Chapter 12
Bridging the Gap Between Operations
and Research to Improve Weather Prediction
in Mountainous Regions
W. James Steenburgh, David M. Schultz, Bradley J. Snyder,
and Michael P. Meyers
Abstract A gap between operational and research meteorologists has existed
since the infancy of weather forecasting and represents an obstacle to progress
in meteorology. This gap is related to the profoundly different perspectives and
professional expectations of operational and research meteorologists. For the
knowledge, observations, tools, and models described in this book to reach their
full potential, the mountain meteorology community must work more effectively to
bridge this gap, as described in this chapter. Essential to this effort are advocates
who are capable of interacting, communicating, and commanding respect with both
the operational and research communities. As a result, the mountain meteorology
community should provide the attention and resources needed to ensure that
future advocates are created from the pool of young scientists and forecasters.
The community should also ensure that knowledge and technological advances
from field programs and other research efforts are effectively transferred into
operations and, at least in North America, explore the development of an integrated
research and forecast center to tackle challenges in mountain hydrometeorology
W.J. Steenburgh ()
Department of Atmospheric Sciences, University of Utah, Salt Lake City, UT, USA
e-mail: [email protected]
D.M. Schultz
Division of Atmospheric Sciences, Department of Physics, University of Helsinki,
Helsinki, Finland
Finnish Meteorological Institute, Helsinki, Finland
Centre for Atmospheric Science, School of Earth, Atmospheric and Environmental Sciences,
University of Manchester, Manchester, UK
B.J. Snyder
Meteorological Service of Canada, Vancouver, Canada
M.P. Meyers
NOAA/National Weather Service, Grand Junction, CO, USA
F. Chow et al. (eds.), Mountain Weather Research and Forecasting,
Springer Atmospheric Sciences, DOI 10.1007/978-94-007-4098-3 12,
© Springer ScienceCBusiness Media B.V. 2013
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and fire–atmosphere prediction. Although the existence of a modest gap reflects a
healthy scientific and forecasting enterprise, these and other gap-bridging activities
and incentives described in this chapter should benefit the entire mountain weather
community, its operational and research sectors, and, via improved forecasts, society
at large.
12.1 Introduction
“One of the greatest obstacles to the progress in meteorology is undoubtedly to be
found in the wide gulf between the mathematical theory on one hand and the applied
science weather-map analysis and forecasting, on the other.” As true now as when
Rossby (1934) said it, the gap between operational and research meteorologists
prevents forecasters from extracting maximum benefit from today’s sophisticated
observations, forecast tools, and numerical models, and inhibits researchers from
fully evaluating weaknesses in current scientific understanding and capabilities.
History suggests that forecast improvements and scientific advances accelerate
when operational and research meteorologists respect their unique perspectives
and interact productively (e.g., Board on Atmospheric Sciences and Climate 2000;
Waldstreicher 2005; Mass 2006; Volkert and Gutermann 2007). If the observations,
models, tools, and knowledge described in this book are to reach their full potential,
the operational and research communities must develop a closer, more integrated
collaboration to address critical challenges for weather prediction in mountainous
regions.
This chapter provides a roadmap for bridging the gap and accelerating progress in
mountain meteorology by examining the differing perspectives of operational and
research meteorologists, identifying ingredients for successful gap bridging, and
providing specific examples upon which to pattern future collaborative efforts. We
refer to a single gap, although in reality there may be multiple gaps in the chain from
basic research to operations. Although the operational and research communities
each stand to benefit from increased collaboration, the primary reason to bridge
the gap is to produce better weather forecasts for the benefit of society. As such, a
gap must also be bridged to decision makers and forecast consumers (Morss et al.
2005), but, for the purposes of this chapter, we focus on improving interactions
between operational and research meteorologists in the mountain meteorology
community.
12.2 Causes of the Gap
Winston Churchill said, “true genius resides in the capacity for evaluation of
uncertain, hazardous, and conflicting information.” If so, Churchill would consider forecasters the epitome of true genius. Every working day forecasters face
uncertainty. They never have all the data they want, are confronted with several
12 Bridging the Gap Between Operations and Research. . .
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computer models (and ensembles) that produce different forecasts, and have
imperfect knowledge of a chaotic atmosphere. Forecast deadlines and the urgent
nature of severe weather demand that forecasters be comfortable making decisions
in the face of uncertainty, even when the data and forecast guidance cannot be fully
analyzed and interpreted. The best forecasters develop schema to quickly organize
the wide array of observational data and model guidance, identify critical issues,
and make good decisions (e.g., Doswell 2004). This ability to make decisions from
limited evidence is known as forecaster intuition, a combination of experience,
conceptual model application, and educated guesses. Such skills are most apparent
in the engaged forecaster (e.g., Roebber et al. 2002; Pliske et al. 2004; Stuart
et al. 2007). Weather forecasting is a scientific endeavor involving hypothesis
formulation, hypothesis testing, and prediction (e.g., Roebber et al. 2004), but it
is inherently less rigorous and more speculative than scientific research.
Although the best forecasters are comfortable dealing with incomplete information and evidence, conscientious scientists are not. Scientists must extensively
test hypotheses and determine the generality of their conclusions. They demand
that conclusions be justified and consider statements based on limited evidence
(i.e., the forecaster’s intuition) to be nothing more than speculation. Just as an
Olympic alpine skier may not make the best Nordic skier, despite both disciplines
requiring strength, cardiovascular fitness, and balance on skis, operational and
research meteorologists, despite their common skills and knowledge in meteorology,
are not necessarily interchangeable.
