for the product is based on the（一行）
thresholdtechnique.
Eachpixel is identified as opaque
2. Algorithm
cloud, semi-transparent cloud or fractional cloud as
described
in the previous section depending on observed
2.1 Overview
Cloud detectionin the reflectance,
CMP algorithm
based onbrightness
temperature,andis brightness
comparison ofobservation
data two
andwavelengths.
clear sky data
temperature
difference
betweenThe
calculated
fromnumerical
weather
prediction(NWP)
tests performed for each level are described in this section,
data. If observation data differ from clear sky data, theand the algorithm is described in the subsequentpixel is cloudy. To discriminate between cloudy and clear
subsections.
pixels, it is desirable for observation variables in clear
Thecloudy
cloud conditions
types identified
in andthis forproduct
do notandto differthe physicalcorrespondto thetheten basic
types commonlyreferenced
reason behinddifference
to be explicit(e.g.,in
temperature,reflectanceand emissivityof theonly
top andsurfaceweatherobservation;
essentially,
thetransmittance
relating cloud
to clouds
and the atmosphere).
categoriesof opaqueand semi-transparent
cloud
Ratherthanencompassing
retrieval
of
these
five
are used. MSC of JMA also provides High-resolution
variables, most of the tests in the CMP algorithm involve
Cloud Analysis Information (HCAI) (Suzue et al. 2016)
the use of brightness temperature or reflectance data withcontaining cloud type information similar to thatsimilar tendencies. Cloud mask tests are based on theindicating
ten basic
cloud oftypes.
cloud mask
algorithm
the NoWCasting (NWC)
Satellite Application Facility (SAF) (Meteo-France,2012) Opaqueand theor semi-transparent
National Oceanic cloud
and atAtmospheric3.1.1.
Levels 1, 2,(NOAA)/National
Environmental
3Administration
and 4
Satellite, Data, and Information Service (NESDIS)(Heidinger and Straka III, 2012).
Radiative transfer calculation using NWP data for* Office of Observation Systems Operation, Observation Department, Japan Meteorological Agency** System Engineering Division, Data Processing Department, Meteorological Satellite Center(Received September 1, 2015, Accepted 20 November 2015)
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METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016
METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016

A 𝑇𝑇𝑇𝑇(11.2) − 𝑇𝑇𝑇𝑇(12.4) test is utilized to discriminate
daytime only, and the 𝑇𝑇𝑇𝑇(3.9) − 𝑇𝑇𝑇𝑇(11.2) test is for lower（二行）

opaque cloud and semi-transparent cloud in these levels.

opaque cloud at nighttime. The 𝑇𝑇𝑇𝑇(8.6) − 𝑇𝑇𝑇𝑇(11.2) test is

Algorithm Theoretical Basis for Himawari-8
Cloud Mask Product
The semi-transparent cloud test is carried out first. If thefor separation of lower fractional
（二行）

cloud

and

result is positive (i.e., the pixel is semi-transparent), thesemi-transparent cloud.
IMAI
opaque cloud test is not carried out. If
the Takahito
result *isand YOSHIDA Ryo**negative, the pixel is given an opaque cloud status.

（三行）– Semi-transparent cloud

The opaque cloud test is carried out first. This is
Abstract

i. Daytime

This >
paper
describes the algorithm theoretical followedbasis for bythetheHimawari-8
Cloudtest,
Mask
𝑇𝑇𝑇𝑇(11.2) − 𝑇𝑇𝑇𝑇(12.4)
𝑇𝑇𝑇𝑇112𝑇𝑇𝑇𝑇124𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜
fractional cloudandProductremaining cloud(CMP) developed by the Meteorological Satellite Center.
CMP
is
part
of
the
Fundamental
Cloudis classified as semi-transparent.
Product, which incorporates cloud phase, type and top altitude from Himawari-8/AHI and has been
– Opaque cloud
operational since 7 July 2015.
Opaque cloud
𝑇𝑇𝑇𝑇(11.2) − 𝑇𝑇𝑇𝑇(12.4)≤ 𝑇𝑇𝑇𝑇112𝑇𝑇𝑇𝑇124𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜
The CMPalgorithm is based on the cloud mask-technique
of NWC-SAF for MSG/SEVIRI andNOAA/NESDIS for GOES-R/ABI. Most cloud detection
tests
involve thresholdmethods based on
𝑇𝑇𝑇𝑇(11.2)− 𝑇𝑇𝑇𝑇(12.4)< 𝑇𝑇𝑇𝑇112𝑇𝑇𝑇𝑇124𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜
radiative
transfer
calculation
using
NWP
data
as
atmosphericprofiles.
The
thresholds
are modified
And
The threshold for 𝑇𝑇𝑇𝑇112𝑇𝑇𝑇𝑇124𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 is derived fromusing offsets determined on the basis of comparison with the MODIS cloud mask product. Initial
𝑅𝑅𝑅𝑅(0.64) > 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚064
results showed that the CMP hit ratio derived from such comparison was more than 85%. The CMP
𝑇𝑇𝑇𝑇112𝑇𝑇𝑇𝑇124𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜
algorithm was applied to SEVIRI data, with results showing hit ratios with the MODIS product(11.2)
𝑇𝑇𝑇𝑇𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 (12.4)- Fractional cloud
𝑇𝑇𝑇𝑇𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠
were =
around85%
for all−seasons.
+ 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂_112_𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜（三行）
𝑇𝑇𝑇𝑇(8.6) − 𝑇𝑇𝑇𝑇(11.2) ≤ 𝑇𝑇𝑇𝑇86𝑇𝑇𝑇𝑇112𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂

1.
IntroductionHere,
𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂_112_𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 is a constant value（一行）
considered to exhibit thehighest skill score in opaque
Himawari-8 is the new geostationary satellite of thecloud separation. The offset is taken from an LUT (seeJapan Meteorological Agency (JMA). It was launched on
the
Appendix
forandinformationon the offset).
7 October
2014
began operationon 7 July 2015. The
satellite carries the Advanced Himawari Imager (AHI),
which is greatly improved over past imagers in terms of
its number of bands and its temporal/special resolution(Bessho et al. 2016). Using AHI data, JMA’s
Meteorological Satellite Center (MSC) developed theHimawari-8 Cloud Mask Product (CMP).
Cloud mask is used to discriminate cloudy pixels
from clear ones in satellite data. Numerous satellite
Fig. 2. require
Flow of cloud
cloud typeproducts
maskdetermination
information. for
ByLevels
way ofexample,Sea 4Surface Temperature (SST) (Yasuda and1, 2, 3 andShirakawa 1999), Clear Sky Radiance (CSR) (Uesawa
2009) and Aerosol Detection (Okawara et al. 2003) are
derivedOpaquein clearpixels. Conversely,
Cloud
3.1.2.
or semi-transparent
cloud atLevelsGrid5, 6,Information (Tokuno 2002) is processed in cloudy pixels.
and 7 and fractional cloud
There were no MSC cloud mask products before thestart of Himawari-8’s operation; each satellite productIn itsthese, 𝑅𝑅𝑅𝑅(0.64)
had
own categories,
cloud mask 𝑇𝑇𝑇𝑇(11.2)
process. −As𝑇𝑇𝑇𝑇(12.4)
cloud mask
is also ,𝑇𝑇𝑇𝑇(3.9) −for
𝑇𝑇𝑇𝑇(11.2)
and 𝑇𝑇𝑇𝑇(8.6)− 𝑇𝑇𝑇𝑇(11.2)
tests arerequiredmost Himawari-8
products,
MSC decided
todevelopas a resource
available for Himawari-8
utilized CMP
to discriminate
semi-transparent,
opaque andproducts
common.
is a test
part forof theFundamental
fractionalincloud.
The CMP𝑅𝑅𝑅𝑅(0.64)
semi-transparent
Cloud
Product(FCP),
which
contains
cloud
cloud covering lower-layer cloud is carriedphase,out intype
theand top height for Himawari-8/AHI pixels (Mouri et al.
2016a, 2016b). FCP has been produced since Himawari-8

