The Data Assimilation System in the ERA-20C Reanalysis

THE DATA ASSIMILATION SYSTEM IN THE

ERA-20C REANALYSIS

ERA-20C: ERA-CLIM pilot reanalysis of the 20th-century using surface observations only

Paul Poli, Hans Hersbach, David Tan, Dick Dee, Carole Peubey, Yannick Trémolet, Elias Holm, Massimo

Bonavita, Lars Isaksen, and Mike Fisher

Outline• Expectations and challenges• ERA-20C system overview• Assimilation method• Evolution of background errors• Post-assimilation diagnostics• Issues• Case studies (1899, 1987)• Conclusions

Poli, ERA-20C Data Assimilation System, EMS 2013

3

How good are forecasts issued from analyses of Ps only?

Day 6 >~ day 3 Day 6 ~ day 3

Day 6 fc error

Day 3 fc error

[K] [K]

[K] [K]


4

Challenge for any climate dataset based on observations: changing observing system

Surface pressure


5

Challenge for any climate dataset based on observations:

changing observing system (cont.)Wind above ocean surface


6

ERA-20C system overview• Resolution as in ERA-20CM, except archive 3-hourly

– 75 surface fields– 14 fields for each of the 91 model levels– 16 fields (+PV, +RH) for each of the 37 pressure levels

• Forcings: as in ERA-20CM• Surface observations assimilated

– Surface pressure from ISPD 3.2.6– Surface pressure and near-surface wind from ICOADS 2.5.1, ocean only

• 4DVAR analysis– Outer loop (short forecasts) at T159 or 125 km– Inner loop (analysis increments) at T95 or 210 km– 24-hour window

• 10 realizations or members, including a control• 6 production streams


7

ERA-20C production streams

• Speed: ~30-40 days/day/stream. Completed in ~200 days. Missing Oct 2009-Dec 2010• During production:

– 3.5 Tb/day, 350 million of meteorological fields.– 2000 4DVAR assimilations daily

• A failure rate as low as 0.1% would imply already 2 manual interventions per day. Home-grown solution to automatically detect model explosion, stop production, halve the model time-step, set the date back, resume production, record the problem, and resume to normal time-step once problematic date is recovered


8

Constructing a history of the pastwith (24-hour) 4DVAR data assimilation

[Pa]

Surface pressure at Montreal, QuebecObservations from ISPD 3.2.6, collection #3004 (Canadian Stations Environment Canada )


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Ensemble of 4DVAR data assimilations:Discretization of the PDF of uncertainties

Surface pressure at Montreal, QuebecObservations from ISPD 3.2.6, collection #3004 (Canadian Stations Environment Canada )

Background forecast, with uncertainties in the model and its forcings (HadISST2.1.0.0 ensemble)

Observationswith uncertainties(some could not be fitted – they are VARQC rejected)

Analysis, with uncertainties

Benefits:1. Estimate automatically our background errors, and update them

2. Provide users with uncertainties estimates (not perfect, but better than … nothing)

Forcing uncertainties

Model uncertaintie

s

Observation uncertaintie

sReanalysis

uncertainties


10

ERA analysis window configurations

ERA-40

ERA-Interim

ERA-20C


11

Observation diversity in ERA-20C

Surface pressure

Wind components


12

1-year ensemble spread, throughout the century+3 h +27h

1900

1960

2000

[hPa]


13

From the ensemble spread, one can estimate background error variances

Estimate of bkg. error stdev. for vorticity at model level 89, for the year 1900

[s**-1]


14

Evolution of background error (std. dev.) Zonal wind near the surface

1900

1960 2000

[m/s]


15

Self-updating background error covariances, throughout the century (updated every 10 days,

based on past 90 days)

Over the course of the century, more observations result in…Smaller background errors, with sharper horizontal structuresAnalysis increments that are smaller, over smaller areas= ERA-20C system adapts itself to the information available

With satellites, radiosondes,… (for comparison)


16

Impact of using our own background errors, instead of those derived for NWP

• N. Hem. extratropics: 1 day of forecast gain• S. Hem. extratropics: 1.5 day of forecast gain• Tropics: brings 12h forecast skill above 60%


17

Background errors: stored also in the observation feedback

Ortelius World map,circa 1570

ERA-20C 1900 weather world map of uncertainty, circa 2013

[hPa]


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Fit to assimilated observations

Southern mid-lat.

Northern mid-lat.

Before assimilation

After assimilation


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Assimilation error assumptions: budget closure

Assumed Actual

Showing only observations in the first 90 minutes of the 24-h window


20

What about error growth within the 24-hour window?

<+1h +23h

19001920

1940

196019802000

[hPa] [hPa]RMS (O-B) RMS (O-A)

+12h <+1h +23h+12h


21

Estimated (and used) pressure observation error biases


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Mean differences between consecutive streams


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Upper-air temperatures

1979 2007 1979 2007

Anomalies (1979-2008)

Analysis increments


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Issues

• Model time-step– On the long (cheap) side, 1 hour instead of 30

minutes (would have doubled the cost of the run)• Observation quality control– Too loose, let a few bad observations in

• Analysis increments far away from observations– Systematic and changing upper-air analysis

increments, causing spurious signal interfering with trends


25

AnalysesForecasts, from 96 hours ahead to 12 hours ahead

Great Storm 16 October 1987, 00 UTC

NWP

ERA-15

ERA-40

ERA-Int

ERA-20C

“It was the worst storm since 1703 and was analysed as being a one in 200 year storm for southern Britain” (Met Office)


26

U.S. East Coast Great Blizzard February 1899

• One of the most intense blizzards in US history

• Subject of earlier research, e.g. Kocin, Paul J., Alan D. Weiss, Joseph J. Wagner, 1988: The Great Arctic Outbreak and East Coast Blizzard of February 1899. Wea. Forecasting, 3, 305–318.

• Maps used for such studies usually based on measurements over the continental US and Canada

• Results from ERA-20C show global picture, with a wave-2 planetary pattern

• Embedded in this system, an extraordinary powerful low, nearly stationary, battered the Atlantic for several days


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Comparison of surface pressure reanalyses for 1-15 February 1899

ERA-20C NOAA/CIRES 20CR


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11 February

1899

Kocin et al., WAF 1987

ERA-20C NOAA/CIRES 20CR


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Application of ERA-20C for comparing with independent observational data records:

e.g. temperatures from ships

• Temperatures from ships biased warm during day-time (measurements contaminated by the ship structures, heated by sun)

• Some data problems in 1980?

Can be traced to 3 individual collections from the feedback archive


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Conclusions• Innovative components in ERA-20C DAS

– Ensemble of SST conditions (HadISST2.1.0.0)

– Variational bias correction of surface pressure observations– 24-hour 4DVAR– Self-updating background error global covariances from ensemble, and cycling local

variances• ERA-20C ensemble production essentially done (missing last few months). A

~700Tb meteorological dataset produced in ~200 days.• Trends are contaminated by systematic analysis increments• Preliminary assessment suggests some capacity at representing interesting

known extreme events, provided they were observed, in spite of low horizontal resolution, very likely thanks to the ensemble, flow- and time-dependent background errors, and 24-hour 4DVAR

• The automatic/self-update of the background errors approach developed and tested in ERA-20C is expected to be extended to ECMWF NWP operations soon


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For more details…

ERA Report 14available from the ECMWF websitehttp://www.ecmwf.int/>> Publications >> ERA Reports >> ERA Report Series

http://www.ecmwf.int/

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The Data Assimilation System in the ERA-20C Reanalysis

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