Presentation on theme: "The THORPEX Interactive Global Ensemble (TIGGE)"— Presentation transcript:
1 The THORPEX Interactive Global Ensemble (TIGGE) Multi-model ensembles andTropical cyclone forecastingRichard Swinbank,with thanks tothe GIFS-TIGGE working group,THORPEX IPO and Met Office & other colleaguesEnsemble TC forecasting workshop, Nanjing, December 2011
2 TIGGE and ensemble forecast products ObjectivesTIGGE archiveEnsemble forecastingProbabilistic forecastingUse of multi-model ensemblesForecast productsPlans for development of Global Interactive Forecast SystemDeveloping links with CBS/SWFDPTropical cyclone forecast products
3 TIGGE THORPEX Interactive Grand Global Ensemble A major component of THORPEX: a World Weather Research Programme to accelerate the improvements in the accuracy of 1-day to 2-week high-impact weather forecastsGEO task WE – “TIGGE and the Development of a Global Interactive Forecast System for Weather”Objectives:Enhance collaboration on ensemble prediction, both internationally and between operational centres & universities.Facilitate research on ensemble prediction methods, especially methods to combine ensembles and to correct systematic errorsEnable evolution towards a prototype operational system, the “Global Interactive Forecast System”
4 TIGGE dataTen of the leading global forecast centres are providing regular ensemble predictions to support research on predictability, dynamical processes and the development of probabilistic forecasting methods.TIGGE data is made available for research after a 48-hour delay. Near real-time access may be granted for specific projects through the THORPEX International Project Office.
5 Summary of TIGGE database (late 2010) CentreEnsemblemembersOutput dataresolutionForecastlengthForecastsper dayFields(out of 73)Start dateBOM*331.50º x 1.50º10 day2553 Sep 07CMA150.56º x 0.56º6015 May 07MSC210.9º x 0.9º16 day563 Oct 07CPTEC0.94º x 0.94º15 day1 Feb 08ECMWF51N200 (Reduced Gaussian)N128 after day 10701 Oct 06JMA9 day161KMA*171.00º x 1.00º4628 Dec 07Météo-France354.5 day6225 Oct 07NCEP4695 Mar 07UKMO240.83º x 0.55º72* Delivery of BoM data currently suspended; KMA resumed Aug 2011
7 Information about TIGGE Major Article in BAMSNew leaflet to publicise TIGGE to researchersContribution in GEO book “Crafting Geoinformation”Tropical cyclone case study in WMO BulletinTIGGE website
8 TIGGE ResearchFollowing the successful establishment of the TIGGE dataset, the main focus of the GIFS-TIGGE working group has shifted towards research on ensemble forecasting. Particular topics of interest include:a posteriori calibration of ensemble forecasts (bias correction, downscaling, etc.);combination of ensembles produced by multiple models;research on and development of probabilistic forecast products.TIGGE data is also invaluable as a resource for a wide range of research projects, for example on dynamical processes and predictability.Up to the end of 2010, 43 articles related to TIGGE have been published in the scientific literature
9 Ensemble forecasting X time Deterministic Forecast Initial Condition UncertaintyXAnalysisAnimation in this slide can help illustrate the ensemble concept. Time runs from left to right so we are forecasting from the analysis to some point in the future which must lie within the climatology. There is a region of uncertainty in the forecast but we do not know what it is. The standard deterministic forecast just gives a point within this region. However we know there is uncertainty in the analysis (click to introduce). By randomly sampling within this uncertainty we can produce a set of forecasts (click to animate) which gives us a sampling of the forecast uncertainty on the right – the only estimate we have of what this forecast uncertainty is.ClimatologytimeForecast uncertainty
10 Ensemble forecasting - effect of Chaos The atmosphere is a chaotic system: “… one flap of a seagull’s wing may forever change the future course of the weather”, (Lorenz, 1963)Up to about 3 days ahead we can usually forecast the general pattern of the weather quite accuratelyBeyond 3 days Chaos becomes a major factorTiny errors in how we analyse the current state of the atmosphere lead to large errors in the forecast – these are both equally valid 4-day forecasts!Lorenz’s famous quote is probably an exaggeration for day-to-day forecasting, but nevertheless typifies the problem. We can never analyse the current state of the atmosphere perfectly, even on much larger scales than a seagull! The two charts shown are from quite a typical 4-day ECMWF ensemble forecast – a more extreme example, but certainly not a rare one. You may like to comment that the differences in initial conditions between them are too small for a forecaster to be able to analyse which is the better. The left chart predicts a deep area of low pressure near NW Ireland bringing strong winds and rain to much of the British Isles; the right chart predicts a much weaker low NE of Scotland, with a ridge of high pressure likely to bring fine weather to the southern half of the British Isles. Clearly in this situation a forecaster who has access to only a single model forecast is in danger of issuing a forecast which could go seriously wrong.Fine details (eg rainfall) have shorter predictabilityWith acknowledgements to Ken Mylne for general Ensemble Forecasting slides
11 Lorenz Model – a simple chaotic system Variations in predictability can be illustrated using the Lorenz (1963) model:Simple non-linear system.
