WORLD METEOROLOGICAL ORGANIZATIONWORLD WEATHER RESEARCH PROGRAMMEFIRST WORKSHOP ON THE
THORPEX INTERACTIVE GRAND GLOBAL ENSEMBLE(TIGGE)

FINAL REPORTWMO/TD-No.1273
WWRP/THORPEX No. 5

Reading, United Kingdom, 1-3 March 2005

This report was edited by David Richardson (Met Office), Roberto Buizza (ECMWF) and Renate
Hagedorn (ECMWF) and compiled with the contribution of some participants, invited speakers andmembers of the organizing committee of the 1st TIGGE Workshop held at ECMWF between the 1st
and the 3rd of March 2005: H Boettger (ECMWF), P Bougeault (ECMWF), E Ebert (BMRC,
Australia), M Ehrendorfer (Univ. Innsbruck, Austria), T Hamill (NOAA/CDC), F Lalaurette (ECMWF),
K Mylne (Met Office), T Paccagnella (ARPA/SMR, Italy), M Roulston (Penn State University, US),M Shapiro (NOAA), Y Takeuchi (JMA, Japan), J Thielen (JRC, Italy), Z Toth (NCEP, USA), L
Treinish (IBM, US), and L Wilson (MSC, Canada).

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Executive Summary
TIGGE, the THORPEX Interactive Grand Global Ensemble, is a key component of THORPEX: a
World Weather Research Programme to accelerate the improvements in the accuracy of 1-day to
2-week high-impact weather forecasts for the benefit of humanity.
The key objectives of TIGGE are:An enhanced collaboration on development of ensemble prediction, internationally andbetween operational centres and universities
New methods of combining ensembles from different sources and of correcting forsystematic errors (biases, spread over-/under-estimation)
A deeper understanding of the contribution of observation, initial and model uncertainties toforecast error
A deeper understanding of the feasibility of interactive ensemble system responding
dynamically to changing uncertainty (including use for adaptive observing, variableensemble size, on-demand regional ensembles) and exploiting new technology for gridcomputing and high-speed data transfer.
Test concepts of a TIGGE Prediction Centre to produce ensemble-based predictions ofhigh-impact weather, wherever it occurs, on all predictable time ranges
The development of a prototype future Global Interactive Forecasting System
This report summarizes the key recommendations of the 1st TIGGE Workshop, held at ECMWF
from 1 to 3 March 2005. This workshop aimed to address the strategy required to achieve thesegoals, with a particular focus on establishing the user requirements for TIGGE and proposing an
infrastructure design to meet these requirements.
It has been concluded that to achieve TIGGE’s key objectives, it is necessary to:
Determine the user requirements for TIGGE data, including the types, volumes, format ofdata, access methods and timeliness
Design the TIGGE infrastructure to meet these requirements
Determine resource requirements and secure necessary funding if required
Establish commitments from data contributors, TIGGE archive centres and prospective
users
Implement proposed infrastructure, collecting, archiving, and providing access to TIGGE
data
Develop and maintain close links with TIGGE users, including other THORPEX subprogrammes, field campaigns and Demonstration Projects, as well as other partners
Have the flexibility to respond to evolving user needs as scientific understanding increases
during the project
It is also necessary that the following data be shared between TIGGE users:
Ensemble forecasts generated routinely (often operationally) at different centres around theworld. This is the core data of the TIGGE archive. The total daily data volume is expected
to be around 200GB, based on a preliminary list of required parameters developed at theworkshop.
Observational data and existing datasets including re-analyses and re-forecasts
Additional special datasets generated during the TIGGE project for specific research andapplications.
With respect to data policy, it is suggested that:
TIGGE data should be available to all users for research purposes
Consideration needs to be given to the issue of real-time access to data, in particular for
demonstration projects and field experiments
The process of obtaining approval for data access should be transparent, stream-lined andreasonably fast
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The user interface for access to the central archives should be user-friendly and shouldmake it as easy as possible for researchers in disciplines not used to dealing withexceptionally high data volumes to obtain subsets of ensemble data.
Open-source sharing of post-processing software (calibration, combination, decisionmaking) should be promoted in order to maximise benefit for both researchers and endusers.
A TIGGE infrastructure needs to be established rapidly to meet specific requirements. In particular
it is proposed to use TIGGE’s capability during the International Polar Year 2007-08 and during the
Beijing Olympics in 2008, and to link TIGGE with the North American Ensemble Forecast System(NAEFS), which is planned to become fully operational by March 2006. The database needs to beavailable to allow research to begin in advance of the above applications, for the TIGGE research
projects and for users in other THORPEX sub-programmes
Given the above requirements for data policy and access, it is suggested that the TIGGE
infrastructure is developed in two phases:
Phase-1, during which data are collected in near-real time (via internet ftp) at a small
number of central TIGGE data archives. This can be implemented now at little cost andcould handle the estimated 200 GB per day data volumes with current network andstorage capabilities
Phase-2, during which data archives are distributed over a number of repositories, insteadof all being held centrally, but efficient and transparent access to users is maintained. Thisis a more flexible solution with the potential to eliminate routine transfers of large data
volumes. But this will require substantial software development over a number of years, incoordination with the WMO Information System, and will require additional funding
To meet the time constraints of user access to TIGGE, it is proposed that Phase-1 is started assoon as possible and that planning for Phase-2 begins in parallel. In particular, the following outline
timetable is proposed for the Phase-1 development and implementation.
Firstly, the THORPEX GIFS-TIGGE Working Group that is intended to coordinate the TIGGE
activity should be established as soon as possible.
Secondly, in 2005, the GIFS-TIGGE WG should:
Complete detailed planning (including costs and resource) and initiate Phase-1
Secure commitment from institutes willing to act as central data archives for TIGGE’s
Phase-1 and from initial contributors to TIGGE database
Agree data exchange protocols and data formats
Finalise list of datasets and parameters to store in the TIGGE archive or provide alternative
access to, based on provisional lists given in this report
Help and encourage contributing centres to begin development to provide data in agreed
formats
Address data access policy, including consideration of requirements for real-time access,particularly for field campaigns and demonstration projects
Consider the strategy required to initiate work on Phase-2 of TIGGE, including funding andresources
Work within THORPEX and with external partners to develop detailed planning for the firstreal-time demonstrations using the TIGGE infrastructure (including evaluation of potential
use during IPY and Beijing Olympics)
It is recommended that a follow-on workshop be held as soon as possible for contributors andPhase-1 archive centres to achieve the relevant above targets.
Thirdly, in 2006:
The Phase-1 TIGGE data archives will begin collecting available ensemble contributions innear-real time4

Links will be available to other already established data archives
Research access will be available to TIGGE data (through the existing accessinfrastructure of the archive centres)
Work will begin on Phase 2 software developments (once required funding is secured)The above timetable should enable TIGGE to contribute to proposed real-time THORPEX support
for:International Polar Year field campaigns in 2007-08
Beijing 2008 Olympics WWRP Research and Development project
The workshop considered that achieving Phase-1 within the above time constraints is feasible, butchallenging, and depends on immediate action and commitments from participation centres.
It is suggested that this report is treated as a detailed proposal for the execution of the
TIGGE project, and as such it is discussed by the WMO THORPEX Executive Board and by
the WMO International Core Steering Committee. It is also suggested that this report ismade available to the other THORPEX Working Groups, which are expected to overseemuch of the research undertaken using the TIGGE data, and that the considerations
outlined in this report are considered during the planning of the research projects
promoted/managed by these sub-programmes.

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Table of Contents
1. Introduction: THORPEX and TIGGE ................................................................................ 8-10
1.1 THORPEX Research .................................................................................................... 8
1.2 TIGGE ........................................................................................................................... 8
1.3 Expected Outcomes of TIGGE ..................................................................................... 9
1.4 Links between TIGGE and other THORPEX Sub-Programme ................................... 10
2. The 1st TIGGE Workshop ............................................................................................... 11
2.1 Workshop Objectives .................................................................................................. 11
2.2 Organization of the Workshop .................................................................................... 11
3. TIGGE’s Key Scientific Questions ................................................................................. 12
4. Potential TIGGE Ensemble Investigations ................................................................... 13-14
4.1 Investigations with Routine Data ................................................................................. 13
4.2 Global New-data Experiments .................................................................................... 13
4.3 Links between Global and Regional EPSs ................................................................. 14
5. Societal and Economic Applications of TIGGE’s Research ...................................... 15-16
5.1 Obstacles to Application of Ensemble Forecast ......................................................... 15
5.2 Strategies for Promotion of Socio-Economic Application ............................................ 15
5.3 Data Requirements for Socio-Economic Applications ................................................ 16
6. TIGGE’s Links with other Projects ............................................................................... 17-18
6.1 The North American Ensemble Forecast System (NAEFS). ....................................... 17
6.2 Links with Hydrological Projects (EFAS and HEPEX) ................................................ 17
6.3 Links to Monthly / Seasonal Data and COPES ........................................................... 18
6.4 THORPEX Campaigns and Demonstration Projects .................................................. 18
7. Calibration, Combination and Verification of TIGGE’s Forecasts .............................. 19-21
7.1 Observations and Analyses ........................................................................................ 19
7.2 Verification .................................................................................................................. 19
7.3 Calibration.................................................................................................................... 21
7.4 Combination................................................................................................................. 21
8. User Requirements: Access and TIGGE’s Data Policy .............................................. 22
9. Data Requirements for TIGGE ....................................................................................... 23-25
9.1 Forecasts .................................................................................................................... 23
9.2 Re-Forecasts .............................................................................................................. 23
9.3 Initial / Boundary Conditions for LAMS ....................................................................... 23
9.4 Observations ............................................................................................................... 24

