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（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

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* 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)
-1-

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

atmospheric profiles is essentially adopted for estimation（二行）
2.2 Dataof clear sky data. As the quality of results obtained fromsuch calculation are Algorithm
not always highenough foruse inTheoretical
Basisfor Himawari-8
Cloud Mask Product2.2.1 Input datacloud detection thresholds, offsets are introduced as（二行） The inputs for the CMP algorithm are Himawari
Standard Data (HSD), the results of radiative transfer
additional threshold terms.
IMAI Takahito* and YOSHIDA Ryo**
calculation, NWP data and surface condition data.
In addition to numerous cloud detection tests, the（三行）
AHI has 16 bands (B01 – B16) with different spatial
CMP algorithm also includes snow/sea ice detection tests,
resolutions as shown on the left of Table 1. HSD full-disk
aerosol detection tests and filters. The algorithm isdata consist of 22,000 x 22,000 pixels for B03, 11,000 x
outlined in Fig. 1.
Abstract
11,000pixelsfor B01, B02Cloudand B04,
5,500 x 5,500
First, a snow/sea
ice detection
performed.
If
This paper
describesstep
the isalgorithm
theoreticalbasis forthe Himawari-8
Maskand
Product
pixels fortheisotherbands.
The size of Cloudeach pixel forsnow or sea(CMP)
ice isdeveloped
detected, bythethepre-final
cloud mask
Meteorological
Satellite Center.
CMP
part ofthe Fundamental
which
incorporates
cloud phase,
altitudefrom
Himawari-8/AHI
and hastobeen
infraredbands(B05– B16) corresponds
2 x 2 pixels
based on theProduct,
results ofall thecloud detection
tests istype
set and top
since
July or2015.
for B01, B02 and B04 and 4 x 4 pixels for B03. The CMP
to clear. Foroperational
pixels where
no 7snow
sea ice is detected,
The
CMP algorithm
is based onIfthe
cloud
technique isof NWC-SAF
MSG/SEVIRI
and(5,500 x
applied to foreachinfrared pixela series of clouddetection
tests is performed.
cloud
is maskalgorithm
NOAA/NESDISfor
GOES-R/ABI.
Most
cloud
detection
tests
involvethresholdmethods
based
on of data
5,500). Cloud mask operation involves the use
detected in any of these, the pre-final cloud mask is set toradiative
transfer
calculation
using
NWP
data
as
atmosphericprofiles.
The
thresholds
aremodifiedfrom the current situation as well as data from 10 and 60
cloudy. Pixels determined as cloudy in top temperature orusing offsets determined on the basis of comparison with the MODIS cloud mask product. Initial
minutes ago to enable calculation of temporal variations
reflectance tests can be modified as clear based on the
results showed that the CMP hit ratio derived from such comparison was more than 85%. The CMP
in variables for the cloud mask test. AHI data for CMP
outcomes of atmospheric absorption tests. A filter isalgorithm was applied to SEVIRI data, with results showing hit ratios with the MODIS productare summarized on the right of Table 1. The CMP
applied for were
the pre-final
cloud
mask,
and
cloudypixelsaround 85% for all seasons.
are divided into cloudy and clear-cloudy mixed pixels in（三行）
algorithm requires brightness temperature Tλ, reflectancethe filter process. The CMP algorithm includes aerosol
Rλ org and radiation intensity data Iλ from HSD, where λdetection based on an ash detection algorithm (Pavolonis
denotes center wavelength of the band.
began operation.
1.
et Introduction
al. 2009). The final cloud mask is generated by
Radiative transfer calculation-based data required for
（一行）
This
paper describes the algorithm theoretical basis for
combining the cloud mask results with those of aerosol
CMP are listed in Table 2.
Himawari-8 is the new geostationary satellite of the
CMP.
detection.
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 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, typeFig.1 CMP algorithm outline(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)-- 21 --

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

Table 1 Specifications of AHI
bands and data required for CMP（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）

Table 2 Data based on radiative transfer calculation
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 CloudsrfMask Product (CMP).
Reflectance Rλ for the top of the atmosphere (TOA)
Cloud mask is used to discriminate cloudy pixels
with clear sky conditions is used as reflective band (B03,from clear ones in satellite data. Numerous satellite
B04 and B05) data. Reflectance is estimated using
products require cloud mask information. By way of
look-up table (LUT) values calculated from RSTAR
example, Sea Surface Temperature (SST) (Yasuda and(Nakajima and Tanaka 1986) with NWP data using the
Shirakawa 1999), Clear Sky Radiance (CSR) (Uesawa
latest forecast values from JMA’s Global Spectral Model2009) and Aerosol Detection (Okawara et al. 2003) are(GSM) and the surface bidirectional reflectancederived in clear pixels. Conversely, Cloud Grid
distribution function (BRDF). Sea surface BRDF
Information (Tokuno 2002) is processed in cloudy pixels.
RλCoxMunk is calculated following Cox and Munk (1954)
There were no MSC cloud mask products before theusing RSTAR with GSM-predicted surface horizontal
start of Himawari-8’s operation; each satellite productwind. Land surface BRDF is calculated using the
had its own cloud mask process. As cloud mask is also
Roujean model (Roujean et al. 1992) with the Moderaterequired for most Himawari-8 products, MSC decided to
Resolution Imaging Spectroradiometer (MODIS) BRDF
develop CMP as a resource available for Himawari-8
product (Strahler et al. 1999). In snow surface conditions,products in common. CMP is a part of the Fundamental
an LUT calculated using the ARTMASS radiative
Cloud Product (FCP), which contains cloud phase, typetransfer model (Aoki et al. 1999) is used.
and top height for Himawari-8/AHI pixels (Mouri et al.
2016a, 2016b). FCP has been produced since Himawari-8

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, theBrightness temperature Tλsrf and radiance Iλsrf at the
pixel is cloudy. To discriminate between cloudy and clear
TOA are used as infrared band (B07, B10, B11, B13, B14
pixels, it is desirable for observation variables in clear
and B15) data. In addition to clear sky condition data,and cloudy conditions to differ and for the physicalradiance calculated for conditions with black body (BB)reason behind the difference
to be explicit (e.g.,cloud on the tropopause Iλtrp and for roughly estimatedtemperature, reflectanceandemissivityof the top andcloud top radiance Iλopq are used for aerosol detection.
transmittance relating
to clouds and the atmosphere).
Estimation of Iλopq is based on Pavolonis et al. (2009).
Rather than encompassing retrieval of these five
For radiative transfer calculation, the fast radiative
variables, most of the tests in the CMP algorithm involve
transfer model known as RTTOV (Radiative Transfer for
the use of brightness temperature or reflectance data with
the Television and Infrared Observation Satellite
similar tendencies. Cloud mask tests are based on the(TIROS) Operational Vertical Sounder; Eyre 1991) withcloud mask algorithm of the NoWCasting (NWC)
NWP data from the GSM is used.
Satellite Application Facility (SAF) (Meteo-France,
Merged satellite and in situ data for the global daily2012) and the National Oceanic and Atmosphericsea surface temperature (MGDSST; Kurihara et al. 2006)
Administration(NOAA)/National
Environmental
are used for sea-surface cloud detection tests. JMA’s
Satellite, Data, and Information Service (NESDIS)
internal snow and sea ice cover products are used for(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)-- 31 --

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

snow/sea surface classification.
（二行）
I λ − I λsrf
ε λstropo = trp
In addition, the following geographical data for
(1)
srf
I
I
−
λ
λ
Theoreticalfor Himawari-8 Cloud Mask Productindividual pixels areAlgorithm
used: sun zenith
angle θBasis
sunZ, sunazimuth angle θsunA, satellite zenith angle θsatZ, satellite（二行） ελsopaque: effective emissivity of single-layer cloud on
the roughly estimated cloud top level in a single-layer
azimuth angle θsatA, sunlight scattering angle at the
IMAI Takahito* and YOSHIDA Ryo**situation. The definition is as follows:
surface or cloud θscat and altitude z. Angles are given in（三行）
degrees unless otherwise stated.
I λ − I λsrf
ε λsopaque = opq
(2)
srf
I
−
I
λ
λ
2.2.2 Output data
Abstract
CMP provides
information
on whether
pixels aretheoreticalclear,
βλstropo, βλsopaque: beta ratioof Mask
ελstropo Productand ελsopaque. The
Thispaper describes
the algorithm
basis
for theHimawari-8
Cloud
definitionof theas follows: Cloud
cloudy or (CMP)
clear-cloudy
mixedwith quality
developedby thealong
Meteorological
Satellite Center.
CMP
is betapart ratioof theis Fundamental
Product,
which
incorporates
cloudphase,
typeand
top
altitudefrom
Himawari-8/AHI
information (high/low) and aerosol information for eachln(1 − ε λx1 ) and has beenxsince algorithm
7 July 2015.
,
β λ1 λ2 =(3)pixel (Tableoperational
3). As the CMP
is applied to each
x
ln(
1
)
ε
−
The
CMP
algorithm
is
based
on
the
cloud
mask
techniqueof
NWC-SAFfor
MSG/SEVIRI
andλ2
infrared pixel, CMP output consists of 5,500 x 5,500
NOAA/NESDIS
for GOES-R/ABI.
Most cloud
pixels for eachfull-disk scene.
The specificationsof detection tests involve threshold methods based onradiative
transfer
calculation
using
NWP
data
as atmosphericprofiles.
Theorthresholds
where x denotes
stroposopaque. are modified
CMP are given in Table 4.
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
Operators
2.2.3 Operators and processed data
algorithm was applied to SEVIRI data, with results showing hit ratios with the MODIS productThis subsection
summarizes
operators
andprocessed
STAT8(v): statistical value of the dataset on the eight
were around 85% for all seasons.
data used in CMP.
pixels (infrared size) surrounding the cloud masking（三行）
pixel, where STAT is replaced by SD for standard
deviation, MAX for the maximum value, MIN for theProcessed data
began operation.
1. Introduction
minimum value and MEAN for the mean value. Only
Rλmod: Rλorg with sun zenith angle correction (Li and（一行）
This paper describes the algorithm theoretical basis fordata on pixels with the same surface condition (sea/land)
Shibata 2006)
Himawari-8
is the new geostationary satellite of theCMP.
as that of the cloud masking pixels can contribute toελstropo: effective emissivity of single-layer cloud on
Japan Meteorological Agency (JMA). It was launched on（一行）
statistical values.
the tropopause (EST), which essentially has the same
7 October 2014 and began operation on 7 July 2015. The
2. Algorithm
tendency as brightness temperature. The spatial
CORR(v1,v2): coefficient of correlation between thesatellite carries the Advanced Himawari Imager (AHI),
continuity of the EST field is superior to that of thedataset of variable v1 and variable v2 in a 5 x 5-pixel
which is greatly improved over past imagers in terms of
2.1 Overview
brightness temperature field because the effects of thesegment centered on the cloud masking pixel
Cloud detection in the CMP algorithm is based on
its number of bands and its temporal/special resolution
surface and the atmosphere are reduced by combining
STATCMA(v): statistical value of the dataset oncomparison of observation data and clear sky data
(Bessho et al. 2016). Using AHI data, JMA’s
observation data and estimated data. The definition is as
high-resolution pixels in a cloud masking pixel, where
calculated from numerical weather prediction (NWP)
Meteorological Satellite Center (MSC) developed thefollows:
STAT is replaced by SD for standard deviation, MAX for
data. If observation data differ from clear sky data, theHimawari-8 Cloud Mask Product (CMP).
the maximum value, MIN for the minimum value andpixel is cloudy. To discriminate between cloudy and clear
Cloud mask is used to discriminate cloudy pixels
Table
3
SummaryofCMP
output.
“HQ”
and
“LQ”
denoteinformationand low).
“Mixed”
pixels,quality
it is desirablefor(high
observation
variablesin clearfrom clear ones in satellite data. Numerous satellite
clear-cloudy
mixed. The numbers
arecloudy
assignedconditions
for each pixelin the output.
andto differ
and for the physicalproducts represents
require cloudmask information.
By wayinofthe tablereason 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 product
Table 4 CMP specifications
cloud 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)-- 41 --