12 Ensemble Forecasting in the Lorenz Model 1. Predictable -deterministic OK2. Predictable at first -probability OK3. Unpredictableclimatology OKThanks to Tim Palmer, ECMWF
13 Ensemble ForecastingThe aim of ensemble prediction system is to represent the uncertainty in the state of the NWP model at all stages through the forecast rangeEach ensemble member is designed to sample a PDF representing uncertainties in the model state. The PDF is usually represented by a control run plus many perturbed forecasts.Initial condition perturbations are designed to represent uncertainties in the initial analysis – closely linked to data assimilation. These grow with time as a result of the chaotic nature of model dynamics.The uncertainty in forecasts also grows as a result of model error. This effect may be represented by perturbing model physics – e.g. stochastic physics schemes.
14 Examples of comparison of ensemble spread & error (from 2010 MOGREPS upgrade) CompareRed – oldBlue − newNote:Ensemble spread is less than RMS errorsNew system both reduces RMS error & increases spreadUnder-spread in surface temperature exaggerated because of representivity errors (point observations not representative of grid-square averages)
15 Under-dispersive forecast Real forecast uncertaintyDeterministic ForecastInitial Condition UncertaintyEstimated forecast uncertaintyXThe ensemble may capture reality less often than it should.Dangerous - false sense of security!Analysistime
16 What makes a good ensemble? Resolution – ability to distinguish between different events.Reliability – the forecast probabilities match the associated observed frequencies.Calibration – apply post-processing so that the statistics match those of a reference dataset. This might include bias-correction and variance adjustment.ObservedThe observed track should lie within the ensemble.
17 Met Office Multi-Model Ensemble One approach to improving the calibration is to use multi-model ensemblesNCEP21 Member3 variables:MSLP2m Temp500mb HeightECMWF51 MemberMet Office24 MemberMet Office MME results courtesyChristine Johnson
18 Reliability DiagramsECMWFUKMOProbability that 2m temperature is greater than the climatological mean at T+240. Globally averaged.MultimodelNCEPMulti-model gives better reliability and reduced sharpness.
19 Brier Skill Scores1 day5 dayBrierProbability of 2m temperature greater than climatological mean.Multi-model gives improvement in reliability and resolution at all lead times.ReliabilityResolution
20 Dissimilarity of ensembles D has large values (high dissimilarity, red) if the between-model variance is large compared to the mean-square-error of the multi-model mean.mslptempD+2D+2D+10D+10
21 Multi-model ensemble forecast real forecast uncertaintyDeterministic ForecastInitial Condition UncertaintyEstimated forecast uncertaintyXUse of multi-model ensembles can improve the sampling of forecast uncertaintyAnalysistime
22 Multi-model ensemble forecasts of T850 Demonstrates benefit of multi-model ensemble, provided that the most skilful models are used.Renate Hagedorn, ECMWF
23 Multi-model ensemble compared with reforecast calibration Reforecast calibration gives comparable benefit to multi-model ensembleChoice of verification data set (in this case, ERA-Interim) could have subtle but significant effect on relative benefitsCalibration could further enhance benefit of multi-model ensembleRenate Hagedorn
24 Precipitation forecasts over USA 24 hour accumulations, data from 1 July 2010 to 31 October 2010.20 members each from ECMWF, NCEP, UK Met Office, Canadian Meteorological Centre.80-member, equally weighted, multi-model ensemble verified as well.Verification follows Hamill and Juras (QJ, Oct 2006) to avoid over-estimating skill due to variations in climatology.Conclusions:ECMWF generally most skillful.Multi-model beats all.Tom Hamill
25 Summary - EPS & multi-model ensembles Ensembles are valuable for forecasting the risks of exceeding thresholds (e.g. for high-impact weather events).But forecasts often need calibration to correct both biases and variability (e.g. to correct under-estimates of forecast spread).The best approach is to address the systematic errors, i.e. reduce model biases and improve the representation of model errors in the EPSs.That is a long-term goal. In the mean time….Use of multi-model ensembles is a pragmatic approach that reduces calibration errors, especially where models have similar skill but different types of systematic errorReforecasts (forecasts of past cases with current NWP models) can be used to estimate, and then correct, model biases
26 Towards the Global Interactive Forecast System (GIFS) Many weather forecast situations are low probability but high risk – unlikely but potentially catastrophic. Probabilistic forecasting is a powerful tool to improve early warning of high-impact events.The objective of the GIFS is to realise the benefits of THORPEX research by developing and evaluating probabilistic products to deliver improved forecasts of high-impact weather.GIFS-TIGGE WG has initiated a GIFS development project to develop & evaluate products, focused onTropical cyclonesHeavy precipitationStrong winds
27 Tropical cyclonesAs a first step, the GIFS-TIGGE working group set up a pilot project for the exchange of real-time tropical cyclone predictions using “Cyclone XML” format.Example of combined TC track forecasts (Met Office + ECMWF)
29 GIFS links with SWFDPGIFS will collaborate with WMO Severe Weather Forecast Demonstration Project (SWFDP) and other FDPs and RDPsto ensure that products address needs of operational forecasters and end users;to provide an environment for the evaluation of prototype products.GIFS will use global-regional-national cascade pioneered by the SWFDP. No single “GIFS centre”.Use of web-enabled technology for generation and distribution of products.
