JOINT WMO TECHNICAL PROGRESS REPORT ON THE GLOBAL DATAPROCESSING AND FORECASTING SYSTEM AND NUMERICAL
WEATHER PREDICTION RESEARCH ACTIVITIES FOR 2014
Japan Meteorological Agency

1.

Summary of highlights(1) The Global Numerical Weather Prediction (NWP) system was upgraded in March 2014. The
number of vertical levels was increased from 60 to 100, and the pressure of the top level was
raised from 0.1 to 0.01 hPa (see 4.2.2.2 (1)). The parameterization schemes of the Global
Spectral Model (GSM), including those for the boundary layer, radiation, non-orographic
gravity waves and deep convection, were also improved (see 4.2.2.2 (2)). The upgrade
incorporated improvements in data assimilation considerations such as the bending angle of
the Global Navigation Satellite System (GNSS) Radio Occultation (RO) (incorporating highaltitude data up to 0.1 hPa) (see 4.2.1.2 (1)), Advanced Microwave Sounding Unit-A (AMSU-A)
14 ch radiance data (with a sensitivity peak near 2 hPa) (see 4.2.1.2 (2)), and zenith total
delay data from the ground-based GNSS (see 4.2.1.2 (3)).
(2) Clear sky radiance data from hyper-spectral infrared sounding conducted via theAqua/Atmospheric Infrared Sounder (AIRS) and the Meteorological Operational Satellite(Metop)/Infrared Atmospheric Sounding Interferometer (IASI) were introduced into the Global
NWP system in September 2014 (see 4.2.1.2 (4)).
(3) Typhoon bogussing in the Global and Meso-scale NWP systems was improved in May and
September 2014 (see 4.2.1.2 (5) and 4.3.1.2 (1)).
(4) The off-line simple biosphere model (SiB) was introduced for land surface analysis of the
Meso-scale NWP system in November 2014.
(5) The horizontal resolution of the One-week Ensemble Prediction System (EPS) was enhanced
from TL319 to TL479 in February 2014. Its update frequency was also increased from once a
day to twice a day, and its ensemble size was approximately halved from 51 to 27, making thetotal ensemble size 54/day as opposed to 51/day (see 4.2.5.1).
(6) High-resolution Precipitation Nowcasts providing short-range prediction of precipitation with aspatial resolution of 250 m were introduced in August 2014 (see 4.4.1.1 (1)).
(7) The horizontal resolution of the Typhoon EPS was enhanced from TL319 to TL479 in March
2014. Its ensemble size was also increased from 11 to 25 (see 4.5.2.1 (1)).
(8) The horizontal resolution of the UV-index prediction model was enhanced from T42 to T106 in
October 2014 (see 4.5.2.1 (9)).
(9) The horizontal resolution of the One-month EPS was enhanced from TL159 to TL319 in March2014 (see 4.6.1.1).

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2.

Equipment in use

On 5 June, 2012, an upgraded version of the computer system used for numericalanalysis/prediction and satellite data processing was installed at the Office of Computer Systems
Operations in Kiyose, which is about 30 km northwest of JMA’s Tokyo Headquarters. The office inKiyose and JMA’s Headquarters are connected via a wide-area network. The computer types usedin the system are listed in Table 2-1, and further details are provided in Narita (2013).
Table 2-1 System computer types
Supercomputers (Kiyose) Hitachi: SR16000 model M1
Number of subsystem 2
Number of nodes

54 physical nodes per subsystem
432 logical nodes per subsystem

Processors

3,456 IBM POWER7 processors (32 per node)

Performance

423.5 TFlops per subsystem (980 GFLOPS per node)

Main memory

55.296 TiB per subsystem (128 GiB per node)High-speed storage*

Hitachi AMS2500 (138 TB for primary, 210 TB for secondary)

Data transfer rate

96 GiB/s (one way) (between any two nodes)

Operating system

IBM AIX Version 7.1* Dedicated storage for supercomputers

Primary Satellite Data Processing Servers (Kiyose): Hitachi EP8000/750
Number of servers

3

Processor

IBM POWER7 (3.0 GHz)

Main memory

128 GiB per server

Operating system

IBM AIX Version 6.1

Secondary Satellite Data Processing Servers (Kiyose): Hitachi EP8000/750
Number of servers

6

Processor

IBM POWER7 (3.0 GHz)

Main memory

128 GiB per server

Operating system

IBM AIX Version 6.1

Foreign Satellite Data Processing Servers (Kiyose): Hitachi HA8000/RS220AK1
Number of servers

6

Processor

Intel Xeon X5670 (2.93 GHz)

Main memory

32 GiB per server

Operating system

Linux

Division Processing Servers A (Kiyose): Hitachi BS2000-2-

Number of servers

16

Processor

Intel Xeon E5640 (2.66 GHz)

Main memory

48 GiB per server

Operating system

Linux

Division Processing Servers B (Kiyose): Hitachi EP8000/520
Number of servers

2

Processor

IBM Power6+ (4.7 GHz)

Main memory

32 GiB per server

Operating system

IBM AIX Version 6.1

Decoding Servers (Kiyose): Hitachi EP8000/750
Number of servers

2

Processor

IBM Power7 (3.70 GHz)

Main memory

64 GiB per server

Operating system

IBM AIX Version 6.1

Mass Storage System (Kiyose)
Shared storage**

Hitachi VFP500N and AMS2500 (754 TB total, RAID 6)

Data bank storage**

Hitachi VFP500N and AMS2500 (2932 TB total, RAID 6)

Backup tape storage

Hitachi EP8000 and L56/3000 (1520 TB total)** Shared by supercomputers and servers

Wide Area Network (between HQ and Kiyose)
Network bandwidth

200 Mbps (two independent 100-Mbps WANs)

3.

Data and Products from GTS and other sources in use

3.1

Observation

A summary of data received through the GTS and other sources and processed at JMA is given in
Table 3-1.
Table 3-1 Number of observation reports in use
SYNOP/SHIP/SYNOP MOBIL
164,000/day
BUOY
33,000/day
TEMP-A/PILOT-A
1,600/day
TEMP-B/PILOT-B
1,600/day
TEMP-C/PILOT-C
1,200/day
TEMP-D/PILOT-D
1,100/day
AIREP/AMDAR
833,000/day
PROFILER
6,500/day-3-

AMSR2
AIRS/AMSUNOAA/AMSU-A
Metop/AMSU-ANOAA/AMSU-B
NOAA/MHS
Metop/MHSMetop/ASCATGOES/CSRMTSAT/CSR
METEOSAT/CSR
GPSRO
AMV
SSMIS
GNSS-PWVAMeDASRadar Reflectivity
Radial Velocity
Typhoon Bogus

3.2

12,000,000/day
280,000/day
850,000/day
570,000/day
510,000/day
5,100,000/day
5,100,000/day
5,100,000/day
1,120,000/day
120,000/day
1,090,000/day
280,000/day
5,000,000/day
16,900,000/day
710,000/day
232,400/day
4,200/day
4,200/day
12/dayForecast products

Grid Point Value (GPV) products of the global prediction model from ECMWF, NCEP, UKMO,
BOM, CMS, DWD and CMA are used for internal reference and monitoring. The products of
ECMWF are received via the GTS, and the other products are received via the Internet.

4.

Forecasting systems

4.1

System run schedule and forecast ranges

Table 4.1-1 summarizes the system run schedule and forecast ranges.
Table 4.1-1 Schedule of the analysis and forecast system
InitialRun schedule
Model
time
(UTC)
(UTC)
Global000225 – 0330
Analysis/Forecast
06
0825 – 0930
121425 – 1530, 1715 – 1800
182025 – 2130
Meso-scale
000055 – 0205
Analysis/Forecast
030355 – 0505
060655 – 0805
090955 – 1105
121255 – 1405
15
1555 – 1705
181855 – 2005
212155 – 2305
Local
00, 01,0035 – 0100, 0135 – 0200,-4-

Forecast
range (hours)
84
84
264
84
39
39
39
39
39
39
39
39
9

Analysis/Forecast

Typhoon Ensemble
Forecast

Ocean Wave
Forecast

Storm Surge
Forecast

One-week Ensemble
Forecast
One-month Ensemble
Forecast

02, 03,04, 05,06, 07,08, 09,10, 11,
12, 13,14, 15,16, 17,18, 19,20, 21,
22, 23
00
06
12
18
00
06
12
18
00
03
06
09
12
15
18
21
00
12
12
12

Seasonal Ensemble
Forecasts

00

0235 – 0300, 0335 – 0400,
0435 – 0500, 0535 – 0600,
0635 – 0700, 0735 – 0800,
0835 – 0900, 0935 – 1000,
1035 – 1100, 1135 – 1200,
1235 – 1300, 1335 – 1400,
1435 – 1500, 1535 – 1600,
1635 – 1700, 1735 – 1800,
1835 – 1900, 1935 – 2000,
2035 – 2100, 2135 – 2200,
2235 – 2300, 2335 – 2400
0305 – 0350
0905 – 0950
1505 – 1550
2105 – 2150
0330 – 0350
0930 – 0950
1530 – 1550, 1840–1850
2130 – 2150
0200 – 0225
0505 – 0525
0800 – 0825
1105 – 1125
1400 – 1425
1705 – 1725
2000 – 2025
2305 – 2325
0310 – 0500
1510 – 1845
1855 – 2015 (every Tuesday
and Wednesday)
1855 – 2015 (every Saturday
and Sunday)
2205 – 2315 (every 5 days)

4.2

Medium-range forecasting system (4 – 10 days)

4.2.1

Data assimilation, objective analysis and initialization

132
132
132
132
84
84
264
84
39
39
39
39
39
39
39
39
264
264
816
432
(7 months)

4.2.1.1 In operation(1) Global Analysis (GA)A four-dimensional variational (4D-Var) data assimilation method is employed in analysis of theatmospheric state for the Global Spectral Model (GSM). The control variables are relative vorticity,
unbalanced divergence, unbalanced temperature, unbalanced surface pressure and the natural
logarithm of specific humidity. In order to improve computational efficiency, an incremental method
is adopted in which the analysis increment is evaluated first at a lower horizontal resolution (TL319)
and is then interpolated and added to the first-guess field at the original resolution (TL959).
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The Global Analysis (GA) is performed at 00, 06, 12 and 18 UTC. An early analysis with a short
cut-off time is performed to prepare initial conditions for operational forecasting, and a cycle
analysis with a long cut-off time is performed to maintain the quality of the global data assimilation
system.
The specifications of the atmospheric analysis schemes are listed in Table 4.2.1-1.
In March 2014, the number of vertical levels was increased from 60 to 100 and the pressure of the
top level was raised from 0.1 to 0.01 hPa.
The global land surface analysis system has been in operation since March 2000 to provide theinitial conditions of land surface parameters for the GSM. The system includes daily global snow
depth analysis, described in Table 4.2.1-2, to obtain appropriate initial conditions for snow
coverage and depth.
Table 4.2.1-1 Specifications of the GA
Analysis scheme
Incremental 4D-Var
Data cut-off time
2 hours and 20 minutes for early run analysis at 00, 06, 12 and 18 UTC
11 hours and 50 minutes for cycle run analysis at 00 and 12 UTC
7 hours and 50 minutes for cycle run analysis at 06 and 18 UTC
First guess
6-hour forecast by the GSM
Grid form,
Reduced Gaussian grid, roughly equivalent to 0.1875˚
resolution and[ 1920 ( tropic ) – 60 ( polar ) ] x 960
number of grids
Vertical levels
100 forecast model levels up to 0.01 hPa + surface
Analysis variables
Wind, surface pressure, specific humidity and temperature
Observation (as of
SYNOP, SHIP, BUOY, TEMP, PILOT, Wind Profiler, AIREP, AMDAR;
31 December 2014) atmospheric motion vectors (AMVs) from MTSAT-2, GOES-13, 15,Meteosat-7, 10; MODIS polar AMVs from Terra and Aqua satellites;
AVHRR polar AMVs from NOAA and Metop satellites; LEO-GEO AMVs;
ocean surface wind from Metop-A, B/ASCAT; radiances from NOAA-15,
18, 19/ATOVS, Metop-A, B/ATOVS, Aqua/AMSU-A, DMSP-F16, 17,
18/SSMIS, TRMM/TMI, GCOM-W/AMSR2 Aqua/AIRS, Metop-A,B/IASI;
clear sky radiances from the water vapor channels (WV-CSRs) of
MTSAT-2, GOES-13, 15, Meteosat-7, 10; GNSS RO bending angle
data from Metop-A, B/GRAS, COSMIC/IGOR, GRACE-A/blackjack,TerraSAR-X/IGOR, zenith total delay data from ground-based GNSS
Assimilation window 6 hours
Table 4.2.1-2 Specifications of snow depth analysis
Methodology
Two-dimensional Optimal Interpolation scheme
Domain and grids
Global, 1˚ × 1˚ equal latitude-longitude grids
First guess
Derived from previous snow depth analysis and USAF/ETAC Global
Snow Depth climatology (Foster and Davy 1988)
Data used
SYNOP snow depth data
Frequency
Daily(2) Typhoon bogussing in the GA-6-

For typhoon forecasts over the western North Pacific, typhoon bogus data are generated torepresent typhoon structures accurately in the initial field of forecast models. These data consist of
information on artificial sea-surface pressure and wind data around a typhoon. The structure is axiasymmetric. First, symmetric bogus data are generated automatically based on the centralpressure and 30-kt wind speed radius of the typhoon. Axi-asymmetric bogus data are thengenerated by retrieving asymmetric components from the first-guess field. Finally, these bogus
profiles are used as pseudo-observation data for the GA.

