ࡱ> %` bjbj ̟̟֋%b b b b b b b v 8ڞv &.zl&"HHHq-s-s-s-s-s-s-$0h3`-b ۻ=ۻۻ-b b HH-2ۻb pHb Hq-ۻq-# b m+H ';&H-T-H&.&h3_2h3m+h3b m+RUTQ--j&.ۻۻۻۻv v v J:WFv v v :Wv v v b b b b b b  HIGH IMPACT WEATHER FORECASTING FOR GEOSS: the global interactive forecast system (GIFS) THORPEX GIFS-TIGGE Working Group *********************Draft********************* October 31 2008  OBJECTIVES. The objective of the future Global Interactive Forecast System (GIFS) is the production of internationally coordinated advance warnings and forecasts for high impact weather events to mitigate loss of life and property, and to contribute to the welfare of all World Meteorological Organization (WMO) nations, with a particular emphasis on least developed and developing countries. Ensemble prediction systems will play a critical role in assessing and mitigating weather and climate related risks by quantifying forecast uncertainty. The architecture of GIFS will be based on the new WMO Information System (WIS). GIFS will use high impact event related data, ensemble-based probabilistic forecast products, and services contributed voluntarily by Numerical Weather Prediction (NWP) centers and other providers around the globe. Providers will control the amount of data and processing they wish to share with the GIFS user community. It is anticipated that in its advanced stage, GIFS will provide guidance on, and coordinate the use of observational, numerical data assimilation, forecasting, and user application resources to ensure the production of the highest quality guidance for high impact weather events. BENEFITS. It is expected that the international coordination of the design, future development, and operation of global observing, data assimilation, numerical modeling, and user application techniques for high impact weather forecasting will yield significant benefits in terms of improvements in the range and quality of services, leading to savings in costs, property, and lives. This will be achieved by (i) a scientifically sound combination of information from existing NWP forecast providers, (ii) the global leveraging of NWP activities that are yet not well coordinated and to a large extent are carried out independently by national and regional forecast centers, and (iii) fostering collaboration between academic and operational institutes in ensemble related scientific research. RESOURCES. GIFS involves a wide range of activities, as discussed below. The development, and then the operational maintenance of data access, product generation, and adaptive NWP procedures can be achieved only with significant volunteer contributions from the participating organizations. To the maximum extent possible, GIFS plans will build and leverage on existing infrastructure by coordinating and augmenting ongoing activities at established NWP centers and other organizations. Still, to realize the full benefits of GIFS, significant additional investments will be necessary from National Hydro-Meteorological Services (NHMS) and other organizations. The investment in GIFS by NHMSs will need to be justified by the ability to deliver improved services that in turn lead to socioeconomic benefits for the nations involved. In return, the ensemble data-providing centers will benefit from receiving reports on verification and product development carried out at the regional and national centers, facilitated by the GIFS infrastructure. STAGES OF DEVELOPMENT. The objectives of GIFS can be achieved only with a carefully planned, multi-year effort that started with the establishment of the THORPEX research program. GIFS builds on the experience accumulated in WMO Severe Weather Forecast Demonstration Projects (SWFDP). The Southern Africa SWFDP project is a good example of the successful delivery and use of operational products to support forecasts of high-impact weather in Southern Africa. GIFS will be one of the first World Meteorological Organization (WMO) Information System (WIS) projects. Given its broad scope, GIFS development work can be organized and advanced in the following (possibly overlapping) stages: TIGGE (2005 onwards). TIGGE (THORPEX Interactive Grand Global Ensemble) is an international data archive where numerical ensemble forecast providers share their data to advance scientific research related to improving high impact weather forecasting. The shared data are made available for research purposes with a time delay at three volunteer archive centers via site specific web interfaces (see http://www.wmo.int/pages/prog/arep/thorpex/). The development of TIGGE is essentially complete, apart from some desired enhancements to the data access portals, and the inclusion of any new global ensemble data that producing centers may wish to share with the user community. When completed, it will be possible to access and download only those subsets of the entire dataset that are of relevance to the users. TIGGE research topics include observing system design, data assimilation, predictability, and forecast applications, contributing to the development of GIFS. The TIGGE data set is particularly suited for studying the benefit of multi-model ensembles, by combining forecasts from different TIGGE data providers. GIFS Development (2008-2012). This initial phase of GIFS entails developing real time access to basic ensemble forecast data and creating an infrastructure for the generation of derived products and the provision of other related services. Access to both data and products will be via a unified web portal. Ensemble forecast data access: The data-sharing protocols developed for TIGGE will be expanded to enhance access to ensemble forecast data. First, for the purpose of GIFS product generation, ensemble data will become accessible directly from the originating centers. This, along with the use of sophisticated data access tools that allow the transfer of only those portions of the entire that set that is needed for any particular application, will lead to significant reductions in data latency, supporting real time forecast applications. Applications may include more complex tasks such as Limited Area Model (LAM) forecasting that require the provision of initial and boundary condition data. Providers can control what type of data, under what conditions, and to what user groups the forecasts are made available; the general policy for GIFS data access is discussed below. High-impact weather product generation: Derived products and other services will be provided to users via an enhanced version of the web interface that is also used for accessing basic ensemble forecast data. Products will be based on ensemble forecasts, possibly including higher resolution unperturbed forecasts, collected potentially from a number of NWP centers. As part of the WIS, a web-oriented Information Technology (IT) infrastructure will be established to facilitate the exchange and sharing of various tasks among data and service providers, as well as access to and dissemination of an expanding suite of products and services to users in less-developed countries with limited telecommunication and other IT capabilities. Products that are commonly requested by the regions or other users and are cpu intensive will be generated on schedule by volunteer data providers (called Data Collection or Product Centers DCPC - in WIS), e.g., global and regional NWP centers or WMO Regional Specialized Meteorological Centers (RSMCs). The products then will be distributed either directly from global NWP centers or cascaded via regional centers to the users. The flow of information will be assisted through the use of metadata by Global Information System Centers (GISCs). Product generation and services will be supported by software submitted to a jointly maintained and developed toolbox, shared by the GIFS community. Documentation of the available tools and products, including verification statistics, will assist the users in selecting from, and using the available products. GIFS implementation (2012 onwards). The implementation phase of GIFS will combine both strands of GIFS development to enable the real time generation of products and services. This will be followed by further enhancements, leading to the formation of an End-to-End GIFS forecast system. At this advanced stage, user requests will influence not only what products or services are provided but, to maximize the value of such products and services, will also adaptively optimize the entire forecast process. GIFS Products: In the Products stage of GIFS various products and services will be generated in an operational environment for use in high impact events by the global forecast community, with a special emphasis on developing nations. The list of products and services will be developed in collaboration with the users to ensure that the products and services are compatible with the often limited telecommunication facilities in developing regions, and that the users are trained and ready to work with the new products. Experience in SWFDPs such as that organized by WMO in Southern Africa in 2006-2007 will be fully utilized. It is expected that after initial testing of some prototype GIFS products, the list of products and services for high impact events will be gradually expanded during the implementation phase of GIFS. End-to-End GIFS: The End-to-End GIFS will involve special protocols and procedures designed for the adaptive, or on demand use of observational, data assimilation, and numerical modeling (e.g., LAM, in collaboration with WWRP Mesoscale Weather Forecasting WG) procedures. The significant broadening of the GIFS project poses special challenges such as the development and acceptance of rules determining priorities for allocating various resources for the End-to-End forecast process related to high impact events. The End-to-End GIFS system, however, offers significant new benefits such as a more direct, two-way interaction between the users and providers of the forecasts, where the entire forecast process may be adaptively shaped to meet user requirements. It is anticipated that different aspects and capabilities of the End-to-End GIFS (e.g., adaptive observations, LAM ensembles, etc.) will be tested and implemented gradually, as they become developed. For a hypothetical example of GIFS activities associated with forecasting a high impact event, see Appendix A. DATA POLICY. Since the aim of GIFS is to facilitate the delivery of public weather services, especially warnings of high-impact weather, its data policy will be developed to allow sufficient distribution of data for products to be generated and cascaded to NHMSs and users, particularly what is necessary for forecasts of high-impact weather events. Weather forecasts, however, are also a valuable commodity for many NHMSs. Therefore in general it will not be feasible to provide unfettered access to forecast data in GIFS. More general access, however, could be permitted for scientific research after a suitable delay, in line with the current TIGGE data policy. Also, real-time access could be allowed for key projects, including the development of agreed upon GIFS capabilities and support for THORPEX field campaigns. Furthermore, data providers will be free to allow wider access to their data on a voluntary basis. See Appendix B for further discussion. DATA ARCHIVING. It is anticipated that the existing TIGGE archive centers will retain the data they accumulated and keep them accessible to the research community for the foreseeable future. Subject to research user requirements, the archive centers will continue accumulating TIGGE data during the entire GIFS-TIGGE project. In addition to the central archive facilities, some ensemble forecast providers and product generating centers may set up their own archive system. Work will be needed to link the three TIGGE archives and possible newly established archives, including those for TIGGE-LAM data with a common web interface that will be developed for real time data access. SCIENCE SUPPORT. It is anticipated that most aspects of the development of GIFS will be based on and/or supported by research carried out in the WMO/WWRP THORPEX research program by the international Data Assimilation and Observing Systems (DAOS), Predictability and Dynamical Processes (PDP), and the WWRP Socio-Economic Research and Applications (SERA) Working Groups (WGs), in collaboration with the GIFS-TIGGE Working Group. There are a number of important open science questions that should be addressed using the TIGGE archive and other datasets, for example: Statistical correction: One issue is the choice of statistical bias correction algorithms for multi-center ensemble forecasts, and the optimal distribution of computer resources for generating real time forecasts (at the maximum resolution possible) vs. hind-casts (the largest number of cases possible) for high quality statistically post-processed ensemble forecasts Multi-center ensembles: We envisage that some of the GIFS products will be based on ensembles run at several different