operation. cloud-beganSemi-transparent
This
paper
describes
the algorithm
theoreticalbasis forAll pixels
otherthan thosein the above
categories
CMP.
（一行）
2. Algorithm
2.1 Overview
Cloud detection in the CMP algorithm is based oncomparison of observation data and clear sky data
calculated from numerical weather prediction (NWP)
data. If observation data differ from clear sky data, thepixel is cloudy. To discriminate between cloudy and clear
pixels, it is desirable for observation variables in clear
andconditions
to differ
and for for
the Levels
physical
Fig.cloudy
3. Flowof cloud typedetermination
reason behind the difference to be explicit (e.g.,5, 6 and 7 in the daytime
temperature, reflectance and emissivity of the top andtransmittance relating to clouds and the atmosphere).
Ratherretrievalof thesefive
The than
𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚encompassing
𝑚𝑚𝑚𝑚064 thresholdis usedto allow
variables, most of the tests in the CMP algorithm involve
semi-transparent cloud to be distinguished from opaque
the use of brightness temperature or reflectance data withcloud in the NWCSAF algorithm (Meteo France 2012).
similar tendencies. Cloud mask tests are based on theThisbased onthe conceptthat NoWCastingsemi-transparent
cloud
cloudis mask
algorithmof the(NWC)
andopaquecloud areFacility
separatedby themaxCiR064
Satellite
Application(SAF)(Meteo-France,2012) and reflectance
the National
Oceanic
Atmosphericthresholdderivedfromand𝑇𝑇𝑇𝑇(11.2). The
Administration(NOAA)/National
Environmental
maxCiR064 threshold
and 𝑇𝑇𝑇𝑇(11.2) havea linear
Satellite,
Data,
and
Information
Service(NESDIS)relationship.
(Heidinger and Straka III, 2012).
Radiative transfer calculation using NWP data for* Office of Observation Systems Operation, Observation Department, Japan Meteorological Agency** System Engineering Division, Data Processing Department, Meteorological Satellite Center(Received September 1, 2015, Accepted 20 November 2015)
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METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016
METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016

A line can be drawn through two points:（二行）

・(223.15, 0.35): Coldest point at 11.2 microns
Algorithm Theoretical Basis for Himawari-8 Cloud Mask Product・(𝑇𝑇𝑇𝑇𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 (11.2),𝑅𝑅𝑅𝑅𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 ): Warmest (surface) point at 11.2
microns（二行）

IMAI Takahito* and YOSHIDA Ryo**
The bi-directional effect of the overcast model
proposed（三行）

by Manalo-Smith et al. (1998) is used for calculation of
𝑅𝑅𝑅𝑅𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 .

AbstractThis
describes
the algorithmbetween
theoretical basis for the Himawari-8 Cloud Mask ProductFigure 4 showsan paperexampleof the relationship(CMP)
developedbytheMeteorologicalCenter. CMP is part of the Fundamental Cloud
𝑇𝑇𝑇𝑇(11.2) and 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚064. The straight line is usedSatellite
to
Product, which incorporates cloud phase, type and top altitude from Himawari-8/AHI and has beenFig. 5. Flow of cloud type determination for Levels 5, 6
distinguish semi-transparent
opaque cloud.
operational sincecloud
7 Julyfrom
2015.
and 7 at twilight
The CMP algorithm is based on the cloud mask technique
of NWC-SAF for MSG/SEVIRI andNOAA/NESDIS for GOES-R/ABI. Most cloud detection tests involve threshold methods based onradiative transfer calculation using NWP data as atmospheric profiles. The thresholds are modified
using offsets determined on the basis of comparison with the MODIS cloud mask product. Initial
iii Nighttime
results showed that the CMP hit ratio derived from such comparison was more than 85%. The CMP
algorithm was applied to SEVIRI data, with results showing hit ratios with the MODIS product
The procedure for nighttime testing involves application
were around 85% for all seasons.

1. Introduction（三行）
of the 𝑇𝑇𝑇𝑇(3.9) − 𝑇𝑇𝑇𝑇(11.2) test to detect lower-layer（一行）
Himawari-8 is the new geostationary satellite of theJapan
Agency𝑇𝑇𝑇𝑇(11.2)(JMA). It
was𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚
launched
Fig. 4.Meteorological
Relationship between
and
𝑚𝑚𝑚𝑚064on
7 October 2014 and began operation on 7 July 2015. The
satellite carries the Advanced Himawari Imager (AHI),
which is greatly improved over past imagers in terms of
its
number of bands and its temporal/special resolution
ii Twilight(Bessho et al. 2016). Using AHI data, JMA’s
MeteorologicalCenterdeveloped
The procedure Satellite
for twilighttests (MSC)
is the sameas thatthe
forHimawari-8 Cloud Mask Product (CMP).
the daytime except for the 𝑅𝑅𝑅𝑅(0.64) test.
Cloud mask is used to discriminate cloudy pixels
from clear ones in satellite data. Numerous satellite- Opaque require
cloud cloud mask information. By way of
products
𝑇𝑇𝑇𝑇(11.2) −Sea
𝑇𝑇𝑇𝑇(12.4)< 𝑇𝑇𝑇𝑇112𝑇𝑇𝑇𝑇124𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜
example,SurfaceTemperature (SST) (Yasuda andShirakawa 1999), Clear Sky Radiance (CSR) (Uesawa
2009)
and Aerosol- Fractional
cloud Detection (Okawara et al. 2003) are
derivedinclear≤ pixels.
Conversely,
𝑇𝑇𝑇𝑇(8.6) − 𝑇𝑇𝑇𝑇(11.2)
𝑇𝑇𝑇𝑇86𝑇𝑇𝑇𝑇112𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 Cloud Grid
Information (Tokuno 2002) is processed in cloudy pixels.
There were no MSC cloud mask products before the- Semi-transparent cloud
start of Himawari-8’s operation; each satellite product
Allitspixelsthose
in the above
categories
hadownothercloudthanmask
process.
As cloudmask is also
required for most Himawari-8 products, MSC decided todevelop CMP as a resource available for Himawari-8
products in common. CMP is a part of the Fundamental
Cloud Product (FCP), which contains cloud phase, type
and top height for Himawari-8/AHI pixels (Mouri et al.
2016a, 2016b). FCP has been produced since Himawari-8

opaque cloud.
began operation.
This paper
describes the algorithm theoretical basis for- Opaquecloud
CMP.
𝑇𝑇𝑇𝑇(11.2) − 𝑇𝑇𝑇𝑇(12.4) < 𝑇𝑇𝑇𝑇112𝑇𝑇𝑇𝑇124𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜（一行）
And
2. Algorithm
𝑇𝑇𝑇𝑇(3.9) − 𝑇𝑇𝑇𝑇(11.2) < 𝑇𝑇𝑇𝑇39𝑇𝑇𝑇𝑇112𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜

2.1 Overview
Cloud detection- Fractional
cloud in the CMP algorithm is based oncomparisonof observation
data andclear sky data
𝑇𝑇𝑇𝑇(8.6)− 𝑇𝑇𝑇𝑇(11.2)≤ 𝑇𝑇𝑇𝑇86𝑇𝑇𝑇𝑇112𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂
calculated from numerical weather prediction (NWP)
data. If observation data differ from clear sky data, the- Semi-transparent cloud
pixel is cloudy. To discriminate between cloudy and clear
All pixels other than those in the above categories
pixels, it is desirable for observation variables in clear
and cloudy conditions to differ and for the physicalreason behind the difference to be explicit (e.g.,temperature, reflectance and emissivity of the top andtransmittance relating to clouds and the atmosphere).
Rather than encompassing retrieval of these fivevariables, most of the tests in the CMP algorithm involvethe use of brightness temperature or reflectance data withsimilar tendencies. Cloud mask tests are based on thecloud mask algorithm of the NoWCasting (NWC)
Satellite Application Facility (SAF) (Meteo-France,2012) and the National Oceanic and Atmospheric
Administration(NOAA)/National
Fig. 6. Flow of cloudtype determinationEnvironmental
for Levels
Satellite, Data, and Information Service (NESDIS)
5, 6 and 7 at nighttime(Heidinger and Straka III, 2012).
Radiative transfer calculation using NWP data for* Office of Observation Systems Operation, Observation Department, Japan Meteorological Agency** System Engineering Division, Data Processing Department, Meteorological Satellite Center(Received September 1, 2015, Accepted 20 November 2015)
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METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016
METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016