30 Tropical cyclone products: Examples from Met Office Uses Julian Heming’s code to identify and track tropical cyclones (TC), originally developed for the deterministic global Unified Model.Tropical Cyclones (TCs) are identified where 850hPa relative vorticity (RV) maxima are greater than a thresholdFor TC tracking, use search radius of 4 degrees for analysis and 5 degrees for forecast positionsIdentified storms that do not match with a named storm or a TC identified at a previous time are counted as TC genesisMet Office TC products courtesyPier Buchanan, Helen Titley and Julian Heming
31 MOGREPS-15 products for Ma-On, 12Z Friday 14th July 2011. Left hand plot: 24 ensemble tracksMiddle plot: strike probability – probability that the storm will be within 75 miles within thhe next 15 days.Right hand plot: MOGREPS ensemble mean (blue), control (cyan), previous observations (red) and deterministic track (green)
32 Products for named and potential storms on a basin by basin basis.
33 StormTracker Ma-On, 12Z Friday 14th July 2011. Black – previous trackWhite – deterministic model forecastPurple UKMO ensemble mean, mustard ECMWF ensemble mean and pink NCEP GEFS ensemble mean.
34 TOMAS: MOGREPS-15 12Z North Atlantic forecast on Wednesday 27th October 2010.
41 Warnings of heavy precipitation Similar products also available forhot & cold temperatures,strong windsPrototype product courtesyMio Matsueda
42 Case 1: heavy rainfall by cyclone YASI (Feb. 2011) + 7-day forecastTRMM dailyprecipitationexceeds the TRMM's 95th percentile.
43 Case 1: heavy precipitation by cyclone YASI + 6-day forecast
44 Case 1: heavy rainfall by cyclone YASI (Feb. 2011) + 5-day forecast
45 Case 1: heavy rainfall by cyclone YASI (Feb. 2011) + 4-day forecast
46 Case 1: heavy rainfall by cyclone YASI (Feb. 2011) + 3-day forecast
47 Case 1: heavy rainfall by cyclone YASI (Feb. 2011) + 2-day forecast
48 Case 1: heavy rainfall by cyclone YASI (Feb. 2011) + 1-day forecast
49 Other possible visualisation: combined warnings Prototype product courtesyMio Matsueda
50 Website: http://icdm2012.csp.escience.cn ICDM workshopWorkshop on Dynamics and Predictability of High-Impact Weather Events and Climate ExtremesKunming, China 6-9 August 2012Organised by IAMAS International Commission on Dynamical MeteorologySponsored by WWRP, WCRP, IUGG, IAMAS, Chinese IUGG committee, CAS, NSFC, LASG/IAP, Nanjing University & research projects.Further information (first announcement) available from me.Website:
51 TIGGE website: http://tigge.ecmwf.int SummarySince October 2006, the TIGGE archive has been accumulating regular ensemble forecasts from leading global NWP centres.The archive is a tremendous resource for the research community at large, particularly on EPS – including use of multi-model & other approaches to reduce systematic errors.As the basis of the development of the future Global Interactive Forecast System, products are being developed to enhance the prediction of high-impact weather, starting with tropical cyclones.GIFS products will be developed & evaluated in conjunction with SWFDP and other regional projects.TIGGE website: