February
Journal of2015
the Meteorological Society of Japan, Vol.
S. KOBAYASHI
93, No. 1, pp. et5−48,
al. 2015
DOI:10.2151/jmsj.2015-001

5The JRA-55 Reanalysis: General Specifications and Basic Characteristics
Shinya KOBAYASHI, Yukinari OTA, Yayoi HARADA
Japan Meteorological Agency, Tokyo, Japan
Meteorological Research Institute, JMA, Tsukuba, Japan

Ayataka EBITA
Meteorological Research Institute, JMA, Tsukuba, Japan

Masami MORIYA, Hirokatsu ONODA, Kazutoshi ONOGIJapan Meteorological Agency, Tokyo, Japan

Hirotaka KAMAHORI, Chiaki KOBAYASHI, Hirokazu ENDO
Meteorological Research Institute, JMA, Tsukuba, Japan

Kengo MIYAOKA and Kiyotoshi TAKAHASHIJapan Meteorological Agency, Tokyo, Japan(Manuscript received 13 March 2014, in final form 18 September 2014)

Abstract
The Japan Meteorological Agency (JMA) conducted the second Japanese global atmospheric reanalysis, called
the Japanese 55-year Reanalysis or JRA-55. It covers the period from 1958, when regular radiosonde observations
began on a global basis. JRA-55 is the first comprehensive reanalysis that has covered the last half-century since
the European Centre for Medium-Range Weather Forecasts 45-year Reanalysis (ERA-40), and is the first one toapply four-dimensional variational analysis to this period. The main objectives of JRA-55 were to address issues
found in previous reanalyses and to produce a comprehensive atmospheric dataset suitable for studying multidecadal
variability and climate change. This paper describes the observations, data assimilation system, and forecast model
used to produce JRA-55 as well as the basic characteristics of the JRA-55 product.
JRA-55 has been produced with the TL319 version of JMA’s operational data assimilation system as of December
2009, which was extensively improved since the Japanese 25-year Reanalysis (JRA-25). It also uses several newly
available and improved past observations. The resulting reanalysis products are considerably better than the JRA-25
product. Two major problems of JRA-25 were a cold bias in the lower stratosphere, which has been diminished, anda dry bias in the Amazon basin, which has been mitigated. The temporal consistency of temperature analysis has also
been considerably improved compared to previous reanalysis products. Our initial quality evaluation revealed problems
such as a warm bias in the upper troposphere, large upward imbalance in the global mean net energy fluxes at the topof the atmosphere and at the surface, excessive precipitation over the tropics, and unrealistic trends in analyzed tropical cyclone strength. This paper also assesses the impacts of model biases and changes in the observing system, andmentions efforts to further investigate the representation of low-frequency variability and trends in JRA-55.
Keywords

reanalysis; data assimilation; meteorological observation; numerical weather prediction

Corresponding author: Shinya Kobayashi, Japan Meteorological Agency, 1-3-4, Ote-machi, Chiyoda-ku, Tokyo
100-8122, Japan
E-mail: s-kobayashi@met.kishou.go.jp©2015, Meteorological Society of Japan

1.

Introduction

Reanalysis of past observations with a consistent,
state-of-the-art data assimilation system aims at
producing a high-quality climate dataset. It can

6Journal of the Meteorological Society of Japanproduce a dataset for numerous types of meteoro­
logical variables, including those for whichobservations are sparse, in a physically consistent,
spatiotemporally regular manner. Reanalyses havebeen widely used for research into the mechanisms of
the earth’s climate system, the study of predictability,
and climate monitoring. Reanalysis has madesignificant contributions to research on synoptic
and planetary scale phenomena such as storm
tracks, blocking, the Madden-Julian oscillation, theEl Ninõ/Southern Oscillation and quasi-biennial
oscillation, and verification of the reproducibilityof these phenomena in forecast models. Continued
improvement of reanalyses is crucial to advanceclimate research and improve climate services.
Reanalysis efforts are continually undertaken at
major numerical weather prediction (NWP) centersand meteorological research institutes (a detailed list
is available at http://www.reanalyses.org). The qualityof recent reanalyses has markedly improved becauseof progress in rescuing and homogenizing past observations and advances in data assimilation techniques.
In Japan, the Japanese 25-year Reanalysis (JRA-25)
was jointly conducted by the Japan Meteorological
Agency (JMA) and the Central Research Institute ofElectric Power Industry (CRIEPI) (Onogi et al. 2007).
Its representation of precipitation distribution, tropical cyclones, and stratocumulus along the western
coast of continents was the best available at that time.
However, it suffered from problems such as a cold
bias in the lower stratosphere and a dry bias in the
Amazon basin. Its reanalysis period, which went backonly to 1979, was not long enough for the studies
of multidecadal variability and climate change. It
also shared problems that are general to reanalysis,
including quality changes that arise from changes ofobserving systems and the reproducibility of fluxes
between climate subsystems such as atmosphere–
ocean and atmosphere–land fluxes (Bengtsson et al.
2007).
Since the JRA-25 data assimilation system has been
built, JMA’s operational NWP system has improvedin many aspects, including revision of the longwave
radiation scheme and introduction of four-dimensional variational analysis (4D-Var) and variational
bias correction (VarBC) for satellite radiances. These
improvements have significantly reduced model
biases, improved the dynamical consistency of analysis fields, and advanced handling of satellite radiances. Ongoing efforts to improve the quality of pastobservations include homogenization of radiosondetemperature observations (Haimberger et al. 2008,

Vol. 93, No. 1

2012) and reprocessing of observations at major meteorological satellite centers.
Taking advantage of these improvements, JMA
has conducted the second Japanese global atmospheric reanalysis, called JRA-55. As mentioned in
the interim report (Ebita et al. 2011), the data cover
the period starting from 1958 when regular radiosonde observations began on a global basis. JRA-55
is the first comprehensive reanalysis to cover the lasthalf-century since the European Centre for Medium-Range Weather Forecasts 45-year Reanalysis(ERA-40, Uppala et al. 2005) and the first to apply
4D-Var to this period. Its main objectives were toaddress the issues identified in previous reanalyses
and to produce a comprehensive atmospheric dataset
suitable for the studies of multidecadal variability andclimate change.
As the comprehensive report on JRA-55, this paper
outlines the general specifications and basic characteristics of JRA-55. Data sources, quality control (QC),
and data selection for observations are explained in
Section 2. The data assimilation system and the forecast model are described in Sections 3 and 4, respectively. Computational environment and production
streams are summarized in Section 5. Section 6 examines the basic performance of the data assimilation
system with fits to observation, analysis increments,and forecast scores. Section 7 focuses on two major
improvements in quality over the JRA-25 product, thetemporal consistency of the temperature analysis, andthe representation of the South American monsoon
system. Section 8 reviews the basic characteristics ofthe products. Conclusions are presented in Section 9.
Appendix B contains a list of the acronyms used inthis paper.
2.

Observations

2.1 Data sources
Observations used in JRA-55 primarily consist of
those used in ERA-40 (Uppala et al. 2005) and thosearchived by JMA. The ERA-40 observational dataset
was supplied to JMA by the European Centre for
Medium-Range Weather Forecast (ECMWF) for use
in the JRA-25 project (Onogi et al. 2007). Observations for the period starting in 1979 are basically thesame ones used in JRA-25. In addition, newly available observational datasets were collected and usedwhenever possible. A detailed list of data suppliers
and the type of data provided by them is given inappendix A.
Reports of surface observation from fixed land
stations (SYNOP) and upper-level observations used

February 2015

S. KOBAYASHI et al.

in the National Centers for Environmental Prediction(NCEP)/National Center for Atmospheric Research(NCAR) reanalysis (Kalnay et al. 1996; Kistler et al.
2001) were used on a supplementary basis for the year
1979 because those observations were incompletein the ERA-40 observational dataset. Wind profile
retrievals surrounding tropical cyclones (TCRs)(Fiorino 2002) were regenerated with improved
QC for best-track data and supplied by Dr. Michael
Fiorino.
Snow depths from Russia, the USA, and Mongolia
were provided by the Russian Research Institutefor Hydrometeorological Information (RIHMI), theUniversity Corporation for Atmospheric Research(UCAR) (NCDC et al. 1981), and the Institute ofMeteorology and Hydrology (IMH), respectively.
These datasets were used in preference to the operational archives in the snow depth analysis component because they have fewer gaps and contain fewer
errors due to miscoded reports from the Global Telecommunication System.
Recently, major meteorological satellite centers
have been reprocessing historical satellite observations with the latest algorithms to produce highquality, homogeneous satellite products. The JMA
Meteorological Satellite Center (MSC) reprocessedatmospheric motion vectors (AMVs) and clear sky
radiances (CSRs) from past Geostationary Meteorological Satellite (GMS) and Multi-functional
Transport Satellite (MTSAT) imagery with the latest
derivation algorithms as of 2009, which was a pilot
project in the Sustained, Coordinated Processing ofEnvironmental Satellite Data for Climate Monitoring(SCOPE-CM) initiative (http://www.wmo.int/pages/
prog/sat/scope-cm_en.php). JRA-55 is the first reanalysis to assimilate the newly reprocessed AMVs andCSRs from these satellites.
2.2 Quality control and data selection
Observational datasets contain poor-quality data
resulting from instrument malfunctions, human errors,
and inadequate metadata. These kinds of poor-quality
data must be identified and excluded before the assimilation step. It is common practice to automatically
exclude observations that are inconsistent with othersor outliers from background fields (JMA 2007; Onogi
et al. 2007). Offline QC is also an effective measure
for reanalysis because analysis of a past period is freefrom constraints of a real-time operation. In addition to poor-quality data, observations unsuitable forassimilation are excluded, such as those that are farless accurate than background, those whose spatial

7

representativeness considerably differs from that ofbackground, or for which background equivalents
cannot be generated with sufficient accuracy.
The following subsections explain the QC and data
selection methods used for each observation type in
JRA-55.
a.

Conventional data
The QC procedure for conventional data was basically the same as the one used for JRA-25, including
a climatological check, track check, consistency
check, gross error check, etc (Onogi et al. 2007).
A notable feature of the QC methods used in JMA’s
data assimilation system is “Dynamic QC,” in which
the threshold value is defined as a variable linearly
depending on the local horizontal gradient andtendency of the first-guess fields (Onogi 1998). Since
JRA-25, the set of QC thresholds has been thoroughly
reviewed and updated (Sakamoto 2009).
Another important QC task was the removal of
duplicates. Because the observational dataset used
for JRA-55 had multiple data sources, there weremany duplicates. However, it is difficult to identify
and completely remove duplicates because metadata (such as location, time and level) did not always
match among different data sources. To minimize
duplications, the ERA-40 observational dataset waspreferred over others in most cases because it is themost consolidated observational dataset among thoseused for JRA-55.
TCRs are assimilated with the same prescribed
observational errors as those used for typhoon bogus
data in JMA’s operational system. The data consist of
6-hourly winds on standard pressure levels from 1000hPa to 400 hPa at the centers of tropical cyclones andat 2° latitude/longitude away from those points in thefour cardinal directions.
Table 1 compares data counts, rejection rates, andutilization rates for each type of conventional observation used in JRA-55 with those used in JRA-25 forthe year 1995; Table 2 does the same with those used
in JMA’s operational system for the year 2010. Data
excluded by blacklisting or preliminary screening are
not included in the statistics.
The number of observations ingested in JRA-55 issmaller than that in JRA-25 for 1995 because JRA-55
ingested conventional observations from JMA’s
archive before August 2002 only over Japan, whereas
JRA-25 ingested all available observations. Anothernotable difference between JRA-25 and JRA-55 is thegreater rejection rates of upper-level observations dueto the tightening of QC thresholds in JRA-55.

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Journal of the Meteorological Society of Japan

Vol. 93, No. 1

Table 1. Data counts and QC statistics for conventional data in JRA-25 and JRA-55 for the year 1995.
Obs type

Parameter

Level

SYNOP
SHIP
BUOY
Upper-level
Upper-level
Upper-level
Upper-level
Upper-level
Aircraft
Profiler (US)

P
P
P
T
T
u
u
Rh
u
u

surfacesurfacesurface~100 hPa
100~1000 hPa~100 hPa
100~1000 hPa
100~1000 hPa
100~1000 hPa
100~1000 hPa

Ingested obs
JRA-25JRA-5514445140
252482026977995055575
12265485
488454512143438
10985799
14082016
805161410615854
239117420484194409306
10642362
39536508853872910932966640827835794

Rejected obs (%)
JRA-25JRA-55
2.16
3.38
2.12
0.96
0.63
0.85
0.49
0.01
1.45
3.95

3.22
7.85
5.55
6.03
5.90
1.76
3.11
1.03
3.80
4.09

Used obs (%)
JRA-25JRA-55
36.11
44.36
8.65
42.31
38.54
42.33
44.62
27.91
14.44
4.16

42.34
43.18
10.45
44.60
41.78
47.56
55.45
31.90
27.99
4.26

Table 2. Data counts and QC statistics for conventional data in JMA’s operational system and JRA-55 for the year 2010.
Obs type

Parameter

Level

SYNOP
SHIP
BUOY
Upper-level
Upper-level
Upper-level
Upper-level
Upper-level
Aircraft
Profiler (US)Profiler (Japan)
Profiler (Europe)

P
P
P
T
T
u
u
Rh
u
u
u
u

surfacesurfacesurface~100 hPa
100~1000 hPa~100 hPa
100~1000 hPa
100~1000 hPa
100~1000 hPa
100~1000 hPa
100~1000 hPa
100~1000 hPa

Ingested obs
Oper
JRA-55
19181647
115282070400186631550
13696594
880825613192314
12451435
34745677
746926522695916
13695644

Unlike operational systems, reanalysis has noconstraint of cutoff time. Thus, JRA-55 ingested more
observations than JMA’s operational system for theyear 2010, except for surface pressure observations
from SYNOP. In JRA-55, surface pressure observations over the Amazon basin were entirely excluded
because of their inconsistency with background fields.
Further detail is given in Section 7.2.
JRA-55 ingested twice as many aircraft observations as JMA’s operational system. Aircraft observations over the continental United States were thinned
to one-fiftieth by preliminary screening in the operational system (JMA 2013b, Section 2.2.2.7), whereas
no preliminary screening was performed for JRA-55
because coverage of earlier aircraft observations is too
sparse to apply this screening. In JMA’s operational
system, temperature data from aircraft are used withbias corrections that are estimated using one-monthstatistics for each aircraft identifier (Sako 2010).

18701759
117303772965036952966
14399844
8953899
13377604
12963016
75256027
746870922696592
13702435

Rejected obs (%)
Oper
JRA-55
2.01
2.70
0.93
3.92
3.97
1.45
1.99
0.93
0.93
5.07
6.78
9.30

2.17
3.36
1.19
3.68
3.99
1.37
2.13
0.88
1.18
5.04
7.32
9.63

Used obs (%)
Oper
JRA-55
30.51
35.73
14.80
39.66
36.07
28.73
37.67
26.38
17.72
4.24
0.80
0.94

29.79
35.53
14.69
38.12
34.34
28.31
37.10
25.27
11.00
4.30
0.79
0.95

However, because many of the older aircraft observations had unknown identifiers, this bias correction method could not be used for the reanalysis.
Therefore, temperatures from aircraft are not used in
JRA-55.
Radiosonde temperature records contain numerous
discontinuities arising from factors such as modifications in radiosonde instrument. In particular,
numerous radiosonde temperature observations
before the 1980s had a significant warm bias mainly
caused by radiation effects (Onogi 2000). It is crucial
to remove these biases before using these records forclimate applications.
JRA-25 used the method of Andræ et al. (2004) tocorrect temperature bias. Their method uses statistics
for each country or region under the assumption thateach uses a similar type of radiosonde to calculate
the bias for four classes of solar elevation. The coefficients are recalculated every month by the departure9

S. KOBAYASHI et al.

Departure (K)February 2015

0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
1958

uncorrected(JRA-25)
bias-corrected(JRA-25)

1964

1970

1976

uncorrected(JRA-55)
bias-corrected(JRA-55)

1982 1988
Year

1994

2000

2006

2012

Fig. 1. Monthly mean background departures for temperature observations from radiosondes at levels near 300 hPa
averaged over the extratropical northern hemisphere from JRA-25 and JRA-55. Uncorrected background departures are shown in solid lines and bias-corrected departures in dotted lines.

statistics of the latest 12 months (Onogi et al. 2007).
JRA-55 performed bias correction for radiosondetemperature using the Radiosonde Observation
Correction using Reanalysis (RAOBCORE) version
1.4 (Haimberger et al. 2008) until the end of 2006,
and RAOBCORE version 1.5 (Haimberger et al.
2012) thereafter. Bias estimates were produced foreach station by applying a statistical break detection
method to the departure values from ERA-40 and
ERA-Interim (Dee et al. 2011). Because bias estimates for the hours of 06 and 18 UTC are not available in RAOBCORE, bias estimates for either 00 or
12 UTC were substituted assuming that biases did not
significantly vary within the daytime and nighttime
hours.
Figure 1 shows time series from JRA-25 andJRA-55 for monthly mean background departures(observations minus background) for temperatureobservations from radiosondes at levels near 300 hPa,
averaged over the extratropical northern hemisphere.
The uncorrected departures exhibit larger warm biases
in early years. These were significantly reduced
by RAOBCORE in JRA-55, whereas differences
between the uncorrected and bias-corrected departuresin JRA-25 are very small, indicating a small impactof the bias correction. The bias-corrected time series
from JRA-55 exhibits seasonal variations because
dependencies of biases on solar elevations were nottaken into account.

b. Satellite radiances(1) Infrared sounders
Because JRA-55 does not simulate the effects ofclouds on satellite radiances, cloud-contaminatedobservations are identified and rejected before theassimilation step. Whereas JRA-25 performed clouddetection using window channels (Sakamoto andChristy 2009), JRA-55 uses an improved cloud detection scheme based on Krzeminski et al. (2009).
Table 3 summarizes the Vertical Temperature
Profile Radiometer (VTPR) and the High-Resolution
Infrared Spectrometer (HIRS) observations used in
JRA-55 (the available period for each type of observation is given in appendix A). While stratospherictemperature channels, of which the central wavenumber is located near 680 cm–1, are not subject
to cloud detection, tropospheric temperature, andhumidity channels are only used in clear sky condition. Channels sensitive to the ground are excluded
over land and sea ice because surface temperature andemissivity estimates over those regions are not very
reliable. All the observations over the Caspian Sea
are excluded for the same reason. In addition, observations near both the ends of scan lines are excluded
because they tend to exhibit large biases because ofvarious reasons such as incomplete radiative transfer
modeling at large viewing angles.
For the Stratospheric Sounding Unit (SSU), observations from all the channels are assimilated.

