1. Reanalysis: Data assimilation aspects
Paul Poli
ECMWF
Meteorological Training Course
Data Assimilation and Use of Satellite Data
29 April 2009

2. Data assimilation in reanalysis
Outline
Definition
Data assimilation in reanalysis
Analysis scheme
Bias correction
Error assignment
Data usage
Reanalysis in the real world
Projects
Users
European Reanalysis: ERA-Interim
Conclusions and Future prospects

3. WHY Reanalysis ? (+ a short definition)
Cannot use operational NWP products for long studies!
ECMWF operational system
RE-analyse past observations with a fixed data assimilation system and model  reanalysis

4. Other strong underlying reasons for reanalysis
Advantages of concentrating and analyze all sources of information [observations] in a unified framework:
We should be able to draw more benefits from all this information than from one subset of it
We don’t have too many observations of the Earth system to help solve the puzzle of natural variability
Reanalyses produce four-dimensional fields which are consistent
In space and in time
Between the various geophysical parameters
Discrepancies point to un-understood areas
The validity of this approach is proven
By validating reanalysis products with independent observations

5. Data assimilation in reanalysis
Outline
Definition
Data assimilation in reanalysis
Analysis scheme
Bias correction
Error assignment
Data usage
Reanalysis in the real world
Projects
Users
European Reanalysis: ERA-Interim
Conclusions and Future prospects

6. Changes in operational NWP systems and input that affect product quality (usually for the better)
Data
Observing system (instrumentation – raw data)
Forcing data: SST, sea-ice, greenhouse gases…
Data processing
Data assimilation
Analysis scheme
Bias correction
Data usage: blacklist, thinning, active/passive (! )
Observation error assignment (! )
NWP forecast model
Physics
Dynamics
Resolution
Misc: computer (! ), code (! ), compiler (! ), settings (! )
Changes that can be minimized in a reanalysis
Requires additional efforts and collaboration

7. Reanalysis in practical terms: NWP forecast model
Use a fixed version (dynamics, physics)
To benefit from the best available parametrizations and modeling
Use a fixed resolution
Must be computationally affordable to cover say 20 years
Implies to run about 10 days of assimilation and forecast per day of reanalysis run
Practical choice, before starting reanalysis production:
Use the near-latest operational model version
Use the resolution that was operational 6-8 years ago
Do not change setup during the run
Be extra careful when changing machine, compiler…
In a reanalysis, one picks a model configuration version and resolution which is best for the purpose – and one tries to stick to it!!

8. Reanalysis in practical terms: data assimilation system
Data assimilation combines information from
Observations
A short-range “background” forecast that carries forward the information extracted from prior observations
Error statistics
Dynamical and physical relationships
4DVAR
This produces the “most probable” atmospheric state (maximum-likelihood estimate)***
***if background and observation errors are Gaussian,
unbiased, uncorrelated with each other; all error covariances
are correctly specified; model errors are negligible within
the 12-h analysis window
background constraint
observation constraint
simulates the observations
In a reanalysis, one tries to use the best data assimilation approach available

9. Reanalysis in practical terms: data assimilation system
4D-Var CONTROL
3D-Var
4D-Var
15 February UTC
All observations
Surface pressure
Observations only
Advances in data assimilation can help extract more information from historic data that could ever be thought possible at the time the observations were collected

10. Reanalysis in practical terms: data assimilation system
Data assimilation combines information from
Observations
A short-range “background” forecast that carries forward the information extracted from prior observations
Error statistics
Dynamical and physical relationships
4DVAR
***if background and observation errors are Gaussian, unbiased, uncorrelated with each other; all error covariances are correctly specified; model errors are negligible within the 12-h analysis window
This produces the “most probable” atmospheric state (maximum-likelihood estimate)***
***if background and observation errors are Gaussian,
unbiased, uncorrelated with each other; all error covariances
are correctly specified; model errors are negligible within
the 12-h analysis window
background constraint
observation constraint
simulates the observations

