Objectives of Total S

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From ERA-40 to the 3rd generation ECMWF
reanalysis ERA-Interim
Sakari Uppala, Dick Dee, Shinya Kobayashi, Adrian
Simmons and J.-N. Thépaut ECMWF
NCEP, 6 November 2007
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Contents

• Background
• Observations for reanalyses
• ERA-40 experience
• ERA-40 ? ERA-Interim
• ERA-Interim early results
• Future directions

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Zonal mean vertical velocity mPa/s 1979-1993
OPERATIONS
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Application of reanalyses (1)

• To improve understanding of
• Weather and climate
• General circulation of atmosphere
• Long term variability and trends
• Tele-connections
• Atmospheric transport
• Hydrological cycle
• Surface processes
• Predictability studies daily ? seasonal
• Extreme weather, storm tracking, tropical
  cyclones,

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Application of reanalyses (2)

• To provide
• Initial states, external forcing or validation
  data for
• Climate model integrations
• Ocean models
• Monthly and seasonal forecasting
• Chemical transport models
• DEMETER, ENSEMBLES, ENACT, CANDIDOZ,
• A substitute for observed statistics

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Reanalysis projects

• NCEP/ NCAR 1948 ? CDAS
• NASA/ DAO 1980 - 1995
• ECMWF, ERA-15 1979 - 1993
• ECMWF, ERA-40 1957 - 2002
• ECMWF, ERA-Interim 1989 ? ECDAS
• JMA, JRA-25 1979 ? CDAS
• In preparation
• NASA/ MERRA 1979 ?
• New coupled NCEP reanalysis 1979 ?
• US Arctic Reanalysis System 2000 ? 2010
• New JRA reanalysis 1957 ?

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Operational forecast performance 1980-2007
Monthly time series Moving average
Northern Hemisphere 500 hPa geopotential ANC
reaching 60
Tropics 850 hPa wind vector ABC reaching 70
Southern Hemisphere 500 hPa geopotential ANC
reaching 60
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Fields retrieved from ECMWF archive May 2003
September 2007
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Survey on the use of ERA-40 from
web http//www.ecmwf.int/research/era/era40sur
vey/
Currently 9500 unique registered users
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Observations for reanalysis
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Observing Systems in ERA-40
1957
2002
METEOSAT Reprocessed Cloud Motion Winds
1982
1988
1979
TOMS/ SBUV
1973
AIRCRAFT DATA
CONVENTIONAL SURFACE AND UPPERAIR OBSERVATIONS
NCAR/ NCEP, ECMWF, JMA, US Navy, Twerle, GATE,
FGGE, TOGA, TAO, COADS,
1973
1979
1987
VTPR
TOVS HIRS/ MSU/ SSU
SSM/I
1991
ERS-1
1995
Cloud Motion Winds
ERS-2
1998
ATOVS AMSU-A
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Radiosonde coverage for 2001
Average number of soundings per day 1189
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Zonal mean number of radiosondes
ERA-15 / L31
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Satellites with VTPR instruments
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First Garp Global Experiment FGGE
1979Definition of the observing system
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TOVS/ATOVS satellite data 1978-2002
YEAR
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Use of atmospheric satellite data in reanalyses
VTPR/ TOVS/ ATOVS VTPR/ TOVS/ ATOVS VTPR/ TOVS/ ATOVS VTPR/ TOVS/ ATOVS VTPR/ TOVS/ ATOVS DMSP GEO
SSU VTPR HIRS MSU AMSU SSM/I
NCEP 1948 ? NESDIS operational T q retrievals NESDIS operational T q retrievals NESDIS operational T q retrievals NESDIS operational T q retrievals NESDIS operational T q retrievals - Oper AMWs
ERA-15 1979-1993 - - 1D-Var retrievals of T q using CCR. Above 100hPa NESDIS retrievals. 1D-Var retrievals of T q using CCR. Above 100hPa NESDIS retrievals. - - Oper AMWs
ERA-40 1957-2002 1c 1c 1c 1c 1c 1D-Var retrievals of TCWV wind speed Operreprocessed AMWs, CSR passively
JRA-25 1979 ? 1c - 1c 1c 1c JMA retrievals of TCWV Operreprocessed AMWs
ERA-Interim 1989 ? 1c - 1c 1c 1c 1c radiances and 1D-Var retrievals of rainy radiances Operreprocessed AMWs, CSR passively
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Quality of Cloud Motion Winds improves
Moving average of daily Sqrt(Urms2Vrms2) Latitu
de band 30N-15N (OBAN, OBFG) levels 300-200 hPa
1979
1993
1979
1993
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METEOSAT Reprocessed Winds
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Sea Surface Temperature and Ice data

