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FOCRAII-9, Beijing 10 th April 2013 © ECMWF ECMWF long range forecast systems Dr. Tim Stockdale European Centre for Medium-Range Weather Forecasts
FOCRAII-9, Beijing 10 th April 2013 © ECMWF Outline Overview of System 4 Some recent research results EUROSIP multi-model forecasts Forecasts for JJA 2013
FOCRAII-9, Beijing 10 th April 2013 © ECMWF System 4 seasonal forecast model IFS (atmosphere) T L 255L91 Cy36r4, 0.7 deg grid for physics (operational in Dec 2010) Full stratosphere, enhanced stratospheric physics Singular vectors from EPS system to perturb atmosphere initial conditions Ocean currents coupled to atmosphere boundary layer calculations NEMO (ocean) Global ocean model, 1x1 resolution, 0.3 meridional near equator NEMOVAR (3D-Var) analyses, newly developed. Coupling Fully coupled, no flux adjustments Sea-ice based on sampling previous five years
FOCRAII-9, Beijing 10 th April 2013 © ECMWF Reduced mean state errors S4 S3 T850U50
FOCRAII-9, Beijing 10 th April 2013 © ECMWF Tropospheric scores Spatially averaged grid-point temporal ACC One month leadFour month lead
FOCRAII-9, Beijing 10 th April 2013 © ECMWF S4 extended hindcast set Scores are smoother and systematically higher with 51 member hindcasts
FOCRAII-9, Beijing 10 th April 2013 © ECMWF S4 extended hindcast set Gain over S3 is now stronger and more robust
FOCRAII-9, Beijing 10 th April 2013 © ECMWF More recent ENSO forecasts are better.... 1981-19951996-2010
FOCRAII-9, Beijing 10 th April 2013 © ECMWF QBO 50hPa 30hPa System 3 System 4
FOCRAII-9, Beijing 10 th April 2013 © ECMWF Problematic ozone analyses
FOCRAII-9, Beijing 10 th April 2013 © ECMWF Land surface Snow depth limits, 1 st April
FOCRAII-9, Beijing 10 th April 2013 © ECMWF Sea ice
FOCRAII-9, Beijing 10 th April 2013 © ECMWF Tropical storm forecasts
FOCRAII-9, Beijing 10 th April 2013 © ECMWF Recent Research
FOCRAII-9, Beijing 10 th April 2013 © ECMWF QBO Period and downward penetration match observations Semi-annual oscillation still poorly represented A big reduction in vertical diffusion, and a further tuning of non-orographic GWD, has given a big additional improvement in the QBO compared to S4.
FOCRAII-9, Beijing 10 th April 2013 © ECMWF QBO forecasts S3 S4 New
FOCRAII-9, Beijing 10 th April 2013 © ECMWF NH winter forecasts 0.319 0.371
FOCRAII-9, Beijing 10 th April 2013 © ECMWF NH winter forecasts Even with 101 members, ensemble mean signal not always well defined
FOCRAII-9, Beijing 10 th April 2013 © ECMWF NH winter forecasts New version has weaker signal, more noise
FOCRAII-9, Beijing 10 th April 2013 © ECMWF NH winter forecasts Forecast skill is above perfect model predictability limit
FOCRAII-9, Beijing 10 th April 2013 © ECMWF EUROSIP A European multi-model seasonal forecast system Operational since 2005 Data archive and real-time forecast products Initial partners: ECMWF, Met Office, Météo-France NCEP an Associate Partner; forecasts included since 2012 Products released at 12Z on the 15 th of each month Aim is a high quality operational system Data policy issues are always a factor in Europe
FOCRAII-9, Beijing 10 th April 2013 © ECMWF Recent changes: variance scaling Robust implementation Limit to maximum scaling (1.4) Weakened upscaling for very large anomalies Improves every individual model Improves consistency between models Improves accuracy of multi-model ensemble mean
FOCRAII-9, Beijing 10 th April 2013 © ECMWF Revised Nino plumes
FOCRAII-9, Beijing 10 th April 2013 © ECMWF Error vs spread (uncalibrated)
FOCRAII-9, Beijing 10 th April 2013 © ECMWF Calibrated p.d.f. ENSO forecasts have good past performance data We can calibrate forecast spread based on past performance We can also allow varying weights for models We have to be very careful not to overfit data at any point. Represent forecast with a p.d.f. This is the natural output of our calibration procedure Easier visual interpretation by user Calibration and combination in general case Ideally apply similar techniques to all forecast values (T2m maps etc) More difficult because less information on past (higher noise levels) Hope to get there eventually …...
