The next generation of regional prediction systems for weather, water and
environmental applications
by Gilbert Brunet1, Tom Keenan2, Jeanette Onvlee3, Michel Béland4, David
Parsons5 and Jocelyn Mailhot4
1. Background
Weather events effecting society typically occur on regional and even local scales. Thus,
the underlying motivation for improving weather prediction on regional scales is to meet
user demands for improvements in the accuracy, lead-time, confidence and utilization of
forecasts of weather that have a significant impact on society, the economy and the
environment. One aspect of this high impact weather is regional disasters. The
frequency of disasters worldwide is illustrated in Fig. 1. Weather events account for
approximately two thirds of the natural disasters in the form of floods, wind storms, slides
(e.g., primarily mud slides and avalanches induced by precipitation), extreme
temperatures (e.g., urban heat wave), and wild fires (typically associated with high
winds, low humidity and high temperatures). Floods and wind storms (primarily tropical
cyclones) are by far the most common type of disasters that impact our planet
accounting for over half of the disasters. The character, intensity and impacts of these
disasters vary on regional and even local scales.
An important aspect of the frequency of natural disasters is how these events have
increased with time. For example, approximately 100 natural disasters per decade
occurred between 1900-1940, while that number has risen to almost 2800 events per
decade in the 1990s (ICSU 2008). The cost of disasters globally to society have reached
nearly 500 billion (US dollars) per decade with the costs of hydro-meteorological
disasters increasing by a staggering factor of 7 in the time period of 1997 to 2007. The
dramatic increase in cost is due to a number of factors including the increase in the
number of disasters, the expanding material wealth of segments of society, the
exponential growth of the world’s population, climate change, globalization that expands
the geographical footprint of any event, changes in land use such as deforestation and
demographic shifts associated with concentrating populations into urban areas, flood
plains, coastal regions and other vulnerable areas.
Extreme
Temperature
3.6%
Slides
5%
Flood
30%
Drought
7%
Wild Fires
3.5%
Epidemic, famine,
insects
14%
Tsunami
0.39%
Windstorm
25%
Figure 1: The frequency of natural
disasters over the globe for the
period 1980 to 2005 from the
OFDA/CRED International Disaster
Data Base.
Earthquake
9%
Volcano
1.6%
The human influences on the climate system have also likely changed the probability of
some types of high impact weather, like extreme temperature events, over the past
decades. Furthermore, many of the observed changes in extremes are by and large
consistent with what the scientific climate community anticipates of human induced
1
climate change. We can expect in the long term is more frequent warm extremes, less
frequent cold extremes and more high impact hydro-meteorological events as a result of
climate change. The latter demands better regional climate predictions in support of
mitigation policy.
Fortunately, weather forecast models, which are the backbone of environmental
prediction systems on these time-scales, have also remarkably improved in recent years
with the forecast skill for many parameters improving at about a day per decade. Thus,
for example, a five-day forecast today generally has more skill than the four-day forecast
of a decade earlier. The improved quality of modern Numerical Weather Prediction
(NWP) systems run operationally at National Meteorological and Hydrological Service
(NMHS) have formed the basis for the development of new specialized environmental
prediction tools to support policies and operational objectives of their respective
government and industrial sector. Improved numerical environmental forecasts bring
significant health and safety and economic benefits to the public, marine, transportation,
agriculture, forests and resource sectors. A critical question, however, is whether this
steady linear improvement in the prediction skill of numerical weather prediction is
sufficient to meet the growing need for atmospheric modeling of the future. Certainly, the
strain has increased as:
i)
Societies struggle to mitigate the rising economic costs of hydrometeorological disasters and the overall increase in the number of disasters.
ii)
The demand has grown for environmental prediction systems as air quality
can degrade with increases in population and industrial capacity, particularly
in urban basis and megacities. Another implication of shifting demographics
is spread of population into forests prone to fires, which increases the need
for fire weather forecasts.
iii)
Water management has also become a more important issue as the scarcity
of potable water spreads and urban and river flooding effects more people as
populations densities have rapidly grown in areas at risk, such as coastal
plains and river valley.
iv)
Member nations further develop alternative energy sources, which enhance
the need for predictions to drive decision support systems for wind, solar and
other new energy sources.
