1 reanalysis: data assimilation aspects paul poli [email protected] ecmwf meteorological training...

1

Reanalysis: Data assimilation aspects

Paul Poli [email protected]

ECMWF

Meteorological Training CourseData Assimilation and Use of Satellite

Data29 April 2009

Outline

I. Definition

II. Data assimilation in reanalysis• Analysis scheme• Bias correction• Error assignment• Data usage

III. Reanalysis in the real world• Projects• Users• European Reanalysis: ERA-Interim

IV. Conclusions and Future prospects

WHY Reanalysis ? (+ a short definition)

• Cannot use operational NWP products for long studies!

32 MPI Validation Report

Figure 1 - Latitude-time section (1979-1993) of the zonally averaged vertical velocity in 500 hPa. Upper panel:ECMWF operational analyses, lower panel: ECMWF reanalyses. Units: mPa/s.

ECMWF operational system 1979-1993

RE-analyse past observations with a fixed data assimilation system and model reanalysis

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

Outline

I. Definition




1. Data– Observing system (instrumentation – raw data)– Forcing data: SST, sea-ice, greenhouse gases…– Data processing

2. Data assimilation– Analysis scheme– Bias correction– Data usage: blacklist, thinning, active/passive (! )– Observation error assignment (! )

3. NWP forecast model– Physics– Dynamics– Resolution– Misc: computer (! ), code (! ), compiler (! ),

settings (! )

Changes in operational NWP systems and input that affect product quality (usually for the better)

Changes that can be minimized in a reanalysis

Requires additional efforts and collaboration

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!!

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

h(x)yRh(x)yx)(xBx)(xJ(x) 1Tb

1Tb

background constraint observation constraint

(x)hh(x) Μ simulates the observations

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

4DVAR

Reanalysis in practical terms: data assimilation system

In a reanalysis, one tries to use the best data assimilation approach available

4D-Var CONTROL

3D-Var 4D-Var

15 February 2005 00 UTC


All observations

Surface pressureObservations 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

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

h(x)yRh(x)yx)(xBx)(xJ(x) 1Tb

1Tb

background constraint observation constraint

(x)hh(x) Μ simulates the observations

This produces the “most probable” atmospheric state (maximum-likelihood estimate)***





4DVAR


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…)

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.

An example where the bias correction is responding to instrument recalibration

global

(AQUA)

Example 2 of variational bias correction: Bias in the observations

Before bias correction After bias correction

From Grody, Vinnikov, Goldberg, Sullivan, and Tarpley

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.

An example where the bias correction is acting properly – and for the good reason

global global

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]

Example 4 of variational bias correction: Bias in the forecast model

Before bias correction After bias correction

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.

An example where the bias correction is reacting – but for the wrong reason

tropics tropics

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

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

Key changes to the observing system

1979: Improved sounding from polar orbiters Winds from geostationary orbit More data from commercial aircraft Drifting buoys

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

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

Time coverage of in situ surface data

1989 2009

Average number of soundings per day: 1609

AB

C

D

EH

I

J

K

M

N

P

Q

UV

Radiosonde coverage for 1958

Ships maintaining fixed locations


AB

C

D

EH

I

J

K

M

N

P

Q

UV



Time coverage of Atmospheric Motion Vector (AMV) data1989 2009

Example of improved data coverage: Reprocessed Atmospheric Motion Vectors from Meteosat

Early 1980s Expanded Low-resolution Winds

Time coverage of radiance data

Comparatively fewer sources

Comparatively many more sources …

1989 2009

Observations available for ERA-Interim 4DVAR

Drifting buoysGPS radio occultation

Radiosondes (wind only)

Atmospheric motion vectors

RadiancesTOTALRadiosondes

Scatterometers

Surface pressure pseudo-observations from Australian Bureau of Meteorology (PAOB)

Aircraft

“Conventional” “Satellite”

Surface

Percentage of data assimilated in ERA-Interim

Sat Sat Sat SatConv Conv Conv Conv Conv

A large number of satellite data are still not assimilated – these represent as many yet untapped resources for future reanalyses of the present

Outline

I. Definition




• 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)

Atmospheric reanalysis: Starting points

• Three centres took initiative in mid 1990s– ERA-15 (1979 - 1993) from Europe – with significant funding from USA