centers using different NWP models. Use of multi-model ensemble products in GIFS will also benefit from experience accumulated in the North American Ensemble Forecast System (NAEFS, Toth et al. 2005). Appendix C provides a brief overview of multi-center ensemble forecasting. Early results from TIGGE demonstrate the potential of multi-model ensembles to improve forecasts of, for example, surface air temperature. However, further research is required to show the impact of multi-model ensembles on predicting other types of high impact weather such as heavy rain and strong winds. One benefit of making TIGGE data widely available to the research community is that universities could provide valuable contributions by developing verification and post-processing methods. It is proposed that, for timely implementation of research findings into operational practice, scientific exploration and technical developments necessary for the development of GIFS proceed in parallel. VALIDATION. The effectiveness of the new techniques, such as various statistical correction methods and multi-center ensembles will need to be evaluated before they are incorporated in GIFS. Similarly, the effectiveness of adaptive systems such as observation targeting and on-demand LAM ensemble forecasts will need to be demonstrated in order to give forecasters and users confidence in those systems. Validation of adaptive systems would be made more tractable by, for example, having a limited list of possible on-demand LAM configurations. Assessment of these novel techniques require further development and application of verification systems, to include both probabilistic scores and appropriate user-oriented verification methods. See Appendix D for further notes on ensemble verification. TECHNICAL ASPECTS. The development of GIFS is not possible without the innovative use of existing and to-be-developed software and other procedures, facilitating a new level of international collaboration. Some of the new technical aspects of GIFS are introduced in Appendix B. COORDINATION. GIFS is a hazard reduction project of the Global Earth Observing System of Systems (GEOSS), fully coordinated under WMO. Research aspects of the program are guided by the World Weather Research Program (WWRP), Commission on Atmospheric Sciences (CAS), while operational and technical aspects are guided by the Commission on Basic Services (CBS) and the new WMO Information System (WIS) recommendations. The CBS Expert Team on Ensemble Prediction Systems (ET-EPS) will help articulate existing operational requirements. The THORPEX Regional Committees will provide feedback regarding the preparedness of the various WMO Regions concerning the expected new types of products and services, including LAM ensemble applications. The proposed operational GIFS configuration will also be closely coordinated with ET-EPS to facilitate timely training for new GIFS products and services. ORGANIZATION. GIFS builds on the cascading and interactive structure developed and successfully used in the Southern Africa SWFDP. Since most events manifest themselves locally, disaster prevention and mitigation efforts occur on a national and regional scale. National Hydro-Meteorological Services (NHMSs) will contribute by articulating the meteorological and climatological data and service requirements for weather and climate related disaster mitigation. To address those requirements, Global NWP centers will provide and statistically enhance ensemble forecast data, and generate some derived probabilistic forecast products out to 15 days lead time. WMO Data Collection or Product Centers (DCPCs) will carry out further critical high impact event related activities, such as collection of targeted observations, production of LAM ensembles for shorter lead times, generation of special products for use by NHMSs in their area of interest, as well as training related to the use of such products. Finally, the NHMSs will disseminate forecast products and provide guidance in their respective service areas. For a more detailed list of anticipated GIFS participants and their possible contributions, see Appendix E. REGIONAL FOCUS. GIFS will focus on the particular regions and types of events anticipated to have high socio-economic impact. At any one time, there might be several forecast high-impact weather events potentially affecting different regions of the globe. As discussed above, GIFS operations will have several levels of coordination, including global, regional, and national, all following the concepts of the new WIS. Of these, regional coordination will be of particular importance, with the following potential components and benefits: special focus on high impact weather events affecting each specific region regional focus for the collection of global ensemble forecast data (a natural development from TIGGE) facility to carry out on-demand forecasts to get more detailed forecast for their region, or sub-region, including the generation of regional ensembles (developed in coordination with TIGGE-LAM project) routine generation of high-impact related weather products for their particular region, based on both global and regional ensembles facility to generate additional non-routine products in response to specific weather conditions and/ or user demand verification of the ensemble forecast products, to provide basic quality information to users observation targeting system focused on improving forecasts for their region and ability of less developed countries within each region to benefit from access to better targeted weather forecasts. CAPACITY BUILDING. While GIFS is expected to improve the skill and utility of weather forecasts for high impact events in general, a particular emphasis of THORPEX is to ensure that least developed and developing countries share in these benefits. Considering that less-developed countries have limited capacity to receive data and limited capacity to process forecast information for their own benefit, GIFS will include significant activities related to capacity building in less developed regions of the world, in collaboration with THORPEX Regional Committees. CHANGE MANAGEMENT IN GIFS. Changes to individual ensemble systems will happen all the time, and these will have an impact on multi-model products. Currently, when changes are made at individual NWP centers, users are warned well in advance, and the impact of changes are described. As GIFS moves from initial product development towards a pre-operational end-to-end NWP system, methods will need to be developed to manage the changes and maintain the integrity of GIFS, and keep users informed. Criteria to assess the impact of any changes, especially in cases where benefits to some users are offset by degradation of other aspects of the forecast performance, will need to be discussed. FIRST STEPS. GIFS development calls for an unprecedented level of international collaboration, along with the use of recently or newly developed software and other IT infrastructure. Therefore, as a risk reduction measure, it is proposed that procedures are first developed and tested in a limited setting. Following the recommendations of the WMO International Workshop on Tropical Cyclones (IWTC-lV, see Appendix F), 1-15 day tropical cyclone forecasting is being used as a prototype for GIFS products developments. This choice limits data exchange and processing needs to tropical cyclone specific information, requiring only a fraction of the bandwidth and computational resources needed for full 3-dimensional atmospheric data processing. This allows for extensive experimentation, facilitating the development of a prototype GIFS-Product system for tropical cyclone prediction. Such a multi-center ensemble forecast system will offer great benefits to the user community for one of the most threatening high impact events on Earth. In addition, an early, Beta test phase of the tropical cyclone aspect of GIFS benefits the THORPEX Pacific Asian Regional Campaign (T-PARC). T-PARC, carried out in conjunction with the International Polar Year (IPY), is a major international field experiment aimed at the study of tropical and extra-tropical cyclones over the Pacific Ocean. Appendix G describes the content and format of tropical cyclone data used for GIFS-TIGGE data exchange, along with a timeline for the development of Tropical Cyclone related GIFS Products. FURTHER DEVELOPMENT. Beyond tropical cyclone data, GIFS will also incorporate extratropical cyclone data and processes as soon as operationally feasible. Due to its utmost importance in hydrologic and other applications, Probabilistic Quantitative Precipitation Forecasting (PQPF) will be considered as a second important application area for GIFS. The exchange of 3-dimensional atmospheric fields and associated processing and product/service generation for other types of high impact weather events will be guided by the experience accumulated in the development of TC and PQPF GIFS Products. Another possible development would be to collaborate with some of the SWFDP projects to include some experimental multi-center ensemble forecast products in their experiments. This would give an early insight into the likely benefits to operational forecasters from the wider distribution of multi-model products in GIFS. Due to its complexity, preparations needed for the development, testing, and possible implementation of the final, End-to-End stage of GIFS will require the longest period. Appendix H lists a proposed timeline, consistent with the longer range plans outlined in the THORPEX International Research Implementation Plan (TIP 2005), for TIGGE and GIFS developments. ROADMAP. After the incorporation of feedback from the broader THORPEX community, the conceptual plan for GIFS described in this document will be presented to the THORPEX International Core Steering Committee for their approval. Next, the technical infrastructure (see Appendix C) necessary for the development of GIFS will have to be designed in detail, followed by the development of implementation plans for various aspects of GIFS, similar to that for TC GIFS referenced in Appendix G. To facilitate planning related to the infrastructure and implementation of GIFS, the GIFS-TIGGE WG proposes to create two focus groups, composed of technical experts from and outside of the GIFS-TIGGE WG. The first focus group will consider access to real time and archived data, and the second will address real time product generation. The focus groups will report to the GIFS-TIGGE WG. For the charge for the two focus groups, see Appendix I. Once the initial design phase for various GIFS applications is over and the plans are approved, the focus groups will facilitate the execution of the plans in coordination with the global, regional, and national GIFS participants. While scientific and technical development will be joint under the focus groups for all GIFS applications, due to differences in weather patterns, preparedness, and user interests, product development will be carried out in sub-groups formed to address the specific needs of various regions or other types of GIFS applications. For further details, see Appendix J. REFERENCES. Buizza, R., M. Miller and T.N. Palmer, 1999: Stochastic representation of model uncertainties in the ECMWF ensemble prediction system, Quart. J. R. Meteorol. Soc., 125, 2887-2908. Candille, G., 2007: The Multi-Ensemble Approach: the NAEFS Example. AGU Abstract, available at: http://adsabs.harvard.edu/abs/2007AGUFMNG32A..07C Cui, B., Toth, Z., Zhu, Y., Hou, D., Unger, D., Beauregard, S., 2006: The Trade-off in Bias Correction between Using the Latest Analysis/Modeling System with a Short, versus an Older System with a Long Archive. The First THORPEX International Science Symposium. Deccember 6-10, 2004, Montral, Canada, World Meteorological Organization, P281-284. De Pondeca, M., and Coauthors, 2007: The Status of the Real Time Mesoscale Analysis at NCEP. Preprints, 22nd Conf. on WAF/18th Conf. on NWP, Park City, UT, Amer. Meteor. Soc., 4A.5. Ebert, B., Z. Toth, M. Charles, and G. Ross, 2008: An XML format for cyclone analyses and forecasts. Available at http://www.bom.gov.au/bmrc/projects/THORPEX/CXML/CXMLspecification.doc Evans, R., M. Harrison, R.J. Graham and K. Mylne, 2000: Joint medium-range ensembles from the Met Office and ECMWF systems, Mon. Weather Rev. 128, 3104-3126. Johnson, C. and R. Swinbank, 2008: Medium-range multi-model ensemble combination and calibration, Q. J. R. Meteor. Soc. (submitted). Also issued as Forecasting Research Technical Report no. 517, Met Office. Matsueda, M. and H.L Tanaka, 2008: Can MCGE outperform the ECMWF ensemble?, SOLA, 4, 77-80. doi:10.2151/sola.2008-020. Palmer, T. et al, 2004: Development of a European multimodel ensemble system for seasonal-to-interannual prediction (DEMETER), Bull. Amer. Meteor. Soc., 85, 853-872. Pappenberger, F., J. Bartholmes, J. Thielen, H. L. Cloke, R. Buizza, and A. de Roo, 2008: New dimensions in early flood warning across the globe using grand-ensemble weather predictions, Geophys. Res. Lett., 35, L10404, doi:10.1029/2008GL033837. Park, Y.