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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: 1.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
Page 1© Crown copyright 2004 Presentation to ECMWF Forecast Product User Meeting 16th June 2005.
Page 1© Crown copyright Operational Use of ECMWF products at the Met Office: Current practice, Verification and Ideas for the future Tim Hewson 17 th June.
User Meeting 15 June 2005 Monthly Forecasting Frederic Vitart ECMWF, Reading, UK.
Training Course 2013– NWP-PR: The Monthly Forecast System at ECMWF 1 Monthly Forecasting at ECMWF Frédéric Vitart European Centre for Medium-Range Weather.
Training Course 2010– NWP-PR: The Monthly Forecast System at ECMWF 1 Monthly Forecasting at ECMWF Frédéric Vitart European Centre for Medium-Range Weather.
Page 1© Crown copyright 2005 Met Office seasonal predictions and applications Richard Graham Chris Gordon, Matt Huddleston, Mike Davey, Alberto Arribas,
HB 1 Forecast Products Users'Meeting, June 2005 Users meeting Summary Performance of the Forecasting System (1) Main (deterministic) model -Outstanding.
Training Course 2012– NWP-PR: The Monthly Forecast System at ECMWF 1 Monthly Forecasting at ECMWF Frédéric Vitart European Centre for Medium-Range Weather.
Page 1© Crown copyright 2005 Use of EPS at the Met Office Ken Mylne and Tim Legg.
Seasonal forecasting: status and plans David Anderson Tim Stockdale, Magdalena Balmasda, Arthur Vidard, Alberto Troccoli, Paco Doblas-Reyes, Kristian Morgensen,
1 The ECMWF Monthly and Seasonal Forecast Systems D. Anderson, M. Balmaseda, L. Ferranti, F. Molteni, T. Stockdale, F. Vitart ECMWF, Reading, UK.
Training Course 2009 – NWP-PR: The Seasonal Forecast System at ECMWF 1 The Seasonal Forecast System at ECMWF Tim Stockdale European Centre for Medium-Range.
Slide 1 Forecast Products User Meeting June 2006 Summary Forecast Products User Meeting: June 2006 Summary.
ECMWF Forecast Products User Meeting, E. Zsoter, June 2006 Severe Weather Forecasts Severe Weather Forecasts Ervin Zsoter.
Page 1© Crown copyright 2004 Seasonal forecasting activities at the Met Office Long-range Forecasting Group, Hadley Centre Presenter: Richard Graham ECMWF.
ECMWF Slide 1Met Op training course – Reading, March 2004 Forecast verification: probabilistic aspects Anna Ghelli, ECMWF.
Discussion of development of operational 1-90 prediction capability Pedro L. Silva Dias National Laboratory for Scientific Computing/LNCC Petrópolis RJ,
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Extended range forecasts at MeteoSwiss: User experience.
AREP GAW Section 12 Air Quality Forecasting Tools.
Training Course 2009 – NWP-PR: Calibration of EPSs 1/42 Calibration of EPSs Renate Hagedorn European Centre for Medium-Range Weather Forecasts.
F. Prates Data Assimilation Training Course April Error Tracking F. Prates.
Page 1 © Crown copyright 2005 ECMWF User Meeting, June 2006 Developments in the Use of Short and Medium-Range Ensembles at the Met Office Ken Mylne.
Training Course 2009 – NWP-PR: Ensemble Verification I 1/33 Ensemble Verification I Renate Hagedorn European Centre for Medium-Range Weather Forecasts.
Robin Hogan Ewan OConnor, Anthony Illingworth University of Reading, UK Clouds radar collaboration meeting 17 Nov 09 Ground based evaluation of cloud forecasts.
ECMWF DA/SAT Training Course, May The Operational Data Assimilation System Lars Isaksen, Data Assimilation, ECMWF Overview of the operational data.
Norwegian Meteorological Institute met.no GLAMEPS: GLAME PS GLAMEPS: Grand Limited Area Model Ensemble Prediction System Towards establishing a European-wide.
The THORPEX Interactive Global Ensemble (TIGGE) Multi-model ensembles and Tropical cyclone forecasting Richard Swinbank, with thanks to the GIFS-TIGGE.
Robin Hogan Ewan OConnor, Anthony Illingworth University of Reading, UK Chris Ferro, Ian Jolliffe, David Stephenson University of Exeter, UK Verifying.
ECMWF User Meeting / 1 Pertti Nurmi Juha Kilpinen Annakaisa Sarkanen ( Finnish Meteorological Institute ) Probabilistic Forecasts.
Slide 1 Forecast Products User Meeting June 2006 Slide 1 ECMWF medium-range forecasts and products David Richardson Met Ops.
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