This vision paper discusses the research underpinnings that are necessary to support
the transition to high resolution and multi-parameter prediction and the implications for
the WMO.
2. Changing paradigms for regional prediction
Prediction systems are in the midst of several paradigm shifts. The first paradigm shift is
a movement to increasing complexity as models are moving from solely predicting
weather towards environmental prediction systems that can include forecasting
capability for atmospheric constituents, soil-vegetation-ecosystems, cryosphere,
hydrology, and the coupling of atmospheric-ocean and even fresh water systems. This
trend increases the utility of the atmospheric forecasting systems, but also improves the
quality of such systems when feedbacks of these diverse processes on atmospheric
2
flows are properly represented. These systems can provide additional information for
decision-making. As an example, these systems can be used to determine the factors
that affect water levels and flows over for hydrographic basins and lake systems,
developing and testing potential new regulation plans and assessing the impacts of
these potential plans on the ecosystem, human and economical interests. Numerous
other examples can be found including forecasts of sea ice in the Arctic, where the IPYTHORPEX cluster of projects played a role, to sand and dust storm forecasts in and
around desert areas, where there is a focused WMO research effort led by WWRP and
GAW.
The second paradigm shift is a movement to non-hydrostatic models (horizontal scales
of order km or less) that begin to directly resolve deep convection. While improvements
in forecast skill of NWP systems with time are indeed impressive, a closer examination
of forecast skill (Fig. 2) provides insight into both user needs and research directions. In
this figure, the anomaly correlation coefficient of predicted versus observed fields are
shown as a function of forecast lead time by the ECMWF system for 500 hPa height,
potential vorticity and precipitation for the European sector. For 500 hPa, the skill shows
that the prediction has utility out to 7 to 8 days. This forecast skill is impressive and, it is
indeed the parameter that is typically shown to illustrate the historic improvement in
forecast skill made by operational NWP. If one examines the skill for anomalies in
potential vorticity, which represents the dynamically active portions of the atmosphere
then approximately three days of skill are lost for this parameter versus the 500 hPa
fields! This loss of skill is important as it is these dynamically active portions of the
atmosphere that are indeed likely to contain active weather and therefore be more
important for users. If one considers the prediction of precipitation anomalies, than the
useable skill is only on the order of 1 or 2 days. Admittedly, even the construction of
precipitation anomalies from observations is difficult so this measure should be treated
with some skepticism, but it does illustrate the weakness such systems have in
quantitative precipitation forecasts and the clear need for improvement. Numerous other
examples of the motivation behind this movement to non-hydrostatic prediction systems
can be found. For example, recent studies have illustrated that numerical models that do
not resolve convective scale often have short comings in representing even the diurnal
cycle of convection and that the inability of these models to capture propagation
characteristics of convection systems is limiting predictive skill.
If convective systems eventually become directly resolved by non-hydrostatic models,
the reasoning is that the vexing problem of convective parameterization will be
eliminated and forecasts will be improved. Note this could be achieved satisfactorily only
at sub-kilometer resolution depending of the application and user requirements. A similar
statement could be made about the forecast improvements that come about from better
representation of orography, coastlines, and other variations in surface conditions. An
example of high resolution flows in an orographic coastal region is shown in Fig. 3. Of
course, in addition to the motivation to improve NWP models by going to finer-scales,
the higher resolution space and time-scale prediction by these higher resolution models
also open a wide variety of new and/or improved services (e.g., local infrastructure
forecasts such as large public events or airports and other transportation hubs,
precipitation forecasts for small basins including flash flood predictions, winds and winddriven waves in coastal regions, input to local storm surge and hydrological models,
temperature and fog forecasts for stable basins).