-Y., R. Buizza, and M. Leutbecher, 2008: TIGGE: preliminary results on comparing and combining ensembles, Q. J. R. Meteor. Soc. (submitted). Also published as ECMWF Technical Memorandum No. 548. Pena, M., and H. van den Dool, 2008: Consolidation of Multi Model Forecasts by Ridge Regression: Application to Pacific Sea Surface Temperature. J. Climate, in press, available at:  HYPERLINK "http://ams.allenpress.com/archive/1520-0442/preprint/2008/pdf/10.1175_2008JCLI2226.1.pdf" http://ams.allenpress.com/archive/1520-0442/preprint/2008/pdf/10.1175_2008JCLI2226.1.pdf TIP, 2005: THORPEX International Research Implementation Plan, World Meteorological Organization (WMO), WMO/TD-No. 1258 WWRP/THORPEX No. 4, available at: http://www.wmo.int/pages/prog/arep/wwrp/new/documents/CD_ROM_implementation_plan_v1.pdf Toth, Z., J. Desmarais, G. Brunet, Houtekamer, Y. Zhu, R. Wobus, R. Hogue, R. Verret, L. Wilson, B. Cui, G. Pellerin, B. Gordon, E. O'Lenic, D. Unger, 2005: The North American Ensemble Forecast System (NAEFS). Proceedings of the 1st THORPEX International Science Symposium, December 2004, Montreal, Canada. Available at HYPERLINK "http://wwwt.emc.ncep.noaa.gov/gmb/ens/papers/toth_naefs_thorpex_montreal.pdf"http://wwwt.emc.ncep.noaa.gov/gmb/ens/papers/ toth_naefs_thorpex_montreal.pdf Toth, Z., and S. Vannitsem, 2002:  HYPERLINK "http://wwwt.emc.ncep.noaa.gov/gmb/ens/papers/ecmwf-finalwordweb.htm" Model errors and ensemble forecasting. Proceedings of the 8th ECMWF Workshop on Meteorological Operational Systems. November 12-16, 2001, Reading, England, 146-154. APPENDICES APPENDIX A GIFS HIGH IMPACT EVENT EXAMPLE Case Selection. Consider the following example in the advanced, End-to-End stage of GIFS. The most recent GIFS threat assessment based on a multi-center bias-corrected and downscaled ensemble forecast system indicates that there is a 10% chance of a major typhoon making a landfall on a densely populated portion of the East Asian coast 8 days in the future. Impact assessment models indicate a significant risk for wide spread damages, including inland flooding, with potentially catastrophic consequences. Adaptive Procedures. In response, all restrictions on the distribution of observational and forecast data related to the typhoon forecast are suspended by all data providers. Considering other potential threats on the global scale, observational resources such as the collection and processing of data from polar orbiting and geostationary weather satellites, available unmanned aircraft and other repositionable platforms and instruments are directed to increase the surveillance of all areas of the atmosphere that may influence the emergence, development, and movement of the would-be typhoon. Special efforts are made at global NWP centers to ensure that all relevant observational information is captured. Numerical model integrations used for predicting the typhoon development are adaptively configured at a number of selected global NWP centers to provide enhanced spatial, temporal, and physical representation of processes affecting the typhoon development. The output of numerical ensemble forecasts undergoes statistical bias correction and downscaling by the global NWP and WMO DCPCs, leading to higher quality numerical predictions for the typhoon. Benefits. Due to the continued enhanced adaptive observational, data assimilation, numerical modeling, and user application procedures it is determined 4 days prior to the potential landfall that the typhoon will stay over water and pose no direct threat to land. Without the special procedures, such a determination could have been made only a day and a half later. As a result, the evacuation of millions of inhabitants and other expensive safety measures are avoided, leading to significant cost savings and other benefits. Special adaptive procedures related to the typhoon are discontinued; the freed-up observational and other resources are redirected to routine high impact forecast procedures, or another major threat if one is identified. APPENDIX B NEW TECHNICAL ASPECTS OF GIFS Initial capabilities for GIFS will be built up gradually, by the development and addition of new features during the development stage of GIFS. The development work is expected to continue after the implementation of initial GIFS capabilities. All development will be done in accordance with the guidelines for the WMO Information System (WIS). The international coordination of these developments offers significant overall cost savings and accelerated implementation for the organizations participating in TIGGE/GIFS. Details of the plans for GIFS are yet to be finalized by the TIGGE focus groups (see Appendix H), but it is anticipated that the new aspects of GIFS will include: ENSEMBLE DATA ACCESS Real time data access. A critical new aspect of GIFS is the ability to provide ensemble forecast data for product generation purposes in real time. This can be achieved by each center making their data accessible directly from their center. This may require the establishment of appropriate hardware and software infrastructure. This is in contrast with TIGGE procedures, where archive centers collect large amounts of ensemble data from all producing centers for subsequent research, rather than for real time data access. In GIFS we will develop facilities to enable products from, for example, multi-model ensembles to be produced and distributed in near real-time. Data distribution policy. The distributed data access arrangement will allow each contributing center to control the type and amount of data they wish to share with the GIFS developer and user communities at any time. Current data policy varies widely across NWP ensemble and product generating centers. While some centers make all ensemble data and derived products publicly available without restrictions, others limit the set of information that they distribute free of charge. Centers wishing to participate in the development and operational implementation of GIFS may need to ease or eliminate some of the current restrictions on their data distribution policies. For example, during the development stage of GIFS, producing centers may grant real time data access to trusted GIFS partners only. In the testing and implementation phase of GIFS, centers may open up their distribution of specific data or derived products to a wider user community. During high impact events, the ensemble producing centers may further relax the restrictions on their data/product distribution. Data exchange format. The use of agreed-upon formats for data exchange is critical for the establishment of (a) a common web interface; (b) shared software development, and (c) common toolbox, and (d) shared data processing. An appropriate format for each data type will be selected considering international data exchange requirements, current practices at NWP providing and TIGGE archive centers, as well as Global Earth Observing System of Systems (GEOSS) and WMO/WIS recommendations. We anticipate the continued use of WMO GRIB2 standards for the distribution of gridded fields. Data distribution format. While the use of a common format for exchanging data among participating centers is necessary, to satisfy different user groups, GIFS centers may need to distribute data in one or more additional formats. For example, netcdf is widely used by the seasonal forecasting and climate modeling communities; a GRIB2 to netcdf converter has been developed to support the provision of netcdf data. Common web user interface. GIFS will use a common web-based user interface with the aim of closing the gap between the providers and users of forecast data. This interface will be developed jointly by the GIFS community using standards including metadata. Following the WIS concept, interoperability across data, transport, and search functions will facilitate the deployment of the system possibly at several GIFS providing or other centers, supporting uninterrupted services. At later stages, web services may become more advanced. For example, data providers will be able to serve the data in multiple formats, beyond the format selected for GIFS data exchange; and users may be able to set up a user profile specifying common elements in their data/product requests. TIGGE-LAM data provision. TIGGE providing centers may support real time LAM ensemble applications by making available global high resolution forecast data for use as initial and boundary conditions by TIGGE-LAM research groups and operational centers. The content of data to be provided by the global ensemble producing centers is currently under discussion. Data archives. Access to archived TIGGE ensemble forecast data will remain important for research and development purposes. As TIGGE continues, the three existing archive centers will continue collecting global ensemble data. The existing archive centers have also agreed to archive high priority data for the TIGGE-LAM project, on a regional basis. (CMA, ECMWF, and NCAR are expected to collect data from and around Asia, Europe, and North America, respectively.) In addition, some global and regional (LAM) NWP data providers may also establish archives for their own data. Access to archived data will be through the common web interface that will also serve requests for real time data, once the interface is developed. GIFS PRODUCTS Voluntary contributions. Data and processing contributions by NWP and other centers are voluntary. The level of commitment by centers participating in GIFS will be according to their interest and resources. Initial commitments may be low, to be increased, if resources and policies permit, based on accumulating experience with GIFS. Data access needs. For the success of GIFS, it is desirable that participating centers consider: During the development phase of GIFS, allowing real time access to all ensemble data necessary for testing statistical processing, product generation, and verification algorithms for other GIFS participants. Further distribution of ensemble data or products by other participants will be subject to special permits that may be granted by the producing centers upon special requests during demonstration or field campaigns; During the implementation and operational use of GIFS, continued real time access to ensemble data and products related to GIFS operations to other GIFS participants, plus consent to the redistribution of such data for high impact weather events as defined by the GIFS community in consultation with relevant WMO bodies. GIFS procedures should be configured flexibly so that temporarily or newly available data can be incorporated into the forecast process with ease. This will allow for the generation of guidance products based on the set of ensemble forecasts that is available at any time. During routine conditions, this set may be limited to ensembles from centers without data distribution restrictions, while during high impact events, it may be expanded with forecasts from the other participating centers. Shared software development Common toolbox. Data processing (e.g., statistical bias correction), verification, and product generation tools (e.g., combining ensembles from different centers and determining probabilities for exceeding a particular threshold value, downscaling to provide calibrated forecasts for major population centers) will be developed by a number of groups working in parallel, as part of a community effort. A toolbox will be set up to which all interested and registered parties can contribute, subject to proper documentation and verification of the methods / algorithms / codes. Protocols will be developed by the focus groups for the exchange of software and other tools (see Appendix H). Rules and standards will be carefully chosen to increase the productivity of the community enterprise. For example, different regional GIFS centers may produce products based on different sets of constituent ensembles (depending on what is most readily available), but these products could all be produced using the same common toolbox. Shared data processing. Data, product, and service requests will be submitted via the common GIFS web user interface and relayed to data producing and processing centers. Raw or pre-processed data will be collected before derived products are made at the product generating center. The derived products are then uploaded to the web either on a fixed schedule (in case of pre-made products), or as a response to a special query. Pre-made vs. on-demand processing. Computationally intensive and/or often requested products (e.g., bias correction of individual ensemble members, or characterization of significant features such as tropical cyclones) may be computed at the producing centers according to an operational schedule. Additional products will be prepared in response to requests, using resources dedicated for such jobs at participating centers (e.g., derivation of exceedance