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9.5 Verification Output Data .............................................................................................. 24
9.6 User Utility Functions .................................................................................................. 24
9.7 Proposed Content of TIGGE Archive .......................................................................... 24
10. TIGGE’s Implementation Plan and Infrastructure Requirements ............................. 26-27
10.1 Historic Data ............................................................................................................. 26
10.2 Data Exchange, Storage and Access for Operational EPSs .................................... 26
10.3 Data Volumes and Archive Structure ........................................................................ 27
11. TIGGE’s Time Tables and Milestones ......................................................................... 28-29
11.1 TIGGE’s Time Tables ............................................................................................... 28
11.2 Links with the North American Ensemble Forecasting System (NAEFS) ................. 28
12. TIGGE’s Funding .......................................................................................................... 30
13. Reference ....................................................................................................................... 31
ANNEX I – List of Participants .............................................................................................. 32
ANNEX II – Workshop Programme ....................................................................................... 34
ANNEX III – Available EPSs and LEPSs .............................................................................. 35
ANNEX IV – Summary of Expressions of Interest ............................................................... 37
ANNEX V – List of Acronyms ................................................................................................ 38

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1 Introduction: THORPEX and TIGGE
Two major research challenges of the 21st century are to reduce and mitigate natural disasters andto realise the societal and economic benefits of improved weather forecasts. THORPEX (THeObserving system Research and Predictability Experiment) is a response to these challenges.
THORPEX will provide the research underpinning the WMO Multi-Hazard Prevention Strategy tohalve the number of deaths due to meteorological, hydrological and climate-related natural
disasters over the next 15 years. THORPEX will contribute to the development of a future truly
integrated Global Interactive Forecast System (GIFS), that will form the basis for the weather
component of the WMO-lead Global Early Warning and Response System for multi-hazard
prevention. The GIFS will provide appropriate risk management tools for decision makers of allnations.
The THORPEX Interactive Grand Global Ensemble (TIGGE) will be developed as a resource tosupport the substantial research effort needed to evaluate the potential of a Global Interactive
Forecast System. TIGGE will provide a framework for international collaboration in thedevelopment of ensemble prediction systems (EPSs) and will facilitate the design of the main
prediction tools in the THORPEX Forecast Demonstration Projects.

1.1 THORPEX research
The core research objectives of THORPEX are described within four sub-programmes:
Predictability and Dynamical Processes; Observing Systems; Data Assimilation and Observing
Strategies; and Societal and Economic Applications. Developments in all these areas will beneeded to achieve the goal of developing truly interactive forecast systems in which observations,
their use in data assimilation, and the design of the numerical modelling system are adapted to therequirements of the users of the forecasts.
The scientific research needed, and the strategy to achieve this, are given in the following
documents, available from http://www.wmo.int/thorpex/publications.html:
M. A. Shapiro and A. J. Thorpe, 2004: THORPEX International Science Plan. Version 3. 2
November 2004, WMO/TD-No. 1246, WWRP/THORPEX No. 2
D. P. Rogers et al., 2005: THORPEX International Research Implementation Plan. Version
1. 14 February 2005. WMO/TD-No. 1258, WWRP/THORPEX No. 4

1.2 TIGGE
The THORPEX Interactive Grand Global Ensemble, TIGGE, is introduced in the THORPEX
Science Plan:“THORPEX will develop, demonstrate and evaluate a multi-model, multi-analysis and multinational ensemble prediction system, referred to as the THORPEX Interactive Grand
Global Ensemble (TIGGE). The TIGGE will integrate user requirements for forecast
information, developments in observing systems, targeting, adaptive data assimilation, andmodel improvements into a multi-model/multi-analysis ensemble prediction system. TIGGE
data bases, containing individual model EPS analyses and forecasts will be required tofacilitate research on the design of best configuration of multi-model/multi-analysis
ensemble forecast systems. TIGGE prototype forecast systems, resulting from thisresearch, would be used to produce experimental real-time forecasts. The skill of theseexperimental forecasts will be evaluated relative to existing techniques. These forecasts
should be tailored according to the severity of the predicted weather hazards. An
operational forecast system of this design would allow international resources to be brought
to bear on the most critical environmental forecast problems of the day.”

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The TIGGE concept is expanded in the THORPEX Implementation Plan; the main points are
summarised below. More information about TIGGE can be found in Chapter 8 of the THORPEX
Implementation Plan.

1.2.1 Basic structure
The initial basic components of TIGGE will be global ensembles run to around 14 days, including
those run currently at a number of operational centres (see list of current systems at Appendix B).
These will be collected in near real time and stored in a common format in a number of central
data servers for access by researchers in operational centres and the academic communities. This
will facilitate research on combination and inter-comparison of different systems; it will becomestraightforward, for example, to compare the value of multi-model ensembles with those based on
perturbations of a single model. Easy access to long series of data is necessary for applications
such as bias correction and the optimal combination of ensembles from different sources; thesewill be accumulated, albeit with ever improving data assimilation and modelling techniques, as the
TIGGE archive grows during the project.

1.2.2 Interactive forecast system
The concept of interactivity will be tested in the TIGGE framework. As a response to the day to daychanges in uncertainty, extra observations could be called on in ‘sensitive areas’, ensemble size
and resolution adjusted, and regional ensembles run as and when needed; all these adapting inreal time to meet user needs. Research on the scientific aspects of interactivity that can be carriedout through TIGGE include:
Are there different ‘optimal’ configurations for different situations (e.g. relative benefits ofresolution and ensemble size); how would the requirements vary from day to day.
Links between global and regional ensembles, including the provision of appropriate
boundary conditions, how global ensembles can be used for ‘on-demand’ requests forregional ensembles, whether global ensembles can guide the real-time composition ofregional ensembles (‘representative members’).
Observation targeting - some methods of sensitive area prediction such as the ETKF will
benefit directly from the large ensemble size available in TIGGE; other methods will not
rely directly on TIGGE. Other aspects of targeting are covered elsewhere in the THORPEX
plans.

1.2.3 THORPEX field demonstration projects and real-time use
TIGGE data could be used in real time during the THORPEX field demonstration projects (FDPs).
The timeframe for these FDPs will allow for 2-3 years of near-real-time TIGGE development before
real-time availability is required. By the time of THORPEX FDPs (in ~2008) enough data andexperience will have been gained to use TIGGE in real time as the main forecast tool, providing
both global and regional quantifications of weather uncertainties to use in decision-making.

1.3 Expected outcomes of TIGGE
The overall expected outcomes of the TIGGE project are stated in the THORPEX Implementation
Plan• Enhanced collaboration on development of ensemble prediction, internationally andbetween operational centres and universities• New methods of combining ensembles from different sources and of correcting forsystematic errors (biases)

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•

•
•

Understanding of the feasibility of interactive ensemble system responding dynamically tochanging uncertainty (including use for adaptive observing, variable ensemble size, ondemand regional ensembles) and exploiting new technology for grid computing and highspeed data transfer.
Test (through FDPs) concept of TIGGE Prediction Centre to produce ensemble-based
predictions of high-impact weather, wherever it occurs, on all predictable time ranges.
A prototype future Global Interactive Forecasting System.

1.4 Links between TIGGE and other THORPEX sub-programmes
TIGGE will fulfil several roles within THORPEX:• It will provide a framework to address scientific questions of predictability and ensemble
design• It will provide a resource for research being carried out under the four research subprogrammes• It will provide a facility to evaluate the concept of a Global Interactive Forecast System(GIFS)• It will contribute key prediction tools to the THORPEX field demonstration projects
Close collaboration will be maintained throughout the project between all THORPEX subprogrammes. It is anticipated that much of the research carried out using the TIGGE data will bedone under the auspices of other sub-programmes.
The TIGGE data facility will need to be designed to meet the differing requirements of each ofthese activities. Outlines of the research issues and resources for each activity are given in the
THORPEX Science and Implementation Plans. These provide initial guidelines for thedevelopment of the TIGGE user requirements. It is expected that the THORPEX Working Groups
that will be established for each sub-programme will further refine the requirements in each area.
Since significant work is needed to develop the TIGGE infrastructure, and this is a necessary prerequisite for much of the THORPEX research, work on TIGGE needs to begin as soon as possible.
While ideally detailed specifications would be obtained from each prospective user group of TIGGE,
it is recognised that some of these will take time to establish. The TIGGE Workshop has drawn on
experts in each THORPEX research area to obtain the best current estimate of user requirements.
It is anyway likely that user requirements will evolve during the lifetime of THORPEX as we better
understand the scientific issues. The TIGGE design will reflect this expectation.

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2 The 1st TIGGE Workshop
The 1st TIGGE workshop was held at ECMWF from 1 to 3 March 2005. The workshop was initiatedby the WMO THORPEX programme, and was co-sponsored by ECMWF, The UK Met Office and
WMO. Seventy delegates from international organizations, national and regional meteorologicaland hydrological services, universities and private companies attended it (Appendix A includes afull list of participants).

2.1 Workshop objectives
The workshop was held to discuss the scientific aims and user requirements in more detail withrepresentatives of the expected TIGGE user community, and to make sure that the TIGGE
infrastructure accurately meets the users’ needs.
One of the objectives of the workshop was to produce a report outlining user requirements and theinfrastructure design, to be presented to the WMO THORPEX Executive Board and InternationalCore Steering Committee (ICSC). This report could be treated as a detailed proposal for the
execution of the TIGGE project. The report could also be made available to the other THORPEX
Working Groups, which are expected to oversee much of the research undertaken using the
TIGGE data. The considerations of this workshop may be useful in planning of these research subprogrammes.