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Journal of the Meteorological Society of Japan

Vol. 93, No. 1

Table 3. VTPR and HIRS/2 observations used in JRA-55. Central wavenumbers are from
McMillin et al. (1973) for VTPR and Werbowetzki (1981) for HIRS/2.
ChannelCentralwavenumber(cm–1)

Land or sea ice

Sea

CloudyClear

Cloudy

Clear

○

○
○

○

○
○
○
○
○
○

VTPR
1
2
3
4
5
6
7
8

668.5
677.5
695.0
708.0
725.0
747.0
535.0
833.0

1500 m**Not used if the surface height is greater than the given value.
Note: Observations from the fields of view (FOVs) 1 and 23, which are located at both ends
of scan lines, and all observations over the Caspian Sea are excluded.
HIRS/2
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

669
680
690
703
716
733
749
900
1030
1225
1365
1488
2190
2210
2240
2270
2360
2515
2660
14500

○

○
○

1500 m*

○

○
○
○
○
○
○

○
○
○
○

*Not used if the surface height is greater than the given value.
Note: Observations from the FOVs 1–3 and 54–56, which are located at both ends of scanlines, and all observations over the Caspian Sea are excluded.

(2) Microwave sounders
Table 4 summarizes microwave sounder observations used in JRA-55. In the microwave region, wavelengths are far larger than the size of water droplets andice crystals; hence, absorption and scattering by thoseparticulates is mostly negligible, except in the presenceof raindrops. In JRA-25, however, cloud detection for
the Microwave Sounding Unit (MSU) was performed

using the scheme for HIRS, the infrared sounder on thesame satellite; consequently, some usable troposphericobservations were rejected. In JRA-55, cloud detection for MSU uses first-guess departures of the window
channel (channel 1) of its own instrument to retain as
many observations as possible.
Cloud and rain detection for the Advanced Microwave Sounding Unit (AMSU)-A is performed using a

February 201511

S. KOBAYASHI et al.

Table 4. Microwave sounder observations used in JRA-55. Frequencies are from Werbowetzki (1981) for
MSU and Robel (2009) for AMSU-A, AMSU-B, and MHS.
ChannelFrequency (GHz)Land or sea ice
Rainy/
Cloudy

Sea

Clear

Rainy

Cloudy

Clear

○

○
○

○
○

○
○
○

MSU
1
2
3
4

50.3
53.74
54.96
57.95

○

Note: Observations from the FOVs 1 and 11, which are located at both ends of scan lines, are excluded.
AMSU-A
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

23.80031.40050.30052.80053.596 ± 0.115
54.40054.94055.500f0 = 57.290344
f0 ± 0.217
f0 ± 0.3222 ± 0.048
f0 ± 0.3222 ± 0.022
f0 ± 0.3222 ± 0.010
f0 ± 0.3222 ± 0.0045
89.000

○
○
○
○
○
○

1500 m*2500 m*
○
○
○
○
○
○
○

○
○
○
○
○
○

○
○
○
○
○
○
○
○

○
○
○
○
○
○
○
○
○
○
○

○
○
○

○
○
○

*Not used if the surface height is greater than the given value.
AMSU-B/MHS
1
2
3
4
5

89.0 ± 0.9/89.0
150.0 ± 0.9/157.0
183.31 ± 1.00/183.311 ± 1.0
183.31 ± 3.00/183.311 ± 3.0
183.31 ± 7.00/190.311

scattering index for all surface types as well as total
column cloud water retrievals over sea. The total
column cloud water retrievals are computed withthe algorithm that has been updated since JRA-25(Okamoto et al. 2007). AMSU-A radiances are
considered to be contaminated by clouds if the total
column cloud water retrieval exceeds 100 g m–2 andby rain if the value exceeds 300 g m–2.
For AMSU-B and the Microwave Humidity
Sounder (MHS), radiances satisfying the following
criterion are considered to be contaminated by rain;
Tb (ch1) − Tb (ch 2) > 3 K

where Tb is the observed brightness temperature. This
criterion was derived from the fact that Tb (ch1) iscolder than Tb (ch2) in clear sky condition due to theeffect of water vapor continuum absorption.
(3) Microwave imagers
In JRA-25, precipitable water contents were first
retrieved from microwave imager radiances with thealgorithm of Takeuchi (2002) and then assimilated
in the atmospheric analysis component. In JRA-55,
radiances from vertically polarized channels of four
frequency bands around 19, 22, 37, and 90 GHz are
directly assimilated over the sea after removing radi-

12

Journal of the Meteorological Society of Japanances contaminated by thick clouds or rain.
Microwave imagers also make observations withhorizontally polarized channels in the same frequency
bands. These different polarized channels have thesame atmospheric transmittances but different surface
emissivities, which enables the retrieval of surface
variables. In the atmospheric analysis component of
JRA-55, however, radiances from horizontally polarized channels are not assimilated because satellite
radiances have no direct influence on surface variables in the JRA-55 data assimilation system, except
through the linearized model operator (Okamoto et al.
2008, 2011).
In the snow depth analysis component, daily snow
cover retrievals from the Special Sensor Microwave/
Imager (SSM/I) and the Special Sensor Microwave
Imager Sounder (SSMIS) are used.
(4) CSRs
Radiances from water vapor channels of geostationary satellite imagers are assimilated in JRA-55(Ishibashi 2009). While some recent geostationary
satellites are equipped with multiple water vapor
channels with differing weighting function peaks, e.g.,
the Spinning Enhanced Visible and Infrared Imager(SEVIRI) 6.2 and 7.3 μm channels on the Meteosat
Second Generation (MSG) satellites, only the channel
sensitive to the upper troposphere is used in JRA-55.
This is because the surface emissivity over land isfixed at 0.9, and the radiance simulation for channels
sensitive to the surface is not sufficiently accurate.
For the water vapor channel of GMS-5, the radiancesimulation uses a corrected spectral response function
proposed by Bréon et al. (1999).
c. AMVs
AMVs from geostationary and polar orbiting satellites have been used since January 1979 and June
2004, respectively. Reprocessed AMVs are usedwhenever available (Appendix A); otherwise operational AMVs are used. A QC procedure similar tothe one described for conventional data is used for
AMVs. In addition, data selection is performed using
the AMV blacklist, which specifies the regions andlevels for each type of sensor where AMVs are to be
used. The quality indicators of Holmlund (1998) arealso used for data selection if available (JMA 2013b).
For most operational AMVs prior to 1995, information on computation method, i.e., from which channel
the wind is derived, is missing or encoded incorrectly.
Data selection for those AMVs was performed as if
they were from infrared channels.

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d.

Scatterometer ocean surface winds
The QC procedure for scatterometer ocean surface
winds consists of the removal of ambiguity in winddirection, a gross error check, and “Group QC”(Ohhashi 2004), which restores winds rejected by thegross error check if they are spatially consistent (JMA
2013b). Background departures are computed using
10 m wind forecasts and then used as the departures
for the lowest model level in the atmospheric analysis
component.
Because winds from the Advanced Scatterometer(ASCAT) have a slow bias relative to the forecast
model in strong winds, winds stronger than 15 m s–1
are not used. This criterion was also applied to reprocessed winds from the Active Microwave Instrument(AMI) (De Chiara et al. 2007), which were retrieved
with the same model function used for ASCAT,
the C-band model function (CMOD) 5.N (Hersbach 2008). For reprocessed winds from SeaWinds(Dunbar et al. 2006), data flagged as being in rain
areas are not used (Ohhashi 2004).
Scatterometer ocean surface winds are also assimilated in the screen-level analysis component.
e. 	Global Navigation Satellite System–Radio Occultation (GNSS-RO) refractivities
The GNSS-RO refractivities have been usedsince July 2006 in JRA-55 but they were not used in
JRA-25. The GNSS-RO observations can be assimilated without bias correction because they are derived
from atmospheric time delays of radio signals, which
can be measured by an atomic clock with high accuracy. The quality of GNSS-RO observations is hardly
affected by surface and weather conditions. JRA-55
assimilates an average of approximately 500 refractivity profiles from the entire globe in every analysis
cycle. Therefore, GNSS-RO observations together
with radiosonde observations are important for
constraining model biases and anchoring VarBC.
Because refractivity is a function of temperature,humidity, and pressure, it can be used to determine theprofiles of these properties. Refractivity data for altitudes up to 30 km are used with 500-m vertical intervals. The assimilation of refractivity data is performed
with a local operator, which only considers temperature, humidity, and pressure at the ray tangent point.
The observation errors are defined as a function ofheight (JMA 2013b).
2.3 Coverage
Figure 2 shows the monthly mean counts of
conventional upper-level observations, surface pres-February 2015610(a) 90N - 60N-1

Count (day )

4000
3000
2000
1000
0
16000

13

S. KOBAYASHI et al.

5

10

104
3

10

102
1

(b) 60N - 20N

10
1958

12000

1964

-1

Count (day )

4000

3200

1976

Aircraft wind
AMV
GNSS-RO

8000
0
5000
4000
3000
2000
1000
0

1970

(c) 20N - 20S

1982

1988

Year

1994

2000

2006

2012

Scatterometer wind
PAOBS

Fig. 3. Global monthly mean counts of aircraft
and satellite winds, PAOBS, and GNSS-RO
refractivities assimilated in the atmospheric analysis component of JRA-55.

(d) 20S - 60S

2400
6

1600

10

0
400

-1

Count (day )800(e) 60S - 90S

300
200

104
3

10
1958

100
0
1958

5

10

1964

1970

1976

1982 1988
Year

Upper windUpper temperature
Upper humidity

1994

2000

2006

2012

Surface pressure
TCR

Fig. 2. Monthly mean counts of conventional
upper-level observations, surface pressures, andTCRs assimilated in the atmospheric analysis
component of JRA-55 in five latitude bands.

sures, and TCRs assimilated in the atmospheric analysis component of JRA-55 in five latitude bands. The
number of upper-level observations reached a peak in
the late 1980s and then began to decline. A marked
decline in the Arctic region was most likely becauseof station closures and cutbacks in the former Soviet
Union. Observation counts picked up in the 2000s
for reasons that may include increasing reports ofsignificant levels, a rise in maximum altitudes, anddecreasing rejection rates following the improvementin observation quality.
For surface pressure observations, there is a
striking change in observation counts after 1967 in
the northern hemisphere and tropics (Figs. 2a, b, c).
Observation counts in 1965 and 1966 dipped sharply
due to the lack of surface observations in Canada,
Mexico, China, Mongolia, India, and most of Europe.
On the other hand, observation counts in southernhemisphere mid-latitudes increased steadily (Fig. 2d).
The tropical record (Fig. 2c) shows occasional

1964

1970

1976

1982

1988

Year
Microwave sounders
Microwave imagers

1994

2000

2006

2012

CSR
Infrared sounders

Fig. 4. Global monthly mean counts of various
types of satellite radiances assimilated in theatmospheric analysis component of JRA-55.

spikes in observation counts, as in 1979 when
the First Global Experiment of the Global Atmospheric Research Programme (FGGE) was held. In
the Antarctic region, observation counts show large
seasonal increases in summer (Fig. 2e).
Figures 3 and 4 show global monthly mean countsof aircraft and satellite observations assimilated in
the atmospheric analysis component of JRA-55.
These types of observation first became available insignificant numbers in 1973, and they have rapidly
increased after the 1990s. Observation counts from
infrared and microwave sounders on polar orbiting
satellites dipped sharply in the 1980s when only asingle polar orbiting satellite was operational.
For AMVs from geostationary satellites, observations first became available from all longitudes in1979 when FGGE was held. Then, only GMS andthe Geostationary Operational Environmental Satellite (GOES) continued observation until Meteosat-2
became operational in the early 1980s. Observations
covered all longitudes again in 1998 when Meteosat-5
began observations over the Indian Ocean.

14

Journal of the Meteorological Society of JapanScaerometer
ocean surface winds

AMV

Vol. 93, No. 1

Boundary condionand forcing fields

Screen-level analysis

Forecast model
Atmosphericforcing

GNSS-RO

Convenonal data

AtmosphericanalysisSnow depth analysis(18Z only)

Snow depth

Microwave imager
snow cover retrievals

Satellite radiancesFig. 5.

3.

Land surface
analysis

Schematic of the main components of the JRA-55 data assimilation system and the data flow among them.

nJ (Δʓ 0 ) = 1 ΔʓT0 B−1Δʓ 0 + 1 ∑ (Hi Δʓi − di )T2
2 i =0

Data assimilation system

Figure 5 illustrates the main components of theJRA-55 data assimilation system and the data flow
between them. Atmospheric analysis, screen-level
analysis, and land surface analysis are performed at00, 06, 12, and 18 UTC, and the snow depth analysis is performed at 18 UTC every day. The forecast
model uses the most recent previous analysis as initialconditions, thereby generating background and atmospheric forcing fields that are necessary for analysis.
Using these fields and observations, analysis fields arethen generated as the most plausible state of the atmosphere. The JRA-55 system is based on the low-resolution (TL319) version of JMA’s operational data
assimilation system as of December 2009 (JMA 2007,2013b), which has been improved extensively sinceJRA-25 (Table 5). Details of the products from theJRA-55 system are given in JMA (2013a, 2014).
3.1 Atmospheric analysis
The atmospheric analysis component of JRA-55 isbased on incremental 4D-Var (Courtier et al. 1994).
The analysis za = zb + Δz is obtained through estimating the analysis increment Δz that minimizes thecost function J given by1R−i ( Hi Δʓ i − di ) + J c (Δʓ 0 )di = yio − Hi (ʓ bi )ʓ bi +1 = M i (ʓ bi ) = M i ( M i−1 ( M i−2 ( M 0 (ʓ b0 ))))
Δʓ i +1 = Mi Δʓ i = Mi Mi−1Mi−2 M0 NΔʓ 0
where y° is observations, zb is the background, and B
and R are the background error covariance and observation error matrices, M and M are the model operator and its linearization, H and H are the observation
operator and its linearization, Jc is the penalty term tosuppress gravity waves, and N is a nonlinear normalmode initialization operator. The subscript i denotes
a time slot, and n is the last one (JMA 2013b). The
state vector z contains the atmospheric state as well as
VarBC parameters, which is applied to satellite radiances. The observation operator H simulates observations from the model state and consists of spatial
interpolation from neighboring grid points to observation points, conversion from model variables to observation quantities with a radiative transfer model andthe like, and VarBC.
a.

4D-Var
4D-Var has a significant advantage over 3D-Var,which was used for JRA-25, in using a forecast
model to propagate initial perturbations and in prop-

February 201515

S. KOBAYASHI et al.
Table 5. Data assimilation systems used for JRA-25 and JRA-55.
JRA-25JRA-55

Base system

JMA’s operational system as of March
2004 (JMA 2002)

JMA’s operational system as ofDecember 2009 (JMA 2007, 2013b)

Horizontal grid system

Gaussian

Reduced Gaussian

Horizontal resolution

T106 (~110 km)

TL319 (~55 km)

Vertical levels

Surface and 40 levels up to 0.4 hPa

Surface and 60 levels up to 0.1
hPa (Iwamura and Kitagawa 2008;
Nakagawa 2009)

Analysis scheme

3D-Var with the T106 inner resolution

4D-Var with the T106 inner resolution

Background error covariances

StaticStatic with the simple inflation factorof 1.8 applied before 1972

Bias correction for satellite radiances

TOVS
Adaptive scheme using 1D-Var analysis departures (Sakamoto and Christy
2009)
ATOVS
Static (until July 2009) and adaptive(thereafter) schemes using radiosonde and supplemental backgroundfields(Kazumori et al. 2004)

VarBC (Derber and Wu 1998; Dee andUppala 2009; JMA 2013b)

Radiative transfer model for satellite
radiances

TOVS: RTTOV-6
ATOVS: RTTOV-7

RTTOV-9.3

Screen-level analysis

2D-OI

2D-OI with the FGAT approach

Land surface analysis

Offline SiB with 6-hourly atmosphericforcing

Offline SiB with 3-hourly atmosphericforcing

Snow depth analysis

2D-OI

2D-OI

Atmospheric analysis

Surface analysis

erly taking account of observation time, both ofwhich are beneficial for producing analysis fields
with greater physical consistency. On the other hand,
4D-Var requires greater computer resources because
time integration of the forecast model is performed aspart of the iteration to minimize the cost function J.
To reduce the computational cost, analysis increments
are produced with the T106 resolution model, while
first guess fields are produced with the TL319 resolution model in JRA-55 (JMA 2007). Minimization of
the cost function J is done using the limited-memory
Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm (Liu and Nocedal 1989) employing the preconditioner by Veersé et al. (2000).

b.

Background error covariances
The background error covariance model used
for JRA-55 is the same that was used for JRA-25(JMA 2007), which is similar to the formulation by
Derber and Bouttier (1999). In this formulation, thecontrol variables consist of relative vorticity, unbalanced divergence, unbalanced mass (temperatureand surface pressure), and the logarithm of specifichumidity, which are represented in spectral spacefor each model level. The model variables are recovered from control variables, which takes into accountthe geostrophic balance with relative vorticity andthe correlation between unbalanced divergence andthe mass variables. There is no correlation between
different spectral coefficients but a full vertical auto-

16

Journal of the Meteorological Society of Japan

Vol. 93, No. 1

Fig. 6. Zonal mean differences between temperature analyses of (a) observing system experiments with andwithout satellite observations, (b) the same as (a) but using background error variances multiplied by 1.8 for the
reduced observing system experiment and (c) reduced observing system experiments with the original and inflated
background error variances, averaged over August 1990.

covariance matrix is defined for each spectral coefficient. Therefore, the resulting autocovariance model
is nonseparable and isotropic in grid space. There
are three differences from the formulation by Derberand Bouttier (1999). First, weakening of the geostrophic balance in the tropics is taken into account byexpressing the regression equations in singular vector
space instead of spectral space. Second, the logarithmof specific humidity, instead of specific humidity, isused as a control variable. Third, error variances arehorizontally constant because variance adjustment is
not performed in grid space.
The background error covariances and regression coefficients used for JRA-55 were statisticallycomputed using the NMC method (Parrish and Derber1992) on 24/48-h forecast differences for a year from
October 2004.
Although the background error covariance matrix B
used for JRA-55 is static, it was increased by 80 % for
the pre-satellite period before 1972 for all the control
variables except the logarithm of specific humidity to
account for the increased background errors. Thisscaling factor was obtained using the diagnostics byDesroziers et al. (2005) on departures from observing
system experiments with and without satellite observations for January and August 1990. Figure 6 showsthat the difference between zonal mean temperature
analysis fields is slightly reduced by the adjustment ofbackground error variances.
c.

VarBC
To effectively assimilate satellite radiances, it isnecessary to properly remove biases stemming fromincomplete instrument calibration or radiative transfer

modeling. Because these biases depend on time andatmospheric state in most cases, bias estimates need tobe continuously updated to adapt to the actual biases.
VarBC uses the state vector z augmented with bias
parameters, and thereby automatically adjusts bias
estimates to maintain consistency with all available
information in the cost function J (Derber and Wu
1998; Dee and Uppala 2009; JMA 2013b). In JRA-55,
VarBC is applied to all satellite radiances.
d.

