Overview of ECMWF, KMA, March 2013 © ECMWF Slide 1 Overview of research and developments at ECMWF Niels Bormann with contributions from Erland Källén, Stephen English, Massimo Bonavita, Lars Isaksen, Richard Forbes, Tony McNally and many others

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 2 ECMWF An independent intergovernmental organisation established in 1975 with 20 Member States 14 Co-operating States

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 3 How ECMWF was established Background 1967European Council of Ministers propose co-operation in science and technology 1969Expert group in meteorology propose ‘European Meteorological Computing Centre’ 1971Report on ‘EMCC’: Net benefit of £100m per annum at 1971 prices Establishment 1975ECMWF convention in force 1978Headquarters building completed 2010Amended convention come into force Start of operational activities 1978Installation of first computer system (CRAY 1-A) 1979Start of operations (N48 grid point model)

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 4 Who are we and what do we do? European CentreWe are an independent international organisation funded by 34 States Medium-RangeUp to fifteen days ahead. Today our products also include monthly and seasonal forecasts and we collect and store meteorological data. Weather ForecastsWe produce world-wide weather forecasts What do we have to achieve this? PeopleAround 260 staff, specialists and contractors EquipmentState-of-the-art supercomputers and data handling systems Experience37 years

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 5

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 6 COUNCIL 20 Member States Organisation of ECMWF DIRECTOR-GENERAL A. Thorpe (UK) Meteorological Division E. Andersson (Sweden) Meteorological Division E. Andersson (Sweden) Computer Division I. Weger (Austria) Computer Division I. Weger (Austria) Operations Deputy Director-General W. Zwieflhofer (Austria) Operations Deputy Director-General W. Zwieflhofer (Austria) Research E. Källén (Sweden) Research E. Källén (Sweden) Administration N. Farrell (Ireland) Administration N. Farrell (Ireland) Data Division J.-N. Thépaut (France) Data Division J.-N. Thépaut (France) Model Division P. Bauer (Germany) Model Division P. Bauer (Germany) Predictability Division R. Buizza (Italy) Predictability Division R. Buizza (Italy) Finance Committee 7 Members Finance Committee 7 Members Technical Advisory Committee 19 Members Technical Advisory Committee 19 Members Scientific Advisory Committee 12 Members Scientific Advisory Committee 12 Members Policy Advisory Committee 5–19 Members Policy Advisory Committee 5–19 Members Advisory Committee of Co-operating States 15 Members Advisory Committee of Co-operating States 15 Members Advisory Committee on Data Policy 5–34 Members Advisory Committee on Data Policy 5–34 Members Atmospheric Composition Division V.-H. Peuch (France) Atmospheric Composition Division V.-H. Peuch (France) (67)(48) (11) (30) (45)(30) (33)

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 7 ~210km ~125km ~63km ~39km ~25km ~16km Evolution of ECMWF forecast skill

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 8 Evolution of ECMWF forecast skill Optimal Interpolation 3DVAR 6 h 4DVAR 12 h 4DVAR Hybrid 4DVAR

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 9 ECMWF scores compared to other major global centres

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 10 Baseline operational system: March 2013 ● Four-dimensional variational data assimilation based on 16 / 80 km horizontal resolution and 91-level vertical resolution (4D-Var). ● 16 km 91-level model for single-valued (deterministic) forecast twice daily. ● Ensemble Prediction System: 51 members  With 32 km (62 levels) to 10 days twice per day  With 65 km (62 levels) coupled to an ocean model out to 15 days (32 days on Mondays and Thursdays at 00 UTC)  Coupled ocean model with horizontally varying resolution (110 km midlat / 33 km tropics) ● Seasonal Forecast System (once a month)  80 km horizontal (91 levels) to 7 months (12 months every quarter) with coupled ocean model (All forecasts are coupled to an ocean wave model)

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 11 Interdependent systems ENS High- Res EDA 4D-Var

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 12 ERA-20CMEnsemble of model integrations, using HadISST2 and CMIP5 forcing T members ERA-20CReanalysis of surface pressure observations T members Available mid 2013 ERA-20CLLand-surface only; forced by ERA-20C T members Available mid 2013 ERA-SATNew reanalysis of the satellite era T511 To replace ERA- Interim Available mid year collaborative research project coordinated by ECMWF, supported by the EC’s FP7: Prepare input observations, model data, and data assimilation systems for a global atmospheric reanalysis of the 20 th century – to begin production in 2014 ERA-Clim