11. Reanalysis in practical terms: bias correction
Variational bias correction (see lecture by Niels Bormann)
Aims at correcting observation and observation operator biases in an automatic, consistent and time-smooth manner
Requires data that are considered not biased:
All in situ: surface stations, radiosondes, aircraft
GPS radio occultation (see lecture by Sean Healy)
So far, only applied to satellite radiance data in reanalysis
Caveat: Not designed to handle model biases!
Variational bias correction has become an important component of global reanalysis because it minimizes the impact of changes in radiance data (new instruments, re-calibration during flight…)

12. Example 1 of variational bias correction: Instrument recalibration
Instrument recalibration (AMSU-A sensor on METOP-A)  changes mean behaviour of the instrument  automatically corrected by the variational bias correction scheme.
global
(AQUA)
An example where the bias correction is responding to instrument recalibration

13. Example 2 of variational bias correction: Bias in the observations
Satellite orbit drift  changes sun angle on the satellite  changes satellite heat budget  changes in-satellite blackbody used for warm target calibration  automatically corrected by the variational bias correction scheme.
From Grody, Vinnikov, Goldberg, Sullivan, and Tarpley
global
global
Before bias correction
After bias correction
An example where the bias correction is acting properly – and for the good reason

14. Example 3 of variational bias correction: MSU instrument bias due to satellite reference calibration blackbody fluctuations
Variational bias estimates for NOAA-14
Actual warm-target temperatures on board NOAA-14 (Grody et al. 2004)
This illustrates the power of reanalysis in identifying instrument errors based on all available information [model and observations]

15. Example 4 of variational bias correction: Bias in the forecast model
Mt Pinatubo eruption  introduced aerosols in stratosphere  stratosphere got warmer  Forecast model uses fixed quantity of aerosols  bias considered to be an observation bias by the bias correction scheme, and hence was corrected for.
tropics
tropics
Before bias correction
After bias correction
An example where the bias correction is reacting – but for the wrong reason

16. Reanalysis in practical terms: data usage
Usage / no usage of data, besides variational QC and first-guess checks, is controlled using simple switches:
Data extraction
Thinning/sub-sampling
Blacklisting/whitelisting
Each of these steps can be a source of error in a reanalysis which concentrates many varied sources of information, whose observation characteristics sometimes even vary over time
Observation monitoring is critical in a reanalysis: it is there to try to catch all the possible things that can go wrong with data usage

17. Observations assimilated in ERA-Interim 4DVAR
Total number of observations over 20 years: exceeds 30x109
Reanalyses have to deal with very large numbers of observations, whose quantity vary over time

18. Key changes to the observing system
1957: Radiosonde network enhanced in
Southern Hemisphere for
International Geophysical Year
1973: NOAA-2 – First operational sounding of temperature and humidity from polar-orbiting satellite
NOAA-2
15 Oct 1972
1979: Improved sounding from polar orbiters
Winds from geostationary orbit
More data from commercial aircraft
Drifting buoys
Today: Additional satellite, aircraft and buoy data. Poorer radiosonde coverage, but better quality
The input data change all the time … but the root problem doesn’t: obtain the best estimate of the atmosphere given all available observations

19. Time coverage of in situ surface data
1989
2009

20. Radiosonde coverage for 1958B C D E H I J K M N P Q U V
Ships maintaining fixed locations
Average number of soundings per day: 1609

21. Radiosonde coverage for 1979B C D E H I J K M N P Q U V
Average number of soundings per day: 1626
Average number of soundings per day: 1626

22. Radiosonde coverage for 2001
Average number of soundings per day: 1189

23. Time coverage of Atmospheric Motion Vector (AMV) data
1989
2009

24. Early 1980s Expanded Low-resolution Winds
Example of improved data coverage: Reprocessed Atmospheric Motion Vectors from Meteosat
Early 1980s Expanded Low-resolution Winds
Early 1980s Expanded Low-resolution Winds

25. Time coverage of radiance data
1989
2009
Time coverage of radiance data
Comparatively many more sources …
Comparatively fewer sources

26. Observations available for ERA-Interim 4DVAR
“Conventional”
“Satellite”
Radiosondes
TOTAL
Radiances
Surface
Atmospheric motion vectors
Aircraft
Radiosondes (wind only)
Scatterometers
GPS radio occultation
Drifting buoys
Surface pressure pseudo-observations from Australian Bureau of Meteorology (PAOB)

