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Acknowledgements to:

Data Assimilation Progress and Plans at ECMWF Lars Isaksen Head of Data Assimilation Section, ECMWF lars.isaksen@ecmwf.int. Acknowledgements to:

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  1. Data Assimilation Progress and Plans at ECMWFLars IsaksenHead of Data Assimilation Section, ECMWFlars.isaksen@ecmwf.int Acknowledgements to: Massimo Bonavita, Elias Holm, Patricia de Rosnay, Joaquin Muñoz Sabater, Clément Albergel, Mike Fisher, Yannick Trémolet, Carla Cardinali, Deborah Salmond, Drasko Vasiljevic, Tomas Kral, and Marta Janiskova

  2. Ensemble of Data Assimilations (EDA) 4D-Var Data assimilation progress and plans at ECMWF Surface analyses Hybrid 4D-Var & EDA Scalability Future plans

  3. Inter-dependent analysis & forecasting system at ECMWF Ensemble Prediction System High-Resolution Forecasting Ensemble of Data Assimilations 4D-Var Data Assimilation

  4. ECMWF HPC systems • Until 2012 IBM Power6 (18400 cores) • Recently upgraded to IBM Power7 (48800 cores) • Operational Forecast and 4D-Var assimilation configuration • 10-day T1279L91 Forecast (16 km horizontal grid) • 12 hour 4D-Var T1279 outer loop T159/T255/T255 inner loop • Upgrade from 91 levels to 137 levels in June 2013 • Operational Ensemble of Data Assimilations (EDA) • 10 member 4D-Var T399 outer loop and T95/T159 inner loop Upgrade to 25 members in June 2013 • 50 member Ensemble Prediction System (ENS) at T639L62 • 15-day forecasts; 50 members; monthly forecasts twice weekly

  5. The increased forecast skill at ECMWF during the last 30 years is primarily due to data assimilation progress Improved forecasts are primarily due to improved analyses 2011 1980

  6. A few Characteristics about the ECMWF 4D-Var • All observations within a 12-hour period (~12,000,000) are used simultaneously in one global (iterative) estimation problem • “Observation – model” values are computed at the observation time at high resolution: 16 km • 4D-Var finds the 12-hour forecast that take account of the observations in a dynamically consistent way. • Based on a tangent linear and adjoint forecast models, used in the minimization process. • 80,000,000 model variables (surface pressure, temperature, wind, specific humidity and ozone) are adjusted 9Z 12Z 15Z 18Z 21Z

  7. Observations and the forecast model are very important parts of data assimilation! But the talk today will focus on data assimilation methods, scalability issues, and other challenges

  8. u-wind increments fc t+12, ~700 hPa • Non-linear finite difference (FD) • TL integration • Accuracy of Tangent-Linear and adjoint important: LINEARITY ISSUES • In the case of physical processes, strong non-linearities or thresholds can occur in the presence of discontinuous/non-differentiable processes

  9. u-wind increments fc t+12, ~700 hPa • Non-linear finite difference (FD) • TL integration • Accuracy of Tangent-Linear and adjoint important: LINEARITY ISSUES • Regularizations help to remove the most important threshold processes in physical parametrizations which can effect the range of validity of the tangent linear approximation

  10. Impact of the linearized physics processes in 4D-VAR • comparisons of 4D-Var against the version without linearized physics included: • positive impact on analysis and forecast • reducing precipitation spin-up problem N.HEM : 700 hPa rel. humidity N.HEM : 500 hPa geopotential Tropics : 700 hPa rel.humidity Anomaly correlation: grey bars indicate significance at 95% confidence level July – September 2011

  11. Background error specification: Ensures that the background model fields are adjusted meteorologically consistently Increments due to a single observation of geopotential height at 1000hPa at 60N with value 10m below the background.

  12. Background error specification: The Balance Operator ensures the height and wind field balance is retained wind increments at 300hPa wind increments 150 metre above surface Increments for a single observation of geopotential height at 1000hPa. Thanks to John Derber for developing this scheme during a 1 year stay at ECMWF in 1994

  13. ECMWF has got an advanced static background error formulation that has been gradually improved over the last 20 years.But it has only a weakly flow-dependent error specification Analysis increments with omega balance, non-linear balance, wavelet formulation Isotropic analysis increments Flow-dependency is now being provided by an Ensemble of Data Assimilation

  14. How to introduce flow-dependent background errors In the ECMWF 4D-Var, the B matrix is defined implicitly in terms of a transformation from the background departure (x-xb) to a control variable χ: (x-xb) = Lχ So that the implied B=LLT. In the current wavelet formulation (Fisher, 2003), the variable transform can be written as: T is the balance operator Σb is the gridpoint variance of background errors Cj(λ,φ) is the vertical covariance matrix for wavelet index j ψj are the set of radial basis function that define the wavelet transform.

