David Parsons. WWRP and THORPEX International Programme Office (WMO)

Transcription

1 David Parsons and THORPEX International Programme Office (WMO) 1 THORPEX Organisational Structure International Project Office International Core Steering Committee Executive Committee Regional Committees Africa Asia Europe North America Southern Hemisphere Predictability and Dynamical Processes Working Group Heini Wernli and Istvan Szunyogh PDP WG Data Assimilation and Observing Strategies Working Group Florence Rabier and Pierre Gauthier DAOS WG Global Interactive Forecasting System THORPEX Interactive Grand Global Ensemble Working Group Zoltan Toth and Philippe Bougeault GIFS-TIGGE WG Links to the SERA and the Joint Verification WGs Six weeks ago the Working Groups and the co-chairs of The Regional Committees met in Geneva to complete a strategic plan of work for the next few years 2 1

2 Predictability ANd Dynamics Of Weather Systems in the Atlantic-European Sector European Windstorms Severe Convection Mediterranean Cyclones Tropical Cyclones DFG funded Research Group PANDOWAE 3 THORPEX Highlights for WGNE THORPEX Interactive Grand Global Ensemble (TIGGE) THORPEX Cluster under the International Polar Year THORPEX Africa and the developing world THORPEX covered elsewhere T-PARC (next presentation -- D. Parsons) Report from Data Assimilation and Observation System Working Group including targeting (Florence Rabier) Year of Tropical Convection (WGNE Co-chairs) Weather-climate interface and THORPEX s role in seamless prediction (Gilbert Brunet) Some aspects of TIGGE in Ensemble Forecasting presentation (Tom Hamill) 4 2

3 TIGGE partners and data flow 5 TIGGE objectives (agreed in March 2005) Enhance international collaboration on ensemble prediction for severe weather Collaboration between operational centres and universities Develop theory and practice of multi-model ensembles Examine the feasibility of interactive ensembles responding dynamically to changing uncertainty Develop the concept of a Global Interactive Forecasting Slide 6 System (GIFS) Slide 6 3

4 What is available now? Operational global ensemble forecasts from ten centres: BoM (Australia), CMA (China), CPTEC (Brazil), ECMWF (Europe), JMA (Japan), KMA (Korea), Meteo-France, MSC (Canada), NCEP (USA), Met Office (UK) Archives start between October 2006 and January 2008 (depending on centres) The depth of the archive is reaching 2 years for the first providers Work to be done, but many user oriented features. Pressure level data + all usual surface fields (e.g. T2m, U10m, MSLP, rainfall) available from all providers More exotic fields (e.g. CAPE, sunshine duration, etc ) Slide 7 available from some providers only, but improving regularly Slide 7 Current Data Portals Functions Registration Search and discover facilities Select data by Initial date/time and forecast time Spatial sub-domain Data provider Atmospheric quantity and level Check volume and download data By agreement, access is open 48h after forecast start time Quicker access is possible in some cases (by THORPEX/IPO determination) 8 4

5 Can MCGE Outperform the ECMWF Ensemble? M. Matsueda and H. L. Tanaka -- SOLA, Multi-Center Grand Ensembles (MCGEs) were constructed by combining five operational medium-range ensemble forecasts: CMC, ECMWF, JMA, NCEP, and UKMO with equal weights and no bias correction and compared to the ECMWF ensemble. Seasonal Root Mean Square Error (RMSE) and the Ranked Probability Score (RPS) were derived for 500 hpa geopotential height over the Northern Hemisphere (20 N-90 N) from December 2006 to November Deterministic and probabilistic verifications that the MCGEs generally outperformed the ECMWF ensemble at least in the medium forecast range (day 6-9) for all seasons. The improvements in the RMSE and the RPS are several percentage points. These are almost comparable with the rate of improvement in a singlecenter ensemble forecast during the latest few years. 9 Recent research results based on TIGGE Acknowledgments to Young-Youn Park, KMA Renate Hagedorn, ECMWF Florian Pappenberger, ECMWF Richard Swinbank et al., UK Met Office Slide 10 Slide 10 5