27. Percentage of data assimilated in ERA-Interim
Sat
Conv
Sat
Conv
Sat
Conv
Conv
Conv
Sat
A large number of satellite data are still not assimilated – these represent as many yet untapped resources  for future reanalyses of the present

28. Data assimilation in reanalysis
Outline
Definition
Data assimilation in reanalysis
Analysis scheme
Bias correction
Error assignment
Data usage
Reanalysis in the real world
Projects
Users
European Reanalysis: ERA-Interim
Conclusions and Future prospects

29. Atmospheric reanalysis: Starting points
Has its origins in datasets produced for the Global Weather Experiment (FGGE)
Widely used, but superseded by use of multi-year operational NWP analyses
Proposed by Roger Daley in 1983 for monitoring the impact of forecasting system changes on the accuracy of forecasts
Adrian Simmons (personal communication)
Proposed for climate-change studies in two journal articles:
Bengtsson and Shukla (1988), Trenberth and Olson (1988)

30. Atmospheric reanalysis: Global products
Three centres took initiative in mid 1990s
ERA-15 ( ) from Europe – with significant funding from USA
NASA/DAO ( ) from USA
NCEP/NCAR ( present) from USA
Second round followed
ERA-40 ( ) from Europe – with significant funding from FP5
JRA-25 ( ) from Japan
NCEP/DOE ( present) from USA
Now in third generation of comprehensive global reanalysis
ERA-Interim ( present) from Europe
JRA-50 ( ) from Japan
NASA/GMAO-MERRA ( present) from USA
NCEP-CFSRR ( ) from USA

31. Atmospheric reanalysis: The user base
Many users:
12000 registered users of ERA public data server
≳5M fields retrieved daily by ECMWF and Member-State users
National mirror sites for ERA in many countries
And many citations:
Paper on NCEP/NCAR reanalysis is most cited paper in geosciences
Paper on ERA-40 is most cited recently in the geosciences
Many references in IPCC Fourth Assessment report

32. Atmospheric reanalysis: Becoming more diverse
Regional reanalysis and downscaling
Long-term reanalysis using only surface-pressure observations
Short-term reanalysis for chemical composition
North American Regional Reanalysis
Maximum gusts
26 December 1999
2m temperature
6UTC, 1 January 1999

33. Atmospheric reanalysis: Some uses
Monitoring of the observing system
Providing feedback on observational quality, bias corrections and a basis for homogenization studies of long data records that were not assimilated
Development of climate models
Providing data for verification, diagnosis, calibrating output,, …
Driving data for users’ models/applications
For smaller-scales (global→regional; regional→local), ocean circulation, chemical transport, nuclear dispersion, crop yield, health warnings, …
Providing climatologies for direct applications
Ocean waves, resources for wind and solar power generation, …
Study of short-term atmospheric processes and influences
Process of drying of air entering stratosphere, bird migration, …
Study of longer-term climate variability/trends
Preferably used with caution in conjunction with observational studies

34. ERA-Interim: after ERA-40 and before the next reanalysis

35. Under the hood of the ERA-Interim reanalysis
Grab observations from archive
Basic pre-processing
Ingest observations to assimilation database
Run 4DVAR (atmosphere) and surface/sea/snow data assimilations
Run the forecast model
Generate monitoring and diagnostics plots

36. Reanalysis and climate monitoring
Benefits of reanalysis products as a proxy for observations are well established
Reanalysis for climate monitoring: physically consistent ECVs (essential climate variables)
In the climate community, reanalysis is still regarded as unsuitable for trend estimation (IPCC)
This view evolves as reanalyses become more consistent and rely on more observations

37. (“Atmospheric reservoir”)
Global water cycle
ERA-40: Excessive precipitation over tropical oceans, exacerbated by Pinatubo eruption
(“Atmospheric reservoir”)
Pinatubo eruption

38. Drifts in AMSU-A tropospheric radiance data
Globally averaged bias estimates for AMSU-A radiances
AQUA
There are clear inconsistencies among the AMSU-A observations -> some instrument issues
Decreasing bias estimates: Reanalysis trend > AMSU-A trend
This is not a drift toward the model climate (model has a cold bias in the troposphere)
Why is the reanalysis getting warmer rather than colder?