– NASA/DAO (1980 - 1993) from USA

– NCEP/NCAR (1948 - present) from USA

• Second round followed– ERA-40 (1958 - 2001) from Europe – with significant funding from FP5

– JRA-25 (1979 - 2004) from Japan

– NCEP/DOE (1979 - present) from USA

• Now in third generation of comprehensive global reanalysis– ERA-Interim (1989 - present) from Europe

– JRA-50 (1958 - 2012) from Japan

– NASA/GMAO-MERRA (1979 - present) from USA

– NCEP-CFSRR (1979 - 2008) from USA

Atmospheric reanalysis: Global products

• 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

Atmospheric reanalysis: The user base

• Regional reanalysis and downscaling

• Long-term reanalysis using only surface-pressure observations

• Short-term reanalysis for chemical composition

Atmospheric reanalysis: Becoming more diverse

North American Regional Reanalysis Maximum gusts

26 December 1999

2m temperature6UTC, 1 January 1999

• 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

Atmospheric reanalysis: Some uses

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

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

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

Global water cycle

ERA-40: Excessive precipitation over tropical oceans, exacerbated by Pinatubo eruption

Pinatubo eruption

(“Atmospheric reservoir”)

Drifts in AMSU-A tropospheric radiance data

• 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?

Globally averaged bias estimates for AMSU-A radiances

AQUA

Anchoring data for the troposphere: Radiosondes

Global mean departures and data counts for radiosonde temperature data

NOAA-15

NOAA-16Warm bias relative to radiosondes

Data count

Anchoring data for the troposphere: Aircraft reports

Global mean departures and data counts for aircraft temperature data

NOAA-15

NOAA-16Unbiased relative to aircraft reports

Aircraft data biased?

Data count

Conclusions on the AMSU-A bias drifts

ERA-Interim ERA-40 JRA-25 NCEP

Global mean temperature anomaliesDrift 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 needbias correction

Validation: Fit to radiosonde wind observations

Updated by Simmons, from Simmons & Hollingsworth (2002)

%

Validation: Forecast skill

Operations

ERA-Interim

Anomaly Correlation (%) for the 500 hPa Geopotential

Height

100% = perfect forecast

60% = limit of use

%

Validation: Fit to 2m land temperature anomalies (K)

ERA sampled as CRUTEM3 (Brohan et al., 2006) following Simmons et al. (2004)

Differences of monthly values from CRUTEM3

CRUTEM3

K

Validation: Fit to temperature profiles from radiosondes

analysis departures background departures

Fit to temperature observations for ERA-Interim and ERA-40

Based on January 2000 radiosonde temperature reports north of 70N

Progress in time consistency: analysis increments

Zonal mean temperature analysis increments for August 2001

ERA-Interim ERA-40

Outline

I. Definition




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

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

Further reading and on-line material

• European reanalysis (ERA):http://www.ecmwf.int/research/era

• Uppala et al. (2005), “The ERA-40 reanalysis”, Q. J. R. Meteorol. Soc., 131 (612), 2961-3012, doi:10.1256/qj.04.176

• NCEP/NCAR reanalysis:http://www.cdc.noaa.gov/data/reanalysis/reanalysis.shtml

• SciDAC Review (2008), “Bridging the gap between weather and climate”, on the web at http://www.scidacreview.org/0801/pdf/climate.pdf with contributions from G. P. Compo and J. S. Whitaker

• Japanese 25-year reanalysis (JRA-25):http://jra.kishou.go.jp

• NASA GMAO Modern Era Retrospective-analysis for Research and Applications (MERRA) http://gmao.gsfc.nasa.gov/research/merra/

• Bengtsson et al. (2007), “The need for a dynamical climate reanalysis”, Bull. Am. Meteor. Soc., 88 (4), 495-501

http://www.ecmwf.int/research/era

http://www.cdc.noaa.gov/data/reanalysis/reanalysis.shtml

http://www.scidacreview.org/0801/pdf/climate.pdf

http://jra.kishou.go.jp/

http://gmao.gsfc.nasa.gov/research/merra/

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 Serverhttp://data-portal.ecmwf.int/data/d/interim_daily/

1 reanalysis: data assimilation aspects paul poli [email protected] ecmwf meteorological training...

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