4.2.1.2 Research performed in the field(1) Assimilation of GNSS RO data bending angle profiles into the Global NWP system
JMA began assimilating Global Navigation Satellite System (GNSS) Radio Occultation (RO)refractivity data into the Global NWP system in March 2007. However, refractivity profiles were notassimilated at levels higher than 30 km, due to the well-documented degradation of retrieval
precision above this height. To avoid such height limitations, the assimilation of bending angle data
to replace refractivity data was started on 18 March 2014. In the new configuration, bending angle
profiles above 30 km are not excluded, and vertical data thinning is removed. A one-dimensional
observation operator provided as part of the Radio Occultation Processing Package (ROPP;
developed by the Radio Occultation Meteorology Satellite Application Facility (ROM-SAF)) was
introduced along with the start of bending angle data assimilation. Observing system experiments
involving the use of the Global NWP system showed that the first-guess near 5 hPa was improvedby the assimilation of bending angle profiles above 30 km. (H. Owada)(2) Assimilation of AMSU-A channel 14 data into the Global NWP system
The assimilation of AMSU-A channel 14 data into the Global NWP system began on 18 March2014 when the system’s top-level pressure was raised from 0.1 to 0.01 hPa (see 4.2.2.2 (1)). The
AMSU-A channel 14 has a sensitivity peak near 2 hPa. Normal distribution for the first-guess (FG)departure of AMSU-A channel 14 radiance data after scan bias correction was verified. (T. Egawa)(3) Assimilation of ground-based GNSS zenith total delay data into the Global NWP
system
JMA began assimilation of ground-based GNSS zenith total delay data into the Global NWP
system on 18 March 2014. Recently, more ground-based GNSS data have been made available
on the Global Telecommunication System (GTS). The stations are distributed globally, though the
majority are located in Europe and the U.S. Observing system experiments involving ground-based
GNSS zenith total delay data using the Global NWP system showed that dry bias in the initial fields-7-was improved and specific humidity fields at 850 hPa were improved for the forecast range of 24hours. Other variables of initial fields, including temperature at 850 hPa, were neutral. (K.
Yoshimoto)(4) Assimilation of Metop/IASI and Aqua/AIRS radiance data into the Global NWP system
Observation data from the Infrared Atmospheric Sounding Interferometer (IASI) and theAtmospheric Infrared Sounder (AIRS) have been operationally assimilated into the Global NWP
system since 4 September 2014. JMA utilizes the IASI dataset (consisting of 616 channels
spatially thinned to one pixel from four for each scan position) and the AIRS dataset (consisting of
324 channels spatially thinned to one from nine for each scan position). Long-wave temperaturesounding channels (around 15 μm) were selected as assimilation targets for accuracy
improvement in the analysis temperature field. There are 69 selected channels for IASI and 76 for
AIRS. For AIRS, 9 channels around 4.4 μm are added at nighttime. Cloud-contaminatedobservations are excluded to avoid the complexity of having to consider the effects of cloud in data
assimilation. A pre-operational experiment revealed that the root mean square (RMS) of the firstguess departure for AMSU-A and MHS was reduced by the introduction of IASI and AIRS,
indicating accuracy improvement for temperature fields in analysis and the first guess. Errors also
exhibit a statistically significant reduction in short-range forecasting. (A. Okagaki)(5) Modification of typhoon bogus generation method
One of the parameters used for modification of the typhoon bogus generation method was
adjusted in September 2014, as it had not previously been updated in line with the improvement of
the inner model’s resolution in 2011. Typhoons with too steep a pressure gradient around theircenter often cannot be represented using the inner model in the same way as observed because
the structure around the center is too small for the model’s horizontal grid spacing. Accordingly,
central pressure is modified depending on the resolution of the inner model in the typhoon bogus
generation process so that the pressure gradient around the typhoon center does not become
overly steep. The adjusted parameter is used in this modification process. As a result of theadjustment, typhoons with a steep pressure gradient around their center can be analyzed more
appropriately. It was also found that computational stability in the minimization process ofvariational data assimilation was occasionally reduced by assimilating typhoon bogus data whose
departure from the first guess was extremely large. For this reason, gross error checking for
typhoon bogus data was introduced to enable the elimination of such extreme data. The observing
system experiment was performed in the summer of 2014. Both typhoon positional error andintensity error were reduced for typhoons with steep-pressure gradients. (Y. Kosaka)-8-4.2.2Model

4.2.2.1 In operation(1) Global Spectral Model (GSM)
The specifications of the operational Global Spectral Model (GSM1403; TL959L100) aresummarized in Table 4.2.2-1.
JMA runs the GSM four times a day (at 00, 06 and 18 UTC with a forecast time of 84 hours and at
12 UTC with a forecast time of 264 hours).
In March 2014, the number of vertical levels was increased from 60 to 100 and the pressure of the
top level was raised from 0.1 to 0.01 hPa. The parameterization schemes, such as those for theboundary layer, radiation, non-orographic gravity waves and deep convection, were also improved.

Table 4.2.2-1 Specifications of the GSM for 11-day forecasts
Basic equations
Primitive equations
Independent variables
Latitude, longitude, sigma-pressure hybrid coordinates, time
Dependent variables
Surface pressure, winds (zonal, meridional), temperature,
specific humidity, cloud water content
Numerical techniques
Spectral (spherical harmonic basis functions) in horizontal,
finite differences in verticalTwo-time-level, semi-Lagrangian, semi-implicit time integration
schemeHydrostatic approximation
Integration domain
Global in horizontal, surface to 0.01 hPa in vertical
Horizontal resolution
Spectral triangular 959 (TL959), reduced Gaussian grid
system, roughly equivalent to 0.1875˚ × 0.1875˚ in latitude andlongitude
Vertical resolution
100 unevenly spaced hybrid levels
Time step
400 seconds
Orography
GTOPO30 dataset, spectrally truncated and smoothed
Gravity wave dragLongwave orographic drag scheme (wavelengths > 100 km)
mainly for stratosphere
Shortwave orographic drag scheme (wavelengths
approximately 10 km) only for troposphere
Non-orographic spectral gravity wave forcing scheme
Horizontal diffusion
Linear, fourth-order
Vertical diffusion
Stability (Richardson number) dependent, local formulation
Planetary boundary layer
Mellor and Yamada level-2 turbulence closure scheme
Similarity theory in bulk formulae for surface layer
Treatment of sea surface
Climatological sea surface temperature with daily analyzed
anomaly
Climatological sea ice concentration with daily analyzed
anomaly
Land surface and soil
Simple Biosphere (SiB) model
RadiationTwo-stream with delta-Eddington approximation for shortwave-9-Convection
Cloud(hourly)Two-stream absorption approximation method for longwave(hourly)
Prognostic Arakawa-Schubert cumulus parameterization
PDF-based cloud parameterization

4.2.2.2 Research performed in the field(1) Upgrade of the GSM
In March 2014, JMA began operation of the upgraded GSM with more vertical levels and a higher
top level. The number of vertical layers was increased from 60 to 100, and the pressure of the top
level was raised from 0.1 to 0.01 hPa. In particular, more layers were applied mainly in the upper
troposphere and the lower stratosphere to improve forecast accuracy for the troposphere andstratosphere, respectively. The main purposes of raising the model top were to allow theassimilation of more satellite observations with sensitivity for the middle atmosphere and to reduce
the effect of pseudo-reflection from the model top in the stratosphere. (H. Yonehara)(2) Revision of physical processes
In March 2014, the parameterization schemes, including those of the boundary layer, radiation,
non-orographic gravity waves and deep convection, were revised to improve the representation of
atmospheric characteristics. The GSM improvements reduced mean errors (ME) and root meansquare errors (RMSE) against analysis for 11-day forecasting of geopotential height for mostpressure levels and all forecast times. Overall improvement was also seen in forecasts of otherelements such as global mean sea level pressure and 850/250-hPa vector winds in theextratropics. The GSM upgrade reduced tropical cyclone track forecast errors in most sea areas of
the world. (H. Yonehara)

4.2.3

Operationally available NWP products

The model output products shown below from the GSM are disseminated through JMA’s radio
facsimile broadcast (JMH) service, GTS and the Global Information System Centre (GISC) Tokyo
website.

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Table 4.2.3-1 List of facsimile charts transmitted via the GTS and JMH
The contour lines (upper-case letters) are: D: dew-point depression (T - Td); E: precipitation;
H: geopotential height; J: wave height; O: vertical velocity (ω); P: sea level pressure; T:
temperature; W: isotach wind speed; Z: vorticity; δ: anomaly from climatology; μ: averageover time.
The other symbols are: a: wind arrows; b: observation plots; d: hatch for dewpoint depression< 3 K; g: arrows for prevailing wave direction; j: jet axis; m: wave period in digits; t:temperature in digits; x: streamlines.
The subscripts in the table indicate: srf: surface; trp: tropopause; digit (ex. 500) pressure in hPa.
The superscripts indicate dissemination channels and time: G: sent to GTS; J: sent to JMH; 12:
for 12 UTC only; 5: statistics for pentad sent once per five days for 00 UTC; m: statistics forthe month sent monthly for 00 UTC.
Model

Area
Analysis

GSM

Asia

HZ500G
T850O700 G

East Asia

HWtab300GJ
HTab500G
HTbd700G
HTbd850GJ

Forecast Time [h]
24
36
48
72

12

96
120

144
168
196

HZ500GJ
T500D700GJ
Ta850O700GJ
PEsrfGJ

PEsrfGAsia

HZ500G
Ta850O700GJ12
PEsrfGJ

PEsrfJ12
HZ500G12
PsrfT850G12

Asia-Pacific HWtaj200HtrpG
HWta250G
G
HWta250
HWta500GG
NW Pacific
x200
x200G
G
x850
x850G
G
N Hem.
HT500
Ocean
Japan
JbgmsrfGJ
Wave
NW Pacific
JbgmsrfGJ
JgmsrfJ
JgmsrfJG5
G5
JCDAS
N Hem.
μHδH100 , μHδH500 , μHδH500Gm, μPδPsrfGm

Table 4.2.3-2 List of GPV products (GRIB2) distributed via the GISC Website
Symbols: H: geopotential height; U: eastward wind; V: northward wind;
T: temperature; R: relative humidity; O: vertical velocity (ω); Z: vorticity; X: stream function;
Y: velocity potential; P: pressure; Ps: sea level pressure; E: rainfall; N: total cloud cover;
Ch: high cloud cover; Cm: middle cloud cover; Cl: low cloud cover.
Model

GSM

Area andresolution

Whole globe, Region II
0.25° × 0.25° (surface)
0.5° × 0.5° (surface, isobar level)
10 hPa, 20 hPa, 30 hPa, 50 hPa, 70 hPa, 100 hPa, 150 hPa,
200 hPa, 250 hPa, 300 hPa, 400 hPa, 500 hPa, 600 hPa,
700 hPa, 800 hPa, 850 hPa, 900 hPa, 925 hPa, 950 hPa,
975 hPa, 1,000 hPa, surface
Surface: U, V, T, R, Ps, P, E, N, Ch, Cm, Cl
200 hPa: U, V, T, R, H, O, X, Y
500 hPa: U, V, T, R, H, O, Z
850 hPa: U, V, T, R, H, O, X, Y
Other levels: U, V, T, R, H, O
0 – 84 every 3 hours,

LevelsElements

Forecast- 11 -

hours
Initial times

90 – 264 every 6 hours (12 UTC)
00 UTC, 06 UTC, 12 UTC, 18 UTC

Table 4.2.3-3 List of GPV products (GRIB) distributed via the GISC website and the GTS
Symbols: D: dew-point depression; E: precipitation; G: prevailing wave direction; H:
geopotential height; J: wave height; M: wave period; O: vertical velocity (ω); P: sea levelpressure; R: relative humidity; T: temperature; U: eastward wind; V: northward wind; X:
stream function; Y: velocity potential; Z: vorticity;
The prefixes µ and σ represent the average and standard deviations of ensemble prediction
results, respectively. The symbols °, *, ¶, §, ‡ and † indicate limitations on forecast hours or
initial times as shown in the notes below.
Model

GSM

GSM

GSM

Destination
Area andresolution
Levels andelements

GISC
Whole globe, 1.25° × 1.25°

GTS, GISC
20°S – 60°N, 60°E – 160°W
1.25° × 1.25°
10 hPa: H, U, V, T
20 hPa: H, U, V, T
30 hPa: H, U, V, T
50 hPa: H, U, V, T
70 hPa: H, U, V, T
100 hPa: H, U, V, T
150 hPa: H, U, V, T§§§§
200 hPa: H , U , V , T , X, Y
250 hPa: H, U, V, T
300 hPa: H, U, V, T, D
400 hPa: H, U, V, T, D
§
§
§
§
§
500 hPa: H , U , V , T , D ,
Z
§
§
§
§
§
700 hPa: H , U , V , T , D ,
O
§
§
§
§
§
850 hPa: H , U , V , T , D ,O, X, Y925 hPa: H, U, V, T, D, O
1,000 hPa: H, U, V, T, D
¶
¶
¶
¶
¶
Surface: P , U , V , T , D ,¶E
0 – 84 every 6 hours§
Additional 96 – 192 every
24 hours for 12 UTC¶
0 – 192 every 6 hours for
12 UTC

GTS, GISC
Whole globe, 2.5° × 2.5°

10 hPa: H, U, V, T
20 hPa: H, U, V, T
30 hPa: H, U, V, T
50 hPa: H, U, V, T
70 hPa: H, U, V, T
100 hPa: H, U, V, T
150 hPa: H, U, V, T
200 hPa: H, U, V, T, X, Y
250 hPa: H, U, V, T
300 hPa: H, U, V, T, R, O
400 hPa: H, U, V, T, R, O
500 hPa: H, U, V, T, R, O, Z
600 hPa: H, U, V, T, R, O
700 hPa: H, U, V, T, R, O
850 hPa: H, U, V, T, R, O, X, Y
925 hPa: H, U, V, T, R, O
1,000 hPa: H, U, V, T, R, O
Surface: P, U, V, T, R, E†

Forecasthours

0 – 84 every 6 hours and
96 – 192 every 12 hours† Except analysis

Initial times

00 UTC, 06 UTC, 12 UTC, 18
UTC

Model

One-week EPS

Destination
Area andresolution
Levels andelements

GISC
Whole globe,
2.5° × 2.5°
250 hPa: µU, µV, σU, σV
500 hPa: µH, σH
850 hPa: µU, µV, µT, σU, σV, σT
1,000 hPa: µH, σH
Surface: µP, σP
0 – 192 every 12 hours

Forecast
hours
Initial times

00 UTC and 12 UTC

00 UTC, 06 UTC, 12 UTC,
18 UTC

10 hPa: H*, U*, V*, T*
20 hPa: H*, U*, V*, T*
30 hPa: H°, U°, V°, T°
50 hPa: H°, U°, V°, T°
70 hPa: H°, U°, V°, T°
100 hPa: H°, U°, V°, T°
150 hPa: H*, U*, V*, T*
200 hPa: H, U, V, T
250 hPa: H°, U°, V°, T°
300 hPa: H, U, V, T, D*‡
400 hPa: H*, U*, V*, T*, D*‡
500 hPa: H, U, V, T, D*‡
700 hPa: H, U, V, T, D
850 hPa: H, U, V, T, D
1,000 hPa: H, U*, V*, T*, D*‡
Surface: P, U, V, T, D‡, E†

0 – 72 every 24 hours and 96– 192 every 24 hours for 12UTC° 0 – 120 for 12 UTC† Except analysis* Analysis only
00 UTC, 12 UTC‡ 00 UTC only

Ocean Wave Model
GISC
75°S – 75°N, 0°E – 359.5°E
0.5° × 0.5°
Surface: J, M, G

0 – 84 every 6 hours,
96 – 192 every 12 hours
00 UTC, 06 UTC, 12 UTC, 18
UTC- 12 -

4.2.4

Operational techniques for application of NWP products

4.2.4.1 In operation(1) Forecast guidance
The application techniques for both the medium- and short-range forecasting systems aredescribed in 4.3.4.1 (1).

4.2.4.2 Research performed in the field

4.2.5

Ensemble Prediction System (EPS)

4.2.5.1 In operation

JMA operates the One-week EPS (WEPS) to support one-week forecasts. The major system
upgrade in February 2014 included enhancement of the horizontal resolution of the EPS modeland revision of its physical processes. It also involved an increase in the frequency of operationfrom once a day to twice a day and an approximate halving of each ensemble size from 51 to 27.
Accordingly, the total ensemble size is now 54/day as opposed to 51/day.
The specifications of the EPS are shown in Table 4.2.5-1. It is composed of one control forecastand 26 perturbed forecasts. Initial perturbations are generated using the singular vector (SV)method (Buizza and Palmer 1995). The tangent-linear and adjoint models used for SV computation
are lower-resolution versions of those used in the 4D-Var data assimilation system for the GSM
until October 2011. The moist total energy norm (Ehrendorfer et al. 1999) is employed for the
metrics of perturbation growth. The forecast model used in the EPS is a low-resolution version of
the GSM1304. Accordingly, the dynamical framework and physical processes involved are
identical to those of the previous version of GSM except for the horizontal resolution. A stochastic
physics scheme (Palmer et al. 2009) is used in the One-week EPS in consideration of model
uncertainties associated with physical parameterizations.
Unperturbed analysis is prepared by interpolating the analyzed field in global analysis (see 4.2.1.1).
The sea surface temperature analysis value is used as a lower boundary condition and prescribedusing the persisting anomaly, which means that the anomalies shown from analysis for the initialtime are fixed during the time integration. The sea ice concentration analysis value is alsoprescribed using the persisting anomaly.
- 13 -

Table 4.2.5-1 Specifications of the One-week EPS
Start of operation
March 2001
Ensemble size
27
Integration
Initial time
00 and 12 UTC
Forecast range
11 daysModel type
GSM1304
TL479 reduced Gaussian grid system roughly
Horizontal resolution
equivalent to 0.375° × 0.375° (40 km) in latitude andlongitude
EPS model
Vertical resolution
60 levels (0.1 hPa)(model top)
Model ensemble
Stochastic physics scheme
methodInner-model resolution Spectral triangular truncation 63 (T63), 40 levels
Norm
Moist total energy
InitialSouthern
Tropics
perturbatio
Northern Hemisphere
Hemisphere(30°S –n (Initial Targeted area
(30°N – 90°N)
(90°S
–
30°S)
30°N)
ensemble
generator |*Simplified physics
Physical process**Full physics
SingularvectorOptimization time
48 hours
24 hours
method) Evolved SV
Used
Number of SVs
25*Simplified physics: initialization, horizontal diffusion, surface fluxes and vertical diffusion**Full physics: as per simplified physics with the addition of gravity wave drag, large-scalecondensation, long-wave radiation and deep cumulus convection

4.2.5.2 Research performed in the field(1) Improvement of One-week EPS
JMA plans to improve the One-week EPS (WEPS) in 2016. The work will include an increase in thenumber of forecast model vertical layers from 60 to 100 (and in the pressure of the top level from
0.1 to 0.01 hPa) and revision of its physical processes, including those of the boundary layer,
radiation, non-orographic gravity waves and deep convection. As a result, the dynamicalframework and physical processes involved will be identical to those of the current GSM (see4.2.2.1 (1)) except for horizontal resolution. The results of an experiment conducted for the periodsof September 2013 and February 2014 showed that the WEPS improvement reduced ME and
RMSE values against analysis for 11-day forecasting of geopotential height in the ensemble mean
forecast for the Northern Hemisphere extra tropics with most pressure levels and all forecast times.
(K. Ochi and M. Kyouda)- 14 -

4.2.5.3 Operationally available EPS products

See 4.2.3.