3
500 hPa Heights
Potential Vorticity
Precipitation
Figure 2: Measures of skill score from the ECMWF forecast system for the anomaly correlation coefficient of 500 hPa,
Potential vorticity, and rainfall as a function of forecast lead time. The scores are shown for the period 2001-2005.
Another paradigm shift that is ongoing for predictive models is the development and
utilization of ensemble prediction systems. Ensemble systems were developed to
capture the uncertainty associated with a specific forecast. A measure of uncertainty is
required since many atmospheric events are highly non-linear, which means that a small
change in the initial state or an error in the model formulation will produce significantly
large changes in forecast. For regional modeling systems, an additional uncertainty
arises from the specification of the lateral boundary conditions by global models.
Building an ensemble system requires knowledge of the error in the initial and lateral
boundary conditions and in each component of the modeling process. Determining and
capturing this uncertainty in the design of the ensemble will require significant research
for these new regional coupled environmental modeling systems.
Given the investment in computational and human resources for such models, critical
questions for researchers are how to optimize the design of new operational high
resolution environmental modeling systems and quantifying the degree of improvement
in predictive skill and the corresponding benefit in services. The complexity of
information will also require new forecast systems to move information from the model to
the user.
4
Figure 3. The model output from the regional MC2 model was run for Vancouver Island
at a horizontal resolution of 2km: 17H forecast valid 26 June 1997 2000 UTC. It shows
near surface wind flow (arrows with scale in knots in lower left corner) superimposed
over topography (gray shades every 500m). Only one arrow for every other grid point is
displayed for each direction.
This vision paper looks at the implications of these ongoing paradigms shifts to the
efforts of CAS research programmes. This paper concentrates on regional prediction for
two reasons: The first is that within the WWRP, the THORPEX programme focuses most
(but not all) of its energy on improvements in global prediction through an extensive
research effort. In contrast, within the WWRP regional prediction is organized through a
more distributed collection of working groups and projects within the WWRP and through
collaborations with the CAS OPAG-Environmental Pollution and Atmospheric Chemistry
(EPAC) and the WCRP. The EPAC efforts within Global Atmospheric Watch efforts also
are highly relevant with work on atmospheric transport models, the urban air quality
through the GURME (GAW Urban Research Meteorology and Environment (GURME)
project and their participation in the Group of Experts on Scientific Aspects of Marine
Environmental Protection (GESAMP). GAW is also where the expertise resides in
pollution and air chemistry measurements, which is inherently critical to enviormental
modeling.
The second reason for selection regional modeling is that the movement towards higher
resolution non-hydrostatic modeling scales is already taking place on regional and
weather time-scales as global modeling systems are limited by computational resources.
With a focus on regional modeling systems, we do not mean to downplay the link of
these efforts to global modeling systems. We will subsequently discuss the
recommendations of the ideas in this vision paper to efforts within THORPEX and the
global aspects of GAW recognizing that in many ways, such limited area models
5
critically depend on the lateral boundary conditions obtained from global models.
Improvements in non-hydrostatic modeling on the regional scale now will benefit global
weather and climate prediction systems in the future. Indeed more and more National
Meteorological and Hydrological Services (NMHSs) are using unified modelling
approach for short and mid-term operational weather forecasting. Figure 4 shows
different grids of the Canadian Global Environmental Multiscale (GEM) NWP model for
applications at different space-time scales. GEM forecasts also better represent
physical, chemical and hydrological processes (cloud formation, rain, snow, etc.) and
surface simulations (lacustrine, forest environments, etc.).
Figure 4. Global Environmental Multiscale (GEM) Miscellaneous Grid Configurations:
The first at the top, a global uniform horizontal resolution grid. In the middle, a variable
horizontal resolution grid with a window over Oklahoma. At the bottom, a very high
resolution window over the Vancouver region.