Radiative transfer model for satellite radiances
Satellite radiances are assimilated using the software package Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV) version 9.3 (Saunders et al. 2008). Compared with RTTOV-6, which
was used for assimilating TOVS data in JRA-25, it
reproduces line-by-line radiances and Jacobians forthe water vapor channels of HIRS with significantly
improved accuracy (Matricardi et al. 2004). The simulation of ocean surface emissivity for microwave
instruments at large viewing angles has also beensignificantly improved (Saunders 2002). Comparedwith RTTOV-7 (Matricardi et al. 2004), which wasused for assimilating Advanced TOVS (ATOVS) data
in JRA-25, it reduces systematic biases in calculated
brightness temperatures for microwave water vapor
channels. In addition, the removal of an inaccurate
representation of the Zeeman effect has significantly
improved the simulation for microwave upper stratospheric temperature channels (Kobayashi et al.
2009).

February 2015

S. KOBAYASHI et al.

3.2 Surface analysis
a. Screen-level analysis
The analysis of screen-level variables (2-mtemperatures, 2-m relative humidities and 10-m
winds) is performed separately from the atmosphericanalysis component. These variables are analyzed
with a univariate 2-dimensional optimal interpolation (2D-OI). In JRA-25, departures were estimatedby comparing observations with the first guess at theanalysis time. In JRA-55, observations are comparedwith the first guess at the actual observation time andthe departures are applied at the analysis time (theso-called first guess at the appropriate time (FGAT)
approach).
Temperature and wind observations from islands
are not used because they are not necessarily representative at the grid scale of JRA-55. Determining
whether an observation is from an island is basedon the 0.25-degree resolution land cover data;consequently, observations from the coast are also
excluded.
Screen-level analysis fields are not used as initialconditions for forecasts.
Land surface analysis
Land surface analysis fields are generated bydriving an offline version of the JMA Simple
Biosphere (SiB) model with forcing fields from theatmospheric model, which is the same method used
for JRA-25 (Onogi et al. 2007). Atmospheric forcing
parameters are the same as those used for JRA-25,
i.e., pressure, temperature, humidity, and wind at thelowest level of the atmospheric model, plus surfacepressure, precipitation, downward solar and longwave radiation fluxes, and total cloud cover. Atmospheric forcing fields have 3-hour resolution in
JRA-55 rather than 6-hour resolution in JRA-25. Thischange more realistically represents the diurnal cycles
of atmospheric forcing.
The snow depth field in the land surface analysis at
00 UTC is replaced with a snow depth analysis fieldevery day.

assigned to the first guess.
● If there is snow only in (B), then the first guess isa snow depth that can bring the ground temperature
down to the freezing point through melting (2.1 cm
at the maximum).
● If there is snow only in (A), then the first guess is 0cm.
● If there is no snow in (A) and (B), then the analysis
is 0 cm without 2D-OI.
In addition, first-guess fields over ice sheets are
replaced with either climatology or 2 cm, whicheveris greater, in every analysis. This is because the offline
SiB does not take into account the physical processesof ice sheets, and consequently snow over ice sheets
tends to accumulate over time.
Satellite snow covers are retrieved in the 0.25° ×
0.25° latitude/longitude grid from microwave imager
radiances with the same scheme as used for JRA-25(Onogi et al. 2007). In the algorithm, regions where
the observation satisfies the following criteria areconsidered to be covered by snow:

Tb (19 GHz , H ) < 265 K
Tb (19 GHz , H ) − Tb (37 GHz , H ) < threshold (H )b.

c.

Snow depth analysis
Snow depth analysis fields are generated once a daywith 2D-OI using SYNOP snow depth observations,which is the same method as the one used for JRA-25(Onogi et al. 2007). First-guess fields are derived
for each grid point using (A) snow depth of the land
surface analysis and (B) satellite snow covers, as
follows:● If there is snow in both (A) and (B), then (A) is17

Tb (19 GHz , V ) − Tb (37 GHz , V ) < threshold (V )where Tb is the brightness temperature, H and V are
the horizontal and vertical channels, respectively.
Thresholds (H, V) should be 0 K for ideal snow but
in practice need to be adjusted depending on the snow
and surface condition. Accordingly, those values were
determined for each region, vegetation, and month.
For periods when no snow cover retrieval is available,monthly climatology for the period 1987–2000 is usedinstead.
4.

Forecast model
A forecast model is employed to run forecasts using
the most recent previous analysis as its initial condition, generating the background and atmosphericforcing fields that are necessary for analysis. A forecast model serves to propagate information of observations in time and place, and therefore its performance has a considerable impact on the quality ofa reanalysis. The forecast model used for JRA-55 isbased on the TL319 spectral resolution version of the
JMA global spectral model (GSM) as of December
2009 (JMA 2007, 2013b), which has been extensively
improved since JRA-25 (Table 6). The following
subsections describe the major changes in the forecast
model since JRA-25.

18

Journal of the Meteorological Society of Japan

Vol. 93, No. 1

Table 6. Forecast models used for JRA-25 and JRA-55.
JRA-25JRA-55

Base model

JMA GSM as of March 2004 (JMA 2002)

JMA GSM as of December 2009 (JMA
2007, 2013b)

Horizontal resolution

T106 (~110 km)

TL319 (~55 km)

Vertical levels

Surface and 40 levels up to 0.4 hPa

Surface and 60 levels up to 0.1 hPa(Iwamura and Kitagawa 2008; Nakagawa
2009)

Horizontal grid system

Gaussian

Reduced Gaussian

Advection scheme

Euerlian

Semi-LagrangianLongwave radiation

Line absorptions
Random band model of Goody (1952)
Water vapor continuum (e-type)
Roberts et al. (1976)
Radiatively active gases
H2O, O3 andCO2 (constant at 375 ppmv)

Line absorptions
Pre-computed transmittance tables andk-distribution (Chou et al. 2001)
Water vapor continuum (e-type andp-type)Zhong and Haigh (1995) with MK_
CKD (Clough et al. 2005)
Radiatively active gases
H2O, O3, CO2, CH4, N2O, CFC-11,
CFC-12 and HCFC-22

Shortwave radiation

Absorptions by H2O, O2, O3 and CO2
Briegleb (1992)

Absorptions by H2O
Briegleb (1992)
Absorptions by O2, O3 and CO2
Freidenreich and Ramaswamy (1999)

Cloud radiation

Longwave
Maximum-random overlap
Shortwave
Random overlap

Longwave
Maximum-random overlap with themethod of Räisänen (1998)
Shortwave
Random overlap

Aerosols

Atmospheric aerosol profiles from WMO(1986) (CONT-I over land and MAR-I
over sea)

Atmospheric aerosol profiles from WMO(1986) (CONT-I over land and MAR-I
over sea) with optical depths adjusted to
2-dimensional monthly climatology

Cumulus convection

Prognostic Arakawa-Schubert

Prognostic
DCAPE

Initialization

Nonlinear normal mode initialization

Not used

Dynamics

Radiation

Arakawa-Schubert

withBoundary conditions and forcing fields
SST and sea ice

COBE-SST (Ishii et al. 2005)

COBE-SST (Ishii et al. 2005)

Ozone

T42L45 version of MRI-CCM1(Shibata et al. 2005)

Until 1978
Climatology
From 1979 onward
T42L68 version of MRI-CCM1 (Shibata et al. 2005)

February 2015

S. KOBAYASHI et al.

4.1 Dynamics
To mitigate the overconcentration of grid points
at high latitudes and lower the computational cost, a
reduced Gaussian grid is adopted for the GSM. The
number of grid points at each latitude is determinedfrom the magnitude of associated Legendre functions,which is negligibly small at high latitudes and in highorders. This method also reduces the computational
cost of Legendre transformation (Juang 2004; JMA
2013b).
GSM is constructed on the framework of asemi-implicit semi-Lagrangian global model. The
general shortcomings of semi-Lagrangian models
are the lack of conservation properties and the heavy
cost of three-dimensional interpolations. To addressthese limitations, Yoshimura and Matsumura (2003)
developed a vertically conservative semi-Lagrangian
scheme in which vertical advection is treated separately from horizontal advection, so that the model
may preserve the conservation of vertically integrated
quantities, such as water vapor, under non-dissipative
conditions. The separate treatment enables the model
to reduce the cost of interpolations and to recover theconservative property (JMA 2013b).
4.2 Radiation
a. Longwave radiation
Longwave radiation fluxes are computed by solving
the equation of radiative transfer for a non-scattering
atmosphere in each predefined spectral band (theband-emissivity method with the diffusivity approximation). To reduce the computational costs, computations for longwave radiation are performed not at
every time step but every three hours (JMA 2007).
In the forecast model used for JRA-25, the longwave spectrum was divided into 4 bands. Band-averaged transmission functions were obtained using therandom band model of Goody (1952) for line absorptions by atmospheric gases. The transmittances due
to water vapor continuum absorption were computedwith the expression by Roberts et al. (1976), which
only considered the self (e-type) continuum resulting
from collisions between water vapor molecules (JMA2002). In the forecast model used for JRA-55, thenumber of spectral bands increased to 9. The basic
framework for computing band-averaged transmission functions was replaced with the method of Chouet al. (2001), which uses a combination of pre-computed transmission tables (Chou and Kouvaris 1991)
and the k-distribution method (Arking and Grossman1972) to compute the transmittances due to line
absorption. The transmittances due to water vapor

19

continuum absorption are computed with the expression by Zhong and Haigh (1995), considers the self
continuum as well as the foreign (p-type) continuum
resulting from collisions between water vapor andnon-water molecules (JMA 2007).
The pre-computed transmittance tables are applied
to the absorption bands that substantially contributeto cooling in the stratosphere and above, i.e., the 15
μm band of carbon dioxide, the 9.6 μm band of ozone,
and major absorption bands of water vapor. The
k-distribution method is applied to other absorption
bands. The rationale for using different approaches
for different absorption bands is as follows. Although
the k-distribution method with linear pressure scaling
is computationally very fast, it is not accurate in the
low-pressure condition where the Doppler broadeningof absorption lines is important. This is due to thefact that the linear pressure scaling used in the k-distribution method, which assumes Lorentz broadening
as the dominant cause of line broadening, does nothold in the low-pressure condition. Compared to theforecast model used for JRA-25, absorptions due toDoppler broadening are more properly represented inthat used for JRA-55 due to the use of transmittance
tables pre-computed for a wide range of pressures andtemperatures.
Absorbing gases considered in the forecast model
used for JRA-25 were only water vapor, ozone, andcarbon dioxide (constant at 375 ppmv). In the forecast model used for JRA-55, five species (methane,
nitrous oxide, chlorofluorocarbon (CFC)-11, CFC-12,
and hydrochlorofluorocarbon (HCFC)-22) are also
taken into account. For ozone and long-lived greenhouse gases (carbon dioxide, methane, nitrous oxide,
CFC-11, CFC-12, and HCFC-22), temporal variations are taken into account. Further detail is given in
Subsections 4.4.c and 4.4.d.
The transmittance tables were computed using theHigh Resolution Transmission (HITRAN) compilation 2000 (Rothman et al. 2003) and the Line-by-Line
Radiative Transfer Model (LBLRTM, Clough et al.
1992; Clough and Iacono 1995). Absorption coefficients due to water vapor continuum are based on the
Mlawer–Tobin–Clough–Kneizys–Davies (MT_CKD)
water vapor continuum absorption model (Clough
et al. 2005).
b.

Shortwave radiation
Shortwave radiation fluxes are computed by a
two-stream method with the δ-Eddington approximation (Joseph et al. 1976; Coakley et al. 1983). To cut
down on the computational costs, computations for20

Journal of the Meteorological Society of Japan

shortwave radiation are not performed at every time
step but hourly (JMA 2007).
In the forecast model used for JRA-25, the shortwave spectrum was divided into 18 bands (sevenbands for the ultraviolet, one band for the visible, andten subbands for the k-distribution method for thenear-infrared) based on Briegleb (1992). This method
took into account the absorptions due to ozone in
the ultraviolet and visible, oxygen (constant at 209,
490 ppmv) in the visible and near-infrared, carbon
dioxide in the 2.7 and 4.3 μm bands and water vapor
in the near-infrared, and the Reyleigh scattering (JMA2002). In the forecast model used for JRA-55, theseparameterizations were updated following Feidenreichand Ramaswamy (1999) except for absorptions due
to water vapor. In this method, the number of spectral
bands increased to 10 for the ultraviolet and 5 for thevisible. In addition, absorption due to the SchumannRunge O2 band is taken into account (JMA 2007).
In the forecast model used for JRA-25, the land
surface albedos (at the top of vegetative canopies) were derived by solving the equation of radiative transfer in vegetative canopies using the opticalproperties of Dorman and Sellers (1989). The ocean
surface albedo was parameterized following Briegleb
et al. (1986), and the sea ice surface albedos were
fixed at 0.8 for the visible and at 0.4 for the near-infrared. In the forecast model used for JRA-55, thesea ice surface albedo is parameterized as functionsof solar zenith angle and skin temperature, so that
correlation (anti-correlation) with solar zenith angle(skin temperature) is taken into account. Reflectivity
over deserts is parameterized as a function of solar
zenith angle following Briegleb et al. (1986). In addition, the snow surface albedo over perennial land ice
was modified and increased by about 10 %.
c.

Cloud radiation
In the longwave flux calculations, clouds are basically treated as blackbodies. Cloud overlap between
different vertical layers is represented assuming the
maximum-random overlap (Geleyn and Hollingsworth 1979) with the method of Räisänen (1998), in
which the effects of cloud cover and cloud emissivity
on cloud overlap are considered separately (Kitagawa
and Murai 2006). Räisänen (1998) pointed out thatthe combined use of effective cloud cover, i.e., theproduct of cloud cover and cloud emissivity, andmaximum-random overlap leads to smaller cloud radiative forcings as vertical resolution becomes higher,
and demonstrated that this dependence on verticalresolution can be significantly reduced by separately

Vol. 93, No. 1

taking into account the effects of cloud cover andcloud emissivity. The cloud emissivity is parameterized as functions of cloud liquid water content, cloud
ice content, and effective radius of cloud particles.
Cloud optical properties used in the shortwave radiation scheme, i.e., the cloud optical depth, the single
scattering albedo, and the asymmetry factor, areparameterized as functions of cloud water path andeffective radius of cloud particles according to Slingo(1989) for water droplets and Ebert and Curry (1992)
for ice crystals (JMA 2007). Cloud overlap in the
shortwave is represented assuming random overlap.
In the forecast model used for JRA-25, the effective radius for water droplets was fixed at 15 μm,
and that for ice crystals was parameterized as functions of temperature following Ou and Liou (1995)(JMA 2002). In the forecast model used for JRA-55,
the effective radii for water droplets are fixed at 10
μm over land and at 13 μm over sea, and that for ice
crystals is parameterized as functions of temperatureand cloud ice content following Wyser (1998) (JMA2007).
d. Aerosols
The aerosol direct effect is taken into accountusing two atmospheric aerosol profiles from WMO(1986), i.e., CONT-I over land and MAR-I over sea,
which are basically the same profiles as those used
for JRA-25. While no temporal variation was taken
into account in JRA-25, JRA-55 uses a 2-dimensional
monthly aerosol optical depth climatology, which isbased on observations from the Moderate Resolution
Imaging Spectroradiometer (MODIS) and the Total
Ozone Mapping Spectrometer (TOMS) (JMA 2013b).
4.3 Cumulus convection
The basic framework of the cumulus convection
scheme is the same that was used for JRA-25 (Onogi
et al. 2007), which is an economical version of the
Arakawa-Schubert scheme (Arakawa and Schubert1974), except that JRA-55 uses the convective triggering mechanism proposed by Xie and Zhang(2000), known as the dynamic CAPE generation rate
(DCAPE) (JMA 2013b).
4.4 Boundary conditions and forcing fields
a. Sea surface temperature (SST)The SST analysis for JRA-55 is the Centennial In
Situ Observation-based Estimates of the Variability ofSSTs and Marine Meteorological Variables (COBE,
Ishii et al. 2005), which is basically the same one used
for JRA-25.

February 2015

S. KOBAYASHI et al.

The SST analysis has a resolution of 1° latitude and1° longitude and uses the OI method. The deviationof the previous day’s analysis from normal, multiplied by 0.95, is used as a first guess. The analysis isperformed daily and uses the marine meteorological
data for the 7-day period from 3 days before to 3 days
after the day of interest. The observed data in a day
are averaged in a 1.5° × 1.5° box before analyzing
data by the OI method (JMA 2007).
b.

Sea ice
Sea-ice concentrations used for JRA-55 are basically daily data produced for COBE-SST, which are
the same as for JRA-25. The sea-ice concentrations
consist of data from Walsh and Chapman (2001) for
the northern hemisphere and climatology for thesouthern hemisphere for the period before October
1978, and microwave imager retrievals by Matsumoto et al. (2006) thereafter. The data by Walsh andChapman (2001) lack sea/lake-ice concentrations in
the Sea of Okhotsk, the Gulf of St. Lawrence, and theGreat Lakes for most of the period before October
1978, but no correction is made for COBE-SST. In
JRA-55, climatology in those regions for the period
1979–1986 was used instead for the period before
October 1978.
In the forecast model, regions where the sea-ice
concentration exceeds 55 % are considered to becompletely covered by sea ice and lower values areinterpreted as zero sea-ice coverage.
c.

Ozone
Three-dimensional daily mean ozone distributions used in JRA-55 for the period after 1979 were
produced separately from the JRA-55 data assimilation system using the T42L68 resolution versionof the chemistry climate model (CCM) developed
at the Meteorological Research Institute (MRICCM1, Shibata et al. 2005). A nudging scheme was
employed to assimilate total column ozone retrievalsfrom TOMS on Nimbus-7 and other satellites for theperiod 1979–2004 and from the Ozone Monitoring
Instrument (OMI) on Aura thereafter. The ozone
profiles were adjusted for each model grid point
once a day at the solar culmination time using theseobservations. More detail is presented in the chemicaltransport model section in JMA (2013b). Wind fields
were also nudged toward 6-hourly wind fields fromJRA-25 to simulate the transport of chemical species
in MRI-CCM1 as realistically as possible. In addition, the time evolution of total chlorine and bromine
were prescribed in the CCM to incorporate the effects

21

of ozone-depleting substances since 1979 (seeKobayashi and Shibata, 2011 for details). This method
was the same as the one used for producing ozone
distributions for JRA-25 (Maki et al. 2008).
The daily ozone distributions before 1978 weretime-interpolated, three-dimensional climatologicalmonthly means for 1980–1984.
d.

Long-lived greenhouse gases
It is important to accurately represent radiative
forcing due to the increase in long-lived greenhousegases in forecast models. In the forecast model used
for JRA-25, only carbon dioxide was taken into
account, and its concentration was fixed at 375 ppmv.
The forecast model used for JRA-55 takes six species
into account (carbon dioxide, methane, nitrous oxide,
CFC-11, CFC-12, and HCFC-22). Their concentrations are globally uniform and temporally varied
based on the data sources listed in Table 7.
5.

