**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.

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**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:

a)  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;

b)  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, 10^th^ and 90^th^ 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.*** Today's 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.

![](media/image1.png){width="4.528472222222222in"
height="4.545138888888889in"}

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.

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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.

![](media/image4.wmf){width="4.8902777777777775in"
height="4.670138888888889in"}

Figure C3: Improvements in RPS of 51-, 154- and 279-member multi-model
ensembles relative to the ECMWF ensemble for 500hPa heights over
20°-90°N 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.

![](media/image5.wmf){width="2.8229166666666665in"
height="1.6145833333333333in"}

![](media/image7.wmf){width="2.8333333333333335in"
height="1.6145833333333333in"}

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 90^th^ 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 90^th^ 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.

![](media/image8.jpeg){width="4.725694444444445in"
height="3.5541666666666667in"}

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:
[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.

![](media/image9.png){width="5.181889763779528in"
height="4.304724409448819in"}

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 90^th^
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

<http://www.wmo.int/pages/prog/www/DPFS/Meetings/ET-EPS_Exeter2006/Doc8(1).doc>).
The fields being verified are Z~500~, T~850~, \|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 Z~500~, T~850~,
\|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
<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
<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):**

a)  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

b)  Statistical enhancement of their global ensemble forecasts (for
    example, bias correction and downscaling)

c)  Product generation based on global ensemble forecasts from various
    centers

d)  Archiving of their (and for the TIGGE Archive Centers, some other
    centers') global ensemble forecasts and/or derived products
    (optional)

**REGIONAL CENTERS (DCPCs)**

a)  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

b)  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

c)  Collect forecast data critical for their operations, including
    boundary conditions for LAM integrations, from global NWP centers

d)  Prepare and give feedback to the global NWP and other providing
    centers regarding the use and utility of their products, including
    possible improvements

e)  Carry out LAM integrations for high impact events for enhancing
    global NWP guidance for 1-3 days lead time

f)  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.

g)  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

h)  Conduct routine verification of forecast products and disseminate
    verification results to NCs, global NWP centers, and WMO.

i)  Organize and conduct training on the use of GIFS products for NHMS
    forecasters in their region

**NATIONAL HYDRO-METEOROLOGICAL SERVICES (NCs)**

a)  Collect observational data relevant for detecting and forecasting
    high impact events

b)  Set forecast product data and service requirements for disaster and
    other high impact event preparation and mitigation

c)  Interpret climatological and meteorological guidance for high impact
    events

d)  Special product generation and outreach to users

e)  Conduct specialized verification of local high impact events

**OTHER GROUPS**

GIFS development efforts will also build on strong collaboration with
the following groups:

a)  THORPEX DAOS (Data Assimilation and Observing Systems) WG --
    Adaptive observing and DA techniques

b)  THORPEX Predictability and Dynamical Processes (PDP) WGs -- Ensemble
    generation and statistical post-processing techniques

c)  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

d)  Socio-Economic Research and Applications (WWRP SERA) WG -- Design of
    products & services, estimation of value added by & cost of GIFS
    products

e)  THORPEX Regional Committees -- Knowledge of regional capabilities
    and requirements and outreach

f)  Severe Weather Forecast Demonstration Projects (CBS SWFDPs) --
    Know-how on operationally viable strategies in each region

g)  WWRP/WGNE (Working Group on Numerical Experimentation) Joint
    Verification WG -- Verification methods for TIGGE forecasts & GIFS
    products

h)  WWRP Nowcasting WG -- Statistical downscaling of ensemble forecasts

i)  WWRP Mesoscale WG -- Seamless forecast suite from hours to weeks

j)  WCRP Seasonal to Interannual Prediction (WGSIP) -- Link with
    seasonal ensemble forecast systems and products for seamless
    prediction from days to seasons

k)  CBS -- Operational systems and requirements

l)  CBS Expert Team on Ensemble Prediction Systems (ET-EPS) -- Training
    for ensemble and probabilistic forecasting

m)  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
<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
(<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:

> \<cyclone\>
>
> \<name\>Katrina\</name\>
>
> \<basin\>Atlantic\</basin\>
>
> \<fix type=\"forecast\" time=\"2005-08-29T00:06:00Z\>
>
> \<latitude\>27.0\</latitude\>
>
> \<longitude\>88.9\</longitude\>
>
> \<maxWindSpeed units=\"kts\"\>135.\</maxWindSpeed\>
>
> \<minimumPressure units=\"hPa\"\>910.\</minimumPressure\>
>
> \</fix\>
>
> \<fix type=\"forecast\" time=\"2005-08-29T00:12:00Z\>
>
> \<latitude\>27.1\</latitude\>
>
> \<longitude\>87.8\</longitude\>
>
> \<maxWindSpeed units=\"kts\"\>130.\</maxWindSpeed\>
>
> \<minimumPressure units=\"hPa\"\>915.\</minimumPressure\>
>
> \</fix\>
>
> \</cyclone\>

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
<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.

![](media/image10.png){width="2.79375in"
height="2.327777777777778in"}![](media/image11.png){width="2.803472222222222in"
height="2.3361111111111112in"}

![](media/image12.png){width="2.8256944444444443in"
height="2.354861111111111in"}

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)**

1)  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*)

2)  Share plans with tropical cyclone user community (Jan 2008 -
    *Completed*)

3)  Incorporate feedback from tropical cyclone user community into final
    plan on data content and format (Feb 2008 - *Completed*)

4)  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*)

5)  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*

6)  Set up Beta test version of common web interface for accessing
    tropical cyclone data from all providing centers (Dec 2008)

7)  Preliminary plans drafted for real time exchange of basic ensemble
    precipitation forecast data (Dec 2008)

8)  Each producing center archives or arranges for the archiving of
    their operational tropical cyclone forecast data (June 2009).

9)  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)

10) 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)

11) Real-time data access through common web interface (June 2010)

12) Archived data (from central and/or decentralized archives)
    accessible through common web interface (Dec 2010)

**GIFS PRODUCTS (2008 onwards)**

1)  Develop methods to combine and statistically improve tropical
    cyclone forecast data and heavy rain data originating from various
    sources (initial toolbox development, Dec 2009)

2)  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*

3)  Review experience with tropical cyclone and heavy rain product
    generation and web services. Develop detailed plans for full
    3-dimensional GIFS-Products (June 2011)

4)  Develop methods to combine and statistically improve 3-dimensional
    multivariate data originating from various sources (toolbox
    development, June 2012)

5)  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)

6)  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)**

1)  Assess adaptively deployable observing, data assimilation, and NWP
    resources that participating organizations will make available for
    End-to-End GIFS

2)  Determine criteria, and develop algorithm for identifying high
    impact forecast events based on special GIFS-Products

3)  Agree on rules for setting priorities for the use of available
    adaptive resources; Identify way and form of forecast user input

4)  Develop objective procedures for allocation of adaptively
    configurable resources based on agreed-upon priorities

5)  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
(<http://www.ecmwf.int/services/grid/simdat/>), the Global Organization
for Earth System Science Portal (GO-ESSP,
<http://go-essp.gfdl.noaa.gov/2008/>), NOAA National Operational Model
Archive and Distribution System (NOMADS,
<http://nomads.ncdc.noaa.gov/>), and NOAA's Community Hydrologic
Prediction System (CHPS,
<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

a)  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?

b)  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

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-   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?

c)  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?

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```
-   Do the different providers have to use the same data
    selection/preparation tools (e.g., NOMADS)?

    -   How sophisticated search/interrogation algorithms should be?

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```
-   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?

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```
-   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

a.  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)?

b.  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?

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-   How can verification results be made part of "metadata" required for
    submissions of forecast procedures?

    a.  Shared use of algorithms / software for operational
        applications.

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-   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?

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-   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?

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```
-   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?

    a.  Product generation at DCPCs (either global NWP ensemble
        producers or other specialized centers).

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-   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?

    a.  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.