3. The scientific challenges of supporting the shifting paradigms in prediction
i)
Weather prediction from minutes to days
Weather forecasting needs to rely in the very short term (few hours) on nowcasting
systems due to the presence of significant and detrimental spin-up in the representation
of dynamical flow and moist processes at the start of a NWP integration. The scope of
nowcasting in the context of environmental prediction is focused on the detection and
forecasting of weather over the 0-6 hour time frame. Nowcasting employs a range of
techniques. Short-term extrapolation is particularly effective at the shorter time periods
and can be realized through a number of techniques: heuristic methods, statistical
approaches, conceptual models, data mining, artificial intelligence, and fuzzy logic
schemes are all employed. Beyond about three hours the use of numerical prediction
techniques becomes increasingly effective. Advancing nowcasting depends on
improving the effectiveness of these various underlying components and how they
operate in combination. A non-exhaustive examination indicates a diverse user
6
community: emergency services, defence forces, security agencies, the transport
industry, hydrologists, the agricultural community, recreational groups, and air quality
agencies regularly depend upon nowcasting services.
Beyond the nowcasting time scale, weather forecasting relies on global and limited area
(e.g., often called regional or mesoscale) numerical weather prediction systems that
typically contain higher resolution than most global modeling systems. The underlying
motivation of limited area modeling is to provide a higher spatial resolution and nonhydrostatic depiction of weather events to match user needs. These limited area models
also can have more frequent temporal updates as these systems are also sometimes
more frequently “run” than global systems. These higher resolution models serve
another important function as they are many NMHSs link to numerical modeling as many
more NMHSs have some form of limited area modeling system whereas a relatively
small number of global modeling producing centres exist.
Research challenges for both nowcasting and limited area modeling with order 1-km
horizontal scale models are discussed in the Strategic Plan for the Implementation of the
WMO’s WWRP effort in the Working Groups on Nowcasting Research and Mesoscale
Weather Forecasting Research. In this context, improvements in prediction capability in
limited area modeling will require fundamental advances in mesoscale environmental
observing capability with emphasis on remote sensing, mesoscale data assimilation, in
the representation of convection and complex terrain, in the treatment of surface
characteristics and processes, and in understanding the mesoscale predictability and in
the barriers limiting predictive skill. The better representation of steep orography will
furthermore require serious adjustments in the numerical schemes of high-resolution
models as well as the modification of some of the physical parameterization schemes.
In addition, the WWRP-THORPEX’s has a multi-model effort called TIGGE-LAM
(THORPEX Interactive Grand Global Ensemble Limited Area Model) that includes efforts
to improve the design of ensemble forecast systems for limited area models to meet the
challenge of deriving accurate probabilistic forecasts through characterizing the
uncertainties due to model error, initial conditions and the time-dependent lateral
boundary conditions as well as reducing/removing model bias.
Despite the different approaches between nowcasting and limited area modeling, the
improvements in nowcasting systems also rest on issues identical or closely related to
those for limited area modeling. For example, the research issues for nowcasting include
characterizing uncertainty by developing probabilistic forecasting and improving
prediction skill of nowcasting systems through improving the initial state, removing bias,
better representation of systematic processes such as those triggered by orography and
understanding small-scale predictability and error growth. The long-term strategies for
multi-hazard nowcasting depend upon improved high resolution NWP model
development, including new numerical algorithms, and data assimilation. Activities that
are deemed high priority for nowcasting research, such as error reductions in radar
measurements, are also directly relevant to limited area modeling. For example,
reduction in radar errors will allow for improvements in the assimilation of radar data into
the analyses that initialized these higher resolution numerical modeling efforts, which
has been demonstrated to improve prediction. Developing this regional modelling
capability will also have significant application in the climate domain where regional
response to anthropogenic warming is a key issue.
