SOLA, 2014, Vol. 10, 78−82, doi:10.2151/sola.2014-016

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Preliminary Results of the JRA-55C,
an Atmospheric Reanalysis Assimilating Conventional Observations Only
Chiaki Kobayashi1, Hirokazu Endo1, Yukinari Ota2, Shinya Kobayashi2, Hirokatsu Onoda2,
Yayoi Harada2, Kazutoshi Onogi2 and Hirotaka Kamahori1
1
Meteorological Research Institute, Tsukuba, Japan
2
Japan Meteorological Agency, Tokyo, Japan

Abstract
As a subset of the Japanese 55-year Reanalysis (JRA-55) project, the Meteorological Research Institute of the Japan Meteorological Agency is conducting a global atmospheric reanalysis that
assimilates only conventional surface and upper air observations,with no use of satellite observations, using the same data assimilation system as the JRA-55. The project, named the JRA-55
Conventional (JRA-55C), aims to produce a more homogeneous
dataset over a long period, unaffected by changes in historicalsatellite observing systems. The dataset is intended to be suitable
for studies of climate change or multi-decadal variability. The
climatological properties deduced from the early results of theJRA-55C are similar to those of the JRA-55 in the troposphereand lower stratosphere, except for high southern latitudes. On thebasis of forecast skill, the quality of the JRA-55C is inferior tothat of the JRA-55, but the JRA-55C has better temporal homogeneity than the JRA-55. The skill of the latter changes during
the JRA-55 period. We have completed 85% of the entire JRA55C calculation as of February 2014. We expect that the JRA55C will contribute to a much better understanding of the impact
of changes in observing systems on climate trends and variability
estimated from the JRA-55.
(Citation: 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, doi:10.2151/
sola.2014-016.)

1. Introduction
To reproduce the past atmospheric states most realistically,major global atmospheric reanalysis datasets have been generated by using all available observational data. These multi-year
reanalysis datasets are essential tools for climate research today.
However, it is known that historical changes of observing systems
cause inhomogeneities in these reanalyses that have discouraged
use of the reanalyses to detect and examine climate variations andclimate trends. Trenberth et al. (2008) have pointed out that, atthe present time, variability on multi-year timescales (especiallydecadal) is not estimated very well in the reanalyses. Much effort
such as quality control of the observations and/or observation data
rescue has been made at reanalysis centers to improve the homogeneity of reanalysis products.
The second Japanese global atmospheric reanalysis, theproduction of which was recently completed by the Japan Meteorological Agency (JMA), has been named the Japanese 55-year
Reanalysis (JRA-55; Ebita et al. 2011) and covers the period from1958 to 2012. Early results from the JRA-55 indicate improvements in many respects relative to the first Japanese reanalysis,
the Japanese 25-year Reanalysis (JRA-25; Onogi et al. 2007). The
inhomogeneities in the JRA-25 product have been diminished inCorresponding author: Chiaki Kobayashi, Climate Research Department,
Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 3050052, Japan. Email: ckobayas@mri-jma.go.jp. ©2014, the MeteorologicalSociety of Japan.

the JRA-55 by reduction of systematic model biases and use ofadvanced assimilation techniques. However, inhomogeneities arestill a concern in JRA-55 because the JRA-55 reanalysis period
includes both the post-1979 period of abundant satellite observations and the pre-satellite period before 1972.
To produce a more time-consistent reanalysis, the JMA
Meteorological Research Institute (MRI) has started to produce a
JRA-55 sub-product, the JRA-55 Conventional (JRA-55C). This
sub-product uses the same data assimilation (DA) system andthe same boundary conditions as the JRA-55 but assimilates
only conventional observations. The JRA-55C is the first fixed
observing system reanalysis to make use of conventional upper
air observations; a pioneering reanalysis with a fixed observing
system was undertaken by the Twentieth Century Reanalysis
Project (20CR, Compo et al. 2011), which used surface-pressure
observations only. The JRA-55C will be provided as an importantcomponent of the “JRA-55 family.” Another sub-product, theJRA-55AMIP, assimilates no observational data and is also provided as a member of the JRA-55 family; it uses the numerical
weather prediction (NWP) model used in the JRA-55 DA systemand prescribes the same boundary conditions as do other membersof the JRA-55 family. In this paper, we provide an overview of theJRA-55C project and early results from the JRA-55C.