Production

The production of JRA-55 took about three years
and was completed in March 2013. Since then, it
has operated on a near-real-time basis as a successor
to the previous JRA-25-based JMA Climate Data
Assimilation System (JRA-25/JCDAS). JRA-55 was
first run using eight nodes of the high-performancecomputer (Hitachi SR11000 model J1) in JMA’s
eighth-generation computer system with a peak
performance of 121.6 GFLOPS per node (JMA 2007).
After June 2012, it was continued with 16 nodes of
the high-performance computer (Hitachi SR16000
model M1) in JMA’s ninth-generation computer
system with a peak performance of 0.98 TFLOPS per
node (JMA 2013b). As the data assimilation system
consists of numerous programs with complicateddependencies on one another, the Supervisor Monitor
Scheduler (SMS) developed by ECMWF is used toefficiently control production tasks.
Initially, the reanalysis period was divided into two
streams (A002, B002). The following technical problems were found at that time, which were corrected byrecalculations (Fig. 7):● From January to June 1958 (A003): Lack of sea/
lake-ice data in the Sea of Okhotsk, the Gulf of St.
Lawrence, and the Great Lakes.
● From December 1974 to August 1980 (A004):
Problem in cloud detection for VTPR radiances.
● From June 1987 to September 1992 (B003):
Misplacement of microwave imager snow cover
retrievals.
Consequently, the streams shown in Fig. 7 have

22

Journal of the Meteorological Society of Japan

Vol. 93, No. 1

Table 7. Data sources of long-lived greenhouse gases.
Molecule

PeriodSourceCO2

1958
1959–1982
1983–20102011–

Law Dome ice core data (Etheridge et al. 2008)
Keeling Mauna Loa observations
WDCGG (WMO 2012)
RCP4.5 (Clarke et al. 2007; Smith and Wigley 2006; Wise et al. 2009)

CH4

–1983
1984–20102011–

20C3M (Meinshausen et al. 2011)
WDCGG (WDCGG 2012)
Constant at the value in 2010

N2O

–1979
1980–20102011–

20C3M (Meinshausen et al. 2011)
WDCGG (WDCGG 2012)
RCP4.5 (Clarke et al. 2007; Smith and Wigley 2006; Wise et al. 2009)

–2005
2006–

20C3M (Meinshausen et al. 2011)
A1 scenario of the Scientific Assessment of Ozone depletion: 2010
(WMO 2010)

CFC-11,
CFC-12,
HCFC-22

Stream A
A003

A002

1 July 1958

1960

Stream B
A004

B002

B003

1 September 1980

1970

1980

B002

1 October 1992

Year

1990

2000

2010

Fig. 7. Streams of JRA-55 production. Shading indicates periods during which recalculation was performed. Solidvertical lines indicate the times of three discontinuities (see text).

three discontinuities: at 00 UTC on 1 July 1958
(A003/A002), 00 UTC on 1 September 1980 (A004/
B002), and 00 UTC on 1 October 1992 (B003/B002).
For other stream changeovers, succeeding streams
were initiated from the last data of the preceding
streams.
6. 	Basic performance of the data assimilation
system
6.1 Background and analysis fits to observations
Data assimilation systems produce not only analyses but also background and analysis departures(observations minus background and observations
minus analysis). These departures contain plenty of
information that is useful for monitoring data assimilation cycles. In particular, background departures
can be used for various purposes such as estimatingbiases in observations and investigating performanceof the forecast model because they are independent ofprescribed background and observation errors. Here,we compare the time series of global mean and rootmean-square (RMS) departure of radiosonde temperatures used in JRA-25 and JRA-55.
Global mean departures from JRA-25 at levels near
30 hPa clearly show a large cold bias in the lower
stratosphere, especially for the period before 1998
when TOVS data were used (Fig. 8a). In JRA-55,
the cold bias has been reduced significantly, which
can be seen clearly from the improved consistency
with radiosonde temperatures. This improvement
was primarily achieved by the proper representationof absorptions due to the Doppler broadening in therevised longwave radiation scheme.
At levels near 250 hPa, global mean departuresfrom JRA-25 exhibit large low-frequency variations(Fig. 8c). Global mean departures from JRA-55 do
not exhibit such large variations but are biased in thenegative direction, indicating a warm bias in the upper
troposphere. Time series from JRA-55 also show ajump in July 2006; thereafter, the warm bias has beenDeparture (K)Departure (K)February 2015
3.5 (a) 30hPa BIAS
JRA-25(BG)
3JRA-55(BG)
2.5
JRA-25(AN)2
JRA-55(AN)
1.5
1
0.5
0
-0.5
1958 1964 1970
0.3 (c) 250hPa BIAS
0.2
0.1
0
-0.1
-0.2
-0.3
-0.4
1958 1964 1970

4 (b) 30hPa RMS
3.5
3
2.5
2
1.5
1976

1976

1982

1982

1988

1988

1994

1994

2000

2000

2006

2006

2012

2012

Departure (K)Departure (K)0.15 (e) 500hPa BIAS
0.1
0.05
0
-0.05
-0.1
-0.15
-0.2
-0.25
1958

23

S. KOBAYASHI et al.

1964

1970

0.45 (g) 850hPa BIAS
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
1958 1964 1970

1
1958

1970

1976

1982

1988

1994

2000

2006

2012

2 (d) 250hPa RMS
1.8
1.6
1.4
1.2
1
0.8
0.6
1958 1964 1970

1964

1976

1982

1988

1994

2000

2006

2012

1976

1982

1988

1994

2000

2006

2012

1976

1982

1988

1994

2000

2006

2012

1.5 (f) 500hPa RMS

1976

1982

1988

1994

2000

2006

2012

1.4
1.3
1.2
1.1
1
0.9
0.8
0.7
0.6
1958

1964

1970

2 (h) 850hPa RMS
1.8
1.6
1.4
1.2
1
1976

1982

1988

1994

2000

2006

2012

0.8
1958

1964

1970

Fig. 8. Global mean and RMS departure of radiosonde temperatures used in JRA-25 and JRA-55. Solid lines show
background departures, and dotted lines represent analysis departures.

reduced, which is most likely due to the assimilationof GNSS-RO observations.
At levels near 500 hPa, global mean departuresfrom JRA-55 demonstrate an improved fit to radiosonde temperatures compared with those fromJRA-25 (Fig. 8e), although they are slightly biased in
the positive direction for the periods before the early
1960s and after the latter half of the 2000s. It can also
be argued that smaller difference between RMS background and analysis departures in JRA-55 indicatessmaller increments and is an evidence of improved
physical consistency among analysis fields (Fig. 8f).
At levels near 850 hPa, global mean departuresfrom both JRA-25 and JRA-55 are on the positiveside throughout the reanalysis period (Fig. 8g), indicating a cold bias in the lower troposphere. Thesetime series also exhibit shifts in the late 1970s, theearly 1990s, and the mid-2000s. It must be notedthat the number and distribution of samples for thesestatistics vary in time; therefore, these shifts might be
attributed to apparent variations as well as the impactsof changes in observing systems. The causes of theseshifts require further investigation.
6.2 Analysis increments
The analysis increment represents the extent towhich observations affect the background throughthe data assimilation system. Although it does notnecessarily represent errors in the background, its
temporal variations provide useful insights regarding
the impact of changes in observing systems on thetemporal consistency of analyses.
Figure 9 compares time-height cross sections forthe global mean monthly temperature increments fromJRA-55 and JRA-25. The increments from JRA-55
are generally smaller than those from JRA-25, andthey show fewer sudden changes in pattern followingchanges in observing systems. The difference between
increments in JRA-25 and JRA-55 demonstrates aclear improvement of the model climatology and an
advance in the bias correction methods for radiosondetemperatures and satellite radiances.

24

Journal of the Meteorological Society of Japan

Vol. 93, No. 1

Fig. 9. Time-height cross sections for the 12-month running mean of the global mean temperature increments from (a)JRA-55 and (b) JRA-25.

However, there are cooling increments in the upper
troposphere that gradually increase as the observing
system is improved. The cooling increments are
particularly apparent at levels near 200 hPa after July
2006 when the GNSS-RO refractivities were introduced into JRA-55. These signs indicate that the forecast model has a warm bias in the upper troposphere.
Another noticeable feature in Fig. 9 is an oscillatory structure in the vertical in the stratosphere.
Modest variations can be observed in this structure
before the early 1960s, during which few radiosonde
observations were available in the upper stratosphere,
and from August 1998 to December 2000, during

which observations from TOVS and ATOVS wereused at the same time. The variations in the increment structure suggest larger uncertainties in theupper stratospheric temperature analysis during these
periods.
Figure 10 compares time-height cross sections forthe global mean monthly specific humidity increments from JRA-55 and JRA-25. The incrementsfrom JRA-55 do not exhibit impacts of changes inobserving systems as clear as those from JRA-25.
However, JRA-55 has significant moistening increments above 850 hPa and drying increments below
it, indicating that the forecast model has a dry bias

February 2015

S. KOBAYASHI et al.

25

Fig. 10. Time-height cross sections for the 12-month running mean of the global mean specific humidity increments from (a) JRA-55 and (b) JRA-25.

in the upper and middle troposphere and a moist bias
in the lower troposphere. The moistening incrementsin the upper and middle troposphere tend to slightly
increase as the number of observations from satellite
water vapor channels increases. Large moistening
increments can also be seen around 800 hPa in boreal
summer during 1971–1978, which are most likely
because of miscoded radiosonde humidity observations and inadequate QC for radiances from the VTPR
water vapor channel. Note that intense drying increments observed in the lower troposphere arise because
supersaturations are removed in the analysis, whereas
they are allowed in the forecast model such as in theregions where stratocumulus occurs.
Figure 11 shows precipitable water increments
averaged over the periods 1958–1964 and 2002–2008.
Humidity observations in the atmospheric analysis component of JRA-55 during the earlier period
are only from radiosondes, whereas those during
the later period consist of many satellite as well asradiosonde observations. As a result, the patterns ofprecipitable water increments in these periods lookquite different, especially over the oceans, where
extensive increments can be observed only in the later
period. Comparison between precipitable waters fromJRA-55 and independent microwave imager retrievals

26

Journal of the Meteorological Society of Japan

Fig. 11.

Vol. 93, No. 1

Precipitable water increments averaged over the periods (a) 1958–1964 and (b) 2002–2008.

over the ocean indicates that the regions where large
moistening increments occurred in the later period
generally correspond to those where JRA-55 exhibits
a dry bias (not shown). The increment pattern in
2002–2008 also suggests that the dry bias in the upperand middle troposphere seen in humidity (Fig. 10) is
mainly from the regions of deep convection.
6.3 Two-day forecast scores
To evaluate the temporal consistency of the productand the impact of changes in observing systems, ashort-range forecast was carried out in JRA-55 from
12 UTC every day. Figure 12 shows the time seriesof RMS errors in these 2-day forecasts at a geopotential height of 500 hPa averaged over the extratropical northern and southern hemisphere from JRA-25,
JRA-55, and the JMA operational system, as verified
against their own analyses. Because the forecasts
were carried out with their own forecast models, thecomparison is not made based on a common standard;February 2015

RMSE (gpm)4030JRA−25

Operational

JRA−55(a) Northern Hemisphere
T63L16, 3D−OI

TL319L60, 4D−Var

T106L21
T213L40

T106L40, 3D−Var

TL319L60

3D−Var

20

TL959L60

4D−Var

10 Upgrade in operational system
050

RMSE (gpm)27

S. KOBAYASHI et al.

40

outer resolution
assimilation scheme

T63L16, 3D−OI(b) Southern Hemisphere

T106L21

T106L40, 3D−Var
T213L40

TL319L60, 4D−Var

3D−Var

TL319L60

30
20

4D−VarUpgrade in operational systemouter resolution
assimilation scheme

1960

1965

1970

1975

1980

1985 1990
Year

1995

2000

2005

TL959L60

2010

Fig. 12. RMS errors of 2-day forecasts of the geopotential height at 500 hPa averaged over the extratropics of the(a) Northern and (b) Southern Hemispheres from JRA-25, JRA-55, and JMA operational system, verified against
their own analyses. Changes in the assimilation scheme and resolution of the outer model are also noted. Eachvalue represents the average for the last 12 months.

nevertheless, it can provide useful insights regarding
the temporal consistency of each product.
The JMA operational system has been improvedin many aspects since JRA-25, including a revisionof the longwave radiation scheme and the introduction of 4D-Var and VarBC. The JRA-55 data assimilation system, which is based on the TL319 resolution version of the operational system as of December
2009, incorporates these improvements and has beenused consistently throughout the reanalysis period.
Thus, variations in the forecast scores of JRA-55
can be attributed solely to the changes in observing
systems and natural variations of atmospheric predictability, whereas forecast scores of the operational
system clearly show the effect of these improvements.
These are evidence of the greater temporal consistency of the JRA-55 product. The forecast scoresof the JRA-55 system are considerably better thanthose of the JRA-25 system, which is based on the

T106 resolution version of the operational system asof March 2004. The improvement of forecast scoresis particularly significant in the southern hemisphere,
which is most likely because of the availability of newsatellite observations as well as the improvement of
the data assimilation system.
The forecast scores of JRA-55 show relatively
large variations that correspond to the introductionof VTPR in 1973; the advent of satellite observing
systems in the late 1970s, ATOVS in 1998, and
GNSS-RO in 2006; and variations in coverage of
TOVS observations, suggesting that performance
under sparse observations is an important concern for
future reanalyses. It should be noted that the forecast
scores in the southern hemisphere tend to be degraded
during the pre-satellite era, whereas the number ofused obsevations rather increased (Figs. 2d, e). This
inconsistency may indicate that the JRA-55 data
assimilation system did not perform well during this28

period.
7.

Improvements from JRA-25

7.1 Temporal consistency of temperature analysis
The representation of low-frequency variability andtrends has been a major issue for reanalysis. Recent
investigations using new-generation reanalyses haveindicated significant improvement in this respect(Santer et al. 2004; Simmons et al. 2004, 2010,2014). As the first reanalysis of the full observing
system covering the last half-century since ERA-40,
JRA-55 is expected to be used for various purposes
including studies of multidecadal variability andclimate change, thus it is very important to evaluate
its temporal consistency. In this section, we compare
low-frequency variability and trends in global mean
temperatures from JRA-55 with those from independent observational datasets and other reanalyses,
including JRA-25.
a.

Near surface over land
Figure 13 compares monthly mean land-surface airtemperature anomalies from the Climatic Research
Unit (CRU) temperature database (CRUTEM4, Joneset al. 2012), the NCEP/NCAR reanalysis, ERA-40,
JRA-25, and JRA-55, averaged over the globe.
Reanalyses are sampled with the same spatial andtemporal coverage as CRUTEM4. The screen-level
analysis method used for JRA-55 is basically thesame as the one used for JRA-25, and the low-frequency variability of 2-m temperature anomalies overland is fairly similar in the two reanalyses. Comparedwith ERA-40, the trend reproduced in JRA-55 iscloser to that in CRUTEM4 but there is a differenceof less than 0.1 K in warming between CRUTEM4and JRA-55 after the 1990s. The difference might berelated to a difference in how observations are used in
CRUTEM4 and JRA-55. Observations on islands andthe coast are not used in the screen-level analysis of
JRA-55, as mentioned in Subsection 3.2.a, and analysis in those areas could be affected by observations
in coastal waters such as reports of surface observation from sea stations (SHIP) and buoy observations(BUOY), and by SST through background fields.
CRUTEM4 is based on observations over land only,
which include those on islands and on the coast.
b.

Lower troposphere to lower stratosphere
Figure 14 compares monthly temperature anomalies from JRA-55, existing reanalyses, and independent observational datasets for four layers from thelower troposphere to the lower stratosphere, averaged

Anomaly (K)Journal of the Meteorological Society of Japan

Vol. 93, No. 1

1
0.5
0
-0.5
1958

1964

1970

1976

1982 1988 1994
Year
NCEP/NCAR
JRA-25
ERA-40JRA-55

2000

2006

2012

CRUTEM4

Fig. 13. Twelve-month running mean land-surface
air temperature anomalies from CRUTEM4,
the NCEP/NCAR reanalysis, ERA-40, JRA-25,
and JRA-55, averaged over the globe. Anomalies for each dataset were defined relative totheir own climatological monthly means over
1961–1990, except JRA-25, for which anomalies
were first computed relative to its own climatological monthly means over 1981–2010 and thenadjusted so that their average over 1979–1990
gave the same value as that of JRA-55. Reanalyses are sampled with the same spatial andtemporal coverage as CRUTEM4.

over 82.5°N to 82.5°S. The independent observational
datasets are the Hadley Centre’s radiosonde temperature product (HadAT2, Thorne et al. 2005) and theRemote Sensing Systems (RSS) V3.3 atmospherictemperature records from the MSU and AMSU microwave sounders (Mears and Wentz 2009a, b).
The JRA-25 forecast model had a significant cold
bias in the lower stratosphere, resulting in large artificial jumps after changes in observing systems or bias
correction methods for satellite radiances. JRA-55
considerably reduces those changes; in particular, thetime series for the three tropospheric layers before
the 1970s shows variations very similar to those in
HadAT2 compared with the NCEP/NCAR reanalysis
and ERA-40.
For the lower stratosphere, the low-frequency
variability reproduced in JRA-55 is closer to that in
HadAT2 and RSS compared with other reanalyses,
whereas the cooling trend observed in JRA-55 issmaller than that in HadAT2. McCarthy et al. (2008)
investigated uncertainty in estimates of multidecadaltrends in HadAT with an automated homogenization
system, and suggested that the homogenization is
likely to underestimate adjustments if observations
from neighboring radiosonde stations have a common
systematic bias. This could partly account for thedifference in cooling trends between HadAT2 andJRA-55. In addition, RAOBCORE v1.4, which wasused in JRA-55 until 2006, has a larger warming trend
in the upper troposphere and a smaller cooling trendFebruary 2015
1.5

1.5

1

1

0.5
0

0.5

(a)

-0.5

-1

-1

0.5
0

(b)

-0.5
0.5
0

(c)

-0.5

Temperature anomaly (K)Temperature anomaly (K)

(a)

0

-0.5

1
0.5
0

(b)

-0.5
-1
1

0.5
0

(d)

-0.5
-1
1958

29

S. KOBAYASHI et al.

0.5
0

(c)

-0.5
1964

1970

1976

1982 1988 1994
Year
NCEP/NCAR
JRA-25
ERA-40JRA-55

2000

2006

2012

HadAT2RSS V3.3

-1
-1.5
1958

1964

1970

1976

1982

1988

1994

2000

2006

2012

Year

Fig. 14. Twelve-month running mean temperature
anomalies for the (a) lower stratosphere, (b)upper troposphere, (c) middle troposphere, and(d) lower troposphere averaged over 82.5°N to
82.5°S. Time series from RSS V3.3 represents
measurements by the MSU channel 4, 3, and2, and the lower tropospheric extrapolations,whereas those from HadAT2, the NCEP/NCAR
reanalysis, ERA-40, JRA-25, and JRA-55 are
MSU equivalent brightness temperatures. Anomalies for each dataset were defined relative totheir own climatological monthly means over
1979–1998.

in the lower stratosphere than the latest version (v1.5)
of RAOBCORE (Haimberger et al. 2012). Therefore,
the possibility of underestimation of a cooling trend in
JRA-55 cannot be ruled out.
c.

