ECMWF 20th Century Reanalysis Using Surface -Only Observations - PowerPoint PPT Presentation

Specification of Background Errors in the ECMWF 20th Century Reanalysis Using Surface -Only Observations (ERA-20C) World rld Map ap, , A.Orteli rtelius us , ci circa a 1570 World map of weather uncertainty in 1900, ERA-20C, circa 2013 Paul Poli, Dick Dee, David Tan, Hans Hersbach, Elias Hólm, Massimo Bonavita, Lars Isaksen, and Mike Fisher 2013 WMO Symposium on Data Assimilation; Poli et al. 1

Reanalysis: Dealing with an uncertain past [Pa] Surface pressure at Montreal, Quebec Observations from ISPD 3.2.6, collection #3004 (Canadian Stations Environment Canada ) 2013 WMO Symposium on Data Assimilation; Poli et al. 2

ERA-20C system  10-member Ensemble of Data Assimilation  24-hour window 4DVAR  ICOADS 2.5.1 and ISPD 3.2.6 provided observations: surface pressures and marine surface winds (a few of those observations were recovered with funding from ERA-CLIM)  Variational bias correction of surface pressure obs.  Model: IFS CY38R1, T159L91 or approx. 125 km hor. res. atmosphere, land, and waves  Forcings: HadISST2 ensemble, otherwise as CMIP-5. Used also to force a model-only integration (ERA-20CM)  All outputs: 3-hourly, 75 surface fields, 14 fields / model levels (91) and selected pressure levels (37))  Archive: 700 Tb of fields and 10 Tb of observation feedback  Data to be copied to http://apps.ecmwf.int/datasets/ 2013 WMO Symposium on Data Assimilation; Poli et al. 3

ERA analysis window configurations ERA-40: 6-hour 3D-Var ERA-Interim: 12-hour 4D-Var ERA-20C: 24-hour 4D-Var 2013 WMO Symposium on Data Assimilation; Poli et al. 4

More observations Smaller ensemble spread Surface pressure 1 o x1 o data count Ensemble spread [hPa] 1900 Marine winds 1 o x1 o data count Ensemble spread [m/s] 1960 2013 WMO Symposium on Data Assimilation; Poli et al. 5

How ERA-20C estimates and uses background errors  Every ry 10 days From past 90-day differences between 3-hr forecasts ensemble members - System generates global background error covariances - For each control variable -  Every ry da day The analysis uses these covariances - Modulating locally the vorticity variance by the local ensemble spread - (after some rescaling, although this scaling is constant and very close to unity in ERA-20C) There is no time-varying, manual adjustment -  Bottom ttom line: e: Only y observati rvation on errors rs are manua uall lly y specifie fied The model stochastic physics (model-specified) and - The several SST ensemble members (data provider-specified) - Are supposed to represent all the other sources of uncertainties * - * Terms and conditions apply. See full report for details. 2013 WMO Symposium on Data Assimilation; Poli et al. 6

Checking the error assumptions & spread-skill Assumed med Actual ual measure ure of of skill ll Fr From om the ensemble mble spread d and d lo local l da daily ly mod odula lation tion 2013 WMO Symposium on Data Assimilation; Poli et al. 7

More observations Smaller background errors Sharper horizontal correlations Changing vertical correlations Vorticity bkg. error std. dev. Vorticity horizontal correlation January July Correlation NWP: satellites, radiosondes , aircraft,… (for comparison) Cross-correlation Single observation analysis between increment gradually smaller, temperature and Ps affecting smaller areas / sigma_Ps ERA-20C system adapts itself to * sigma_T the information available Vertical cross-correlations between Ps and Temperature evolve also. …Haven’t yet made sense of these… 2013 WMO Symposium on Data Assimilation; Poli et al. 8

Model bias exposed … by the vertical correlations Temperature Analysis Increments Temperature Anomalies 1979 2007 1979 2007 2013 WMO Symposium on Data Assimilation; Poli et al. 9

2013 WMO Symposium on Data Assimilation; Poli et al. 10

Conclusions about ERA-20C & Outlook  Inno novati ative ve componen onents ts 24 24-ho hour ur 4DVAR analys ysis is - Self-upd updatin ting g backgro ground und error r global bal covarianc iances es, , with h local, l, - cycling ing adjustmen ustment t of varianc ances es =Testbed bed for NWP developments opments Varia iati tion onal al bias corre recti tion on of surfac ace e pressure sure observatio rvations -  Ense semb mble le produc ucti tion on essentia ntiall lly y comple lete te ~700Tb b dataset t produc duced ed in ~200 days - GOOD OD: : Seems to repre resent sent fairl rly known wn extreme eme events, s, provided vided - they were observe rved d (in spite e of low hori rizon zontal tal resolution)… very likely ely thanks ks to the ensemb mble, le, flow- and time-depe depende ndent nt background ground error ors, s, and 24-hou our r 4DVAR BAD: Trends ds are contami amina nated ted by systematic matic analysis sis increments ements - We think nk we need to be more re aggress ssive ive with h the bias correc ection, tion, to - present the analysis with unbiased departures (… à la 20CR) If this is confir firmed, d, we would ld redo a singl gle member ber reanaly alysi sis, , - prob obabl ably y with a 100- day “blitz run”, fixing also a few other problems 2013 WMO Symposium on Data Assimilation; Poli et al. 11

Thank you for your attention -- for more details: ERA Report 14 available from the ECMWF website >> Publications >> ERA Reports >> ERA Report Series http://www.ecmwf.int/publications/library/do/references/show?id=90833 Published at the same time as the production completed. 2013 WMO Symposium on Data Assimilation; Poli et al. 12

ERA-20C: 1899-2009 in 200* days * approximately http://www.wikipedia.org Authors: Alvin Lee and Elmor 1914 1930 1942 1989 2012  Speed: between ~30-40 (2 nodes) and 60-80 (4 nodes) days/day/stream. Still missing Oct 2009-2010  During production 3.5 Tb/day, 350 million of meteorological fields, 2000 24-h 4DVAR assimilations run daily  A failure rate as low as 0.1% would have implied 2 manual interventions per day.  Home-grown solution to automatically detect model explosion , stop production, halve the model time- step, etc… 2013 WMO Symposium on Data Assimilation; Poli et al. 13

Forecast scores • N. Hem. extratropics: 1 day of forecast gain • S. Hem. extratropics: 1.5 day of forecast gain • Tropics: brings 12h forecast skill above 60% 2013 WMO Symposium on Data Assimilation; Poli et al. 14

Fit to observations Before assimilation Southern mid-lat. Northern mid-lat. After assimilation 2013 WMO Symposium on Data Assimilation; Poli et al. 15

Differences at stream boundaries 2013 WMO Symposium on Data Assimilation; Poli et al. 16