4.3

Short-range forecasting system (0 – 72 hrs)

4.3.1

Data assimilation, objective analysis and initialization

4.3.1.1 In operation(1) Meso-scale Analysis (MA)A 4D-Var data assimilation method has been employed since 19 March, 2002, for mesoscale
analysis of atmospheric conditions (Meso-scale Analysis, or MA). The MA was replaced with a new
4D-Var called the JNoVA (Honda et al. 2005) in April 2009. The JNoVA is based on JMA’s nonhydrostatic model (JMA-NHM; Saito et al. 2006), which is a current mesoscale forecast model (theMeso-Scale Model, or MSM). The analysis domain was expanded in March 2013. The
specifications of the MA are detailed in Table 4.3.1-1.
Table 4.3.1-1 Specifications of the MA
4D-Var formulation
Incremental 4D-Var using a nonlinear forward model in the inner stepwith low resolution
Data cut-off time
50 minutes for analysis at 00, 03, 06, 09, 12, 15, 18 and 21 UTC
Observation (as of
SYNOP, SHIP, BUOY, TEMP, PILOT, Wind Profiler, Weather Doppler
31 December 2014) radar (radial velocity, reflectivity), AIREP, AMDAR; AMVs from MTSAT2; radiances from NOAA-15, 18, 19/ATOVS, Metop-A, B/ATOVS,
Aqua/AMSU-A, DMSP/SSMIS-F16, 17, 18, TRMM/TMI, GCOMW/AMSR2; WV-CSR of MTSAT-2; radar-raingauge analyzed
precipitation; precipitation retrievals from TRMM/TMI, DMSP-F16, 17,
18/SSMIS, GCOM-W/AMSR2; Total Precipitable Water Vapor fromground-based GNSSFirst guess
3-hour forecast produced by the MSM
Domain(Outer step)
configuration
Lambert projection; 5 km at 60°N and 30°N, 817 × 661
Grid point (1, 1) is at the northwest corner of the domain.
Grid point (565, 445) is at 140°E, 30°N.
(Inner step)
Lambert projection; 15 km at 60°N and 30°N, 273 × 221
Grid point (1, 1) is at the northwest corner of the domain.
Grid point (189, 149) is at 140°E, 30°N.
Vertical levels(Outer step) 50 levels up to 22 km (consistent with the forecast model
setting)(Inner step) 40 levels up to 22 km
Analysis variables
Wind, potential temperature, surface pressure and pseudo-relative
humidity
Assimilation window 3 hours(2) Typhoon bogussing of the MA- 15 -

The method employed is the same as that in the GA (see 4.2.1.1 (2)).
(3) Local Analysis (LA)
Local Analysis (LA), which was introduced in August 2012, produces initial conditions for the Local
Forecast Model (LFM) at a horizontal resolution of 2 km. For the provision of initial conditions to thehigh-resolution forecast model targeting small-scale severe weather events, the LA is designed toallow rapid production and frequent updating of analysis at a resolution of 5 km. An analysis cyclewith hourly three-dimensional variational (3D-Var) data assimilations is executed each time for theprevious three-hour period to incorporate information from newly received observations in eachcase. High-resolution NWP’s capacity to capture small-scale variations in topography is expected
to help a reduction of representativeness errors in surface observation assimilation. In association,
the LA also assimilates automated surface station (AMeDAS) data ahead of other operational data
assimilation systems at lower resolutions to appropriately reflect the effects of local-scale
environments near the surface. The analysis domain was expanded so that the Japan and its
surrounding areas can be covered and the update frequency was enhanced to every hour in May
2013. The specifications of the LA are detailed in Table 4.3.1-2.
Table 4.3.1-2 Specifications of the LA
Analysis cycle
The three-hour analysis cycle repeats hourly assimilation with 3D-Var
and one-hour forecasts.
Data cut-off time
30 minutes for analysis at 00, 01, 02, 03, 04, 05, 06, 07, 08, 09, 10, 11,
12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22 and 23 UTC
Observation (as of
SYNOP, SHIP, BUOY, AMeDAS, TEMP, PILOT, Wind Profiler, Weather
31 December 2014) Doppler radar (radial velocity, reflectivity), AIREP, AMDAR and Total
Precipitable Water Vapor from ground-based GNSSFirst guess
Initial fields produced by the latest MSM
DomainLambert projection; 5 km at 60°N and 30°N, 633 × 521
configuration
Grid point (1, 1) is at the northwest corner of the domain.
Grid point (449, 361) is at 140°E, 30°N
Vertical levels
50 levels up to 22 km
Analysis variables
Wind, potential temperature, surface pressure, pseudo-relative humidity
and ground potential temperature

4.3.1.2 Research performed in the field(1) Introduction of gross-error checking for typhoon bogus data in the MA
It was found that computational stability in the minimization process of variational data assimilation
was occasionally reduced by assimilating typhoon bogus data whose departure from the first guess
was extremely large. Accordingly, gross error checking for typhoon bogus data to eliminate suchextremes was introduced on 22 May 2014. (Y. Kosaka)- 16 -

4.3.2Model

4.3.2.1 In operation(1) Meso-Scale Model (MSM)
JMA has operated the MSM since March 2001. Its main roles are disaster prevention and aviation
forecasting. The JMA-NHM was adopted as the MSM in September 2004, and 15- or 33-hour
forecasts have been provided every 3 hours, i.e., 8 times a day, since May 2007. The forecast
domain was expanded in March 2013. The forecast range at all the initial times was extended to 39
hours in May 2013. The specifications of the MSM are listed in Table 4.3.2-1.
Table 4.3.2-1 Specifications of the MSM
Basic equations
Fully compressible non-hydrostatic equations
Independent variables
Latitude, longitude, terrain-following height coordinates, time
Dependent variables
Momentum components in three dimensions, potential
temperature, pressure, mixing ratios of water vapor, cloudwater, cloud ice, rain, snow and graupel, number
concentration of cloud ice
Numerical techniques
Finite discretization on Arakawa-C-type staggeredcoordinates, horizontally explicit and vertically implicit time
integration scheme, fourth-order horizontal finite differencing
in flux form with modified advection treatment for monotonicity
Projection and grid size
Lambert projection, 5 km at 60°N and 30°N
Integration domain
Japan, 817 × 661 grid points
Vertical levels
50 (surface to 21. 8 km)
Forecast times
39 hours from 00, 03, 06, 09, 12, 15, 18 and 21 UTC
Initial fields
4D-Var analysis with mixing ratios of cloud water, cloud ice,
rain, snow and graupel derived from preceding forecasts
considering consistency with the analysis field of relative
humidity
The MSM runs three hours before the initial time for spin-up.
Lateral boundary
00–45 hour forecasts by the GSM initialized at 00/06/12/18
UTC for (03, 06)/(09, 12)/(15, 18)/(21, 00) UTC forecasts
Orography
Mean orography smoothed to eliminate shortest-wave
components
Horizontal diffusion
Linear, fourth-order Laplacian + nonlinear damper
Targeted moisture diffusion applied to grid points where
excessive updrafts appear
Convection
Kain-Fritsch convection scheme
Cloud
Three-ice bulk cloud microphysics
Lagrangian treatment for rain and graupel precipitation
Radiation (short wave)
Two-stream with delta-Eddington approximation (every 15
minutes)Radiation (long wave)
Table look-up and k-distribution methods (every 15 minutes)
Cloudiness
Cloud water and cloud cover diagnosed using a partial
condensation scheme
Gravity wave dragNo parameterization scheme included
PBL
Improved Mellor-Yamada Level 3 scheme
Similarity theory adopted for surface boundary layer
Land surface
Ground temperature predicted using a four-layer ground- 17 -

Surface state

model
Evaporability predicted initialized by climatological values
depending on location and season
Observed SST (fixed during time integration) and sea-ice
distribution
Climatological values of evaporability, roughness length andalbedoSnow cover over Japan analyzed daily(2) Local Forecast Model (LFM)
Making use of the new powerful supercomputer system installed in June 2012, operation of aforecast model called the LFM with an even higher resolution was launched in August 2012 alongwith LA. The new model has 2-km horizontal grid spacing and 60 vertical layers up to a height ofapproximately 20.2 km above the surface, and is designed to produce more detailed forecasts withemphasis on predicting localized and short-lived severe events. The LFM is specifically intended toprovide very short-range forecasts for the period of nine hours ahead and other periods, and toallow rapid and frequent forecast updates based on initial conditions with the latest observations
assimilated by the LA. The forecast domain was expanded so that the Japan and its surrounding
areas can be covered and the update frequency was enhanced to every hour in May 2013. The
specifications of the LFM are listed in Table 4.3.2-2.
Table 4.3.2-2 Specifications of the LFM
Basic equations
Fully compressible non-hydrostatic equations
Independent variables
Latitude, longitude, terrain-following height coordinates, time
Dependent variables
Momentum components in three dimensions, potential
temperature, pressure, mixing ratios of water vapor, cloud water,
cloud ice, rain, snow and graupel
Numerical techniques
Finite discretization on Arakawa-C-type staggered coordinates,
horizontally explicit and vertically implicit time integration
scheme, fourth-order horizontal finite differencing in flux form
with modified advection treatment for monotonicity
Projection and grid size
Lambert projection, 2 km at 60°N and 30°N
Integration domain
Japan, 1531 × 1301 grid points
Vertical levels
60 (surface to 20. 2 km)
Forecast times
9 hours from 00, 01, 02, 03, 04, 05, 06, 07, 08, 09, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22 and 23 UTC
Initial fields
The LA produces initial conditions through the three-hour
analysis cycle based on hourly assimilation with 3D-Var andone-hour forecasts.
Lateral boundary
00–13 hour forecasts produced by the latest MSM
Orography
Mean orography smoothed to eliminate shortest-wave
components
Horizontal diffusion
Linear, fourth-order Laplacian + nonlinear damper
Targeted moisture diffusion applied to grid points where
excessive updrafts appear
Convection
No parameterization scheme included
Cloud
Three-ice bulk cloud microphysics
Lagrangian treatment for rain and graupel precipitation
Radiation (short wave)
Two-stream with delta-Eddington approximation (every 15- 18 -

Radiation (long wave)
Cloudiness
Gravity wave dragPBL
Land surface

Surface state

minutes)
Table look-up and k-distribution methods (every 15 minutes)
Cloud water and cloud cover diagnosed using a partial
condensation scheme
No parameterization scheme included
Improved Mellor-Yamada Level 3 scheme
Similarity theory adopted for surface boundary layer
Ground temperature predicted using a four-layer ground model
Evaporability predicted initialized by climatological values
depending on location and season.
Observed SST (fixed during time integration) and sea-ice
distribution
Climatological values of evaporability, roughness length andalbedoSnow cover over Japan analyzed daily

4.3.2.2 Research performed in the field

4.3.3

Operationally available NWP products

4.3.4

Operational techniques for application of NWP products

4.3.4.1 In operation(1) Forecast guidance
Forecast guidance is utilized to issue warnings, advisories, information and weather forecasts. Five
operational techniques are routinely used to determine guidance from NWP model output: Kalman
Filter, artificial neural network, multiple linear regression, logistic regression, and diagnostic
methods. These techniques are applied to grid-point values from the GSM and the MSM in order toreduce systematic errors in NWP models and extract useful information such as probabilities andcategorical/diagnostic values.
The Kalman filter technique is used to determine precipitation probability, average precipitation
amounts,

maximum/minimum

temperatures,time-series

temperature,maximumwind

speed/direction, gust speed/direction, time-series wind speed/direction, average and minimum
visibility, and probability of minimum visibility values being less than 5,000 or 1,600 meters.
Maximum wind speed/direction and time-series wind speed/direction guidance were updated inJune 2013, and maximum/minimum temperature guidance and time-series temperature guidancewere improved in March 2014.

- 19 -

The artificial neural network technique is used to determine sunshine durations, minimum humidity,
cloud amounts, cloud base heights, point-type snowfall depths, and maximum 1- and 3-hourcumulative precipitation. Maximum 1- and 3-hour cumulative precipitation guidance is determinedby multiplying average precipitation guidance for each grid square by an optimum ratio determinedusing the artificial neural network. Point-type snowfall depth guidance was improved in November
2013.
The multiple linear regression technique is used to determine maximum 24-hour cumulative
precipitation, for which guidance is determined from average 24-hour cumulative precipitation
guidance. The artificial neural network technique was previously used to derive maximum 24-hourcumulative precipitation guidance as well as 1- and 3-hour cumulative precipitation guidance, but
this approach occasionally resulted in overestimation. To reduce unnaturally large precipitation
amounts, the multiple linear regression technique was adopted in March 2013.
The logistic regression technique is used to determine thunderstorm probability, particular cloudbase heights and gusting, and is also applied for turbulence indexing. Guidance for thunderstorm
probability involves use of the Lagged Average Forecast (LAF) method for improved reliability. The
turbulence index called TBindex (Kudo 2011) is formulated from a number of other indices used tocomprehensively predict various kinds of turbulence, and was upgraded in August 2014.
The diagnostic method is used to determine grid-type maximum snowfall depths, weather
categories and visibility distribution guidance, the latter of which supports the determination ofvisibility values based on the use of extinction coefficients for raindrops, snowfall, cloud water andhumidity. The diagnostic method is also applied to the CB cloud amount, the CB top height and theaircraft icing index for area forecasts. The CB cloud amount and top height are determined via the
deep convection discrimination technique used in the Kain-Fritsch convective scheme, in which thecloud top and base heights are estimated from the surrounding temperature, humidity and updraft
conditions. The icing index is formulated by multiplying a distribution function of icing fortemperature with one for dew-point depression based on the results of statistical research
performed for the area around Japan.
(2) Hourly Analysis
JMA Hourly Analysis involves three-dimensional evaluation of temperature and wind fields with agrid spacing of 5 km to provide real-time monitoring of weather conditions. The latest MSM
forecast is used as the first guess, and observational information is added through assimilation.
The 3D-Var data assimilation method is adopted as the analysis technique. The hourly product ismade within 30 minutes of the end of each hour, and is provided to operational forecasters andaviation users. The specifications of the Hourly Analysis schemes are listed in Table 4.3.4-1.

