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Work Package 4 „Workshop on Observation errors “ Vienna

The "Workshop on Observation Errors" held on April 19-20, 2012, in Vienna aimed to optimize the use of observational data in reanalysis. It focused on developing data monitoring tools for uncertainty assessment, consistent error bounds for data input, and methods for detecting inconsistencies among input datasets through bias correction techniques. Contributors from ECMWF, UKMET, UBERN, EUMST, UNIVIE, RIHMI, and FFCUL discussed effective approaches for bias estimation and correction, emphasizing the importance of quality control and data homogenization in improving the reliability of reanalysis products.

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Work Package 4 „Workshop on Observation errors “ Vienna

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  1. Work Package 4 „Workshop on Observation errors“Vienna Leopold Haimberger Overview 19 April 2012

  2. WP4 Objectives • Ensure optimal use of observations in reanalysis • Provide data monitoring tools to support uncertainty assessments • Detect inconsistencies in and among input datasets employing departure statistics from (pilot) reanalyses • Reduce uncertainties in reanalysis through bias correction of input • Explore opportunities for variational bias correction of selected in-situ observation types • Improvement of ocean observations for reanalysis • Provide consistent and meaningful error bounds • for all data input to reanalysis • employing departure statistics

  3. WP4 contributors • ECMWF: OFA interface, (variational) biasestimation • UKMET: Oceanobservations, earlysatellites • UBERN: Upperairdatabiascorrection • EUMST: Satellitedataerrorestimates • UNIVIE: Offline/online upperairdatabiasestimation, WP4 lead • RIHMI: Homogenizationof time series • FFCUL: Pressuredatabiascorrection

  4. WP4 Workshop in Vienna • Discuss uncertainty estimation methods • Methods for quantifying observation errors • Bias estimation and correction of observations • Impact of observation errors on reanalysis • Impact of uncertainties of surface boundary conditions on reanalysis • Methods for estimating and reducing the uncertainty of reanalysis products • Choose quantities to be stored in observation archive, specify an archive plan for uncertainty estimates • 19-20 April 2012

  5. In situ upperairbiascorrectionactivitiesat Univ. Vienna Leopold Haimberger, Marco Milan, Lorenzo Ramella-Pralungo, Christina Tavolato 19 April 2012

  6. Outline • Mainly on T adjustmentsfrom 1958 onwards • RAOBCORE/RICH homogenizationsystem • Somediagnosticsoftheadjustmentsystem • Breaksize estimation • Adjustmentensembles, sensitivityexperiments • Comparisonwithsatellitedata • Other parametersthan T • Back toearly 20th century ->Lorenzo R. • Variationalbiascorrectionofradiosondes • Wind direction -> Christina T. • Temperature -> Marco M.

  7. Observation Feedback • Background (y-Hxb) andanalysisdeparturestatisticsfrompilotassimilationsandreanalyses • Credo: Departurestatisticshave high potential for QC/BC • So farweuseddeparturestatisticsfrom • ERA-Interim (ODB files) • ERA-40 (BUFR files) • IGRA (bgcalculated „offline“ frominterpolatedgriddedbgfields) • CHUAN v1 (bgcalculated offline, z-level wind datainterpolatedto p-levels) • Departurestatisticsfrom 20th Century Reanalyses v2 • Obs fromabovearchives • Departuresfrominterpolatedensemblemeananalysis(sinceanalysisindependentof RS data)

  8. 1969-72 obs-bgat 100 hPa >3K

  9. Trends 1964-1984

  10. Obs-bg(merged ERA-40/ERA-Interim) RecentShifts Annual cycleofbias Shifts

  11. RAOBCORE break detection

  12. Homogenisationmethods • RAOBCORE „Radiosonde Observation CorrectionusingReanalysis“ • Detectsinhomogeneities in observationrecordsfromy-H(x) (obs-bg) time series • Obs-bg time series also usedforobsadjustment • 1100 Stations, back to 1958 • RICH „Radiosonde Innovation Composite Homogenization“ • Relies on breakpointsdetectedby RAOBCORE but usesneighboringrecordsforadjustmentestimation • RICH-obscompares 10-30 neighboringobsrecords • RICH-t compares 10-30 neighboringbg-obsrecords