probabilities, given a bias corrected set of ensemble forecasts). On-demand products would need to be requested from a standard menu of product types, domains, etc. Incoming queries will be screened to eliminate requests with excessive computational, bandwidth, or algorithmic requirements. Probabilistic forecast guidance. Internally, GIFS will contain ensemble-based information about future high impact weather events in probabilistic form. The information can then be reformulated to various related products (for example, 10th and 90th percentile values) depending on the specifics of external data requests. Links with seasonal prediction. The Working Group on Seasonal to Interannual Prediction (WGSIP) of the World Climate Research Program (WCRP) considers plans for more effective preparation and use of seasonal forecasts, through the Climate-system Historical Forecast Project (CHFP). It is desirable that GIFS (days 1-14 days) and CHFP (1-24 month) predictions are developed in a coordinated fashion, leading to improvements also in the intermediate, 10-60 days Intra-Seasonal (IS) time range. We envisage opportunities in the following areas: temporal and spatial integrals (as compared to products instantaneous in time), including time mean, and temporal frequency of events, to be used as products for extended range forecasts; seamless suite of forecasts from days to seasons, facilitating concurrent use of forecasts on multiple time scales; and introduction of netcdf data format to accommodate the needs of the climate research community. END-TO-END GIFS Adaptive procedures. Adaptive observational, data assimilation, numerical modeling, and user application procedures, where resources are adaptively shifted to maximize forecast quality and impact for selected high impact events, requires careful planning and development work. A range of procedures are already in use, such as special tropical and winter storm related observations, limited area numerical modeling, and other activities. Existing procedures, however, are often inflexible and constitute the exception, not the norm in the numerical forecast process. Interdisciplinary collaboration among atmospheric, engineering, and IT specialists will be required to develop the scientific and technical basis for adaptive enhancements to the NWP process. The cost and value of these adaptive enhancements will need to be carefully assessed. High impact event selection. High impact events during the End-to-End phase of GIFS will be identified using user specified thresholds and procedures. Beyond GIFS ensemble product-based automated alarms, adaptive GIFS procedures can also be directly requested by user groups based on their subjective evaluation of weather related hazards. Prioritization of and resource allocation for high impact events. The GIFS system will be developed to serve the interests of the entire global user community, including less developed and developing nations and regions. How available observational and other resources can be used in an equitable manner to best serve the global user community will require social science research and testing of proposed alternatives. While some processes will have to be coordinated on the global level, requiring additional planning and coordination, others will be best undertaken by the WMO DCPCs, which can allocate their own resources to satisfy the unique needs of each region. Targeted observations. GIFS will develop and if they add value, operationally implement global and regional targeted observational procedures to collect high impact forecast specific observational data from adaptively deployable platforms and instruments. Targeted data processing. GIFS will develop and if they add value, operationally implement adaptive data processing and assimilation methods (e.g., enhanced use of satellite observational data in areas sensitive to high impact events, possibly including new data types developed for such applications). Adaptive numerical forecasting techniques. GIFS will develop and if they add value, operationally implement methods to adaptively configure NWP methods to maximize value for high impact events. This may include an increase in the spatial and temporal resolution of forecasts, the realism (and computational cost) of physical parameterization schemes, ensemble members, etc, to maximize their value for high impact event forecasting. Downscaling. Todays global NWP models lack the horizontal resolution required by most users. GIFS will support both dynamical (Limited Area Modeling, LAM) and statistical approaches to render the GIFS forecasts more useful for a wide variety of applications. User-driven product generation. Going beyond a routine set of products, End-to-End GIFS will offer the potential for generating additional case-dependent products and services. More resource-intensive requests will be limited to authorized users. Reference downscaling analysis. GIFS will promote the sharing of procedures and products related to the generation of observationally based fine resolution analysis fields needed for the statistical downscaling of GIFS forecasts, as well as for the verification of all downscaled forecast products on regional and possibly on the global scale. User specific verification. Beyond the basic verification built into the GIFS product generation procedures at the producing centers, GIFS users are also encouraged to share their experience with the use and performance of GIFS products and services. Feedback, including special verification statistics may contribute to improvements in GIFS procedures, potentially benefiting the entire GIFS user community. Capacity building. Special efforts need to be extended toward identifying the best practices for engaging less developed countries in the GIFS process. This will involve the assessment of regional forecast needs, telecommunication limitations, forecaster training, and help with the outreach to the user community in less developed countries. Collaboration with the WWRP Mesoscale Working Group, as well as the seasonal and climate change forecast communities may yield mutual benefits as most users must base their operational decisions on forecasts on multiple time scales. APPENDIX C MULTI-CENTER ENSEMBLE FORECASTING INTRODUCTION. Ensemble forecast systems that generate all their members with the same version of a single NWP model typically exhibit under-dispersion. This is often attributed to the lack of representation of model-related uncertainties in such ensemble forecasts. In the past, three main approaches have been tested to remedy this problem. First, stochastic perturbations can be used to account for random type of model errors (e.g., Buizza et al. 1999). Second, various versions of the same model(s) can be used to generate various members in an ensemble (Houtekamer et al.). And third, forecasts (possibly an ensemble of forecasts) generated using completely different NWP models can be combined into a multi-center ensemble. Such an ensemble should capture some of the uncertainty related to both the model and the initial conditions. Regime- or case-dependent model errors, that are difficult or impossible to statistically correct, may also be reduced as long as regime dependent biases vary among the models (Toth and Vannitsem, 2002). The aim of this appendix is to summarize the current research and operational experience of using multi-model ensembles based on TIGGE and NAEFS ensemble forecasts, and to discuss the prospects for additional operational multi-center ensemble products. PREVIOUS RESULTS. In the context of seasonal forecasting, it has been shown that the combination of ensemble forecasts from different models results in more skill than the single model ensembles considered separately (e.g. DEMETER project, see Palmer et al, 2004). This improvement is not just a result of the increased ensemble size, but is also due to complementary information provided by the different climate forecast systems (i.e., data assimilation and numerical model). Studies exploring more sophisticated methods to weight the different ensembles found modest or no advantages over using equal weights with typically available sample size (see, e.g., Pena and van den Dool 2008, and references therein). Possibly the first attempt at combining operationally generated global ensemble weather forecasts (Zhu et al. 1996, personal communication) showed the largest benefit from combining ECMWF and NCEP ensembles for cases when the performance of the two systems was similar (Fig. C1, days 6-8 range). Other studies (e.g. Evans et al, 2000) have also indicated benefits of multi-model ensembles for medium-range forecasts. Results from some more recent studies on weather ensemble combination are summarised below.  Fig C1: Ranked Probability Skill Score (RPSS) for NH 500 hPa geopotential height for the 14-member NCEP ensemble (10 members from 0000 UTC, 4 members from previous 1200 UTC cycle) and a 14-member randomly selected subset of the ECMWF ensemble (previous 1200 UTC cycle) for the December 1995 February 1996 period. Event thresholds for the RPSS are the 10, 20, etc percentile values from the NCEP-NCAR reanalysis climatology. EARLY RESULTS FROM TIGGE In conjunction with TIGGE, a few research studies have been carried out to investigate the benefits of using multi-model ensembles that combine results from some of the TIGGE models. Park et al (2008) compared the skills of all the single-model ensembles available in the TIGGE archive for several different periods. They primarily focused on comparing single-model ensemble forecasts of 500 hPa geopotential height and 850 hPa temperature, each verified against their own analyses. In addition, they investigated the skill of several different multi-model combinations of single-model ensembles. An example is shown in Fig C2, for the Rank Probability Skill Score (RPSS) of the ECMWF and Met Office (UKMO) forecasts of 500 hPa geopotential height and 850 hPa temperature, together with results from the corresponding two-model ensemble with and without bias correction. The RPSS is based on 10 climatologically equally likely categories. The relatively low apparent skill of the UKMO forecasts of 850 hPa temperature is attributable to the systematic differences between the UKMO analyses and the ERA-40 climatology that was used as reference data. The 500 hPa NH extratropical height shows a small benefit from the multi-model (with bias correction improving short-range skill but degrading week-2 skill). The 850 hPa tropical temperature results show significant benefit from bias-corrected multi-model ensemble, but not from the non-bias-corrected multi-model. Qualitatively similar results were found with other combinations of models for other periods; multi-model forecasts only gave small benefits for forecasts of NH 500 hPa height, but generally better results for tropical 850 hPa temperatures.   SHAPE \* MERGEFORMAT  SHAPE \* MERGEFORMAT  Figure C2: Combination results for a) NH 500 hPa height and b) Tropical 850 hPa temperature, for June-August 2007 (86 cases), showing average scores of EC (ecmwf, dashed red line), UKMO (ukmo, dashed blue line), combined EC and UKMO (eu, solid black line) and bias-corrected combined EC and UKMO (eu_bc, solid blue line), with biases estimated using a 30-day training period.  Figure C3: Improvements in RPS of 51-, 154- and 279-member multi-model ensembles relative to the ECMWF ensemble for 500hPa heights over 20-90N for three-month periods from December 2006 to November 2007. Matsueda and Tanaka (2008) compared the ECMWF ensemble with three different multi-model ensembles, each of which combined forecasts from CMC, ECMWF, JMA, NCEP and UKMO. The first, 51-member, ensemble was designed to be the same size as the ECMWF ensemble; it was created by taking 10 members from each model (11 from ECMWF). The second ensemble was 154 members, all the members then available from the 12 UTC forecasts from each centre. The size of the third ensemble was 279, comprising all members available each day. Figure C3 plots RPSS of the ensemble forecasts of 500 hPa height, relative to the ECMWF ensemble. Verification was performed against the ECMWF analyses. These results show relatively small benefit from the multi-model ensemble; in some cases there is a detriment in the early stages of the forecasts. The 51-member ensemble shows that a small benefit results from using multiple models, but keeping the same sized ensemble - even though the ECMWF model is the most skilful. The 154-member ensemble shows most benefit, while the 279-member ensemble suffers from some of its component members being taken from older forecasts.     