- 20 -

Table 4.3.4-1 Specifications of the Hourly Analysis
Analysis scheme
3D-VarData cut-off time
20 minutes
First guess
2, 3 or 4-hour forecast by the MSM
DomainLambert projection, 5 km at 60°N and 30°N, 721 × 577 grid points
configuration
Grid point (1, 1) is at the northwestern corner of the domain.
Grid point (489, 409) is at 140°E, 30°N.
Vertical levels
50 forecast model levels
Analysis variables
Wind, temperature, surface wind and surface temperature
Observation (as of
AMeDAS, Wind Profiler, Weather Doppler radar (radial velocity), AIREP,
31 December,
AMDAR, and AMVs from MTSAT-2
2014)
Post-processing
Surface filtering (followed by adjustment of the increment within thePBL)

4.3.4.2 Research performed in the field(1) Forecast guidance
Maximum wind speed/direction guidance and maximum snowfall amount guidance for one-week
forecasts have been developed. The Kalman filter and diagnostic methods are used to determine
maximum wind and snowfall amounts, respectively, for each One-week EPS ensemble member.
Guidance for thunderstorm probability will be improved in 2015. To enhance forecast accuracy,some predictors have been changed and all coefficients of logistic regression have beenrecalculated over recent periods.

4.4

Nowcasting and Very-short-range Forecasting systems (0 – 6 hrs)

Since 1988, JMA has routinely operated a fully automated system of precipitation analysis andvery short-range forecasting to monitor and forecast local severe weather conditions. In addition tothese, JMA has issued Precipitation Nowcasts since June 2004, Thunder Nowcasts since May
2010 and Hazardous Wind Potential Nowcasts since May 2010. High-resolution Precipitation
Nowcasts (JMA’s latest nowcasting product) were introduced in August 2014.
The products are listed below.
(1) High-resolution Precipitation Nowcasts (incorporating forecasts of 5-minute cumulative
precipitation, 5-minute-interval precipitation intensity and error range estimation based on
extrapolation and spatially three-dimensional forecasting covering the period up to 60 minutes
ahead)- 21 -

(2) Precipitation Nowcasts (incorporating forecasts of 10-minute cumulative precipitation and 5minute-interval precipitation intensity based on extrapolation covering the period up to 60
minutes ahead)(3) Thunder Nowcasts (incorporating forecasts of thunder and lightning activity based on lightning
detection network system observation covering the period up to 60 minutes ahead)(4) Hazardous Wind Potential Nowcasts (incorporating forecasts of the probability of hazardous
wind conditions such as tornadoes covering the period up to 60 minutes ahead)(5)

Radar/Raingauge-Analyzed

Precipitation(R/A)*(incorporating

one-hourcumulative

precipitation based on radar observation calibrated half-hourly using raingauge measurementsfrom JMA’s Automated Meteorological Data Acquisition System (AMeDAS) and other available
data such as those from rain gauges operated by local governments)(6) Very-Short-Range Forecasts of precipitation (VSRFs) (incorporating forecasts of one-hourcumulative precipitation based on extrapolation and prediction by the MSM and LFM (see4.3.2.1) and covering the period from one to six hours ahead)*Referred to before 15 November, 2006, as Radar-AMeDAS precipitation.

4.4.1

Nowcasting system (0 – 1 hrs)

4.4.1.1 In operation(1) High-resolution Precipitation Nowcasts
High-resolution Precipitation Nowcasts include prediction of 5-minute cumulative precipitation/5minute-interval precipitation intensity and error range estimation up to an hour ahead. Initial
precipitation intensity distribution is determined via three-dimensional analysis of storms using
radar echo intensity, Doppler velocity and upper-air observation data. Precipitation intensity iscalibrated using raingauge observation data, and precipitation distribution is predicted based on
calculation using methods such as kinetic and dynamic estimation. Generation of convective
clouds triggered by convergence is also considered. The specifications are summarized in Table
4.4.1-1.
High-resolution Precipitation Nowcasts are provided to local weather offices and to the public inorder to enable close monitoring of heavy rain areas and support disaster prevention activities.
Table 4.4.1-1 Specifications of the High-resolution Precipitation Nowcast model
Forecast process
 Kinetic: non-linear motion/intensity extrapolation
 Dynamic: vertically one-dimensional convective model enabling
calculationrelating to raindrop generation, precipitation andevaporation
Movement vector
 Precipitation system, cell and rain intensity trend motion vectors
estimated using cross-correlation pattern matching and discreteinterpolation- 22 -

Time step

Grid form
Resolution andforecast time
Number of grids
InitialUpdate interval Dual-Doppler wind
5 minutes (low-resolution three-dimensional prediction)
1 minute (high-resolution three-dimensional prediction)
1 second (vertically one-dimensional convective model)
Cylindrical equidistant projection
Approx. 250 m over land and coasts 00 − 30 minutes ahead
1km over land and coasts 35 − 60 minutes ahead
1km from the coasts 00 − 60 minutes ahead
16,660,800
Analyzed precipitation distribution determined from radar, raingauge
and upper-air observation
Every 5 minutes(2) Precipitation Nowcasts
Precipitation Nowcasts predict 10-minute accumulated precipitation and 5-minute-interval
precipitation intensity by extrapolation up to one hour ahead. Initial precipitation intensity
distribution is derived from radar data obtained at 5-minute intervals, and is calibrated by raingauge
observation. Using estimated movement vectors, these forecasts predict precipitation distribution
on the basis of extrapolation within three minutes of radar observation. These processes areplanned to be replaced with smoothing the output of High-resolution Precipitation Nowcasts. The
specifications are summarized in Table 4.4.1-2.
Precipitation Nowcasts are provided to local weather offices and to the public to help clarify
precipitation transition and to support disaster prevention activities.
Table 4.4.1-2 Specifications of the Precipitation Nowcast model
Forecast process
Non-Linear motion/intensity extrapolation
including the generation and lifecycle estimation of storm cells as wellas orographic rainfall trend prediction
Movement vector
Precipitation system and/or cell motion estimated using the crosscorrelation pattern matching and discrete interpolation
Time step
5 minutes
Grid form
Cylindrical equidistant projection
Resolution
Approx. 1 km
Number of grids
2,560 × 3,360
InitialCalibrated radar echo intensities
Forecast time
60 minutes ahead, updated every 5 minutes(3) Thunder Nowcasts
Thunder Nowcasts predict thunder and lightning activity up to one hour ahead. Initial activity
distribution is derived from lightning detection network system observations obtained at 10-minuteintervals. Using estimated movement vectors, these forecasts predict activity distribution on thebasis of extrapolation within three minutes of radar observation. The specifications are summarizedin Table 4.4.1-3.

- 23 -

Thunder Nowcasts are provided to local weather offices and to the public. They are utilized tounderstand thundercloud transfer and to advise people to stay in or go to safe places in order toavoid lightning strikes.
Table 4.4.1-3 Specifications of Thunder Nowcast model
Forecast process
Extrapolation
Movement vector
As per the Precipitation Nowcast system
Grid form
Cylindrical equidistant projection
Resolution
Approx. 1 km
Number of grids
2,560 × 3,360
Initial4-level activity of thunder and lightning based on lightning detection
network system observation
Forecast time
60 minutes ahead, updated every 10 minutes(4) Hazardous Wind Potential Nowcasts
Hazardous Wind Potential Nowcasts predict the probability of hazardous wind conditions such as
tornadoes up to one hour ahead. Initial probability distribution is established using radarmeasurements including Doppler radar data obtained at 10-minute intervals and severe weather
parameters calculated from Numerical Weather Prediction. Using estimated movement vectors,
these forecasts predict probability distribution on the basis of extrapolation within three minutes ofradar observation. The specifications are summarized in Table 4.4.1-4.
Hazardous Wind Potential Nowcasts are provided to local weather offices and to the public. They
are utilized to understand the transition of high potential areas for hazardous wind and to callattention to hazardous wind conditions.
Table 4.4.1-4 Hazardous Wind Potential Nowcast model
Forecast process
Extrapolation
Movement vector
As per the Precipitation Nowcast system
Grid form
Cylindrical equidistant projection
Resolution
Approx. 10 km
Number of grids
256 × 336
Initial2-level presumed hazardous wind probabilities
Forecast time
60 minutes ahead, updated every 10 minutes

4.4.1.2 Research performed in the field(1) Development of High-Resolution Precipitation Nowcasts
 A new nowcasting product called High-resolution Precipitation Nowcasts was introduced inAugust 2014 (see 4.4.1.1 (1)).

(2) Employment of finer-resolution Doppler velocity data for Hazardous Wind Potential
Nowcasts- 24 -

 The resolution of Doppler velocity data from radars used for Hazardous Wind Potential
Nowcasts to detect meso cyclones was enhanced from 500 to 250 m in November 2014. The
statistical model used to estimate hazardous wind potential was also revised utilizing the latest
observation data.

4.4.2

Models for Very-short-range Forecasting Systems (1 – 6 hrs)

4.4.2.1 In operation(1) Radar/Raingauge-Analyzed Precipitation (R/A)
Radar/Raingauge-Analyzed Precipitation (R/A) is a type of precipitation distribution analysis with aresolution of 1 km, and is derived on a half-hourly basis. Radar data and raingauge precipitation
data are used to make R/A. The radar data consist of intensity data from 46 weather radars
operated by JMA and the Ministry of Land, Infrastructure, Transport and Tourism (MLIT), and theraingauge precipitation data are collected from more than 10,000 raingauges operated by JMA,
MLIT and local governments.
After collecting this information, the radar intensity data are accumulated to create one-hour
accumulated radar precipitation data. Each set of this data is calibrated with the one-hour
accumulated raingauge precipitation data. R/A is a composite of all calibrated and accumulated
radar precipitation data. The initial field for extrapolation forecasting is the composite of thecalibrated radar intensity data.
(2) Very-Short-Range Forecasts of precipitation (VSRFs)
The extrapolation forecast and precipitation forecast from the MSM and the LFM (see 4.3.2.1) are
merged into the Very-Short-Range Forecast of precipitation (VSRFs). The merging weight of the
MSM/LFM forecast is nearly zero for a one-hour forecast, and is gradually increased with forecast
time to a value determined from the relative skill of MSM/LFM forecasts. The specifications of theextrapolation model are detailed in Table 4.4.2-1.
Table 4.4.2-1 Specifications of extrapolation model
Forecast process
Extrapolation
Physical process
Enhancement and dissipation
Movement vector
Precipitation system movement evaluated using the cross-correlation
methodTime step2 – 5 minutes
Grid form
Oblique conformal secant conical projection
Resolution
1 km
Number of grids
1,600 × 3,600
InitialCalibrated radar echo intensities- 25 -

Forecast time

Up to six hours from each initial time (every 30 minutes = 48 times/day)

VSRFs products are issued about 20 minutes after radar observation to support local weather
offices that issue weather warnings for heavy precipitation, and are used for forecast calculation of
applied products such as the Soil Water Index and the R/A Runoff Index.

4.4.2.2 Research performed in the field(1) Radar/Raingauge-Analyzed Precipitation (R/A)
 Delayed analysis for archive data creation has been conducted since January 2014. Thisapproach involves the use of more observation data than real-time analysis.
(2) Very-Short-Range Forecasts of precipitation (VSRFs)
 Several improvements were introduced in May 2014, including:‑ Derivation of mid-term movement vectors based on a large-scale precipitation system‑ Estimation of storm cell life cycles from trends of precipitation intensity calculated using
numerical weather prediction models

4.5

Specialized numerical predictions

4.5.1

Assimilation of specific data, analysis and initialization (where applicable)

4.5.1.1 In operation(1) Global Ocean Data Assimilation System
The Global Ocean Data Assimilation System (named MOVE/MRI.COM-G; Usui et al. 2006)
developed by the Meteorological Research Institute of JMA is in operation at JMA. Its
specifications are shown in Table 4.5.1-1.
Table 4.5.1-1 Specifications of the Global Ocean Data Assimilation System
Basic equations
Primitive equations with free surface
IndependentLat-lon coordinates and σ-z hybrid vertical coordinatesvariables
Dependentu, v, T, S, SSH
variables
Numerical
Finite difference both in the horizontal and in the verticaltechnique
Grid size
1˚ (longitude) × 1˚ (latitude, smoothly decreasing to 0.3˚ toward theequator) grids
Vertical levels
50 levels
Integration domain
Global oceans from 75°N to 75°S
Forcing data
Heat, water and momentum fluxes are calculated using data from- 26 -

Observational data
Operational runs

the JMA Climate Data Assimilation System (JCDAS).
Sea-surface and subsurface temperature and salinity and sea
surface height
Two kinds of run (final and early) with cut-off times of 33 days and 2day, respectively, for ocean observation data

Outputs of MOVE/MRI.COM-G are used to monitor and diagnose tropical ocean status. Somefigures based on MOVE/MRI.COM-G output are published in JMA’s Monthly Highlights on Climate
Systemandprovidedthrough

the

Tokyo

ClimateCenter

(TCC)

website(http://ds.data.jma.go.jp/tcc/tcc/index.html). The data are also used as oceanic initial conditions for
JMA’s coupled ocean-atmosphere model (JMA/MRI-CGCM).
(2) High-resolution sea surface temperature analysis for global oceans
Objective analysis is conducted to produce high-resolution data on daily sea surface temperatures(SSTs) in global oceans on a 1/4˚ × 1/4˚ grid for ocean information services. These data are also
used to provide boundary conditions for short- to medium-range NWP models and the ocean data
assimilation system for the North Pacific Ocean. SST data obtained from polar-orbiting satellites(AVHRRs on the NOAA series and Metop; Windsat on Coriolis; and AMSR2 on GCOM-W) areused together with in-situ SST observation data. The analysis data are available on the NEARGOOS Regional Real Time Database (http:// ds.data.jma.go.jp/gmd/goos/data/database.html).

4.5.1.2 Research performed in the field

4.5.2

Specific models

4.5.2.1 In operation(1) Typhoon Ensemble Prediction System (Typhoon EPS)
JMA operates the Typhoon EPS to support five-day tropical cyclone (TC) track forecasts. A major
system upgrade in March 2014 included enhancement of the horizontal resolution of the EPS
model, revision of its physical processes and an increased ensemble size. The current system
involves 25 forecasts run up to four times a day from base times at 00, 06, 12 and 18 UTC with aforecast range of 132 hours, and is operated when any of the following conditions is satisfied:

A TC of tropical storm (TS*) intensity or higher is present in the RSMC Tokyo - Typhoon
Center’s area of responsibility (0°–60°N, 100°E–180°).

A TC is expected to reach TS intensity or higher in the area within the next 24 hours.

A TC of TS intensity or higher is expected to move into the area within the next 24 hours.

- 27 -

* A TS is defined as a TC with maximum sustained wind speeds of 34 knots or more and less than 48 knots.