2. Outline of the JRA-55C
The JRA-55C covers the period from November 1972, when
the JRA-55 starts to use satellite data, to 2012. A 55-year reanalysis dataset that assimilates conventional observations only can beprovided by using the JRA-55 products from the pre-satellite era(1958−1972) and the JRA-55C products from 1972−2012.
The observational data assimilated in JRA-55C are land
and marine surface data (reported as SYNOP, SHIP, and BUOY
defined by the World Meteorological Organization for observation exchanges), upper air data (observed by radiosondes, pilot
balloons, and wind profilers) and tropical cyclone wind retrievals(TCR) (Fiorino 2002). The reason these observational data wereadopted is that they existed throughout the period covered by thereanalysis. Australian manual surface pressure bogus data (PAOB)
and aircraft data are not used in the JRA-55C, because they did
not exist during the earlier part of the reanalysis period.
The DA system used in the JRA-55C is a four-dimensionalvariational data assimilation (4D-Var) system, exactly the same
system used in the JRA-55. The model used in the DA system hasa horizontal resolution of TL319 (= 60 km), with 60 layers in thesigma-pressure hybrid vertical coordinate. The top of the atmosphere is 0.1 hPa. For DA, the scaling factor for the backgrounderror covariance matrix (B-matrix) is 1.8 times the scaling factor
used in the JRA-55 satellite era through the period covered by
the JRA-55C. This larger scaling factor is also used during the
pre-satellite era of the JRA-55. The quality of analysis is acknowledged to be largely dependent on the background error covariancematrix when the number of observational data is small (Whitaker
et al. 2009). The scaling factor was determined from assimilation
experiments prior to JRA-55 production. The JRA-55C and JRA55 boundary conditions, such as historical sea surface temperatures (SSTs) and distributions of the concentrations of ozone,
other greenhouse gases, and aerosols, are exactly the same. The

Kobayashi et al., Preliminary Results of the JRA-55C
three dimensional daily mean ozone produced by a DA system ofchemistry climate model was used for radiation scheme of NWP
model of JRA-55 for the period after 1979 to take into accountthe historical change of ozone concentration. The daily ozone
distributions before 1978 are time-interpolated three dimensional
climatological monthly means over 1980 to 1984. The historicalchange of greenhouse gas concentration is also taken into account.
The aerosols used for JRA-55 are monthly climatology.
The production is executed with three separate streams.
Stream A covers the period from November 1972 to August 1980
and is identical to the period associated with stream A in JRA-55.
Stream B covers the period from September 1980 to the middleof 2005 (undecided). Stream C covers recent years, beginning in
the middle of 2005. Stream A has been completed and Streams B
and C are being calculated. The computations for 36 years werecompleted as of February 2014.
For reference, we show the number of radiosonde temperatureobservations in Fig. 1 by five latitude bands. The number of theobservations in the mid-latitude of the northern hemisphere islarger than the other areas. The number peaked in the 1970s and1980s, and decreased in 1990s. The numbers of observations inthe stratosphere are increase as time advances. The time variationof the numbers may affect the quality of the JRA-55C reanalysis.
Still, the variation is smaller than that in JRA-55, which assimilates various types of satellite observations. Details will be presented in the JRA-55 description paper which we are preparing.

