Page 1© Crown copyright 2004 ECMWF Forecast Products Users Meeting 15th June 2006

Page 2© Crown copyright 2004 New Development in Monthly Range prediction at the Met Office Bernd Becker, Paul M. James. Met Office monthly forecast suite Products from the Monthly Outlook Grosswetterlagen Analysis

Page 3© Crown copyright 2004 Monthly Forecasting System Coupled ocean-atmosphere integrations: a 51-member ensemble is integrated for 32 days every week. Atmospheric component: IFS with the latest operational cycle30r1 and with a T159L62 resolution (320 * 161) Oceanic component: HOPE (from Max Plank Institute) with a zonal resolution of 1.4 degrees and 29 vertical levels Coupling: OASIS (CERFACS). Coupling every ocean time step (1 hour) Perturbations: Atmosphere: Singular vectors + stochastic physics Ocean: SST perturbations in the initial conditions + wind stress perturbations during data assimilation. Hindcast statistics: 5-member ensemble integrated over 32 days during the past 12 years. Representing a 60-member ensemble. Running every week

Page 4© Crown copyright 2004 Q5 Q4 Q3 Q2 Q1 T Q1 Q2 Q3 Q4 Q5 Harvesting the Ensemble(1): Rank Ordering Ignore shape in the baseline Rank ordering the hind cast Slicing into equally large chunks Counting the forecast members in each category Warm and dry Cold and wet P

Page 5© Crown copyright 2004 Post processing 1. Data Volume reduction Derive properties of the PDF 2. Interpolation to 10 UK climate regions Down scaling 3. Calibration with historical data Bias correction 4. Interpretation of the histogram Deterministic quintile category 5. Mapping Deterministic value

Page 6© Crown copyright 2004 Example UK temperature forecast for 10 climate districts

Page 7© Crown copyright 2004 Holiday planner June/July 2006 Tmax Precipitation Week 1Week 2Week 3&4

Page 8© Crown copyright 2004 UK average Skill scores… CSI: critical success index Q: odds ratio, Yules Q. PSS: Peirce Skill Score HSS: Heidke Skill Score GSS: Gerrity skill Score BS: Brier Score BSS: Brier skill Score ROC: Area under the ROC curve..are derived from a 5 * 5 * 10 contingency table. Each cell records matching: T mean, days Observation / Forecast category and the probability with that the category was predicted Scores are calculated per category, figures in graph below are averaged over 5 categories.

Page 9© Crown copyright 2004 Harvesting the Ensemble (2) : Grosswetterlagen (GWL) (Paul James work) A subjective classification of 29 large-scale weather types, conceived by Baur et al. (1940s), revised by Hess and Brezowsky (50s to 70s) and maintained by the German Weather Service (to present) GWL patterns are characteristic synoptic circulation types, covering most of Europe and N.E. Atlantic while focused on Central Europe GWL events must last at least 3 days – they define regimes Conceptually one of the best classification systems in existence ( Note that GWLs are a form of clustering into a fixed number of possible states, where the clusters have distinct synoptic meaning with consistent large-scale characteristics ) but: Subjective, probably non-homogeneous over time Large-scale patterns often inconsistent outside of Central Europe objectiveNot applicable to NWP etc. unless they can be made objective

Page 10© Crown copyright 2004 Examples of GWL-Composites for mid-June, based on ERA40 MSLP Contours, Precipitation Colour- Fill Fields 2m-Temperature Anomaly Circles

Page 11© Crown copyright 2004 Empirical Objective-GWL Classification Method (1) Form GWL-Composites MSLP and Geopotential Height at 500 hPa (Z500) ERA40, Use the official (subjective) GWL catalogue for this Separate composites for Winter and Summer half-years, sinusoidally-weighted, centred on mid-January / mid-July (2) Pattern Correlations Correlate daily MSLP / Z500 fields with each GWL base composite Highest correlating GWL taken as GWL for this day Apply subsequent temporal filtering ( logical steps ) to set most appropriate GWL regime (must last at least 3 days each)