The specifications of the Typhoon EPS are shown in Table 4.5.2-1. A low-resolution version of the
GSM1304 is used in this EPS and in the One-week EPS (see 4.2.5.1). Accordingly, the dynamicalframework and physical processes involved are identical to those of the previous GSM except forhorizontal resolution. Unperturbed analysis is conducted by interpolating the target field in the GA.
The results of sea surface temperature and sea ice analysis are referenced for the lower boundary
condition, and the initialized condition is prescribed using the persisted anomaly. Accordingly,
anomalies shown based on analysis for the initial time are fixed during time integration. As with the
One-week EPS, initial perturbations are also generated using the SV method, but theconfigurations are different.
Table 4.5.2-1 Specifications of the Typhoon EPS
Start of operation
February 2008
Ensemble size
25
Integration
Initial time
00, 06, 12 and 18 UTC
Forecast range
132 hours
Model type
GSM1304
TL479 reduced Gaussian grid system roughly equivalent
Horizontal resolution
to 0.375° × 0.375° (40 km) in latitude and longitude
EPS model
Vertical resolution
60 levels (0.1 hPa)(model top)
Model ensemble method Stochastic physics scheme
Inner-model resolution
Spectral triangular truncation 63 (T63), 40 levels
InitialNorm
Moist total energy
perturbation
Vicinities of up to 3 TCs
Northwestern Pacific(Initial
Targeted areain the Typhoon Center’s(20°N – 60°N, 100°E – 180°)
ensemblearea of responsibility
generator |Physical process
Simplified physics
Full physics
Singular
Optimization time
24 hours
vector
EvolvedSVNotusedmethod)Number of SVs
10
10 for each TC(2) Environmental emergency response system
JMA acts as a Regional Specialized Meteorological Center (RSMC) for Environmental Emergency
Response in WMO Regional Association (RA) II, and is responsible for the preparation anddissemination of transport model products on exposure and surface contamination involving
accidentally released radioactive materials. An operational tracer transport model is run at therequest of National Meteorological Services in RA II and the International Atomic Energy Agency(IAEA) to offer RSMC support for environmental emergency response.
A Lagrangian method is adopted for the transport model, and large numbers of tracers are
released at certain times and locations in line with pollutant emission information provided as partof related requests. Effects on three-dimensional advection and horizontal/vertical diffusion, dry
and wet deposition and radioactive decay are computed from three-hourly outputs of the high- 28 -

resolution global model (TL959L60). The standard products of the RSMC involve maps on
trajectories, time-integrated low-level concentrations and total deposition up to 72 hours ahead.
As part of the CTBTO-WMO Backtracking Response System, JMA is responsible for providing
atmospheric backtracking products to the Comprehensive Nuclear-Test-Ban Treaty Organization(CTBTO) in its role as a Regional Specialized Meteorological Center. JMA developed an
atmospheric backtracking transport model and built up a response system that receives e-mailnotifications from CTBTO, executes backtracking calculations and provides the resulting products
in line with the procedure defined in WMO no. 485. JMA began operation of the backtracking
system in December 2009. Backtracking over a period up to 50 days can be provided on an
operational basis. At the 2013 meeting of the Expert Team on Emergency Response Activities (ETERA), a representative from CTBTO asked RSMCs to adapt their operational system for a new
type of request form and to adopt an SFTP file transfer protocol rather than using FTP. JMA had
developed such a system by 11 June 2014.
(3) Ocean-wave forecasting models
JMA operates three numerical wave models: the Global Wave Model (GWM), the Coastal Wave
Model (CWM) and the Shallow-water Wave Model (SWM), all of which are classified as thirdgeneration wave models. The GWM and the CWM are based on the MRI-III, and were developed
at JMA’s Meteorological Research Institute and updated to create new versions in May 2007. The
models’ specifications are given in Table 4.5.2.1 (3)-1.
An assimilation scheme developed by JMA for wave models was incorporated into the GWM andthe CWM in October 2012.
The SWM is based on the WAM, which was modified at the National Institute for Land andInfrastructure Management of MLIT and put into operation under a cooperative framework with
MLIT’s Water and Disaster Management Bureau. The target area is limited, and is currently being
expanded. The models’ specifications are given in Table 4.5.2.1 (3)-2.
Table 4.5.2.1 (3)-1 Specifications of the ocean-wave prediction model
Model name
Global Wave Model
Coastal Wave Model
Model type
Spectral model (third-generation wave model)
Spectral components 900(25 frequencies from 0.0375 to 0.3 Hz and 36 directions)
Grid form
Equal latitude-longitude grid on spherical coordinates
Grid size
0.5° × 0.5° (720 × 301)
0.05° × 0.05° (601 × 601)
Integration domain
Global 75°N – 75°S
Coastal Sea of Japan 50°N –20°N, 120°E – 150°E
Time step
Advection term: 10 minutes
Advection term: 1 minuteSource term: 30 minutes
Source term: 3 minutes
Forecast time
84 hours from 00, 06 and 18 UTC 84 hours from 00, 06, 12 and 18
264 hours from 12 UTC
UTC- 29 -

Boundary conditions
Initial conditions
Wind field

Global Wave Model
Hindcast
Global Spectral Model (GSM)
Bogus gradient winds (for typhoons in the western North Pacific)

Table 4.5.2.1 (3)-2 Specifications of the ocean-wave prediction model
Model nameShallow-water Wave Model
Model type
Spectral model (third-generation wave model)
Spectral components 1,260(35 frequencies from 0.0418 to 1.1 Hz and 36 directions)
Grid form
Equal latitude-longitude grid on spherical coordinates
Grid resolution
1’ × 1’Areas
Domain nameGrid size
Integration domain

Time step
Forecast time
Boundary conditions
Initial conditions
Wind field

Tokyo Bay

37 × 43

Ise Bay

61 × 43

Harima-Nada
Osaka Bay
Ariake Sea
Shiranui Sea
Off Niigata

79 × 49

Sendai Bay

37 × 43

Off Tomakomai

121 × 43

Suo-Nada
Iyo-Nada
Aki-Nada
Hiuchi-Nada

109 × 67

Off Shimane

67 × 31

Ishikari Bay

49 × 43

Off Ishikawa

49 × 67

43 × 73
55 × 37

103 × 73

35.05°N – 35.75°N
139.55°E – 140.15°E
34.35°N – 35.05°N
136.45°E – 137.45°E
34.05°N – 34.85°N
134.15°E – 135.45°E
32.05°N – 33.25°N
130.05°E – 130.75°E
37.80°N – 38.40°N
138.35°E – 139.25°E
37.75°N – 38.45°N
140.90°E – 141.50°E
42.00°N – 42.70°N
141.00°E – 143.00°E
33.30°N – 34.40°N
131.00°E – 132.80°E
33.60°N – 34.80°N
132.60°E – 134.30°E
35.25°N – 35.75°N
132.55°E – 133.65°E
43.10°N – 43.80°N
140.70°E – 141.50°E
36.20°N – 37.30°N
136.00°E – 136.80°E

Advection term: 1 minuteSource term: 1 minute39 hours from 03, 09, 15 and 21 UTC
Coastal Wave Model
Hindcast
Meso-Scale Model (MSM)
Bogus gradient winds (for typhoons in the western North Pacific)

Wave model products are adopted by various domestic users (such as governmental organizations
and private weather companies) via the Japan Meteorological Business Support Center (JMBSC),
whereas SWM products are only used within JMA and MLIT’s Regional Development Bureaus.
GWM products are available within JMA’s WMO Information System for National Meteorologicaland Hydrological Services (NMHSs), and are also disseminated to several countries via GTS.

- 30 -

(4) Storm-surge model
JMA operates a numerical storm surge model to predict storm surges in coastal areas of Japan
using sea-surface wind and pressure fields inferred by the MSM. In the case of tropical cyclones(TCs), storm surges for six scenarios are predicted in consideration of TC track forecast errors. In
addition to the MSM, TC bogus data corresponding to five tracks (center, faster, slower andrightmost/leftmost of the TC track forecast) are used for each scenario. Data on astronomical tides
are required for the prediction of storm tides (i.e., the sum of storm surges and astronomical tides).
Astronomical tides are estimated using an ocean tide model and added linearly to storm surges.
The model’s specifications are given in Table 4.5.2.1 (4)-1.
Table 4.5.2.1 (4)-1 Specifications of the numerical storm-surge model
Basic equations
Two-dimensional shallow-water equations
Numerical technique
Explicit finite difference method
Integration domain
Coastal areas of Japan(117.4°E – 150.0°E, 20.0°N – 50.0°N)
Grid size
Adaptive Mesh Refinement (AMR) method
45 seconds (longitude gradually doubling to 12
minutes toward offshore areas) × 30 seconds(latitude gradually doubling to 8 minutes toward
offshore areas)
Boundary conditions
Modified radiation condition at open boundaries
and zero normal flows at coastal boundaries
Forecast time
39 hours
Forcing data
Meso-Scale Model (MSM)
Bogus data for TCs around Japan
Astronomical tides
Ocean tide model (Egbert and Erofeeva 2002)
and data assimilation of harmonic constants at
tide stations using the ensemble transform
Kalman filter (ETKF)
JMA developed a storm surge model for the Asian region in 2010 in collaboration with Typhoon
Committee Members providing tidal observation and sea bathymetry data. Since 1 June, 2011,
horizontal maps of predicted storm surges have been published on JMA’s Numerical Typhoon
Prediction website. Since 5 June, 2012, time-series charts of predicted storm surges have been
published. The storm surge model uses the GSM for meteorological forcing. In the case of TCs,
storm surges are predicted up to 72 hours ahead using a simple parametric TC track (center) in
addition to the GSM. The model’s specifications are given in Table 4.5.2.1 (4)-2. JMA added 41
stations for time-series charts and total number became 51 stations in 2014.
Table 4.5.2.1 (4)-2 Specifications of the Numerical storm-surge model (Asian region)
Basic equations
Two-dimensional linear shallow-water equations
Numerical technique
Explicit finite difference method
Integration domain
Coastal areas of Asia
(95.0°E – 160.0°E, 0.0°N – 46.0°N)
Grid size
2 minutes × 2 minutes
Boundary conditions
Modified radiation condition at open boundaries andzero normal flows at coastal boundaries- 31 -

Forecast time
Forcing data
Astronomical tides

72 hours
Global Spectral Model (GSM)
Bogus data for TCs (center)
Not included(5) Ocean data assimilation system for the North Pacific Ocean
An ocean data assimilation system for the North Pacific is operated to represent ocean
characteristics such as the movement of the Kuroshio current in the mid/high latitudes of the North
Pacific with the specifications shown below. Data on ocean currents and several layers ofsubsurface water temperatures (products of this system) are available on the NEAR-GOOS
Regional Real Time Database (http:// ds.data.jma.go.jp/gmd/goos/data/database.html).
Table 4.5.2.1 (5)-1 Specifications of the ocean data assimilation system for the
North Pacific Ocean
Basic equations
Primitive equations with free surface
Independent variables
Lat-lon coordinates and σ-z hybrid vertical coordinates
Dependent variablesu, v, T, S, SSH
Numerical technique
Finite difference both in the horizontal and in the vertical witha three-dimensional variational (3D-Var) data assimilation
systemGrid size(1) Western North Pacific model
0.1° longitude × 0.1° latitude in the seas off Japan,
decreasing to 0.166° toward the northern and easternboundaries with the North Pacific model(2) North Pacific model
0.5° longitude × 0.5° latitude
Vertical levels
54
Integration domain(1) Western North Pacific model
From 15°N to 65°N between 115°E and 160°W(2) North Pacific model
From 15°S to 65°N between 100°E and 75°W
Forcing data
Heat, water and momentum fluxes from the Japanese 55year Reanalysis (JRA-55) and from the control run of Onemonth Ensemble Prediction System
Observational dataSea-surface and subsurface temperature/salinity, sea surface
height, sea ice concentration
Operational runs
10-day assimilation and 30-day prediction are implemented
every day(6) Sea-ice forecasting model
JMA issues information on the state of sea ice in the seas off Japan. A numerical sea-ice model
has been run to predict sea ice distribution and thickness in the seas off Hokkaido (mainly in thesouthern part of the Sea of Okhotsk) twice a week in winter since December 1990 (see Table
4.5.2.1 (6)-1).
Table 4.5.2.1 (6)-1 Specifications of the numerical sea-ice prediction model
Dynamical processes
Viscous-plastic model (MMD/JMA 1993) –- 32 -

Physical processes
Dependent variables
Grid size and time step
Integration domain
Initial time and forecast time
Initial condition

considering wind and seawater stress on sea ice,
Coriolis force, force from the sea surface gradient andinternal force
Heat exchange between sea ice, the atmosphere andseawaterConcentration and thickness
12.5 km and 6 hours
Seas around Hokkaido
168 hours from 00 UTC (twice a week)
Concentration analysis derived from MTSAT and
NOAA satellite imagery and thickness estimated byhindcasting

Grid-point values of the numerical sea-ice model are disseminated to domestic users. Sea ice
conditions for the coming seven days as predicted by the model are broadcast by radio facsimile(JMH) twice a week.
(7) Marine pollution transport model
JMA operates the numerical marine-pollution transport model in the event of marine-pollution
accidents. Its specifications are shown in Table 4.5.2.1 (7)-1. The ocean currents used for themodel’s input data are derived from the results of the ocean data assimilation system for the North
Pacific Ocean.
Table 4.5.2.1 (7)-1 Specifications of the marine pollution transport model
Area
Western North Pacific
Grid size
2 – 30 km (variable)
Model type3-dimensional parcel model
Processes
Advection caused by ocean currents, sea surface winds andocean waves
Turbulent diffusion
Chemical processes (evaporation, emulsification)(8) Aeolian dust prediction model
JMA has operated an Aeolian dust prediction model since January 2004 to enable forecasting ofAeolian dust distribution. In November 2014, the model was updated to a new version based on an
Earth-system model (MRI-ESM1; Yukimoto et al. 2011; Yukimoto et al. 2012) for global climatechange research. The model consists of an atmospheric global circulation model (AGCM) called
MRI-AGCM3 and a global aerosol model known as MASINGAR mk-2, which are linked with acoupler library called Scup (Yoshimura and Yukimoto 2008). The method of dust emission flux
calculation was updated to encompass the scheme of Tanaka and Chiba (2005). The model isdirectly coupled with a low-resolution version of the GSM, and makes use of several GSM
parameters without temporal or spatial interpolation (Tanaka et al. 2003). The model’s
specifications are given in Table 4.5.2.1 (8)-1.

- 33 -

Table 4.5.2.1 (8)-1 Specifications of the Aeolian dust prediction model
Basic equations
Eulerian model coupled with the Global
Spectral Model
Numerical technique
3D semi-Lagrangian transport and dust
emission calculation from surface
meteorology
Integration domain
GlobalGrid size
TL159 (1.125°)
Vertical levels
40 (surface – 0.4 hPa)
Initial time and forecast time
96 hours from 12 UTC (once a day)
Boundary conditions
Similar to those of the Global Spectral Model
Forcing data (nudging)
Global analysis (GA) and forecasts of the
Global Spectral Model (GSM)
Sea surface temperature (MGDSST)(9) Ultraviolet (UV) index prediction system
JMA has operated a UV-index prediction system since May 2005. The UV index is calculated using
a chemical transport model that predicts the global distribution of ozone and a radiative transfer
model. In October 2014, the ozone chemistry model was updated to a new version of thechemistry-climate model (MRI-CCM2; Deushi and Shibata 2011), which is part of MRI-ESM1. The
model’s components are coupled with Scup, and its horizontal resolution has been enhanced fromT42 to T106 (see Table 4.5.2.1 (9)-1 for model specifications).
The radiative transfer model (Aoki et al. 2002) calculates the UV index in the area from 122°E to
149°E and from 24°N to 46°N with a grid resolution of 0.25° × 0.20°. The Look-Up Table (LUT)
method is adopted in consideration of the computational cost involved. The basic parameters of
LUT are the solar zenith angle and total ozone predicted using the CTM. The clear sky UV index iscorrected for aerosols (climatology), distance from the sun, altitude and surface albedo(climatology). The forecast UV index is also corrected for categorized weather forecasting. The
specifications of the radiative transfer model for the UV index are given in Table 4.5.2.1 (9)-2.
Table 4.5.2.1 (9)-1 Specifications of the chemical transport model in the UV index
prediction system
Basic equations
Eulerian model coupled with the Global Spectral Model
Numerical technique
3D semi-Lagrangian transport and chemical reaction
Integration domain
GlobalGrid size
T106 (1.125°)
Vertical levels
64 (surface – 0.01 hPa)
Initial time and forecast
48 hours from 12 UTC (once a day)time
Boundary conditions
Similar to those of the Global Spectral Model
Forcing data (nudging)
Global analysis (GA) and forecasts of the Global Spectral
Model (GSM)
Observational data
Column ozone from OMI/NASA- 34 -