3. Preliminary results of the JRA-55C
3.1 Evaluation of synoptic variability
a. Difference of 6-hourly snapshots
The differences between the JRA-55 and JRA-55C 6-hourly
fields are illustrated by the differences of geopotential heights at
500 hPa at 12 UTC on 25 August 1993 (Fig. 2a). The differenceindicates the impact of satellite observations on the reanalysis
field. The height differences are smaller than 15 m in the extra-tropics in the Northern Hemisphere (NH), except for a part of
Arctic Ocean and a part of the eastern North Pacific Ocean (in thevicinity of the Gulf of Alaska) where observations are relatively
sparse. In contrast, large differences are apparent throughout mostof the extra-tropics in the Southern Hemisphere (SH). The difference between the JRA-55 and the ERA-Interim (Dee et al. 2011),
which is a reanalysis dataset produced by European Centre for
Medium-Range Weather Forecasts, is smaller than the difference
between the JRA-55 and JRA-55C (Figs. 2a, b). Since the satellite
observations are assimilated in both the JRA-55 and ERA-Interim,
we can therefore say that the impact of satellite observations on
the analysis field is greater than the impact of differences in thedata assimilation system in the SH.
Local anomaly correlations of 6-hourly geopotential height at
500 hPa between reanalyses are illustrated in Figs. 2c, d. The area

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of high correlation between JRA-55C and JRA-55 covers mostof the earth, except for high latitude areas of the SH (60°S−90°S)
and the eastern part of the South Pacific Ocean, where conventional observations are sparse. The area of high correlation between
JRA-55 and ERA-Interim covers most of the earth. Similarcorrelation pattern can be found at most of the levels in the troposphere (not shown).
b. 2-day forecast score
The quality of reanalysis data can be assessed from the accuracy of forecasts made using the reanalysis data as the initialconditions, because the skill of a NWP forecast is sensitive tothe initial conditions. As was the case with the JRA-55, 9-day
forecasts were projected on a daily basis using reanalysis data at
12 UTC as the initial conditions using the NWP model in the DA
system. Figure 3 shows the root-mean-square error (RMSE) of
the 500 hPa geopotential height forecasts at day 2 verified against
the corresponding geopotential heights from the analyses of theJRA-55C, JRA-55, JRA-25, and the historical JMA Operational
systems.
The RMSE scores of the JRA-55C were larger than those of
the JRA-55 but smaller than those of the JRA-25 in the NH before
1998. The difference of the scores between the JRA-55 and JRA55C is likely due to the impact of satellite observations. Because
the impact of satellite observations increased from year to year,
the skill of the JRA-55 gradually improved. In contrast, the scoresof the JRA-55C were comparable to those of the JRA-55 in thelate 1970s.
It is inevitable that the quality of the JRA-55C, which assimilates conventional observations only, will be inferior to that of
the JRA-55, which assimilates all available observational data,
including satellite observations. The quality of the JRA-55C werecomparable level to the JRA-55 in the late 1970s, as stated above.
In terms of consistency, however, the quality of the JRA-55C was
more homogeneous than that of the JRA-55 during the reanalysis
period.
In the SH, the RMSE scores of the JRA-55C were larger thanthose of the JRA-25 from the early 1980s. This result suggests that
assimilation of satellite observations was more effective than the
improvement of the assimilation system from the JRA-25 to JRA55. Because the quality of the JRA-55 and JRA-25 over the SH
depends largely on satellite observations, the skills of the JRA-55
and JRA-25 improved gradually during the reanalysis period. In
contrast, the scores of the JRA-55C showed no large improvement
during the reanalysis period. The quality of the JRA-55C was
rather homogeneous compared to the quality of the JRA-55 andJRA-25 during this period. The RMSE scores of the JRA-55C
were smaller during the 1970s than after 1980, despite the factthat the number of surface pressure observations increased over
the SH, particularly in higher latitudes. The fact that the RMSE
scores did not decrease with increasing numbers of observations
is contrary to expectations. One possible explanation is that obser-

Fig. 1. The number of radiosonde temperature observations used in JRA-55C in five latitude bands.
a) Time series of data counts at levels near 500
hPa, and latitude-pressure cross sections of data
counts for b) the 1960s, c) the 1980s and d) the
2000s (excluding the year 2003 and 2004). Units
are day−1. Color bar boundaries are 0, 100, 200,
300, 400, 600, 800, 1000, 1200 and 1400 day−1.
The numbers before 1972 are provided by JRA55. The five latitude bands are 90°N−60°N,
60°N−20°N, 20°N−20°S, 20°S−60°S and 60°S−
90°S.

