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The THORPEX Interactive Global Ensemble (TIGGE) Multi-model ensembles and Tropical cyclone forecasting Richard Swinbank, with thanks to the GIFS-TIGGE.

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Presentation on theme: "The THORPEX Interactive Global Ensemble (TIGGE) Multi-model ensembles and Tropical cyclone forecasting Richard Swinbank, with thanks to the GIFS-TIGGE."— Presentation transcript:

1 The THORPEX Interactive Global Ensemble (TIGGE) Multi-model ensembles and Tropical cyclone forecasting Richard Swinbank, with thanks to the GIFS-TIGGE working group, THORPEX IPO and Met Office & other colleagues Ensemble TC forecasting workshop, Nanjing, December 2011

2 TIGGE and ensemble forecast products TIGGE Objectives TIGGE archive Ensemble forecasting Probabilistic forecasting Use of multi-model ensembles Forecast products Plans for development of Global Interactive Forecast System Developing links with CBS/SWFDP Tropical 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 forecasts GEO 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 errors Enable evolution towards a prototype operational system, the Global Interactive Forecast System

4 TIGGE data Ten 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) Centre Ensemble members Output data resolution Forecast length Forecasts per day Fields (out of 73) Start date BOM*331.50º x 1.50º10 day2553 Sep 07 CMA150.56º x 0.56º10 day26015 May 07 MSC210.9º x 0.9º16 day2563 Oct 07 CPTEC150.94º x 0.94º15 day2551 Feb 08 ECMWF51 N200 (Reduced Gaussian) N128 after day day2701 Oct 06 JMA510.56º x 0.56º9 day1611 Oct 06 KMA*171.00º x 1.00º10 day24628 Dec 07 Météo-France351.50º x 1.50º4.5 day26225 Oct 07 NCEP211.00º x 1.00º16 day4695 Mar 07 UKMO240.83º x 0.55º15 day2721 Oct 06 * Delivery of BoM data currently suspended; KMA resumed Aug 2011

6 TIGGE Archive Usage (NCAR + ECMWF)

7 Information about TIGGE Major Article in BAMS New leaflet to publicise TIGGE to researchers Contribution in GEO book Crafting Geoinformation Tropical cyclone case study in WMO Bulletin TIGGE website

8 TIGGE Research Following 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 time Forecast uncertainty Climatology Initial Condition Uncertainty X Deterministic Forecast Analysis

10 Ensemble forecasting - effect of Chaos The atmosphere is a chaotic system: … one flap of a seagulls wing may forever change the future course of the weather, (Lorenz, 1963) Tiny 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! Up to about 3 days ahead we can usually forecast the general pattern of the weather quite accurately Beyond 3 days Chaos becomes a major factor Fine details (eg rainfall) have shorter predictability With 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 OK 2. Predictable at first - probability OK 3. Unpredictable climatology OK Thanks to Tim Palmer, ECMWF

13 Ensemble Forecasting The aim of ensemble prediction system is to represent the uncertainty in the state of the NWP model at all stages through the forecast range Each 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) Compare Red – old Blue new Note: Ensemble spread is less than RMS errors New system both reduces RMS error & increases spread Under-spread in surface temperature exaggerated because of representivity errors (point observations not representative of grid-square averages)

15 time Estimated forecast uncertainty Initial Condition Uncertainty X Deterministic Forecast Analysis Under-dispersive forecast Real forecast uncertainty The ensemble may capture reality less often than it should. Dangerous - false sense of security!

16 What makes a good ensemble? Observed 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. The observed track should lie within the ensemble.

17 Met Office Multi-Model Ensemble NCEP 21 Member ECMWF 51 Member Met Office 24 Member 3 variables: MSLP 2m Temp 500mb Height Met Office MME results courtesy Christine Johnson One approach to improving the calibration is to use multi-model ensembles

18 ECMWF Multimodel NCEP UKMO Reliability Diagrams Multi-model gives better reliability and reduced sharpness. Probability that 2m temperature is greater than the climatological mean at T+240. Globally averaged.

19 Brier Reliability Resolution Probability of 2m temperature greater than climatological mean. Brier Skill Scores Multi-model gives improvement in reliability and resolution at all lead times. 1 day 5 day

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. mslptemp D+2 D+10 D+2 D+10

21 time Estimated forecast uncertainty Initial Condition Uncertainty X Deterministic Forecast Analysis Multi-model ensemble forecast real forecast uncertainty Use of multi-model ensembles can improve the sampling of forecast uncertainty

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 ensemble Choice of verification data set (in this case, ERA-Interim) could have subtle but significant effect on relative benefits Calibration could further enhance benefit of multi-model ensemble Renate Hagedorn

24 Precipitation forecasts over USA 24 hour accumulations, data from 1 July 2010 to 31 October 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 error Reforecasts (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 on Tropical cyclones Heavy precipitation Strong winds

27 Tropical cyclones As 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)

28 CXML track exchange

29 GIFS links with SWFDP GIFS will collaborate with WMO Severe Weather Forecast Demonstration Project (SWFDP) and other FDPs and RDPs to 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 Hemings 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 threshold For TC tracking, use search radius of 4 degrees for analysis and 5 degrees for forecast positions Identified storms that do not match with a named storm or a TC identified at a previous time are counted as TC genesis Met Office TC products courtesy Pier Buchanan, Helen Titley and Julian Heming

31 MOGREPS-15 products for Ma-On, 12Z Friday 14 th July Left hand plot: 24 ensemble tracks Middle 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 14 th July Black – previous track White – deterministic model forecast Purple UKMO ensemble mean, mustard ECMWF ensemble mean and pink NCEP GEFS ensemble mean.

34 TOMAS: MOGREPS-15 12Z North Atlantic forecast on Wednesday 27 th October 2010.

35 Yasi MOGREPS Forecasts

36 Comparison of deterministic forecast with MOGREPS-15 ensemble mean

37 Comparison of MOGREPS, ECMWF and multi-model ensemble.

38 Tropical cyclone products from MRI/JMA

39 New tropical cyclone product: Strike probability time-series at a city Tetsuo Nakazawa

40 Use of MOGREPS-15 products in SWFDDP

41 Warnings of heavy precipitation Prototype product courtesy Mio Matsueda Similar products also available for hot & cold temperatures, strong winds

42 Case 1: heavy rainfall by cyclone YASI (Feb. 2011) + 7-day forecast TRMM daily precipitation exceeds the TRMM's 95 th 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 courtesy Mio Matsueda

50 ICDM workshop Workshop on Dynamics and Predictability of High- Impact Weather Events and Climate Extremes Kunming, China 6-9 August 2012 Organised by IAMAS International Commission on Dynamical Meteorology Sponsored 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 Summary Since 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:

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