2.2 Organisation of the workshop
During the first half of the workshop, 14 invited speakers gave overview talks on some key aspects
of TIGGE, such as the design of global and regional ensemble prediction systems, the simulationof initial and model uncertainties, the combination and calibration of forecasts produced bydifferent systems, the verification of weather forecasts, high-impact weather and societal andeconomic applications, infrastructure and architecture of forecasting systems (Appendix A includes
the workshop programme).
During the second half of the workshop, delegates joined four working groups designed to discuss
the following issues:
WG1: Design of TIGGE experiments and model systems
WG2: Post-processing and verification
WG3: Applications in TIGGE
WG4: Infrastructure of TIGGE
Plenary sessions were held to provide feedback between the working groups and to draw someconclusions on three key areas: key scientific questions, ensemble design experiments andsocietal and economic applications. Conclusions and recommendations on these three areas are
summarised, respectively, in sections 3, 4 and 5.

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3 TIGGE’s key scientific questions
The overall purpose, goals and expected outcomes of TIGGE, as given in the THORPEX Scienceand Implementation Plans are summarised in Section 1. The workshop considered the strategy
required to achieve these goals. As outlined in Section 1, TIGGE is expected to be a resource for awide range of users. Research on fundamental issues of predictability and ensemble design,
information on analysis uncertainty, potential of interactive forecast systems and applications tousers will all make use of TIGGE data. Sections 5 and 6 review the requirements in detail from theperspectives of ensemble design and end-user applications. Consideration is focused particularly
on issues that need access to multi-centre ensemble data. However, the infrastructure and accessfacilities provided as a necessary part of the TIGGE infrastructure will have more generalapplication to a broad range of THORPEX research activities that do not specifically require multiensemble data. This may be particularly valuable for users outside the main operational centres,such as academics, and those working on end-user applications.
During the workshop, the following questions were discussed:
Is there value in combining ensemble forecasts from multiple NWP centres?
Is model-related uncertainty better captured in multi-centre ensembles?
Can an ‘optimal’ approach to the simulation of observation./initial uncertainties be identified?
What is the best way to combine ensembles from multiple centres?
Will less skilful sub-components add value to an overall product?
Does a multi-centre EPS possess value beyond an ensemble of high-resolution multicentre operational control forecasts?
To what extent are multi-centre perturbations appropriate?
Can we learn about analysis uncertainty from TIGGE and hence improve DA procedures
and existing EPSs?
Do the benefits of combining ensembles from multiple centres justify the additional
operational costs?
Can a single system be improved to a higher level by learning from a multi-centre EPS?
During the workshop, it was pointed out that these questions are relevant for a variety of situations,such as tropical storm track forecasts, or other high-impact weather situations, and that theanswers may be different for different applications. It was also pointed out that the interfacing of
LAMs with the global TIGGE data set was considered especially important for high-impact weather.
The workshop noted that answers to the questions posed above would depend on the time andspace scales and lead time considered, and also on the type of high-impact events studied.
Metrics for the evaluation of the results should include those related to practical applications.
Particular weather phenomena suggested for further studies, including those with LAMs, included
tropical storms, heavy precipitation events, etc., possibly in selected “test-beds” over specified
areas and time periods, to be agreed upon by the collaborating investigators. It is the expectationof the workshop that the scientific investigations and experiments carried out with the help of the
TIGGE data archive will also contribute to advancing our knowledge of atmospheric predictability.

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4 Potential TIGGE ensemble investigations
TIGGE offers a unique opportunity to investigate ensemble forecasting related issues that can beaddressed only through the use of multi-centre ensemble prediction data sets.

4.1 Investigations with routine TIGGE data
In order to address the key scientific questions listed in section 3, the workshop proposed thefollowing experiments for the consideration of the THORPEX research community.
The first group of experiments are based on the use of the routine ensemble data sets that areproposed to be part of the TIGGE archive, and should address the following two key issues:
Is forecast uncertainty better captured with a multi-centre approach?
o How much analysis/forecast error is explained by single vs multi-centre ensembles?
o To what extent do perturbations describe analysis error? (Check perturbations
against known properties of analysis error).
o Does the multi-centre approach provide a better description of uncertainty
associated with moist/precipitation processes?
Explore and explain cases when sub-components of the TIGGE ensemble generated bydifferent models bifurcate.

4.2 Global new-data experiments
The second group of experiments considered by the workshop involved the coordinated generationof additional ensemble forecast data as part of a multi-centre inter-comparison.

4.2.1 Multi-Model / Multi-Analysis Ensemble (MUMMA)
A set of cross-model cross-analyses experiments was proposed, in which each centre’s model
would be run from the analyses of each of the other centres as well as from its own analysis. If N
centres participate, this would yield NxN forecasts. The properties of the N forecasts from each
initial condition may provide further insight into characteristics of model error. Consideration of the
N forecasts using the same model may provide information about the impacts of using the different
analyses available at the various centres. Integrations can be carried out in near-real time
(preferred for operational feedback) or retrospectively for limited period of time (30-90 days?),possibly at low resolution (e.g. ~T150), to limit cost requirements. Difficulties in actually interfacing
analyses and models were recognized and discussed. This experiment was referred to as
MUMMA .
In particular, the workshop considered MUMMA suitable for testing the following sciencehypotheses related to multi-centre ensemble forecasting:
The control initial fields (operational analyses) from the different NWP centres provide agood description and sampling of uncertainty in the initial conditions (when the same modelis used)The use of different models provides a good description of model related forecast
uncertainty (when the same initial conditions are used)
The complete MUMMA ensemble will better describe forecast uncertainty in general ascompared to an ensemble generated at a single centre.
The combination of analyses from multiple centres provides an improved estimate of theinitial state, and better quantification of the analysis uncertainty
The immediate requirement to enable MUMMA is that analyses from contributing centres are madeavailable within TIGGE on ~20 pressure levels at the resolution available at contributing centres.

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It was pointed out that one of the key difficulties in realizing this project would be the treatment of
surface field: one approach that could be followed is to integrate each model using upper-air fields
from analyses generated by the other centres, but using surface data from its own analysis. The
fact that this project may face this and other technical problems, and require considerable
resources should be considered in the planning phase. It was also mentioned that a similar
initiative might be being undertaken or considered by WGNE, and has been suggested that before
proceeding with any MUMMA project, the potential link with WGNE is investigated.

4.2.2 Controlled initial perturbation experiments
An experiment using a single model (possibly developed and/or installed at the Earth Simulator) tointegrate all available perturbation fields was proposed. This would allow:
the comparison of the multi-centre TIGGE ensemble with a same-size single-model system
in which only the initial conditions are different between members• The comparison of the effectiveness of different perturbation techniques that are used
operationally at different centres, eliminating the effect of different initial conditions and
NWP models as used in operations (Buizza et al 2005, Bourke et al. 2004)
evaluation of the relative importance of ensemble size and resolution by for example
running the system with 350 members at T100 and with 50 members at T200 (i.e.
comparing systems with similar computational cost).

4.3 Links between global and regional EPSs
In view of the emphasis of THORPEX on high-impact weather, the interfacing of global ensemble
prediction systems with LAM ensembles was considered to be of particular importance. In that
context, a key question that TIGGE should address is the following:Will the use of TIGGE data as boundary conditions in LAM ensembles (for existing systems,
see table in Appendix B) lead to enhanced guidance for high impact weather events?
Regional models require lateral boundary conditions from a global model at high temporal
resolution and either on all model levels or on pressure levels, depending on the specifications of
the regional model. Transmission and archiving of this data (full model fields for all ensemble
members) is not currently feasible for TIGGE. However, the following proposals are feasible andshould be considered:
Retain the required global fields at the originating centre for a limited time (e.g. 2 days),during which LAM groups can access the data if they wish (for example if the situation isparticularly interesting). Archiving of these data would be the responsibility of the LAM
group; the data would not be archived by the global centre.
Retain for a longer period (and possibly transmit and archive in TIGGE) a limited and predefined set of “frames” that provide the necessary boundary conditions for given regions.
This is significantly less data than the full fields but does not allow flexible choice of LAM
region.
For particular high-impact cases, chosen in real time, archive the full model fields in TIGGE.
This would allow many LAM groups to carry out case study work on significant events.
For a pre-selected period (e.g. 1 month), archive required global fields at originating
centres and transfer (by tape) to TIGGE archive, to allow intensive study of use of TIGGE
global models to drive regional ensembles.
The first proposal in the above list is a potential real-time/operational configuration. Such a systemprovides part of the interactive component of TIGGE since it allows for specific on-demand action
whenever severe weather seems imminent. It is suggested that in the planning phase these
proposals are discussed with groups and consortia involved with limited area activities (e.g.
HIRLAM, COSMO-LEPS).

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5 Societal and economic applications of TIGGE research
There is a wide range of users in both the public and private sectors who could potentially act aspartners in translating the information contained in TIGGE products into socio-economic benefits.
Organizations in the following broad areas were identified by the workshop as being possible
partners in TIGGE applications research:
Civil Protection and Aid Agencies: e.g. preparation for and response to floods, storms,
tropical and mid-latitude cyclones, heat-waves, wild-land fires, snowfall, landslides,
atmospheric release of hazardous materials, and diseases with a weather-dependent
component (including airborne disease).
Resource and infrastructure management: e.g. water management, energy management,
agriculture, forestry, fishing, health services, transportation, construction, retail, finance,
tourism and sporting events.
It is anticipated that these sectors could exploit medium-range forecast information in various ways,
uncluding:Preparation for deployment of resources: e.g. emergency disaster aid, forest fire-fighters,
and equipment.
Planning and scheduling of possible actions: e.g., evacuation planning, elective proceduresin hospitals, making additional staff and equipment available at short notice, power-station
maintenance.
Monitoring of potential events: e.g. placement of mobile Doppler radar units.
Scheduling of personnel shifts: e.g. city workers for ploughing and gritting.
Increasing frequency of local, high-resolution modelling and/or monitoring: e.g. increase
frequency of local flood forecasting models.