Middle to top stratosphere
Figure 15 compares monthly temperature anomalies from JRA-25, JRA-55 and two independent observational datasets for the middle, upper,
and top stratosphere, averaged over 75°N to 75°S.
The independent datasets are the Met Office SSU
dataset (Nash and Forester 1986; Nash 1988; Shineet al. 2008) and the National Oceanic and Atmospheric Administration (NOAA) the Center for Satellite Application and Research (STAR) SSU dataset
version 1.0 (Wang et al. 2012). These two SSU datasets display strikingly different time series, suggestingJRA-25JRA-55

Met Office SSU
STAR SSU V1.0

Fig. 15. Twelve-month running mean temperature anomalies for the (a) top, (b) upper and (c)middle stratosphere averaged over 75°N to 75°S.
Time series from the Met Office SSU dataset andSTAR SSU dataset V1.0 represent measurements
by the SSU channels 3, 2, and 1, whereas thosefrom JRA-25 and JRA-55 are SSU equivalentbrightness temperatures. Anomalies for eachdataset were defined relative to their own climatological monthly means over 1980–1994.

a clear need for better understanding observational
characteristics of the SSUs to obtain more reliable estimates of stratospheric temperature trends(Thompson et al. 2012). In JRA-55, radiances from all
the channels of SSUs were directly assimilated with
VarBC.
The impact of changes in observing systems on
the JRA-55 time series is reduced compared with
JRA-25 but the low-frequency variations are smaller
than those of the SSU datasets, especially in the top
stratosphere. The forecast model used for JRA-55
does not take into account interannual variations of
volcanic aerosols, solar constant, and stratosphericwater vapor concentrations. Because VarBC cannot
distinguish between observational and forecast model
biases, availability of anchoring observations such asradiosonde and GNSS-RO is a key factor in keeping
analysis fields from drifting toward model clima-

30

Journal of the Meteorological Society of Japan

tology. In the upper stratosphere and above, however,such observations are sparse. Consequently, VarBC
only partially corrects biases in observations fromstratospheric temperature channels, and even drifts
towards the model climatology, showing smaller time
variations in Fig. 15.
Trends represented in the two SSU datasets are
in reasonable agreement in the top stratosphere,
and JRA-55 time series also shows a similar trend.
However, the time series for the middle and upper
stratosphere show significant discrepancies between
the two SSU datasets. Trends in JRA-55 are closer tothe Met Office SSU dataset in the middle stratosphereand closer to the STAR SSU dataset in the upper
stratosphere. These differences could be attributed
to both uncertainty in the homogeneity of the SSU
datasets and underestimation of the interannual variability in JRA-55. Unless their relative contributions
are clarified, it is difficult to make further commentson reliability of any of these trends. From the viewpoint of reanalysis, the reliability of stratospherictemperature variations could be improved by taking
account of the missing factors in the forecast model,
correcting known problems in calibration (Nash andSaunders 2013) and improving radiative transfer
modeling for upper stratospheric temperature channels (Kobayashi et al. 2009), thereby reducing thedependence on VarBC.
7.2

Representation of the South American monsoon
systemIn JRA-25, surface pressure observations with erroneous station heights generated spurious anticyclonic
circulation anomalies in the lower troposphere over
the Amazon basin, which resulted in a dry bias inthis region (Fig. 16c). Accordingly, suspect observations were excluded from preliminary experiments for
JRA-55. However, the anomalies did not disappear,
and there still remained a dry bias in the South American monsoon region (Fig. 16d).
It is known that circulation patterns in the rainyseason in this region are characterized by 1) water
vapor transported southwest from the ocean into thecontinent and then transported southeast along theeast side of the Andes to Bolivia and 2) water vapor
carried around the edge of the South Atlantic subtropical high that flows into southeastern Brazil (Raiaand Cavalcanti 2008). However, JRA-25 and preliminary experiments for JRA-55 both showed that water
vapor fluxes into the continent were partly blocked by
anticyclonic circulation increments (Figs. 16f, g),which appeared to partly account for the dry bias.

Vol. 93, No. 1

Those anticyclonic anomalies stemmed from positive surface pressure increments, and intensified mostat 18 UTC, the time at which mesoscale convective
systems start to form in this region (early afternoon in
local time) (Figs. 17a, b, c, d).
The root cause of the positive surface pressure
increments has not been identified, but it might berelated to biases in surface pressure observations,errors in station heights and the fact that the solar
semidiurnal tide reproduced in the JMA GSM is about
10° of longitude ahead of the observed tide (D. Hotta
2010, personal communication). Surface pressure
increments from JRA-55 experiments clearly exhibited a westward propagating component of wavenumber 2 in the tropics, of which peaks were locatednear the nodes of the solar semidiurnal tide. Anotherpossible cause is that surface pressure observationsin the sea surrounding South America are sparse,and consequently surface pressure increments tend to
localize on land.
There may also be a feedback mechanism that reinforces positive surface pressure increments in the data
assimilation system. Once the land surface is driedup, the increase in sensible heat flux causes warmingin the lower troposphere, which results in decreased
background surface pressures that in turn reinforce
positive surface pressure increments. In addition,observational constraint on soil moisture in the land
surface analysis of JRA-55 is imposed only by snow
depths through snow depth analysis, which might bea reason why the feedback mechanism is maintained.
Because solving the root cause of this problem
requires a long-term effort outside the scope of theJRA-55 project, we entirely excluded surface pressure observations over the Amazon basin. In an experiment with this solution, surface pressure increments
over the Amazon basin mostly disappeared (Figs.
17e, f, g, h). The experiment also produced only small
total column water vapor flux increments (Fig. 16h)
and reproduced a precipitation distribution closer tothose of the Global Precipitation Climatology Project(GPCP) version 2.2 (Adler et al. 2003) and the Global
Precipitation Climatology Centre (GPCC) full datareanalysis version 6.0 (Schneider et al. 2011) (Figs.
16a, b, e).
8.

Basic characteristics of JRA-55

8.1 Global energy budget
In reanalysis, the energy balance is not exactly
preserved due to analysis increments. The degree towhich the actual energy balance is reproduced canserve as an indicator of the quality and usability of theFebruary 2015

S. KOBAYASHI et al.

31

Fig. 16. (a–e) Monthly mean precipitation and (f–h) total column water vapor flux increments and their divergencefrom (a) GPCP V2.2, (b) GPCC V6, (c, f) JRA-25, and preliminary experiments for JRA-55 with surface pressure
observations assimilated (d, g) and excluded (e, h) over the Amazon basin, averaged over November 1979. Total
column water vapor flux analyses are also shown in (c–e).

reanalyses for many purposes (Trenberth et al. 2011).
Observations related to Earth’s energy balance havealso substantial uncertainties, especially at the Earth’s
surface, where satellites cannot make direct measurements. Wild et al. (2013) proposed new best estimates for the components of the global mean energybalance along with their uncertainty ranges, which
they estimated by combining surface observations
from the global energy balance archive (GEBA) andthe Baseline Surface Radiation Network (BSRN) withthe radiation budgets from simulations performedin Coupled Model Intercomparison Project Phase 5(CMIP5). Here, we evaluate the energy balance reproduced in JRA-55 using the estimates by Wild et al.
(2013), and we examine differences from the Clouds

and the Earth’s Radiant Energy System (CERES)
observations and JRA-25.
Tables 8 and 9 list the components of the global
mean annual energy balance from Wild et al. (2013),
JRA-25 and JRA-55 for the top of the atmosphere(TOA) and the Earth surface, respectively. The valuesof JRA-55 mostly are within the uncertainty rangesof Wild et al. (2013), except for the outgoing thermal
flux, hydrological cycle components and net energy
fluxes. The most notable change from JRA-25 is thatthe surface downward thermal flux is closer to thebest estimate, most likely due to the improved representation of water vapor continuum absorption in therevised longwave radiation scheme.
The TOA reflected solar flux also becomes closer

32

Journal of the Meteorological Society of Japan

Vol. 93, No. 1

Fig. 17. Monthly mean surface pressure increments from preliminary experiments with surface pressure observations assimilated (a–d) and excluded (e–h) over the Amazon basin, averaged over (a, e) 00 UTC, (b, f) 06 UTC, (c,g) 12 UTC, and (d, h) 18 UTC data for November 1979.

February 201533

S. KOBAYASHI et al.
Table 8. Global annual mean energy balance at the TOA (W m–2). Values fromJRA-25 and JRA-55 are for the period 2002–2008, whereas those from Wild et al.
(2013) represent present-day climate conditions at the beginning of the 21st century with their uncertainty ranges in parentheses.

Incoming solar
Solar reflected
Thermal outgoing
Residual (downward)

Wild et al. (2013)

JRA-25JRA-55340 (340, 341)
100 (96, 100)
239 (236, 242)

341
95
255
–7.9

341
100
251
–10.0

Table 9. Global annual mean energy balance at the surface (W m–2). Values fromJRA-25 and JRA-55 are for the period 2002–2008, whereas those from Wild et al.
(2013) represent present-day climate conditions at the beginning of the 21st century with their uncertainty ranges in parentheses.

Solar down
Solar reflected
Solar absorbed surface
Solar absorbed atmosphere
Residual (downward)
Thermal down
Thermal up
Sensible heat
Evaporation

Wild et al. (2013)

JRA-25JRA-55185 (179, 189)
24 (22, 26)
161 (154, 166)
79 (74, 91)
0.6 (0.2, 1.0)
342 (338, 348)
397 (394, 400)
20 (15, 25)
85 (80, 90)

197
25
172
75
–11.6
327
399
20
91

189
26
164
77
–11.2
338
400
20
93

to the best estimate, due primarily to improvement of
the clear-sky shortwave radiation scheme. In addition,
revision of the reflectivity over deserts and the snow
surface albedo over perennial land ice also increased
the TOA reflected solar flux through increase of thesurface reflected solar flux.
The incoming solar fluxes from JRA-25 andJRA-55 differ slightly from the best estimate of Wild
et al. (2013). This is because the best estimate is basedon the estimated solar irradiance (1360.8 ± 0.5 Wm–2) of Kopp et al. (2005) and Kopp and Lean (2011),
whereas the previous estimate (1365 W m–2, Kopp
and Lean 2011) was used for JRA-25 and JRA-55.
Figure 18 shows the TOA radiation flux components from JRA-55 and their differences from the
CERES-Energy Balanced and Filled (EBAF) flux
dataset edition 2.7 (Loeb et al. 2009) and JRA-25.
The TOA reflected solar fluxes of JRA-55 have generally increased from those of JRA-25, especially over
the Amazon basin and desert/semidesert regions.
Figure 19 shows the TOA cloud radiative effects(obtained from clear-sky and all-sky flux differences)from JRA-55 and their differences from CERES and

JRA-25. The TOA solar flux reflected from clouds
significantly increased over the Amazon basin in
JRA-55 (Fig. 19c), which indicates that improved
reproducibility of convective activities reduced negative biases in the TOA reflected solar fluxes over theregion. An increase over desert and semidesert regions
is due to revision of the albedo. Although this revision
reduces negative biases over the Sahara, it exacerbates
positive biases over other regions. It thus appears tobe necessary to reevaluate the assumption of the same
albedo for the Sahara and those regions. Moreover,there still remain positive biases over the tropics andsubtropics, and negative biases over the Antarctic
Ocean, which have been known since JRA-25 as
pointed out by Trenberth and Smith (2008). The
current shortwave radiation scheme assumes a random
cloud overlap within the cloudy fraction of a model
grid, which may lead to too much reflection of solar
radiation over convective regions, where clouds tend
to overlap maximally in the vertical (Kitagawa andYabu 2002), which is most likely related to the positive biases over the tropics and subtropics.
For the outgoing thermal fluxes, a significant reduc-34

Journal of the Meteorological Society of Japan

Vol. 93, No. 1

Fig. 18. (a–c) Reflected solar fluxes, (d–f) outgoing thermal fluxes, and (g–i) net radiation fluxes at the TOA from(a, d, g) JRA-55, and their differences from (b, e, h) CERES-EBAF ed. 2.7 and (c, f, i) JRA-25, averaged over
2002–2008.

tion of positive biases can be seen over the Amazon
basin where a dry bias has been mitigated, most likely
due to the increase of precipitable water. A modest
reduction of positive biases can also be seen overmost of the mid-latitudes. However, there still remain
positive biases in deep convection regions over
the tropics, particularly over the Indian Ocean, thewestern Pacific, the Intertropical Convergence Zone
and the South Pacific Convergence Zone, where thebiases become worse than JRA-25. This is most likely
due to underestimation of cloud radiative effects in
those regions (Figs 19e, f).
Because of the biases in the TOA reflected solar
and outgoing thermal fluxes, the TOA net radiation
fluxes from JRA-55 have too little absorption in thetropics and too little emission in the extratropics, afeature that has been seen since JRA-25. These biases
may reduce activity in storm tracks and consequently
reduce poleward energy transport (Trenberth andFasullo 2010). Because these biases broadly correspond to the biases in the cloud radiative effects (Fig.

19), they are most likely related to representation ofclouds (such as distribution, height and optical properties) and the cloud overlap assumptions made in
the forecast model. There results clearly indicate theimportance of improving representation of clouds forreproducing a more accurate energy balance.
8.2 Precipitation
It is crucially important to better understand mechanisms of each component of the hydrological cycle,
such as precipitation, evaporation, atmospheric transport of water vapor, river runoff, for further advancing
climate monitoring and climate modeling. Reanalysis
can generate these variables as a synthesis of observations and modeling. However, forecast models
have their own uncertainty, and corrections by observations introduce artificial source or sink into thewater budget, which in turn leads to the spin-up (orspin-down) problem of the hydrological cycle. Also,changes in observing systems have a significant
impact on the representation of hydrological cycle inFebruary 2015

S. KOBAYASHI et al.

35

Fig. 19. Cloud radiative effects (obtained from clear-sky and all-sky flux differences) on (a–c) reflected solar
fluxes, (d–f) outgoing thermal fluxes and (g–i) net radiation fluxes at the TOA from (a, d, g) JRA-55, and theirdifferences from (b, e, h) CERES-EBAF ed. 2.7 and (c, f, i) JRA-25, averaged over 2002–2008.

reanalyses. Because of these weaknesses, it has been
pointed out that great caution is needed when usinghydrological variables from reanalyses, especiallymodel diagnostics such as precipitation and evaporation (Bosilovich et al. 2011; Trenberth et al. 2011).
Here, we examine main characteristics of the hydrological cycle in JRA-55 using GPCP V2.2 and otherreanalyses, including JRA-25.
Figure 20 shows the climatology of global precipitation distributions in JRA-55, JRA-25, ERA-Interim,
ERA-40, the Modern-Era Retrospective Analysis for
Research and Applications (MERRA, Rienecker et al.
2011), and GPCP as an observational dataset. While
precipitation in middle and high latitudes are underestimated in most reanalyses, this feature is improvedin JRA-55, especially in the Pacific and Atlantic
Oceans north of 30°N. On the other hand, JRA-55
overestimates precipitation in the tropics comparedwith GPCP. The regions where JRA-55 overestimates
precipitation tend to exhibit the spin-down problem(precipitation is excessive immediately after thestart of forecasts and then gradually decreases) (notshown). They also roughly correspond to the regions
where large moistening increments occur (Fig. 11b).
Therefore, the excessive precipitation in the tropics in
JRA-55 is most likely related to the dry bias and thespin-down problem of the forecast model in regionsof deep convection.
Figure 21 shows the time series of global monthly
mean precipitation anomalies and their spatial
anomaly correlation with GPCP. Although GPCP has
no trend for the past 30 years, most of the reanalyses
have strong trends, especially ERA-40 and MERRA.
The time series from JRA-55 exhibits little trend andremains stable. The time series from the TwentiethCentury Reanalysis (20CR, Compo et al. 2011) iseven more stable, but its spatial anomaly correlations
are not as high as the other latest generation reanalyses, most likely due to its sole reliance on surfacepressure observations. Spatial anomaly correlations for JRA-55 are improved from JRA-25 by 0.1
throughout the study period, and exceed 0.6 after36

Journal of the Meteorological Society of Japan

Vol. 93, No. 1

Fig. 20. Climatological annual mean precipitations in (a) JRA-55, (b) JRA-25, (c) ERA-Interim, (d) ERA-40, (e)MERRA, and (f) GPCP V2.2, averaged over 1980–2001.

the late 1980s (Fig. 21b). It should be noted that theabrupt increase in the late 1980s in most of the reanalyses is most likely due to the introduction of humidity
observations from the SSM/I instrument on the
Defense Meteorological Satellite Program satellites.
Until then, spatial anomaly correlations for MERRA,ERA-Interim and the Climate Forecast System
Reanalysis (CFSR, Saha et al. 2010) are higher thanthe others. Another important feature in Fig. 21b is thehigher correlation in strong El Niño years, especiallyin 1997 and 1998 when the correlation was as high as
0.8. After the late 1990s, correlation coefficients for
JRA-55, ERA-Interim and MERRA remained high atnearly 0.7, the highest among all reanalyses. The large

increasing trend in spatial anomaly correlations for
JRA-55 indicates that the reproducibility of precipitation has a great dependence on satellite observing
systems compared with the other latest generation
reanalyses, and suggests that there is a clear needfor improving the reproducibility of precipitation,
especially for the period before satellite observations
increased substantially in the late 1990s.
8.3 Tropical cyclones
In JRA-25, TCRs were assimilated for the first timein reanalyses, contributing to a better representationof tropical cyclones than other reanalyses of the time
(Hatsushika et al. 2006). However, the TCRs used

February 201537

S. KOBAYASHI et al.

Precip. Anomalies (mm/day)

(a)
0.3
0.2
0.1
0.0
−0.1
−0.2
−0.3
1960

1965

1970

1975

1980

1985
Year

1990

1995

2000

2005

2010

1965

1970

1975

1980

1985
Year

1990

1995

2000

2005

2010

Spacial Cor. of Pre. Anomalies

(b)
0.8

GPCP V2.2
JRA−55JRA−25
ERA−40ERA−Int
CFSR
MERRA
NCEP/NCAR
20CR

0.7
0.6
0.5
0.4
1960

Fig. 21. (a) Global monthly mean precipitation anomalies from reanalyses and GPCP V2.2, and (b) 12-monthrunning mean spatial anomaly correlations against GPCP V2.2. Anomalies for each dataset were defined relative
to their own climatological monthly means over 1980–2001.

100
90
Detection rate (%)

in JRA-25 contained some duplications and location
errors that originated in best-track data; accordingly,
for JRA-55 the TCRs were regenerated with improved
QC for best-track data.
Figure 22 shows global detection rates of tropical
cyclones in JRA-55, JRA-25, and ERA-Interim. The
observational definition of a tropical cyclone is a tropical disturbance with maximum sustained wind speedof 34 knots (17.5 m s–1) or greater, but this criterion
is not applicable to grid point data. Instead, the detection criterion for isobaric fields by Hatsushika et al.
(2006) was employed. Horizontal resolutions of thegrid point data used in this study are 1.25° in JRA-55
and JRA-25 and 1.5° in ERA-Interim, but the samedetection criterion was applied for them because thedifference did not significantly affect the result. The
detection rate in JRA-55 is around 95 % from the
1950s to the 1980s, after which it gradually decreases
to 85–90 % in the 2000s, while the detection rate in
JRA-25 is almost constant at 90 % except during thelate 2000s (Fig. 22). As shown in Figs. 2, 3 and 4, thenumber of observations increases with time, particularly satellite data, which should lead to increasing

80
70
JRA-55JRA-2560

ERA-Int
50
1958

Fig. 22.

