SOLA, 2011, Vol. 7, 149−152, doi:10.2151/sola.2011-038

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The Japanese 55-year Reanalysis “JRA-55”: An Interim Report
Ayataka Ebita1, Shinya Kobayashi1, Yukinari Ota1, Masami Moriya1, Ryoji Kumabe1, Kazutoshi Onogi1,
Yayoi Harada1, Soichiro Yasui1, Kengo Miyaoka1, Kiyotoshi Takahashi1, Hirotaka Kamahori2,
Chiaki Kobayashi2, Hirokazu Endo2, Motomu Soma1, Yoshinori Oikawa1, and Takahisa Ishimizu1
1
Japan Meteorological Agency, Tokyo, Japan
2
Meteorological Research Institute of JMA, Tsukuba, Japan

Abstract
The Japan Meteorological Agency (JMA) started the second
Japanese global atmospheric reanalysis project named the Japanese 55-year Reanalysis (JRA-55). It covers 55 years, extending
back to 1958, when the global radiosonde observing system wasestablished. Many of the deficiencies found in the first Japanesereanalysis, the Japanese 25-year Reanalysis (JRA-25), have been
improved. It aims at providing a comprehensive atmospheric dataset that is suitable for studies of climate change or multi-decadal
variability, by producing a more time-consistent dataset for alonger period than JRA-25.
Production of JRA-55 started in 2010, and computations formore than 16 years have been completed as of August 2011. The
entire JRA-55 production will be completed in early 2013 andthereafter JRA-55 will be continued as a new JCDAS on real timebasis. This paper is a brief report to introduce the JRA-55 reanalysis project. The data assimilation and prediction (DA) system usedin JRA-55 is introduced and compared to that used in JRA-25.
Early results of JRA-55 are presented and discussed, showing
general improvements.

1. Introduction
It is a crucially important requirement to estimate the climatefeatures of the global atmosphere with high accuracy in terms ofboth quality and quantity for climate studies and monitoring. Reanalysis of past observations with a fixed, state-of-the-art numerical weather prediction (NWP*) system is a powerful approach tofulfill such a requirement.
Global reanalyses have been conducted at major NWP centerssuch as the NCEP/NCAR reanalysis (Kalnay et al. 1996; Kistler
et al. 2001), NCEP/DOE renalaysis (Kanamitsu et al. 2002) andERA-40 (Uppala et al. 2005) by ECMWF. JMA and CRIEPI also
completed the JRA-25 reanalysis (Onogi et al. 2007), and thereafter JMA has been operating the JMA Climate Data Assimilation
System (JCDAS), to continue producing consistent climate dataon real time basis. These reanalyses have been providing fundamental data sets for climate monitoring and researches. However,to extend the range of application, further improvement is needed.
In particular, neither data availability nor long-term consistency
in data quality is sufficient for detecting and examining long-termclimate trends and/or multi-decadal climate variability (Trenberth
et al. 2008). This clearly shows the need for updating a reanalysis
dataset with a state-of-the-art system and recently new reanalyses
have become available from ECMWF (Dee et al. 2011), NASA
(Rienecker et al. 2011), NCEP (Saha et al. 2010) and NOAACIRES (Compo et al. 2011).
Since the time of the JRA-25 production, the JMA operational
NWP system achieved some major updates, including increase ofresolution, introduction of a new radiation scheme to the forecast
model, and four-dimensional variational data assimilation (4D-Var)with Variational Bias Correction (VarBC). With these updates,
the current system is able to handle satellite radiances better andto produce less-biased analysis fields compared to the JRA-25
Corresponding author: Ayataka Ebita, Japan Meteorological Agency,
1-3-4 Otemachi, Chiyoda-ku, Tokyo 100-8122, Japan. E-mail: ebita@met.
kishou.go.jp. ©2011, the Meteorological Society of Japan.

system, and expected to overcome the deficiencies in the stratospheric temperature analysis such as negative bias in JRA-25.
To address the issues identified in JRA-25, we started thesecond Japanese reanalysis project, JRA-55, in which the analysis
period is extended substantially from that for JRA-25 with themore advanced DA system. In this paper, we report an overview
of the JRA-55 project and progress as of August 2011. In Section 2,
the models used in JRA-55 are outlined and compared with thoseused in JRA-25. In Section 3, several early results of JRA-55 areintroduced and compared with the results of JRA-25. In Section 4,
advantages and remaining deficiencies in JRA-55 are summarized,
and future plans are presented.