6 Upper air variables Significant differences in quality between the systems Up to 3 days differences in probabilistic forecast skill Agreement between spread and skill is the most variable aspect and has a strong impact on probabilistic skill scores In the Tropics the spread is underestimated by almost all systems Impact of the verification analysis Relatively little impact in the extra-tropics (as long as the analysis comes from one of the best systems) Large impact in the Tropics (and difficult to decide which is the best analysis) Skill of multi-model system versus single-model systems Only marginal improvement in the extra-tropics Significant improvement in the Tropics (subject to significant bias corrections) How about the surface weather? Recently Johnson and Swinbank found that multi-model forecasts of T2m outperform significantly any single model EPS Interpreted as a proof that the variety of physics (soil, vegetation, PBL) between the models captures better the uncertainty in surface parameters Renate Hagedorn is currently trying to reproduce these results at ECMWF Results depend strongly from the verification analysis (at variance from upper air variables) Comparison with actual observations is necessary for T2m Incidentally: T2m from TIGGE database at Fcst time=0 is Slide 12 NOT an analysis of T2m temperature (it is an intelligent vertical interpolation) - do not use it for verification! Slide 12 6

7 Verification of T2m against observations T-2m, 250 European stations (60 cases) Multi-Model ECMWF Met Office NCEP CRPSS Solid: no BC Slide Lead time / days Slide 13 Verification of T2m against observations T-2m, 250 European stations (60 cases) Multi-Model ECMWF Met Office NCEP CRPSS Dotted: 30d-BC Solid: no BC Slide Lead time / days Slide 14 7

8 Verification of T2m against observations T-2m, 250 European stations (60 cases) Multi-Model ECMWF Met Office NCEP CRPSS Dashed: REFC-NGR Dotted: 30d-BC Solid: no BC Slide Lead time / days Slide 15 Preliminary conclusions for T2m (very tentative!) Results are sensitive to the choice of verifying analysis Generally speaking, MM is better than any single model Generally speaking, MM superiority comes from ECMWF, and ECMWF alone is better than any MM without ECMWF Calibration using recent forecasts reduces the superiority of the MM but does not change the above conclusions Calibration using a special set of re-forecasts may offset completely the superiority of the MM (?) The superiority of the MM may also be challenged if uncertainty in soil moisture is added in the single systems 8

9 Additional work Confirm results on longer time series, with more observations Examine other weather parameters Rainfall, 10m wind, clouds, etc. Examine impact of multi-model on applications (end-toend forecast systems) Obvious example is with ensemble hydrological forecasts forced by TIGGE, and initial results are supporting superiority of MM Use TIGGE MM as a benchmark to improve single-model systems Real scientific progress would be to encapsulate all aspects of uncertainty in a single, optimal system: Slide TIGGE 17 can help us to locate and repair the deficiencies of existing operational EPSs Slide 17 Multimodel Superensemble using TIGGE data over China T. N. Krishnamurti, Anitha D. Sagadevan, Arindam Chakraborty & A. K. Mishra Department of Meteorology, Florida State University Tallahassee, Florida USA 18 9

10 Multimodel Superensemble Experiment with TIGGE Data Training Period: February 2008 to April 2008 Forecast Period: May 2008 July 2008 Center Ensemble Members Model Resolution Forecast Length ECMWF 51 N day (Reduced Gaussian) ECMWF 51 N day (Reduced Gaussian) UKMO x 0.83 Deg 15 day JMA x 1.25 Deg 9 day NCEP x 1.00 Deg 16 day CMA x 0.56 Deg 10 day CMC x 1.00 Deg 16 day BOM x 1.50 Deg 10 day MF x 1.50 Deg 2.5 day KMA x 1.00 Deg 10 day CPTEC x 1.00 Deg 15 day Details of TIGGE Models: for FSU multimodel suite only 5 members of TIGGE archive were used : ECMWF, UKMO, CMA, NCEP and BOM 19 Training Phase Weights Forecast Phase Training Time Series Model 1 Model 2 Model 3 Model 4 Model N G Multiple Linear Regression: N train S i 1 ( i O i Minimization of error term G. Statistical weights obtained in the training phase are passed on to the forecast phase. ) 2 a 1 a 2 a 3 a 4 a N Forecast Time Series Superensemble Forecasts: S N i 1 a ( Fi F i) O F => Forecasts O=>Observatio ns a i => Weights. Overbar represents climatology. i Forecasts In addition to removing the bias, the superensemble scales the individual model forecasts contributions according to their relative performance in the training period 20 in a way that, mathematically, is equivalent to weighting them. 10

11 Influence of Asian Monsoon (in Eastern side) and Tropical System Frank (Fengshen) in Western side on 26 th Jun RMS Errors and Spatial Correlations of Day 1 and Day 2 Precipitation Forecasts valid on 26 th Jun 2008 Tropical System Frank (Fengshen) Day 1 Day