The

𝑇𝑇𝑇𝑇39𝑇𝑇𝑇𝑇112𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜

and

𝑇𝑇𝑇𝑇86𝑇𝑇𝑇𝑇112𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂（二行）Cloud particle phase discrimination involves the use ofthresholds are used to detect opaque cloud and fractional

cloud type and satellite data on variables such as

Algorithm Theoretical Basis for Himawari-8
Cloud Mask Productcloud, respectively, at nighttime.
brightness temperature. For opaque（二行）

𝑇𝑇𝑇𝑇39𝑇𝑇𝑇𝑇112𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜

cloud,

therelationships linking 𝑇𝑇𝑇𝑇(11.2) , 𝑇𝑇𝑇𝑇(8.6) − 𝑇𝑇𝑇𝑇(11.2) ,IMAI Takahito* and YOSHIDARyo**
𝑅𝑅𝑅𝑅(0.64) and𝑅𝑅𝑅𝑅(1.6) are used to determine the particle= 𝑇𝑇𝑇𝑇𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 (3.9) − 𝑇𝑇𝑇𝑇𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 (11.2)+ 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂_39_𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜（三行）

phase. For other types, the phase is the same as that of thelocal radiative center (LRC) if the LRC is opaque;otherwise, the ice phase is attributed to semi-transparent
Abstract

This𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂paper
basis forCloud
MasktoProduct𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 describes the algorithm theoretical cloud
𝑇𝑇𝑇𝑇86𝑇𝑇𝑇𝑇112𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂
and thethe Himawari-8
water phase isattributed
fractional cloud.
(CMP) developedbytheMeteorological
Satellite
Center.
CMP
is
part
of
the
Fundamental
Cloud= 𝑇𝑇𝑇𝑇𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 (8.6) − 𝑇𝑇𝑇𝑇𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 (11.2)
Product, which incorporates cloud phase, type and top altitude from Himawari-8/AHI and has been
3.2.1. Cloud type discrimination results+ 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂_86_𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂
operational
since 7 July 2015.𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂The CMP algorithm is based on the cloud mask technique of NWC-SAF for MSG/SEVIRI andNOAA/NESDIS
for GOES-R/ABI.𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂Most
cloud detection
tests involve
methods
based on involves
For opaque
cloud thresholdpixels, phase
discrimination
and 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂_86_𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 are
𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂_39_𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜
radiative
transfer
calculation
using
NWP
data
as
atmosphericprofiles.
The
thresholds
aremodifiedthe use of the satellite data described in this section. The
constant values considered to have the highest skill scoreusing offsets determined on the basis of comparison with the MODIS cloud mask product. Initial
phase is ice for semi-transparent cloud pixels and waterin separation of opaque cloud. These biases are contained
results showed that the CMP hit ratio derived from such comparison was more than 85%. The CMP
forshowing
fractionalhitcloud
pixels.
in an LUT (seethe Appendix
for further
information
algorithm
was applied
to SEVIRI
data, onwith results
ratioswith the MODIS productbiases).
were around 85% for all seasons.
（三行）
3.2.2. 𝑻𝑻𝑻𝑻(𝟏𝟏𝟏𝟏𝟏𝟏𝟏𝟏. 𝟐𝟐𝟐𝟐) test

3.2. Cloud phase product1. Introduction
This section describes （一行）cloud particle phase classification,
Himawari-8 is the new geostationary satellite of thewhich is also based on the threshold technique. The flow
Japan Meteorological Agency (JMA). It was launched onshown in2014
Fig. and7. began operation on 7 July 2015. The
7is Octobersatellite carries the Advanced Himawari Imager (AHI),
which is greatly improved over past imagers in terms of
its number of bands and its temporal/special resolution(Bessho et al. 2016). Using AHI data, JMA’s
Meteorological Satellite Center (MSC) developed theHimawari-8 Cloud Mask Product (CMP).
Cloud mask is used to discriminate cloudy pixels
from clear ones in satellite data. Numerous satellite
products require cloud mask information. By way of
example, Sea Surface Temperature (SST) (Yasuda andShirakawa 1999), Clear Sky Radiance (CSR) (Uesawa
2009) and Aerosol Detection (Okawara et al. 2003) are
derived in clear pixels. Conversely, Cloud Grid
Information (Tokuno 2002) is processed in cloudy pixels.
There were no MSC cloud mask products before thestart of Himawari-8’s operation; each satellite producthad
its7.own
cloud
maskphaseprocess.
As cloud mask is also
Fig.
Flow
of cloudprocessing
required for most Himawari-8 products, MSC decided todevelop CMP as a resource available for Himawari-8
products in common. CMP is a part of the Fundamental
Cloud Product (FCP), which contains cloud phase, type
and top height for Himawari-8/AHI pixels (Mouri et al.
2016a, 2016b). FCP has been produced since Himawari-8

began
operation.
For opaque
cloud, the phase is ice when 𝑇𝑇𝑇𝑇(11.2) is
This
paper
describes
the algorithm
basis forbelow freezing temperature
without theoreticalan ice nucleus
andCMP.
water when 𝑇𝑇𝑇𝑇(11.2) is above freezing temperature.
（一行）
2. Algorithm
・Ice particle cloud

𝑇𝑇𝑇𝑇(11.2)< 243.15
2.1 Overview
Cloud detection in the CMP algorithm is based oncomparisonof observation
data and clear sky data
・
Water droplet
cloud
calculated
fromnumerical
weather
prediction (NWP)
𝑇𝑇𝑇𝑇(11.2) > 273.15
data. If observation data differ from clear sky data, thepixel is cloudy. To discriminate between cloudy and clear
3.2.3 𝑻𝑻𝑻𝑻(𝟖𝟖𝟖𝟖. 𝟔𝟔𝟔𝟔) − 𝑻𝑻𝑻𝑻(𝟏𝟏𝟏𝟏𝟏𝟏𝟏𝟏. 𝟐𝟐𝟐𝟐) test
pixels, it is desirable for observation variables in clear
and cloudy conditions to differ and for the physicalAs described
2.3,difference
the ice or towaterreasonbehind inthe
be droplet
explicitphase(e.g.,istemperature,and ofemissivityof the−top
andattributed onreflectancethe basis
the 𝑇𝑇𝑇𝑇(8.6)
𝑇𝑇𝑇𝑇(11.2)
transmittancedifference,
relating towhichcloudsand the atmosphere).
temperatureis generally
larger for ice
Ratherthanencompassing
retrievalofthese fivecloud than for water cloud.
variables, most of the tests in the CMP algorithm involvethe use of brightness temperature or reflectance data with・Ice particle cloud
similar tendencies. Cloud mask tests are based on the
𝑇𝑇𝑇𝑇(11.2) of the NoWCasting (NWC)
cloud𝑇𝑇𝑇𝑇(8.6)
mask −algorithm
𝑇𝑇𝑇𝑇𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 (8.6)− 𝑇𝑇𝑇𝑇𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐(Meteo-France,
Satellite Application >Facility(SAF)
𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 (11.2)
2012) and the National
Oceanic and Atmospheric+ 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂_86_𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚
Administration(NOAA)/National
Environmental
Satellite,
Data,
and
Information
Service(NESDIS)The value of 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂_86_𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 is set in reference
to an(Heidinger and Straka III, 2012).
Radiative transfer calculation using NWP data for* Office of Observation Systems Operation, Observation Department, Japan Meteorological Agency** System Engineering Division, Data Processing Department, Meteorological Satellite Center(Received September 1, 2015, Accepted 20 November 2015)
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METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016
METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016

