Reanalysis at the Japan Meteorological Agency - Copernicus Climate Change Service
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Reanalysis at the Japan Meteorological Agency
Shinya Kobayashi
Japan Meteorological Agency (JMA)
Koichi Yoshimoto, Masashi Harada, Kengo Miyaoka, Yoshiaki Sato
(JMA)
Tosiyuki Nakaegawa, Chiaki Kobayashi, Yayoi Harada, Hirotaka Kamahori
(JMA Meteorological Research Institute)
and many colleagues
Fifth International Conference on Reanalysis (ICR5), 13–17 November 2017, Rome 1
Role of reanalysis in JMA’s climate services
Observations Seasonal ensemble prediction system
Satellite
JRA reanalysis
Upper air
Climate monitoring,
Data assimilation
extreme event analysis
Land surface
Homogeneous,
high-quality El Niño monitoring
climate dataset
Sea surface
JRA-55
Boundary condition Study of past severe
(1958 – present)
weather events
and many others
2
A brief history of the Japanese Reanalysis
• JRA-25 (1979-2004, JMA/CRIEPI) and JCDAS (2005-2014.1)
– produced with JMA NWP system as of 2004.3
• JRA-55 (1958 to present)
– produced with more sophisticated JMA NWP system as of 2009.12
– assimilated newly obtained past observations
– used in various JMA’s climate services since its completion in 2014
Continued on a near-real-time basis
JRA-55 (4D-Var)
JRA-25/JCDAS (3D-Var)
Surface, radiosondes, tropical cyclone retrievals, windprofilers
Aircraft
Polar orbiting satellites
Geostationary satellites
IGY FGEE GNSS
1960 1970 1980 1990 2000 2010
3
Use of JRA-55 in the wider community
• JRA-55 data are available to the
public from the following data
servers:
– JMA Data Dissemination System
(JDDS)
– Data Integration & Analysis System
(DIAS)
– NCAR
– ESGF (Ana4MIPs, CREATE-IP)
– ECMWF (in preparation)
• More details can be found at
the JRA website:
– http://jra.kishou.go.jp/
4
Improvement of temporal consistency
(global mean temperature anomalies)
Lower stratosphere JRA-25
• A large jump at the time of switching from
TOVS to ATOVS
• Different RT models and bias correction
schemes were used for TOVS and ATOVS.
JRA-55
• A single RT model and bias correction
Upper troposphere scheme is used consistently for all satellite
radiances.
• Temporal consistency is considerably
improved.
Middle troposphere
Lower troposphere
Hadley Centre radiosonde dataset
RSS microwave sounder dataset
Twelve-month running mean temperature anomalies averaged over 82.5N to 82.5S
5
Impact of changes in observing systems
Global mean specific humidity increments
VTPR TOVS SSM/I AMSU-B
JRA-55
• Moistening increments above 850
hPa and drying increments below
that level due to model biases
• The moistening increments gradually
SSM/I AMSU-B increased as more satellite water
JRA-25 vapour observations were assimilated.
• Resulted in overestimated moistening
trends at those layers
6
JRA-55 family
• Having a deeper understanding of model biases and impact of changing
observing systems is important for evaluating and improving temporal
consistency of reanalysis.
• To this end, different types of product have been produced with the common
NWP system.
• JRA-55 (JMA)
– Full observing system reanalysis
– Available from JMA, DIAS, NCAR, ESGF
– Poster by Y. Harada (Section 4 on Wed.)
• JRA-55C (MRI/JMA)
– Using conventional observations only
– Available from DIAS, NCAR
– Poster by C. Kobayashi (Section 4 on Wed.)
• JRA-55AMIP (MRI/JMA)
– AMIP-type simulation RMS errors of 2-day forecasts of
geopotential height (gpm) at 500hPa
– Available from DIAS, NCAR averaged over the northern hemisphere
Adapted and updated from C. Kobayashi (2014)
7
Japanese Reanalysis for Three Quarters of a Century
(JRA-3Q)
• Reanalysis period: 1947 to present
• Provisional specifications
– Resolution: 55 km, 60 layers (JRA-55) -> 40 km, 100 layers (JRA-3Q)
– Incorporating many improvements from the operational NWP system
• Overall upgrade of physical processes
• New types of observation (ground-based GNSS, hyperspectral sounders)
– Improved SST (Poster by M. Harada, Section 3 on Tue.)
• COBE-SST2 (1-deg, up to 1985) & MGDSST (0.25 deg, from 1985 onward)
– Improved observations
• Observations newly rescued and digitised by ERA-CLIM and other projects
• Improved satellite observations through reprocessing
• JMA’s own tropical cyclone bogus data
• Production schedule
– Q1 2019: start production
– Q1 2021: complete production for the 1991 – 2020 normal period
– Q1 2022: complete production for the whole period 8
Surface net energy flux (January 2016)
Global mean net flux (W m-2)
JRA-3Q Exp JRA-55
August 2015 -12.0 -16.5
January 2016 4.6 -3.6
• JRA-55 has a bias of -11.8 W m-2
(Kobayashi et al. 2015).
• This bias is almost halved in JRA-3Q Exp.
9
3Q for your attention!
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