5.1 Obstacles to Application of Ensemble Forecasts
To allow and encourage these potential users to participate effectively in TIGGE-oriented research,
the workshop identified several issues that need to be addressed:
A lack of awareness of ensemble-based medium-range forecasts among potential endusers.
A tradition of end-users dealing with forecasts presented deterministically and a lack ofexperience in exploiting probabilistic forecast skill.
The need for end-users to formulate appropriate responses to relatively low probability,
albeit highly significant, warnings issued at lead times of several days.
A lack of verification of ensemble forecasts against quantities of interest and relevance toend-users.
The non-quantitative formulation of some ensemble-based products.
Restricted access to ensemble data products.
The high data volumes associated with ensemble forecasts, and data interfaces that are
unsuitable for end-users.
The availability of ensemble-based forecasts in forms suitable for end-users and specificweather-risk problems. This is both a problem of visualization and dissemination and also
applies to deterministic forecasts.
An incomplete characterization of the global climatology of severe weather in conjunction
with the model climatologies of severe weather.
Periodic changes and updates to numerical models that possibly diminish the robustness of
post-processing and verification methods.

15

5.2 Strategies for Promotion of Socio-economic Applications
The workshop made the following recommendations and suggestions for how the issues outlined
above might be addressed:
Awareness and visibility of ensemble-based forecasts should be raised. Two means offurthering this aim could be the creation and dissemination of brochures and establishing a
web site aimed at potential end-users.
A workshop format should be developed to stimulate discussion between meteorologists
and potential end-users in how information contained in ensemble forecasts might betranslated into socio-economic benefits. Such workshops should be held in multiple
locations and targeted on specific applications or sectors to reach communities ofpotential end-users.
Collaborative projects between meteorologists, academics and end-users in the public andprivate sectors should be promoted. Projects such as the Direct and Inverse Modelling inEnvironmental End-to-End Prediction (DIME) project at the London School of Economics
and the European Flood Alert System (EFAS) developed and tested during the European
Commission’s Framework 6 programme may provide useful models for this type of joint
research.
Post-doctoral researchers should be placed within end-user organizations to facilitate a
two-way exchange of information and experience concerning the integration of ensemblebased forecasts into decision-making. Such efforts should yield an understanding of

the decision-making process, quantitative assessment of the impact of forecast
information for specific weather-sensitive decisions, and a definition of the metrics
for measuring forecast utility.
An online catalogue of databases of observations relevant to potential end-users should beestablished. Examples include hydrological observations and energy demand.
An effort to characterize the global climatology of severe weather in both observations andthe models should be made to help end-users interpret low probability forecasts ofsevere events and allow tools such as the Extreme Forecast Index to be extended to thewhole globe.
The TIGGE project should be used as an opportunity to study and possibly quantify the
impact of numerical model changes on the value of post-processing techniques.

The TIGGE project should develop an early prototype deployment of some subsetof the above ideas. This will serve to demonstrate the concepts and potential value
to the intended beneficiaries as well as provide a test-bed for the technical andscientific requirements.
Implications for the design of the TIGGE facility are included in Section 8 on access and data
policy.

5.3 Data Requirements for Socio-Economic Applications
The draft list of variables given in section 9.7 was considered by the workshop to be acceptable forsocio-economic applications research, including driving hydrological models. In addition, it would
be advantageous to have a higher temporal frequency than 6 hours in the first 72 hours of theforecasts. The actual temporal frequency should be consistent with the spatial resolution of themodel.
If ensemble forecast information is to be translated into socio-economic benefits it is of paramountimportance that all barriers to the use of TIGGE data be minimised, whether these barriers areadministrative, technical or caused by a lack of understanding as to what the data are and how
they should, and should not, be used. The success of socio-economic applications will depend
upon data being available, accessible and usable.

16

6 TIGGE’s links with other projects
TIGGE could benefit from strong links with existing international projects, in particular with closeinteractions with the North American Ensemble Forecast System, hydrological projects,
monthly/seasonal projects and ad-hoc demonstration projects. Some ideas on why these links
should be promoted are highlighted hereafter.

6.1 The North American Ensemble Forecast System (NAEFS)
The North American Ensemble Forecast System (NAEFS) is a multinational project being
developed by the Meteorological Service of Canada (MSC), the National Meteorological Service of
Mexico (NMSM), and the US National Weather Service (NWS), with the UK Met Office joining in
2006. The project involves the exchange and sharing of global ensemble forecasts among theparticipating countries. The NAEFS’ Initial Operating Capability was established in September
2004.
NAEFS plans to become fully operational by March 2006, and further refinements are expected to
be implemented in the following years. Each set of ensemble forecasts (currently from MSC and
NWS) will be statistically post-processed to reduce systematic errors. The bias-corrected
constituent ensembles will be combined into a joint ensemble that will be used to generate forecast
products for intermediate and end users. All software products used in the processing of ensemble
forecast data, as well as in the generation of products are shared among the participatingorganizations, and used consistently at each centre. As a result, forecasts issued by theparticipating national meteorological services will be seamless both in time and space.
TIGGE will co-ordinate with NAEFS, and will benefit from the research, development andexperience of multi-model ensembles accumulated within that project. Not surprisingly, the TIGGE
database and the associated research plans share many of the design features of the NAEFS
project, since both are based on the use of combined multi-centre ensemble forecasts. The newtechniques and tools that are being developed for NAEFS will also be shared with the THORPEX
research and user community. It is expected that TIGGE-related THORPEX research will greatly
expand the capabilities of multi-centre ensemble forecasting and that the enhanced techniques willfind their operational use in the NAEFS and its successors.
The workshop considered it important to minimise any duplication of effort between TIGGE
research activities and the operational NAEFS. Wherever possible, infrastructure should be
integrated, software shared and research and development activities co-ordinated between the two
projects. This needs to be addressed at the outset of TIGGE to avoid costly parallel developments.
Formal contact between TIGGE/THORPEX and NAEFS should be established as soon as possible
to discuss the issues highlighted above.

6.2 Links with hydrological projects (EFAS and HEPEX)TIGGE could foster the development of closer links between meteorological and hydrologicalinstitutions, thus helping the realization of integrated meteo- and hydro-logical forecasting systems.
To investigate these opportunities, TIGGE should explore the possibility to establish links with two
existing meteo- and hydro-logical initiatives:
EFAS, the European Flood Alert System
HEPEX, the Hydrological Ensemble Prediction Experiment
These two initiatives aim to bring the international hydrological and meteorological communities
together to develop reliable forecasting tools that can be used with confidence by the emergency
management and water resources sectors to make decisions that have important consequences17for public health and safety, and the economy. The TIGGE data-set should be designed taking into
consideration the requirements of these and similar initiatives.

6.3 Links to monthly/seasonal data and COPES
Co-ordination between THORPEX and the WCRP/COPES initiative, to address the observationaland modelling requirements for the prediction of weather and climate for two weeks and beyond, is
one of the core objectives stated in the THORPEX Science Plan.
The workshop noted that a number of centres are exploring the concept of “unified” or “seamless”
forecast production spanning a range of timescales from days to seasons. End users would benefit
from a more consistent and standardised product suite. Potential benefits for modelling centres areboth scientific and technical, with expected resource savings.
There is significant potential for mutual benefits in infrastructure, data access and applications
development from collaboration and coordination between activities on different time-scales. The
links being established between THORPEX and COPES should include consideration of theseissues at an early stage.

6.4 THORPEX Campaigns and Demonstration Projects
THORPEX will conduct regional and global campaigns to demonstrate and evaluate newobserving technologies and interactive forecast systems. THORPEX will explore the opportunities
to carry out these campaigns in conjunction with major international programmes such as theInternational Polar Year (IPY) and the African Monsoon Multi-disciplinary Analysis (AMMA).
THORPEX Demonstration Projects are specifically intended to benchmark the societal andeconomic benefits of the THORPEX programme.
Possible demonstrations include
The Beijing Olympics in 2008, in collaboration with the WWRP Research and Development
Project (B08RDP) on use of short-range ensemble forecasts
Demonstration of the usefulness of ensemble forecasts for risk-reduction strategies inAfrican countries most vulnerable to weather-related diseases
Improvements of forecasts of sand and dust storms (in collaboration with the WWRP Sandand Dust Storm Experiment)
It is important that assessment of the benefits to end users (through coupling to impacts models or
specific user applications) is included in at least some of these demonstrations.
The Campaigns and Demonstrations will provide guidance to relevant bodies including the WMO
Commission for Basic Systems (CBS), the World Weather Watch (WWW) and operational forecast
centres on improvements to forecast systems, and optimisation of global and regional observingsystems.
TIGGE will be a major resource for many of these field campaign and demonstration activities. The
TIGGE framework may, for example, be able to provide:
Straightforward, consistent and real-time access to required forecast information to all
involved in a given activity
Advance warning of potential significant events based on multi-centre ensemble predictions
Specific indications of where and when additional observations would be particularly useful(by generating and providing access to sensitive area prediction data through the TIGGE
infrastructure).
Predictions of when it may be especially important to be able to run regional/mesoscaleensembles (this might give some flexibility if a centre is not able to commit to continuous
running of a regional system).

18

Provision of boundary conditions for high resolution regional ensembles
The appropriateness and feasibility of these options will need to be assessed for each activity.

19

7 Calibration,
forecasts

combination

andverification

of

TIGGE’sOnce a set of ensemble forecasts has been generated it then becomes necessary to verify theforecasts. TIGGE will archive forecasts and observations to enable individual users to perform awide range of diagnostics and verification studies. If the verification indicates that there aresystematic errors then some adjustment (calibration) is necessary to optimize the forecasts.
Because TIGGE will involve multiple ensemble forecast systems, techniques will be needed to
combine them into synthesized forecast products. To assist TIGGE researchers in theirinvestigations and data users in their choice of optimal ensemble configuration, it is recommended
that a library of open-source verification, calibration, and combination routines be made available
along with the data.