・Satellite zenith angle (𝜃𝜃𝜃𝜃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 ): every 10 degrees from 0 to 90
LUT, and is a constant considered to have the highest（二行）

skill score in ice particle cloud detection. The score is・ Azimuth difference between sun and satellite ( 𝜑𝜑𝜑𝜑{𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠−𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠} )Algorithm Theoretical Basis for Himawari-8
Cloud Mask Product(degrees) : 0, 2.5, 5, 10, 15, 25, 35, 45, 55, 65, 75, 85, 95, 105, 115,
calculated from the Calipso cloud phase product (Hu el al.
（二行）

125, 135, 145, 155, 165, 170, 175, 177.5, 1802009) and NWP data (see the Appendix for information
IMAI Takahito* and YOSHIDA Ryo**on the constant).
（三行）

ii. Cloud・Water droplet cloud・Optical thickness (τ): 0.25, 0.50, 1, 2, 4, 8, 16, 32, 64, 96, 128

・Cloud height: 3 km
𝑇𝑇𝑇𝑇(8.6) − 𝑇𝑇𝑇𝑇(11.2)
Abstract
This paper
the algorithm theoretical basis for the Himawari-8 Cloud Mask Product≤ 𝑇𝑇𝑇𝑇𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐describes
𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 (8.6) − 𝑇𝑇𝑇𝑇𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 (11.2)(CMP) developedbytheMeteorological Satellite Center.
CMP radius
is part (𝒓𝒓𝒓𝒓of the) andFundamental+ 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂_86_𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤
shape forCloud
ice cloudiii. Effective
𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆
Product, which incorporates cloud phase, type and top altitude from Himawari-8/AHI and has been・Effective radius (μm): 10, 20, 30, 40
operational since 7 July 2015.
𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂_86_𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤
set inonreference
The value of The
is based
spherical
CMP algorithm isthe cloudtomask・Shape:
techniqueof NWC-SAF for MSG/SEVIRI and
for GOES-R/ABI.
Most
cloud detection tests involve threshold methods based on
an LUT, andNOAA/NESDIS
is a constant considered
to have thehighestradiative
transfer
calculation
using
NWP
datais as atmosphericprofiles.
modified
iv. Effective
radiusTheandthresholds
shape for are
water
droplet cloudskill score inice particle
cloud
detection.
The
score
using offsets determined on the basis of comparison with the MODIS cloud mask product. Initial・Effective radius (μm): 4, 8, 16, 32
calculated from the Calipso cloud phase product (Hu et al.
results showed that the CMP hit ratio derived from such comparison was more than 85%. The CMP・Shape:spherical
2009) and NWP
data was(see applied
the Appendix
for information
algorithm
to SEVIRI
data, with results
showing
hit ratios with the MODIS producton the constant).
were around 85% for all seasons.
（三行）
v. Other conditions

3.2.4. 𝑹𝑹𝑹𝑹(𝟎𝟎𝟎𝟎. 𝟔𝟔𝟔𝟔𝟔𝟔𝟔𝟔) and 𝑹𝑹𝑹𝑹(𝟏𝟏𝟏𝟏. 𝟔𝟔𝟔𝟔) tests
1. Introduction（一行）
If the values of 𝑅𝑅𝑅𝑅(0.64)
for water droplet cloud and ice
Himawari-8 is the new geostationary satellite of theparticle cloud are the same, the value of 𝑅𝑅𝑅𝑅(1.6) for ice
Japan Meteorological Agency (JMA). It was launched on
will 2014be smaller
thanoperationthat for on
water
droplet7cloudOctoberand began
7 July2015.cloud.
The
This characteristic
discrimination
the twocloud
satellite
carries theallows
Advanced
Himawari ofImager(AHI),
types. is Basedon this,overconsistent
cloud
phase
which
greatly improved
past imagers
in terms
of
its
number of bands
and its temporal/special
resolutionof
determinationis achieved
through comparison(Besshoal. 2016).
Using values
AHI anddata, radiative
JMA’s
𝑅𝑅𝑅𝑅(0.64) / et𝑅𝑅𝑅𝑅(1.6)
observation
Meteorological
Satellite
Center(MSC)
developed
thetransfer calculation.
Himawari-8 Cloud Mask Product (CMP).
In this method, an LUT is created using the RSTAR
Cloud mask is used to discriminate cloudy pixels
radiative transfer model with various conditions offrom clear ones in satellite data. Numerous satellite
𝑅𝑅𝑅𝑅(0.64) and𝑅𝑅𝑅𝑅(1.6).
the radiativetransferBycalculation,
products
requirecloudIn mask
information.
way ofthereflectances
of
ice
and
water
phase
cloud
at
0.64
and
example, Sea Surface Temperature (SST) (Yasuda andShirakawa
Clear Sky
(CSR)
(Uesawa
1.6 microns1999),
are acquired.
TheRadiancereflectanceis varied
with
2009)
and Aerosol
Detection(Okawara
al. 2003)cloudarethe conditionsof cloud
altitude,cloud etthickness,derivedinclear
pixels.
Conversely,
Cloud
Grid
particle shape/radius, positional relationship between the
Information (Tokuno 2002) is processed in cloudy pixels.
sun and the satellite, atmospheric profile and surface
There were no MSC cloud mask products before thereflectance. The calculation conditions are summarized
start of Himawari-8’s operation; each satellite productbelow.
had
its own cloud mask process. As cloud mask is also
required for most Himawari-8 products, MSC decided todevelop
CMPconditions
as a resource available for Himawari-8i.
Geometric
products in common. CMP is a part of the Fundamental
Cloudzenith
Product
contains
phase,
・Sun
angle(FCP),(𝜃𝜃𝜃𝜃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 ):which
every 10degrees cloudfrom 0 to
90 typeand top height for Himawari-8/AHI pixels (Mouri et al.
2016a, 2016b). FCP has been produced since Himawari-8

・Surface albedo (𝐴𝐴𝐴𝐴𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 ) (𝑅𝑅𝑅𝑅(0.64)): 0.02, 0.05, 0.1, 0.2, 0.3, 0.5

began operation.
・Surfacealbedo (𝐴𝐴𝐴𝐴𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 ) (𝑅𝑅𝑅𝑅(1.6)): 0.02, 0.05, 0.1, 0.2, 0.3, 0.5, 0.7,

1.0This paper describes the algorithm theoretical basis for

CMP.

・Atmospheric profile: standard for mid-latitude summer（一行）

2. Algorithm
The value shown below is then set to the LUT.
2.1𝑅𝑅𝑅𝑅Overview{𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖/𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤} �𝜆𝜆𝜆𝜆, 𝜏𝜏𝜏𝜏, 𝑟𝑟𝑟𝑟𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 , 𝜃𝜃𝜃𝜃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 , 𝜃𝜃𝜃𝜃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 , 𝜑𝜑𝜑𝜑{𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠−𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠} , 𝐴𝐴𝐴𝐴𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 �
Cloud detection in the CMP algorithm is based oncomparison
observation
sky data
The cloud ofparticlephase isdata
set andfromclear
the LUT.
The
calculated
fromnumerical
weather
prediction(NWP)
procedure is as outlined below.
data. If observation data differ from clear sky data, thepixel is cloudy. To discriminate between cloudy and clear
i. The values of 𝜃𝜃𝜃𝜃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 , 𝜃𝜃𝜃𝜃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 , 𝜑𝜑𝜑𝜑{𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠−𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠} and 𝐴𝐴𝐴𝐴𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 for
pixels, it is desirable for observation variables in clear
every
pixel conditions
are determined
based
and
and cloudy
to differ
andonfornavigation
the physicalwhite(e.g.,skyancillarydata. Here,
𝐴𝐴𝐴𝐴𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 is fromreason behindthe difference
to MODIS
be explicit
temperature,reflectanceemissivityof the�𝜆𝜆𝜆𝜆,top𝜏𝜏𝜏𝜏, 𝑟𝑟𝑟𝑟andalbedodata (Moody
et al.and
2005).
𝑅𝑅𝑅𝑅{𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖/𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤}
𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 �transmittanceclouds and the atmosphere).
isalso obtainedrelatingfrom thetoLUT.
Rather than encompassing retrieval of these fivevariables, most of the tests in the CMP algorithm involveii. Values of 𝜏𝜏𝜏𝜏 corresponding to each value of 𝑟𝑟𝑟𝑟𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 are
the use of brightness temperature or reflectance data withdetermined based on comparison of 𝑅𝑅𝑅𝑅(0.64) andsimilar tendencies. Cloud mask tests are based on the𝜏𝜏𝜏𝜏, 𝑟𝑟𝑟𝑟𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 �.of the NoWCasting (NWC)
𝑅𝑅𝑅𝑅{𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖/𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤}
cloudmask�0.64,
algorithm
�1.6, 𝑟𝑟𝑟𝑟𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒(SAF)� correspondingto 𝜏𝜏𝜏𝜏 isAvalue of
𝑅𝑅𝑅𝑅{𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖/𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤}
Satellite
Application
Facility(Meteo-France,2012)determined.
and the National Oceanic and Atmospheric
thenAdministration(NOAA)/National
Environmental
Satellite,
Data,
and
Information
Service(NESDIS)foriii. 𝑅𝑅𝑅𝑅(1.6) and 𝑅𝑅𝑅𝑅{𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖/𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤} �1.6, 𝑟𝑟𝑟𝑟𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 � are compared(Heidinger and Straka III, 2012).
Radiative transfer calculation using NWP data for* Office of Observation Systems Operation, Observation Department, Japan Meteorological Agency** System Engineering Division, Data Processing Department, Meteorological Satellite Center(Received September 1, 2015, Accepted 20 November 2015)
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METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016
METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016