2. Data assimilation and prediction system
Table 1 summarizes the DA system and boundary conditionsof JRA-55 together with those of JRA-25 for comparison. It isbased on the JMA operational NWP system as of December 2009
(JMA 2007), into which many improvements have been implemented since the time of the JRA-25 production, including higher
spatial resolution, introduction of the new radiation scheme, and
4D-Var with VarBC for satellite radiances. Long term quality of
JRA-55 is expected to be improved by introduction of green housegases with time varying concentrations.
Observational data being used in JRA-55 are listed with theiravailable period in Fig. 1. In addition to the data assimilated into
JRA-25, many kinds of newly obtained observational data areassimilated. Atmospheric Motion Vector (AMV) data from GMS,MTSAT and METEOSAT are newly reprocessed after JRA-25.
Clear Sky Radiance (CSR) data from GMS and MTSAT repro­
cessed at MSC/JMA will be used for the first time in reanalyses.
The production is being executed with three separate streams(Fig. 1). Stream A covers old years with few satellite data. Stream
B covers the period from 1980 to 2002, with AMV from geostationary satellites, radiances from vertical sounders (TOVS andATOVS) and microwave imagers (SSM/I), and ocean surface
winds from microwave scatterometers (ERS and QuikSCAT)
onboard polar orbiter satellites. Stream C covers recent years from2003 with many kinds of satellite data. Streams A and B are being
calculated as of August 2011.
JRA-55 covers the period from pre-satellite era to recent
years with many kinds of varying satellite data as shown in Fig.
1. It is recognized that analysis quality largely depends on the
background error covariance matrix (B-matrix) when/where thenumber of observational data is small (Whitaker et al. 2009).
Therefore, for years before 1972 with no satellite data, the Bmatrix specified in the data assimilation system needs to be adjusted. We estimated an appropriate scaling factor for the B-matrix
used in the pre-satellite era to be 1.8 from departure statistics ofa set of assimilation experiments with and without satellite data.
Figure 2 shows bias and RMS difference between radiosonde temperature measurements and the background and analyzed values
from assimilation experiments for the extratropical southernhemisphere. It can be seen that covariance scaling gives a positive
impact in reducing RMS of background departures.
In JRA-25, land surface in the Amazon basin has a dry bias
due to unrealistically high pressure area formed in the lower troposphere. A similar problem occurred in preliminary experiments*All acronyms are defined in Table S-1 of the supplement.

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Ebita et al., The Japanese 55-year Reanalysis “JRA-55”: An Interim Report

Table 1*. Comparison of the DA systems, and boundary conditions between JRA-55 and JRA-25.
Years
Model version
Resolution
Time integration

JRA-25JRA-5526 years (1979−2004)

55 years (1958−2012)as of Mar. 2004
operational

as of Dec. 2009
operational

T106L40 (~120 km)(top layer at 0.4 hPa)

TL319L60 (~60 km)(top layer at 0.1 hPa)

Eulerian

Semi-LagrangianLong-wave
radiationLine absorption
Statistical band model
Water vapor continuum
e-typeLine absorption
Table lookup+ K-distribution
Water vapor continuum
e-type + P-type

Assimilation
scheme3D-Var(with T106 resolution)

4D-Var(with T106 inner model)

Backgrounderror covariance

Constant

Simple inflation factor(× 1.8) applied before 1972

Bias correction(radiosonde)

Andrae et al. (2004)

RAOBCORE v1.4
(Haimberger 2007)

Bias correction
Sakamoto and Christy(satellite radiance)(2009)

VarBC (Dee 2005)

Land surface
analysis

Offline SiB(6 hourly atmosphericforcing)

Offline SiB(3 hourly atmosphericforcing)

Green house
gases

CO2
(constant at 375 ppmv)

CO2, CH4, N2O, CFC-11,
CFC-12, HCFC-22(daily interpolation ofannual values)

Ozone

Daily 3-D ozone(−1978)(MRI-CCM1; T42L45)
Monthly climatology(Shibata et al. 2005)(1979−)New daily 3-D ozone(MRI-CCM1; T42L68)(Shibata et al. 2005)

AerosolAnnual climatology for Optical depth
continental and maritime Monthly 2-D climatology
Optical properties, verticalprofileAnnual climatology forcontinental and maritime

SST / sea ice

COBE SST(Ishii et al. 2005)

COBE SST(Ishii et al. 2005)

with a prototype version of the JRA-55 system, and we found that
this problem stemmed from a mismatch between surface pressure observations and the background values. The cause of themismatch has not been fully understood, but is likely related tomis-specified station height, bias in observations and deficiencies
in the diurnal cycle reproduced by the forecast model. While thisproblem needs further investigation, we simply discard surfacepressure observations around the Amazon basin to prevent thisproblem.