7.1 Observations and analyses
Observations and analyses of meteorological variables must form an important part of the TIGGE
dataset. When verifying against analyses a weighted blend of analyses from several operational
centers may be a fairer verification field than single-model analyses to avoid incestuous false skill,
particularly in the early forecast range and in data-sparse regions (this is a debatable issue thatshould be investigated under TIGGE).
In order to produce statistically significant verification results, particularly for severe weather andother rare events, it is necessary to have a large number of samples spanning both time and space.
However, to explore the dependence of forecast performance on region, season, etc., it is also
desirable to have sample sets that are relatively homogeneous. Care must be taken to balancethese two considerations.
Verification techniques are now beginning to account for uncertainty in the verification data.
Therefore observation and analysis error statistics should also be part of the TIGGE dataset.
Research should continue on investigating the best ways to make use of information on
observational uncertainty. Use of re-sampling techniques in verification can help to quantify the
impact of small data samples for rare events, and also of inhomogeneities in forecast datasets
caused by changes in operational ensembles.

7.2 Verification
To provide information on forecast quality to help address many of the scientific questions listed in
Section 3, TIGGE should host (or be instrumental in the establishment of) a website that contains alimited set of verification statistics for individual and multi-model ensembles. It is recommended
that the WMO standard for ensemble forecast verification (section III of the WMO Manual of GDPS,
Attachment II.7, Table F) be used as a baseline. A verification website following this standard isalready hosted for WMO by the Japan Meteorological Agency and could form a prototype for a
TIGGE website. The recommended scores and diagrams for probability forecasts are:Reliability diagrams
ROC diagrams
Brier skill score(Continuous) Ranked Probability skill score
Relative economic value
Ensemble spread should be assessed using:
Rank histogram
Spread vs. skill

20

The WMO standard defines "reliability tables" (contingency tables) as the mechanism for exchangeof results, from which all of the probabilistic scores and diagrams can be calculated. In addition tothese probability-based metrics the verification should also include assessment of the ensemble
mean in a similar fashion to traditional deterministic verification:
RMSE
Anomaly correlation
Spatial variance
The WMO standard variables to be verified include:
PMSL +/- 1, +/- 2 S.Ds w.r.t. climatology
Z500 +/- 1, +/- 2 S.Ds w.r.t. climatology|V|850 with thresholds of 10, 15, 25 m/s
T850 anomalies with 2, 4, 8 C w.r.t. climatology
Precipitation at 1, 5, 10, 25 mm/24h verified against observations from the GCOS network
To provide further verification for weather near the surface, which is where the greatest impact isfelt, we additionally recommend the verification of:
T2m +/- 1, +/- 2 S.Ds w.r.t. climatology|V|10m with thresholds of 5, 10, 15 m/s
The climatology referred to by the WMO standard is a "centre specified climatology". Since the
TIGGE dataset will include analyses of these fields from multiple operational centres it would be
preferable to use a weighted blending of operational analyses as the reference state used forverification and for definitions of climatology. Alternatively, a re-analysis dataset (e.g. ERA-40)
could be used for climatology. For station verification, the long-term station climatology should be
used
Verification software, compatible with the TIGGE archive dataset, should be prepared for thevariables and scores in the above list. This will provide the basis for consistent evaluation of
TIGGE and needs to be done as soon as possible, and made available to all participants in TIGGE.
This may eventually be a more consistent and effective way of carrying out the verification both for
WMO and for TIGGE research projects, and could be automated to update monthly on the TIGGE
dataset.
Statistics will be accumulated monthly for forecast periods of 24h, 48h, etc. Monthly results will besaved in a format that enables aggregation to longer time periods. The spatial domains over which
verification should be done are specified following the WMO standard:
Northern Hemispheric extratropics (90°N – 20°N)
Tropics (20°N – 20°S)
Southern Hemispheric extratropics (20°S – 90°S)
The reporting of confidence intervals with the verification statistics is highly recommended to help
assess whether estimated ensemble skill is statistically significant, and whether differences inensemble performance are significant.
The verification products specified above represent only a basic set. TIGGE also encourages thedevelopment and use of additional verification methods including feature-based and user-oriented
verification methods.
Verification of extreme (rare) events remains a difficult problem, but one which is extremely
important for meeting THORPEX goals. These fall well outside of the ±1, ±2 standard deviations
w.r.t. long-term climatology specified above. Because of the small sample size, and also becauseof difficulties in making accurate observations in extreme events, there are large uncertainties
associated with the verification results. Rather than relying on uncertain statistics, research groups
should examine the TIGGE ensembles for the severe weather events that are of particular interest21

to them. Further research, possibly involving the statistical community, is required to develop
methods of estimating ensemble skill for rare events.

7.3 Calibration
Here, calibration refers to the statistical adjustment of ensemble forecasts based upon acomparison of prior forecasts and observations. Calibration can apply corrections for biases in boththe mean forecasts from individual models and the spread in ensembles. Within TIGGE research isparticularly required in calibration methods for multi-model ensembles, which is closely linked withwork on combination described in section 7.4 below.
There is a tension between the desire of operational forecast centers to calibrate using shorttraining periods and the potential for greater improvement by using longer training periods (Hamill
et al. 2004). TIGGE will include NOAA/CDC's reforecast dataset (Hamill et al. 2005) which will
enable comparisons between probability forecasts made using low resolution models with large
training datasets and those made using the latest EPS systems (single and multi-model) but withshorter training sets.

7.4 Combination
Combination refers to the generation of probabilistic forecast products based on multiple ensemble
forecast systems with variable quality. TIGGE will investigate methods for optimal combination,building on related work done by the seasonal forecasting community. Some techniques such as
Bayesian model averaging comprise both calibration and combination. As with verification andcalibration, there is a tension between the need for large datasets to ensure statistical significanceand avoid over-fitting based on small samples, and the need for systems to adjust to changes inoperational ensembles.

22

8 User requirements: access and TIGGE’s data policy
The workshop made the following recommendations for data policy and access to TIGGE data:
The data policy should ensure that ensemble data are available to any user willing to
publish the results of research involving the data. This policy should include real-time datarequired for demonstrations. The process of obtaining approval for data access should betransparent, streamlined and reasonably fast
To meet user needs, data access must go beyond simple bulk access (e.g. ftp of GriB files).
Ensemble variables most likely to have the most direct socio-economic benefits should be
re-archived in such a way that time series of forecasts for limited regions or single locations
can be efficiently extracted. Self-training systems capable of making such a selection
adaptively should be considered.
Regional subsets of ensemble data should be stored at regional data centres. These
regional data centres would improve the service offered to local researchers by offering a
selection of product tailored for potential applications in that region and making themquicker to access in developing countries.
The user interface for making data queries to the central and regional re-archives should bedesigned to be user-friendly and should make it as easy as possible for researchers in
disciplines not used to dealing with exceptionally high data volumes to obtain subsets ofensemble data. This may require the support of multiple application views layered

above the “meteorological access”
Ready access to information explaining the data sets should be available. This information
should be designed to be usable by non-meteorologists. Information concerning the model(centre, resolution, version etc.) used to produce data should be included as meta-data as

well as the impact of those meteorological aspects of the model on end-user
applications. Background information and guidelines will be required for users to makeappropriate use (and understand limitations) of the data.
Open-source sharing of post-processing software (calibration, combination, verification anddecision-making) should be promoted in order to maximise benefit for both researchers andend-users.

23

9 Data requirements for TIGGE
The success of TIGGE depends greatly on the data sets made available to the user community. In
order to achieve the goals of the project, not only the forecasts produced during the project, butalso re-forecasts and observational data sets have to be provided. This section summarises thedata requirements identified at the workshop, based on the scientific research and application
requirements presented in other sections of this report.

9.1 Forecasts
The operationally generated ensemble forecasts will form the core of the TIGGE database. Thisdata set will be used to address basic scientific questions related to the performance of single vs.
multi-models. A preliminary list of parameters to be archived in the common TIGGE database isgiven in section 9.7. Those tables, specifying the proposed content of the archive separately forsurface and upper air levels, should be circulated in the TIGGE community for further
consideration and eventual approval by the GIFS-TIGGE working group.
In order to perform extended studies on initial/evolving perturbations and the “Multi-Analyses-MultiModel” experiments described in section 4.2.1, the full fields of the operational control run andperturbed analyses are also needed (on model or pressure levels). The immediate and non-trivial
requirement to enable MUMMA is that analyses from contributing centres are made available
within TIGGE on ~20 pressure levels at the resolution available at contributing centres.

9.2 Re-Forecasts
A number of forecast data sets for past periods exist and should be offered to the TIGGE
community. This includes e.g. the NOAA-CIRES Climate Diagnostic Center’s ensemble re-forecast
data set, which will be part of TIGGE. This data set consists of 15-member, 15-day ensemble
forecasts from 1979 - current, created from an unchanging T62 version of NCEP’s medium-range
forecast model, see Hamill et al. (2004, 2005) for details. Furthermore, both historical data(including verification) from operational ensemble forecast systems (ECMWF, NCEP, etc.), and reanalysis datasets (including forecasts, e.g. ERA-40, NCEP/NCAR) could be made available for
TIGGE research. It is proposed to link to all these and any other data sets (NCEP re-analyses etc.)from the TIGGE website, whereas - at least for the time being - the actual access and download ofthe data should be realized via the original data access mechanisms of the respective data
supplier. However, if during the project specific access standards are developed, it is desirable toapply such standards to the existing data sets as well.