all values of 𝑟𝑟𝑟𝑟𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 . If the results all indicate water, theCloud phase is expressed as 1-byte integers as indicated（二行）
phase is marked as water; if they all indicate ice, thebelow.

Algorithm Theoretical Basis for Himawari-8 Cloud Mask Productphase is marked as ice; otherwise, the phase is marked as
unknown or mixed.
4. Data format（二行）-128: Error pixel associated with satellite data errors and
IMAI Takahito* and YOSHIDA
similar Ryo**（三行）

0: Clear pixels in cloud mask

1: Ice phase
Data are provided in flat binary format, with those Abstractof
2: Water phasetheasalgorithm
theoretical 3:
basis
for the orHimawari-8
Cloud Mask Productcloud type andThis
cloudpaper
phasedescribes
expressed
1-byte integers.
Unknown
mixed phase(CMP)
developedbytheMeteorological
Satellite
Center.
CMP
is
part
of
the
Fundamental Cloud
Data are written in files following the order from
Product, which incorporates cloud phase, type and top altitude from Himawari-8/AHI and has been
5. Validation
northwest tooperational
southeast withsince5,500
7 Julypixels
2015. and 5,500 lines.
The pixel sequenceis thealgorithm
same as isthatof theHimawari
The CMPbasedon thecloud mask technique of NWC-SAF for MSG/SEVIRI andNOAA/NESDIS
for GOES-R/ABI. Most cloud detection
involve thresholdmethodsCenter
based onof JapanThe tests
Meteorological
Satellite
Standard Data
format.
radiative transfer calculation using NWP data as atmosphericprofiles.
The
thresholds
aremodified
Meteorological Agency carried out cloud phase validation
using offsets determined on the basis of comparison with the MODIS cloud mask product. Initial
4.1. Cloud type values
using the MODIS level 2 product over a period of two
results showed that the CMP hit ratio derived from such comparison was more than 85%. The CMP
weeks
fromhit20 ratios
July to2 August
2015.productThe comparison
algorithm was applied to SEVIRI data, with results
showingwith
the MODIS
Cloud typewere
is expressed
as 1-byte
integers as indicatedindices used were the capture ratio (Eq. 5-1), the false
around 85%
for all seasons.
below.

（三行）
alarm ratio (Eq. 5-2) and the undetected error ratio (Eq.

1. Introduction-128:
Error pixel associated with satellite data errors and（一行）
similar
Himawari-8 is the new geostationary satellite of the
0: Clear pixel in cloud mask
Japan Meteorological Agency (JMA). It was launched on
1:
Extremelyhigh
7 October2014
andopaquebegancloud
operation on 7 July 2015. The
2:High opaquesatellite
carries cloudthe Advanced Himawari Imager (AHI),
3:
Relatively
highimproved
opaque cloudtheimagers
middle in
layer
which
is greatly
over inpast
terms ofits Relatively
number oflow
bands
and cloudits temporal/special
resolution
4:
opaquein the middle layer(Bessho
et al.
2016). Using AHI data, JMA’s
5:
Low opaquecloud
Meteorological
Satellite
6: Very low opaque cloud Center (MSC) developed theHimawari-8 Cloud Mask Product (CMP).
7: Extremely low opaque cloud
Cloud mask is used to discriminate cloudy pixels
8: Extremely high semi-transparent cloudfrom clear ones in satellite data. Numerous satellite
9:High semi-transparent
cloud information. By way of
products
require cloud mask
10:
Relatively
high semi-transparent
cloud inthe middle
example,Sea Surface
Temperature (SST)(Yasudaand
Shirakawa 1999), Clear Sky Radiance (CSR) (Uesawa
layer
2009)
and Aerosol
Detection (Okawara
et al.
2003)
are
11:
Relatively
low semi-transparent
cloudin themiddlederivedinclear
pixels.
Conversely,
Cloud
Grid
layer
Information (Tokuno 2002) is processed in cloudy pixels.
12: Low semi-transparent cloud
There were no MSC cloud mask products before the13: Very low semi-transparent cloud
start of Himawari-8’s operation; each satellite product14:
low semi-transparent
had Extremely
its own cloudmask process. Ascloud
cloud mask is also
15:
Fractionalcloudrequired for most Himawari-8 products, MSC decided todevelop CMP as a resource available for Himawari-8
products
in phasecommon.
CMP is a part of the Fundamental
4.2.
Cloud
valuesCloud Product (FCP), which contains cloud phase, type
and top height for Himawari-8/AHI pixels (Mouri et al.
2016a, 2016b). FCP has been produced since Himawari-85-3) as calculated from a contingency table. Table 1
began operation.
shows
an example of an ice phase contingency table.
This paper describes the algorithm theoretical basis for
CMP.
𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂（一行）(𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 5 − 1)
Capture ratio =𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 + 𝑋𝑋𝑋𝑋𝑋𝑋𝑋𝑋
2. Algorithm
False
alarm ratio =2.1 Overview

𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂(𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜5 − 2)
𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 + 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂

Cloud detection in the CMP algorithm is based on
𝑋𝑋𝑋𝑋𝑋𝑋𝑋𝑋 data and clear sky datacomparisonerrorof ratioobservation(𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜5 − 3)
Undetected=
𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂
calculated from numerical+ 𝑋𝑋𝑋𝑋𝑋𝑋𝑋𝑋
weather prediction (NWP)
data. If observation data differ from clear sky data, thepixel is cloudy. To discriminate between cloudy and clear
pixels, it is desirable for observation variables in clear
Table 1. Contingency table in case of ice phaseand cloudy conditions to differ and for the physicalOO represents
the numberof cases
which bothreasonbehind thedifference
to bein explicit(e.g.,
temperature,reflectanceandemissivity
of
the
top
and
Calipso or MODIS and cloud phase results indicate
transmittance
relating
to clouds
and the ofatmosphere).
ice phase. OX
represents
the number
cases inRather than encompassing retrieval of these fivewhich cloud
ice CMP
phasealgorithm
but Calipso
orvariables,
most typeof theindicatestests in the
involve
MODIS
does not. XO
and XX or
represent
the data
inversetheuse of brightness
temperaturereflectancewithsimilartendencies. Cloud mask tests are based on thesituations.
cloud mask algorithm of the NoWCasting (NWC)
Satellite Application Facility (SAF) (Meteo-France,2012) and the National Oceanic and Atmospheric
Administration(NOAA)/National
Environmental
Satellite, Data, and Information Service (NESDIS)(Heidinger and Straka III, 2012).
Radiative transfer calculation using NWP data for* Office of Observation Systems Operation, Observation Department, Japan Meteorological Agency** System Engineering Division, Data Processing Department, Meteorological Satellite Center(Received September 1, 2015, Accepted 20 November 2015)
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METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016
METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016

Figures 8 (a) and (b) show the results of cloud phase
References:（二行）
validation. Although the capture ratio for the ice phase is

Algorithm Theoretical Basis for Himawari-8
Cloud Mask Product
Baum, B. A., W. P. Menzel, R. A. Frey, D. Tobin, R. E.

comparatively favorable, the undetected error ratio and（二行）

false alarm ratio are large for the unknown/mixed phase.
Holz, S.A. Ackerman, A. K. Heidinger, and P. Yang,
Ryo** cloud top property refinements for
The capture ratio for the latter phase is alsoIMAI
low.Takahito* and YOSHIDA
2012: MODIS（三行）

MSC has not carried out validation for the cloud typeproduct because data in the Calipso and MODIS level 2

Collection 6.