－ 4 －

METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016segment containing 5 snow pixels; 3: cold surface pixels:（二行）
those with a low surface temperature (Tsrf < 263 K or Tsrf
MEAN for the mean value. 4 x 4 original B03 data and 2x 2 original B01, B02,
B04 data are
included in thecloud< 268 K in snow
neighbor
and a low altitude (z <Algorithm
Theoretical
Basisfor Himawari-8
Cloud
Maskpixels)
Product
masking pixel. Due to its frequent appearance in this（二行）
1,500 m); and 4: mountain pixels: those centered in 3 x
3-pixel segments with a high standard deviation of
paper, MEANCMA(Rλorg) is expressed simply as Rλ.
IMAI Takahito* and YOSHIDA Ryo**
altitude (SD8((zcenter-z) x dT / dz) > 1.0, where zcenter
ABS(v): absolute value of the variable v（三行）
denotes the altitude of the center pixel).
NWC(v): value of the variable v on the neighboring
warmest center (NWC), which is the pixel with thehighest T10.4 value and the same surface condition in a 21
2.4 Cloud mask tests
Abstractx 21-pixel segmenton the cloudmasking pixel.
This centeredpaper describes
the algorithm
theoretical basis for the Himawari-8 Cloud Mask Product
The surface (CMP)
conditionis classified
as eithersea or landSatellite
in
2.4.1 Snow/sea
developedby theMeteorologicalCenter.
CMP is ice
partdetectionof the Fundamental Cloud
Product,
which
incorporates
cloudphase,
typeand
top
altitudefrom
Himawari-8/AHI
beensky over
NWC calculation. The optical depth of the NWC is the
Discrimination between cloud andandhasclear
operational
since 7The
JulyNWC2015.is expected to bethinnest amongits neighbors.
snow/sea ice with a single test is challenging because
The
CMP
algorithm
is
based
on
the
cloud
mask
techniqueof NWC-SAF
MSG/SEVIRI
and for bothclear or to have thinner cloud than the cloud masking
mostobservation
variablesforhave
similar values
NOAA/NESDISfor
GOES-R/ABI.
Most
cloud
detection
tests
involvethresholdmethods
basedonpixel.
conditions. Accordingly, a number of tests involving
top
radiative
transfer
calculation
using
NWP
data
as
atmosphericprofiles.
The
thresholds
aremodifiedreflectance, top temperature and cloud transmittance are
LRC(v): value of the variable v on the local radiative
using offsets determined on the basis of comparison with the MODIS cloud mask product. Initial
applied simultaneously. As top reflectance tests are
center (LRC), which is the nearest local maximum of theresults showed that the CMP hit ratio derived from such comparison was more than 85%. The CMP
stropoessential, snow/sea ice detection is performed for pixels
field from the cloud masking pixel. If the cloud
ε11.2
algorithm was applied to SEVIRI data, with results showing hit ratios with the MODIS productwith a sun zenith angle of less than 85.0 degrees. If all ofmasking pixeliscloudy,
the
LRC
is
the
highestorwere around 85% for all seasons.
the following conditions are satisfied, the pixel is marked
thickest cloud pixel in the cloud cluster to which the（三行）
as clear sky over a snow surface:cloud masking pixel belongs. If the LRC is cloudy, thecloud masking pixel is likely to be cloudy insrf(4)
began operation. R1.6 < R1.6
1.
Introduction
considerationof spatial continuity. In contrast, if the LRC（一行）
This paper describes the algorithm theoretical basis foris clear, the cloud masking pixel is also likely to be clear.
R −R
Himawari-8 is the new geostationary satellite of the
CMP.
NDWI = 0.64 1.6 > 0.3 + 0.15 ⋅{cos(θ scat ) + 1}2 (5)∆(v):
temporaldifferenceofvariablev.
− v onR0.64 + R1.6 （一行）Japant Meteorological Agency (JMA). It wasvlaunchedt0
t 0 −t7 October 2014 and began operation on 7 July 2015. The
2. Algorithmmax(v1, v2,…,vn): maximum value of v1, v2, ..., vn
T3.9 −T 10.4
satellite carries the Advanced Himawari Imager (AHI),< 10.0(6)which is greatly improved over past imagers in terms of
2.1 Overview cos(θ sunZ )2.3 Scene classification
Cloud detection in the CMP algorithm is based on
its number of bands and its temporal/special resolution
In some cloud mask tests, thresholds and equations
srf
5.0 <T 10.4< data
286.15comparisonT10ofand clear sky (7)data(Bessho et al. 2016). Using AHI data, JMA’s.4 −observation
depending on geometric and surface conditions arecalculated from numerical weather prediction (NWP)
Meteorological Satellite Center (MSC) developed therequired.
T 10.4data−T 12.differ
data. If observation
theHimawari-8 Cloud Mask Product (CMP).
4 < 2.0from clear sky data,(8)Based on geometric conditions, pixels are classified
pixel is cloudy. To discriminate between cloudy and clear
Cloud mask is used to discriminate cloudy pixels
as day (θsunZ < 85), night (θsunZ > 93), twilight (85 ≤ θsunZ
0.2 +observation
0.45 ⋅ {cos(θ scat
) + 0.55}2in clear
R0.64 > Rit0srf.64is or
R0.64 > forpixels,desirablevariablesfrom clear ones in satellite data. Numerous satellite(9)≤ 93) or sunglint (θsunZ < 75 and 0.1 < R0.64CoxMunk).
and cloudy conditions to differ and for the physicalproducts require cloud mask information. By way of
Based on surface conditions, pixels are first classified
reason behind the difference to be explicit (e.g.,
example, Sea Surface Temperature (SST) (Yasuda andas sea, land, snow, sand or vegetation. Snow pixels are
R0.86 >and
0.2emissivity of the top(10)temperature, reflectanceandShirakawa 1999), Clear Sky Radiance (CSR) (Uesawa
those in which snow or sea ice has been detected in thetransmittance relating to clouds and the atmosphere).
2009) and Aerosol Detection (Okawara et al. 2003) arelast four days. For the polar night area, JMA’s snow andHere, NDWI denotes Normalized Differential Water
Rather than encompassing retrieval of these five
derived in clear pixels. Conversely, Cloud Gridsea ice cover products are used for snow classification.
Index.
variables, most of the tests in the CMP algorithm involve
Information (Tokuno 2002) is processed in cloudy
pixels.
For detection of clear sky over sea ice, the following
Based on Black Sky Albedo (BSA) RλBSA estimatedthe use of brightness temperature or reflectance data with
There were no MSC cloud mask products before theusing the MODIS BRDF product (Strahler et al. 1999),
conditions are used:similar tendencies. Cloud mask tests are based on thestart of Himawari-8’s
operation; each satelliteBSA
product
BSA
land (0.1 ≤ R0.64≤ 0.3), sand (0.3 ≤ R0.64 ) and
anal
(11)
cloud mask SST
algorithm
had its own cloud
mask
process.
As
cloud
mask
is
also
< of
277the.15NoWCasting (NWC)
vegetation (R0.64BSA < 0.1) are distinguished. Four types
Satellite Application Facility (SAF) (Meteo-France,
required for most Himawari-8 products, MSC decided toof surfaces are then identified: 1: Coast pixels: those insrf(12)2012) and the R
Nationaland Atmosphericdevelop CMP as a resource available for Himawari-8
1.6 < R1Oceanic.6
sea areas near land/snow/sand/vegetation pixels or
Administration(NOAA)/National
Environmental
products in common. CMP is a part of the Fundamental
land/snow/sand/vegetation pixels near sea pixels; 2: snow
> 0.and
3 + 0.15⋅{cos(θ scat )Service
+ 1}2
NDWI
(13)
Satellite,
Data,
Information(NESDIS)
Cloud Product (FCP), which contains cloud phase, typeneighbor pixels: those centered in a 16 x 16-pixel(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)-- 51 --