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 13 MACC Daily Service Provision Air quality Global Pollution Aerosol UV index Biomass burning (Monitoring Atmospheric Composition and Climate)

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 14 Other Activities ● Training Courses –Numerical methods –Data assimilation & use of satellite data –Parametrization of diabatic processes –Predictability, diagnostics and long-range forecasting –Use and interpretation of ECMWF products –Computer user training courses ● Seminars –Research Seminars (annually) –Meteorological Operational Systems (biennial) –Large-scale Computing (biennial) ● Workshops

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 15 Supercomputers at ECMWF ● ECMWF has a long history of using High Performance Computing in NWP – : Cray (Cray-1A, XMP, YMP, C90, T3D) – : Fujitsu (VPP700, VPP700E, VPP5000) – today: IBM (Power4, Power5, Power6, Power7)

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 16 Highlights of recent operational implementations

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 17 Ensemble of data assimilation 10 members of 2 inner-loop 4D-Var’s at T159/255 L91, T399 outer lops Perturbations from observations, SST, SPPT; noise filtering, scaling

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 18 Ensemble of data assimilation Impact in analysis 9 May UTC analysis, TC Aere log(Pa) p [Pa] (M. Bonavita et al.)

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 19 Ensemble of data assimilation Flow-dependent background errors from EDA used in high-resolution 4DVAR. Impact on high-resolution forecast skill: Geopotential height normalized forecast error differences experiment-control 11 Jan – 30 Mar 2010 (M. Bonavita et al.)

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 20 Microwave sounder observation errors Comprehensive study on spatial and spectral observation error correlations for AMSU- A/B, MHS, AIRS, IASI, SSM/I, AMSR-E, TMI by Bormann et al. (QJRMS). Inter-channelSpatial

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 21 Microwave sounder observation errors Error estimates for AMSU-A Relative reduction in 500 hPa geopotential height forecast errors Forecast range [days] Reduce OE bad Reduce OE good

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 22 Aircraft data bias correction 200 hPa temperature bias (L. Isaksen et al.)

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 23 NPP CrIS infrared sounder ATMS microwave sounder CERES radiation budget OMPS ozone sounder VIIRS imager

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 24 NPP: ATMS AMSU-AATMS 22-channel microwave sounder combining AMSU-A (temperature) and MHS (humidity) heritage channels, with 3 new channels. Launched on Suomi-NPP 28 October Temperature sounding channels compared to AMSU-A:  Higher noise  Smaller footprint  Oversampled  Use 3x3 footprint averaging for temperature-sounding channels

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 25 NPP ATMS: Comparison to AMSU-A (for ATMS 3x3) (Departure statistics for data after QC, Dec 2011; global over sea)

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 26 NPP ATMS: Forecast impact ATMS bad ATMS good T511 experiments over two different seasons: 15 Dec 2011 – 6 Feb June 2012 – 31 August 2012 Assimilation of temperature and humidity channels over sea. Combined scores over two seasons (102 cases): N.Hem, Z 500 hPaS.Hem, Z 500 hPa

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 27 Satellite section Pre-SAC, Sept 2012 NPP ATMS: Striping ATMS, ch 11, Obs-FG N19 AMSU-A, ch 10, Obs-FG NWP systems are very powerful for detecting even small instrument anomalies.

Overview of ECMWF, KMA, March 2013 © ECMWF Slide NPP CrIS (T. McNally et al.)