Table 4.5.2.1 (9)-2 Specifications of the radiative transfer model in the UV index
prediction system
Basic equations
Radiative transfer equations for multiple scattering andabsorption by atmospheric molecules and aerosols
Numerical technique
Doubling and adding method
Spectral region and
280 – 400 nm and 0.5 nm
resolution(10) Photochemical oxidant information advisory service
JMA has provided a photochemical oxidant information advisory service since August 2010. The
information is produced by combining numerical prediction of photochemical oxidants in the
troposphere/stratosphere and statistical guidance induced from model outputs associated with past
events. The latter is produced based on verification of the model’s output against observations toenable quantification of oxidant levels for operational forecasters.
Numerical prediction of photochemical oxidants is carried out using a global chemistry-climate
model (MRI-CCM2). The specifications of the global chemistry model are given in Table 4.5.2.1(10)-1. JMA plans to introduce a high-horizontal-resolution regional chemical transport model(NHM-Chem; Kajino et al. 2012) to improve the photochemical oxidant information advisory service
in 2015. NHM-Chem is driven with meteorological fields predicted using JMA-NHM offline. The
lateral boundary conditions of NHM-Chem are given by MRI-CCM2.
Table 4.5.2.1 (10)-1 Specifications of the global chemistry-climate model for thephotochemical oxidant information prediction system
Basic equations
Eulerian model coupled with the Global Spectral Model
Numerical technique
3D semi-Lagrangian transport and chemical reaction
Integration domain
GlobalGrid size
T106 (1.125°)
Vertical levels
48 (surface – 0.01 hPa)
Initial time and forecast
72 hours from 12 UTC (once a day)time
Boundary conditions
Similar to those of the Global Spectral Model
Emission inventories
EDGAR, GEIA (for Global) and REAS (for East-Asia)
Forcing data (nudging)
Global analysis (GA) and forecasts of the Global Spectral
Model (GSM)(11) Mesoscale air pollution transport model
JMA issues photochemical oxidant information for relevant prefectures on days when high oxidant
concentration is expected. This information is based on statistical guidance for oxidant
concentration using weather elements and pollutant observation data as input. In addition to thisstatistical guidance, a mesoscale atmospheric transport model (Takano et al. 2007) is applied tooxidant concentration forecasting with a grid interval of 10 km, in which MSM output is used to
calculate the transport of highly concentrated pollutant masses in the air. Using the oxidant
forecast from the atmospheric transport model with the initial time at 03 UTC, photochemical- 35 -

oxidant information is issued hourly for 04 – 09 UTC for the northern part of the Kyushu region and
the Kanto region including the Tokyo metropolitan area.
(12) Regional Atmospheric Transport Model (RATM) for volcanic ash
JMA introduced the Volcanic Ash Fall Forecast (VAFF) based on the Regional AtmosphericTransport Model (RATM) in March 2008 (Shimbori et al. 2009). This is a 6-hour prediction of areas
where ash is expected to fall as a result of volcanic eruptions in Japan, and is issued in principle
when an ash plume reaches a height of 3,000 m above the crater rim or when the JMA Volcanic
Alert Level is three or higher. The specifications of RATM, for which the outputs of the MSM areused, are given in Table 4.5.2.1 (12)-1. Quantitative forecasting of ash-fall depth has beendeveloped and is currently under trial operation (see 4.5.2.2 (8)).
Table 4.5.2.1 (12)-1 Specifications of RATM for volcanic ash
Model type
Lagrangian description
Number of tracer particles 100,000
Time step
3 minutes
Forecast time
6 hours from the time of eruption
Initial condition
Eruption column based on observational reports including
eruption time and plume height, and continuance of volcanicash emissions
Meteorological fieldMeso-Scale Model (MSM)
Processes
3D advection, horizontal and vertical diffusion, volcanic-ash
fallout, dry deposition and washout(13) Global Atmospheric Transport Model (GATM) for volcanic ash
Since 1997, JMA has been providing information on volcanic ash clouds to airlines, civil aviation
authorities and related organizations in its role as the Volcanic Ash Advisory Centre (VAAC) Tokyo.
JMA introduced the Global Atmospheric Transport Model (GATM) in December 2013 as an 18hour prediction of areas where ash clouds are expected in the area of responsibility as a result of
volcanic eruptions. The forecast is normally updated every six hours (00, 06, 12 and 18 UTC) for
as long as ash clouds are identified in satellite imagery. In July 2014, GATM was extended toprovide 24-hour prediction on a trial basis for verification of 24-hour forecast efficacy in areas withvolcanic ash clouds.
The specifications of the GATM are given in Table 4.5.2.1 (13)-1.
Table 4.5.2.1 (13)-1 Specifications of GATM for volcanic ash
Model type
Lagrangian description
Number of tracer particles 40,000
Time step
10 minutes
Forecast coverage
18 hours*1 from the time of MTSAT-2*2 observation*1 24 hours on a trial basis (18 hours for regular operation)*2 Scheduled for replacement by Himawari-8 from MTSAT-2
on 7 July 2015
Initial condition
Location of volcanic ash particles based on the area and- 36 -

Meteorological field
Processes

maximum altitude of volcanic ash cloud observed by satellite
Global Spectral Model (GSM)
3D advection, (horizontal and vertical diffusion,) volcanic-ash
fallout, dry deposition and washout

4.5.2.2 Research performed in the field(1) Storm surge model
Wave setup sometimes plays a predominant role in storm surges at Japanese ports facing theopen ocean, but this effect is not included in the current storm surge model. JMA is currently
evaluating a number of methods that can be operationally used to estimate sea level rises caused
by wave setup using wave conditions predicted in wave model products.
(2) Sea-ice forecasting model
A new ocean forecast model and a new ocean data assimilation system for the North Pacific
Ocean have been developed (see 4.5.2.1 (5)). JMA introduced ocean current data produced as aresult of these two developments into the sea-ice forecast model in March 2011, and is currently
verifying calculated sea ice data against observation data.
(3) Aeolian dust prediction model
A data assimilation system with the local ensemble transform Kalman filter (LETKF) for aerosols
using satellite sensors (Sekiyama et al. 2010; Yumimoto et al 2014) has been developed.
Verification and improvement of the system will be carried out toward operational application.
(4) UV index prediction system
A data assimilation system with the LETKF for stratospheric ozone has been developed (Sekiyama
et al. 2011; Nakamura et al. 2013), and is scheduled to enter into operation in 2017.
(5) Regional chemical transport model
JMA plans to introduce a high-horizontal-resolution regional chemical transport model (NHMChem; Kajino et al. 2012) to improve the photochemical oxidant information advisory service in2015. The Agency is currently evaluating its forecast guidance for the photochemical oxidant
information advisory from the regional model.
(6) An ensemble forecast system for ocean waves- 37 -JMA has embarked on the development of an ensemble wave forecast system in response to
demand for stochastic wave forecasts up to a week ahead. The Agency has run the system inquasi-operational mode since 29 May 2013 for the purposes of verification and further
improvement, and plans to issue week-range wave forecasts in the second quarter of 2016.
(7) Volcanic ash concentration forecast
Despite the importance of volcanic ash concentration forecasting in the world of aviation, no
method for such prediction has yet been developed. JMA is currently evaluating a forecast method
involving calculation with weight coefficients for individual particles, based on the comparison of
actual results with observation data for past eruptions.
(8) Quantitative forecasting of volcanic ash-fall depth (VAFF)
As described in 4.5.2.1 (12), a VAFF update is scheduled for spring 2015 (Hasegawa et al., 2015).
Thereafter, three types of VAFF will be sequentially provided: VAFFs (Scheduled) will be issued
periodically based on an assumed eruption for active volcanoes, VAFFs (Preliminary) will be brief
forecasts issued within 5 − 10 minutes of an actual eruption, and VAFFs (Detailed) will be moreaccurate forecasts issued within 20 − 30 minutes of an actual eruption. The updated VAFFs will
provide information on expected ash/lapilli fall areas and/or amounts, using the RATM with theoutputs of the LFM and MSM.

4.5.3

Specific products operationally available(1) Numerical storm surge prediction products
Time series representations of predicted storm tides/astronomical tides and forecast time on
predicted highest tides for the coastal area in Japan are disseminated to local meteorologicalobservatories. This information is used as a major basis for issuing storm surge advisories andwarnings.
(2) Aeolian dust products operationally available
Predicted distributions of the surface concentration and total amount of Kosa in eastern Asia areprovided online (http://www.jma.go.jp/en/kosa/index.html) once a day.
(3) UV index products operationally available- 38 -

Distributions and time series representations of predicted UV index information are provided online(http://www.jma.go.jp/en/uv/index.html) twice a day.

4.6

Extended-range forecasts (ERFs) (10 – 30 days)

4.6.1

Models

4.6.1.1 In operation

JMA operates One-month Ensemble Prediction System (One-month EPS) once a week and thecurrent system was upgraded in March 2014. The numerical prediction model applied for thissystem is a low-resolution version (TL319) of the GSM (Table 4.6.1.1-1). For the lower boundary
condition of the model, initial SST anomalies which are estimated using the high-resolution daily
SSTs (see 4.5.1.1(2)) are fixed during the 34-day time integration. Soil moisture, soil temperatureand snow depth are predicted by the model, and their initial states are provided by the land data
assimilation system.
An ensemble consists of 50 members per week – 25 member runs for each of the 34 days ofensemble prediction from two consecutive days. Thus, initial perturbations are produced by
combining the breeding of growing mode (BGM) method and the LAF method. A stochastic physics
scheme (Buizza et al. 1999), which is same as in the One-week EPS (see 4.2.5.1), is used inconsideration of model uncertainties associated with physical parameterizations.
Table 4.6.1.1-1 Specifications of the one-month EPS
Atmospheric model GSM1304
Integration domain
Global, surface to 0.1 hPa
Horizontal
TL319 (reduced Gaussian grid system, approx. 0.5625º Gaussian grid,
resolution
55 km)
Vertical levels
60 (surface to 0.1 hPa)
Forecast time
816 hours from 12 UTC
Ensemble size
50 members
Perturbation
Combination of breeding of growing mode (BGM) method and lagged
generator
averaged forecast (LAF) method
Perturbed area
Northern Hemisphere (20ºN – 90ºN) and tropics (20ºS – 20ºN)
Model ensemble
Stochastic physics scheme
method

4.6.1.2 Reanalysis project

In March 2013, JMA completed the second Japanese global reanalysis, known formally as JRA-55(Kobayashi et al. 2015) and informally as JRA Go! Go! (as “go” is the Japanese word for “five”), toprovide a comprehensive atmospheric dataset suitable for the study of climate change and multidecadal variability. The data cover a period of 55 years extending back to 1958 when regular- 39 -

radiosonde observations became operational on a global basis. The data assimilation system forJRA-55 is based on the TL319 version of JMA’s operational data assimilation system as ofDecember 2009, which has been extensively improved since the JRA-25 dataset was produced(Onogi et al. 2007). JRA-55 is the first global atmospheric reanalysis in which four-dimensionalvariational assimilation (4D-Var) was applied to the last half century including the pre-satellite era.
Its production also involved the use of numerous newly available and improved past observations.
The resulting reanalysis products are considerably better than those based on the JRA-25 dataset.
Two major problems with JRA-25 were a lower-stratosphere cold bias, which has now been
reduced, and a dry bias in the Amazon basin, which has been mitigated. The temporal consistencyof temperature analysis has also been considerably improved. JMA continues the production of
JRA-55 dataset on a near-real-time basis with the data assimilation system used for this dataset.

4.6.2

Operationally available NWP model and EPS ERF products

A model systematic bias was estimated as an average forecast error calculated from hindcast
experiments for the years from 1981 to 2010. The bias is removed from forecast fields, and gridpoint values are processed to produce several forecast materials such as ensemble means andspreads.
Gridded data products for one-month forecast are provided via the Tokyo Climate Center (TCC)
website (http://ds.data.jma.go.jp/tcc/tcc/index.html). Details of these products are shown in Table
4.6.2-1, and map products provided via the TCC are shown in Table 4.6.2-2.
Table 4.6.2-1 Gridded data products (GRIB2) for one-month forecasts provided via the TCC
websiteDetails
Level
Area
Base time &(hPa)forecast times
Ensemble Sea level pressure*
Glob Base time:mean
and its anomalyal
00 UTC ofvalue of
2.5˚ Wednesday
Rainfall amountforecast×and its anomaly
members
2.5˚ Forecast
Temperature and its
Surf, 850, 700
time:2,3,4…,31,
anomaly
32 days fromRelative humidity
850
base time
Geopotential height
500, 100and its anomaly
Wind (u, v)
850, 200

Individual
ensemble

Stream function andits anomaly
Velocity potential
and its anomaly
Sea level pressure*

850, 200
200-

Rainfall amount-- 40 -

Base time :00UTC ofmembers

Temperature*
Relative humidity

Surf,1000, 850, 700, 500, 300, 200, 100
1000, 850, 700, 500, 300

Geopotential height*

1000, 850, 700, 500, 300, 200, 100

Wind (u,v)

1000, 850, 700, 500, 300, 200, 100

Stream function

850, 200

Velocity potential

200

Tuesday andWednesday
Forecast time:
0,1, 2,...,31,32
days from basetime

* Geopotential height, sea level pressure and temperature are calibrated by subtracting thesystematic error from the direct model output.
Table 4.6.2-2 Map products for one-month forecasts provided via the TCC website
Forecast time
Parameter
Ensemble mean
Averages ofGeopotential height at 500 hPa and its anomaly
days 3 – 9, 10 Temperature at 850 hPa and its anomaly– 16, 17 – 30,
Sea level pressure and its anomaly
Stream function at 200 hPa and its anomaly
3 – 30Stream function at 850 hPa and its anomaly
Velocity potential at 200 hPa and its anomaly
Precipitation and its anomaly
Temperature at 2 m and its anomaly
Sea surface temperature (prescribed)

4.7Long range forecasts (LRF) (30 days up to two years)

4.7.1

Models

4.7.1.1 In operation

JMA operates Seasonal Ensemble Prediction System (Seasonal EPS) using an atmosphere-ocean
coupled model (JMA/MRI-CGCM; Yasuda et al. 2007) for three-month, warm/cold season and El
Niño outlooks. The 51-member ensemble is used for the three-month forecast issued every monthand for the warm/cold season forecasts issued five times a year (in February, March, April,
September and October). The El Niño outlook is also issued based on the same model results.
The JMA/MRI-CGCM was developed by the Meteorological Research Institute and the Climate
Prediction Division of JMA. Its specifications are shown in Table 4.7.1-1. The model is initializedwith atmospheric and oceanic analysis using the JMA Climate Data Assimilation System (JCDAS)
and MOVE/MRI.COM-G, respectively. Land surface climatological conditions are used as the initialvalues for the CGCM, and a land surface model coupled to the AGCM is used for the prediction ofland surface conditions. The climatological distribution of sea ice is used as the lower boundary
condition. The EPS adopts a combination of the LAF method and the initial perturbation method
described below. Nine-member ensemble predictions are made every five days, and atmosphericinitial perturbations for each initial date are obtained using the BGM method. Oceanic initial
perturbations are obtained with MOVE/MRI.COM-G (see 4.5.1.1 (1) for details) forced by thesurface heat and momentum fluxes of atmospheric initial perturbation fields using the BGM method.
- 41 -

Table 4.7.1-1 Specifications of the seasonal EPS
Model
JMA/MRI-CGCM (Yasuda et al. 2007)
Oceanic component
Identical to the model for MOVE/MRI.COM-G
Atmospheric
Basic equations
Primitive equations
components
DomainGlobalResolution
TL95, 40 vertical levels
Convection scheme
Arakawa-SchubertLand surface
SiB of Sellers et al. (1986)
processes
Coupling
Coupling interval
1 hourFlux adjustment
Monthly heat and momentum flux
adjustment
Forecast period
7 months
Model run frequency Once every 5 days
Perturbation
Combination of the breeding of growing mode (BGM) method
generator
and the LAF method

4.7.2

Operationally available EPS LRF products

JMA provides gridded data and map products for three-month forecasts every month. Warmseason (June-July-August; JJA) forecasts are issued in February, March and April, and coldseason (December-January-February; DJF) forecasts are issued in September and October.
A model systematic bias was estimated for use as an average forecast error calculated fromhindcast experiments for the 30 years from 1981 to 2010. The bias is removed from forecast fields,
and grid-point values are processed to produce several forecast materials such as ensemblemeans and spreads.
The following model output products (Tables 4.7.2-1 and 4.7.2-2) for three-month and warm/coldseason

forecasts

areprovided

via

the

Tokyo

ClimateCenter

(TCC)

website(http://ds.data.jma.go.jp/tcc/tcc/index.html).
Table 4.7.2-1 Gridded data products (GRIB2) for three-month and warm/cold-season
forecasts provided via the TCC website
Details
Level
Area
Base time &(hPa)forecast time
Ensemble
Sea level pressure*,
Base time:mean, its
its anomaly and spread
00 UTC around theanomaly,
15th of each monthRainfall amount,and spread its anomaly and spread
Global(standard
2.5˚ ×Sea surface temperature*
Forecast times:
deviation)
2.5˚
and its anomaly
values ofOne- and threeTemperature*, its
Surf, 850
forecast
month averages foranomaly and spread
members
targeted terms
Geopotential height*, its
500
anomaly and spread- 42 -Wind (u, v), its anomaly
850, 200and spread
Individual
Sea level pressure* and its
Base time:ensembleanomaly
00 UTC on eachmembers
initial date ofRainfall amount and its
prediction
anomaly(every 5 days)
Sea surface temperature*and its anomaly
Forecast times:
Temperature* and its
Surf, 850, 500, 200
One-monthanomaly
averages for
Relative humidity and its
850
targeted terms
anomaly
Specific humidity and its
850
anomaly
Geopotential height* and850, 500, 300,
its anomaly
200, 100
Wind (u,v) and its anomaly
850, 500, 200* Geopotential height, sea level pressure, temperature and sea surface temperature are calibrated
by subtracting the systematic error from the direct model output.
Table 4.7.2-2 Map products for three-month and warm/cold-season forecasts provided via
the TCC website
Forecast time
Parameter
EnsembleThree-month forecast:
Geopotential height at 500 hPa, related anomaly andmean, its
Averages of first month,spreadanomaly
second month, third
Temperature at 850 hPa, its anomaly and spreadand spread month, and three months Sea level pressure, its anomaly and spread
Stream function at 200 hPa, its anomaly and spread
Warm/cold season
Stream function at 850 hPa, its anomaly and spread
forecast:
Wind (u,v) anomaly at 850 hPa
Averages of three
Velocity potential at 200 hPa, its anomaly and spreadmonths (JJA or DJF)
Precipitation, its anomaly and spread
Temperature at 2 m, its anomaly and spread
Sea surface temperature and its anomaly

5.