9.3 Initial / boundary conditions for LAMS
In order to run limited area models with initial and boundary conditions from the TIGGE data set in general - model level data have to be provided. Since it is not feasible to transfer model level
data from all participating EPS’s to the central data repositories, other strategies for supplying
LAMs with the necessary forcing data have to be developed. A possible solution would be toprovide the full model level data only at the site of the producing centre and only for a limited
period (up to 2 days after production). The LAM groups then have to download the forcing data
(global model levels or frames) directly from the individual EPS centres they are interested in.
Additionally, it was mentioned at the workshop that not all LAMs need model level data, but some
LAMs can be run with a suitable number of pressure level fields. In order to address such needs,
the archive should contain more pressure levels than needed for diagnostic and verification
purposes only.

24

Since one of the main goals of TIGGE is to address severe weather and LAMs are an integral partin the process of improving severe weather predictions, it might be useful to circulate a specificquestionnaire amongst the LAM community for an exact definition of the specific LAM
requirements.

9.4 Observations
Various observational data sets are necessary for a thorough assessment of the TIGGE forecasts.
In order to account for uncertainties in observations, observational error statistics should be madeavailable where possible. For an efficient verification process it is desirable to have easy access tothe following observational data sets:
Surface network (GTS)Radiosonde network
Gauge precipitation
Furthermore, the TIGGE database / website should provide the capability to link to regional anduser-specific observational data sets.

9.5 Verification output data
In order to avoid duplication of verification efforts amongst the TIGGE user community, the TIGGE
database could include intermediate verification output data, e.g. contingency tables, which could
be used by individual users to calculate individual scores. WMO has already initiated a similar
effort for verification of global ensemble prediction systems, and JMA is hosting the corresponding
website (access is restricted to the participating centres). It should be investigated whether thefacilities at JMA can be used and extended to incorporate a TIGGE verification output database.

9.6 User utility functions
In order to allow and encourage more user-relevant verification, tables of individual user utility
functions should be made available. However, this information should be well documented to avoid
inappropriate usage.

9.7 Proposed content of TIGGE archive
The following tables contain the proposed list of variables to be archived in the common TIGGE
database. The proposed content encompasses basically all variables identified for ensemble
exchange between MSC and NCEP, i.e. in NAEFS (see section 6.1). However, it does not mirror
the exact content and definitions of NAEFS, in particular to avoid existing inconsistencies in thedefinitions between NCEP and MSC. It is strongly recommended to agree on common archive
definitions regarding the content, units and aggregation method of the archived fields. Furthermore,
the proposed archive goes over and above the agreed exchange in NAEFS to accommodate user
requirements from the application and LAM communities. It might also be necessary to adapt theoutput frequency of selected parameters to specific end-user needs, e.g. for the hydrologicalcommunity.

9.7.1 Surface data
It is proposed to archive 19 (+2) surface fields.

25

Parameter

Abbrev

Level

Unit

Mean sea level pressure
Surface Presure
10m U-velocity
10m V-velocity
2m temperature
2m dew point temperature
2m max temperature
2m min temperature
Total precipitation(liquid+frozen)
Snow fall

MSLP
SP
10U
10V
2T
2D
MX2T
MN2T
TP

MSL
surface
10m
10m
2m
2m
2m
2m
surfacePaPam s**-1
m s**-1
K
K
K
K
m

SF

surfaceSnow depthSDsurface

Total cloud cover
Total column water
Surface latent heat flux
Surface sensible heat flux
Surface solar radiation
Surface thermal radiation
Sunshine duration
Convective available potential
energyOrography (Geopotential at thesurface)Land-sea mask

TCC
TCW
SLHF
SSHF
SSR
STR
SUND
CAPE

surfacesurfacesurfacesurfacesurfacesurfacesurfacesurfacem of waterequivalentm of waterequivalent
0-1
kg m**-2
W m**-2 s
W m**-2 s
W m**-2 s
W m**-2 ss
J kg**-1

GHsurface

m**2s**-2

LSM

surface0-1

Outputfrequency
6h
6h
6h
6h
6h
6h
6h
6h
6h

Accum
inst
inst
inst
inst
inst
inst
det_lodet_loacc_st6hacc_st

6h

inst

6h
6h
6h
6h
6h
6h
6h
6h

inst
inst
acc_stacc_stacc_stacc_stacc_stinst

t_0 (and poss. inst
t_res-change)
t_0 (and poss. inst
t_res-change)

inst: instantaneous output
det_lo: determined over period from last output time to current output time
acc_st: accumulated over period from start of forecast to current output time (or alternatively
accumulated from last output time to current output time; to be decided)

9.7.2 Upper air data
It is proposed to archive 5 parameters on 9 pressure levels, i.e. 45 fields.
Parameter
Temperature
Geopotential
U-velocity
V-velocity
Specific Humidity

Abbre
v
T
G
U
V
Q

Level

Unit

Output frequ.

Comments

L9
L9
L9
L9
L9

K
m**2 s**-2
m s**-1
m s**-1
kg kg**-1

6h
6h
6h
6h
6h

inst
inst
inst
inst
inst

L9: at levels 1000, 925, 850, 700, 600, 500, 300, 250, 200 hPa

26

10 TIGGE’s
implementation
requirements

plan

and

infrastructureThis Section describes some details of the infrastructure requirements to set up and manage the
TIGGE data sets. These are based on the data and access requirements summarised in theprevious two sections.
Datasets needed for TIGGE projects are of three main types:
Existing (historic) data
Data to be collected routinely in near-real time from the participating centres
Special datasets for specific sub-projects collected either in near-real time for selectedperiods or archived in delayed mode, possible by exchange on tape.

10.1 Historic data
This consists of data that is already available from various centres, for example the CDC
reforecast data, ERA-40, NCEP reanalysis, and historical EPS forecasts and verification results.
Observation datasets, including satellite data, are also available.
It is recommended that the TIGGE archive contain data catalogues and documentation for as
many of these existing datasets as possible, but that the data themselves would not be archived aspart of TIGGE. Users are expected to make requests to the source organization for access tothese datasets.
To facilitate access to historical data, access methods, and formats (GRIB, NetCDF etc) should be
documented. Also, existing or future common data access methods and tools should be applied
wherever possible. These data would be free for registered research users.

10.2 Data exchange, storage and access for operational EPSs
This is the routine (often operational) ensemble data generated every day by a number of NWP
centres. Two possible general structures were defined for the storage and access to TIGGE data,a “Phase-1” structure, which could be implemented now at little cost, and a “Phase-2” structure,
which would be possible to implement within several years. These are briefly described in thissection:

10.2.1 TIGGE Phase-1
Data would be exchanged in “near-real time”, which means within a few hours to a few days, via
FTP over the Internet. The data would be collected and archived at one or more central datarepositories (ECMWF, NCAR, and CMA have so far expressed interest in retaining the entire
dataset) Regional subsets of data may also be archived at centres which have expressed aninterest in taking on the task of providing a data service for their region (e.g. SAWS)
Each contributing centre would be responsible for providing a backup archive of the data, which it
contributes to the archive.
In addition, complementary datasets may be exchanged in delayed mode for use in THORPEXTIGGE demonstration projects, for example boundary conditions over local areas for limited areamodels. It is not considered feasible to routinely archive sufficient data from global ensembles in
the central archive to provide boundary conditions to LAMs, due to the enormous data volumes.

27

10.2.2 TIGGE Phase-2
Within several years, and following significant software development, it will become possible to set
up distributed data repositories, in coordination with the WMO/WIS initiative. These must provide
efficient and transparent data delivery to users, and may eventually reduce or eliminate to need totransfer large quantities of data on a daily basis to centralized archive centres. It is expected that
Phase 1 will continue during the development and demonstration period for phase 2, for at least 2
years. Funding will be required to support software development.

10.3 Data volumes and archive structure
By the time the TIGGE archive starts, ECMWF (which runs the largest and highest-resolution
ensemble of all centres) will have increased the resolution of its ensemble model to T399.
Assuming 75 forecast fields, 40 timesteps per run for two runs per day, Gaussian grid, 50+1
ensemble members, and including full archive of the analysis on both model and pressure levels,
this leads to an archive volume of 130 Gb per day. With respect to this, addition of ensembles
from up to 9 other centres will increase the data volume by 50-80 Gb per day, giving a total dailyvolume of about 200 Gb. This is judged to be feasible within current network constraints. Sendingout more than 2 or 3 copies of the ECMWF ensemble would be problematic, however. It is
expected that input volumes from other centres will increase rapidly over the next few years as
upgrades to their ensembles are implemented.
Data exchange should be standardized in terms of parameters (parameter definitions may vary
between models), physical units, format and file naming conventions.
A requirement for data processing and archiving at higher temporal resolution (3-hourly or hourly)
for the first 3 days was identified for some applications (e.g. driving hydrological models). Inclusionof high temporal resolution data for the whole forecast period could further increase the data
volume, while implementation of a variable temporal resolution would complicate the archive andaccess structures.
The requirement for provision of boundary conditions for LAM integrations can be handled
separately from the central archive by accessing full model resolution data while they are online(within a few hours of the model run). Archive service for these data could be made available at alater stage. Data exchange of full model data on model levels is not possible in near-real time;demonstration datasets could be distributed by tape.
A large subset of users may want data in the form of temporal series of a single parameter or asmall number of parameters rather than as a set of full global fields for each day. This leads to arequirement for the rearchiving of a subset of the parameters as timeseries, which is feasible andmay benefit from technical advances.
Post-processing of archive data will be required before delivery to users. Routines are needed forgrid conversion, format conversion, and for the extraction of sub-areas, parameters and levels, forexample. At ECMWF, access through the MARS system would be available to registered researchusers, and the more popular data sets can be made available through public servers.
Applications and tools for data processing tailored to the needs of specific users can be preparedand shared among users. In this way, the effort required for development of data processing tools
can be shared among participants. These tools will need to be catalogued and documented. The
option for storing user-generated data should be considered.
It was noted that the NOAA Operational Model Archive Distribution System (NOMADS) data
access software project, developed at NOAA, may also have addressed some of these issues andcollaboration with that project may be useful.
Applications are potentially CPU-intensive and they may require additional resources.