3. Quality of JRA-55 early results
Computations for more than 16 years have been completed
as of August 2011. We describe some early results of JRA-55
in which deficiencies in JRA-25 have been removed or reduced
significantly.
Figure 3 shows vertical profiles of global mean bias and RMS
differences between radiosonde temperature measurements andthe background and analyzed values from JRA-55 and JRA-25.
Both bias and RMS difference of JRA-55 are reduced from thoseof JRA-25. In particular, large negative biases (note that ‘OBS
minus BG/AN’ are drawn) found in the lower stratosphere of
JRA-25 have been removed. This improvement is due to the introduction of the new radiation scheme, which was implemented into
the operational system in December 2004.
Temporal variability of the stratospheric temperature analysis
was assessed using microwave sounder measurements processed

Fig. 1*. List of observational data available for JRA-55 with their period(top) and streams of JRA-55 production (bottom). 55 years are separated
into three parts of years depending on availability of satellite data type.
Note that the final list is subject to change depending on progress in development.

Fig. 2. Bias and RMS difference between radiosonde temperature measurements and the background (solid lines) and analyzed values (dottedlines) from experiments with all observations (black), no satellite (red) andno satellite with covariance scaling (blue) for the extratropical southernhemisphere for August 1990.

by RSS (Mears and Wentz 2009). The result for 11 years from1980 showed that the trend of JRA-55 is in better agreementwith that of RSS compared to that of JRA-25, and that unrealistic
variation seen in the time series for JRA-25 was reduced significantly in JRA-55 (Fig. 4). These improvements are primarily due
to the introduction of VarBC (Dee 2005) and indicate an improved
performance in handling satellite radiances.
Figure 5 shows the time series for (a) precipitation, (b)evaporation, (c) runoff, and (d) soil wetness at the root level averaged over the Amazon basin. As mentioned in Section 2, JRA-25
has a dry bias over the Amazon basin and the time series for
precipitation from JRA-25 shows a clear underestimation (Fig.
5a) compared to the GPCP Ver. 2.1 (Adler et al. 2003). The timeseries for precipitation from JRA-55 shows a better agreementwith GPCP, and the time series for other hydrological variables
also demonstrate an improved seasonal cycle compared to that of
JRA-25 (Figs. 5b, c, d). Although verification with independent
observations is needed, the water cycle of the Amazon basin is
expected to be represented more realistically in JRA-55.
Precipitation from reanalyses was assessed by Bosilovich et al.
(2008) using Taylor diagrams and it was demonstrated that the*All acronyms are defined in Table S-1 of the supplement.

SOLA, 2011, Vol. 7, 149−152, doi:10.2151/sola.2011-038

Fig. 3. Vertical profiles of global mean bias and RMS difference between
radiosonde temperature measurements and the background (solid lines)
and analyzed values (dotted lines) from JRA-25 (black) and JRA-55 (red)for January 1981.

151

Fig. 6. Taylor diagrams for the annual mean correlations and standard deviation of precipitation from JRA-25 (black) and JRA-55 (red) with GPCP
Ver 2.1 for the globe (left), global land (center) and global ocean (right).
Each dot represents a 12-month average for each year, 1980−1990.

Fig. 4. Time series of global mean, monthly mean temperature anomaly at
the lower stratosphere. Results are processed microwave sounder measurements (RSS Ver. 3.2; blue) and its equivalents calculated from JRA-25(black) and JRA-55 (red). Anomalies of RSS and JRA-25 were defined
relative to the 1979−1998 climatology of their own, while anomalies of
JRA-55 were computed using the JRA-25 climatology and adjusted insuch a way that the anomaly averaged over 1980 to 1990 was the same asthat of JRA-25.

Fig. 7. Time series for the RMS error of 2-day forecast of geopotential
height at 500 hPa verified against its own analysis for NH (top) and SH(bottom). Gray lines indicate the scores of operational forecast, black lines
JRA-25, and red lines JRA-55. A 12-month running mean is applied forsmoothing. Each forecast is started using each analysis or reanalysis data.
Note that red lines are drawn only for completed years of JRA-55.

Fig. 5. Time series of surface hydrological variables in the Amazon basin;(a) precipitation, (b) evaporation, (c) runoff, and (d) soil wetness.