Verification of prognostic products

5.1

Annual verification summary

5.1.1

NWP prognostic products

Objective verification of prognostic products is operationally performed against analysis andradiosonde observations according to WMO/CBS recommendations. The results of monthly
verification for 2014 are presented in Tables 5.1.1-1 – 5.1.1-20. All verification scores are only forprediction from 1200 UTC initials.
Table 5.1.1-1 Root mean square errors of geopotential height at 500 hPa against analysis (m)Northern Hemisphere (20–90˚N)
Hours

Jan

Feb

Mar

Apr

May

Jun

- 43 -

Jul

Aug

Sep

Oct

Nov

Dec

Ave

24
72
120

8.0
7.7
7.1
6.7
6.2
6.3
6.0
5.7
6.1
6.4
6.9
7.3
24.9 25.0 23.5 21.7 20.3 19.2 18.3 16.9 19.9 21.4 23.9 25.1
47.6 49.8 46.4 44.2 42.9 38.1 36.4 32.3 39.8 44.9 48.9 50.2

6.7
21.7
43.5

Table 5.1.1-2 Root mean square errors of geopotential height at 500 hPa against analysis (m)Southern Hemisphere (20–90˚S)
Hours
24
72
120

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Ave

7.5
7.8
8.0
7.9
8.4
8.6
8.9
9.3
8.7
7.9
7.4
7.3
25.4 26.3 26.8 28.3 29.5 29.1 31.7 31.7 30.1 27.1 23.6 23.4
47.4 51.0 54.3 56.1 62.6 59.4 61.4 61.7 58.5 54.9 47.0 46.2

8.1
27.8
55.0

Table 5.1.1-3 Root mean square errors of geopotential height at 500 hPa against observations (m)North America
Hours
24
72
120
ob. num.

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Ave

12.7 13.1 12.1 12.6 11.0 11.3 10.8 10.2 10.9 11.8 11.7 12.0
32.1 26.9 24.9 23.4 19.9 19.6 16.9 15.2 18.8 23.2 26.4 27.7
57.3 54.8 45.9 44.8 36.0 35.2 30.6 28.7 32.4 44.2 53.1 55.2
92
92
93
93
93
93
92
93
93
94
93
92

11.7
22.9
43.2
92.8

Table 5.1.1-4 Root mean square errors of geopotential height at 500 hPa against observations (m)Europe/North Africa
Hours
24
72
120

Jan Feb Mar Apr May Jun
Jul Aug Sep Oct Nov Dec
14.6 15.4 14.2 13.7 11.3 10.0 10.7
9.9 11.3 12.0 13.5 14.2
30.7 35.4 31.7 28.3 21.4 21.1 22.8 18.7 22.2 21.6 25.8 31.5
51.4 56.9 60.4 47.6 38.9 38.1 39.2 27.3 40.6 42.0 51.7 62.5
ob. num.
60
62
60
60
60
61
59
58
57
58
58
57

Ave
12.6
25.9
46.4
59.2

Table 5.1.1-5 Root mean square errors of geopotential height at 500 hPa against observations (m)
Asia
Hours
24
72
120

Jan Feb Mar Apr May Jun
Jul Aug Sep Oct Nov Dec Ave
12.4 13.7 12.0 12.9 12.2 11.6 11.8 11.3 11.0 11.4 12.6 12.7 12.1
21.0 22.9 21.2 21.4 23.2 18.6 19.7 17.5 18.4 21.2 22.1 23.5 20.9
34.6 37.2 35.5 35.8 39.8 30.0 30.0 28.3 29.9 37.9 40.8 38.2 34.8
ob. num.
106 106 107 109 108 106 107 107 107 109 111 109 107.7
Table 5.1.1-6 Root mean square errors of geopotential height at 500 hPa against observations (m)Australia/New Zealand
Hours
24
72
120

Jan Feb Mar Apr May Jun
Jul Aug Sep Oct Nov Dec
13.1 13.6 15.1 16.4 16.8 18.2 17.2 17.5 15.4 18.0 18.6 17.4
21.5 17.9 20.6 24.2 24.4 25.8 28.9 25.1 25.8 26.2 25.6 22.6
40.1 33.4 31.4 42.5 40.5 43.7 41.7 35.1 50.2 42.5 36.4 33.1
ob. num.
12
14
13
13
12
12
12
12
13
13
13
14

Ave
16.4
24.1
39.2
12.8

Table 5.1.1-7 Root mean square errors of geopotential height at 500 hPa against observations (m)Northern Hemisphere (20–90˚N)
Hours

Jan

Feb

Mar

Apr

May

Jun
- 44 -

Jul

Aug

Sep

Oct

Nov

Dec

Ave

24
72
120

13.4 14.2 13.0 13.2 12.0 12.0 12.0 11.4 11.5 12.1 12.6 13.0 12.5
28.5 28.3 26.7 24.7 22.4 21.2 20.6 18.7 21.4 23.7 25.4 27.9 24.1
49.7 51.1 49.2 44.9 41.2 37.6 36.4 31.5 39.2 45.3 49.7 53.3 44.1
ob. num.
352 356 354 357 355 355 352 351 346 350 356 350 352.8
Table 5.1.1-8 Root mean square errors of geopotential height at 500 hPa against observations (m)Southern Hemisphere (20–90˚S)
Hours
24
72
120

Jan Feb Mar Apr May Jun
Jul Aug Sep Oct Nov Dec
12.0 12.6 13.2 13.8 14.8 14.9 14.6 14.7 13.5 13.6 13.3 13.5
21.7 21.8 23.3 24.1 24.9 26.3 29.2 27.4 25.2 23.0 21.4 21.0
38.8 39.5 38.2 44.3 48.9 46.5 47.4 46.2 45.9 43.3 36.2 35.0
ob. num.
39
39
39
39
39
39
40
36
38
40
40
41

Ave
13.7
24.1
42.5
39.1

Table 5.1.1-9 Root mean square of vector wind errors at 250 hPa against analysis (m/s)
Northern Hemisphere (20–90˚N)
Hours
24
72
120

Jan

Dec

Ave

3.5
3.3
3.3
3.4
3.4
3.4
3.3
3.4
3.2
3.2
3.3
3.1
8.0
7.7
7.6
7.7
7.7
7.9
8.0
7.8
7.9
7.7
7.8
7.8
12.9 12.6 12.3 12.5 12.8 12.7 12.7 12.0 12.8 12.9 13.4 13.1

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

3.3
7.8
12.7

Table 5.1.1-10 Root mean square of vector wind errors at 250 hPa against analysis (m/s)
Southern Hemisphere (20–90˚S)
Hours
24
72
120

Jan

Dec

Ave

3.3
3.3
3.4
3.3
3.4
3.4
3.3
3.3
3.3
3.1
3.2
3.2
8.7
8.4
8.8
8.2
8.6
8.4
8.5
8.4
8.4
7.8
7.8
7.9
13.5 13.9 14.7 14.0 15.0 14.6 13.7 13.7 13.8 13.3 13.0 12.8

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

3.3
8.3
13.8

Table 5.1.1-11 Root mean square of vector wind errors at 250 hPa against observations (m/s)
North America
Hours
24
72
120
ob. num.

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Ave

6.1
5.5
5.9
5.9
5.5
6.3
5.5
5.5
5.5
5.6
5.6
5.6
11.4
9.1
9.3
9.3
8.9 10.2
8.7
9.0
9.2
9.6
9.4
9.8
17.1 15.1 14.4 15.0 13.2 14.2 12.6 13.2 13.5 15.6 15.4 15.9
90
91
91
92
93
91
92
92
92
93
92
92

5.7
9.5
14.6
91.8

Table 5.1.1-12 Root mean square of vector wind errors at 250 hPa against observations (m/s)
Europe/North Africa
Hours
24
72
120
ob. num.

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Ave

5.5
5.5
5.0
4.9
4.9
4.9
5.0
5.1
5.3
4.9
5.3
5.1
9.3
9.6
9.0
8.4
8.2
8.7
9.4
9.1
9.3
8.8
9.5
9.6
14.8 14.8 16.2 13.4 13.5 14.3 15.0 12.4 15.6 14.4 16.0 16.8
63
64
62
61
61
62
60
59
59
60
60
59

5.1
9.1
14.8
60.8

Table 5.1.1-13 Root mean square of vector wind errors at 250 hPa against observations (m/s)
Asia
Hours
24
72

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Ave

4.8
7.2

5.2
7.9

5.9
8.9

6.4
6.3
9.6 10.1

6.3
9.7

5.9
9.4

5.9
9.5

5.3
8.4

5.0
8.3

4.9
7.7

4.6
6.9

5.5
8.6

- 45 -

120
ob. num.

9.9 11.2 11.9 12.8 14.7 13.1 12.5 12.7 12.0 12.1 11.9
140 139 136 135 136 136 138 138 140 140 141

9.9 12.1
139 138.2

Table 5.1.1-14 Root mean square of vector wind errors at 250 hPa against observations (m/s)
Australia/New Zealand
Hours
24
72
120
ob. num.

Jan

Feb

Mar

Apr

May

Jun

Jul

5.4
5.4
5.2
5.3
5.5
5.4
5.2
8.3
7.8
7.7
8.6
8.6
7.9
8.2
11.3 11.0 10.5 12.2 12.8 12.1 11.9
21
23
23
21
19
21
20

Aug

Dec

Ave

4.7
5.2
5.5
6.0
5.7
7.2
7.5
7.7
8.4
8.7
9.6 12.3 11.5 12.7 11.7
22
18
17
16
17

Sep

Oct

Nov

5.4
8.1
11.6
19.8

Table 5.1.1-15 Root mean square of vector wind errors at 250 hPa against observations (m/s)
Northern Hemisphere (20–90˚N)
Hours
24
72
120
ob. num.

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Ave

5.2
5.1
5.5
5.6
5.6
5.7
5.4
5.5
5.2
5.0
5.0
4.9
5.3
8.9
8.6
8.9
8.9
9.1
9.3
9.1
9.2
9.0
8.7
8.7
8.5
8.9
13.5 13.5 13.7 13.3 13.9 13.6 13.3 13.0 13.7 13.8 13.9 13.6 13.6
394 400 388 388 388 388 389 388 388 391 391 388 390.1

Table 5.1.1-16 Root mean square of vector wind errors at 250 hPa against observations (m/s)
Southern Hemisphere (20–90˚S)
Hours
24
72
120
ob. num.

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Ave

5.3
5.6
5.8
5.5
5.8
5.7
5.8
5.4
5.8
5.5
5.9
5.6
8.6
8.4
9.3
8.8
9.4
8.4
9.3
8.7
8.7
8.7
8.6
8.7
12.0 12.4 13.3 12.8 14.0 13.4 13.9 12.6 13.0 13.0 12.5 12.0
46
47
47
46
45
45
46
44
42
43
43
45

5.6
8.8
12.9
44.9

Table 5.1.1-17 Root mean square of vector wind errors at 850 hPa against analysis (m/s)
Tropic
Hours
24
72
120

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Ave

1.6
2.9
3.7

1.5
2.8
3.5

1.5
2.6
3.4

1.4
2.5
3.1

1.4
2.4
3.0

1.4
2.5
3.2

1.5
2.7
3.4

1.4
2.6
3.4

1.4
2.7
3.6

1.4
2.5
3.2

1.4
2.5
3.2

1.4
2.5
3.2

1.4
2.6
3.3

Table 5.1.1-18 Root mean square of vector wind errors at 250 hPa against analysis (m/s)
Tropic
Hours
24
72
120

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Ave

3.4
6.4
8.3

3.3
6.1
7.9

3.2
5.8
7.6

3.1
5.7
7.4

2.9
5.2
6.9

3.0
5.4
7.1

3.2
5.6
7.0

3.1
5.7
7.1

3.1
5.6
7.2

2.9
5.5
7.0

3.0
5.6
7.2

3.2
5.8
7.6

3.1
5.7
7.4

Table 5.1.1-19 Root mean square of vector wind errors at 850 hPa against observations (m/s)
Tropic
Hours
24
72
120

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Ave

3.7
4.3
4.8

3.5
4.1
4.5

3.4
4.0
4.4

3.3
3.7
4.2

3.1
3.5
3.8

3.4
3.9
4.2

3.5
4.0
4.6

3.4
3.8
4.3

3.4
4.1
4.7

3.4
3.9
4.4

3.3
3.8
4.2

3.6
4.1
4.4

3.4
3.9
4.4

- 46 -

ob. num.

62

65

70

70

70

70

70

69

67

69

70

68

68.3

Table 5.1.1-20 Root mean square of vector wind errors at 250 hPa against observations (m/s)
Tropic
Hours
24
72
120
ob. num.