J. Appl. Meteor. Clim., 51, 1145-1163.

Eyre J. R., 1991: A fast radiative transfer model for

products are not appropriate for comparison.
Abstractsatellite sounding systems., ECMWF Research Dept.
This paper describes the algorithm theoretical basis
forMemo.
the Himawari-8
Cloud Mask Product
Tech.
176.
(CMP) developed by the Meteorological Satellite Center.
CMP
is
part
of
the
Fundamental
Cloud
Hu Y., D. Winker, M. Vaughan,
B. Lin,A. Omar, C.
Product, which incorporates cloud phase, type and top altitude from Himawari-8/AHI and has been
Trepte, D. Flittner, P. Yang, S. L. Nasiri, B. Baum, R.
operational since 7 July 2015.
Holz, W.ofSun,
Z. Liu, Z.
S. Young,and
K. Stamnes,
The CMP algorithm is based on the cloud mask technique
NWC-SAF
forWang,
MSG/SEVIRINOAA/NESDIS for GOES-R/ABI. Most cloud detection
tests involve
based onJ. Huang,and R.threshold
Kuehn, methods
2009: CALIPSO/CALIOP
radiative transfer calculation using NWP data as atmosphericprofiles.
The
thresholds
aremodified
Cloud Phase Discrimination Algorithm. J. Atmos.
using offsets determined on the basis of comparison with the MODIS cloud mask product. Initial
Oceanic Technol., 26, 2293-2309.
results showed that the CMP hit ratio derived from such comparison was more than 85%. The CMP(a) results
ImaiT., and hit
R. Yoshida,
2016:
theoretical basis
algorithm was applied to SEVIRI data, withshowingratios with
the Algorithm
MODIS productfor Himawari-8 Cloud Mask Product. Meteorologicalwere around 85% for all seasons.
（三行） Satellite Center Technical Note, 61, 1-17.

1. Introduction（一行）
Himawari-8 is the new geostationary satellite of theJapan Meteorological Agency (JMA). It was launched on
7 October 2014 and began operation on 7 July 2015. The(b)satellite carries the Advanced Himawari Imager (AHI),
which is greatly improved over past imagers in terms of
its number of bands and its temporal/special resolution(Bessho et al. 2016). Using AHI data, JMA’sFig. 8 (a) Satellite
Comparison
of JMAcloudphase theMeteorologicalCenter(MSC)
developed
Himawari-8
CloudL2Mask
Product (CMP).
to MODIS
product
Cloud mask is used to discriminate cloudy pixels
Fig. 8 (b) Comparison of JMA cloud phasefrom clear ones in satellite data. Numerous satellite
to Calipso
product
products
require L2
cloudmask information. By way of
example, Sea Surface Temperature (SST) (Yasuda andShirakawa 1999), Clear Sky Radiance (CSR) (Uesawa
2009) and Aerosol Detection (Okawara et al. 2003) are
derived in clear pixels. Conversely, Cloud Grid
Acknowledgements:
Information (Tokuno 2002) is processed in cloudy pixels.
There were no MSC cloud mask products before the
The authors gratefully acknowledge the two anonymous
start of Himawari-8’s operation; each satellite productreviewers
helpfulthis
had its ownwho
cloudprovided
mask process.
As comments
cloud mask on
is alsomanuscript,
thank theproducts,
NASA LangleyResearchrequired for andmostalso
Himawari-8
MSC decided
todevelopAtmospheric
CMP as a resource
available
for for
Himawari-8
CenterScience Data
Centerproviding
productsin common.
CMP
a partof theFundamental
the
MODIS
and Calipso
dataisusedin thestudy.
Cloud Product (FCP), which contains cloud phase, type
and top height for Himawari-8/AHI pixels (Mouri et al.
2016a, 2016b). FCP has been produced since Himawari-8

Meteo France, 2012: Algorithm Theoretical Basisbegan
operation.
Document
for “Cloud Products” (CMa-PGE01 V3.2,
This
paper
describes
the algorithm v2.2).
theoretical basis for
CT-PGE02 v2.2, & CTTH-PGE03
CMP.
Moody, E. G., M. D. King, S. Platnick, C. B. Schaaf, and
（一行）
F.
Gao,
2005:
Spatially
complete global spectral
2. Algorithm
surface albedos: Value-added datasets derived from
MODIS land products. IEEE Trans. Geosci.
2.1Terra
Overview
Cloud detection
the CMP algorithm is based on
RemoteSens., 43, in
144-158.
comparison
clearformulations
sky data
Nakajima, T.,ofandobservation
M. Tanaka,data
1986:andMatrixcalculated
fromnumerical
weather
prediction(NWP)
for the transferof solar radiation in a plane-paralleldata. If observation data differ from clear sky data, thescattering atmosphere. J. Quant. Spectrosc. Radiat.
pixel is cloudy. To discriminate between cloudy and clear
Transfer,
13-21. for observation variables in clear
pixels,it is 35,
desirable
Pavolonis,
2010: GOES-R
Advanced
ImagerandcloudyM.,conditions
to differ
and forBaseline
the physical(ABI)behind
Algorithm
For
reasonthe Theoreticaldifference toBasisbe Document
explicit (e.g.,temperature,emissivity of the top andCloud Typereflectanceand Cloud andPhase.
transmittance
relating
to clouds
and theSmith, M. N., G.
L. Smith,
S. N. Tiwari,
andatmosphere).
W. F. Staylor,
Ratherthanencompassing
retrievaloffive
1998: Analytical forms of bidirectional thesereflectancevariables, most of the tests in the CMP algorithm involvefunctions for application to Earth radiation budget
the use of brightness temperature or reflectance data withstudies, Journal of Geophysical Research, 103 (D16)
similar tendencies. Cloud mask tests are based on the19733-19751.
cloud
mask algorithm of the NoWCasting (NWC)
Strabala,K, S. A. Ackerman,
W. P.(Meteo-France,
Menzel, 1994:
Satellite I.Application
Facility and
(SAF)
2012)
the Nationaland Data.
Atmospheric
Cloudand
Properties
inferred Oceanicfrom 8-12-μm
J. Appl.
Administration(NOAA)/National
Environmental
Meteor., 33, 212-229.
Satellite,
Data,
and
Information
Service(NESDIS)
Suzue, H., T. Imai, and K. Mouri, 2016: High-resolution(Heidinger and Straka III, 2012).
Radiative transfer calculation using NWP data for* Office of Observation Systems Operation, Observation Department, Japan Meteorological Agency** System Engineering Division, Data Processing Department, Meteorological Satellite Center(Received September 1, 2015, Accepted 20 November 2015)
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METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016
METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016

Cloud Analysis Information derived from Himawari-8（二行）
data. Meteorological Satellite Center Technical Note,61, 43-51.