12 Onset of South China Sea Monsoon RMS Errors and Spatial Correlations of Day 3 and Day 5 Precipitation forecasts valid on May 3 rd 2008 Onset of South China Sea Monsoon Day 3 Day

13 EPS 6 EPS 1 EPS 3 ECMWF EPS 2 EPS 5 EPS 4 EPS 7 25 EPS 1 EPS 2 ECMWF EPS 3 EPS 4 EPS 5 EPS 6 EPS

14 EPS 6 EPS 1 EPS 7 ECMWF EPS 2 EPS 4 EPS 3 EPS 5 27 The Future: Area Model) 1. TIGGE-LAM (Limited The general approach will be similar to TIGGEglobal: archive the operational forecasts of a large number of LAM-EPS systems TIGGE archive centres have agreed to host this additional dataset Access for research will be open 24h after forecast start time (vs 48h for TIGGE global) Practical aspects still under discussion (grids, etc..) Vision of adaptive LAM ensembles for developing based on early warning from global TIGGE Slide 28 TIGGE-LAM led by Tiziana Paccagnella Slide 28 14

15 The Future: 2. The Global Interactive Forecast System (GIFS) A document describing a vision of an operational version of TIGGE, the GIFS, has been agreed by the TIGGE partners and the THORPEX Exec Com last week This will be submitted to the THORPEX International Core Steering Committee at its November 2008 meeting, then to CBS Although we have agreed a roadmap, the implementation agenda is still very uncertain. A number of issues need to be solved before the operational forecast centres can agree to exchange forecasts in real Slide time 29 for operational multi-model products Slide 29 GIFS issues Science: TIGGE results have not yet demonstrated unambiguously the benefits of multi-model systems. More research is needed. As research progresses, the superiority of MM may decrease Resource: Much hardware and manpower is needed to develop reliable exchange mechanisms for real-time production. Resources will be made available by the operational centres only if real benefits are expected, based on research results Operational continuity: How to manage smoothly operational changes occurring at different times for the various components, guarantying smooth progress of MM skill and proper user information? The interactivity will be even more difficult Data policy: Many TIGGE providers will want to protect their commercial revenues. Negotiations will be needed to agree a scheme satisfying all partners. Usage may Slide be 30 restricted to severe weather warnings. Slide 30 15

16 A symbolic initial step towards the GIFS Starting with severe weather products is a good idea! Most TIGGE partners have agreed to exchange data on tropical cyclone tracks in real-time, for the whole duration of the T-PARC experiment (June March 2009) A special format (CXML) has been developed to make these data more easy to interpret and use by the academic community Data are available in real time from providers or from NCAR archive at More information on CXML project web site Slide 31 dex.html Slide 31 Key Message for WGNE TIGGE is a major research infrastructure and every effort has been made to make it userfriendly -- However, efforts to develop software tools are underfunded at some archive centres. About 100 serious users currently: THIS IS NOT ENOUGH -- Any action that WGNE can undertake to increase research usage and answering the research questions posed by TIGGE would be appreciated. Demonstration project based on TIGGE Tropical Cyclones tracks A TIGGE Users Meeting will take place in the frame of the next THORPEX Conference (Monterey, 4-8 May 2009) Slide 32 Will include tutorials and discussions with users to get feedback and decide about priorities for service improvements Slide 32 16

17 Acknowledgments BoM: Beth Ebert CMA: Shi Peiliang, Yang Xin, Lang Honglian, Tian Hao ECMWF: Baudouin Raoult, Manuel Fuentes, Joerg Urban JMA: Shintaro Yokoi, Yoshiaki Takeuchi KMA: Young-Youn Park NCEP: Zoltan Toth, Gordon Brent NCAR: Doug Schuster, Dave Stepaniak, Nathan Wilhelmi, Luca Cinquini, Steve Worley UKMO: Richard Swinbank, Simon Thompson UNIDATA: Steve Chiswell, Tom Slide Yoksas 33 FSU: T. N. Krishnamurti et al Slide 33 The IPY-THORPEX Cluster 10 individual projects (see WMO Bulletin Oct. 2007) The objectives of the IPY-THORPEX Cluster proposal are: Explore use of satellite data and optimised observations to improve high impact weather forecasts (form a Polar Trec and/or provide additional observations in real time to the WMO GTS) Better understand physical/dynamical processes in polar regions Achieve a better understanding of small scale weather phenomena Utilise improved forecasts to the benefit of society, the economy and the environment Utilise of TIGGE for polar prediction Meteorologisk Institutt met.no 17