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Fig. 2. Geopotential heights at 500 hPa at 12 UTC on 25
August 1993 from reanalyses (upper panels) and local
anomaly correlation coefficient of 6-hourly geopotential
height at 500 hPa between reanalyses in August 1993 (lower
panels). a) The 500 hPa geopotential heights of JRA-55C(contours) and height differences between the JRA-55C
and JRA-55 (shadings). b) Same as a) but for ERA-interim(contours) and height differences between the ERA-interim and JRA-55 (shadings). Units are geopotential meters(gpm). The contour interval is 120 gpm. c) Local anomaly
correlation coefficient between JRA-55C and JRA-55. d)Same as c) but between JRA-55 and ERA-interim.

Fig. 3. Time series of the RMSE of 2-day forecasts of geopotential heightat 500 hPa verified against the corresponding analysis for (a) the NH(20°N−90°N) and (b) SH (20°S−90°S). Units are geopotential meters(gpm). A 12-month running mean was applied for smoothing. “Operation”
indicates the JMA operational forecast started from the JMA operational
analysis at the indicated time. Each forecast is started from the data produced by the corresponding reanalysis. Note that green lines are drawn
only for years during which the JRA-55C forecast process has been completed.

vational coverage was too sparse to correctly represent synoptic
weather patterns during this period.
3.2 Representation of climatology and variability
a. Globally averaged annual-mean surface temperature on land
Globally averaged annual-mean surface air temperature (SAT)
is frequently monitored as an index of global warming in climate
studies. Figure 4 presents time series of the globally averaged
SAT anomalies at a height of 2 m over land estimated by the JRA55, JRA-55C, and Climatic Research Unit temperature dataset
version 4 (CRUTEM4, Jones et al. 2012), which is a gridded
dataset of global historical near-surface air temperature anomalies
over land produced by the Met Office Hadley Centre and the
Climatic Research Unit at the University of East Anglia. The
globally averaged temperatures in Fig. 4 were calculated from theland grids of the DA system where CRUTEM4 data existed. The
time series of JRA-55 and JRA-55C are generally consistent with
the CRUTEM4 time series. The correlation coefficient between
the JRA-55C and CRUTEM4 anomalies was 0.96 for the period
1973−2002.
b. Precipitation over East Asia
The precipitation products of the JRA-55 and JRA-55C arebased on 6-hour forecasts from each analysis. Although precipitation is not a directly assimilated variable, precipitation provides
an integrated evaluation of the performances of the DA system.
Figures 5a, b, c, d illustrate the distributions of seasonally aver-Fig. 4. Time series of globally averaged land surface temperature anomalies (K). The anomalies are departures from the monthly climatology
averaged from 1971 to 2000. The globally averaged values have been calculated from the land grids of the DA system where CRUTEM4 data exist.
A 13-month running mean has been applied for smoothing.

aged precipitation for June, July, and August (JJA) over the East
Asia region estimated from the Global Precipitation Climatology
Project (GPCP) version 2.1 (Huffman et al. 2009), JRA-55, JRA55C, and JRA-25, respectively. A Taylor diagram (Taylor 2001)of the spatial patterns of the precipitation is also shown in Fig. 5e.
The precipitation band associated with the Baiu front is similar in
the JRA-55 and JRA-55C and is more realistically reproduced by
the latter than by the JRA-25. The JRA-55 and JRA-55C precipitation patterns within the dashed rectangle were better correlatedwith the GPCP than the JRA-25 and ERA-Interim precipitation
patterns, as shown in the Taylor diagram (Fig. 5e). The normalized
standard deviations of the JRA-55 and JRA-55C indicate greater
consistency with the GPCP than those of the JRA-25 and ERAInterim. These results indicate that the upgraded DA system usedin the JRA-55 improves the climatological precipitation patternover the East Asia region during JJA, although areas with relatively sparse observations, such as high latitudes of the SH, indicate
lower correlations (not shown).
c. Zonal mean temperature and zonal wind
Figure 6 depicts the climatological zonally averaged temperature and zonal wind speed for JJA (1980−2000 mean). The
difference in climatological temperatures reproduced from theJRA-55 and JRA-55C was smaller than 0.2K in the troposphere
over the tropics and in the NH, whereas the differences between
the JRA-55 and ERA-Interim were slightly larger than thosebetween the JRA-55 and JRA-55C. Similarly, the differences ofKobayashi et al., Preliminary Results of the JRA-55C