Skill score varies with the offset (Fig A-1). The offset

Algorithm Theoretical Basis for Himawari-8
Cloud Mask Productvalue with which the highest skill score is achieved is（二行）

adopted. In this example, the offset value is 2K.
IMAI Takahito* and YOSHIDA Ryo**（三行）

Appendix:

Abstract

This paper describes the algorithm theoretical basis for the Himawari-8 Cloud Mask Product
Values for 𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶_𝟏𝟏𝟏𝟏𝟏𝟏𝟏𝟏𝟏𝟏𝟏𝟏_𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐,(CMP)
developedby the Meteorological
𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶_𝟖𝟖𝟖𝟖𝟖𝟖𝟖𝟖_𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆
𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆, Satellite Center. CMP is part of the Fundamental Cloud𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶_𝟑𝟑𝟑𝟑𝟑𝟑𝟑𝟑_𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐𝒐,
Product, which incorporates cloud phase, type and top altitude from Himawari-8/AHI and has been
𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶_𝟖𝟖𝟖𝟖𝟖𝟖𝟖𝟖_𝒇𝒇𝒇𝒇𝒇𝒇𝒇𝒇𝒇𝒇𝒇𝒇
and since𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶𝑶_𝟖𝟖𝟖𝟖𝟖𝟖𝟖𝟖_𝒘𝒘𝒘𝒘𝒘𝒘𝒘𝒘𝒘𝒘𝒘𝒘𝒘𝒘𝒘𝒘𝒘𝒘𝒘𝒘
operational
7 July 2015.
The CMP algorithm is based on the cloud mask technique of NWC-SAF for MSG/SEVIRI andNOAA/NESDIS
for GOES-R/ABI.
Most cloud detection tests involve threshold methods based onof offset values
is based on comparison
Determination
radiative
transfer
calculation
using
NWP
data as atmospheric profiles. The thresholds are modified
between the cloud classification of the Calipso cloudusing offsets determined on the basis of comparison with the MODIS cloud mask product. Initial
phase product (Hu et al. 2009) and cloud type identified
Fig. A-1. was
Biasmorevaluesfor85%.
T39T112opaque
results showed that the CMP hit ratio derived from such comparison
than
The CMP andusing the algorithm
method describedin
Subsections
3.1.1
and
was applied to SEVIRI data, with results showing
ratios This
with theskill hitscores.
test MODIS
is for productopaque cloud
3.1.2. The wereskill aroundscore is85%
adoptedasseasons.
a comparison index.
for alldetection at nighttime using T(3.9) – T(11.2)（三行）
By way of example, the 2 x 2 contingency table shownas described in 3.1.2.
below is used for the opaque cloud detection
began operation.
1.(𝑇𝑇𝑇𝑇39𝑇𝑇𝑇𝑇112𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜)
Introduction
described in Chapter 3.1.2. Skill（一行）
This paper describes the algorithm theoretical basis forscore is defined using the following equation:
Himawari-8 is the new geostationary satellite of the
CMP.
In practice, the offset value is acquired for each value ofJapan Meteorological Agency (JMA). It was launched on（一行）
𝑇𝑇𝑇𝑇(11.2)
andsurfacelandtype. This offset tables are(𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂+𝑋𝑋𝑋𝑋𝑋𝑋𝑋𝑋)−𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆
7𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆October
2014 and began operation on 7 July 2015. The
2. Algorithm
𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 =𝑁𝑁𝑁𝑁 𝑁Advanced
𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆
shown in Figs A-2 (a), A-2 (b), A-2 (c), A-2 (d) and A-2
satellite carries the
Himawari Imager (AHI),(e).
In this study, data from Meteosat-9’s SEVIRI
which is greatly improved over past imagers in terms of
2.1 Overview(𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 + 𝑋𝑋𝑋𝑋𝑋𝑋𝑋𝑋)(𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 + 𝑋𝑋𝑋𝑋𝑋𝑋𝑋𝑋)× (𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 +𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂)its+ temporal/special× (𝑋𝑋𝑋𝑋𝑋𝑋𝑋𝑋 +𝑋𝑋𝑋𝑋𝑋𝑋𝑋𝑋)
Sc =number of bands
Cloudweredetectionin the CMP
algorithm
is based The
on
its
and
resolution
imagerused because
𝑇𝑇𝑇𝑇(8.6)
was required.
𝑁𝑁𝑁𝑁
𝑁𝑁𝑁𝑁
comparison
of anobservation
data ofandeight
clearweeks
sky data(Bessho et al. 2016). Using AHI data, JMA’s
study covered
overall period(two
𝑁𝑁𝑁𝑁 = 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 + 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 + 𝑋𝑋𝑋𝑋𝑋𝑋𝑋𝑋+ 𝑋𝑋𝑋𝑋𝑋𝑋𝑋𝑋 Center (MSC) developed thecalculated
fromnumerical
weather
prediction(NWP)
Meteorological
Satellite
weeks during each of winter 2011 – 2012 (December anddata. If observation data differ from clear sky data, theHimawari-8 Cloud Mask Product (CMP).
January), spring 2012 (March and April), summer 2012
pixel is cloudy. To discriminate between cloudy and clear
Cloud mask is used to discriminate cloudy pixels(June and July) and autumn 2012 (September andpixels, it is desirable for observation variables in clear
fromones in satellite
Tableclear
A-1. Contingency
table data. Numerous satellite
October)).
the future, tothedifferoffset andvalueforwill
updatedandcloudyInconditions
thebephysicalproducts
require cloudmask ofinformation.
By wayOO represents
the number
cases in which
bothofbased onbehind
additionalof accumulated
data. (e.g.,reasontheyearsdifference
to be AHI
explicit
example, Sea Surface Temperature (SST) (Yasuda andCalipso
and
cloud
type
results
indicate
opaquecloud.
temperature, reflectance and emissivity of the top andShirakawa 1999), Clear Sky Radiance (CSR) (Uesawa
OX represents
theDetection
number ofcases inetwhichcloudtransmittance relating to clouds and the atmosphere).
2009)
and Aerosol(Okawara
al. 2003)
areRather than encompassing retrieval of these five
derivedinclear
pixels.
Conversely,
Cloud
Grid
type indicates opaque cloud but Calipso does not. XOvariables, most of the tests in the CMP algorithm involve
Information (Tokuno 2002) is processed in cloudy pixels.
and XX represent the inverse situations.
the use of brightness temperature or reflectance data with
There were no MSC cloud mask products before thesimilar tendencies. Cloud mask tests are based on thestart of Himawari-8’s operation; each satellite productcloud mask algorithm of the NoWCasting (NWC)
had its own cloud mask process. As cloud mask is also
Satellite Application Facility (SAF) (Meteo-France,
required for most Himawari-8 products, MSC decided to2012) and the National Oceanic and Atmosphericdevelop CMP as a resource available for Himawari-8
Administration(NOAA)/National
Environmental
products in common. CMP is a part of the Fundamental
Satellite, Data, and Information Service (NESDIS)
Cloud Product (FCP), which contains cloud phase, type
(Heidinger and Straka III, 2012).
and top height for Himawari-8/AHI pixels (Mouri et al.
Radiative transfer calculation using NWP data for
2016a, 2016b). FCP has been produced since Himawari-8* Office of Observation Systems Operation, Observation Department, Japan Meteorological Agency** System Engineering Division, Data Processing Department, Meteorological Satellite Center(Received September 1, 2015, Accepted 20 November 2015)
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METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016
METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016

（二行）

Algorithm Theoretical Basis for Himawari-8 Cloud Mask Product（二行）

IMAI Takahito* and YOSHIDA Ryo**（三行）

Abstract

This paper describes the algorithm theoretical basis for the Himawari-8 Cloud Mask Product(CMP) developed by the Meteorological Satellite Center. CMP is part of the Fundamental Cloud
Product, which incorporates cloud phase, type and top altitude from Himawari-8/AHI and has been
operational since 7 July 2015.
The CMP algorithm is based on the cloud mask technique of NWC-SAF for MSG/SEVIRI andNOAA/NESDIS for GOES-R/ABI. Most cloud detection tests involve threshold methods based onradiative transfer calculation using NWP data as atmospheric profiles. The thresholds are modified
using offsets determined on the basis of comparison with the MODIS cloud mask product. Initial
results showed that the CMP hit ratio derived from such comparison was more than 85%. The CMP
algorithm was applied to SEVIRI data, with results showing hit ratios with the MODIS productwere around 85% for all seasons.
（三行）