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Ave

5.4
7.2
8.4
63

5.2
6.8
8.0
66

4.9
6.5
7.7
71

4.8
6.5
7.6
72

4.7
6.2
7.4
72

4.7
6.4
7.7
72

5.1
6.8
8.1
73

4.8
7.3
8.6
71

4.9
6.8
8.1
68

4.6
6.2
7.6
70

4.5
6.1
7.4
71

5.0
6.8
8.2
70

4.9
6.6
7.9
69.9

Verification for One-week EPS is performed against analysis according to the Manual on GDPFS(WMO-No. 485). The Brier Skill Score (BSS) for seasonal (DJF: December-January-February,
MAM: March-April-May, JJA: June-July-August, SON: September-October-November) and annual
averages in 2014 (December in 2013) are shown in Tables 5.1.1-21 - 5.1.1-26.
Table 5.1.1-21 BSS for geopotential height at 500 hPa over the Northern Hemisphere (20–90˚N)
Hour

Z500 anomaly +1.0 standard
deviation
MA
Annu
DJF
JJA SONM
al

Z500 anomaly +1.5 standard
deviation
MA
Annu
DJF
JJA
SON
M
al

Z500 anomaly +2.0 standard
deviation
MA
Annu
DJF
JJA
SON
M
al

24

0.918

0.844

0.745

0.846

0.838

0.912

0.827

0.695

0.815

0.812

0.902

0.808

0.619

0.771

0.775

72

0.783

0.722

0.595

0.688

0.697

0.758

0.674

0.531

0.630

0.648

0.730

0.612

0.447

0.576

0.591

120

0.604

0.533

0.394

0.483

0.504

0.562

0.463

0.314

0.396

0.433

0.521

0.372

0.214

0.326

0.358

168

0.421

0.343

0.216

0.272

0.313

0.377

0.275

0.144

0.166

0.240

0.357

0.203

0.066

0.076

0.175

Ho
ur

Z500 anomaly -1.0 standard
deviation
MA
Annu
DJF
JJA SONM
al

Z500 anomaly -1.5 standard
deviation
MA
Annu
DJF
JJA
SON
M
al

Z500 anomaly -2.0 standard
deviation
MA
Annu
DJF
JJA
SON
M
al

24

0.899

0.850

0.747

0.838

0.833

0.867

0.817

0.711

0.819

0.803

0.828

0.767

0.671

0.786

0.763

72

0.748

0.703

0.602

0.682

0.684

0.697

0.636

0.556

0.637

0.632

0.625

0.552

0.505

0.572

0.563

120

0.564

0.493

0.407

0.465

0.482

0.501

0.395

0.344

0.397

0.409

0.406

0.281

0.282

0.319

0.322

168

0.390

0.294

0.221

0.249

0.289

0.321

0.201

0.171

0.188

0.221

0.235

0.089

0.136

0.126

0.146

Table 5.1.1-22 BSS for temperature at 850 hPa over the Northern Hemisphere (20–90˚N)
Hour

T850 anomaly +1.0 standard
deviation
MA
Annu
DJF
JJA SON
M
al

24

0.799

0.699

72

0.627

0.532

120

0.473

0.366

0.62
7
0.40
9
0.23
5
0.09
6

0.71
6
0.52
8
0.34
9
0.18
3

T850 anomaly +1.5 standard
deviation
MA
Annu
DJF
JJA SON
M
al

T850 anomaly +2.0 standard
deviation
MA
Annu
DJF
JJA SON
M
al

0.710

0.766

0.615

0.537

0.639

0.639

0.752

0.459

0.350

0.460

0.505

0.524

0.571

0.428

0.285

0.413

0.424

0.542

0.237

0.047

0.184

0.252

0.148
0.263

0.002
0.112

0.356

168

0.326

Ho
ur

T850 anomaly -1.0 standard
deviation
MA
Annu
DJF
JJA SON
M
al

24

0.818

0.199

0.650

0.459

0.64
5

0.201

0.643

0.411
0.274

0.246

0.106

0.098

0.025

0.226
0.076

0.247
0.106

0.374

0.044

0.246

0.072

0.067
-0.050T850 anomaly -1.5 standard
deviation
MA
Annu
DJF
JJA SON
M
al

T850 anomaly -2.0 standard
deviation
MA
Annu
DJF
JJA
SON
M
al

0.780

0.728

0.475

0.169

- 47 -

0.482

0.477

0.13
8

0.439

0.145

0.143

72

0.660

0.502

0.279

0.48
0

0.480

0.601

0.321

0.005

0.308

0.306

0.521

120

0.502

0.339

0.129

0.31
0

0.320

0.436

0.163

0.128

0.147

0.154

0.370

168

0.345

0.180

0.002

0.13
3

0.164

0.264

0.020

0.235

0.013

0.009

0.194

0.01
6
0.16
3
0.27
4

0.611

0.039

-0.036

0.674

0.186

-0.163

0.733

0.307

-0.280

Table 5.1.1-23 BSS for geopotential height at 500 hPa over the Tropics (20˚S–20˚N)
Hour

Z500 anomaly +1.0 standard
deviation
MA
Annu
DJF
JJA
SON
M
al

Z500 anomaly +1.5 standard
deviation
MA
Annu
DJF
JJA
SON
M
al

Z500 anomaly +2.0 standard
deviation
Annu
DJF MAM JJA
SON
al

24

0.739

0.570

0.454

0.567

0.631

0.416

0.567

72

0.538

0.204

0.002

0.175

0.441

-0.158

0.387

0.075

0.303

-0.265

0.231

0.027

0.166

-0.222

0.108

0.506
0.042
0.119
0.123

0.407

0.286

0.341

0.056
0.389
0.240

0.464
0.347
0.210

0.551
0.626
0.603

0.104
0.389
1.127
0.538

0.069
1.436
0.970
0.525

0.147
1.311
1.338
1.281

0.187
-0.687

120

0.385

0.036

0.003

168

0.207

0.023

0.001

Ho
ur

Z500 anomaly -1.0 standard
deviation
MA
Annu
DJF
JJA
SON
M
al

Z500 anomaly -1.5 standard
deviation
MA
Annu
DJF
JJA
SON
M
al

Z500 anomaly -2.0 standard
deviation
Annu
DJF MAM JJA
SON
al

24

0.752

0.607

0.512

0.518

0.597

0.703

0.601

0.461

0.481

0.561

0.674

0.580

0.463

0.433

0.537

72

0.524

0.460

0.273

0.312

0.392

0.442

0.459

0.216

0.256

0.343

0.400

0.425

0.219

0.178

0.306

120

0.303

0.309

0.215

0.221

0.262

0.180

0.271

0.160

0.164

0.194

0.143

0.216

0.150

0.088

0.149

168

0.022

0.201

0.148

0.147

0.129

0.197

0.170

0.090

0.076

0.035

0.280

0.102

0.065

0.004

-0.029-0.801-0.559

Table 5.1.1-24 BSS for temperature at 850 hPa over the Tropics (20˚S–20˚N)
Hour

T850 anomaly +1.0 standard
deviation
MA
Annu
DJF
JJA
SON
M
al

T850 anomaly +1.5 standard
deviation
MA
Annu
DJF
JJA
SON
M
al

T850 anomaly +2.0 standard
deviation
Annu
DJF MAM JJA SON
al

24

0.604

0.543

0.473

0.418

0.447

0.406
0.047
0.285
0.315

0.469

0.334

0.383

0.323

0.020
0.208
0.161

0.007
0.200
0.208

0.285
0.633
0.555

0.396

0.206

0.309

0.193

0.213
0.408
0.309

0.137
0.363
0.352

0.526
0.929
0.725

0.295

72

0.383

0.154

120

0.266

0.000

168

0.190

0.014

Ho
ur

T850 anomaly -1.0 standard
deviation
MA
Annu
DJF
JJA
SON
M
al

T850 anomaly -1.5 standard
deviation
MA
Annu
DJF
JJA
SON
M
al

T850 anomaly -2.0 standard
deviation
Annu
DJF MAM JJA SON
al

24

0.619

0.380

0.372

0.339

0.428

0.590

0.251

0.229

0.174

0.311

0.559

72

0.369

0.226

0.202

0.185

0.245

0.334

0.094

0.077

0.066

0.143

0.304

120

0.264

0.139

0.130

0.101

0.158

0.229

0.000

0.025

0.006

0.065

0.203

0.151

0.057

0.017

0.045

0.008

0.132

168

0.188

0.069

0.135
0.012
0.056

0.076

0.051

0.156

0.320

-0.0080.198-0.049

0.128

0.096

0.002

0.254

-0.2110.147-0.199

0.095

0.061
0.078
0.169
0.218

0.037
0.135
0.155
0.174

0.119
0.152
0.183
0.229

-0.156-0.388-0.3230.116-0.015-0.076-0.122

Table 5.1.1-25 BSS for geopotential height at 500 hPa over the Southern Hemisphere (20–90˚S)
Hour

Z500 anomaly +1.0 standard
deviation
MA
Annu
DJF
JJA SONM
al

Z500 anomaly +1.5 standard
deviation
MA
Annu
DJF
JJA SONM
al- 48 -

Z500 anomaly +2.0 standard
deviation
Annu
DJF MAM JJA
SON
al

0.88
3
0.70
6
0.48
2
0.26
9

24

0.897

0.906

0.914

0.898

0.904

0.871

72

0.734

0.744

0.759

0.746

0.746

0.693

120

0.523

0.532

0.558

0.556

0.542

0.462

168

0.317

0.319

0.372

0.358

0.341

0.253

Ho
ur

Z500 anomaly -1.0 standard
deviation
MA
Annu
DJF
JJA SONM
al

Z500 anomaly -1.5 standard
deviation
MA
Annu
DJF
JJA SON
M
al

24

0.888

0.894

0.903

0.897

0.896

0.863

72

0.682

0.703

0.721

0.711

0.704

0.628

120

0.447

0.468

0.513

0.501

0.482

0.371

168

0.258

0.266

0.312

0.314

0.287

0.193

0.87
8
0.66
0
0.41
1
0.209

0.900

0.880

0.884

0.827

0.854

0.866

0.843

0.848

0.730

0.705

0.709

0.601

0.649

0.645

0.624

0.630

0.515

0.481

0.485

0.353

0.391

0.420

0.357

0.380

0.327

0.269

0.279

0.165

0.184

0.250

0.143

0.186

Z500 anomaly -2.0 standard
deviation
Annu
DJF MAM JJA
SON
al

0.884

0.872

0.874

0.836

0.861

0.855

0.846

0.849

0.670

0.656

0.653

0.562

0.615

0.625

0.593

0.599

0.438

0.423

0.411

0.313

0.354

0.359

0.340

0.342

0.239

0.233

0.218

0.156

0.165

0.170

0.158

0.162

Table 5.1.1-26 BSS for temperature at 850 hPa over the Southern Hemisphere (20–90˚S)
Ho
ur
24
72
120
168

T850 anomaly +1.0 standard
deviation
Annu
DJF MAM JJA SON
al

T850 anomaly +1.5 standard
deviation
Annu
DJF MAM JJA SON
al

0.82
6
0.62
6
0.45
1
0.30
7

0.78
8
0.56
0
0.38
6
0.24
7

0.802

0.801

0.607

0.614

0.409

0.433

0.234

0.271

0.78
0
0.59
2
0.40
8
0.24
8

0.802
0.610
0.425
0.265

0.759

0.756

0.536

0.541

0.329

0.349

0.164

0.206

0.74
0
0.52
9
0.33
5
0.18
4

T850 anomaly +2.0 standard
deviation
MA
Annu
DJF
JJA SON
M
al

0.761

0.763

0.723

0.712

0.542

0.507

0.465

0.465

0.350

0.333

0.257

0.277

0.200

0.203

0.109

0.145

0.68
0
0.44
1
0.23
3
0.09
3

0.719
0.469
0.275
0.137

T850 anomaly -1.0 standard
deviation
Annu
DJF MAM JJA SON
al

T850 anomaly -1.5 standard
deviation
Annu
DJF MAM JJA SON
al

T850 anomaly -2.0 standard
deviation
Annu
DJF MAM JJA SON
al

168

0.84
4
0.66
3
0.51
0
0.39
1

0.83
2
0.66
8
0.53
3
0.44
3

0.85
5
0.71
6
0.62
0
0.55
6

5.2

Research performed in the field

6.

Plans for the future (next 4 years)

6.1

Development of the GDPFS

6.1.1

Major changes expected in the next year

Ho
ur
24
72
120

0.754

0.749

0.587

0.594

0.413

0.446

0.263

0.297

0.77
1
0.62
5
0.46
9
0.32
5

0.779
0.617
0.459
0.319

0.656

0.641

0.497

0.506

0.351

0.376

0.231

0.247

0.70
8
0.57
9
0.44
8
0.33
0

0.709
0.563
0.427
0.313

0.499

0.440

0.377

0.340

0.282

0.261

0.197

0.183

0.60
2
0.51
4
0.43
1
0.36
4

0.599
0.487
0.399
0.325

(1) A new framework consisting of a regional forecast model and a data assimilation system(ASUCA and ASUCA-Var) will be installed in the Local NWP system.
(2) Clear sky radiance data from the Megha-Tropiques/Sondeur Atmosphérique du Profild'Humidité Intertropicale par Radiométrie (SAPHIR) will be assimilated into the Global NWP
system.
- 49 -

(3) Surface pressure data from METARs will be assimilated into the Global NWP system.
(4) Clear sky radiance data from the Global Precipitation Measurement (GPM)/GPM Microwave
Imager (GMI) will be assimilated into the Global and Meso-scale NWP systems.
(5) Improvements to the Seasonal EPS will include enhancement of the horizontal and verticalresolutions of the atmospheric model from TL95 to TL159 and from 40 to 60 levels,
respectively, expansion of the target area to the whole globe in the oceanic model, andintroduction of a sea-ice model.
(6) The horizontal resolution of the Aeolian dust model will be enhanced from TL159 to TL479.
(7) A high-horizontal-resolution regional chemical transport model will be introduced.

6.1.2

Major changes expected in the next four years(1) Atmospheric motion vectors and clear sky radiance data from Himawari-8’s Advanced
Himawari Imager (AHI) will be assimilated into the Global, Meso-scale and Local NWP
systems.
(2) The vertical resolution of both the One-week EPS and the Typhoon EPS will be enhanced
from 60 to 100 layers.
(3) The vertical resolution of the MSM will be enhanced.
(4) A SiB will be incorporated into the MSM.
(5) An urban canopy will be incorporated into the SiB of the MSM.
(6) Surface observation data will be assimilated into the MA.
(7) A new framework consisting of a regional forecast model and a data assimilation system(ASUCA and ASUCA-Var) will be installed in the Meso-scale NWP system.
(8) A data assimilation system with the local ensemble transform Kalman filter will be introduced
in aerosol and stratospheric ozone analysis.
(9) A global ensemble forecast system for ocean waves will be put into operation. The model has
a grid resolution of 1.25 with 27 members.

6.2

Planned research Activities in NWP, Nowcasting, Long-range Forecasting andSpecialized Numerical Predictions

6.2.1 Planned Research Activities in NWP

6.2.2 Planned Research Activities in Nowcasting(1) Application of Himawari-8 highly-frequent multiband data to improve Thunder Nowcasts (see4.4.1.1).

- 50 -

6.2.3 Planned Research Activities in Long-range Forecasting

6.2.4 Planned Research Activities in Specialized Numerical Predictions(1) Probability forecasts for volcanic ash
JMA is currently exploring methods to meet the needs of probability forecasts for volcanic ash as
described in the International Airways Volcano Watch (IAVW) roadmap.

7.

Consortium

8.

References

Aoki, Te., Ta. Aoki, M. Fukabori and T. Takao, 2002: Characteristics of UV-B Irradiance at Syowa
Station, Antarctica: Analyses of the Measurements and Comparison with Numerical
Simulations. J. Meteor. Soc. Japan., 80, 161–170.
Buizza, R. and Palmer, T. N., 1995: The singular-vector structure of the atmospheric global
circulation. J. Atmos. Sci., 52, 1434–1456.
Deushi, M., and K. Shibata, 2011: Development of an MRI Chemistry-Climate Model ver. 2 for the
study of tropospheric and stratospheric chemistry, Papers in Meteorology and Geophysics,
62, 1 – 46.
Egbert, G. and S. Erofeeva, 2002: Efficient Inverse Modeling of Barotropic Ocean Tides. J. Atmos.
Oceanic Technol., 19, 183–204.
Ehrendorfer, M., R. M. Errico and K. D. Raeder, 1999: Singular-Vector Perturbation Growth in a
Primitive Equation Model with Moist Physics. J. Atmos. Sci., 56, 1627–1648.
Foster, D. J. and R. D. Davy, 1988: Global Snow Depth Climatology. USAF-ETAC/TN-88/006.
Scott Air Force Base, Illinois, p. 48.
Hasegawa, Y., A. Sugai, Yo. Hayashi, Yu. Hayashi, S. Saito and T. Shimbori, 2015: Improvementsof volcanic ash fall forecasts issued by the Japan Meteorological Agency. J. Appl. Volcanol.
(in press).
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