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 29 New prognostic cloud microphysics scheme What has changed…..? New Cloud Scheme (operational from 9 th Nov 2010, Cy36r4 onwards) 2 prognostic cloud variables + vapour Parametrized sources and sinks Includes convective detrainment Ice/water a diagnostic fn(temperature) Diagnostic precipitation 5 prognostic cloud variables + water vapour Ice and water now independent Snow/rain now advected with the wind Snow impacts radiation scheme More physically based, greater realism Significant change to degrees of freedom Previous Cloud Scheme (Tiedtke scheme operational ) (R. Forbes)

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 30 Previous cloud scheme ice water path (IWP) (ice only) New cloud scheme ice + stratiform snow (IWP+SWP) New scheme total IWP agrees well with CloudSat dataset in extra-tropics Disagreement in tropics due to model convective snow not yet included…. CloudSat (IWP+SWP) (Frank Li, Duane Waliser, JPL) New cloud scheme – prognostic snow g m -2 (R. Forbes) Two thirds of the frozen water path is in precipitating snow particles (now seen by radiation scheme)

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 31 New cloud scheme - Precipitation Improved precipitation skill Precipitation supplementary headline SEEPS score improved (due to new cloud scheme and convection changes at Cy36r4) Precipitation fields smoother due to prognostic variables. Global (1-SEEPS) score (1 July – 9 November 2010) as a function of lead time for Cy36r4 (red) versus Cy36r2 (blue) (higher values = better skill) (R. Forbes, T. Haiden)

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 32 The most significant change in the new scheme is to the physical representation of supercooled liquid water and mixed-phase cloud. Previous scheme: diagnostic fn(T) split between ice and liquid cloud (a crude approximation of the wide range of values observed in reality). New scheme: parametrizes super-cooled liquid water production and ice deposition rate which can give a wide range of supercooled liquid water (SLW) for a given T. PDF of liquid water fraction of cloud for the diagnostic mixed phase scheme (dashed line) and the prognostic ice/liquid scheme (shading) New cloud scheme – mixed-phase cloud (R. Forbes)

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 33 Obs Cloud FractionObs Liquid Water ContentObs Ice Water Content New cloud scheme – mixed-phase cloud realism Arctic cloud case study (MPACE) – typical of SLW topped cloud with ice fallout Previous cloud scheme (36r3) – too little SLW throughout cloud IFS Cloud Fraction IFS Liquid Water ContentIFS Ice Water Content IFS Cloud Fraction IFS Liquid Water ContentIFS Ice Water Content New cloud scheme (revised 37r3+) – SLW at cloud top with ice fallout as obs (M. Ahlgrimm, R. Forbes)

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 34 Upcoming and future developments

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 35 The ECMWF Strategy 2011–2020

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 36 Deterministic system: upgrade from 91 to 137 levels Expected mid-2013

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 37 Global model resolution 10 km 2015

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 38 Data Assimilation Developments ● Variational assimilation (4DVar) – Increased window length and resolution ● Ensemble of Data Assimilations (EDA) –Increased ensemble size (10 → 25) ● Seamless EDA/EPS X0X0 X +12h 4DVar Δx2Δx2 Δx1Δx1 Δx3Δx3 Δx4Δx4 Ensemble initial perturbations for EPS → Flow dependent co- variances for 4DVar →

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 39 Results based on a two-layer quasi-geostrophic model indicate that increasing the length of the analysis window is beneficial, even with a simple model error representation. Long-window, weak-constraint 4D-Var (M. Fisher)

Overview of ECMWF, KMA, March 2013 © ECMWF Model Division: Resolution upgrades (2012: L137, 2015: T2047, etc.) Non-hydrostatic model core Physical parameterizations: Radiation, clouds, convection, land surface, boundary layer, gravity wave drag; linearized models Data Division: Long-window 4D-Var (model error), EDA, EnKF New instruments (e.g., GCOM-W1, Megha Tropiques, etc.), sampling, errors Reanalysis: ERA-Clim (coupling) Predictability Division: Resolution upgrades (2013: L92, 2016: T1023, etc.) Link EDA-EPS, stochastic physics Ocean/sea-ice model, coupling Atmospheric Composition Division: MACC-II → GMES Atmospheric Service/Copernicus Technical: Scalability (data assimilation, model) COPE, OOPS, OpenIFS Current research projects

Overview of ECMWF, KMA, March 2013 © ECMWF Slide 41 Longer-term future Ensemble Prediction System High- Resolution Forecasting Ensemble of Data Assimilations 4D-Var Data Assimilation A much more integrated system …

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Overview of ECMWF, KMA, March 2013 © ECMWF Slide 43 Interdependent systems ENS High- Res EDA 4D-Var