Figure C4 Brier skill scores for a) mean sea level pressure greater than the climatological mean, b) 2m temperature greater than the climatological mean, c) 2m temperature greater than 90th percentile. They grey lines show the bias-corrected single-model ensembles (ECMWF, Met Office and NCEP) and the black lines show three difference multi-model ensembles: simple combination (dotted), weighted (dashed), weighted and variance adjusted (solid). The data are globally averaged over 120 days ending 29 April 2008. Johnson and Swinbank (2008) investigated the benefit of a 3-model ensemble, using ECMWF, NCEP and UKMO ensembles. Figure C4 shows Brier Skill Scores for mean sea level pressure and surface air (2m) temperature, verifying the skill of forecasts relative to climatological percentiles defined using ERA-40 data. Each forecast was bias-corrected and forecasts were verified against a multi-model analysis (average of the three T+0 fields). Three variations of multi-model ensemble were assessed: first, each ensemble was weighted equally; second, each ensemble was weighted to take account of its estimated RMS error, and third, both weights and variance of each ensemble were adjusted. Fig C4a shows that the skill in forecasting sea level pressure greater than the climatological mean is very similar for both the ECMWF and multi-model ensembles. Fig C4b compares scores for forecasts of 2m temperature, relative to the mean; in this case all three multi-model ensembles give a significant improvement over any single ensemble. The largest benefit of multi-model ensembles is shown for forecasts of 2m temperature greater than the 90th percentile (Fig C4c). The results show relatively small impacts from varying the ensemble weighting, consistent with the earlier work by, for example, Pena and van den Dool 2008. In summary, results from TIGGE have demonstrated only limited benefit of multi-model ensembles for forecasts of 500 hPa height and sea level pressure. On the other hand, multi-model techniques give better benefit for 2m temperature and, to a lesser extent, 850 hPa temperature. One possible explanation for the differencdes in the effectiveness of the multi-center approach is that large scale dynamical fields are generally consistently forecast by current NWP models, there is less consistency between models for near surface variables, as these forecasts are more dependent on details of physical parameterizations. Another possible explanation, is that benefits form multi-center combination are more significant when ensembles with comparable skill are combined from different centers whereas the benefits are less clear when poorer performing ensembles are added to a better performing system. The verification statistics do seem to be sensitive to the verification data and climatological reference data. Although we have only shown examples of one type of score from each study, all the studies showed clearer benefits of multi-model ensembles for probabilistic scores (RPS, Brier) than verification of ensemble means.  Figure C5: For a point on the river Jiu (in Romania) where flooding was observed, the 5th and 95th percentile of river discharge predictions are shown for the different forecasts with a 5-day lead time. The dashed horizontal lines show four flood warning thresholds. Observed discharges refer to simulations based on observed meteorological input. APPLICATIONS Beyond the derivation of probabilistic weather forecasts, ensembles have a wide variety of other applications. They can be used in decision support systems to explore the sensitivity of user relevant outcomes to forecast weather conditions. Pappenberger at el (2008), for example, applied the TIGGE ensemble forecasts to flood forecasting. They applied both single-model and multi-model ensembles to the prediction of a particular flood event in Romania in October 2007. Results illustrate that, in this case, warnings could have been issued as early as 8 days before the event; a comparison of 5-day forecasts is shown in Fig C5. The subsequent forecasts provided increasing insight into the range of possible flood conditions. This case study illustrates the potential value of the TIGGE archive and the multi-model ensembles approach to raise preparedness and thus to reduce the negative socio-economic impact of floods. NAEFS RESULTS The concept of the North American Ensemble Forecast System (NAEFS, Toth et al. 2005) was based on earlier experience that suggested benefits from the combination of ensembles with similar skill from different centers. In NAEFS, the Meteorological Service of Canada (MSC) and the US National Centers for Environmental Prediction (NCEP) global ensembles are both bias corrected and then combined (Cui et al., 2006). For continental US applications, the NCEP ensemble is combined with the NCEP high resolution forecast, and both ensembles are also statistically downscaled to a 5 km grid, using the Real Time Mesoscale Analysis (RTMA, Pondeca et al. 2007), and then combined. The combined ensemble exhibits a significant benefit over the single center ensemble from either organizations. For 2m temperature, for example, the gain in predictability is a day or more at and beyond 2-day lead time (see Fig. C6). As expected, the gain in predictability is typically largest when the ensembles combined have similar sized forecast errors. As for statistical bias correction and downscaling, their effect, as seen in Fig. C6, is very robust. It is worth noting that traditional bias correction is most effective for the first 5-6 days, after which the use of a relatively small sample size is a limiting factor (Cui et al. 2006) For additional NAEFS verification statistics, see results at: HYPERLINK "http://www.emc.ncep.noaa.gov/gmb/yzhu/html/opr/naefs.html"http://www.emc.ncep.noaa.gov/gmb/yzhu/ html/opr/naefs.html and also in Candille 2007.  SHAPE \* MERGEFORMAT  Figure C6: Continuous Ranked Probability Score (CRPS) for 2m temperature forecasts verified against the 5x5 km observationally based RTMA analysis over the continental US. Red line (with open circles) represents 20-member raw NCEP ensemble; blue (open square) and green (full circle) lines represent bias corrected and downscaled CMC and NCEP 20-member ensembles respectively (the latter combined with information from a higher resolution control forecast); black line (plus sign) represent 40-member combined bias corrected and downscaled CMC and NCEP ensembles (NAEFS). DISCUSSION AND CONCLUSIONS Results from seasonal ensemble forecasting, as well as early weather ensemble investigations and TIGGE or NAEFS-based studies are consistent in that the combination of ensembles with similar skill yield multi-center ensembles with significantly improved performance. This suggests that typically there is independent information in the ensembles produced at various centers. As Johnson and Swinbank (2008), and Pena and van den Dool (2008) explain, if the constituent ensembles had more similar kinds of error, the benefit of multi-model ensembles would be less. In reality, one (or more) of the ensembles may exhibit performance superior to that of the others. In such cases, typically little or no benefit is observed from combining ensembles with equal weights when compared to the performance of the best ensemble. However, a multi-center ensemble formed without the inclusion of the best ensemble system may still be competitive with the best single-center system. Such a combined system can well serve the user community in case the best performing system is not available. Results so far from the TIGGE studies have shown that there is some significant benefit from multi-model ensembles for surface air temperature, with only marginal benefits for 500 hPa height and sea level pressure. From the point of view of the user, the most important quantities may be rainfall, wind, temperature and visibility, so it is encouraging if forecasts of such weather-related quantities have more potential to benefit from multi-model ensemble techniques. The large improvement in the forecast skill of temperatures above the 90th percentile (Fig. C4c) may indicate better potential from multi-model techniques for forecasting more extreme weather events. As for statistical corrections, the results suggest that bias correction with the current methods and sample size is useful for forecasts out to 5-6 days lead time, while downscaling can have a significant impact, depending on the variable considered, at all lead times. There is some scope for further research to improve the combination and calibration methods; for example, use of a large hindcast dataset should allow improvements to the bias correction methods. Early results from TIGGE and NAEFS are very promising. Though whether the benefits from the multi-center approach outweigh production costs has yet to be clearly demonstrated, it is expected that many of the products planned for GIFS will be based on ensemble forecasts from multiple, rather than a single, NWP centers. While multi-center ensemble forecasting is a reasonably well established technique for seasonal forecasting, the benefits for medium- or short-range forecasts are the subject of active research. One of the main research areas for the TIGGE project is the further development of multi-center ensemble methods and evaluation of the benefits from the use of different data assimilation, numerical modeling, and ensemble generation techniques as they manifest themselves when ensemble forecasts are combined from various NWP centers. Further research under the THORPEX program over the next few years should demonstrate which types of products would benefit most from multi-model ensemble techniques. APPENDIX D VERIFICATION OF TIGGE AND GIFS ENSEMBLE FORECASTS Verification is required to assess the accuracy, reliability, and utility of any forecast system for both development and application purposes. First, verification must provide feedback to the developers so the forecast systems can be optimized. Second, verification is essential for the users to optimize their applications. Depending on the type of ensemble forecasts, products, applications, and the purpose of verification, various forecast variables need to be verified against NWP analysis fields, observationally based high resolution analysis fields, and/or observations, using a host of verification procedures and metrics. As GIFS involves ensemble forecast and probabilistic product development aimed at practical applications, verification must be an integral part of GIFS developments and must be in the form of probabilistic evaluation as any new methods will have to be carefully evaluated before being offered for use. Regarding the performance of the constituent ensembles contributed by the ten global ensemble producing centers, the GIFS-TIGGE WG endorsed the ensemble verification recommendations of the ET-EPS for use in TIGGE. JMA has very kindly volunteered to include TIGGE ensemble verification along with the standard ensemble verification they currently do for CBS. According to this process, each center verifies its ensemble forecasts against its own analyses and sends deterministic statistics and reliability tables to JMA. JMA then computes and plots a common set of scores and displays them on a password-protected web site (for details see  HYPERLINK "http://www.wmo.int/pages/prog/www/DPFS/Meetings/ET-EPS_Exeter2006/Doc8(1).doc" http://www.wmo.int/pages/prog/www/DPFS/Meetings/ET-EPS_Exeter2006/Doc8(1).doc). The fields being verified are Z500, T850, |V|850, and MSLP, but not precipitation or other surface fields. All TIGGE ensemble producing centers should follow this protocol for providing their deterministic statistics and reliability tables to JMA. In addition to Z500, T850, |V|850, MSLP, and surface precipitation, it was also recommended that surface temperature and 10m wind speed be verified. A major focus of GIFS is to find the procedures to produce the best probabilistic forecast products that can be derived from the ensembles collected by TIGGE. Some related early results are discussed in Appendix C. JMA suggested that their center could possibly carry out some basic verification of multi-center forecasts based on a combination of TIGGE data. This is a much larger task than making plots from statistics tables sent by national centers, and will require some effort to design and implement. Much more work is required in this area to investigate optimal statistical correction and combination strategies. This work is expected to be carried out by researchers in universities and national centers. It is especially important to assess the accuracy of raw and post-processed ensembles for surface variables such as precipitation, temperature, and wind, since this is where the greatest impact of "extreme" weather is experienced. This is a more difficult task since model analyses (at least of verification quality) are generally not produced for surface fields. Mesoscale analyses are starting to become available in some countries and regions. These high resolution gridded fields, along with station observations, should also be used to verify ensemble forecasts where possible. Considering that GIFS will be developed for different regional applications, the Regional Centers (DCPCs) will play an important role. They will gather the relevant verification data and provide ongoing and specialized verification results for