39. Anchoring data for the troposphere: Radiosondes
Global mean departures and data counts for radiosonde temperature data
NOAA-15
NOAA-16
Warm bias relative to radiosondes
Data count

40. Anchoring data for the troposphere: Aircraft reports
Global mean departures and data counts for aircraft temperature data
NOAA-15
NOAA-16
Unbiased relative to aircraft reports
Data count
Aircraft data biased?

41. Conclusions on the AMSU-A bias drifts
Drift in NOAA-15 Ch6 (0.5K per decade) is probably due to instrument errors
(Mears et al 2008)
Smaller drifts in other channels are due to warm bias in aircraft reports (Ballish and Kumar 2008)
Slight excess warming in the upper troposphere is probably present in all reanalyses
Aircraft data need
bias correction
ERA-Interim
ERA-40
JRA-25
NCEP
Global mean temperature anomalies

42. Validation: Fit to radiosonde wind observations

43. Validation: Forecast skill
Operations %
Anomaly Correlation (%) for the 500 hPa Geopotential Height
100% = perfect forecast 60% = limit of use
ERA-Interim %
Updated by Simmons, from Simmons & Hollingsworth (2002)

44. Validation: Fit to 2m land temperature anomalies (K)
Differences of monthly values from CRUTEM3
CRUTEM3 K
ERA sampled as CRUTEM3 (Brohan et al., 2006) following Simmons et al. (2004)

45. Validation: Fit to temperature profiles from radiosondes
Fit to temperature observations for ERA-Interim and ERA-40
analysis departures
background departures
Based on January 2000 radiosonde temperature reports north of 70N

46. Progress in time consistency: analysis increments
Zonal mean temperature analysis increments for August 2001
ERA-Interim
ERA-40

47. Data assimilation in reanalysis
Outline
Definition
Data assimilation in reanalysis
Analysis scheme
Bias correction
Error assignment
Data usage
Reanalysis in the real world
Projects
Users
European Reanalysis: ERA-Interim
Conclusions and Future prospects

48. Summary & Important concepts
Reanalysis does not produce “gridded fields of observations”
But it enables to extract information from observations in one, unique, theoretically consistent framework
Reanalysis sits at the end of the meteorological research and development chain that encompasses
observation collection [measurement],
observation processing,
numerical weather prediction modelling, and
data assimilation
Unlike NWP, a very important concept in reanalysis is the consistency in time
Reanalysis is bridging slowly, but surely, the gap between the “weather scales” and the “climate scales”
Resolution gets finer
Covers longer time periods

49. Current status of reanalysis & Future outlook
Reanalysis has developed into a powerful tool for many users and applications
It relies on the combined expertise of a large user community and feedback
An extended reanalysis project such as ERA-40 takes 7-10 years to complete
It is worth repeating as all ingredients continue to evolve:
Models are getting better
Data assimilation methods are getting better
Observation processing is improving
The technical/scientific infrastructure for running & monitoring improves constantly
With ERA-Interim, we made good progress in key problematic conceptual areas:
Dealing with biases and changes in the radiance observing system
Major challenges for a future reanalysis project:
Bringing in additional observations (not dealt with in ERA-Interim)
Dealing with model bias (ultimately responsible for problems with trends)
Coupling with ocean and land surface
Providing meaningful uncertainty estimates for the reanalysis products

50. Further reading and on-line material
European reanalysis (ERA):
Uppala et al. (2005), “The ERA-40 reanalysis”, Q. J. R. Meteorol. Soc., 131 (612), , doi: /qj
NCEP/NCAR reanalysis:
SciDAC Review (2008), “Bridging the gap between weather and climate”, on the web at with contributions from G. P. Compo and J. S. Whitaker
Japanese 25-year reanalysis (JRA-25):
NASA GMAO Modern Era Retrospective-analysis for Research and Applications (MERRA)
Bengtsson et al. (2007), “The need for a dynamical climate reanalysis”, Bull. Am. Meteor. Soc., 88 (4),

51. ERA-Interim product availability
Currently 1989 Jan until 2009 Feb, with monthly updates
Resolution: T255L60, 6-hourly (3-hourly for surface)
Analysis + forecast products; monthly averages
Access to products:
Member state users: full access via MARS
All users: web access via ECMWF Data Server