－ 5 －

METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016of cloud to be greater than those of land and sea surfaces.
(14) （二行）
As determination of reflectance requires a certain level ofsunlight, thesetests Mask
are largely
performed for day pixels.
Algorithm Theoretical Basis for Himawari-8
Cloud
Product
T 10.4−T 12.4< 2.0(15) （二行）
To reduce the effect of Rayleigh scattering, a band with alonger wavelength is desirable. If any of the following
R0.64 > R0srf.64
IMAI Takahito(16) * and YOSHIDA Ryo**
conditions is satisfied for sea pixels other than those with
（三行）
sunglint, the pixel is marked as cloudy:
R0.86 > 0.2(17)
R0.86 > R0thr.86 + dR0coast(22).86 + offset
Here, SSTanal is the MGDSST (Kurihara et al. 2006). Abstract
thr
coast
This paper describes the algorithm theoretical basis for R
the Himawari-8
Cloud
Mask Product+ offset(23)
1.6 > R1.6 + dR1.6
2.4.2 Top temperature
tests by the Meteorological Satellite Center. CMP(CMP) developed
is part of the Fundamental Cloud
Product,
which
incorporates
cloudphase,
type and top altitudecoastfrom Himawari-8/AHI and has been
If the observed
top
temperature
is lowerthan
the clear
Here, dRλ
= 0.03 is a correction term added for
operational
7 Julyto
2015.
sky value, thepixel is sinceconsidered
be cloudy because
TheisCMP
algorithm
is based onwhere
the cloudtechniquepixels.of NWC-SAF for MSG/SEVIRI andthe target of CMP
cloudin the troposphere,
the maskcoast
NOAA/NESDISfor
GOES-R/ABI.
Most
cloud
detection
basedonFortests
landinvolve
pixels,thresholdthe cloudmethods
detection
condition
is asupper atmosphere is essentially colder than the lower
radiative
transfer
calculation
using
NWP
data
as
atmosphericprofiles.
The
thresholds
aremodifiedfollows:
atmosphere. If the following condition is satisfied forusing offsets determined on the basis of comparison with the MODIS cloud mask product. Initialland, sand and vegetation pixels, the pixel is marked as
srf
coast
fwd
+ dRmore
dR85%.
offsetresults showed that the CMP hit ratio derived from suchRcomparisonthan
CMP (24)
0.64 > R0.64 was
0.64 +
0.64 +The
cloudy:algorithm was applied to SEVIRI data, with results showing hit
ratios
with
the
MODIS
product
Here, dR0.64coast = 0.03 is a correction term added forcool
allseasons.
T10.4 <were
T10srf.4 around
offset+ dT10elv.485%+ dTfor+(18)
10.4
coast pixels. dR0.64fwd represents correction to offset the（三行）
difficulty of R0.64srf estimation when the sunlight
Here, dT10.4elv represents correction for the altitudedifference between the NWP model (zNWP) and the actual
scattering angle (θscat) at the surface is small.
began operation.
1.
Introduction
value.
For snow neighbor pixels, dT10.4cool is used to2(25)
θ scat ) theoretical
dR0fwd=04 + 0.29the⋅ [cos(+ 0.68] basis
（一行）
This
paper0.describes
algorithm
for.64
offset the difficulty of expression for extremely cool land
Himawari-8 is the new geostationary satellite of theCMP.
at night in the NWP model. The expressions are as
For sunglint pixels where sea surface reflectance isJapan Meteorological Agency (JMA). It was launched on（一行）
follows:
greater than that of cloud and gross testing is not
7 October 2014 and began operation on 7 July 2015. The
2. Algorithm
effective, a reflectance ratio is used as follows:dT
satellite carrieselvthe Advanced Himawari
Imager (AHI),=
dT10.4 ( z − z NWP ) ⋅(19)
R0.64 > 0.06
(26)
which is greatly
improved over past
2.1 Overview
dz imagers in terms of
Cloud detection in the CMP algorithm is based on
its number of bands and its temporal/special resolution
srf
1 T3.9 − T10.4data and clear sky data

5
.
0
;
255
−
K
≤
T
comparison(Bessho
et al. 2016). Using AHI data,
JMA’s
10.4
0 <of observation< R0.64dT10cool(20)(27).4 = 
srf
srf
0.4 ⋅ (255 Center; T10.4 < developed
255 K− T10.4 ) (MSC)
calculated fromnumerical
prediction (NWP)
Meteorologicalthe0.15
cos(θ sunZweather)
− 5.0 −Satellite
data. If observation data differ from clear sky data, theHimawari-8 Cloud Mask Product (CMP).
Here, the temperature lapse rate of the international
pixel is cloudy. ToTdiscriminate
clear
Cloud mask is used to discriminate cloudy pixels
3.9 < 320.0 between cloudy and(28)dTpixels, it is desirable for observation variables in clear
from clear ones in satellite data. Numerous satellite.
standard atmosphere (-6.49 [K/km]) is used for
In addition, a gross test of 3.9 μm reflectance is also
and cloudy conditions to differ and for the physicalproducts require cloud mask information. By dz
way ofused in water cloud detection for day and twilight pixels.
reason behind the difference to be explicit (e.g.,
example, Sea Surface Temperature (SST) (Yasuda andFor sea pixels, cloud detection condition is as follows:
If the following conditions are satisfied, the pixel istemperature, reflectance and emissivity of the top andShirakawa 1999), Clear Sky Radiance (CSR) (Uesawa
marked as cloudy:
anal
(21)
SST < SST
offsettransmittance relating to clouds and the atmosphere).
2009) and Aerosol
Detection+ (Okawara
et al. 2003)
are
_ day
T3.9 − encompassing
T10.4 > T3thr+ offset of these (29)Rather thanfivederived in clear pixels. Conversely, Cloud Grid
.9−10.4 retrieval
Here, SST is derived from an LUT using T10.4, T10.4- T8.6,
variables, most of the tests in the CMP algorithm involve
Information (Tokuno 2002) is processed in cloudy pixels.
T10.4-T11.2, T10.4, -T12.4 and the satellite zenith angle.
T10.4temperature> 240.0 or reflectance data(30)the use of brightness
with
There were no MSC cloud mask products before theUpper thick cloud can be easily detected using thesesimilar tendencies. Cloud mask tests are based on thestart of Himawari-8’s operation; each satellite productFor sand pixels, the following condition is added to avoid
tests, but thin cloud detection remains challenging due tocloud mask algorithm of the NoWCasting (NWC)
had its own cloud mask process. As cloud mask is also
erroneous detection:
relatively high brightness temperatures caused by theSatellite Application Facility (SAF) (Meteo-France,
required for most Himawari-8 products, MSC decided tocontribution of radiance from below the cloud.

1 and Atmospheric2012) and the National Oceanic
develop CMP as a resource available for Himawari-8
T8.6 − T10.4 > −4.5 − 1.5 ⋅ 
− 1
(31)
Administration(NOAA)/National
Environmental
products in common. CMP is a part of the Fundamental
cos(
)
θ
satZ


2.4.3 Top reflectance tests
Satellite, Data, and Information Service (NESDIS)
Cloud Product (FCP), which contains cloud phase, typewhere T3.9-10.4thr_day is the clear sky brightness temperature
These tests leverage the tendency for the reflectance(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

T10srf.4 − 5.0 <T 10.4< 277.15* 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)-- 61 --

－ 6 －

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

as follows:
difference for the daytime. The contribution from earth（二行）
emissions and reflected sunlight is combined in theT12.4 − T3.9 > T12srf.4 − T3srf.9 + offset
following expressions:
Algorithm Theoretical Basis for Himawari-8
Cloud Mask Product（二行）

thr _ day
srf
T3=
T3srf
.9 −10.4.9 − T10.4+ 0.7 ⋅ R

CoxMunk
3.9

srf
3.9

T= T

srf
10.4

−T

T10.4 > 240.0

(40)

srf
T8.6 − T3.9 > T8srf.6 − T3.9 + offset(41)(32) * and YOSHIDA Ryo**⋅ cos(θ sunZ ) + 7.0 IMAI Takahito
The fourth is for sand pixels at night. The conditions（三行）

are as follows:(over sea)
thr _ day
3.9 −10.4(39)+ 0.4 ⋅ R

BSA
0.64

⋅ cos(θ sunZ )+ 2.0 + 36.0 ⋅ cos(θ sunZ ) ⋅ [cos(θ scat ) − 0.41]

2(33)Abstract

srf
srf
This paper describes the algorithm theoretical basis for theT Himawari-8
Mask
Product− T .9 > TCloud(42).4 − T3.9(over land) by the Meteorological Satellite Center. CMP 10(CMP) developed
is.4 part 3ofthe10Fundamental
Cloud
Product,
whichthickincorporates
cloud phase,type and top altitude from Himawari-8/AHI and has been
These tests
facilitate
cloud detection
for day
T10.4 > 240.0(43)
operational
since 7are
Julyassociated2015.
pixels because
thicker clouds
with higher
The CMP algorithm is based on the cloud mask technique of NWC-SAF for MSG/SEVIRI andreflectance of sunlight.
NOAA/NESDIS for GOES-R/ABI. Most cloud detection
tests involvethreshold2.4.5 Cloudabsorption
tests methods based onradiative
transfer
calculation
using
NWP
data
as
atmosphericprofiles.
The
thresholds
modifiedCirrus pixels are detected areusing
the radiation
2.4.4 Top emissivity tests
using offsets determined on the basis of comparison with the MODIS cloud mask product. Initialabsorption difference between different bands caused by
These tests leverage emissivity differences between
results showed that the CMP hit ratio derived from such comparison was more than 85%. The CMP
clouds. As it is assumed in these tests that some radiation
cloud and surfaces. To extract emissivity information
algorithm was applied to SEVIRI data, with results showing hit ratios with the MODIS productpasses through the target cloud, thick cloud cannot befrom satellite
data,brightness
temperature
differenceswere around 85% for all seasons.
detected. Three brightness temperature difference tests
between different bands are calculated. As upper cloud（三行）
are applied.
and daytime lower cloud can be easily detected using top
The first is for all pixels except the coast type. If thetemperature and reflectance tests, the targets of these tests
began operation.
1. Introduction
followingconditions are satisfied, the pixel is marked asarelower cloud at night and twilight pixels. For detection
（一行）
This
paper
describes the algorithm theoretical basis forcloudy:of lower cloud, it is desirable to use atmospheric window
Himawari-8 is the new geostationary satellite of the
CMP.
bands that have a variety of interaction tendency with
(T10.4 − T12.4 ) > （一行）T10thr.4−12.4 + offset(44)
Japan Meteorological Agency (JMA). It was launched on
cloud. Four brightness temperature difference tests are
7 October 2014 and began operation on 7 July 2015. The
2. Algorithmused.
SD8 (T10.4 ) > 0.3(45)satellite carries the Advanced Himawari Imager (AHI),
The first is for vegetation pixels at night and twilight.
which is greatly improved over past imagers in terms of
2.1 Overview
If the following conditions are satisfied, the pixel isT10.in310CMP.0 algorithm is based(46)onCloud detection
its number of bands and its temporal/special resolution
4 <themarked as cloudy:
comparison of observation data and clear sky data
(Bessho et al. 2016). Using AHI data, JMA’s
0.3 (MSC) developed theT12srf.4 < T10srf.weather(47)
calculated from numericalprediction (NWP)
Meteorological
Satellite
Center4+ offset
T10.4 − T8.6 > 3.7 +(34)
θ satZ(CMP).
)
data. If observation data differ from clear sky data, theHimawari-8 Cloud Maskcos(
ProductHere,pixel is cloudy. To discriminate between cloudy and clear
Cloud mask is used to discriminate cloudy pixels
T10.4 − 260.0
 srf
1− T srf ) ⋅ for;270.0variables
K ≤ T10srf.4 in clear
pixels,it is(T desirable
observationfrom clear ones in satellite
Numerous satellite> 1.data.
5
T10thr.4−12.4 =  10.4 12.4 T10srf.4 − 260.0
(35)
(48)
srf
θ satZmask)
and cloudy
to differ ;andforthephysicalproducts requirecos(
cloud
information. By way of conditions
<
0
T
270
.
0
K
10.4