1968

1978

1988
Year

1998

2008

Global detection rates of tropical cyclones
in JRA-55, JRA-25, and ERA-Interim.

detection rates with time. In ERA-Interim, the detection rate increases from 60 % in the 1980s to 75–80 %in the 2000s.
Figure 23 shows the global mean maximum 10 m
wind speed of tropical cyclones detected in JRA-55,
JRA-25, and ERA-Interim. A weakening trend isJournal of the Meteorological Society of Japan
60

80

50

70
Wind speed (kt)Maximum 10 m wind speed (kt)

38

40
30
20

JRA-55JRA-25

10

60
50
40

TCRs in JRA-55
TCRs in JRA-25
Best-track

30

ERA-Int

20

0
1958

1968

1978

1988
Year

1998

2008

1958

1968

1978

1988
Year

1998

2008

Fig. 24. Global mean wind speed of the TCRs
assimilated in JRA-55 and JRA-25, andmaximum sustained wind speed reported in thebest–track data.

Fig. 23. Global mean maximum 10 m windspeeds around detected tropical cyclone center in
JRA-55, JRA-25, and ERA-Interim.

100

Existing rate (%)

evident in JRA-55 with maximum 10 m wind speeds
decreasing from around 35 kt in the 1960s to 25 kt
in the 2000s. On the other hand, JRA-25 shows notrend in the mean wind speed of tropical cyclones. For
ERA-Interim, in which no artificial tropical cyclone
information like TCRs is assimilated, it is reasonable
that the increasing trend in wind speed leads to anincreasing trend in the detection rate. The intensifying
trend of tropical cyclones in ERA-Interim directlyreflects increasing observations and improving representation of tropical cyclones. The weakening trend oftropical cyclones in JRA-55 goes against the effect ofincreasing observations.
Figure 24 shows the global mean annual windspeeds of TCRs assimilated in JRA-55 and JRA-25,
together with the maximum sustained wind speeds
from best-track data for reference. For JRA-55, thesehave almost constant values of 40–50 kt with notrend from the 1960s to the 1980s and are consistent
with the best-track data, after which they decrease
to around 20–25 kt in the 2000s, which is inconsistent with the best-track data. On the other hand, theglobal mean annual wind speeds of TCRs assimilatedin JRA-25 were almost constant around 25 kt except
in the late 2000s, resulting in constant detection rates
throughout the study period.
The TCRs were retrieved from position, maximum
sustained wind speed, radius of 30-kt winds (R30) and
12-hour motion. Figure 25 shows the annual variation in the existing rates of R30 in the best-track data.
Since the R30 records were only available after thelate 1980s, R30s were taken from a look-up table inJRA-25 instead. In JRA-55, on the other hand, R30s
were taken not from the look-up table but from theVol. 93, No. 1

75
50
25
0
1958

1968

1978

1988
Year

1998

2008

Fig. 25. Existing rates of the R30 in the best-trackdata.

best-track data. When the R30 record was not available, a predefined fixed value was used for R30.
However, there is a considerable difference between
the predefined fixed value and R30s in the best-track
data; consequently, TCR speeds vary significantly
depending on whether the R30 record is available ornot. Comparing Figs. 24 and 25, a negative correlation is evident between TCR speed in JRA-55 andavailability of R30 records.
Although TCRs assimilated in JRA-55 show theartificial weakening trend, they are still useful for
representing tropical cyclones in the correct locations,which can be confirmed by the fact that high detection rates are maintained in JRA-55. Thus, JRA-55
can provide a proper dataset for a case study of anindividual tropical cyclone. However, the trend of

February 2015

tropical cyclone frequency and intensity in JRA-55 ismost likely affected by the artificial weakening trendof TCRs. Further examination is currently underway
to assess the impact of the artificial weakening trendof TCRs on the representation of tropical cyclones in
JRA-55.
9.

39

S. KOBAYASHI et al.

Conclusions

JRA-55 has been produced with the low-resolution(TL319) version of JMA’s operational data assimilation system as of December 2009, which incorporated
many improvements that had been achieved sinceJRA-25, including the revised longwave radiation
scheme, 4D-Var and VarBC for satellite radiances.
In addition, newly available homogenized observations were used in JRA-55 whenever possible.
These improvements have resulted in products that
produce better fits to observations, reduced analysis
increments and improved forecast scores. These are
evidence of the greater physical consistency of theJRA-55 product.
A cold bias in the lower stratosphere, one of the
major problems of JRA-25, has been diminishedowing primarily to the proper representation ofabsorptions due to the Doppler broadening in therevised longwave radiation scheme. The surface
downward thermal flux has drawn closer to theobservational best estimate, most likely due to theimproved representation of water vapor continuum
absorption in longwave radiation. The temporalconsistency of temperature analysis has improved
considerably from previous reanalyses, thanks to
improved model climatology, newly available bias
estimates to homogenize radiosonde temperatures
and advances in handling satellite radiances. A dry
bias in the Amazon basin, another major problem of
JRA-25, has been mitigated temporarily by excluding
surface pressure observations over the Amazon basin.
However, more work is needed to solve this problem.
Our initial quality evaluation has revealed problems such as a warm bias in the upper troposphere,
a cold bias in the lower troposphere and a negative
bias in precipitable waters in deep convection regions.
The warm bias in the upper troposphere gradually
decreases as the observing system is improved. The
impact of changes in observing systems is particularly apparent in July 2006 when the GNSS-RO
refractivities were introduced into JRA-55. In orderto improve the temporal consistency of product, thesemodel biases must be further reduced. Some of thediagnostic fields from the forecast model still exhibit
large biases, e.g., excessive precipitation over thetropics, excessive (insufficient) TOA reflected solar
flux over the tropics and subtropics (the Antarctic
Ocean), excessive outgoing thermal flux in deep
convection regions, and an overall imbalance of 10
W m–2 upward in the global mean net energy fluxes
at the TOA and the surface. For these diagnostic fields
to be useful in climate applications, the parameterizations of physical processes must be further improved.
Our work also showed that tropical cyclone strength
analyzed in JRA-55 has unrealistic trends. This has
most likely stemmed from the retrieval method for
TCR, which highlights anew the importance of homogeneity of observations in reanalysis.
To aid further study of the reproducibilities of
low-frequency variability and trends in JRA-55, as
well as the impacts of model biases and changes ofobserving systems, MRI has produced a reanalysis
assimilating conventional observations only, called
JRA-55C (Kobayashi et al. 2014), and an Atmospheric Model Intercomparison Project (AMIP)-typesimulation, called JRA-55AMIP. These are producedwith the same NWP system as the one used forJRA-55, and they are made available together with
JRA-55 products as the JRA-55 family. Intercomparisons among the JRA-55 family are expected tocontribute to spotting problems in the data assimilation system and forecast model that can lead to
improvement of future reanalyses.
The high-resolution ocean surface boundary condition is also worth consideration for future reanalyses.
Nakamura et al. (2008) suggested the importanceof mid-latitude oceanic fronts for the troposphericcirculation and its variability. The representation of
mid-latitude oceanic fronts in COBE-SST, which
was used for JRA-55, is insufficiently accurate due
to its moderate resolution (1° × 1°) and its sole reliance on in-situ observations. For the period during
which satellite observations are available, the use ofhigh-resolution SST using all available satellite dataneeds to be explored.
It is essential to improve quality and availability ofobservational datasets for further advances in reanalysis. Several international activities are retrieving
historical observations and reprocessing satellite
observations, such as European Reanalysis of Global
Climate Observations (ERA-CLIM, http://www.
era-clim.eu/) and SCOPE-CM. Observational datasets
to be made available through these activities are indispensable for improving reanalyses.
Acknowledgments
The JRA-55 project was conducted in cooperation

40

Journal of the Meteorological Society of Japan

with many divisions of JMA. The data assimilation
system used for JRA-55 is an achievement of many
years of development at the Numerical Prediction
Division. MSC reprocessed AMVs and CSRs frompast GMS and MTSAT imagery. The three-dimensional daily ozone distributions were produced incooperation with the Atmospheric Environment Division. In addition, many individuals from the Climate
Prediction Division and MRI contributed to quality
evaluation of the product from the start of the production of JRA-55.
JRA-55 is also supported by many individuals
and organizations in collecting observations. The
JRA-55 observational dataset consists primarily of
the ERA-40 observational dataset which was suppliedby ECMWF. Dr. Michael Fiorino of NOAA regenerated TCRs, and also provided us with the software forgenerating them on a near real-time basis. Furthermore, various kinds of observations were supplied byorganizations listed in Appendix A.
Satellite radiances were assimilated with RTTOV9.3 which was developed within the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on
Numerical Weather Prediction (NWP SAF). Dr. Roger
Saunders of the Met Office supplied us with the latest
radiative transfer coefficients for VTPR and GMS-5.
The JRA-55 advisory committee, with Prof. Toshiki
Iwasaki of Tohoku University as chairman, provided
us with valuable advice on broad aspects of reanalysis
from the outset of the project. In addition, 27 external
researchers participated in quality evaluation prior torelease of the product.
The JRA-55 project was realized with support fromthese individuals and organizations. We sincerely
appreciate their invaluable contributions.
Appendix A: Observational data sources
for JRA-55
The suppliers of observations used in JRA-55, thetype of data and the period for which the data wereused are given in Table A1.
Appendix B: Acronyms
1D-Var
20C3M
20CR
2D-OI
3D-Var
4D-VarAMI

One-dimensional variational analysis
Twentieth century climate in coupledmodelsTwentieth Century Reanalysis
Two-dimensional OI
Three-dimensional variational analysis
Four-dimensional variational analysis
Active Microwave Instrument

AMIP
AMSR-E
AMSU
AMV
ASCAT
ASPS
ATOVS
BFGS
BSRN
BUOY
C/NOFS
CAPE
CCM
CDAAC
CERES
CFSR
CHAMP
CLASS
CMIP5
CMOD
COBE
COSMICCRIEPICRU
CRUTEM4
CSR
DCAPE
DOE
EBAF
ECMWF
EORC
EOS
ERA
ERA-40ERA-CLIM

Vol. 93, No. 1

Atmospheric Model Intercomparison
Project
Advanced Microwave Scanning Radiometer for EOS
Advanced Microwave Sounding Unit
Atmospheric motion vector
Advanced Scatterometer
Advanced Scatterometer Processing
SystemAdvanced TOVS
Broyden-Fletcher-Goldfarb-Shanno(algorithm)
Baseline Surface Radiation Network
Report of a buoy observation
Communications/Navigation Outage
Forecasting System
Convective available potential energy
Chemistry climate model
COSMIC Data Analysis and Archive
CenterClouds and the Earth’s Radiant Energy
SystemClimate Forecast System Reanalysis
Challenging Mini-satellite Payload
Comprehensive Large Array-data
Stewardship System
CoupledModelIntercomparison
Project Phase 5
C-band model function
Centennial In Situ Observation-based
Estimates of the Variability of SSTs
and Marine Meteorological Variables
Constellation Observing System forMeteorology, Ionosphere, and Climate
Central Research Institute of ElectricPower Industry
Climatic Research Unit
CRU temperature dataset
Clear sky radiance
Dynamic CAPE generation rate
Department of Energy
Energy Balanced and Filled (fluxdataset)European Centre for Medium-Range
Weather Forecast
Earth Observation Research Center
Earth Observing System (NASA)
ECMWF Reanalysis
A 45-year ERA from September 1957
to August 2002
European Reanalysis of Global
Climate Observations

February 201541

S. KOBAYASHI et al.

Table A1. Observational data sources for JRA-55. Observations shown in plain cells were added or reprocessed after JRA-25,
whereas those in shaded cells are the same as the ones used in JRA-25.
Data supplier
Conventional data
ECMWF

Data type and suppliers’ identifiers

PeriodNote

GAME and SCSMEX

Jan 1958–Aug 2002
Jan 1961–
Apr 1998–Oct 1998

NCEP/NCARSYNOP and upper-level observations

Jan 1979–Dec 1979

M. Yamanaka
M. Fiorino

Radiosondes from Indonesia
TCRs

Nov 1991–May 1999
Jan 1958–

RIHMI

Snow depths from Russia

Jan 1958–Dec 2008

UCAR
Monthly Surface
Meteorological
Data in China
IMH
Satellite radiances

Snow depths from USA

Jan 1958–Aug 2011

Kalnay et al. (1996)
Kistler et al. (2001)
Okamoto et al. (2003)
Fiorino (2002)
http://meteo.ru/english/climate/snow.php
NCDC et al. (1981)

Snow depths from China

Jan 1971–Dec 2006

Digitized from printed matters

Snow depths from Mongolia

Jan 1975–Dec 2007

VTPR
HIRS and SSU
MSU and AMSU
SSM/I
AMSU and MHS
SSM/I
AMSU and MHS
SSM/I and SSMIS
TMI
CSR
Reprocessed CSRs from GMS-5, GOES 9 and
MTSAT-1R
Reprocessed TMI Version 7
Reprocessed AMSR-E Version 3
CSRs from the Meteosat series

Jan 1973–Feb 1979
Nov 1978–Dec 2000
Nov 1978–May 2003
Jun 1987–Dec 2004
Aug 1998–
Jul 1987–
Jun 2003–
Mar 2006–
Dec 2011–
Jun 2005–
Jul 1995–Dec 2009

JMA

ECMWF
NOAA/NCDCNOAA/CLASS

JMA

JMA/MSC
JAXA, NASAJAXA
EUMETSAT
AMVs
ECMWF
JMA
JMA/MSC

GMS, Meteosat and GOES
GMS, MTSAT, Meteosat and GOES
MODIS
Reprocessed GMS, GOES 9 and MTSAT-1R
Reprocessed Meteosat-2

EUMETSAT

Reprocessed Meteosat-3 to -7

Meteosat-5 and -7
Scatterometer ocean surface winds
ESA
Reprocessed AMI (ERS.ASPS20.N)
Hersbach (2008)
Reprocessed SeaWinds from QuikSCAT (QSCAT_JPLLEVEL_2B_V2)
JMA
ASCAT
GNSS-RO refractivities

CDAAC
JMA

Reprocessed CHAMP, SAC-C, COSMIC,GRACE, Metop-A, TerraSAR-X and C/NOFSCOSMIC, GRACE, Metop, TerraSAR-X andC/NOFS

Uppala et al. (2005)

Uppala et al. (2005)

Feb 1998–Dec 2011
Jun 2002–Oct 2011
Jan 2001–Aug 2009
Jan 1979–Dec 1997
Dec 1979–Dec 1980
Jan 1998–
Jun 2004–
Jan 1979–Nov 1979
Mar 1987–Sep 2009
May 1982–Aug 1988
Jan 1989–Dec 2000
Aug 1988–Nov 1988
Jan 2001–Feb 2001

Uppala et al. (2005)

van de Berg et al. (2002)

May 1997–Jan 2001

De Chiara et al. (2007)

Jul 1999–Nov 2009

Dunbar et al. (2006)

Jan 2008–

Jul 2006–Jun 2012
Jun 2012–

42

ERS
ESA
EUMETSAT

Journal of the Meteorological Society of Japan

European Remote Sensing Satellite
European Space Agency
European Organisation for the
Exploitation of Meteorological Satellites
FAA
Federal Aviation Administration
FGAT
First guess at the appropriate time
FGGE
First GARP Global Experiment
FOV
Field of viewGAME
GEWEX Asia Monsoon Experiment
GARP
GlobalAtmosphericResearch
Programme
GEBA
Global energy balance archive
GEWEX
Global Energy and Water Cycle
Experiment
GMS
Geostationary Meteorological Satellite
GNSS-RO
Global Navigation Satellite System–
Radio Occultation
GOES
Geostationary Operational Environmental Satellite
GPCC
Global Precipitation Climatology
CentreGPCP
Global Precipitation Climatology
Project
GRACE
Gravity Recovery and Climate Experiment
GSM
Global spectral model
HadAT
Hadley Centre’s radiosonde temperature product
HIRS
High-Resolution Infrared Spectrometer
HITRANHigh Resolution Transmission
IMH
InstituteofMeteorology
andHydrology (Mongolia)
JAXA
Japan Aerospace Exploration Agency
JCDAS
JMA Climate Data Assimilation
SystemJMA
Japan Meteorological Agency
JPL
Jet Propulsion Laboratory
JRA-25
Japanese 25-year Reanalysis
JRA-55
Japanese 55-year Reanalysis
JRA-55AMIP JRA-55 AMIP-type simulation
JRA-55C
JRA-55 sub-product assimilating
Conventional observations only
LBLRTM
Line-by-Line
Radiative
Transfer
Model
MERRA
Modern-Era Retrospective Analysis
for Research and Applications
MHS
Microwave Humidity Sounder
MODIS
Moderate Resolution Imaging Spectroradiometer
MRI
Meteorological Research InstituteVol. 93, No. 1(JMA)
Meteorological Satellite Center (JMA)
Meteosat Second Generation
Microwave Sounding Unit
Mlawer–Tobin–Clough–Kneizys–
Davies (water vapor continuum
absorption model)
MTSAT
Multi-functional Transport Satellite
NASA
National Aeronautics and Space
Administration
NCAR
National Center for AtmosphericResearch
NCDC
National Climate Data Center
NCEP
National Center for Environmental
Prediction
NOAA
National Oceanic and Atmospheric
Administration
NWP
Numerical weather prediction
NWP SAF
Satellite Application Facility on
Numerical Weather Prediction
NWS
National Weather Service
OI
Optimal interpolation
OMI
Ozone Monitoring Instrument
PAOBS
Pseudo Surface Pressure Observations
produced by Australia
QC
Quality control
QuikSCAT
Quick Scatterometer
R30
Radius of 30-kt winds
RAOBCORE Radiosonde Observation Correction
using Reanalyses
RCP
Representative Concentration Pathway
RIHMI
Russian Research Institute for Hydrometeorological Information
RMS
Root-mean-square
RSS
Remote Sensing Systems
RTTOV
Radiative Transfer for the TIROS
Operational Vertical Sounder
SAC-C
Satélite de Aplicaciones Cientificas-C
SCOPE-CM Sustained, Coordinated Processingof Environmental Satellite Data for
Climate Monitoring
SCSMEXSouth China Sea Monsoon Experiment
SEVIRI
Spinning Enhanced Visible andInfrared Imager
SHIP
Report of surface observation from asea station
SiB
Simple Biosphere (model)
SMS
Supervisor Monitor Scheduler
SSM/I
Special Sensor Microwave/Imager
SSMIS
Special Sensor Microwave Imager
Sounder
SST
Sea surface temperature
MSC
MSG
MSU
MT_CKD