  15. How to introduce flow-dependent background errors Cj(λ,φ)are full vertical covariance matrices, function of (λ,φ). They determine both the horizontal and vertical background error correlation structures; In standard 4D-Var T and Cjare computed off-line using a climatology of EDA perturbations. Σb is computed by random sampling of the static B matrix (randomization procedure, Fisher and Courtier, 1995) How do we make this error covariance model flow-dependent? We look for flow-dependent EDA estimates of Σband Cj(λ,φ)

  16. Ensemble of data assimilations (EDA) 10 members; 2 inner-loop 4D-Var at T95/159; T399 outer loop; L91 Perturbed observations and SST; Stochastically perturbed physics

  17. The benefit of an Ensemble of Data Assimilations • In general to estimate analysis uncertainty • To improve the initial perturbations in the Ensemble Prediction (implemented June 2010) • To calculate static and seasonal background error statistics • To estimate flow-dependent background error variances in 4D-Var - “errors-of-the-day” (implemented May 2011) • To improve QC decisions and improve the use of observations in 4D-Var (implemented May 2011) • To update the static background-error covariance statistics based on the latest EDA (implemented June 2012) • To estimate flow-dependent background error variances for unbalanced variables (June 2013) • For online estimation of background error covariances (June 2013)

  18. How is the Ensemble of Data Assimilations (EDA) used to provide flow-dependent background error estimates • Run an ensemble of , say 10, 4D-Var analyses with perturbed observations, Sea Surface Temperature fields and model physics. • Form differences between pairs of analyses (and short-range forecast) fields. • These differences will have the statistical characteristics of analysis (and short-range forecast) error. Yellow shading where the short-range forecast is uncertain: give observations more weight in these regions.

  19. Sampling noise from the 10 member EDA needs to be filtered Raw Ensemble StDev Vorticity level 64 Filtered Ensemble StDev Vorticity level 64

  20. EDA needs to be calibrated to become statistically consistent • We performs an online calibration (Ensemble Variance Calibration; Kolczynsky et al., 2009, 2011; Bonavita et al., 2011) • Calibration factors depend on latitude bands and parameter • Calibration factors also depend on the size of the expected error

  21. Analysis Forecast Analysis Forecast SST+εiSST The 4D-VAR&EDA hybrid implementation at ECMWF EDA Cycle xb+εib i=1,2,…,10 xb xa xf+εif xb xa+εia y+εio EDA scaled Var Variance post-process Variance Recalibration Variance Filtering EDA scaled variances εifraw variances 4DVar Cycle

  22. In May 2011 ECMWF implemented EDA based flow-dependent background error variance in 4D-Var - our first hybrid DA system The 10-member EDA has been used to estimate the background error variance in the deterministic 4D-Var. This is the first step towards the implementation of a fully flow-dependent representation of background error covariance. EDA based background error variance for Surface pressure hPa Hurricane Fanele, 20 January 2009

  23. Impact of EDA based variances in hybrid 4D-Var Impact on high-resolution forecast skill Geopotential height normalized forecast error differences experiment-control 11 Jan – 30 Mar 2010

  24. To update the static background-error covariance statistics based on the latest EDA (implemented June 2012) Resolution upgrades and more observations since last update resulted in sharper structure functions: reduced correlation length scales both horizontally and vertically

  25. Static background-error covariance statistics (for Jb) updated, based on latest EDA (implemented June 2012) Impact on high-resolution forecast skill Vector wind normalized forecast error differences experiment-control 8 June – 7 July 2011

  26. Improved statistical noise filtering of EDA variances (implemented June 2012) • Based on two extended 50 member EDA experiments, we can apply a more direct strategy: • Under the assumption of sampling noise as a random process • Time average sampling noise spectrum samples • Compute raw filters and time average to smooth out noise

  27. Improved statistical noise filtering of EDA variances (implemented June 2012) Impact on high-resolution forecast skill Vector wind normalized forecast error differences experiment-control 10 January to 29 April 2011

  28. EDA-based flow-dependent background errors for unbalanced control vector variables (Tu,Du,LNSPu) - June 2013 Average unbalanced temperature (st.dev. in Kelvin) top surface 90 N 90 S

  29. Impact of EDA-based unbalanced control vector Reduction in Geopotential RMSE (95% confidence, RAOBs) SH NH 200 hPa 500 hPa 1000 hPa Based on 90 days in 2012; T511; CY38r1

  30. EDA based flow-dependent online update of background error covariances (B) – also planned for June 2013 • B is computed in a post-processing step of a 25 member EDA • EDA perturbations from the past 12 days are used, with an exponential decay factor • Updated B is used in HRES 4D-Var • EDA still uses static error variances and B: fully interactive (EnKF type) system will be tested next

  31. Impact of online B Reduction in Geopotential RMSE - 95% confidence NH SH 50 hPa Period: Feb - June 2012 T511L91, 3 Outer Loops (T159/T255/T255) Verified against operational analysis 100 hPa 200 hPa 500 hPa 1000 hPa

  32. Further 4D-Var related upgrades (planned for Dec 2013) • 24-hour 4D-Var with over-lapping window • Run twice daily, but using observations for the last 24 hours • Increase of inner loop resolution • Most likely from T159/T255/T255 to T255/T255/T399 • Increased and improved use of conventional observations • Retuning of observation errors for both conventional and satellite data