Page 12© Crown copyright 2004 Objective-GWLs in Ensemble Forecasts Run objective-GWL algorithm on each ensemble member Yields a set of 51 catalogues of daily GWLs Compare e.g. mean frequency of occurrence of each GWL against hindcast and climatological observed (e.g. ERA40) frequencies Added Value: Indicates probable dates for changes of regime Can form the basis for a meaningful synoptic clustering of possible outcomes Shows the specific influence of synoptic-scale circulation anomalies in the forecast Communicates the ensemble outcomes in a very effective way to synoptic meteorologists

Page 13© Crown copyright 2004 Histogram of GWL

Page 14© Crown copyright 2004 Objective-GWLs in Ensembles: Verification Method has been running weekly on the monthly forecast since Rigorous verification method will be needed Quick first-order verification on most probable daily GWL (GWL having the most ensemble members each day) has been made using following daily scores: 2 points when GWL correct 1 points when a near-neighbour GWL predicted (subjectively, each GWL has about 5 near-neighbours) 0 points when GWL wholly incorrect ( 23 out of 29 GWLs, resp.) Add up points to the end of May Random chance should give a mean of about 3 points per day over 10 forecast weeks (ie. 0.3 pts per day per forecast)

Page 15© Crown copyright 2004 Objective-GWLs in Ensembles: Verification Skill on THORPEX timescales No obvious deterministic skill beyond about 16 days * * But probabilistic breakdown of GWL frequencies may contain skill Scores Forecast Day ( T+x )

Page 16© Crown copyright 2004 Post processing: Rank Ordering Method Data Volume reduction before transfer to The Met Office: Calculate 1.Tercile/Quintile boundaries from the Hindcast ensemble 2.Tercile/Quintile populations from the Forecast ensemble 3.Maximum, Mean and Minimum from Forecast and from Hindcast 4.Forecast Tercile/Quintile averages 5.Average in time to week 1, 2 and 3&4. UK Forecast: Interpolation to points representing UK climate regions 2.Calibration with historical UK climate region observations 3.Interpretation of the Histogram, Ensemble mean or Mode in cases with large spread, derive deterministic forecast tercile/quintile 4.Mapping Tercile/Quintile average onto calibration PDF to derive deterministic forecast value

Page 17© Crown copyright 2004 Post processing: Grosswetterlagen Method Correlate daily MSLP / Z500 fields with each GWL base composite Highest correlating GWL taken as GWL for this day Apply subsequent temporal filtering ( logical steps ) to ascertain most appropriate GWL regime lasts at least 3 days Run objective-GWL algorithm on each ensemble member Yields a set of 51 catalogues of daily GWLs Compare e.g. mean frequency of occurrence of each GWL against hindcast and climatological observed (e.g. ERA40) frequencies

Page 18© Crown copyright 2004 Holiday planner for June/July 2006

Page 19© Crown copyright 2004 Holiday planner for June/July 2006 Wind Wk 1 Wk 2 Wk 3&4

Page 20© Crown copyright 2004 Future Work: port Standardised Verification system (SVS) to R, compare with other verification packages More streamlined More communication More efficient Exploit daily data: Environmental Stress index (Heat stress) Monsoon onset Period statistics, days above a threshold Description of the histogram/PDF in an analytical form, derived from Mean, Standard Deviation, Skewness and Kurtosis More complete description of the PDF Less data to carry around

Page 21© Crown copyright 2004 Conclusion The monthly forecasts model runs are produced at ECMWF, products are derived at the Met Office, operationally. Europe is a difficult region to predict at long time range. The Monthly Outlook is a powerful tool to provide forecast guidance up to a month ahead in many areas. Grosswetterlagen analysis: indicates probable dates for changes of regime can form the basis for a meaningful synoptic clustering of possible outcomes shows the specific influence of synoptic-scale circulation anomalies in the forecast communicates the ensemble outcomes in a very effective way to synoptic meteorologists