FOCRAII-9, Beijing 10 th April 2013 © ECMWF Nino 3.4 plume and p.d.f.
FOCRAII-9, Beijing 10 th April 2013 © ECMWF P.d.f. interpretation P.d.f. based on past errors The risk of a real-time forecast having a new category of error is not accounted for. E.g. Tambora volcanic eruption. We plot 2% and 98%ile. Would not go beyond this in tails. Risk of change in bias in real-time forecast relative to re-forecast. Bayesian p.d.f. Explicitly models uncertainty coming from errors in forecasting system Two different systems will calculate different pdf’s – both are correct Validation Rank histograms show pdf’s are remarkably accurate (cross-validated) Verifying different periods shows relative bias of different periods can distort pdf – sampling issue in our validation data.
FOCRAII-9, Beijing 10 th April 2013 © ECMWF Forecasts for JJA 2013
FOCRAII-9, Beijing 10 th April 2013 © ECMWF ECMWF forecast: ENSO Past performance
FOCRAII-9, Beijing 10 th April 2013 © ECMWF EUROSIP forecast: ENSO Past performance
FOCRAII-9, Beijing 10 th April 2013 © ECMWF ECMWF forecast: JJA 2mT Tercile probabilities ACC skill (1981-2010)
FOCRAII-9, Beijing 10 th April 2013 © ECMWF ECMWF forecast: JJA precip Tercile probabilities ACC skill (1981-2010)
LRF Training, Belgrade 13 th - 16 th November 2013 © ECMWF Sources of predictability and error in ECMWF long range forecasts Tim Stockdale European Centre.
WCRP OSC 2011: Strategies for improving seasonal prediction © ECMWF Strategies for improving seasonal prediction Tim Stockdale, Franco Molteni, Magdalena.
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© Crown copyright Met Office Andrew Colman presentation to EuroBrisa Workshop July Met Office combined statistical and dynamical forecasts for.
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Page 1© Crown copyright 2004 Seasonal forecasting activities at the Met Office Long-range Forecasting Group, Hadley Centre Presenter: Richard Graham ECMWF.
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Available products for Seasonal forecasting J.P. Céron – Direction de la Climatologie.
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The Potential for Skill across the range of the Seamless-Weather Climate Prediction Problem Brian Hoskins Grantham Institute for Climate Change, Imperial.
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Numerical Weather Prediction Models Prepared by C. Tubbs, P. Davies, Met Office UK Revised, delivered by P. Chen, WMO Secretariat SWFDP-Eastern Africa.
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Figures from “The ECMWF Ensemble Prediction System”
Use and Interpretation of ECMWF Products. The behaviour of the atmosphere is governed by a set of physical laws which express how the air moves, the process.
© Crown copyright Met Office NEMOVAR status and plans Matt Martin, Dan Lea, Jennie Waters, James While, Isabelle Mirouze NEMOVAR SG, ECMWF, Jan 2012.
MINERVA workshop, GMU, Sep MINERVA and the ECMWF coupled ensemble systems Franco Molteni, Frederic Vitart European Centre for Medium-Range.
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GOVST III, Paris Nov 2011 ECMWF ECMWF Activities on Coupled Forecasting Systems Status Ongoing research Needs for MJO Bulk formula in ocean models Plans.
© Crown copyright Met Office Decadal Climate Prediction Doug Smith, Nick Dunstone, Rosie Eade, Leon Hermanson, Adam Scaife.
The Seasonal Forecast System
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