JRA-25 precipitation had a good correlation with GPCP. Figure 6
shows Taylor diagrams for the annual mean correlations and standard deviation of global precipitation from JRA-25 and JRA-55
with GPCP data. It can be seen that the JRA-55 precipitation, inparticular over land, has an even better correlation with GPCP
than that of JRA-25. However, the normalized standard deviation
based on JRA-55 is slightly degraded from that based on JRA-25,
as a manifestation of overestimated precipitation compared to

GPCP (not shown).
The quality of reanalysis data can be assessed from the accuracy of the forecast made from a reanalysis as an initial condition.
Figure 7 shows the RMS error of 500 hPa geopotential height
forecasts at day 2 verified against its own analysis for JRA-55,
JRA-25 and the historical JMA operational systems. Although thecomparison is currently available only for the rather short period
from 1980 to 1990, scores of JRA-55 are much better than thoseof JRA-25 in each of the hemispheres. Since the NWP systemused in JRA-25 was based on the operational system as of March
2004, the scores of JRA-25 are better than those of the JMA
operational analysis for the period before 2004, but the opposite
is the case for 2005 and afterward. Scores of JRA-55 are expected
to be comparable to the operational system around 2009 because
the model used in JRA-55 is based on the operational system as ofDecember 2009. The change in the SH of JRA-25 in 1998 is dueto the replacement of TOVS to ATOVS (Onogi et al. 2007).
Wind retrievals surrounding tropical cyclones (TCR) data
(Fiorino 2002) were used in JRA-25 to analyze tropical cyclones(TCs) homogeneously around the globe. Hatsushika et al. (2006)
confirmed the accuracy of TC analysis in JRA-25. We can expect
more accurate reanalysis of TC with a higher resolution, because
TCR data are assimilated in JRA-55 as well. Bessho et al. (2010)
used preliminary experimental products of JRA-55 to examine

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Ebita et al., The Japanese 55-year Reanalysis “JRA-55”: An Interim Report

the predictability of Vera (“Isewan Typhoon” in Japanese), which
landed the middle part of Japan and caused more than 5000 deathsin 1959. They performed meso-scale analysis and forecast experiments of wind, storm surge, and precipitation due to Vera using a
regional model with initial and boundary conditions taken fromthe JRA-55 experiment. Vera was reproduced quite well withrespect to position and strength, which indicates high potential applicability of the JRA-55 products to researches on extreme events
such as tropical cyclones.

4. Summary and future plans
Early results of quality assessment have suggested that manyof deficiencies in JRA-25 have been diminished or reducedin JRA-55. Specifically a large temperature bias in the lower
stratosphere was significantly reduced by introduction of the newradiation scheme to the forecast model. The VarBC contributes tothe diminution of unrealistic temperature variations found in thelower-stratospheric time series based on JRA-25. The dry land
surface problem in the Amazon basin in JRA-25 was mitigated.
Overall quality of JRA-55 is much improved as shown in forecast
scores compared with those of JRA-25 for the completed years,
owing to continuous development of the JMA operational NWP
system as evident from the improvement in the operational scoresin recent years. Global precipitation in JRA-55 is, however, still
somewhat overestimated compared to GPCP as well as in JRA-25,
which requires further investigation.
The entire JRA-55 production is expected to be completed
in early 2013. Thereafter, JRA-55 will be continued as a new
JCDAS on real time basis and replace the current JRA-25-based
JCDAS. Performance of JRA-55 is monitored carefully until thecompletion of the production and the quality will be further investigated. A comprehensive report of the JRA-55 reanalysis will be
published after the completion of the project.
While JRA-55 will use as many types and numbers of observational data as possible to give the best instantaneous field
estimate, we have a plan to produce JRA-55 subset products
assimilating conventional observational data only (JRA-55C),
without any satellite data. It aims at retaining consistency for longyears, even if its analysis quality may be inferior to JRA-55. Thisset of reanalyses is expected to contribute to addressing some of
the issues of the current reanalyses such as impact of changing
observing systems on representation of long-term climate trends
and variability.

Acknowledgements
Most of the observational data used in JRA-55 are thoseused in JRA-25. Additionally, newly reprocessed METEOSATand GMS data were supplied by EUMETSAT and MSC/JMA
respectively. Snow depth data over the United States, Russia andMongolia were supplied by UCAR, RIHMI and IMH respectively.
We are grateful to all the observational data providers for making
their data available to JRA-55.
The JRA-55 project has been supported by a large number of
the JMA staff members in many aspects including planning of the
project, preparation of observational data, testing the data assimilation system, production and verification. We would like to thankall the individuals for providing us with their invaluable help andadvice.

Supplement
All acronyms are defined in Table S-1 of the supplement.

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Manuscript received 19 July 2011, accepted 10 September 2011
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