28

11 TIGGE’s timetable and milestones
The following outline timetable and links are proposed. It is suggested that this schedule isdiscussed and approved by the THORPEX GIFS-TIGGE Working Group that needs to beestablished as soon as possible.

11.1 TIGGE’s time table
In 2005, the GIFS-TIGGE WG should:
Complete detailed planning (including costs and resource) and initiate Phase-1
Secure commitment from institutes willing to act as central data archives for TIGGE’s
Phase-1 and from initial contributors to TIGGE database
Agree data exchange protocols and data formats
Finalise list of datasets and parameters to store in the TIGGE archive or provide alternative
access to, based on provisional lists given in this report
Help and encourage contributing centres to begin development to provide data in agreed
formats
Address data access policy with the aim to provide as far as possible real-time access forresearch use
Consider the strategy required to initiate work on Phase-2 of TIGGE, including funding andresources
Work within THORPEX and with external partners to develop detailed planning for the firstreal-time demonstrations using the TIGGE infrastructure (including IPY and Beijing
Olympics)
It is recommended that a follow-on workshop be held as soon as possible for contributors andPhase-1 archive centres to achieve the relevant above targets.
In 2006:
the Phase-1 TIGGE data archives will begin collecting available ensemble contributions innear-real time
links will be available to other already established data archives
research access will be available to TIGGE data (through the existing access infrastructureof the archive centres)
work will begin on Phase 2 software developments (once required funding is secured)
In 2007 and 2008 the TIGGE infrastructure should be sufficiently established to provide real-timeaccess to TIGGE forecast data (and possibly access through the TIGGE infrastructure toobservation targeting information including sensitive area predictions) to contribute to proposed
THORPEX support for:
International Polar Year field campaigns
Beijing 2008 Olympics WWRP Research and Development project11.2 Link with NAEFS
Operational NAEFS products will be available in early 2006 and so will be available for real-timeuse during IPY and future campaigns. The above timetable is designed to complement this byproviding access to additional ensemble data for use and evaluation during these THORPEX
demonstrations.
Plans are underway for the NAEFS to include ensemble forecasts from the Met Office in 2006; theFleet Numerical Meteorology and Oceanography Centre (FNMOC) of the US Navy also intends toparticipate. NAEFS could be considered as a forerunner to a potential operational application of
TIGGE. As more NWP centres join, this could evolve into an important part of the future GIFS,

29

shaped by new scientific results, and benefiting from a new level of coordination among major
NWP centres that will support it.
To achieve this TIGGE and NAEFS developments need to be co-ordinated as soon as possible.
Formal contact between TIGGE/THORPEX and NAEFS should be established as soon as possible
to minimise costly parallel developments and to maximise potential mutual benefits.

30

12 TIGGE’s funding
TIGGE work will incur specific costs: telecommunications, data servers, additional disk space inthe organizations serving as global or regional data repositories, and labour costs.
It is expected that a significant fraction of these costs will be supported by the partner institutions
as “in kind contributions”. However a remaining fraction may not be covered in this way. In Phase1, disk space and manpower will be needed at the global repositories. The development of theneeded software to access data from the distributed TIGGE system in Phase 2 will probably
require some specific work by specialized analysts, and could become the common property of thepartner institutes. The associated costs can be estimated only after detailed plans are developedfor Phase 2. At that point it may make sense to establish mechanisms to share costs associatedwith this development. The use of the WMO trust fund established by THORPEX ICSC or of similar
instruments was noted as a possibility. The partner institutions may also develop specificproposals to funding agencies (e.g. the EU FP6 programme). After a more detailed evaluation of
the costs, a decision will be needed as to the amount of required contributions in order to start the
project on a safe basis.

31

13 References
Buizza, R., Houtekamer, P. L., Toth, Z., Pellerin, G., Wei, M., & Zhu, Y., 2005:A comparison of the
ECMWF, MSC and NCEP Global Ensemble Prediction Systems.Mon. Wea. Rev., in press.
Bourke, W., Buizza, R., & Naughton, M., 2004: Performance of the ECMWF and the BoM
Ensemble Systems in the Southern Hemisphere. Mon. Wea. Rev. 132, 2338-2357.
Hamill, T.M., J.S. Whitaker, and S.L. Mullen, 2005: Reforecasts, an important new data set for
improving weather predictions. Bull. Amer. Meteor. Soc., submitted.
Hamill, T.M., J.S. Whitaker, and X. Wei, 2004: Ensemble re-forecasting: improving medium-range
forecast skill using retrospective forecasts. MWR, 132, 1434-1447
Jordan C. Alpert and J. Wang, A Client Application for Real Time NOMADS at NCEP To
Disseminate NOAA’s Information Data Base, 17.5, 21st IIPS, AMS 2005. Available at:
http://ams.confex.com/ams/pdfpapers/83062.pdf
Rogers, D. P. et al., 2005: THORPEX International Research Implementation Plan. Version 1. 14
February 2005. WMO/TD-No. 1258, WWRP/THORPEX No. 4. Available fromhttp://www.wmo.int/thorpex/publications.html
Shapiro, M. A. and A. J. Thorpe, 2004: THORPEX International Science Plan. Version 3. 2
November 2004, WMO/TD-No. 1246, WWRP/THORPEX No. 2. Available fromhttp://www.wmo.int/thorpex/publications.html
Toth, Z., J. Desmarais, G. Brunet, Houtekamer, Y. Zhu, R. Wobus, R. Hogue, R. Verret, L. Wilson,
B. Cui, G. Pellerin, B. Gordon, E. O'Lenic, D. Unger, 2005: The North American Ensemble
Forecast System (NAEFS). Proceedings of the first THORPEX International Science Symposium,
Montreal, Canada, December 6-10, 2004. [Available at:
http://wwwt.emc.ncep.noaa.gov/gmb/ens/papers/toth_naefs_thorpex_montreal.pdf.]

32

ANNEX I
List of ParticipantsAndersson, Erik
Barnes, Andrew
Berner, Judith
Boettger, Horst
Bohra, Ashwini
Bougeault, Philippe
Broecker, Jochen
Brown, Barbara
Buizza, Roberto
Burridge, David
Casati, Barbara
Chen, Dehui
Chessa, Piero
Clarke, Liam
Coelho, Caioda Silva Dias, Pedro
Dell'Acqua, Matteo
Ebert, BethEhrendorfer, Martin
Enomoto, Takeshi
Estrada, Bartolome
Franz, Kristie
Frogner, Inge-Lise
Hagedorn, Renate
Hamill, Tom
Hofstadler, Fredi
Iversen, Trond
Lalaurette, François
Lawrence, Andrew
Lee, Woo-Jin
Leutbecher, Martin
Marsigli , Chiara
Montani, Andrea
Morse, Andy
Motz, Martin
Mylne, Ken
Ndarana, Thando
Paccagnella, Tiziana
Pailleux, Jean
Palmer, Tim
Parsons, Dave
Persson, Anders
Raoult, Baudouin
Richardson, David
Roulston, Mark
Shapiro, Mel
Silva Dias, Maria
Stephenson, David
Swinbank, Richard
Takeuchi, Yoshiaki
Talagrand, Olivier

ECMWF
IBM
ECMWF
ECMWF
NCMRWF
ECMWF
LSE
NCAR
ECMWF
WMO
MSC
CMA
SAR Sardinia
LSE
U Reading
U Sao Paolo
ECMWF
BMRC
U Innsbruck Austria
Earth simulator
INM
U California Irvine
NMINorway
ECMWF
CDC
ECMWF
U Oslo
ECMWF
ECMWF
KMA
ECMWF
ARPA
ARPA
U Liverpool
IBM
Met Office
SAWS
ARPA
Meteo-France
ECMWF
NCAR
SMHI
ECMWF
Met OfficePenn State
NOAA
CPTEC
U Reading
Met Office
JMA
LMDe.andersson@ecmwf.int
andrew.barnes@uk.ibm.com
j.berner@ecmwf.int
horst.boettger@ecmwf.int
akbohra@ncmrwf.gov.in
philippe.bougeault@ecmwf.int
j.broecker@lse.ac.uk
bgb@ucar.edu
roberto.buizza@ecmwf.int
d.m.burridge@btinternet.com
barbara.casati@ec.gc.ca
chendh@cma.gov.cn
chessa@sar.sardegna.it
l.clarke@lse.ac.uk
c.a.d.s.coelho@reading.ac.uk
pldsdias@master.iag.usp.br
m.dellacqua@ecmwf.int
e.ebert@bom.gov.au
martin.ehrendorfer@uibk.ac.at
eno@jamstec.go.jp
orfila@inm.es
franzk@uci.edu
inger.lise.frogner@met.no
renate.hagedorn@ecmwf.int
tom.hamill@noaa.gov
alfred.hofstadler@ecmwf.int
trond.iversen@geo.uio.nofrancois.lalaurette@ecmwf.int
a.lawrence@ecmwf.int
wjlee@kma.go.kr
m.leutbecher@ecmwf.int
cmarsigli@smr.arpa.emr.it
amontani@smr.arpa.emr.it
a.p.morse@liverpool.ac.uk
martin_motz@uk.ibm.com
ken.mylne@metoffice.gov.uk
thando@weathersa.co.za
t.paccagnella@smr.arpa.emr.it
jean.pailleux@meteo.fr
tim.palmer@ecmwf.int
parsons@ucar.edu
anders.persson@smhi.seb.raoult@ecmwf.int
david.s.richardson@metoffice.gov.uk
roulston@met.psu.edu
mshapiro@ucar.edu
assuncao@cptec.inpe.br
d.b.stephenson@reading.ac.uk
richard.swinbank@metoffice.gov.uk
ytakeuchi@met.kishou.go.jp
talagrand@lmd.ens.fr