February 2015

SSU
STAR
SYNOP
TCR
TIROS
TMI
TOA
TOMS
TOVS
TRMM
UCAR
VarBC
VTPR
WDCGG
WMO

S. KOBAYASHI et al.

Stratospheric Sounding Unit
Center for Satellite Applications andResearch
Report of surface observation from afixed land station
Wind profile retrieval surrounding
tropical cyclones
Television and Infrared Observation
Satellite
TRMM Microwave Imager
Top of the atmosphere
Total Ozone Mapping Spectrometer
TIROS Operational Vertical Sounder
TropicalRainfall
Measurement
Mission
University Corporation for Atmospheric Research
Variational bias correction
Vertical Temperature Profile Radiometer
World Data Centre for Greenhouse
Gases
World Meteorological Organization
References

Adler, R. F., G. J. Huffman, A. Chang, R. Ferraro, P.-P.
Xie, J. Janowiak, B. Rudolf, U. Schneider, S. Curtis,
D. Bolvin, A. Gruber, J. Susskind, P. Arkin, and E.
Nelkin, 2003: The Version-2 Global Precipitation
Climatology Project (GPCP) monthly precipitation
analysis (1979-present). J. Hydrometeor., 4, 1147–
1167.
Andrae, U., N. Sokka, and K. Onogi, 2004: The radiosonde temperature bias corrections used in ERA-40.
ECMWF ERA-40 Project Report Series, 15,
ECMWF, UK, 34 pp. [Available at http://www.
ecmwf.int/en/research/publications/.]
Arakawa, A., and W. H. Schubert, 1974: Interaction of acumulus cloud ensemble with the large-scale environment, Part I. J. Atmos. Sci., 31, 674–701.
Arking, A., and K. Grossman, 1972: The influence of line
shape and band structure on temperatures in planetaryatmospheres. J. Atmos. Sci., 29, 937–949.
Bengtsson, L., P. Arkin, P. Berrisford, P. Bougeault, C.
K. Folland, C. Gordon, K. Haines, K. I. Hodges,
P. Jones, P. Kallberg, N. Rayner, A. J. Simmons, D.
Stammer, P. W. Thorne, S. Uppala, and R. S. Vose,
2007: The need for a dynamical climate reanalysis.
Bull. Amer. Meteor. Soc., 88, 495–501.
Bosilovich, M. G., F. R. Robertson, and J. Chen, 2011:
Global energy and water budgets in MERRA. J.
Climate, 24, 5721–5739.
Bréon, F.-M., D. Jackson, and J. Bates, 1999: Evidence of
atmospheric contamination on the measurement of the43

spectral response of the GMS-5 water vapor channel.
J. Atmos. Oceanic Technol., 16, 1851–1853.
Briegleb, B. P., 1992: Delta-Eddington approximation forsolar radiation in the NCAR community climate
model. J. Geophys. Res., 97, 7603–7612.
Briegleb, B. P., P. Minnis, V. Ramanathan, and E. Harrison,
1986: Comparison of regional clear-sky albedos
inferred from satellite observations and modelcomputations. J. Climate Appl. Meteor., 25, 214–226.
Chou, M.-D., and L. Kouvaris, 1991: Calculations of transmission functions in the infrared CO2 and O3 bands.
J. Geophys. Res., 96, 9003–9012.
Chou, M.-D., M. J. Suarez, X.-Z. Liang, and M. M.-H. Yan,
2001: A thermal infrared radiation parameterization for atmospheric studies. Technical report series
on global modeling and data assimilation, NASA/
TM-2001-104606, 19, National Aeronautics andSpace Administration (NASA) Goddard Space Flight
Center, USA, 68 pp. [Available at http://ntrs.nasa.
gov/.]
Clarke, L., J. Edmonds, H. Jacopy, H. Pitcher, J. Reilly, andR. Richels, 2007: Scenarios of greenhouse gas emissions and atmospheric concentrations. Sub-report
2.1A of Synthesis and Assessment Product 2.1 by
the U.S. Climate Change Science Program and the
Subcommittee on Global Change Research. Department of Energy (DOE), Office of Biological & Environmental Research, USA, 154 pp. [Available at
http://library.globalchange.gov/products.]
Clough, S. A., and M. J. Iacono, 1995: Line-by-line calculation of atmospheric fluxes and cooling rates 2. Application to carbon dioxide, ozone, methane, nitrous
oxide and the halocarbons. J. Geophys. Res., 100,
16519–16535.
Clough, S. A., M. J. Iacono, and J.-L. Moncet, 1992: Lineby-line calculations of atmospheric fluxes and coolingrates: Application to water vapor. J. Geophys. Res.,97, 15761–15785.
Clough, S. A., M. W. Shephard, E. J. Mlawer, J. S.
Delamere, M. J. Iacono, K. Cady-Pereira, S. Boukabara, and P. D. Brown, 2005: Atmospheric radiativetransfer modeling: a summary of the AER codes. J.
Quant. Spectrosc. Radiat. Transfer, 91, 233–244.
Coakley, J. A., Jr., R. D. Cess, and F. B. Yurevich, 1983: The
effect of tropospheric aerosols on the earth’s radiation budget: a parameterization for climate models. J.
Atmos. Sci., 40, 116–138.
Compo, G. P., J. S. Whitaker, P. D. Sardeshmukh, N.
Matsui, R. J. Allan, X. Yin, B. E. Gleason, R. S.
Vose, G. Rutledge, P. Bessemoulin, S. Brönnimann,
M. Brunet, R. I. Crouthamel, A. N. Grant, P. Y.
Groisman, P. D. Jones, M. C. Kruk, A. C. Kruger, G.
J. Marshall, M. Maugeri, H. Y. Mok, Ø. Nordli, T. F.
Ross, R. M. Trigo, X. L. Wang, S. D. Woodruff, andS. J. Worley, 2011. The Twentieth Century Reanalysis
Project. Quart. J. Roy. Meteor. Soc., 137, 1–28.

44

Journal of the Meteorological Society of Japan

Courtier, P., J.-N. Thépaut, and A. Hollingsworth, 1994: A
strategy for operational implementation of 4D-Var,using an incremental approach. Quart. J. Roy. Meteor.
Soc., 120, 1367–1387.
De Chiara, G., R. Crapolicchio, and P. Lecomte, 2007:
ERS-1/2 scatterometer new products: mission
reprocessing and data quality improvement. Paper
presented at the 2nd Space for Hydrology Workshop,
Geneva, Switzerland, November 12-14, 2007. [Available at http://earth.esa.int/hydrospace07/programme.
html.]
Dee, D. P., and S. Uppala, 2009: Variational bias correctionof satellite radiance data in the ERA-Interim reanalysis. Quart. J. Roy. Meteor. Soc., 135, 1830–1841.
Dee, D. P., S. M. Uppala, A. J. Simmons, P. Berrisford, P.
Poli, S. Kobayashi, U. Andrae, M. A. Balmaseda, G.
Balsamo, P. Bauer, P. Bechtold, A. C. M. Beljaars,
L. van de Berg, J. Bidlot, N. Bormann, C. Delsol, R.
Dragani, M. Fuentes, A. J. Geer, L. Haimberger, S. B.
Healy, H. Hersbach, E. V. Hólm, L. Isaksen, P. Kållberg, M. Köhler, M. Matricardi, A. P. McNally, B. M.
Monge-Sanz, J.-J. Morcrette, B.-K. Park, C. Peubey,
P. de Rosnay, C. Tavolato, J.-N. Thépaut, and F.
Vitart, 2011: The ERA-Interim reanalysis: configuration and performance of the data assimilation system.
Quart. J. Roy. Meteor. Soc., 137, 553–597.
Derber, J., and F. Bouttier, 1999: A reformulation of the
background error covariance in the ECMWF global
data assimilation system. Tellus A, 51, 195–221.
Derber, J. C., and W.-S. Wu, 1998: The use of TOVS cloudcleared radiances in the NCEP SSI analysis system.
Mon. Wea. Rev., 126, 2287–2299.
Desroziers, G., L. Berre, B. Chapnik, and P. Poli, 2005:
Diagnosis of observation, background and analysis
error statistics in observation space. Quart. J. Roy.
Meteor. Soc., 131, 3385–3396.
Dorman, J. L., and P. J. Sellers, 1989: A global climatologyof albedo, roughness length and stomatal resistancefor atmospheric general circulation models as represented by the Simple Biosphere Model (SiB). J. Appl.
Meteor., 28, 833–855.
Dunbar, R. S., T. Lungu, B. Weiss, B. Stiles, J. Huddleston,
P. S. Callahan, G. Shirtliffe, K. L. Perry, C. Hsu, C.
Mears, F. Wentz, and D. Smith, 2006: QuikSCAT
science data product user’s manual. Version 3.0,
D-18053 - Rev A, Jet Propulsion Laboratory (JPL),
USA, 85 pp.
Ebert, E. E., and J. A. Curry, 1992: A parameterization of
ice cloud optical properties for climate models. J.
Geophys. Res., 97, 3831–3836.
Ebita, A., S. Kobayashi, Y. Ota, M. Moriya, R. Kumabe,
K. Onogi, Y. Harada, S. Yasui, K. Miyaoka, K. Takahashi, H. Kamahori, C. Kobayashi, H. Endo, M.
Soma, Y. Oikawa, and T. Ishimizu, 2011: The Japanese 55-year Reanalysis “JRA-55”: an interim report.
SOLA, 7, 149–152.

Vol. 93, No. 1

Etheridge, D. M, L. P. Steele, R. L. Langenfelds, R. J.
Francey, J.-M. Barnola, and V. I. Morgan, 1998:
Historical CO2 records from the Law Dome DE08,
DE08-2, and DSS ice cores. Trends: A Compendiumof Data on Global Change. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory,
DOE, USA. [Available at http://cdiac.ornl.gov/trends/
co2/lawdome.html.]
Fiorino, M., 2002: Analysis and forecasts of tropical
cyclones in the ECMWF 40-year reanalysis (ERA40). 25th Conference on Hurricanes and TropicalMeteorology, CA, USA, 261–264.
Freidenreich, S. M., and V. Ramaswamy, 1999: A new
multiple-band solar radiative parameterization forgeneral circulation models. J. Geophys. Res., 104,
31389–31409.
Geleyn, J.-F., and A. Hollingsworth, 1979: An economical
analytical method for the computation of the interaction between scattering and line absorption of radiation. Beitr. Phys. Atmos., 52, 1–16.
Goody, R. M., 1952: A statistical model for water-vapour
absorption. Quart. J. Roy. Meteor. Soc., 78, 165–169.
Haimberger, L., C. Tavolato, and S. Sperka, 2008: Towardelimination of the warm bias in historic radiosondetemperature records--Some new results from acomprehensive intercomparison of upper-air data. J.
Climate, 21, 4587–4606.
Haimberger, L., C. Tavolato, and S. Sperka, 2012: Homogenization of the global radiosonde temperature dataset
through combined comparison with reanalysis background series and neighboring stations. J. Climate,25, 8108–8131.
Hatsushika, H., J. Tsutsui, M. Fiorino, and K. Onogi, 2006:
Impact of wind profile retrievals on the analysis oftropical cyclones in the JRA-25 reanalysis. J. Meteor.
Soc. Japan, 84, 891–905.
Hersbach, H., 2008: CMOD5.N: A C-band geophysicalmodel function for equivalent neutral wind. ECMWF
Technical Memorandom, 554, ECMWF, UK, 20 pp.
[Available at http://www.ecmwf.int/en/research/publications/.]
Holmlund, K., 1998: The utilization of statistical properties of satellite-derived atmospheric motion vectors
to derive quality indicators. Wea. Forecasting, 13,1093–1104.
Ishibashi, T., 2009: Assimilation of WV-CSRs from fivegeostationary satellites in the JMA global 4D-Var
system. CAS/JSC WGNE Res. Activ. Atmos. Oceanic
Modell., 39, 13–14. [Available at http://www.wcrpclimate.org/WGNE/blue_book.html.]
Ishii, M., A. Shouji, S. Sugimoto, and T. Matsumoto, 2005:
Objective analyses of sea-surface temperature andmarine meteorological variables for the 20th century
using ICOADS and the KOBE collection. Int. J.
Climatol., 25, 865–879.
Iwamura, K., and H. Kitagawa, 2008: An upgrade of theFebruary 2015

S. KOBAYASHI et al.

JMA operational global NWP model. CAS/JSC
WGNE Res. Activ. Atmos. Oceanic Modell., 38, 3–4.
[Available at http://www.wcrp-climate.org/WGNE/
blue_book.html.]
JMA, 2002: Outline of the operational numerical weather
prediction at the Japan Meteorological Agency. WMO
Numerical Weather Prediction Progress Report. JMA,
Japan, 158 pp.
JMA, 2007: Outline of the operational numerical weather
prediction at the Japan Meteorological Agency. WMO
Technical Progress Report on the Global Data-Processing and Forecasting System and Numerical
Weather Prediction, JMA, Japan, 194 pp. [Available
at http://www.jma.go.jp/jma/jma-eng/jma-center/nwp/
nwp-top.htm.]
JMA, 2013a: JRA-55 product users’ handbook: 1.25 degree
latitude/longitude grid data. JMA, Japan, 22 pp.
[Available at http://jra.kishou.go.jp/JRA-55/index_
en.html.]
JMA, 2013b: Outline of the operational numerical weather
prediction at the Japan Meteorological Agency.
WMO Technical Progress Report on the Global
Data-processing and Forecasting System (GDPFS)
and Numerical Weather Prediction (NWP) Research,
JMA, Japan, 188 pp. [Available at http://www.jma.
go.jp/jma/jma-eng/jma-center/nwp/nwp-top.htm.
Japan Meteorological Agency, 2014: JRA-55 product users’
handbook: Model grid data. JMA, Japan, 31 pp.
[Available at http://jra.kishou.go.jp/JRA-55/index_en.
html.]
Jones, P. D., D. H. Lister, T. J. Osborn, C. Harpham, M.
Salmon, and C. P. Morice, 2012: Hemispheric andlarge-scale land surface air temperature variations: An
extensive revision and an update to 2010. J. Geophys.
Res., 117, D05127, doi:10.1029/2011JD017139.
Joseph, J. H., W. J. Wiscombe, and J. A. Weinman, 1976:
The delta-Eddington approximation for radiative flux
transfer. J. Atmos. Sci., 33, 2452–2459.
Juang, H.-M. H., 2004: A reduced spectral transform for the
NCEP seasonal forecast global spectral atmosphericmodel. Mon. Wea. Rev., 132, 1019–1035.
Kalnay, E., M. Kanamitsu, R. Kistler, W. Collins, D.
Deaven, L. Gandin, M. Iredell, S. Saha, G. White,
J. Woollen, Y. Zhu, M. Chelliah, W. Ebisuzaki, W.
Higgins, J. Janowiak, K. C. Mo, C. Ropelewski, J.
Wang, A. Leetmaa, R. Reynolds, R. Jenne, and D.
Joseph, 1996: The NCEP/NCAR 40-year reanalysis
project. Bull. Amer. Meteor. Soc., 77, 437–471.
Kazumori, M., H. Owada, and K. Okamoto, 2004: Improvements of ATOVS radiance-bias correction scheme at
JMA. CAS/JSC WGNE Res. Activ. Atmos. Oceanic
Modell., 34, 15–16. [Available at http://www.wcrpclimate.org/WGNE/blue_book.html.]
Kistler, R., E. Kalnay, W. Collins, S. Saha, G. White, J.
Woollen, M. Chelliah, W. Ebisuzaki, M. Kanamitsu, V. Kousky, H. van den Dool, R. Jenne, and M.

45

Fiorino, 2001: The NCEP-NCAR 50-year reanalysis:Monthly means CD-ROM and documentation. Bull.
Amer. Meteor. Soc., 82, 247–267.
Kitagawa, H., and S. Murai, 2006: A revised radiation
scheme for cloud treatments in the Japan Meteorological Agency Global Spectral Model. CAS/JSC WGNE
Res. Activ. Atmos. Oceanic Modell., 36, 17–18.
[Available at http://www.wcrp-climate.org/WGNE/
blue_book.html.]
Kitagawa, H., and S. Yabu, 2002: Impact of a revised
parameterization of cloud radiative forcings on
Earth’s radiation budgets simulation. CAS/JSC WGNE
Res. Activ. Atmos. Oceanic Modell., 32, section 4.
[Available at http://www.wcrp-climate.org/WGNE/
blue_book.html.]
Kobayashi, C., H. Endo, Y. Ota, S. Kobayashi, H. Onoda, Y.
Harada, K. Onogi, and H. Kamahori, 2014: Preliminary results of the JRA-55C, an atmospheric reanalysis assimilating conventional observations only.
SOLA, 10, 78–82.
Kobayashi, C., and K. Shibata, 2011: Evaluation of dynamical contribution to lower stratospheric ozone trends
in northern mid-latitudes over the last three decades(1980-2006) using a chemical transport model. J.
Meteor. Soc. Japan, 89, 363–376.
Kobayashi, S., M. Matricardi, D. Dee, and S. Uppala, 2009:
Toward a consistent reanalysis of the upper stratosphere based on radiance measurements from SSU
and AMSU-A. Quart. J. Roy. Meteor. Soc., 135,
2086–2099.
Kopp, G., G. Lawrence, and G. Rottman, 2005: The Total
Irradiance Monitor (TIM): science results. The Solar
Radiation and Climate Experiment. Springer., 230,
129–139.
Kopp, G., and J. L. Lean, 2011: A new, lower valueof total solar irradiance: evidence and climate
significance. Geophys. Res. Lett., 38, L01706,
doi:10.1029/2010GL045777.
Krzeminski, B., N. Bormann, G. Kelly, T. McNally, and
P. Bauer, 2009: Revision of the HIRS cloud detection at ECMWF. EUMETSAT/ECMWF Fellowship
Programme Research Report, 19, ECMWF, UK, 15
pp. [Available at http://www.ecmwf.int/en/research/
publications/.]
Liu, D. C., and J. Nocedal, 1989: On the limited memory
BFGS method for large scale optimization. Math.
Programming, 45, 503–528.
Loeb, N. G., B. A. Wielicki, D. R. Doelling, G. L. Smith,
D. F. Keyes, S. Kato, N. Manalo-Smith, and T. Wong,
2009: Toward optimal closure of the earth’s top-of-atmosphere radiation budget. J. Climate, 22, 748–766.
Maki, T., A. Ebita, T. Ishimizu, K. Nagata, M. Ikegami, and
T. Sasaki, 2008: The daily 3D ozone produced by
the chemical transport model for JRA-25. Extended
Abstracts of the Third WCRP International Conference on Reanalysis, Tokyo, Japan. [Available at http://46