ii)
Data assimilation and the explosion in remote sensing capability
7
Effective regional environmental modelling and prediction depend upon accurate timely
and well distributed observations of the earth's environment coupled with effective
techniques for the quality control and analysis of these observations. Satellite and other
remote sensing coupled with data assimilation are key ingredients of successful
prediction systems. Modern satellite and other remote sensors provide orders of
magnitude increase in the scope of environmental observations with an accuracy,
coverage and resolution essential for mesoscale data assimilation. The capacity to
undertake effective use of these observations in analysis and environmental prediction
models is essential to realise improved regional environmental forecasts, but also
provides dividends for seasonal to inter-annual climate forecasts, and increasing the
accuracy of climate data sets. The assimilation of surface-based radar observations also
shows benefits to forecast skill, which brings challenges to the design of assimilation and
modelling systems and also in the international exchange of radar data. In general the
need to investigate on the strengths and limitations (e.g., operational constraints) of
different data assimilation approaches, including cross-fertilization with ensemble
forecasting techniques, and observation impact studies will be tantamount for the future
of regional forecasting.
iii)
Coupled numerical prediction
Coupled Numerical Weather and Environmental Prediction (NWEP) will provide linkage
between the land, hydrosphere and atmosphere (and its chemistry). The CAS Vision
Papers on the recommendations of the EC Research Task Team, on OceanAtmosphere, on Polar Issues and on Environmental Prediction all directly or implicitly
detail the how Members needs will be met by the development and implementation of
environmental prediction systems and the associated research challenges that need to
be met in order to develop or improve those systems. Concentrating on coupled systems
means that the feedbacks between these miscellaneous processes will be represented
so that prediction of a variety of parameters will be improved. One example is the impact
of aerosol on the atmospheric and surface radiation exchanges, which will in turn
produce circulations that will modify the local concentrations of aerosols through
effecting mixing and advection. Thus we will not reiterate those areas in detail, but rather
mention of a few of the associated research challenges. Major research and
development challenges will need to be addressed to extend NWP systems to coupled
NWEP. These areas are:
Chemistry: New satellite instruments (like high spectral resolution sounders such as
AIRS and IASI, EOS instruments) will provide a wealth of information on the chemical
state of the atmosphere for air quality regional issue, including urban scale. Most
currently available measurements are made at the surface, but the new measurements
will provide information throughout the depth of the atmosphere. This opens the door to
much wider application of atmospheric chemistry models for air quality forecasting and
other purposes. This will require data assimilation into coupled atmosphere/chemistry
models, including middle atmosphere capability as a tool for integrating space and
ground-based measurement and providing a base capacity for chemical modeling
activity, including ozone monitoring. Challenges in the pathway to coupled atmosphericchemical modeling include the huge additional computational costs (a low-complexity
chemical scheme contains some few tens of species for which the conservations
equations need to be integrated in addition to the seven atmospheric variables) and, in
particular, the requirement for emission inventories covering the entire modeling domain.
8
Ocean and Coasts: The international ARGO program is providing routine soundings of
the ocean equivalent to (or better than) the global radiosonde network provides for the
atmosphere. Together with new satellite measurements the ocean surface ARGO will
revolutionize observation of the global ocean. The data assimilation techniques used for
atmospheric models can be extended to assimilate these data into coupled regional
ocean-atmosphere models. This will have important applications for understanding and
forecasting ocean regional circulation, ice coverage and wave activity. For regional
coupling efforts could be extended to large fresh water bodies and coastal areas.
Hydrology: Building on efforts in the WWRP MAP D-PHASE project, where nowcasting
and deterministic atmospheric models were linked to deterministic and ensemble
hydrological forecast models, more efforts need to focus on research and development
for coupled atmosphere-hydrological modeling. More intercomparison-type of numerical
experiments with coupled atmosphere-hydrology models need to be promoted through
regional research (e.g., HYMEX) and forecast demonstration projects, including the use
of ensemble prediction systems (e.g., HEPEX). The atmosphere-hydrology problems
need to be tackled regionally for very short to sub-seasonal time range with proper
downscaling and post-processing techniques. Also, methods will have to be developed
in collaboration with the hydrological community, which can efficiently handle the
requirement for calibration of run-off models in each catchment separately.