  33. Land Surface Analysis High quality surface and near surface weather products • - Snow depth analysis • New 2D Optimum Interpolation (OI) (operational) • Ground data (SYNOP and other NRT data) • High resolution NESDIS/IMS snow cover data • - Soil Moisture analysis • Extended Kalman Filter (EKF) (Operational) • Uses screen level parameters analysis • - Satellite data related to Soil Moisture • METOP-ASCAT and SMOS Monitoring operational • ASCAT data assimilation (operational late 2013) • - Validation activities (EUMETSAT H-SAF) NESDIS/IMS snow cover (16 Jan. 2012) ASCAT ECMWF January 2010 SMOS

  34. Use of SYNOP and National Network data SYNOP 06 UTC January 23 2013 National snow data National networks data: GTS: Sweden (>300), Romania(78), The Netherlands (33), Denmark (43), Finland (183) FTP: Hungary (61)

  35. ASCAT soil moisture product Advanced Scatterometer on MetOP A/B (launched in 2006/2012) Active microwave instruments operating at C-band ASCAT operational EUMETSAT soil moisture product Soil Moisture Monitoring Dec 2011-Jan 2012

  36. SMOS: Soil Moisture and Ocean Salinity • ESA Earth Explorer mission ; RD developments: Global assimilation of SMOS brightness temperatures in the ECMWF Simplified Extended Kalman Filter in research mode - Soil moisture from SMOS is expected operational in 2014/2015 - Future SMAP (NASA) in 2015 Sensitivity of TB to soil moisture (K/0.01 m3m-3) +250 -250 0 Averaged soil moisture product, June 2010 ( m3m-3) -250

  37. Aircraft temperature bias correction at ECMWF Based on the variational bias correction scheme developed at ECMWF Each aircraft is bias corrected individually using three predictors First predictor: constant temperature correction at cruise level Second/third predictors: function of the vertical aircraft velocity (dp/dt) to account for ascend/descend bias conditions It works: The aircraft departures biases and standard deviations are reduced; RAOB biases are reduced too Implemented in operations November 2011

  38. Improved o-b and o-a fit to aircraft temperature data Aircraft temperature bias correction Black lines with aircraft bias correction applied, red curves without aircraft bias correction EUCOS E-SAT

  39. Aircraft temperature bias correction Bias correction results in reduced temperature biases for RAOB data and for GPS-RO data

  40. Analysis equation: Xbbackground, Xaanalysis, yobservations K is Kalman gain matrix, H observation operator OI (Observation Influence) and DFS (Degrees of Freedom for Signal) The impact of observations to the analysis FSO (Forecast Sensitivity to Observations; FEC: Forecast Error Contribution) Contribution of observations to the reduction of (24h) forecast error Observation information content metrics used for wind observation impact evaluation

  41. Wind DFS and FSO per datum as function of altitude The wind information is most important for the analysis at 50-100 hPa, and for the forecast at 100-200 hPa

  42. Data Assimilation on future computer architectures Scalability is an important issue • How will the future computer architectures look? • Will we be able to use future parallel computers efficiently for Data Assimilation? • Can we modify our Data Assimilation methods to utilize future computer architectures better? • How scalable is ECMWF’s 4D-Var on todays computer architectures?

  43. ECMWF sustained historic computer performanceAn increase by a factor of 10,000,000 in 30 years Increase is primarily due to more cores (1 to 50000 in 30 years) Future increase in performance will almost certainly come from more cores 1979 2013

  44. Lars Isaksen Annual Seminar, ECMWF, 2011 Scalability of T1279 Forecast and 4D-Var Speed-up Operations 48 Nodes User Threads on IBM Power6

  45. Scalability of T1279 Forecast and 4D-Var Speed-up Operations 48 Nodes Traj_1 & Traj_2 Traj_0 User Threads on IBM Power6

  46. Scalability of T1279 Forecast and 4D-Var Speed-up FC model has 2000000 grid columns Operations 48 Nodes Min_1&2 have 89000 grid columns Min_0 has 36000 grid columns User Threads on IBM Power6

  47. ContinuousObservationProcessingEnvironment(COPE) • Shortens the time critical path by performing observation pre-processing and screening as data arrive • Improve scalability by removing most observation related tasks from time critical path • Reduce risk of failures in the analysis during the time critical path • Enables near real-time quality control and monitoring of observations • More modular software • A hub Observation Data Base (ODB) will be central to this approach

  48. Object-Oriented Prediction System – The OOPS project • Data Assimilation algorithms manipulate a limited number of entities (objects): • x (State), y (Observation), • H (Observation operator), M (Model), H*& M*(Adjoints), • B & R (Covariance matrices), etc. • To enable development of new data assimilation algorithms in IFS, these objects should be easily available & re-usable • More Scalable Data Assimilation • Cleaner, more Modular IFS

  49. OOPS  More Scalable Data Assimilation • One execution instead of many will reduce start-up - also I/O between steps will not be necessary • New more parallel minimisation schemes • - Saddle-point formulation • For long-window, weak-constraint 4D-Var: Minimization steps for different sub-windows can run in parallel as part of same execution with the saddle-point formulation

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