reason behind the difference to be explicit (e.g.,
example, Sea Surface Temperature (SST) (Yasuda and
The second is for night pixels. The conditions are as
The second is for all pixels. The conditions are astemperature, reflectance and emissivity of the top andShirakawa 1999), Clear Sky Radiance (CSR) (Uesawa
follows:
follows:
transmittance relating to clouds and the atmosphere).
2009) and Aerosol Detection (Okawara et al. 2003) are
srf
srf
srf
cool
(T8.6 − Tthan
−
>
−
+
T
T
T
offsetT8srf
offset(36)
Ratherretrievalthese (49)fivederivedT10inclear
pixels.
Conversely,
Cloud
Grid
.4
3.9
10.4
3.9
10.4 ) >encompassing.6 − T10.4 + dT
8.6−10.4 + ofvariables, most of the tests in the CMP algorithm involve
Information (Tokuno 2002) is processed in cloudy pixels.
Here, dT8.6-10.4cool = 0.4 is a correction term for snow
>
240
.
0
(37)
the use of brightness temperature or reflectance data with
There were noT10
MSC
cloud
mask
products
beforethe.4neighbor pixels with T10.4srf < 250 K.
similar tendencies. Cloud mask tests are based on thestart of Himawari-8’s operation; each satellite productFor sand, land or vegetation pixels, following condition is
The third is for night pixels. The conditions are as
cloud mask algorithm of the NoWCasting (NWC)
had its own cloud mask process. As cloud mask is also
added to avoid erroneous detection:
follows:
Satellite Application Facility (SAF) (Meteo-France,
required for most Himawari-8 products, MSC decided to1
T3.9 − TT3srf− T10srf.4 +Oceanic
dT3cool+ offset
(50)
2012)
and
the
Atmosphericdevelop CMP as a resource availablefor Himawari-8
10.4 >.9National.9−10.4 andT8.6 − T10.4 > −4.5 − 1.5 ⋅ 
− 1
(38)
Administration(NOAA)/National
Environmental
products in common. CMP is cos(a partof )the Fundamental
θ satZ
Here, dT3.9-10.4cool is a correction term for snow neighborSatellite, Data, and Information Service (NESDIS)
Cloud Product (FCP), which contains cloud phase, type
pixels.
The third is for sea pixels at night. The conditions are(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)-- 71 --

－ 7 －

METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.61, MARCH, 2016srf
srf
max(0,−(T3srf;250 ≤ T10srf.4 ≤ 255 K.9 − T10.4 ) − 0.5 ⋅ T10.4 + 129.0)
dT3cool.9 −10.4 = 
srf
srf
srf
; T10srf.4 < 250 K
 − (T3.9 − T10.4 ) − 0.15 ⋅ T10.4 + 41.5

(51) （二行）

SD8 (T10.4 ) > 0.6 + offset

Algorithm Theoretical Basis for Himawari-8(T )Mask− T Product
MAXCloud

(56)

8
10.4
10.4
（二行）
> noise(T10.4 )(57)
2.4.6 Atmospheric absorption tests
2
Clear pixels are detected using the radiation
IMAI Takahito* and YOSHIDA Ryo**
Here, noise(T10.4) represents sensor noise estimated fromabsorption difference between different bands caused by（三行）
atmospheric conditions. Two brightness temperatureinterpolation of Table 5, which gives brightness
difference tests are applied.
temperature calculated from the signal noise ratio (SNR)
The first is for detection in relation to the inversionof radiance. If T10.4srf > 240, the following test is added:
Abstract
layer using bands
withdifferent
sensitive
altitudes. theoretical
If theThis
paper
describes
the algorithm
basis for the Himawari-8
Mask
Product; night/ twilight0.2Cloud)SD8 (TCMP
T−>
following conditions
are satisfied,theMeteorologicalpixel is markedSatelliteas(CMP) developedby theCenter.
is
part
of
the
Fundamental
Cloud (58)

10.4
3.9
0
.
4
;
day

Product,
which
incorporates
cloudphase,
typeand
top
altitudefrom
Himawari-8/AHI
and
has
been
clear:
operational since 7 July 2015.
The second is a reflectance test for daytime sea pixels.
T7.3 CMP− T10.4algorithm> 0.5 is based on the cloud(52) mask technique of NWC-SAF for MSG/SEVIRI and
The
The conditions are as follows:
NOAA/NESDIS
for GOES-R/ABI. Most cloud detection tests involve threshold methods
based on
srf
srf
0
R
offsetSD8 ( R0.profiles.
>
+
⋅
+
)
0
.
008
0
.
03
< 250.calculation
T10.4transfer(59)
radiative
using NWP(53)data as atmosphericThe
thresholds
aremodified86
0.86
using offsets determined on the basis of comparison with the MODIS cloud mask product. Initial
This test is performed for pixels marked as cloudy based
R0.86 − MIN
1 The CMP
results showed that the CMP hit ratio derived from such comparison
was
more
8 (R
0.86 )than 85%.
>(60)
on top temperature or reflectance tests.
algorithm was applied to SEVIRI data, with results showing hit ratioswith the SNR
MODIS( I 0.86product)2
The second
involves
detection
for
very
thick
water
were around 85% for all seasons.
Here, SNR(I0.86) = 420.
vapor. As most water vapor is concentrated in the lowest（三行）
The third is a brightness temperature test for land
layer of the troposphere, observation of significant water
pixels other than the mountain type. The conditions are as
vapor indicates that the pixel is clear sky. If the following
began operation.
1. Introduction
follows:
conditions
are satisfied, the pixel is marked as cloudy:（一行）This paper describes the algorithm theoretical basis for
srf
srf
1.0 ; night / twilight 
T
−
T
>
T
−
T
+
offset(54)
Himawari-8
newgeostationary
satellite of the
CMP.
12.4
10is
.4 the 12.4
10.4
SD8 (T10.4 ) >  + offset (61)Japan Meteorological Agency (JMA). It was launched on
; day
（一行）

2.0
− T10.4 operation> 1.5 on 7 July 2015.(55)
7 October 2014Tand
The
2. Algorithm
12.4 began
1.0 ; night / twilightsatellite carries the Advanced Himawari Imager (AHI),
SD8 (T10.4 − T3.9 ) > (62)which is greatly improved over past imagers in terms of
2.1 Overview
; day
2.0
2.4.7 Spatial uniformity tests
Cloud detection in the CMP algorithm is based on
its number of bands and its temporal/special resolution
The fourth is a reflectance test for daytime land pixels
In addition to scene-by-scene and pixel-by-pixel tests,
comparison of observation data and clear sky data
(Bessho et al. 2016). Using AHI data, JMA’s
other than the mountain type. The conditions are as
spatial and temporal consistency tests are also performed.
calculated from numerical weather prediction (NWP)
Meteorological Satellite Center (MSC) developed thefollows:
Six tests involving spatial extent are used. Based on
data. If observation data differ from clear sky data, theHimawari-8 Cloud Mask Product (CMP).
the tendency by which cloud top and the boundary DT  between cloudy and clear
pixel is cloudy. To discriminate
Cloud mask is used to discriminate cloudy pixels
DR0.86 > f  10.4  +offset(63)
between cloudy and clear areas is rougher than that of the
pixels, it is desirable DR
for 0observation
variables in clear
from clear ones in satellite data. Numerous satellite.86 
ground or sea surface, cloud is detected using spatial
and cloudy conditions to differ and for the physicalproducts require cloud mask information. By way ofHere,variations of brightness temperature or reflectance.
reason behind the difference to be explicit (e.g.,
example, Sea Surface Temperature (SST) (Yasuda and
The first is a brightness temperature test for sea pixels.
Dv = max(
MAX 8 (v)and− v,emissivity
v − MIN 8 (ofv))the top(64)temperature,reflectanceandShirakawa 1999), Clear Sky Radiance (CSR) (Uesawa
If the following conditions are satisfied, the pixel istransmittance relating to clouds and the atmosphere).
2009) and Aerosol Detection (Okawara et al. 2003) are
and f(v) is estimated from interpolation of the values
marked as cloudy:
Rather than encompassing retrieval of these five
derived in clear pixels. Conversely, Cloud Gridshown in Table 6.
variables, most of the tests in the CMP algorithm involve
Information (Tokuno 2002) is processed in cloudy pixels.
thebrightness
use of brightness
temperature or reflectance data with
There were no MSC cloud mask
products
beforetheTable 5 Sensor noise for different
temperatures
similar 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 to
6 Variable
dependency 2012)of thresholdcloud Oceanic and Atmosphericand the forNational
develop CMP as a resource Table
availablefor 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)-- 81 --