Journal of the Meteorological Society of Japan

wcrp.ipsl.jussieu.fr/Workshops/Reanalysis2008/
abstract.html.]
Matricardi, M., F. Chevallier, G. Kelly, and J.-N. Thépaut,2004: An improved general fast radiative transfer
model for the assimilation of radiance observations.
Quart. J. Roy. Meteor. Soc., 130, 153–173.
Matsumoto, T., M. Ishii, Y. Fukuda, and S. Hirahara, 2006:
Sea ice data derived from microwave radiometer forclimate monitoring. Proceedings of the 14th Conference on Satellite Meteorology and Oceanography,
Atlanta, USA, 2.21–2.21.
McCarthy, M. P., H. A. Titchner, P. W. Thorne, S. F. B. Tett,
L. Haimberger, and D. E. Parker, 2008: Assessing
bias and uncertainty in the HadAT-adjusted radiosonde climate record. J. Climate, 21, 817–832.
McMillin, L. M., D. Q. Wark, J. M. Siomkajlo, P. G. Abel,
A. Werbowetzki, L. A. Pritchard, D. S. Crosby, H. M.
Woolf, R. C. Luebbe, M. P. Weinreb, H. E. Fleming,
F. E. Bittner, and C. M. Hayden, 1973: Satellite
infrared soundings from NOAA spacecraft. NOAA
Technical Report NESS, 65, NOAA, USA, 112 pp.
Mears, C. A., and F. J. Wentz, 2009a: Construction of theRemote Sensing Systems V3.2 atmospheric temperature records from the MSU and AMSU microwave
sounders. J. Atmos. Oceanic Technol., 26, 1040–1056.
Mears, C. A., and F. J. Wentz, 2009b: Construction of the
RSS V3.2 lower tropospheric dataset from the MSU
and AMSU microwave sounders. J. Atmos. Oceanic
Technol., 26, 1493–1509.
Meinshausen, M., S. J. Smith, K. Calvin, J. S. Daniel, M.
L. T. Kainuma, J.-F. Lamarque, K. Matsumoto, S. A.
Montzka, S. C. B. Raper, K. Riahi, A. Thomson, G. J.
M. Velders, and D. P. P. van Vuuren, 2011: The RCP
greenhouse gas concentrations and their extensions
from 1765 to 2300. Climatic Change, 109, 213–241.
Nakagawa, M., 2009: Outline of the high resolution global
model at the Japan Meteorological Agency. RSMCTokyo-Typhoon Center Technical Review, 11, JMA,
Japan, 1–13. [Available at http://www.jma.go.jp/jma/
jma-eng/jma-center/rsmc-hp-pub-eg/techrev.htm.]
Nakamura, H., T. Sampe, A. Goto, Q. Ohfuchi, and S.-P.
Xie, 2008: On the importance of midlatitude oceanic
frontal zones for the mean state and dominant variability in the tropospheric circulation. Geophys. Res.
Lett., 35, L15709, doi:10.1029/2008GL034010.
Nash, J., 1988: Extension of explicit radiance observations
by the stratospheric sounding unit into the lower
stratosphere and lower mesosphere. Quart. J. Roy.
Meteor. Soc., 114, 1153–1171.
Nash, J., and G. F. Forrester, 1986: Long-term monitoringof stratospheric temperature trends using radiancemeasurements obtained by the TIROS-N series of
NOAA spacecraft. Adv. Space Res., 6, 37–44.
Nash, J., and R. Saunders, 2013: A review of stratosphericsounding unit radiance observations in support ofclimate trends investigations and reanalysis. Fore-Vol. 93, No. 1

casting Research Technical Report, 586, Met Office,UK, 58 pp. [Available at http://www.metoffice.gov.
uk/learning/library/publications/science/weather-science-technical-reports.]
NCDC, NWS, and FAA, 1981: NCDC TD3200 U.S. cooperative summary of day, 1890(1948)-cont. Research
Data Archive at NCAR, Computational and Information Systems Laboratory, USA. [Available at http://
rda.ucar.edu/datasets/ds510.0.]
Ohhashi, Y., 2004: Assimilation of QuikSCAT/SeaWinds
ocean surface wind data into the JMA global data
assimilation system. RSMC Tokyo Typhoon Center
Technical Review, 7, JMA, Japan, 22-27. [Available at
http://www.jma.go.jp/jma/jma-eng/jma-center/rsmchp-pub-eg/techrev.htm.]
Okamoto, K., M. Kazumori, T. Egawa, T. Ishibashi, H.
Owada, A. Murata, and H. Nishihata, 2011: Recent
development of satellite data assimilation at JMA.
Proceedings of the Seventeenth International TOVS
Study Conference, Monterey, USA, 145–154. [Available at http://cimss.ssec.wisc.edu/itwg/itsc/.]
Okamoto, K., H. Owada, T. Egawa, and T. Ishibashi, 2008:
Assimilation of radiance data at JMA: recent developments and prospective plans. Proceedings of the
Sixteenth International TOVS Study Conference,
Angra dos Reis, Brazil, 145–154. [Available at http://
cimss.ssec.wisc.edu/itwg/itsc/.]
Okamoto, K., H. Owada, Y. Sato, and T. Ishibashi, 2007:
Use of satellite radiances in the global assimilation
system at JMA. Proceedings of the Fifteenth International TOVS Study Conference, Maratea, Italy,
162–171. [Available at http://cimss.ssec.wisc.edu/
itwg/itsc/.]
Okamoto, N., M. D. Yamanaka, S.-Y. Ogino, H. Hashiguchi, N. Nishi, T. Sribimawati, and A. Numaguchi,
2003: Seasonal variations of tropospheric wind overIndonesia: comparison between collected operational
radiosonde data and NCEP reanalysis for 1992–99. J.
Meteor. Soc. Japan, 81, 829–850.
Onogi, K., 1998: A data quality control method using forecasted horizontal gradient and tendency in a NWP
system: Dynamic QC. J. Meteor. Soc. Japan, 76,
497–516.
Onogi, K., 2000: The long-term performance of the radiosonde observing system to be used for ERA-40.
ECMWF ERA-40 Project Report Series, 2. ECMWF,
UK, 77 pp. [Available at http://www.ecmwf.int/en/
research/publications/.]
Onogi, K., J. Tsutsui, H. Koide, M. Sakamoto, S.
Kobayashi, H. Hatsushika, T. Matsumoto, N.
Yamazaki, H. Kamahori, K. Takahashi, S. Kadokura,
K. Wada, K. Kato, R. Oyama, T. Ose, N. Mannoji,
and R. Taira, 2007: The JRA-25 reanalysis. J. Meteor.
Soc. Japan, 85, 369–432.
Ou, S.-C., and K.-N. Liou, 1995: Ice microphysics andclimatic temperature feedback. Atmos. Res., 35,

February 2015

S. KOBAYASHI et al.

127–138.
Parrish, D., and J. Derber, 1992: The National Meteorological Center’s spectral statistical interpolation analysis
system. Mon. Wea. Rev., 120, 1747−1763.
Raia, A., and I. F. A. Cavalcanti, 2008: The life cycle of theSouth American monsoon system. J. Climate, 21,6227–6246.
Räisänen, P., 1998: Effective longwave cloud fraction andmaximum-random overlap of clouds: a problem and asolution. Mon. Wea. Rev., 126, 3336–3340.
Rienecker, M. M., M. J. Suarez, R. Gelaro, R. Todling, J.
Bacmeister, E. Liu, M. G. Bosilovich, S. D. Schubert,
L. Takacs, G.-K. Kim, S. Bloom, J. Chen, D. Collins,
A. Conaty, A. da Silva, W. Gu, J. Joiner, R. D. Koster,R. Lucchesi, A. Molod, T. Owens, S. Pawson, P.
Pegion, C. R. Redder, R. Reichle, F. R. Robertson, A.
G. Ruddick, M. Sienkiewicz, and J. Woollen. 2011:
MERRA: NASA’s modern-era retrospective analysis
for research and applications. J. Climate, 24, 3624–
3648.
Robel, J. (ed.), 2009: NOAA KLM user’s guide withNOAA-N, -N’ supplement. February 2009 revision,
NOAA, USA. [Available at http://www.ncdc.noaa.
gov/oa/pod-guide/ncdc/docs/klm/index.htm.]
Roberts, R. E., J. E. A. Selby, and L. M. Biberman, 1976:
Infrared continuum absorption by atmospheric water
vapor in the 8-12-μm window. Appl. Opt., 15, 2085–
2090.
Rothman, L. S., A. Barbe, D. Chris Benner, L. R. Brown,
C. Camy-Peyret, M. R. Carleer, K. Chance, C. Clerbaux, V. Dana, V. M. Devi, A. Fayt, J.-M. Flaud, R.
R. Gamache, A. Goldman, D. Jacquemart, K. W.
Jucks, W. J. Lafferty, J.-Y. Mandin, S. T. Massie,
V. Nemtchinov, D. A. Newnham, A. Perrin, C. P.
Rinsland, J. Schroeder, K. M. Smith, M. A. H. Smith,
K. Tang, R. A. Toth, J. Vander Auwera, P. Varanasi,
and K. Yoshino, 2003: The HITRAN molecular spectroscopic database: Edition of 2000 including updates
through 2001. J. Quant. Spectrosc. Radiat. Transfer,82, 5–44.
Saha, S., S. Moorthi, H.-L., Pan, X. Wu, J. Wang, S. Nadiga,
P. Tripp, R. Kistler, J. Woollen, D. Behringer, H.
Liu, D. Stokes, R. Grumbine, G. Gayno, Y.-T. Hou,
H.-Y. Chuang, H.-M. H. Juang, J. Sela, M. Iredell, R.
Treadon, D. Kleist, P. van Delst, D. Keyser, J. Derber,
M. Ek, J. Meng, H. Wei, R. Yang, S. Lord, H. van den
Dool, A. Kumar, W. Wang, C. Long, M. Chelliah, Y.
Xue, B. Huang, J.-K. Schemm, W. Ebisuzaki, R. Lin,P. Xie, M. Chen, S. Zhou, W. Higgins, C.-Z. Zou, Q.
Liu, Y. Chen, Y. Han, L. Cucurull, R. W. Reynolds, G.
Rutledge, and M. Goldberg, 2010: The NCEP climate
forecast system reanalysis. Bull. Amer. Meteor. Soc.,
91, 1015–1057.
Sakamoto, M., 2009: An update to the quality control
thresholds of the conventional observing system for
global data assimilation. CAS/JSC WGNE Res. Activ.

47

Atmos. Oceanic Modell., 39, 33–34. [Available at
http://www.wcrp-climate.org/WGNE/blue_book.html.]
Sakamoto, M., and J. Christy, 2009: The Influences of
TOVS radiance assimilation on temperature andmoisture tendencies in JRA-25 and ERA-40. J.
Atmos. Oceanic Technol., 26, 1435–1455.
Sako, H., 2010: Assimilation of aircraft temperature datain the JMA global 4D-Var data assimilation system.
CAS/JSC WGNE Res. Activ. Atmos. Oceanic Modell.,40, 33–34. [Available at http://www.wcrp-climate.org/
WGNE/blue_book.html.]
Santer, B. D., T. M. L. Wigley, A. J. Simmons, P. W. Kållberg, G. A. Kelly, S. M. Uppala, C. Ammann, J. S.
Boyle, W. Brüggemann, C. Doutriaux, M. Fiorino,
C. Mears, G. A. Meehl, R. Sausen, K. E. Taylor, W.
M. Washington, M. F. Wehner, and F. J. Wentz, 2004:
Identification of anthropogenic climate change using
a second-generation reanalysis. J. Geophys. Res., 109,
D21104, doi:10.1029/2004JD005075.
Saunders, R., 2002: RTTOV-7 science and validation report.
EUMETSAT NWP SAF, 51 pp. [Available at http://nwpsaf.eu/deliverables/rtm/.]
Saunders, R., 2008: RTTOV-9 science and validation report.
EUMETSAT NWP SAF, 74 pp. [Available at http://nwpsaf.eu/deliverables/rtm/.]
Schneider, U., A. Becker, P. Finger, A. Meyer-Christoffer,
B. Rudolf, and M. Ziese, 2011: GPCC full datareanalysis version 6.0 at 0.5°: monthly land-surface
precipitation from rain-gauges built on GTS-based
and historic data. doi:10.5676/DWD_GPCC/FD_M_
V6_050. [Available at ftp://ftp.dwd.de/pub/data/gpcc/
html/fulldata_v6_doi_download.html.]
Shibata, K., M. Deushi, T. T. Sekiyama, and H. Yoshimura,
2005: Development of an MRI chemical transportmodel for the study of stratospheric chemistry. Pap.
Geophys. Meteor., 55, 75–119.
Shine, K. P., J. J. Barnett, and W. J. Randel, 2008: Temperature trends derived from stratospheric sounding
unit radiances: The effect of increasing CO2 on theweighting function. Geophys. Res. Lett., 35, L02710,
doi:10.1029/2007GL032218.
Simmons, A. J., P. D. Jones, V. da Costa Bechtold, A.
C. M. Beljaars, P. W. Kållberg, S. Saarinen, S. M.
Uppala, P. Viterbo, and N. Wedi, 2004: Comparisonof trends and low-frequency variability in CRU,
ERA-40, and NCEP/NCAR analyses of surface
air temperature. J. Geophys. Res., 109, D24115,
doi:10.1029/2004JD005306.
Simmons, A. J., P. Poli, D. P. Dee, P. Berrisford, H. Hersbach, S. Kobayashi, and C. Peubey, 2014: Estimating
low-frequency variability and trends in atmospherictemperature using ERA-Interim. Quart. J. Roy.
Meteor. Soc., 140, 329–353.
Simmons, A. J., K. M. Willett, P. D. Jones, P. W. Thorne,
and D. P. Dee, 2010: Low-frequency variations insurface atmospheric humidity, temperature, and48

Journal of the Meteorological Society of Japan

precipitation: Inferences from reanalyses and monthly
gridded observational data sets. J. Geophys. Res., 115,
D01110, doi:10.1029/2009JD012442.
Slingo, A., 1989: A GCM parameterization for the shortwave radiative properties of water clouds. J. Atmos.
Sci., 46, 1419–1427.
Smith, S. J., and T. M. L. Wigley, 2006: Multi-gas forcing
stabilization with Minicam. Energy J., 27, 373–391.
Takeuchi, Y., 2002: Algorithm theoretical basis document(ATBD) of the algorithm to derive total water vapor
content from ADEOS-II/AMSR. EORC Bull., 9, 3–7.
[Available at http://sharaku.eorc.jaxa.jp/AMSR/doc/
index.html.]
Thompson, D. W. J., D. J. Seidel, W. J. Randel, C.-Z. Zou,
A. H. Butler, C. Mears, A. Osso, C. Long, and R. Lin,
2012: The mystery of recent stratospheric temperature
trends. Nature, 491, 692–697.
Thorne, P. W., D. E. Parker, S. F. B. Tett, P. D. Jones, M.
McCarthy, H. Coleman, and P. Brohan, 2005:
Revisiting radiosonde upper-air temperatures from1958 to 2002. J. Geophys. Res., 110, D18105,
doi:10.1029/2004JD005753.
Trenberth, K. E., and J. T. Fasullo, 2010: Simulation ofpresent-day and twenty-first-century energy budgetsof the Southern Oceans. J. Climate, 23, 440–454.
Trenberth, K. E., J. T. Fasullo, and J. Mackaro, 2011: Atmospheric moisture transports from ocean to land andglobal energy flows in reanalyses. J. Climate, 24,
4907–4924.
Trenberth, K. E., and L. Smith, 2008: Atmospheric energy
budgets in the Japanese reanalysis: Evaluation andvariability. J. Meteor. Soc. Japan, 86, 579–592.
Uppala, S. M., P. W. Kållberg, A. J. Simmons, U. Andrae,
V. Da Costa Bechtold, M. Fiorino, J. K. Gibson, J.
Haseler, A. Hernandez, G. A. Kelly, X. Li, K. Onogi,
S. Saarinen, N. Sokka, R. P. Allan, E. Andersson, K.
Arpe, M. A. Balmaseda, A. C. M. Beljaars, L. Van De
Berg, J. Bidlot, N. Bormann, S. Caires, F. Chevallier,A. Dethof, M. Dragosavac, M. Fisher, M. Fuentes,
S. Hagemann, E. Hólm, B. J. Hoskins, L. Isaksen,
P. A. E. M. Janssen, R. Jenne, A. P. McNally, J.-F.
Mahfouf, J.-J. Morcrette, N. A. Rayner, R. W. Saunders, P. Simon, A. Sterl, K. E. Trenberth, A. Untch,
D. Vasiljevic, P. Viterbo, and J. Woollen, 2005: The
ERA-40 re-analysis. Quart. J. Roy. Meteor. Soc., 131,
2961–3012.
van de Berg, L., J. Gustafsson, and A. Yildirim, 2002:

Vol. 93, No. 1

Reprocessing of atmospheric motion vectors from
Meteosat image data. ECMWF ERA-40 Project
Report Series, 3, ECMWF, UK, 159–168. [Available
at http://www.ecmwf.int/en/research/publications/.]
Veersé, F., D. Auroux, and M. Fisher, 2000: Limited-memory BFGS diagonal preconditioners for a data
assimilation problem in meteorology. Optimizationand Engineering, 1, 323–339.
Walsh, J. E., and W. L. Chapman, 2001: 20th-century
sea-ice variations from observational data. Annals of
Glaciology, 33, 444–448.
Wang, L., C.-Z. Zou, and H. Qian, 2012: Construction ofstratospheric temperature data records from stratospheric sounding units. J. Climate, 25, 2931–2946.
Werbowetzki, A. (ed.), 1981: Atmospheric sounding user’s
guide. NOAA Technical Report NESS, 83, NOAA,
USA, 82 pp.
Wild, M., D. Folini, C. Schär, N. Loeb, E. G. Dutton, and G.
König-Langlo, 2013: The global energy balance froma surface perspective. Climate Dyn., 40, 3107–3134.
Wise, M., K. Calvin, A. Thomson, L. Clarke, B. Bond-Lamberty, R. Sands, S. J. Smith, A. Janetos, and J.
Edmonds, 2009: Implications of limiting CO2 concentrations for land use and energy. Science, 324, 1183–
1186.
WMO, 1986: A preliminary cloudless standard atmosphere
for radiation computation, World Climate Programme(WCP), 112, 53 pp.
WMO, 2010: Scientific Assessment of Ozone Depletion:
2010. Global Ozone Research and Monitoring
Project. 52, WMO, Switzerland, 572 pp.
WMO, 2012: WMO WDCGG data summary. 36, JMA,
Japan, 100 pp. [Available at http://ds.data.jma.go.jp/
gmd/wdcgg/products/publication.html.]
Wyser, K., 1998: The effective radius in ice clouds. J.
Climate, 11, 1793–1802.
Xie, S., and M. Zhang, 2000: Impact of the convection triggering function on single-column model simulations.
J. Geophys. Res., 105, 14983–14996.
Yoshimura, H., and T. Matsumura 2003: A Semi-Lagrangian
scheme conservative in the vertical direction. CAS/
JSC WGNE Res. Activ. Atmos. Oceanic Modell., 33,
19–20. [Available at http://www.wcrp-climate.org/
WGNE/blue_book.html.]
Zhong, W., and J. D. Haigh, 1995: Improved broadband
emissivity parameterization for water vapor coolingrate calculations. J. Atmos. Sci., 52, 124–138.