• Before November 1981 (HADISST1, Met Office)
• Monthly SST analysis based on ship and buoy
  measurements
• Sea ice extent has large uncertainty
• From November 1981
• Retrievals of SST from Advanced Very High
  Resolution Radiometer after cloud clearing
• Buoy and ship data
• OI or 2D-Var weekly SST analysis (R. Reynolds,
  NCEP)
• Background is the previous SST analysis
• Bias correction applied to satellite SSTs
• Sea ice extent determined from ice concentrations
  retrieved from SSMR and SSMI instruments
• Interpolation to daily values

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Nov 1981
NOAA/ NCEP weekly 2D-Var Satellite, Ship and buoy
Met Office monthly HADISST1 Ship and buoy
30N
30S
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ECMWF reanalyses
ERA-40 1957-2002
ERA-15 1979-1993

• Improved data assimilation system
• Assimilating model T159L60
• Optimum Interpolation ? 3D-Var FGAT
• Analysis of O3
• More extensive use satellite radiances (from CCR
  ? Level 1c radiances)
• ERA-15 experience ? ERA-40 blacklist
• More comprehensive use of conventional
  observations
• Use of Meteosat reprocessed winds, CSR data
  passive
• Improved SST ICE dataset
• Ocean wave height analysis

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Model levels
Levels added
ERA-15/ L31
ERA-40/ L60
65 km
0.1 hPa
13 LEVELS
10 hPa
7 LEVELS
100 hPa
9 LEVELS in PBL
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Handling of biases in ERA-40

• Radiosonde temperature biases 1980 onwards
• VTPR, TOVS, SSMI and ATOVS radiances
• ERS scatterometer wind bias correction

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Radiosonde temperature bias OB-FG (1994, South
West Canada)
(4 solar elevation angle intervals and the mean)
Without correction
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Need to homogenize radiosonde biases in time
(example Haimberger, 2005 using ERA-40
feedback data)
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Pinatubo
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Quality of Analysis Background
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Global 500 hPa Temperature analysis mean
increment and STD
VTPR introduced
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Tropical TCWV analysis mean increment and STD
VTPR
Tropical mean TCWV (kgm-2) background and analysis
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background
analysis
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ERA-40 SATOB U- Wind 850 00 UTC Tropics RMS
(m/s) OB-FG OB-AN 15 days MA
Number of used observations per day
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Global OB-BG STD/ mean MSU-Ch 4 Max energy 90
hPa
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Analysis of ozone
The three-dimensional ozone field is consistent
both with available ozone observations and the
dynamical state of the atmosphere
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Monthly mean values
ERA-40 analysis TOMS and SBUV data assimilated
1979-1988 and 1991-2002.
Ground-based measurements From NOAA/ CMDL and
not used in analysis.
No data assimilated in 1989 and 1990.
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Mean differences between ERA-40 and ERA-15
T2m (K) July 1989
10m wind speed January 1989
Contour interval 0.5ms-1 Yellow/red indicates
ERA-40 windier than ERA-15
Contour interval 2K Yellow/red indicates ERA-40
warmer than ERA-15
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Moisture analysis
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Aspects of tropical humidity analysis in ERA-40
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Tropical oceans SST ?? TCWV
SST analysis
K
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Detection of tropical cyclones
kgm-2
(from Richard Allan)
-4 -2 0 2 4
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Climate variability and trends
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Detection of tropical cyclones
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Debate on the trends in tropical cyclone intensity

• Ryan L. Sriver and Matthew Huber, 2006,
  Geophysical Research Letters
• Low frequency variability in globally
  integrated tropical cyclone power dissipation
• R. N. Maue and R. E. Hart, 2007, comment to the
  previous
• Ryan L. Sriver and Matthew Huber, 2007, Reply
  to comment

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Trend and variability in two-metre temperature
Linear trend (1979-2001) CRUTEM2v 0.31OC/deca
de ERA-40 0.28OC/decade
NCEP 0.19OC/decade
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Trend and variability in lower stratospheric
temperature
Linear trend MSU-4 - 0.39OC/decade
ERA-40 - 0.30OC/decade
NCEP - 0.82OC/decade
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Satellite radar altimeter 1992-2003 (Davis et al.
Science 2005 Vol 308 No. 5730)
Surface elevation change rate (cm per
year) 1992 ? 2003
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ERA-40 Atlas 1979-2001
http//www.ecmwf.int/research/era/ERA-40_Atlas/doc
s/index.html
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Quasi-Biennial Oscillation
Quasi biennal oscillation Equatorial band
2S-2N Monthly mean zonal wind anomaly (m/sec) to
1979-2001 climate
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FORECAST PERFORMANCE1957-2002
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Anomaly correlations of 500hPa height forecasts
Northern Hemisphere