－ 8 －

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

The fifth is a brightness temperature difference test（二行）
T∆thr= ∆10 (T10srf.4 )(78)
10 10.4
for all pixels except the coast type. The conditions are as
follows:
Algorithm Theoretical Basis for Himawari-8
The second
Cloud Mask
involvesProductbrightness temperature
ABS[{NWC (T10.4 ) − NWC (T12.4 )} − (T10.4 − T12.4 )] > offset
uniformity within a 60-minute time frame for twilight sea(65) （二行）
pixels that were cloudy 60 minutes previously. Figure 2
T10.4 − T12.4 < 1.0
IMAI Takahito(66) * and YOSHIDA Ryo**shows two-dimensional daytime frequency distribution（三行）
for Δ60T10.4 and Δ60(T10.4-T12.4) over sea. Both Δ60T10.4
T10.4 < 300.0(67)
and Δ60(T10.4-T12.4) tend to be larger in (b) than in (a).
Based on the results, pixels satisfying at least one of the
The sixth is a test of brightness temperature
Abstract
conditions
shown
below remain
cloudy.ProductThis condition
distribution correlation
betweenthe water
vapor band
andThis paper
describes
the algorithm
theoreticalbasis for the
Himawari-8
Cloud Mask
results incorrectof 96.9%of actually
the window
banddeveloped
for all bypixels.
The brightness(CMP)
the Meteorological
Satellite Center.
CMP
is partidentificationof the Fundamental
Cloud
Product,
which
incorporates
cloudphase,
typeand
top
altitudefrom
Himawari-8/AHI
and
has
been
cloudy pixels and false identification 6.5% of actuallytemperature distribution of these bands has a tendency
operationalsince
7 July 2015.
clear pixels as cloudy.
similar to thatof spatial
variationin thin upper cloud
The
CMP
algorithm
is
based
on
the
cloud
mask
technique of NWC-SAF for MSG/SEVIRI andareas. The conditions are as follows:∆ 60 threshold(T10.4 ) < 2methods.5(79)
NOAA/NESDIS for GOES-R/ABI. Most cloud detection tests involve
based on) > 0.7 using NWP(68)CORR(Ttransfer
radiative
data as atmospheric profiles. The thresholds are modified
7.3 , T10.4 calculation∆ 60 (T10.cloud
1.0
using offsets determined on the basis of comparison with the MODIS
Initial (80)
4 − T12mask.4 ) < product.
SDshowed> 0.the
5 CMP hit ratio derived(69) from such comparison was more than 85%. The CMP
results
8 (T7.3 ) thatalgorithm was applied to SEVIRI data, with results showing hit ratios with the MODIS product
wereSDaround85% for all seasons.
(70)
8 (T10.4 ) > 0.5
（三行）

TPW
> 0.3 cmcos(θ satZ )1. Introduction

(71)

（一行）
z
<
2000
m
(72)
Himawari-8 is the new geostationary
satellite oftheJapan Meteorological Agency (JMA). It was launched on
Here, TPW is total precipitable water in NWP data.
7 October 2014 and began operation on 7 July 2015. The
satellite carries the Advanced Himawari Imager (AHI),
2.4.8 Temporal uniformity tests
which is greatly improved over past imagers in terms of
Brightness temperature temporal variation tends to
its number of bands and its temporal/special resolution
increase when a pixel changes from cloudy to clear or(Bessho et al. 2016). Using AHI data, JMA’sfrom clear to cloudy. Based on this tendency, two tests
Meteorological Satellite Center (MSC) developed theare applied.
Himawari-8 Cloud Mask Product (CMP).
The first involves brightness temperature drop within
Cloud mask is used to discriminate cloudy pixels
a 10-minute time frame. This corresponds to situations infrom clear ones in satellite data. Numerous satellite
which clear pixels change to cloudy or cloud rises or
products require cloud mask information. By way ofthickens as time advances. If the following conditions areexample, Sea Surface Temperature (SST) (Yasuda andsatisfied, the pixel is marked as cloudy:
Shirakawa 1999), Clear Sky Radiance (CSR) (Uesawa< T∆thr+offset et al. 2003)(73)2009) and∆Aerosol(Okawaraare
10 (T10.4 )Detection
10 10.4
derived in clear pixels. Conversely, Cloud Grid
T10.4 2002)
< 330
.0
(74)
Information (Tokuno
is processedin cloudy pixels.
t0
There were no MSC cloud mask products before the< 330.0 each satellite productT10.4 t −10operation;(75)
start of Himawari-8’s
0
had its own cloud mask process. As cloud mask is also
srf0(76)torequired for mostT10
Himawari-8
MSC decided.4 t < 330.products,0develop CMP as a resource available for Himawari-8
T10srf.4 CMP
< 330
(77)
products in common.
is a.0part of the Fundamental
t0 −10
Cloud Product (FCP), which contains cloud phase, type
Here,and top height for Himawari-8/AHI pixels (Mouri et al.
2016a, 2016b). FCP has been produced since Himawari-8

began operation.
This paper describes the algorithm theoretical basis for
CMP.
Fig.2 Two-dimensional frequency distribution in terms of（一行）
the daytime 60 min. temporal differences in T10.4 (Δ60T10.4)
2. Algorithm
and T10.4-T12.4 (Δ60(T10.4-T12.4)) over sea: (a) for pixels
remaining cloudy; (b) for the pixels turning from cloudy to
2.1 Overview
clear within the 60-minute period. The white line indicates
Cloud detection in the CMP algorithm is based on
the threshold of the test for twilight sea pixels, andcomparison of observation data and clear sky data
numbers represent the cumulative frequency for each area.
calculated from numerical weather prediction (NWP)
data. If observation data differ from clear sky data, theFor twilight land pixels, cloudy pixels cannot be
pixel is cloudy. To discriminate between cloudy and clear
discriminated using Δ60(T10.4-T12.4) as they can over land
pixels, it is desirable for observation variables in clear(Figure 3). However, using the following condition,and cloudy conditions to differ and for the physical
75.2% of actually cloudy pixels are correctly identified:reason behind the difference to be explicit (e.g.,
2.0∆.0 the top(81)∆ 60 (T10.4reflectance− T12.4 ) < −andtemperature,emissivity
and
60 T10.4 + 5of
transmittance 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 AtmosphericFig.3 As per Figure
2 but for results overland
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)-- 91 --

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

than those found in a spatial uniformity test for the
2.4.9 Filters（二行）
eight surrounding pixels in a 3 x 3-pixel segment.
Five filters are applied to the pre-final cloud mask in
Theoretical Basis for Himawari-8
The fourth
CloudfilterMaskchanges
Product
isolated cloudy pixels asorder to maintain its Algorithm
spatial continuity.
identified in a cloud detection test using band 7 (3.9 μm)
The first extends cloudy areas in twilight zones (75.0（二行）
data to clear. Band 7 data have lower SNR values thandeg. < θsunZ < 89.0 deg.). If clear pixels around cloudy
IMAI Takahito* and YOSHIDA Ryo**others. This filter corrects misclassification caused by
pixels have a brightness temperature and reflectance（三行）
this uncertainty. If the following conditions are satisfied,similar to those of the cloudy pixels, the filter changes
the pixel is marked as clear:
the clear pixels to cloudy. First, cloud clusters are createdby combining adjacent cloudy pixels in the twilight area.
 The pixel is cloudy as determined in tests related to
Abstract
For each cluster,
averagebrightness
temperature(Gross testof 3.9Maskµm reflectanceThis thepaper
describes
the algorithm
theoretical basis3.9
forµm
thedata.
Himawari-8
Cloud
Product
T10.4AVE and(CMP)(Section
third and fourth
top
averagereflectancewithMeteorologicalsun zenith angle
developedby theSatellite Center.
CMP 2.4.3);
is part second,of the Fundamental
Cloud
modAVE
Product,
which
incorporates
cloudphase,
typeand
top
altitudefrom
Himawari-8/AHI
and
has
been
emissivity tests (Section 2.4.4); third cloudcorrection R0.64
are calculated. If a clear pixel x
7 July 2015.
absorption test (Section 2.4.5).)
adjacent tooperationala pixel insincea cloudcluster satisfies the
The
CMP
algorithm
is
based
on
the
cloud
mask
techniqueof NWC-SAF
for MSG/SEVIRI
following conditions, x is marked as cloudy: All eightsurrounding
pixels in aand3x3-pixel
NOAA/NESDISfor
GOES-R/ABI.
Most
cloud
detection
tests
involvethresholdmethods
based onsegment are clear.
xθ scattransfer< 150
radiative
calculation using NWP(82)data as atmosphericprofiles.
The thresholds
The fifth
filter changes
isolated areclearmodified
pixels to cloudy.
using offsetsx determined on the basis of comparison with the MODIS cloud mask product. InitialIfthefollowingconditionsaresatisfied,
the pixel isAVEmod(83) from such comparison was more than 85%. The CMP
showedthe⋅ CMP
hit ratio derived
MEANresults
R0mod) >that
1.05
CMA ( R0.64.64
classified as cloudy:
algorithm was applied to SEVIRI data, with results showing hit ratios with the MODIS productx
 The pixel is clear.
mod for all seasons.
were around
85%
(84)
MEAN
CMA ( R0.64 ) > 0.3
 All eight surrounding pixels in a 3 x 3-pixel（三行）
segment are cloudy.
x(85)
T10AVE.4 − 0.5 < T10.4
1. IntroductionxAVE
T10.4 < T（一行）10.4 + 0.5(86)
Himawari-8 is the new geostationary satellite of the
For pixels adjacent to those set as cloudy after this filter’sJapan Meteorological Agency (JMA). It was launched on
application, the above conditions are reapplied. If the
7 October 2014 and began operation on 7 July 2015. The
conditions are newly satisfied for individual pixels, thesatellite carries the Advanced Himawari Imager (AHI),
cloudy area is extended accordingly.
which is greatly improved over past imagers in terms of
The second filter changes coast pixels from cloudy to
its number of bands and its temporal/special resolution
clear in order to correct misclassification resulting from
(Bessho et al. 2016). Using AHI data, JMA’s
spatial uniformity tests. If the following conditions are
Meteorological Satellite Center (MSC) developed thesatisfied, the pixel is marked as clear:
Himawari-8 Cloud Mask Product (CMP).
 The pixel is cloudy as determined in spatial
Cloud mask is used to discriminate cloudy pixels
uniformity tests (Section 2.4.7).
from clear ones in satellite data. Numerous satellite
 More than 10% of the eight surrounding pixels in a
products require cloud mask information. By way of3 x 3-pixel segment are clear.
example, Sea Surface Temperature (SST) (Yasuda and More than 50% of the 48surrounding pixels in a 7
Shirakawa 1999), Clear Sky Radiance (CSR) (Uesawa
x 7-pixel segment are clear.
2009) and Aerosol Detection (Okawara et al. 2003) are
The third filter changes snow neighbor pixels fromderived in clear pixels. Conversely, Cloud Grid
clear to cloudy in order to correct misclassifications
Information (Tokuno 2002) is processed in cloudy pixels.
resulting from spatial uniformity tests. If the following
There were no MSC cloud mask products before theconditions are satisfied, the pixel is marked as cloudy:
start of Himawari-8’s operation; each satellite product The pixel is adjacent to a snow pixel.
had its own cloud mask process. As cloud mask is also
 The pixel is cloudy as determined in spatial
required for most Himawari-8 products, MSC decided touniformity tests.
develop CMP as a resource available for Himawari-8
 More than 10% of the eight surrounding pixels in a
products in common. CMP is a part of the Fundamental
3 x 3-pixel segment are clear.
Cloud Product (FCP), which contains cloud phase, type
 A cloud detection test shows no cloudy pixels otherand top height for Himawari-8/AHI pixels (Mouri et al.
2016a, 2016b). FCP has been produced since Himawari-8