33

Tennant, Warren
Thielen, Jutta
Thorpe, Alan
Tolasz, RadimToth, Zoltan
Treinish, Lloyd
Vitart, Frederik
Wilson, Laurie
Yoden, Shigeo
Zsoter, ErvinSAWS
JRC-ISPRA
U Reading
CHMI
NCEP
IBM
ECMWF
MSC
U KyotoHungary Met Service

34

tennant@weathersa.co.za
jutta.thielen@jrc.it
a.j.thorpe@reading.ac.uk
tolasz@chmi.cz
zoltan.toth@noaa.gov
lloydt@us.ibm.com
f.vitart@ecmwf.int
lawrence.wilson@ec.gc.ca
yoden@kugi.kyoto-u.ac.jp
zsoter.e@met.hu

ANNEX II
Workshop Programme
Tuesday 1 March 2005
9.00- 9.15:
Registration
9.15- 9.30:
D Marbouty: Welcome and opening
9.30-10.15: D Richardson: Introduction to THORPEX / TIGGE
10.15-11.00 R Buizza: Overview of global operational ensembles
11.15-12.00 M Ehrendorfer: Initial perturbations in ensemble prediction
12.00-12.45 T Palmer: Model imperfections
14.15-15.00 T Hamill: Calibration/combination
15.00-15.45 E Ebert: Validation
16.00-16.45 T Paccagnella: Limited area ensemble systems
16:45-17:30 K Mylne: Applications: forecaster perspective, training
Wednesday 2 March 2005
9.15-9.45
J Thielen: Applications: weather driven natural hazards
9.45 - 10.15 M Roulston: Applications: economics, energy, risk management
10.45.11.15 Z Toth: NAEFS: scientific and technical issues
11.15-11.45 F Hofstadler: Infrastructure and tools
11.45-12.15 R Hagedorn: User experience and requirements
12.15-12.45 L Treinish: architecture for a User-centric Global Interactive Forecast System
testbed14.15-17.00

Working groups

Thursday 3 March 2005
9.00-10.30
Plenary: preliminary reports from WGs and new brief
11.00-13.00 Working groups: revision of recommendations
14.30-17.00 Plenary: revised reports from WGs, and final discussion
Friday 4 March 2005
9.00-17.00
Organising Committee, speakers, WG chairs only

35

ANNEX III
Available EPSs and LEPSs
This table summarizes some key characteristics of the global and limited-area ensemble prediction
systems run operationally and in research mode.
Status

Runs/day Domain

Resolution

Lead
Time(hr)Members
run

2
1
2
2
1
1
1
1
4
?
?

globe
globe
globe
globe
globe
globe
globe
globe
globe
globe
globe

TL119
T106
T126
TL255
T119
T106
T106
TL149
T126-T62
T80
T62

240
240
360
240
240
216
192
240
384
384
336

UK Met Office 2

globe

90km

NCEP-SREF

2

North
AmericaSRNWP-PEPS 4

Pert method

33
33
15
51
17
25
17
17
11
9
17

1
1
1
1
1
1
1
2
1
1
1

SVs
SVs+BVs
EOF
SVs
BVs
BVs
BVs
EnKF
BVs
BVs
BVs

336

20

1

ETKF

32 km

63

15

Europe7-22 km30

18

COSMO-LEPS 1

Europe

10 km

120

10

2 (ETA, RSM)
NCEP global EPS
18
EuropeanModelsEU global models
Selected
ECMWF
globalEPS
1(LokalModell ,COSMO) members

PEACE Meteo
France1

Globe

TL358, ST2.4 60

11

1 (ARPEGE)

BMRC
CMA
CPTEC
ECMWF
GLOBALOPERATIONAL FNMOC
JMA
KMA
MSC
NCEP
NCMRWF
GLOBALSAWS
RESEARCHLAM
OPERATIONALLAM
RESEARCH/
Num models

SYSTEM

Targeted SVs

NOR LAMEPS 1

Europe

20 km

72

41

2
(HIRLAM- TEPS, based
TEPs)targeted SVs

UK Met Office 2

Euro-Atlantic 24 km

72

16

1 (UK
Model)

INM-SREPS

4

Euro-Atlantic 0.25 deg

72

64(lagged
system)BMRC

1

65S-17N;
65E-185

0.5 deg72

16

1 (LAPS)BMRC global EPS,
using DA cycles withpert-obs

CMC-EPS

1

North
America

28 km

48

20

1
(CMC
mod)

reg CMC global EPS,
using targeted SVs

DMI-HIRLAM(1)

1

Euro-Atalntic 0.6 deg

72

51

DMI-HIRLAM

DMI-HIRLAM(2)

1

Euro-Atlantic 0.2 deg

72

5

HMS LAMEPS 1

Europe

12 km

54

11

SWISS LEPS

Europe10 km

72variable72

6

SAR_MME

1

Euro-Atlantic 0.25 deg

36on

Unified UKMO-EPS, using
ETKF
4 (ECMWF.UKMO
4 (Hirlam, HRM, AVN-NCEP, GMEMM5, UM)
DWD)

ECMWF-EPS
ECMWFControl,using pert moist
DMI-HIRLAM
physics
Meteo-France
PEACE
using
ALADINtargeted SVs
Selected
ECMWF
1 (Lokal Modell global
EPS
COSMO)
members
ICs/BCs from AVN
3(MM5, andIFS
BOLAM, RAMS) deterministic fcs

PIED_SE

1

Europe

7 km

48

3

4
(LM-DWD,aLmo
MeteoSwiss,
LAMI, IFS)
L:AM and IFS

17

MM5

Outer 36 km

UWME(Univerityof
Washington) 2

Pacific
Nortwest

Nested 12 km

MAP D-phase

Europe

?

120

37

Under
Under
development development

8 global models
Under development

ANNEX IV
Summary of Expressions of Interest
An informal survey of the centres represented at the meeting was conducted. The WG discussions
indicated various possible levels of involvement for any centre: Provider of ensemble forecasts,
user of ensembles forecasts, global repository and distribution centre for TIGGE products, regional
repository and distribution centre for TIGGE products. The Table below summarizes the firstexpressions of interest received during the meeting. It is does not constitute a commitment ofthese Centres, nor does it constitute a full survey of the potential participants in TIGGE. It is
included here to indicate the already substantial level of interest in participation in the TIGGE
project.

CentreProvider

User

BMRC
CPTEC
UKMO
Meteo-France
MS Canada
KMA

x
x
x
x
x
x

x
x
x (verification)
x
x
x

CMA

x

x

ECMWF

x
+
software

SMHI
NCAR
ARPA
NCMRWF
Earth Simulator
Center
NCEP
South Africa

MARS

GlobalRepositoryxxx (in future)
x
x

x

x

CDC
JRC

x (reforecasts)

in Phase 2

in Phase 2(subject tofunding)x (to beconfirmed)xxxx(verification)x(agriculture)x

JMA

Regional
Repositoryxx (to beconfirmed)
with Univ.
Tsukuba?

xx

In addition, a strong interest to use TIGGE data was indicated by Univ Innsbruck, LMD (France),
and the HEPEX project.

38

ANNEX V
List of Acronyms
BMRC
CAS
CDC
CMA
CMC
COSMO
CPTEC
DIME
DMI
ECMWF
EFAS
EPS
ETKF
FNMOC
GEPS
GIFS
GTS
HEPEX
HMS
ICSC
INM
JMA
JRC
KMA
LAM
LEPS
MARS
MSC
MUMMA
NAEFS
NCAR
NCEP
NCMRWF
NNSM
NOAA
NOR
NWS
PEACE
PIED-SE
SAR
SAWS
SRNWP
THORPEX
TIGGE
UWME
WGNE
WMO
WCRP
WWRP

Bureau of Meteorology Research Centre
WMO Commission for Atmospheric Sciences
Climate Diagnostic Center
China Meteorological Administration
Canadian Meteorological Centre
COnsortium of Small-scale MOdelling
Centro de Previsao de Tempo e Estudos Climaticos
Direct and Inverse Modelling in Environmental end-to-end prediction
Danish Meteorological InstituteEuropean Centre for Medium-Range Weather Forecasts
European Flood Alert System
Ensemble Prediction System
Ensemble Transform Kalman Filter
Fleet Numerical Meteorology and Oceanography Center
Global EPS
Global Interactive Forecast System
Global Telecommunication Service
Hydrological Ensemble Prediction ExperimentInternational Core Steering Committee
Japan Meteorological Agency
Joint Research Center
Korea Meteorological Administration
Limited Area Model
LAM Ensemble Prediction System
Meteorological Archive System
Meteorological Service of Canada
MUlti-Model Multi-Analysis
North American Ensemble Forecasting System
National Center for Atmospheric Research
National Centers for Environmental Prediction
National Centre for Medium-Range Weather Forecasting
National Meteorological Service of Mexico
National Oceanic and Atmospheric Administration
Norwegian?
US National Weather Services
SArdinian Regional meteolorigcal service
South-African Weather Service
Short-Range Numerical Weather Prediction
The Observing system Research and Predictability EXperiment
THORPEX Interactive Grand Global Ensemble
University of Washington Mesoscale Ensemble??
Working Group on Numerical Experimentation (joint WCRP, CAS)World Meteorological Organization
WMO World Climate Research Programme
WMO World Weather Research Programme39