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Anomaly correlations of 500hPa height forecasts
Australia/New Zealand

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ERA-Interim 1989 ? to continue as CDAS ?
ERA-40 1957-2002

• Data-assimilation system
• T159L60 ? T255L60 / 12 hour 4D-Var
• New humidity analysis and improved model physics
• Satellite level-1c radiances
• Better RTTOV and improved use of radiances
  especially IR and AMSU
• Assimilation of rain affected radiances through
  1D-Var
• Variational bias correction
• Improved use of radiosondes
• Bias correction and homogenization based on
  ERA-40
• Correction of SHIP/ SYNOP surface pressure biases
• Use of reprocessed
• - Meteosat winds
• - GPS-RO data CHAMP / UCAR 2001 ?, GRACE and
  COSMIC
• - GOME O3 profiles 1995 ?
• New set of Altimeter wave height data 1991?

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MSU Ch2 Global mean bias corrections in
ERA-Interim
NOAA-10
NOAA-11
NOAA-12
NOAA-14
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Problem with transition SSU AMSUA at model top
Global mean temperature at 1hPa
JRA-25
ERA-40
ERA-Interim

• ERA-Interim more consistent with SPARC than
  ERA-40
• Important work by S. Kobayashi has improved SSU
  AMSUA consistency
• Jumps are unavoidable as long as model errors are
  large and only partially constrained by
  observations

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Improvement of the Brewer-Dobson circulation

• Transport of water vapour into the lower
  stratosphere is more reasonable (Excessive in
  ERA-40 see Uppala et al., QJ 2005)
• Mean age of air is much closer to observed in
  ERA-Interim (EXP471 in Monge-Sanz, JGR 2007)

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ERA-75?
ERA-Interim

• Could start 2011 depending on resources
• 1938 ? 2013 and continue as CDAS
• Important components
• Recovery, organization and homogenization of
  observations
• Improved SST ICE dataset
• Variational analysis technique aimed for
  reanalysis
• Comprehensive adaptive bias handling
• Handling of model biases
• Coupled atmospheric-ocean reanalysis?

!!
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Inter-satellite biasesSSU uncorrected radiance
departures (ERA-40)

• Global mean differences between observed and
  simulated SSU radiances in ERA-40 show large
  inconsistencies between different satellites
• These inter-satellite biases are thought to be
  mainly due to changes in cell pressure that
  occurred during the lifetime of each satellite

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Inter-satellite biasesSSU inconsistencies
between NOAA-6 and NOAA-7

• Simultaneous Nadir Overpass (SNO)
• The SNO technique compares observations from
  different satellites which happen to be viewing
  the same place at the same time
• Use of the SNO technique shows that weighting
  functions for SSU channels on different
  satellites are not identical
• However RTTOV is based on a single transmittance
  dataset for each channel and applies the
  transmittance to all the instruments

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SSU estimated changes in cell pressure
Cell pressure evolution by satellite (estimated
from modulation frequency records)

• SSU makes use of a pressure modulation technique
  to measure the radiation emitted from the
  absorption band of CO2
• Instrument response is rather sensitive to
  changes in cell pressure
• Due to a sealing problem, cell pressure changes
  significantly during the lifetime of each
  instrument

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Impact of cell pressure changes on instrument
response

• The outgassing from the cell effectively raises
  the weighting function
• This is thought to be the main cause of the
  biases in the SSU radiances
• SSU transmittances will be recalculated for each
  satellite, taking into account the estimated cell
  pressure changes
• An effort to collect all relevant information on
  the SSU instrument is currently being made in
  collaboration with the Met Office

Dependence of weighting functions on cell pressure
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Transition from SSU to AMSU-A in ERA-40Both
could not be used simultaneously
There was a major discrepancy between SSU Ch3 on
NOAA-14 and AMSU-A Ch14 on NOAA-15, especially
in polar winter Many AMSU-A data were initially
rejected by the first-guess check in ERA-40
SSU Ch3 was blacklisted after 3 July 1999
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Representation of the Zeeman effect in RTTOV
Impact on stratospheric temperature analysis
0.1hPa
200hPa
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Consistency between AMSU-A and SSUMean
departures over Antarctic
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4D-Var control
3D-Var surface pressure observations only
4D-Var surface pressure observations only
(Uppala S, A Simmons, D Dee, P Kållberg and J-N
Thépaut, Atmospheric reanalyses and climate
variations in the book Climate variability and
extremes during the past 100 years, Advances in
Global Change Research, Springer (2007, in press,
Eds. Brönnimann, S., J. Luterbacher, T. Ewen, H.
F. Diaz, R. Stolarksi, and U. Neu)