their region (see Appendix E). This will include training in the interpretation of verification results to enable the national centers to appropriately use this information in their forecasting process. National centers will also want to do their own verification, particularly for high impact weather events. THORPEX Regional Committees are asked to assist the Regional Centers to obtain the observational data necessary for effective ensemble forecast verification. Standardization of the verification process across the various centers is desirable, as it helps to ensure best practice is followed, and facilitates regional comparisons and sharing of downstream applications. A toolbox of verification software will be developed to assist both regional and national centers in their verification efforts. Since most centers already verify their own ensembles, they are asked to consider contributing their software to the toolbox. As part of GIFS developments, Focus Group 2, that reports to the GIFS-TIGGE WG, will consider how to organize, consolidate, document, etc. the verification routines needed to build and maintain a verification toolbox for the use of the GIFS and wider user community. APPENDIX E ANTICIPATED GIFS PARTICIPANTS AND POTENTIAL CONTRIBUTIONS GIFS will build on past experience with the SWFDP (see  HYPERLINK "http://www.wmo.ch/pages/prog/www/DPFS/Reports/SWFDP-SG_Geneva2008_rev.pdf" http://www.wmo.ch/pages/prog/www/DPFS/Reports/SWFDP-SG_Geneva2008_rev.pdf), cascading forecast data from global NWP centers to regional centers and thence to national services (NHMSs). The product generation and distribution functions necessary for the implementation of the GIFS will be carried out within the WMO Information System (WIS) framework, (see  HYPERLINK "http://www.wmo.ch/pages/prog/www/WIS-Web/Centres.html" http://www.wmo.ch/pages/prog/www/WIS-Web/Centres.html) using, when possible, existing organizational structures and commitments. Global NWP centers and the current Regional Specialized Meteorological Centers (RSMCs), will function as Data Collection or Product Centers (DCPCs), while related metadata to facilitate wide access to the new information produced by GIFS will be available from Global Information System Centers (GISCs) in WIS. National Centers (NCs) serve the data and product needs of their country. A particular emphasis of GIFS will be to provide support for NCs in less developed countries. Regional centers will cater to the specific needs of their region, producing and distributing an appropriate set of products for forecasting severe and high-impact weather, and conducting routine verification of those products. For less developed nations the regional centers will provide guidance in interpreting products, and cascade training where appropriate. In many cases, the regional centers will run regional LAM ensembles as well as distributing products based on global NWP models. Use of multi-model ensemble products in GIFS will benefit from experience accumulated in the North American Ensemble Forecast System (NAEFS, Toth et al. 2005), a trilateral operational multi-center ensemble forecast system. GIFS planning and development will also benefit from the Tropical Cyclone forecasting related GIFS prototype (Appendix F). For the success of GIFS, it is critical that the procedures, algorithms, and codes are shared through the GIFS toolbox, allowing the entire community to benefit from, as well as contribute to each piece of the infrastructure, as they wish and can. The possible contributions of global, regional and national centers and various other groups are indicated below. GLOBAL NWP CENTERS (DCPCs): Provision of their global ensemble forecasts for high impact event forecast guidance out to 15 days, as well as boundary conditions for LAM ensemble integrations Statistical enhancement of their global ensemble forecasts (for example, bias correction and downscaling) Product generation based on global ensemble forecasts from various centers Archiving of their (and for the TIGGE Archive Centers, some other centers) global ensemble forecasts and/or derived products (optional) REGIONAL CENTERS (DCPCs) Coordinate disaster and other high impact event related activities among NHMSs in each region. The regions are in a position to organize their own resources and data needs such that the data exchange and archiving of relevant data is controlled regionally Collect observational data and prepare observationally based high resolution regional analysis products relevant for high impact events (such as the Real Time Meso-scale Analysis RTMA of the US National Weather Service, De Pondeca et al. 2007) for verification and statistical bias correction and downscaling applications Collect forecast data critical for their operations, including boundary conditions for LAM integrations, from global NWP centers Prepare and give feedback to the global NWP and other providing centers regarding the use and utility of their products, including possible improvements Carry out LAM integrations for high impact events for enhancing global NWP guidance for 1-3 days lead time Design, prepare, and provide forecast products, services and guidance to alert national hydro-meteorological services to likely high-impact weather events in their region. These products and services, based on both global and regional ensemble forecasts, can assist less developed countries within each region to benefit from access to better targeted weather forecasts. Specialize in forecast data processing and product generation for selected type(s) of high impact events, possibly serving other regions with specially prepared products as part of international sharing of responsibilities under GIFS Conduct routine verification of forecast products and disseminate verification results to NCs, global NWP centers, and WMO. Organize and conduct training on the use of GIFS products for NHMS forecasters in their region NATIONAL HYDRO-METEOROLOGICAL SERVICES (NCs) Collect observational data relevant for detecting and forecasting high impact events Set forecast product data and service requirements for disaster and other high impact event preparation and mitigation Interpret climatological and meteorological guidance for high impact events Special product generation and outreach to users Conduct specialized verification of local high impact events OTHER GROUPS GIFS development efforts will also build on strong collaboration with the following groups: THORPEX DAOS (Data Assimilation and Observing Systems) WG Adaptive observing and DA techniques THORPEX Predictability and Dynamical Processes (PDP) WGs Ensemble generation and statistical post-processing techniques North American Ensemble Forecast System (NAEFS) Experience with operational Canadian US multi-center ensemble system, offering an example of fast operational implementation of multi-center research results Socio-Economic Research and Applications (WWRP SERA) WG Design of products & services, estimation of value added by & cost of GIFS products THORPEX Regional Committees Knowledge of regional capabilities and requirements and outreach Severe Weather Forecast Demonstration Projects (CBS SWFDPs) Know-how on operationally viable strategies in each region WWRP/WGNE (Working Group on Numerical Experimentation) Joint Verification WG Verification methods for TIGGE forecasts & GIFS products WWRP Nowcasting WG Statistical downscaling of ensemble forecasts WWRP Mesoscale WG Seamless forecast suite from hours to weeks WCRP Seasonal to Interannual Prediction (WGSIP) Link with seasonal ensemble forecast systems and products for seamless prediction from days to seasons CBS Operational systems and requirements CBS Expert Team on Ensemble Prediction Systems (ET-EPS) Training for ensemble and probabilistic forecasting Hydrologic Ensemble Prediction Experiment (HEPEX) Hydrologic applications of GIFS ADDENDIX F GIFS/TIGGE-RELATED EXCERPTS FROM THE SUMMARY OF MAJOR RECOMMENDATIONS FROM THE SIXTH WMO INTERNATIONAL WORKSHOP ON TROPICAL CYCLONES (IWTC-VI) For the full text of recommendations, see  HYPERLINK "http://severe.worldweather.org/iwtc/" http://severe.worldweather.org/iwtc/ . On the distribution of all numerical ensemble forecast data related to tropical cyclones - One of nine Major recommendations: In light of the benefits yielded by the multi-model consensus approach, the sharing of all ensemble and deterministic forecasts issued by the different Numerical Weather Prediction (NWP) centres has been recognised by the IWTC-VI as a top priority. Details: The WMO should take all necessary action to: a) improve the communication between operational centres and facilitate the dissemination of all tropical cyclone-related NWP products, such as the deterministic and ensemble forecasts (including the full set of ensemble runs), and b) make them available to all RSMCs, TCWCs and researchers in real-time. On collaboration with THORPEX activities, including TIGGE/GIFS and T-PARC - One of nine major recommendations: IWTC-VI considers that the tropical cyclone community should engage and cooperate with the THORPEX activities of relevance to the tropics, especially the THORPEX Pacific Asian Regional Campaign (T-PARC) and the Interactive Grand Global Ensemble/Global Interactive Forecast System, which aims in particular to develop generic probabilistic forecast products from a global archive of ensemble forecasts originating from a number of NWP centres. On improved tropical cyclone related forecasts and techniques - One of nine major recommendations IWTC-VI strongly recommends that greater efforts be put into intensity and structure prediction of tropical cyclones. The development of dynamical models, including coupled ocean-atmosphere models, statistical-dynamical models and all methodologies aimed at improving the skill in intensity and size prediction (and resulting wind and rainfall fields) should be strongly encouraged. Details: Research on multi-model consensus and single-model ensemble approaches, which show promise for intensity prediction, should be encouraged. Links with seasonal / interannual prediction: In addition, we encourage that group of experts, under the auspices of WMO, document the purpose and goals of seasonal forecasting and the uncertainties in these forecasts. It is recognized that there are significant interactions among ENSO, the MJO, and other global-scale circulations such as the Quasi-Biennial Oscillation (QBO) that are not well understood or forecast. The IWTC recommends that the WMO assists least-developed and developing countries so as to engage in hazard assessment, risk mapping, and tropical cyclone simulation exercises, to be conducted especially in highly vulnerable coastal and inland areas. Information derived could then be used by NHMSs disaster managers, local governments and communities to better manage and strengthen national disaster and mitigation plans. APPENDIX G CYCLONE XML FORMAT FOR THE EXCHANGE OF NWP ENSEMBLE DATA FOR TROPICAL (AND EXTRA-TROPICAL) CYCLONES A new Cyclone XML (or CXML) format has been developed for the exchange of NWP ensemble data for tropical and extra-tropical cyclones. A summary of CXML content and format is given below. A detailed proposal for CXML, with samples of cyclone forecasts, can be found in Ebert et al. (2008). Observationally based analysis and subjective and numerical forecast information on tropical cyclones is routinely produced by National Hydro-Meteorological Centers (NHMCs), a number of Regional Specialized Meteorological Centers (RSMCs), Tropical Cyclone Warning Centers (TCWCs), and other Numerical Weather Prediction (NWP) centers and forecast agencies. Currently the different producing centers use a variety of formats to convey tropical cyclone related information. At the 2006 WMO International Workshop on Tropical Cyclones ( HYPERLINK "http://severe.worldweather.org/iwtc/" http://severe.worldweather.org/iwtc/), the research and operational communities recommended the improved sharing of all tropical cyclone-related NWP products, such as the deterministic and ensemble forecasts (including the full set of ensemble runs). It was suggested that WMO "coordinate with the NWP and major operational centres (RSMCs and TCWCs) in order to define a set of resolvable tropical cyclone characteristics to be provided and timely disseminated by the NWP centres through the GTS (e.g. centre location, minimum sea level pressure, max wind, wind radii by quadrants, etc) and define the appropriate standardised format". Existing standard formats for exchanging tropical cyclone data include the table-based formats WMO BUFR and CREX, the 1-line text-based "TC vital statistics", and the slightly more expansive text-based Automated Tropical Cyclone Format (ATCF). BUFR is the officially endorsed format of WMO, and in principle can handle ensemble forecasts. However, the requirement for a decoder to read the data makes it difficult for users outside of large meteorological centers. The text formats are more easily read but are still somewhat cryptic, and neither of them is currently configured to represent ensemble forecasts. Recent years have seen an increase in the use of web services and service-oriented architectures to deliver information using clear and unambiguous description languages. The eXtensible Markup Language (XML) is a widely used human-legible text format that encloses data within self-describing tags, and has become an internet standard in recent years. Because of its descriptive nature XML is ideally suited for representing cyclone data. Sample TC data written in XML might look something like: Katrina Atlantic 135. 