began operation.
2.4.10 Detection of cloudy-clear mixed situations
This paper describes the algorithm theoretical basis for
Cloudy pixels are classified as either cloudy or
CMP.
cloudy-clear mixed. If the following condition is satisfied,（一行）
the pixels is marked as cloudy-clear mixed:
2. Algorithm
T10.4 − T12.4 ≥ 2.0(87)
2.1 Overview
If the following condition is satisfied, the pixel is
Cloud detection in the CMP algorithm is based onclassified as cloudy:
comparison of observation data and clear sky data
2.0
(88)
calculated fromT10numericalprediction (NWP).4 − T12.4 <weather
data. If observation data differ from clear sky data, thepixel is cloudy. To discriminate between cloudy and clear
2.4.11 Aerosol detection
pixels, it is desirable for observation variables in clear
For products derived with clear pixels such as SST
and cloudy conditions to differ and for the physicaland CSR, CMP needs to detect all objects that block
reason behind the difference to be explicit (e.g.,radiative transfer from surface to satellite except for thetemperature, reflectance and emissivity of the top andatmosphere. As aerosols may go undetected in cloudtransmittance relating to clouds and the atmosphere).
mask tests, a volcanic ash detection algorithm (Pavolonis
Rather than encompassing retrieval of these fiveet al. 2009) consisting of a detection part, a confidence
variables, most of the tests in the CMP algorithm involve
setting part and a confidence adjustment part is used.
the use of brightness temperature or reflectance data with
The conditions for aerosol detection are as shown
similar tendencies. Cloud mask tests are based on the
below. Examples of variables for aerosol detection are
cloud mask algorithm of the NoWCasting (NWC)
given in Figure 4.
Satellite Application Facility (SAF) (Meteo-France,stropo≥ 0.Oceanic02(89)2012) and the εNationaland Atmospheric
11.2
Administration(NOAA)/National
Environmental
stropo
≥
0
.
02
(90)
Satellite, Data, εandInformation
Service(NESDIS)
8.6(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

0.0 < β 12stropo.4 / 11.2 < 1.0

ε 11stropo≤ 0.10 , and the white area is non-ash confidence.
(91) （二行）.2

CSUM is a summation
Algorithm Theoretical Basis for Himawari-8
Cloud Mask
of CPIX
Product
and CLRC for five grades of
0.0 < LRC ( β12stropo(92) （二行）.4 / 11.2 ) < 1.0

confidence {0: high; 1: moderate; 2: low; 3: very low, 4:
non-ash}.
0.0 < β 8stropo
IMAI Takahito(93) * and YOSHIDA Ryo**.6 / 11.2 < 10.0
Confidence adjustment involves nine processes（三行）
performed successively from A to I as summarized in
0.0 < LRC ( β 8stropo(94).6 / 11.2 ) < 10.0
Table 7.
The confidence information is set using CPIX, CLRC and
Abstract Pixels with high and moderate confidence CSUM are
CSUM as confidence
set
areas withlow quality,
gradesconfidence(0:This flags.
paper Threedescribes
theofalgorithm
theoreticalbasisasforaerosol
the Himawari-8
CloudhighMaskand
Product
respectively,
CMPofoutput(Table 3). An
example ofhigh; 1: moderate;
non-ash)by
arethesetMeteorologicalfor CPIX and CLRC
(CMP) 2:developed
Satellite Center.
CMP inis partthe Fundamental
Cloud
Product,
which
incorporates
cloudphase,
typeand
top
altitudefrom
Himawari-8/AHI
and
has
been
aerosol detection is shown in Figure 6. The leftmost partbased on the conditions for the variables
operational
since
7
July
2015.
shows plots in an HSD line pixel grid. The center andstropostropostropoand LRC ( β 8.6 / 11.2 ), LRC ( β12.4 / 11.2 )) ,( β 8stropo.6 / 11.2 , β 12.4 / 11
.2 ) CMP(algorithmTheis based on the cloud maskrightmost
techniquepartsof NWC-SAF
for similar
MSG/SEVIRI
are 2D plots
to thoseandin Figure 5
NOAA/NESDISfor
GOES-R/ABI.
Most
cloud
detection
tests
involvethresholdmethods
based
on indicate
showing the detection conditions. Thesefigures
respectively. The conditions are summarized in Figure 5.
radiative
calculation
NWP data
profiles.
The thresholds
modifiedcorrespondence
betweenthe cloudarearea
and detection
The red area
is high,transfer
the orange
area isusing
moderate,
the as atmosphericusing offsets determined on the basis of comparison with the MODIS cloud mask product. Initialconditions.
stropogreen area isresultsmoderate
if ε 11if
10 andhitnon-ash> 0.CMPshowedthat
ratio derived
from such comparison was more than 85%. The CMP.2 thealgorithm 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
Fig.4
Examples of aerosol
detection variables
02 UTCinonthe
21stCMP
Mar. 2015
Cloudfordetection
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 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
cloudphase,
type
Fig.5 Conditions for setting of the three confidence grades for 02 UTC on 21 Mar. 2015
(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

（二行）

Table 7 Confidence adjustment

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 GridstropoFig. 6 Example of aerosol detection (left), ( LRC ( β 8stropo
space (center) and
LRC
β
),
(
))
.
6
/
11
.
2
12
.
4
/
11
.
2
variables, 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 werestropo
no MSC cloud mask products before the( β 8.6 / 11.2 , β12stropo) space (right). The data (c0) – (c3) and (d0) – (d4) plotted in each color are: (c3): no conditions met;
.
4
/
11
.
2
similar tendencies. Cloud mask tests are based on thestart of Himawari-8’s operation; each satellite productcloud mask algorithmof the NoWCasting (NWC)
had its own cloud mask
process.
As
cloud
mask
is
also
(c2): only ε 11stropo
condition met; (c1): only ε 8stropo
condition met; (c0): ε 11stropoand ε 8stropo
conditions met; (d4): identified
.6
.2
.2
.6
Satellite Application Facility (SAF) (Meteo-France,
required for most Himawari-8 products, MSC decided toandas low
the quality;
National
Oceanic asand
Atmosphericdevelop CMP
as a quality;
resource
available
Himawari-8
as non-ash(d3):
identifiedfor
as very
low quality; (d2):2012)
identified(d1): identified
moderatequality(NOAA)/National
Environmental
products in(d0):common.
CMP
is quality.
a part ofFundamental
identified
as high
Thethescene
is for 02 UTC on 21Administration
Mar. 2015.
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, 2016detection in the pre-final cloud mask. A low ratio of false
2.5 Offsets（二行）
alarms for cloudy conditions is therefore desirable.
To compensate for the uncertainty of clear sky data in
First, offsets
resultingratios of 0.005, 0.01, 0.02,
Algorithm offsets
Theoretical
Basis asfor Himawari-8
Cloud
MaskinProductradiative transfer calculation,
are added
0.04, 0.06, 0.08, 0.1, 0.12, 0.15, 0.2, 0.3, 0.5, 0.8 andadditional terms for main variable thresholds in cloud（二行）
0.99 for false alarms of cloudy conditions in each cloudmask tests. The offsets are determined by making the
IMAI Takahito* and YOSHIDA Ryo**mask test are calculated. In the relevant test, the offset
pre-final cloud mask similar to the actual cloud mask. It（三行）
value is discretized and a histogram showing numbers of
is desirable to use a low-earth-orbit satellite cloud
clear pixels in actual cloud mask data is created. A
product as actual cloud mask data because of their highcumulative histogram is made by gathering histogram
quality and rich data content. This type of cloud mask Abstractisa cloudy-side
bin.ProductBy normalizing
based on the Aqua/MODIS
cloud mask
as actual
This paper describes
theproductalgorithm
theoretical databasisfromfor theHimawari-8discretized
Cloud Mask
the cumulativeon the total
number ofdata. Offsets(CMP)
are setdeveloped
for five surfaces
land, snow/sea
by the (sea,Meteorological
Satellite Center.
CMP is histogram
part of thebased
Fundamental
Cloud
Product,
which
incorporates
cloudphase,
typeand
top
altitudefrom
Himawari-8/AHI
and
has
been
clear pixels and subtracting the result from 1, ratios ofice, sand and vegetation), two sun zenith angle conditions
operational
7 Julyfalse alarms for cloudy conditions in each discretized bin(θsunZ < 90 deg.,
90 deg.since≤ θsunZ) and2015.
four satellite zenith
The
CMP
algorithm
is
based
on
the
cloud
mask
techniqueof NWC-SAF
MSG/SEVIRI
arecalculated
as per the forgreen
line shown and
in Figure 7.
angle conditions (sec(θsatZ) < 3, 3 ≤ sec(θsatZ) < 5, 5 ≤NOAA/NESDISfor
GOES-R/ABI.
Most
cloud
detection
tests
involvethresholdmethods
Following the dataset of false alarmbased
ratiosonfrom thesec(θsatZ) < 7, 7 ≤ sec(θsatZ)). To reduce the effect ofradiative
transfer transfer
calculation
using NWP
data as atmospheric
The bin,thresholds
modifiedcloudy-sideprofiles.
discretized
offset arevaluescorresponding
approximationin radiative
calculation,offsets
using offsets determined on the basis of comparison with the MODIS cloud mask product. Initial
to the first discretized bin with ratios of more than 0.005,
depending on several conditions are used.
results showed that the CMP hit ratio derived from such comparison was more than 85%. The CMP
0.01, 0.02, 0.04, 0.06, 0.08, 0.1, 0.12, 0.15, 0.2, 0.3, 0.5,
As it is practically impossible to tune the offsets foralgorithm was applied to SEVIRI data, with results showing hit ratios with the MODIS product0.8 and 0.99 for false alarms of cloudy conditions areeach cloud mask
test
independently,
the
NOAA/NESDIS
were around 85% for all seasons.
derived.
method (Heidinger and Straka III 2012) is adopted.（三行）
Cloud mask tests are essentially used for cloud detection
Second, the hit ratio (see Table 8) of the pre-final
and contain an "or" condition for the result of thecloud mask for each ratio of false alarms for cloudy
began operation.
1. Introduction
pre-final
cloud mask. Accordingly, misclassification of
conditions is calculated. An offset corresponding to the（一行）
This paper describes the algorithm theoretical basis forcloudy as clear (B in Table 8) in one test can be remedied
false alarm ratio for cloudy conditions with the maximum
Himawari-8 is the new geostationary satellite of the
CMP.
by cloud detection in another, while misclassification of
hit ratio of the pre-final cloud mask (with the condition
Japan Meteorological Agency (JMA). It was launched on（一行）
clear as cloudy (C in Table 8) will result in false cloudthat the ratio is lower than 0.3) is adopted.
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
Fig. Detection
7 Example(Okawara
of false alarm
forarecloudy conditions
in a top
temperature
test andfor landtransmittance
relating
to clouds
the atmosphere).
2009) and Aerosol
et al.ratios
2003)
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.
Table 8 Hit ratio definition
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

