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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.

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The Data Assimilation System in the ERA-20C Reanalysis

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  1. 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, YannickTrémolet, Elias Holm, Massimo Bonavita, Lars Isaksen, and Mike Fisher

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

  3. How good are forecasts issued from analyses of Ps only? [K] [K] Day 6 fc error Day 3 fc error [K] [K] Day 6 ~day 3 Day 6 >~day 3 Poli, ERA-20C Data Assimilation System, EMS 2013

  4. Challenge for any climate dataset based on observations: changing observing systemSurface pressure Poli, ERA-20C Data Assimilation System, EMS 2013

  5. Challenge for any climate dataset based on observations: changing observing system (cont.)Wind above ocean surface Poli, ERA-20C Data Assimilation System, EMS 2013

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

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

  8. Constructing a history of the pastwith (24-hour) 4DVAR data assimilation [Pa] Surface pressure at Montreal, Quebec Observations from ISPD 3.2.6, collection #3004 (Canadian Stations Environment Canada ) Poli, ERA-20C Data Assimilation System, EMS 2013

  9. Ensemble of 4DVAR data assimilations:Discretization of thePDF of uncertainties Surface pressure at Montreal, Quebec Observations from ISPD 3.2.6, collection #3004 (Canadian Stations Environment Canada ) Observations with uncertainties (some could not be fitted – they are VARQC rejected) Analysis, with uncertainties Background forecast, with uncertainties in the model and its forcings (HadISST2.1.0.0 ensemble) Benefits: 1. Estimate automatically our background errors, and update them 2. Provide users with uncertainties estimates (not perfect, but better than … nothing) Poli, ERA-20C Data Assimilation System, EMS 2013

  10. ERA analysis window configurations ERA-40 ERA-Interim ERA-20C Poli, ERA-20C Data Assimilation System, EMS 2013

  11. Observation diversity in ERA-20C Surface pressure Wind components Poli, ERA-20C Data Assimilation System, EMS 2013

  12. 1-year ensemble spread, throughout the century [hPa] +3 h +27h 1900 1960 2000 Poli, ERA-20C Data Assimilation System, EMS 2013

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

  14. Evolution of background error (std. dev.) Zonal wind near the surface [m/s] 1900 1960 2000 Poli, ERA-20C Data Assimilation System, EMS 2013

  15. Self-updating background error covariances, throughout the century (updated every 10 days, based on past 90 days) With satellites, radiosondes,… (for comparison) • Over the course of the century, more observations result in… • Smaller background errors, with sharper horizontal structures • Analysis increments that are smaller, over smaller areas • = ERA-20C system adapts itself to the information available Poli, ERA-20C Data Assimilation System, EMS 2013

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

  17. Background errors: stored also in the observation feedback [hPa] Ortelius World map,circa 1570 ERA-20C 1900 weather world map of uncertainty, circa 2013 Poli, ERA-20C Data Assimilation System, EMS 2013

  18. Fit to assimilated observations Before assimilation Southern mid-lat. Northern mid-lat. After assimilation Poli, ERA-20C Data Assimilation System, EMS 2013

  19. Assimilation error assumptions: budget closure Assumed Actual Showing only observations in the first 90 minutes of the 24-h window Poli, ERA-20C Data Assimilation System, EMS 2013

  20. What about error growth within the 24-hour window? RMS (O-B) RMS (O-A) [hPa] [hPa] 1900 1920 1940 1960 1980 2000 <+1h +12h +23h <+1h +12h +23h Poli, ERA-20C Data Assimilation System, EMS 2013

  21. Estimated (and used) pressure observation error biases Poli, ERA-20C Data Assimilation System, EMS 2013

  22. Mean differences between consecutive streams Poli, ERA-20C Data Assimilation System, EMS 2013

  23. Upper-air temperatures Analysis increments Anomalies (1979-2008) 1979 2007 1979 2007 Poli, ERA-20C Data Assimilation System, EMS 2013

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

  25. Forecasts, from 96 hours ahead to 12 hours ahead Analyses Great Storm 16 October 1987, 00 UTC NWP “It was the worst storm since 1703 and was analysed as being a one in 200 year storm for southern Britain” (Met Office) ERA-15 ERA-40 ERA-Int ERA-20C Poli, ERA-20C Data Assimilation System, EMS 2013

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

  27. Comparison of surface pressure reanalyses for 1-15 February 1899 ERA-20C NOAA/CIRES 20CR Poli, ERA-20C Data Assimilation System, EMS 2013

  28. Kocin et al., WAF 1987 11 February 1899 ERA-20C NOAA/CIRES 20CR Poli, ERA-20C Data Assimilation System, EMS 2013

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

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

  31. For more details… ERA Report 14available from the ECMWF website http://www.ecmwf.int/ >> Publications >> ERA Reports >> ERA Report Series Poli, ERA-20C Data Assimilation System, EMS 2013

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