  13. Choice ofsegmentsforbreaksizeestimation in RICH

  14. A breaksizeestimationexample Bethel, Alaska, 198906 1st Iteration (9 neighbors) 2nd Iteration (30 neighbors)

  15. Standard errorof sample means RAOBCORE: Meansof Background departures, RICH-obs: Meansofdifferencebetweentwostationrecords RICH-tau: Meansofdifferencebetweenbg-obsoftwostations Stddev (K)

  16. Standard errorof sample means, depending on remoteness

  17. Differencebetween RAOBCORE and RICH breaksizeestimates • This differenceis larger thanestimatesabove, • likely due toundetectedbiases in thereferenceseries • due totoolittledatafor RICH mainlyat high altitudes

  18. RAOBCORE adjustedtrends 1964-1984

  19. RICH-tau adjustedtrends 1964-84

  20. RICH-obsadjustedtrends 1964-1984

  21. Station climatology adjustment Mean T of neighbouring homogenized „reliable“ series actual (bg-obs) T bg Mean T of most recent part of tested series Expected (bg-obs) (bg-obs)i obsi Bg used for interpolation x,y

  22. Obs-bg after adjustment

  23. Can webelieve in theadjustedvalues? • Improvedspatiotemporalconsistencyisonlynecessary but not sufficientfor temporal homogeneity. • How sensitive areresultstovariationofuncertainparameters in theadjustmentsystem? • Are adjustedseriesconsistentwithsimilardatasets?

  24. Parameters oftheadjustmentsystem • Break detectionefficiency(Howmanybreaks) • Selectionofneighbors (Howmany) • Weightingofneighborswithdistance • Minimum numberofgoodvalues • RICH-obsor RICH-tau • Choice ofparameters, rangessubjective • Conditionof high spatiotemporalconsistencyoftrendslimitsspreadofmeantrends • See Haimberger et al. (2012, rev, JC) • Not yet a probabilisticapproach but a start

  25. Ensemble of RICH adjustments Haimberger et al. 2011, subm. to JC RICH-tau RICH-obs RAOBCORE ERA40/Interim an Unadjusted Spreadthroughvariationofparameters in RICH, e.g. numberofneighbors, weightingwithdistance, minimumrequiredgoodvaluesforadjustment

  26. Removalofsignalexperiments • Shiftsintroduced in all time series such thatclimatesignal (blueline) isreducedtozero • RAOBCORE/RICH canrecoverclimatesignal in thetropicsifbreakpointsareknown

  27. Tropical temperaturevariability Santer et al. (2005), Science RATPAC HadAT AR4 climatemodels Temperaturevariability Trends, 1=0.12K/10a

  28. Warming in Tropics (20S-20N)

  29. Lowertroposphericseries, Tropics Fullseries Black is HadCRUT3 Differenceseries

  30. Adjustmentof ERA-40 backgroundTransition from ERA-40 to ERA-Interim in 1979 coincideswith FGGE, Satelliteintroduction. MakesestimateofshiftdifficultUncertaintyDT ~0.1-0.5K

  31. Lowerstratosphericseries, Tropics Fullseries Differenceseries

  32. Hovmöllerplotunadjusted-adjusted RS, LS

  33. RICHobs-RAOBCORE

  34. RSS-RAOBCORE

  35. Upperair wind trends? Vautard et al. Nature 2010 850 hPa

  36. Wind speedbiases Obs-bg, USA/Canada composite

  37. To Do List • Temperatureadjustmentswithannualcycle • Adjustments back to pre-1958 forbothtemperatureand wind usingsurfacedataonlyreanalyses • Offline homogenizationof wind datatocomplementvariationalapproach • Adjustmentdatabaseforconventionalbiascorrection, breakpointdatabasetosupportvariationalbiascorrection

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