910. 130. 915. Compared with other formats, the advantage of the XML format is that the meaning of each datum is quite clear. Its general structure is defined by a separate online schema, which is used to validate (check) each data file for structural and obvious data errors. Data encoded in XML is easily interpreted by many internet applications, making it ideal to support interoperability between meteorological and other agencies. Clarity of the information encoded in XML is achieved by using a format more verbose than those of other text formats. However, given that tropical cyclone data is feature-centered and highly condensed, the extra requirement on storage and transmission of the data is not deemed significant. XML is increasingly being used in a variety of meteorological applications, most commonly to represent metadata and warnings. The development of appropriate schemas for meteorological data will be taken up by a newly formed WMO Expert Team on Assessment of Data Representation Systems (ET-ADRS). Prior to this work, no existing XML formats were found suitable for tropical cyclone data and none of the many experts we consulted was aware of any such format (although many expressed interest in using an XML format for TCs). The name of the XML format for cyclones is Cyclone XML (CXML). If desired in the future, the format may be modified in the future to comply with the Geography Markup Language (GML, an XML-based language designed to encode geographic information as an application of the ISO standard) by using GML syntax where appropriate, and XML otherwise. CXML includes metadata describing the source of the cyclone data, the time of observations or forecast initiation, and a link to more complete metadata for the data producer, which will be written in accordance with the WMO Core Metadata Profile. The data incorporates a variety of information describing the position and intensity of one or more detected and/or forecast cyclones. Its content is based on that of the ATCF and BUFR formats for TC data exchange. Cyclone phase information has also been included. Note that the extensibility of XML makes it easy to include additional features in the future applicable, for example, for extratropical cyclones. Reading from XML files is done using a "parser", which extracts the data. Writing a parser is a technical task, however, numerous libraries written and tested by experts in various languages are freely available to facilitate working with XML data. Since XML is just text, writing data in XML can be done fairly easily using most languages. Further technical details on CXML, including samples of data and software, can be found online at  HYPERLINK "http://www.bom.gov.au/bmrc/projects/THORPEX/CXML/index.html" http://www.bom.gov.au/bmrc/projects/THORPEX/CXML/index.html. During 2008, seven of the TIGGE data providers have been exchanging tropical cyclone forecast data, primarily to support the T-PARC campaign. Several centers have also started to develop tools to read the CXML data and develop diagnostics that combine the ensemble forecast tracks. Figure F1 shows an early example combining Met Office and ECMWF forecasts. Both ensembles correctly showed hurricane Gustav passing to the west of New Orleans, but with differing tracks after landfall when the hurricane became much weaker.  SHAPE \* MERGEFORMAT  SHAPE \* MERGEFORMAT   SHAPE \* MERGEFORMAT  Fig. F1: Forecast strike probabilities, calculated using CXML data, for hurricane Gustav for a) UK Met Office, b) ECMWF and c) both ensembles. APPENDIX H TIMELINE FOR GIFS DEVELOPMENT (with Proposed Dates) A list of tasks associated with the developments needed for real time ensemble data access, product generation, and End-to-End GIFS is provided below. Note that the timeline for data access and product generation is a proposed schedule that will have to be refined and confirmed as part of a detailed technical planning process, with critical input from the two GIFS focus groups (see Appendix H). ENSEMBLE DATA ACCESS (2008 onwards) TIGGE/GIFS WG agrees on what tropical cyclone properties (e.g., geographical position, intensity, etc) will be shared from ensemble forecasts, and in what format (e.g., GML, special form of XML format Dec 2007 - Completed) Share plans with tropical cyclone user community (Jan 2008 - Completed) Incorporate feedback from tropical cyclone user community into final plan on data content and format (Feb 2008 - Completed) Each global ensemble producing center identifies position and other agreed upon properties of tropical cyclones in their high resolution control and ensemble member forecasts (May 2008 - Completed) Each providing center posts tropical cyclone data in real time in agreed upon format on their ftp server to allow access by other centers and the general user community during and after TPARC (July 2008 - Completed) Note: Points 1-5 satisfy GIFS plans and requirements for tropical cyclone data Set up Beta test version of common web interface for accessing tropical cyclone data from all providing centers (Dec 2008) Preliminary plans drafted for real time exchange of basic ensemble precipitation forecast data (Dec 2008) Each producing center archives or arranges for the archiving of their operational tropical cyclone forecast data (June 2009). Review data format, common web interface, and archiving experience accumulated during Tropical Cyclone prototype of GIFS. Develop detailed plans for real time data sharing during the development and operational phases of GIFS (June 2009) Providing centers make available 3-dimensional multivariate high resolution control and ensemble forecast data through an ftp site in agreed-upon format(s) (Dec 2009) Real-time data access through common web interface (June 2010) Archived data (from central and/or decentralized archives) accessible through common web interface (Dec 2010) GIFS PRODUCTS (2008 onwards) Develop methods to combine and statistically improve tropical cyclone forecast data and heavy rain data originating from various sources (initial toolbox development, Dec 2009) Develop procedures to support enhanced GIFS web interface for tropical cyclone forecasting: (a) request derived tropical cyclone product via common web interface; (b) generate products using tools from the toolbox; (c) provide requested information to users (web display of data, product, or services, Dec 2010). Note: Points 1-2 complete development of the product generation stage of GIFS for tropical cyclone forecasting Review experience with tropical cyclone and heavy rain product generation and web services. Develop detailed plans for full 3-dimensional GIFS-Products (June 2011) Develop methods to combine and statistically improve 3-dimensional multivariate data originating from various sources (toolbox development, June 2012) Develop procedures to support enhanced GIFS web interface for full 3-dimensional forecast data: (a) request derived products via common web interface; (b) generate products using tools from the toolbox; (c) provide requested information to users (web display of data, product, or services, June 2013) Review GIFS product generation experience and make necessary adjustments and improvements. Develop draft plan for End-to-End GIFS (Dec 2013) TASK LIST FOR END-TO-END GIFS (2012 onwards) Assess adaptively deployable observing, data assimilation, and NWP resources that participating organizations will make available for End-to-End GIFS Determine criteria, and develop algorithm for identifying high impact forecast events based on special GIFS-Products Agree on rules for setting priorities for the use of available adaptive resources; Identify way and form of forecast user input Develop objective procedures for allocation of adaptively configurable resources based on agreed-upon priorities Implement SOA, including an enhanced web-based user interface, to carry out adaptive procedures 6) Test adaptive End-to-End GIFS procedures; review experience; adjust procedures as needed APPENDIX I CHARGE FOR GIFS-TIGGE FOCUS GROUPS To develop detailed technical plans for the infrastructure necessary for the development of GIFS, and for the implementation of its various phases (eg, generation of different products, such as TC, PQPF, etc, as well as the later End-to-End GIFS), the GIFS-TIGGE WG proposes to create two focus groups: Focus Group 1 (FG-1): Access to and distribution of real time and archived ensemble data Focus Group 2 (FG-2): Ensemble-based products and services for high impact events The subject areas for the two focus groups are described in further detail below. The two focus groups will closely coordinate their work, especially in the development of a common web interface for data and products. Participation in the focus groups is voluntary and open to anyone interested and willing to contribute to the development of GIFS. An effort will be made to connect with, and solicit participation from related ongoing efforts, such as SIMDAT ( HYPERLINK "http://www.ecmwf.int/services/grid/simdat/" http://www.ecmwf.int/services/grid/simdat/), the Global Organization for Earth System Science Portal (GO-ESSP,  HYPERLINK "http://go-essp.gfdl.noaa.gov/2008/" http://go-essp.gfdl.noaa.gov/2008/), NOAA National Operational Model Archive and Distribution System (NOMADS,  HYPERLINK "http://nomads.ncdc.noaa.gov/" http://nomads.ncdc.noaa.gov/), and NOAAs Community Hydrologic Prediction System (CHPS,  HYPERLINK "http://www.nws.noaa.gov/ohd/hrl/chps/index.html" http://www.nws.noaa.gov/ohd/hrl/chps/index.html). The focus groups will act as advisors to the GIFS-TIGGE WG. The focus groups will follow guidance provided by, and report to the GIFS-TIGGE WG, and will closely interface and coordinate with other WIS efforts. The WG will be responsible for making final decisions, including topics overlapping the areas considered by both groups. The focus groups will carry out their activities via email, occasional teleconferences, and face to face meetings that will preferably be scheduled in association with other THORPEX meetings. It is anticipated that after the planning phase, the focus groups will continue and coordinate the execution of the plans. TOPIC AREAS Focus Group 1 (FG-1): Access to and distribution of real time and archived ensemble data Real time access (by other producing centers, other DCPCs, other users) to global ensemble forecast data from 10 NWP centers where such data are generated (BOM, CMA, CPTEC, ECMWF, JMA, KMA, MeteoFrance, MSC, NCEP, UKMetOffice). Can ensemble data necessary for generation of derived products be exchanged rapidly in order to support operational product generation? If so, how, and at what cost? Is the solution scalable (i.e., can we start with products requiring small amounts of data and scale it up based on success with initial endeavor?) What are the technical implications of changing data policy protocols to support the development and generation of GIFS products? Is WMO GRIB2 the proper format? GRIB2 allows multiple pathways to encode data/ information. Do we need to insist on one particular approach to encoding data in GRIB2 format, in order to use uniform messages (as with data prepared specifically for the TIGGE archive system)? If this is not done, how can we ensure that decoding and application software will work as expected, irrespective of encoding variations? If strictly uniform GRIB2 messages are required, is it easy to convert at each center data into common format? Archiving of ensemble forecast data and products derived from them Ensemble forecast data Do we expect that the archives would also be used in operations (i.e., for bias correction)? If so, would the best place to store the data be where it would be used operationally (i.e., at generating centers)? (overlapping with FG-2) To eliminate non-stationarity in operational forecast systems, hind-cast datasets likely will become available. Would the natural place to archive these be at producing centers? Once bias-corrected ensemble forecasts become available (e.g., NAEFS), will these forecasts be added in place, or in addition to the raw ensemble members in the archives? (overlapping with FG-2) Will TIGGE archive centers continue to offer access to their archive for foreseeable future? Will they continue collecting new data? Will they limit archival to the existing TIGGE variable list? Or are they interested in including additional data such as new variables, bias corrected fields, derived