Urban meteorology: It will become increasingly important at mesoscale resolutions to
include a realistic representation of the effects of large cities for reliable prediction of
flows and dispersion in complex urbanized environments. New land cover databases are
needed to describe the spatial distribution and variability of urban areas and a general
classification method based on satellite imagery are needed. In this context highresolution numerical modelers will need to develop techniques how to treat surfaces
where the roughness elements are (much) taller than the lowest model level (typically
10m for a 1km horizontal resolution model). It will also be necessary to develop a
methodology to characterize the spatial and temporal distributions of anthropogenic heat
emissions from large metropolitan areas. This will have to be tested over major world
cities using detailed observations from urban field campaigns.
Biogeochemical and Ecosystem: Efforts will need to build on comprehensive regional
earth system capability to encompass physical and biogeochemical work involved in
ecosystem science. Knowledge and tools that are essential for managing marine
resources and natural ecosystems with spatially and temporally varying characteristics
of information are required for tactical and strategic natural resource management
decisions. Application areas include:
 Biogeochemical and ecosystem modelling
 Local Impacts related to Climate Change and Acidification on Marine
Ecosystems
iv)
Verification and assessment
Objective verification of high-resolution coupled models poses many challenges. The
usual conventional scores may not be informative enough and new and more
appropriate tools need to be developed. For example, the chaotic nature and short
9
lifetime cell of individual convective cells make traditional verification scores ineffective
as small differences in the timing or location carries major penalties. Thus the goals for
verification research as defined by the Joint (CAS-WCRP) Working Group on Forecast
Verification Research included in the Strategic Plan for the WWRP include verification
techniques targeted toward high-resolution forecasts such as spatial forecast verification
methods and verification methods for weather extremes, including severe weather
warnings. These verification techniques are geared as much to assessing the
information content of a forecast as to determining the level of skill, which is useful to
determine errors in the timing, location and structure of convective systems for example.
Such assessment and verification techniques for these limited area models require highresolution observation networks, such as surface meso-networks, radar and satellite
imagery. Satellite imagery can provide detailed data to verify various aspects of the
mesoscale models (e.g., cloud cover, outgoing radiation fluxes, surface parameters).
Satellite data coupled with surface measurements and remote sensing profiling
techniques are needed to verify air chemistry predictions at high resolutions. When small
domains are considered, satellite imagery from polar-orbiting satellites becomes
particularly important as compared to geostationary satellites. Thus verification using
remote sensing is a critical research area as well as extending verification techniques for
probability distributions to verify ensemble forecasts.
v) Information delivery through sophisticated forecast systems.
The effective use of high resolution and seamless regional environmental systems
depends not only upon the modeling capability but the ability to provide information that
impacts user decision making. Information delivery is ultimately the primary reason for
NMS existence and this step of the forecast process is a key stage requiring end-to-end
approaches to and engagement,
3. Conclusions and recommendations
The following conclusions and recommendations arise from examination of user needs
and research challenges for multi-parameter high resolution prediction.
3.1 Members would greatly benefit from the utilization of new and/or improved
observational, modeling and forecast systems for high-resolution regional
prediction for weather, water and environment. Members needs in many areas from
emergency services, health, water resource management and a large number of
economic sectors would be served by the development of new and improvements
existing capabilities for high resolution prediction systems for high impact weather, water
and environmental events. These capabilities match changing societal needs such as
associated with urbanization and increased movement of populations into areas of risk.
