status and plans Shinya Kobayashi a , Yukinari Ota a , Yayoi Harada a - - PowerPoint PPT Presentation

The Japanese 55-year Reanalysis (JRA-55): status and plans

Shinya Kobayashia, Yukinari Otaa, Yayoi Haradaa, Ayataka Ebitaa, Masami Moriyaa, Hirokatsu Onodaa, Kazutoshi Onogia, Hirotaka Kamahorib, Chiaki Kobayashib, Hirokazu Endob, Kiyotoshi Takahashia, Kengo Miyaokaa, and Ryoji Kumabea

a Japan Meteorological Agency (JMA) b Meteorological Research Institute (MRI), JMA

7-11 October 2013 1 Sixth WMO Symposium on Data Assimilation

Outline

• Outline of JRA-55 (nicknamed “JRA Go! Go!”)

– Observational data – Data assimilation system

• Basic performance of the data assimilation

system

• Early results of quality assessment
• JRA-55 family
• Release schedule
• Future plans
• Summary

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• The second Japanese global reanalysis conducted by JMA
• The first comprehensive global atmospheric reanalysis that

applies 4D-Var to the last half century

• Aiming at providing a comprehensive atmospheric dataset

that is suitable for studies of climate change and multi- decadal variability

Outline of JRA-55

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JRA-55 (4D-Var) JRA-25/JCDAS (3D-Var)

Surface, radiosondes, tropical cyclone retrievals, windprofilers Polar orbiting satellites Geostationary satellites GNSS Aircraft 1960 1970 1980 1990 2000 2010 IGY FGEE COMPLETED

Observational data

• The major data source

– The ERA-40 observational dataset supplied by ECMWF

• Homogenization

– Radiosonde Observation Correction using Reanalyses (RAOBCORE) v1.4 (Haimberger 2008)

• Reprocessed satellite observations

– GMS, GOES-9 and MTSAT-1R (MSC/JMA)

• Sustained, Coordinated Processing of

Environmental Satellite Data for Climate Monitoring

– METEOSAT (EUMETSAT), TMI (NASA and JAXA), AMSR-E (JAXA), QuikSCAT (NASA/PO.DAAC), AMI (ESA), GNSS/RO (UCAR)

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Chronology of types of observational data assimilated in JRA-55

Data assimilation system

JRA-25/JCDAS JRA-55 Version Operational as of Mar 2004 Operational as of Dec 2009 Resolution T106L40 (~ 120 km) top layer at 0.4 hPa TL319L60 (~ 60 km) top layer at 0.1 hPa Assimilation scheme 3D-Var 6-hour time window T106 resolution 4D-Var 6-hour time window T106 inner model Background error covariances are inflated by 1.8 before 1972 Satellite radiance bias correction Adaptive but not variational (Sakamoto and Christy 2009) Variational Bias Correction (VarBC) (Dee 2005) Long-wave radiation scheme

Line absorption

Statistical band model

Water vapor continuum

e-type only

Line absorption

Table lookup + K-distribution

Water vapor continuum

e-type + P-type Green house gases CO2 only (constant at 375 ppmv) CO2, CH4, N2O, CFC-11, CFC-12, HCFC-22 (historical concentrations)

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Basic performance

• f the data assimilation system

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Time series for the RMS errors of 5-day forecasts of geopotential height (gpm) at 500hPa verified against its own analysis

(hPa)

Temporal consistency of temperature analysis

7-11 October 2013 Sixth WMO Symposium on Data Assimilation 7 Anomalies are calculated with respect to their own averages for the years from 1980 to 2001.

JRA-55 ERA-40 JRA-25 ERA-Interim (Year) Time series of global mean temperature anomalies

A dry land surface problem in the Amazon basin

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Total Column Water Vapor flux (kg/m/s) & divergence (kg/m2) for Nov 1979 Top: analysis Bottom: increment

No Ps over the Amazon basin No Ps over the Amazon basin Control Control

• Possible causes

–bias in surface pressure

• bservations

–mis-specified station height –deficiencies in the diurnal cycle reproduced by the forecast model –lack of constraint on soil moisture

• Quick fix for JRA-55

Surface pressure observations are simply discarded over the Amazon basin.

JRA-55 family

• JRA-55 (JMA)

– Full observing system reanalysis

• JRA-55C (MRI/JMA)

– Fixed observing system reanalysis – Using conventional observations only

• surface, radiosondes, tropical cyclone retrievals and windprofilers
• JRA-55AMIP (MRI/JMA)

– AMIP type run (with no observations assimilated)

• Providing a range of products using the common

base NWP system for investigating impact of changing observing systems and model biases

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Representation of the Quasi-Biennial Oscillation

7-11 October 2013 Sixth WMO Symposium on Data Assimilation 10

JRA-55 JRA-55AMIP

(AGCM Simulation)

JRA-55C Time series for zonal wind (m/s) averaged for the equatorial band between 5S and 5N

• JRA-55 and JRA-55C provide a consistent representation of QBO, whereas

JRA-55AMIP does not.

• Upper observations are important for our system to represent QBO.

Chiaki Kobayashi

Product availability

• Available for research purposes from

– JMA Data Distribution System (JDDS)

• http://jra.kishou.go.jp/JRA-55/index_en.html
• A JRA-25 user account can be used to download the JRA-55 product as

well.

– Data Integration and Analysis System (DIAS)

• http://dias-dss.tkl.iis.u-tokyo.ac.jp/acc/storages/filelist/dataset:204

– NCAR

• in preparation, available soon
• Release schedule

– 1.25 degree latitude/longitude grid data from Oct 2013 – Near real-time product from Feb 2014 – Model grid data (TL319L60) from Mar 2014

• Provision of the JRA-25/JCDAS data will be ceased at the end
• f Feb 2014.

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Future plans

• Production of JRA-55 will be continued on a near real-time

basis.

• Production of JRA-55C and JRA-55AMIP will be completed and

release of basic products is planned in FY2014.

• The JRA-55 papers are in preparation;

– Part 1 general specification and basic characteristics

• To be submitted by Nov 2013

– Part 2 reproducibility of atmospheric circulation and climate variability

• To be submitted in mid 2014
• Production of a 5-km downscaling dataset over Japan is

underway and will be completed in FY2015.

• Examinations on issues identified in JRA-55, such as

performance of data assimilation system under reduced

• bserving systems, model biases and so on.

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Summary

• Production of JRA-55 has been completed. Early results of

quality assessment have suggested that many of deficiencies in JRA-25 have been diminished or reduced in JRA-55.

• Temporal consistency of temperature analysis of JRA-55 has

the best performance with few jumps among the reanalyses.

• Inter-comparison among the “JRA-55 family” provides an
• pportunity

for quantitative assessment regarding representation

• f

climatic trends and low-frequency variations.

• For further improvement of temporal consistency, issues such

as performance of data assimilation system under reduced

• bserving systems and model biases need to be addressed.

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Thank you!

7-11 October 2013 14 Sixth WMO Symposium on Data Assimilation

Backup slide

7-11 October 2013 Sixth WMO Symposium on Data Assimilation 15

(hPa)

Temporal consistency of temperature analysis (Part 2)

7-11 October 2013 Sixth WMO Symposium on Data Assimilation 16 Anomalies are calculated with respect to their own averages for the years from 1980 to 2001.

JRA-55 NCEP/NCAR CFSR (NOAA NCEP) MERRA (NASA GMAO) (Year) Time series of global mean temperature anomalies