products? Would distributed archiving (where the same data would not be archived at 3 locations but would be divided among more archive centers, with unified web interface) offer advantages (total volume of archived data could increase)? Which producing centers are interested in contributing to distributed archive concept by providing access to their raw, bias corrected, hind-cast, and/or derived forecast products? What should be the relationship between TIGGE archives and other archives such as producing centers (e.g., producing center could be primary archive, TIGGE archive center could be back-up facility)? Derived products What is the need for archiving these? (overlapping with FG-2) What is the best place for their archival? (overlapping with FG-2) Consider that some (most, or most often used?) products may have small size. Is archival at producing centers a good option? Archive at centers that use products? Archive at both places or neither place? Common web interface (overlapping with Focus group 2) for accessing data from producing centers (and TIGGE archive centers, if possible), requesting products (pre-generated, or generated on the fly) from product generating centers, and displaying products prepared by product generating centers. Can access to real time and archived data be made transparent to user via a common web interface? What software is needed to achieve this? What data selection tools should be used? Do the different providers have to use the same data selection/preparation tools (e.g., NOMADS)? How sophisticated search/interrogation algorithms should be? Is there free software available for some of this, or could one be jointly developed and shared among participants? Do we need one or more centers that run the web page? Should pre-made products be shipped to these centers for web display? How should on-the-fly products be displayed? Are they first sent to the web center? How should different centers communicate with each other? Focus Group 2 (FG-2): Ensemble-based products and services for high impact events Choice of products and algorithms. What specific products should be developed related to Tropical Cyclone and precipitation forecasting (highlighted in the GIFS plan as first and second application areas, respectively)? What products based on multi-center ensemble forecasts should be targeted next for development? What algorithms should be used for generating the agreed-upon products? Should each producing center be asked to run a bias correction algorithm on their ensemble? Should there be a requirement that each center uses the same bias correction algorithm, or should the focus be on the quality of the output? What are the calibration requirements (sample size, hind-cast)? Shared development of algorithms and software for unified data access and web interface, and especially for product generation How can a group of experts from diverse operational and research institutions contribute to the development of a toolbox shared by the GIFS community? How should a plan for the architecture of a multicenter ensemble product generation system be designed to provide a common framework yet allow for creativity? What tools are needed and available for joint or coordinated software development? Who controls what is accepted in toolbox? What metadata should accompany software/algorithm submissions? What tests must be passed? How can verification results be made part of metadata required for submissions of forecast procedures? Shared use of algorithms / software for operational applications. How can updates to software be transitioned into operations? How can the integrity of operations be guaranteed when more than one center contributes to certain products? What are the rules for changing operational practices? When is advance notification of other centers enough? When will the other centers agreement be needed prior to an implementation at one of the participating centers? How to map one- or two-way dependencies in diagram of existing and possible future operational processes? Is all of this doable in a multi-center and community-based collaborative work, or too much? Product generation at DCPCs (either global NWP ensemble producers or other specialized centers). Is focusing first on scheduled, then on on-demand product generation a sensible approach? How will pre-generated products be shipped to web centers for display? Will the operation of product generation be controlled / coordinated among various entities? What file format etc. will be used to exchange derived products, considering their use (e.g., web display, etc)? Can all products critical to users be generated on a routine basis? If not, can web-based services, supported by on-demand product generation, be set up in a multi-center operational environment? How can real-time communication among different contributors (e.g., center running the web page, centers providing raw data, centers processing the raw data and deriving products) be arranged? Common web interface (overlapping with Focus Group 1, for details, see FG-1 description) APPENDIX J SUB-GROUPS FOR PRODUCT DEVELOPMENT As mentioned earlier, GIFS product development will build on the success of the Southern Africa SWFDP, which is currently being expanded to include an additional 11 countries beyond the 5 original participants. It is critical for the success of GIFS that the various regions are empowered to tackle their unique problems, considering also their special IT and other limitations. GIFS development will build on the experience regarding existing operational systems and requirements by consulting with CBS experts as to what is technically possible at any given time. At the same time, GIFS will work on expanding the current possibilities by incorporating new THORPEX research results that can be transitioned into operations in the future. The global ensemble producing centers have a strong interest in improving forecasts in their own and possibly in some other specific regions. Naturally, RSMCs have a vested interest in improving weather forecasts for their own region. In addition, RSMCs have special knowledge about high impact forecast needs of their region, and are often connected to one or more of the global ensemble producing centers. Based on the above considerations, it is proposed that GIFS product design and development efforts are carried out in cooperation with selected regions that could most benefit from GIFS development. The various global and regional centers may choose to participate in product design and testing in one or more of these regions. So far, four regions have been identified (not in priority order, centers with potential interest also noted): Southern Africa (linked with ongoing SWFDP, UK Met Office, NCEP); South America (linked with a developing SWFDP, with a potential for fast technology transfer as GIFS can engage in early design phase, CPTEC, NAEFS), Southeast Asia (KMA), and the South Pacific Islands (BOM). Initially, product development will focus on the GIFS prototypes for Tropical Cyclone and Precipitation prediction, two high impact weather events that affect both developed and developing regions. If opportunities and needs arise, product development sub-groups can also be formed to support other downstream applications related to, for example, health and food availability. For maximum inter-comparability of methods and results, it is recommended that for product development, all regions and applications have access to the same identical ensemble data from the ten global ensemble producing centers. Therefore it is requested that the global ensemble producing centers make their ensemble data available in real time for the product development sub-groups, with the data distribution policy conditions discussed in Appendix B. This will serve multiple objectives, including (a) allowing the testing of real time data access mechanisms; (b) engaging forecasters at global and regional centers in product development; (c) engaging global centers in product development; (d) providing regular feedback from RSMCs on product design and quality; and (e) contributing to forecaster training on the regional level. Many of these objectives would be severely compromised if the ensemble data were not made available in real time. It is anticipated that the regionally oriented product design concept described above would provide the fastest path to product development for all regions concerned, offering the best use of regional resources and the best services for each regions special product needs.     PAGE  PAGE 1 SUMMARY The objective of the future Global Interactive Forecast System (GIFS) is the production of internationally coordinated advance warnings and forecasts for high impact weather events to mitigate loss of life and property, and to contribute to the welfare of all World Meteorological Organization (WMO) nations, with a particular emphasis on least developed and developing countries. It is expected that the international coordination of the design, future development, and operation of global observing, data assimilation, numerical modeling, and user application techniques for high impact weather forecasting will yield significant improvements in the availability and quality of services, leading to a range of socio-economic benefits, including saving property and lives. GIFS will be developed through the volunteer contributions of national, regional, and international organizations, requiring significant investment from National Hydro-Meteorological Services and other organizations. The initial GIFS capabilities will include real time access to ensemble forecast data and the provision of derived products and services via a unified web portal. As a prototype, GIFS products will be first developed in support of probabilistic tropical cyclone warning services. This will be followed by the introduction of products and services for precipitation and other high impact events in the Products stage of GIFS. In its final, End-to-End stage, GIFS will feature two-way interactions between the users and providers of the forecasts. The forecast process, potentially including the observing, data assimilation, ensemble, and user application systems, will be adaptable in order to provide the best prediction services for high impact events. This document outlines the conceptual framework upon which more detailed implementation plans can be developed for GIFS. (a) (b) 8+ days gain NCEP/GEFS raw forecast NAEFS forecast Combination of bias corrected & downscaled NCEP (hires control included) & CMC ensembles (b) (a) (c) [  2 4 5 ? 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( [ g j t   " 8               hYyhYyhYyhRp,h hRp,6hihRp,6 hRp,56 h@hRp, hRp,5 hr5hf h0hrhrI WXY9:<G/157:+-24vw]9 a d f ҿҴƫҧңҟқғҎzzҟҁ hE_hRp,h8ZhRp,5 h35 hRp,5h_ hRp,5h8hYyh/h_h4shRp,0Jj9hRp,U hM1hRp,jhRp,UhfhRp,hYyhYy0Jj8hYyhYyU hYyhYyjhYyhYyU0:FeL$ & FEƀ+ɦ)a$gdRp,L$ & FEƀ+ɦ)a$gdRp,F4e]]$a$gdRp,L$ & FEƀ+ɦ)a$gdRp,L$ & FEƀ+ɦ)a$gdRp,44zeL$ & FEƀ+ɦ)a$gdRp,L$ & FEƀ+ɦ)a$gdRp,zeL$ & FEƀ+ɦ)a$gdRp,L$ & FEƀ+ɦ)a$gdRp,seL$ & FEƀ+ɦ)a$gdRp,L$ & FEƀ+ɦ)a$gdRp,s]eL$ & FEƀfʦ)a$gdYyL$ & FEƀ+ɦ)a$gdRp,8 9 f  QL$ & FEƀ+ɦ)a$gdRp,$a$gdRp, $h^ha$gdRp,L$ & FEƀfʦ )a$gdRp, 2!~!eL$ & FEƀ+ɦ)a$gdRp,L$ & FEƀ+ɦ)a$gdRp,~!!!!!!W"e]TLL$a$gd8 HgdRp,$a$gdRp,L$ & FEƀfʦ)a$gdYyL$ & FEƀ+ɦ)a$gdRp,f !!!!d""""Y#`#$9$$,%/%0%j%%%%%y&&='i'o'p'''''((((((-(((((((((())) )!)hS#hRp,0Jj0;hRp,U hm{hRp,jhRp,U hmhRp,hmhRp,5\hmhRp,5 hr?5 hRp,5hhRp,5 h8hfhfh8 h8h8h8h85hYyhRp,4W""2#eL$ & F!Eƀfʦ)a$gd8L$ & F!Eƀfʦ)a$gd82#$$eL$ & F!Eƀfʦ)a$gd8L$ & F!Eƀfʦ)a$gd8$$j%eL$ & F!Eƀfʦ)a$gd8L$ & F!Eƀfʦ)a$gd8j%%5&eL$ & F!Eƀgʦ)a$gd8L$ & F!Eƀgʦ)a$gdf5&u&'eL$ & F!Eƀgʦ )a$gd8L$ & F!Eƀgʦ )a$gd8'9''eL$ & F!Eƀgʦ )a$gd8L$ & F!Eƀgʦ )a$gd8''((-(X((( )!)f]QQQ]]] $ Ha$gdRp, HgdRp,K HC$EƀgdRp,L$ & F!Eƀgʦ )a$gd8 !)))****+,,,s,t,/.0.1...000000001 $`a$gdRp,$a$gdRp, HgdRp,!))**,s,t,,1.n.o.q..000000000{1111U22q3}3334 4 4 4 44xxsnsg hX}56 hr?5 hRp,5h"i*hRp,mH sH h=hRp,mH sH  h'hRp,h'hRp,mH sH h9/VhRp,mH sH hRp,mH sH h'hRp,56mH sH h\>KhRp,mH sH  hS;rhRp,hmhRp,6 h?6hRp,hRp,OJQJhXPhRp,6hRp,h?6hRp,56%11U2V23 4 4s4t45n7_:<>>>>?;?$ O7$8$H$]^a$gdRp,$dd[$\$a$gdRp, $ Ha$gdRp, HgdRp,$ W^`Wa$gdRp,gdRp, $ a$gdRp,4r4s4t444445B5C5M5Q5X5\5]5d5e5n5o5p5}555557777788;;<<>e@@οo$h vhRp,CJOJQJaJnH tH $hYehRp,CJOJQJaJnH tH hYehRp,0J#jQ<hYehRp,UmHsHjhYehRp,UmHsH hYehRp,hRp,OJQJhYehRp,OJQJhShS6OJQJhSOJQJhRp,hihRp,6 hRp,56';?Y?????@3@c@@@@xCEnIKMMNNN Hgd[)$dd[$\$a$gdp$dd[$\$a$gdRp,$ O7$8$H$]^a$gdRp,@@@@RBSBBBwC0D4DDDDDDDEEEEEFFF8IlIIII1J8JgJJ%K&K2KmKoKpKqKKKKKKLLM˼Ҹ˱ҥ҂~zh`hh"thF hp0JjZ=hpU hM1hphpjhpUhTx= h<hRp,hShYehRp,6 hohRp, hYehRp,hRp,$hYehRp,CJOJQJaJnH tH ,h vhRp,CJOJQJaJmH nH sH tH 0MMMM@MGMnMMMMMMMMMMMMMMMMNNNNNNN9N:N;NWNNNNNNNNNNNNNNNiOrPtPuP컷 h1 ~hRp, hRp,6hihRp,6 hRp,56 h5h whRp,5hhRp,hgghRp, hM1hpj?h`UjO?hpUj>hpU"jhpUmHnHsH tHuhpjhpUh"th`2NNNNNtPuPvPP{Q|QM & F HEƀ+ɦ)gdRp, HgdRp, $ Ha$gdRp, uPvPPPPPpQyQQQ6R?RRSSSSSSST2TUUVVWWWWWWYZZV]d]n]s]x]y]]]]A`J`K`L`M`q`aaabdd᭨jhy3hRp,Uh>hRp,5 hRp,56 h5h whRp,5 h'.5h^ h! 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