3.2 The research programmes of CAS have a strong role to play in the
development of these new observational, modeling and forecast system
capabilities. At the dawn of this new century, significant research and development
challenges remain to be met before acceptable regional meteorological and
environmental forecasts can be produced. The weather research community has the
mandate to plan and develop improved versions of NWP models, at the regional and
local scales in collaboration with the NMHSs. These challenges are associated with the
10
paradigm shift to km-scale non-hydrostatic models and multi-parameter and coupled
prediction systems.
3.3 CAS programmes already play an important role in the research and
development activities in this observations, modeling and forecast systems area,
however, at a minimum greater coordination is required between these CAS
efforts. As already discussed, the Strategic Plan for the WWRP presents a research
agenda for these high resolution coupled modeling systems through the activities of the
relevant WWRP Working Groups (Nowcasting Research, Mesoscale Forecasting
Research and the Joint Working Group on Forecast Verification Research). Due to the
overlapping and interrelationship of the research priorities of these groups, the same
Strategic Plan calls for these groups to work more closely together in the future. In
addition, CAS has projects closely related to the goals discussed in this document such
as the OPAG EPAC efforts in urban air quality forecasting (GURME), EPAC participation
in the Scientific Aspects of Marine Environmental Protection (GESAMP), the EPACWWRP collaboration on the Sand and Dust Storm Warning and Advisory Assessment
Project, the EPAC and WWRP participation in the Shanghai Multi-hazard Early Warning
System project, THORPEX efforts on TIGGE-LAM, regional prediction aspects of the
IPY-THORPEX cluster of projects and the past (e.g. MAP D-PHASE) and future (e.g.,
HYMEX and HEPEX) role of WWRP in coupled hydrological prediction. There is
currently no coordination mechanism between these regional projects although all deal
with aspects of regional prediction systems and would clearly benefit from synergy
established between efforts on the improvement of assimilation and modeling systems.
3.4 The potential benefits and challenges are so great that we recommend that
CAS deliver to the next WMO Congress a research and development programme
focused on delivering improved capabilities for coupled regional prediction
coupled modeling systems for weather, water and environmental parameters such
as air quality. These plans established are ambitious and will focus work on basic
predictability, data assimilation, and advancing model physics. However, these plans do
not address the broad range of coupled models. This programme would be a
comprehensive long-term effort modeled after THORPEX, which has focused more (but
certainly not exclusively) on global prediction systems. The effort would also likely need
to be Member supported through the establishment of a Trust Fund. The programme
would likely incorporate the existing Working Groups of the WWRP that focus on higher
resolution km-scale prediction issues but also develop and implement a broad plan to
attack the range of research activities needed to extend regional prediction into a multidisciplinary effort. The programme would also include links to THORPEX, regional
modeling efforts of WCRP and serve a co-ordination, but not necessarily oversight
function for relevant projects discussed in 3.3 as these projects were established and
function well under existing efforts. Close links to the efforts of GAW-EPAC will need be
established or incorporated into a unified CAS programme. The other CAS Vision
Papers provide guidance to this vision.
3.5 The challenges of regional coupled modeling for weather, hydrological
application, ice, and environmental parameters such as aerosol and atmospheric
constituents pose unique challenges to the WMO as many observational and even
modeling efforts for environmental systems lie outside of the NMHSs in some
countries.
11
Author Affiliations:
1
Recherche en Prévision Numérique, Service météorologique du Canada
Bureau of Meteorological Research Centre, The Centre for Australian Weather and
Climate Research.
3
KNMI, Royal Netherlands Meteorological Institute
4
Service météorologique du Canada
5
World Meteorological Organization
2
References
WMO/TD-No. 1XXX, 2009, Strategic plan for the implementation of the WMO’s World
Weather Research Programme (WWRP): 2009-2017, p. 1-1XX.
WMO/TD-No. 1496, 2009, Challenge and opportunities in research to enable improved
products and new services in climate, weather, water and environment, p.1-24.
12