with a constraint to avoid detection with an extremely
2.6 Quality information（二行）
biased ratio of clear-sky and cloudy pixels. The constraint
Two grades are used to express cloud mask quality forrequires thatCloudthe number
clear-sky pixel hits in cloud
Algorithm
Theoretical
Basisfor Himawari-8
MaskofProducteach of clear and cloudy
pixels (i.e.,high-quality
clear,
mask results obtained using a clear-sky offset should be
low-quality clear, high-quality cloudy and low-quality（二行）
greater than 0.8 times that obtained using an all-sky
cloudy). Low quality is assigned to filtered pixels
IMAI Takahito* and YOSHIDA Ryo**
offset. The cloudy offset is calculated in the same way
regardless of their clear or cloudy status, while quality for（三行）
with the clear sky offset but to maximize the cloudy hit
non-filtered pixels is based on the safety margin. For
ratio (see Table 8). Correspondence between the threecloudy pixels, high quality is assigned if the difference
offsets and cloud mask quality information is shown in
between the observation value and the threshold of the
Abstract
8. the Himawari-8 Cloud Mask Productmain variables Thisin thelast describes
test is greaterthan the safety
paper
the algorithm
theoretical Figure
basis formargin. For(CMP)
clear pixels,
highbyqualityis assigned if Satellitethedeveloped
the MeteorologicalCenter. CMP is part of the Fundamental Cloud
altitude from Himawari-8/AHI and has beendifferences Product,
betweenwhich
the incorporates
observation cloudvaluephase,and type
the and top
3. Results
since 7 inJulythreshold ofoperational
the main variables
all2015.
tests are greater thanThe
CMP
algorithm
isbased on the cloud mask3.1
techniqueNWC-SAF for MSG/SEVIRI andthe safety margin.
Outputof
exampleNOAA/NESDISfor
GOES-R/ABI.
Most
cloud
detection
tests
involvethresholdmethods
The safety margin for snow/sea ice detection is set as
An example of CMP
outputfor 02 based
UTC onon 30 Mar.
srf radiative transfer calculation using NWP data as atmospheric profiles. The thresholds are modified
0.2 x R1.6 for testing of variable R1.6. Calculation of the2015 is shown in Figure 9.
using offsets determined on the basis of comparison with the MODIS cloud mask product. Initialsafety margin for a test with an offset is similar to
A comparison of CMP with clear, all-sky and cloudy
results showed that the CMP hit ratio derived from such comparison was more than 85%. The CMP
calculation of the offset based on the false alarm ratio for
offsets against the MODIS cloud product is shown inalgorithm was applied to SEVIRI data, with results showing hit ratios with the MODIS productcloudy conditions.
To
avoid
confusion,
theoffsetFigure 10. The clear offset produces more cloudy pixels
were around 85% for all seasons.
described in the previous subsection is referred to as the（三行）
with MODIS clear detection (cyan points) than the others,
all-sky offset, and this offset plus a safety margin forand the cloudy offset produces more clear pixels withclear sky or cloudy conditions is referred to as the
MODIS cloud detection (red points) than the others. The
began operation.
1. Introduction
clear-skyoffset or the cloudy offset. While the all-sky
result produced with the all-sky offset is more consistent（一行）
This paper describes the algorithm theoretical basis foroffset is calculated to maximize the hit ratio (see Table 8)in the MODIS result than the others. In these scenes, theHimawari-8 is the new geostationary satellite of theCMP.
of the pre-final cloud mask, the clear-sky offset is
hit ratio (see Table 8) is 83.2% for the clear-sky offset,
Japan Meteorological Agency (JMA). It was launched on（一行）
calculated to maximize the clear hit ratio (see Table 8)
85.6% for the all-sky offset and 80.6% for the cloudy-sky
7 October 2014 and began operation on 7 July 2015. The
2. Algorithm
offset.
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).
Fig.used
8 Correspondence
three offsets
andiscloud
mask
information
pixel
cloudy.
To quality
discriminate
between cloudy and clear
Cloud mask is
to discriminatebetween
cloudythe
pixelspixels, 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 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
Fig. 9 (a) Brightness temperature in B13, (b) cloud mask, (c) aerosol detection, (d) surface information and (e) SST
Satellite, Data, and Information Service (NESDIS)
Cloud Product (FCP), which contains cloud phase, typeused for cloud mask at 02 UTC on 30 Mar. 2015
(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

（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 on
Fig. 10radiative
Comparisonof CMP
with clear
offset (center)
offsetagainst thetransfer
calculation
usingoffset
NWP(left),data asall-sky
atmosphericprofiles.and
Thecloudy
thresholds
are(right)
modified
MODISusing
cloudoffsets
product
in scenes from
UTCoftocomparison
09 UTC on 17
Mar.
determinedon the00basis
withthe2015
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 product3.2 Long-term
4. Summary
wereevaluation
around 85% for all seasons.
The CMP algorithm was applied to Meteosat Second（三行）
Generation (MSG)/Spinning Enhanced Visible andMSC’s Himawari-8 cloud mask product (CMP) has
InfraRed Imager (SEVIRI) data for long-term evaluation.
been produced since Himawari-8 began operation in Julybegan operation.
1.
AsIntroduction
evaluation periods, two weeks from each season were2015.
The CMP algorithm involves snow/sea ice
（一行）
This
paper
describes
the filtering
algorithmand
theoreticalbasis forselected (i.e., 28 December 2011 – 10 January 2012, 28
detection, clouddetection,
aerosol detection
Himawari-8 is the new geostationary satellite of the
CMP.
March – 10 April 2012, 27 June – 10 July 2012 and 27
based on an algorithm proposed by NWC-SAF andJapan Meteorological Agency (JMA). It was launched on（一行）
September – 10 October 2012). Table 9 shows the
NOAA-NESDIS. Most of the cloud detection tests used
7 October 2014 and began operation on 7 July 2015. The
2. Algorithm
resulting hit ratios compared with those of theinvolve threshold methods based on radiative transfer
satellite carries the Advanced Himawari Imager (AHI),
Aqua/MODIS product. The value was consistent at
calculation using NWP data for atmospheric profiles. The
which is greatly improved over past imagers in terms of
2.1 Overview
around 85% for all seasons.
thresholds are modified using offsets determined based
Cloud detection in the CMP algorithm is based on
its number of bands and its temporal/special resolution
on comparison with the MODIS cloud mask product. The
comparison of observation data and clear sky data
(Bessho et al. 2016). Using AHI data, JMA’s
initial results showed that the CMP hit ratio derived from
calculated from numerical weather prediction (NWP)
Meteorological Satellite Center (MSC) developed thesuch comparison was more than 85%. Application of theTable 9 Hit ratios of the CMP algorithm for MSG/SEVIRI
data. If observation data differ from clear sky data, theHimawari-8 Cloud Mask Product (CMP).
CMP algorithm to SEVIRI data showed that hit ratios
data compared with those of the Aqua/MODIS product for
pixel is cloudy. To discriminate between cloudy and clear
Cloud mask is used to discriminate cloudy pixels
were around 85% for all seasons.
each season (winter: 28 December 2011 – 10 January
pixels, it is desirable for observation variables in clear
from clear ones in satellite data. Numerous satellite
2012; spring: 28 March – 10 April 2012; summer: 27
and cloudy conditions to differ and for the physicalproducts require cloud mask information. By way ofJune – 10 July 2012; autumn: 27 September – 10 October
Acknowledgement
reason behind the difference to be explicit (e.g.,
example, Sea Surface Temperature (SST) (Yasuda and2012)
temperature, reflectance and emissivity of the top andShirakawa 1999), Clear Sky Radiance (CSR) (Uesawa
The authors are grateful to NWC-SAF andtransmittance relating to clouds and the atmosphere).
2009) and Aerosol Detection (Okawara et al. 2003) are
NOAA/NESDIS for providing the cloud mask product
Winter Spring Summer Autumn
All
Rather than encompassing retrieval of these five
derived in clear pixels. Conversely, Cloud Gridalgorithm. MSG/SEVIRI data from EUMETSAT were
0.86
0.85
0.85
0.85
0.85
All region
variables, most of the tests in the CMP algorithm involve
Information (Tokuno 2002) is processed in cloudy pixels.
used as input data for the prototype product, and output
0.86
0.85
0.85
0.86
0.85
Sea
the use of brightness temperature or reflectance data with
There were no MSC cloud mask products before thedata were evaluated within the framework of the Cloud
0.86
0.81
0.72
0.86
0.81
Snow/Ice
similar tendencies. Cloud mask tests are based on thestart of Himawari-8’s operation; each satellite product
Retrieval Evaluation Workshop (CREW).
0.87
0.86
0.86
0.89
0.87
Sand
cloud mask algorithm of the NoWCasting (NWC)
had its own cloud mask process. As cloud mask is also
0.86
0.84
0.83
0.83
0.84
Vegetation
Satellite Application Facility (SAF) (Meteo-France,
required for most Himawari-8 products, MSC decided toReference
0.80
0.82
0.86
0.84
0.84
Others2012) 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
Aoki, T., M. Fukabori and A. Uchiyama, 1999:
Satellite, Data, and Information Service (NESDIS)
Cloud Product (FCP), which contains cloud phase, type
Numerical Simulation of the Atmospheric Effects on(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