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Fig. 5. JJA mean climatological precipitation (mm day−1) ofa) GPCP Ver 2.1, b) JRA-55C, c) JRA-55, and d) JRA-25. e)Taylor diagram of the correlation coefficients and standard
deviations of the differences of precipitation determinedfrom reanalyses and observations in the dashed rectangle.
The period for the statistics is 1980−2000.

Fig. 6. Latitude-pressure cross section of zonally averaged(a, b) temperature (K) and (c, d) zonal wind speed (m s−1)
for JJA (1980−2000 mean). Contours denote temperatureand zonal wind speed for (a, c) JRA-55C and (b, d) JRA55. Shading indicates the difference (a, c) between JRA55C and JRA-55 and (b, d) between ERA-interim andJRA-55. Contour interval is 1 K for temperature and5 m s−1 for zonal wind speed. Each climatology corresponds to the 1980−2000 average.

climatological zonal wind speeds reproduced from the JRA-55
and JRA-55C were small in the troposphere compared with thecorresponding differences between the JRA-55 and ERA-Interim.
However the zonal wind difference between the JRA-55 andJRA-55C was large in the upper stratosphere (1 hPa to 10 hPa),
especially in the winter hemisphere. The temperature difference
between the JRA-55 and JRA-55C (Fig. 6a) also showed large
difference in the upper stratosphere corresponding to the zonal
wind difference. The climatological zonal wind and temperatureof the JRA-55AMIP displayed a similar difference pattern with
the JRA-55 in the upper stratosphere during the winter hemisphere(not shown), which is warm bias in mid-latitude and cold bias in
the high-latitude in the winter hemisphere. We hypothesize thatthe difference between the JRA-55 and JRA55C was caused by
the paucity of conventional data and systematic NWP model bias
in the upper stratosphere. While the climatological temperature
difference between the JRA-55 and ERA-Interim have a layered
structure in the upper stratosphere, this would be related to the difference of data processing of the brightness temperature from thehighest-peaking channels of satellites between these DA systems.

d. Quasi-Biennial Oscillation
Figure 7 displays a time-height cross section of mean zonal
wind speeds averaged between 10°S and 10°N, for JRA-55, JRA55C, JRA-55AMIP, and 20CR (Compo et al. 2011). The QuasiBiennial Oscillation (QBO) in the middle-to-lower stratosphere isapparent in the JRA-55 and JRA-55C but not in the JRA-55AMIP.
The QBO is also absent from the 20CR, which uses the NCEP
Global Forecast System to assimilate only surface pressure observations. These results suggest that radiosonde observations play animportant role in reproducing the QBO in reanalyses. One reason
why the JRA-55AMIP does not represent the QBO could be that
the NWP model has not yet adopted a non-stationary gravity wave
parameterization. According to Kawatani and Hamilton (2013),
only four of the models that were a part of the Coupled Model
Intercomparison Project Phase 5 (which included parameterizationof non-stationary gravity waves) realistically simulated the QBO
spontaneously. The JMA is presently studying adaptation of thisparameterization to an operational NWP model.
The zonal wind difference between the JRA-55 and JRA55C was large in the upper stratosphere compared to that in thelower stratosphere. Both the JRA-55 and JRA-55C indicated the82

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Fig. 7. Time-height cross section of equatorial (5°S−5°N)
zonal mean U wind component from 1958 to 2005 shownin a) JRA-55, b) JRA-55C, c) JRA-55AMIP, and d) 20CR.
Units are m s−1.

semi-annual oscillation (SAO) like variation in the upper stratosphere, although the amplitude of the variation of the JRA-55C
was smaller than that of the JRA-55. The amplitude of the JRA55AMIP was smaller than that of the JRA-55C.