18 The - THORPEX IPY cluster -THORPEX IPY Cluster (T.E. Nordeng, coordinator) ARCMIP Arctic Regional Climate Model Intercomparison Project (K. Detholf, Alfred-Wegener Institute) STAR Storm Studies of the Arctic (J. Hanesiak, U Manitoba) GFDex Greenland Flow Distortion experiment (I. Renfrew, U. East Anglia) TAWEPI Thorpex Arctic Weather and Environmental Prediction Initiative (Ayrton Zadra, Environment Canada) Norwegian IPY-THORPEX (J.E. Kristjansson, U Oslo) GREENEX (H. Olafsson, Iceland & DLR) Impacts of surfaces fluxes on severe Arctic storms, climate change and coastal orographic processes (W. Perrie, BIO Canada)) T-PARC THORPEX Pacific Asian Regional Campaign (D. Parsons, NCAR) Concordiasi Use of IASI data (F. Rabier, Meteo-France) Greenland Jets (A. Dombrack, DLR) Meteorologisk Institutt met.no Polar-GEM*: domain description of RPN s research model** maximum sea-ice cover consideration Polar-GEM (15km) Fig.: Arctic sea-ice concentration climatology from , at the approximate seasonal maximum level. Image provided by National Snow and Ice Data Center, University of Colorado. LAM domain considerations No ice flow through lateral boundaries LBCs on mountain ranges (touchy) Zone of interest far enough from LBs Great Lakes not included Computation efficiency - Smaller domain = faster - Use of FFTs => restriction on NI * GEM = Global Environmental Multiscale ** Thanks to Bertrand Denis (MRD/EC) Meteorologisk Institutt met.no 18

19 Meteorologisk Institutt met.no Meteorologisk Institutt met.no 19

20 Intercomparison of temperature and humidity profiles for SHEBA domain, winter (Rinke, Dethloff et al., Climate Dyn., 2006) Temperature, winter, total Specific humidity, winter pressure [hpa] temperature total_hirham total_arcsym total_coamps total_rca total_remo total_regcm total_crcm ensemblemean ecmwf pressure [hpa] specific humidity [g/kg] Remarkable scatter between model temperature and humidity profiles (due to different radiation, cloud, PBL, and soil schemes) Wrong surface fluxes, influence cloud formation and cloud properties Temperature scatter in the order of 3 C (the range of climate scenarios) Key processes to be improved: clouds, land surface- and boundary layer processes, coupling to ice-ocean Meteorologisk Institutt met.no total_hirham total_arcsym total_coamps total_rca total_remo total_regcm ensemblemean ecmwf African Regional Committee Activities THORPEX Workshop and meetings of the Working groups, Geneva, September Aïda Diongue Niang and Andre Kamga 40 20

21 T-NAWDEX THORPEX- North Atlantic Waveguide and Downstream Impact Experiment address the triggering of waveguide disturbances by different processes and the disturbances subsequent downstream evolution study of the downstream impacts of the waveguide disturbances over Europe, the Mediterranean, and northern Africa To be performed in 2011/2012 in conjunction with HYMEX 41 HYMEX: Main Scientific Topics Better understanding of the intense events: processes and contribution to the trend 42 21

22 T-NAWDEX Planning (1) Use HALO-Demonstration Mission HALO-THORPEX as nucleus for field phase 43 T-NAWDEX Planning Planning Workshop Spring 2009 Formulation of the Science Plan scientific aims and questions methods to address the questions (theoretical, numerical, experimental) responsible persons Implementation Plan National/International Cooperation and fund raising 44 22

23 First Announcement Third THORPEX International Science Symposium and TIGGE User Workshop 4 to 8 May 2009 Portola Hotel, Monterey, California Organising Committee Jim Hansen (NRL) - Chair Istvan Szunyogh (U. Maryland) Programme Chair Rolf Langland (NRL) Richard Swinbank (UK Met Office) Florence Rabier (Meteo France) Tetsuo Nakazawa (JMA/MRI) Huw Davies (ETH) Gilbert Brunet (MSC) Eugene Poolman (SA Weather Service) Dan Hodyss (NRL) David Parsons (WMO) David Burridge (WMO) 45 23