1. Introduction

began operation.
（一行）
This paper describes the algorithm theoretical basis for
Himawari-8 is the new geostationary satellite of the
CMP.
Japan Meteorological Agency (JMA). It was launched on（一行）
7 October 2014 and began operation on 7 July 2015. The
2. Algorithm
satellite carries the Advanced Himawari Imager (AHI),
which is greatly improved over past imagers in terms of
2.1 Overview
Cloud detection in the CMP algorithm is based on
its number of bands and its temporal/special resolution
comparison of observation data and clear sky data
(Bessho et al. 2016). Using AHI data, JMA’s
calculated from numerical weather prediction (NWP)
Meteorological Satellite Center (MSC) developed thedata. If observation data differ from clear sky data, theHimawari-8 Cloud Mask Product (CMP).
pixel is cloudy. To discriminate between cloudy and clear
Cloud mask is used to discriminate cloudy pixels
pixels, it is desirable for observation variables in clear
from clear ones in satellite data. Numerous satellite
and cloudy conditions to differ and for the physicalproducts require cloud mask information. By way ofreason behind the difference to be explicit (e.g.,
example, Sea Surface Temperature (SST) (Yasuda andtemperature, reflectance and emissivity of the top andShirakawa 1999), Clear Sky Radiance (CSR) (Uesawa
transmittance relating to clouds and the atmosphere).
2009) and Aerosol Detection (Okawara et al. 2003) are
Rather than encompassing retrieval of these five
derived in clear pixels. Conversely, Cloud Gridvariables, most of the tests in the CMP algorithm involve
Information (Tokuno 2002) is processed in cloudy pixels.
the use of brightness temperature or reflectance data with
There were no MSC cloud mask products before thesimilar tendencies. Cloud mask tests are based on thestart of Himawari-8’s operation; each satellite productcloud mask algorithm of the NoWCasting (NWC)
had its own cloud mask process. As cloud mask is also
Satellite Application Facility (SAF) (Meteo-France,
required for most Himawari-8 products, MSC decided toFig. A-2
2012) and the National Oceanic and Atmosphericdevelop CMP as a resource available for Himawari-8(a)BiasvaluesforT3.9T11.2opaque(b)BiasvaluesforT8.6T11.2fractional
Administration(NOAA)/National
Environmental
products in common. CMP is a part of the Fundamental
Satellite,
Data,
and
Information
Service(NESDIS)Cloud
Product(FCP),
which
contains
cloudphase,
type(c) Bias values for T11.2T12.3opaque (d) Bias values for ice phase (e) Bias values for water phase(Heidinger and Straka III, 2012).
and top height for Himawari-8/AHI pixels (Mouri et al.
Determination of these values is based on land type and T(11.2)
Radiative transfer calculation using NWP data for
2016a, 2016b). FCP has been produced since Himawari-8* Office of Observation Systems Operation, Observation Department, Japan Meteorological Agency** System Engineering Division, Data Processing Department, Meteorological Satellite Center(Received September 1, 2015, Accepted 20 November 2015)
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METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016

雲タイプ・相アルゴリズム記述書（二行）
Algorithm
Theoretical Basis for Himawari-8 Cloud Mask Product毛利 浩樹*、泉 敏治**、鈴江 寛史*、吉田 良*（二行）

IMAI Takahito* and YOSHIDA Ryo**（三行）

要旨
雲タイプ、相プロダクトは基本雲プロダクトを構成する１つである。基本雲プロダクトは雲
マスク、雲タイプ、雲の相、および雲頂高度からなる。気象庁ではひまわり８号を機に様々な２Abstract
次プロダクトが含んでいる雲マスク、雲タイプ、相、雲頂高度プロダクトを独立させ、基本雲プ
This paper describes the algorithm theoretical basis for the Himawari-8 Cloud Mask Product
ロダクトとして一つにまとめ上げた。雲タイプ、相プロダクトは不 f 透明、半透明、断片雲とい(CMP) developed by the Meteorological Satellite Center. CMP is part of the Fundamental Cloudった雲タイプと、水、氷、未定・混合といった雲の相を出力する。解析手法は過去に実績があり、Product, which incorporates cloud phase, type and top altitude from Himawari-8/AHI and has been
比較的理解しやすい NWCSAF の雲タイプアルゴリズムに主に基づいている。雲の相に関して、operational since 7 July 2015.
2015 年 7 月 20 日から 8 月 2 日において気象庁気象衛星センタープロダクトと MODIS（MYD06L2）
The CMP algorithm is based on the cloud mask technique of NWC-SAF for MSG/SEVIRI andプロダクト及び Calipso（CAL_LID_L2_01kmCLay-ValStage1-V3-30）との比較を実施したところ、
NOAA/NESDIS for GOES-R/ABI. Most cloud detection tests involve threshold methods based on
氷相では 80%以上の捕捉率となった。
radiative transfer calculation using NWP data as atmospheric profiles. The thresholds are modified
using offsets determined on the basis of comparison with the MODIS cloud mask product. Initial
results showed that the CMP hit ratio derived from such comparison was more than 85%. The CMP
algorithm was applied to SEVIRI data, with results showing hit ratios with the MODIS productwere around 85% for all seasons.
（三行）

1. Introduction（一行）
Himawari-8 is the new geostationary satellite of theJapan Meteorological Agency (JMA). It was launched on
7 October 2014 and began operation on 7 July 2015. The
satellite carries the Advanced Himawari Imager (AHI),
which is greatly improved over past imagers in terms of
its number of bands and its temporal/special resolution(Bessho et al. 2016). Using AHI data, JMA’s
Meteorological Satellite Center (MSC) developed theHimawari-8 Cloud Mask Product (CMP).
Cloud mask is used to discriminate cloudy pixels
from clear ones in satellite data. Numerous satellite
products require cloud mask information. By way of
example, Sea Surface Temperature (SST) (Yasuda andShirakawa 1999), Clear Sky Radiance (CSR) (Uesawa
2009) and Aerosol Detection (Okawara et al. 2003) are
derived in clear pixels. Conversely, Cloud Grid
Information (Tokuno 2002) is processed in cloudy pixels.
There were no MSC cloud mask products before thestart of Himawari-8’s operation; each satellite producthad its own cloud mask process. As cloud mask is also
required for most Himawari-8 products, MSC decided todevelop CMP as a resource available for Himawari-8
products in common. CMP is a part of the Fundamental
Cloud Product (FCP), which contains cloud phase, type
and top height for Himawari-8/AHI pixels (Mouri et al.
2016a, 2016b). FCP has been produced since Himawari-8* 気象衛星センターデータ処理部システム管理課

began operation.
This paper describes the algorithm theoretical basis for
CMP.
（一行）
2. Algorithm
2.1 Overview
Cloud detection in the CMP algorithm is based oncomparison of observation data and clear sky data
calculated from numerical weather prediction (NWP)
data. If observation data differ from clear sky data, thepixel is cloudy. To discriminate between cloudy and clear
pixels, it is desirable for observation variables in clear
and cloudy conditions to differ and for the physicalreason behind the difference to be explicit (e.g.,temperature, reflectance and emissivity of the top andtransmittance relating to clouds and the atmosphere).
Rather than encompassing retrieval of these fivevariables, most of the tests in the CMP algorithm involvethe use of brightness temperature or reflectance data withsimilar tendencies. Cloud mask tests are based on thecloud mask algorithm of the NoWCasting (NWC)
Satellite Application Facility (SAF) (Meteo-France,2012) and the National Oceanic and Atmospheric
Administration(NOAA)/National
Environmental
Satellite, Data, and Information Service (NESDIS)(Heidinger and Straka III, 2012).
Radiative transfer calculation using NWP data for

気象研究所気象衛星・観測システム研究部***
Officeof Observation Systems Operation, Observation Department, Japan Meteorological Agency- 31 ** System Engineering Division, Data Processing Department, Meteorological
Satellite Center(Received September 1, 2015, Accepted 20 November 2015)-1-－ 31 －