4. Summary
To create a more temporally homogeneous reanalysis product,
the MRI/JMA has started a sub-project, the JRA-55C, whichassimilates conventional observations only, but uses the same DA
system and the same boundary conditions as the JRA-55. The climatological properties inferred from the early results of the JRA55C are similar to the climatological properties of the JRA-55 in
the troposphere and lower stratosphere, except for high latitudesof the SH. On the basis of forecast skill of 500 hPa heights,
however, the quality of the JRA-55C as initial conditions wasinferior to that of the JRA-55. The scores of the JRA-55C during
1980−1998 were comparable to those of the JRA-55 in the 1970sin the extra-tropics of the NH, the implication being that the JRA55C is more homogeneous than the JRA-55. It is expected that thequality of the analysis will be consistent throughout the reanalysis
period.
The entire JRA-55C production will be completed in the
middle of 2014. Thereafter, the product will be provided to themeteorological community together with the products of theJRA-55 and JRA-55AMIP. As a member of this “JRA-55 family”
the JRA-55C provides a unique reanalysis dataset and contributes
to understanding the impacts of observing system changes on theestimation of climate trends and variability.

Acknowledgments
The JRA-55C project has been supported by many JMA staff
members. We would like to thank all these individuals for providing us with their invaluable help and advice.

References
Compo, G. P., and co-authors, 2011: The Twentieth Century
Reanalysis Project. Quart. J. Roy. Meteor. Soc., 137, 1−28,
doi:10.1002/qj.776.

Dee, D. P., and co-authors, 2011: The ERA-Interim reanalysis:
Configuration and performance of the data assimilation
system. Quart. J. Roy. Meteor. Soc., 137, 553−597.
Ebita, A., and co-authors, 2011: The Japanese 55-year reanalysis“JRA-55”: An interim report. SOLA, 7, 149−152, doi:
10.2151/sola.2011-038.
Fiorino, M., 2002: ‘Analysis and forecasts of tropical cyclones
in the ECMWF 40-year reanalysis (ERA-40)’. Extended
abstract of 25th Conference on Hurricanes and TropicalMeteorology, 261−264.
Huffman, G. J., R. F. Adler, D. T. Bolvin, and G. Gu, 2009:
Improving the global precipitation record: GPCP Version
2.1. Geophys. Res. Lett., 36, L17808, doi:10.1029/2009GL
040000.
Jones, P. D., D. H. Lister, T. J. Osborn, C. Harpham, M. Salmon,
and C. P. Morice, 2012: Hemispheric and large-scale land
surface air temperature variations: An extensive revision
and an update to 2010. J. Geophys. Res., 117, D05127, doi:
10.1029/2011JD017139.
Kawatani, Y., and K. Hamilton 2013: Weakened stratospheric quasibiennial oscillation driven by increased tropical mean upwelling. Nature, 497, 478−481, (23 May 2013) doi:10.1038/
nature12140.
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, doi:10.2151/jmsj.85.369.
Taylor, K. E., 2001: Summarizing multiple aspects of model
performance in a single diagram. J. Geophys. Res., 106(D7),7183−7192, doi:10.1029/2000JD900719.
Trenberth, K. E., T. Koike, and K. Onogi, 2008: Progress andprospect for reanalysis for weather and climate. Eos, 89,234−235.
Whitaker, J. S., G. P. Compo, and J.-N. Thépaut, 2009: A comparison of variational and ensemble-based data assimilation
systems for reanalysis of sparse observations. Mon. Wea.
Rev., 137, 1991−1999, doi:10.1175/2008MWR2781.1.
Manuscript received 6 February 2014, accepted 11 April 2014
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