Snow Albedo with a Multiple Scattering Radiative（二行）
Mouri, K., H. Suzue, R. Yoshida, and T. Izumi, 2016b:
Transfer Model for the Atmosphere-Snow System,
Algorithm Theoretical
Basis
Document of Cloud top
Journal of the MeteorologicalSocietyof Japan,BasisVol. 77,
Algorithm
Theoreticalfor Himawari-8
Cloud Mask
Product（二行）
height product. Meteorological Satellite Center TechnicalNo. 2, 595-614
Note, 61, 33-42.
IMAI Takahito* and YOSHIDA Ryo**
Bessho, K., K. Date, M. Hayashi, A. Ikeda, T. Imai, H.
（三行）
Nakajima, T. and M. Tanaka, 1986: Matrix formulation
Inoue, Y. Kumagai, T. Miyakawa, H. Murata, T. Ohno, A.
for the transfer of solar radiation in a plane-parallel
Okuyama, R. Oyama, Y. Sasaki, Y. Shimazu, K. Shimoji,
scattering atmosphere, J. Quant. Spectrosc. Radiat.
Y. Sumida, M. Suzuki, H. Taniguchi, H. Tsuchiyama, D.
Abstract
Transfer,
Uesawa, H. This
Yokota,R. Yoshida,
2016:theoreticalAn
paperand
describes
the algorithm
basis for 35,
the 13-21.
Himawari-8 Cloud Mask Productintroduction(CMP)
to Himawari-8/9- Japan's
new-generation
developed by
the Meteorological
Satellite Center. CMP is part of the Fundamental Cloud
Product,
which incorporates
phase,Soc.
type and top
altitude N.,
fromY.Himawari-8/AHI
been 2003:
Okawara,Yoshizaki andandM.hasTokuno,
geostationary
meteorologicalsatellites. cloud
J. Meteor.
operational since 7 July 2015.
Development of Aerosol Products from GMS/VISSR andJapan, 94, doi:10.2151/jmsj.2016-009.
The CMP algorithm is based on the cloud maskNOAA/AVHRR
technique of NWC-SAF
for MSG/SEVIRIand Satellite
Image Data,
Meteorological
NOAA/NESDISfor
GOES-R/ABI.
Most
cloud
detection
tests
involvethresholdmethods
basedonCenter Technical Note, 42, 43-52 (in Japanese).
Cox, C. and W. Munk, 1954: Measurement of theradiative
transfer
calculation
using
NWP
data
as
atmosphericprofiles.
The
thresholds
aremodifiedroughness of the sea surface from photographs of theusing offsets determined on the basis of comparison with the MODIS cloud mask product. Initial
Pavolonis, M, and J. Sieglaff, 2009: GOES-R Advanced
sun's glitter, J. Opt. Soc. Amer., 44(n), 838-850.
results showed that the CMP hit ratio derived from such comparison was more than 85%. The CMP
Baseline Imager (ABI) Algorithm Theoretical Basisalgorithm was applied to SEVIRI data, with results showing hit ratios with the MODIS product
Document for Volcanic Ash (Detection and Height).
Eyre, J.R., were1991:aroundA fastradiative
transfermodel
for85% for all seasons.
Available atsatellite sounding systems. ECMWF Tech. Memo. 176,（三行）
http://www.star.nesdis.noaa.gov/goesr/docs/ATBD/VolAs
28pp
h.pdf.
began operation.
1.
Introduction
Heidinger,
A. and W. C. Straka III, 2012: Algorithm（一行）This paper
theandalgorithm
theoretical basis
Roujean,J.-L.,describes
M. Leroy,
P. Y. Deschamps,
1992:forA
Theoretical Basis Document ABI Cloud Mask. Available
Himawari-8 is the new geostationary satellite of the
CMP.
bidirectionalreflectance model of the Earth's surface foratJapan Meteorological Agency (JMA). It was launched on（一行）
the correction of remote sensing data, J. Geophys. Res.,
http://www.star.nesdis.noaa.gov/goesr/docs/ATBD/Cloud
7 October 2014 and began operation on 7 July 2015. The
2. Algorithm
97, 20455-20468.
_Mask.pdf.
satellite carries the Advanced Himawari Imager (AHI),
which is greatly improved over past imagers in terms of
2.1 Overview
Strahler, A. H., J.-P. Muller, MODIS Science Team
Kurihara, Y., T. Sakurai, and T. Kuragano, 2006: Global
Cloud detection in the CMP algorithm is based on
its number of bands and its temporal/special resolution
Members, 1999: MODIS BRDF/Albedo Product:daily sea surface temperature analysis using data fromcomparison of observation data and clear sky data
(Bessho et al. 2016). Using AHI data, JMA’s
Algorithm Theoretical Basis Document Version 5.
satellite microwave radiometer, satellite infraredcalculated from numerical weather prediction (NWP)
Meteorological Satellite Center (MSC) developed the
Available atradiometer and in-situ observations. Sokko Jiho, 73, 1-18
data. If observation data differ from clear sky data, theHimawari-8 Cloud Mask Product (CMP).
http://modis-land.gsfc.nasa.gov/brdf.html.
(in Japanese).
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
Tokuno, M., 2002: Advanced Satellite Cloud Grid
Li, J. and K. Shibata, 2006: On the Effective Solarand cloudy conditions to differ and for the physicalproducts require cloud mask information. By way ofInformation Data, Meteorological Satellite Center
Pathlength. Journal of the Atmospheric Sciences 2006
reason behind the difference to be explicit (e.g.,
example, Sea Surface Temperature (SST) (Yasuda andTechnical Note,40, 1-24 (in Japanese).
63:4, 1365-1373
temperature, 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
Uesawa, D., 2009: Clear Sky Radiance (CSR) ProductMeteo-France, 2012: Algorithm Theoretical Basis
Rather than encompassing retrieval of these five
derived in clear pixels. Conversely, Cloud Gridfrom MTSAT-1R, Meteorological Satellite Center
Document for “Cloud Products” (CMa-PGE01v3.2,
variables, most of the tests in the CMP algorithm involve
Information (Tokuno 2002) is processed in cloudy pixels.
Technical Note, 52, 39-48.
CT-PGE02 v2.2 & CTTH-PGE03 v2.2). Available atthe use of brightness temperature or reflectance data with
There were no MSC cloud mask products before the
http://www.nwcsaf.org/indexScientificDocumentation.ht
similar tendencies. Cloud mask tests are based on thestart of Himawari-8’s operation; each satellite productYasuda, H., Y. Shirakawa, 1999: Improvement of theml.
cloud mask algorithm of the NoWCasting (NWC)
had its own cloud mask process. As cloud mask is also
Derivation Method of Sea Surface Temperature from
Satellite Application Facility (SAF) (Meteo-France,
required for most Himawari-8 products, MSC decided to
GMS-5 Data, Meteorological Satellite Center Technical
Mouri, K., T. Izumi, H. Suzue, and R. Yoshida, 2016a:
2012) and the National Oceanic and Atmosphericdevelop CMP as a resource available for Himawari-8Note, 37, 19-33 (in Japanese).
Algorithm Theoretical Basis Document of Cloud
Administration(NOAA)/National
Environmental
products in common. CMP is a part of the Fundamental
Type/Phase Product. Meteorological Satellite Center
Satellite, Data, and Information Service (NESDIS)
Cloud Product (FCP), which contains cloud phase, typeTechnical Note, 61, 19-31.
(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

（二行）

Algorithm Theoretical
Himawari-8 Cloud Mask Product
ひまわりBasis
8 号for
雲マスクプロダクト（二行）

IMAI Takahito* and YOSHIDA Ryo**今井 崇人*、吉田良**（三行）

Abstract

This paper describes the algorithm theoretical basis for the Himawari-8 Cloud Mask Product
要旨Center. CMP is part of the Fundamental Cloud(CMP) developed by the Meteorological Satellite
Product, which incorporates cloud phase, type and top altitude from Himawari-8/AHI and has been
本誌は、気象衛星センターで開発された、ひまわり８号雲マスクプロダクト（CMP）の算出アルゴリズムのoperational since 7 July 2015.
詳細を記述したものである。CMP は、ひまわり８号基本雲プロダクト（雲タイプ、雲相、雲頂高度を含む）の
The CMP algorithm is based on the cloud mask technique of NWC-SAF for MSG/SEVIRI and一部であり、2015
年 7 月 7for日から正式運用されている。
NOAA/NESDISGOES-R/ABI. Most cloud detection tests involve threshold methods based on
CMP のアルゴリズムは、NWC-SAFとusing
NOAA/NESDISMSG/SEVIRI用に開発した雲マス
radiative transfer calculation
NWP dataがそれぞれ
as atmosphericprofiles. とTheGOES-R/ABI
thresholds are
modifiedクアルゴリズムに基づく、閾値を用いた複数の雲検出テストによるものである。これらの閾値は、数値モデル
using offsets determined on the basis of comparison with the MODIS cloud mask product. Initial
データを入力とした放射伝達計算によって決定された後、MODISの雲マスクプロダクトとの比較によって決定
results showed that the CMP hit ratio derived from such comparison
was more than 85%. The CMP
されるオフセットによって修正されたものである。CMPの初期結果を
algorithm was applied to SEVIRI data, with results
showing hitMODIS
ratiosプロダクトと比較した結果、適中with the MODIS productwerearound85%
forallseasons.
率は 85 %以上であることが示された。また、本アルゴリズムを SEVIRI データに適用した結果、四季を通じて（三行）
適中率が 85 %程度であることがわかった
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

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* 気象庁観測部観測課観測システム運用室* Office of Observation Systems Operation, Observation Department, Japan Meteorological Agency**System
気象衛星センターデータ処理部システム管理課**
Engineering Division, Data Processing Department, Meteorological Satellite Center(Received September 1, 2015, Accepted 20 November 2015)
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