The gap between operations and research originates from these profoundly
different worldviews, which allow forecasters and researchers to succeed within
their individual communities by meeting the expectations of their colleagues and
organizations, but make crossing boundaries difficult. Forecasting is challenging for
most researchers, who are unable to fully ponder and investigate under the deadline
of getting the forecast out. In contrast, research and publication are challenging for
forecasters—the intuition that works well for them in day-to-day operations cannot
be relied upon in formal publications to convince a critical audience. Those who
accommodate and accept these two worldviews recognize that forecaster experience
and practice cannot always be justified with a citation or easily quantified through
calculations, but that evidence and logic are essential for both weather forecasting
and scientific research.
12.3 Perceptions of the Gap
The gap between operational and research meteorologists has been recognized since
at least the early twentieth century (e.g., Rossby 1934; Bergeron 1959) and remains
considerable even today (Doswell et al. 1981; Ramage 1993; Doswell 2007). Snyder
and Loney (2007) reported that operational meteorologists in the Meteorological
Service of Canada (MSC) and the National Weather Service (NWS) of the United
States believe that there are three major contributors to the current gap.
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• Limited exposure of researchers to the operational forecast environment. Many
of today’s young research scientists have limited (if any) experience with
synoptic weather analysis and have never worked a forecast shift, so they have
little exposure to the unique demands of operational weather forecasting, as
argued by Doswell (1986). This lack of exposure to operations is ironic because
many of their scientific forebears began their careers as weather forecasters.
For example, analysis and forecasting was essential to the scientific advances of
the Bergen School (Friedman 1989, 1999), and many distinguished atmospheric
scientists of the mid to late twentieth century served as weather observers or
forecasters in World War II [e.g., Heinz Lettau (Besser 2008), Edward Lorenz
(Palmer 2008), Sverre Petterssen (Bundgaard 1979; Petterssen 2001), Richard
Reed (Reed 2003), Frederick Sanders (Sanders 2008), Joanne Simpson (Lewis
1995)]. Nearly all universities emphasize atmospheric dynamics and physics at
the expense of applications and forecasting. When taught, weather analysis and
forecasting is often decoupled from dynamics and physics, when, in practice,
forecasting (and research) requires a broad view that integrates across these
atmospheric subdisciplines (e.g., Doswell et al. 1981; Doswell 1986).
• A lack of training and opportunities for forecasters to participate in research.
Forecasters need training and encouragement to read the meteorological
literature, identify scientific articles relevant to their job, and participate in
research (e.g., Doswell et al. 1981; Doswell 1986). Many forecast organizations
have positions designed to address this need (e.g., the NWS Science and
Operations Officer), but administrative obstacles and other work demands
frequently limit the time available for scientific research and forecaster training.
As a result, many forecasters are unable to keep up with recent progress in the
atmospheric sciences or maintain the mathematical and theoretical knowledge
needed to communicate effectively with the research community. Exacerbating
this problem are bureaucratic rules that prevent or limit forecaster participation
in research and training projects, a lack of release time from forecast shifts
for research and training, and limited rewards and funding for forecaster
participation in research.
• The need for improved methods to transfer results from research to operations
(e.g., Smith et al. 2001; Stuart et al. 2006; Snyder et al. 2006; Rauhala and Schultz
2009). Because of contrasting cultures and professional expectations, forecasters
and researchers prefer different modes of communication. Researchers typically
disseminate research results in journal articles and conference presentations.
Such approaches appeal to scientists and university faculty, but are not favored
by students and forecasters who prefer concrete examples and active participation
(Roebber 2005; Stuart et al. 2007). In fact, only 40% of the US students who
responded to an American Meteorological Society (AMS) member survey said
that they “read printed research literature on a daily or weekly basis” (Stanitski
and Charlevoix 2008). Further, forecasters consider reading journal articles
and attending scientific conferences amongst the least effective training modes
and consider collaborative forecasting with experts (i.e., “double banking”),
weather-event simulators, and residency training courses with close and regular
12 Bridging the Gap Between Operations and Research. . .
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Fig. 12.1 Annual threat scores for 24-h, 1-in. (2.54-cm), day 1 quantitative precipitation forecasts
produced by the NCEP North American Mesoscale (NAM) model, Global Forecast System (GFS),
and forecasters at the HPC (Courtesy HPC)
interaction with scientific experts to be the most effective training modes (Snyder
and Loney 2007). Such responses are not unexpected as forecasters would much
rather learn in ways that directly relate to their job rather than through ways
that researchers learn best, and many training programs may not contain enough
operationally relevant material for forecasters (e.g., Smith et al. 2001). Therefore,
such differences in learning contribute to creating the gap.
Addressing these three issues head-on is essential for the mountain meteorology
community to extract maximum societal benefit from today’s sophisticated observations and forecast tools. Although concerns that numerical weather prediction
and automation will eliminate humans from the forecast process have been raised
since at least the 1950s (e.g., Harper 2008, pp. 229–231; 2009), well-trained and
fully engaged forecasters continue to play “a clear role in the forecast process by
contributing a wealth of knowledge, tools, and techniques that cannot be duplicated
by computers or [numerical weather prediction]” (McCarthy et al. 2007). This
is particularly true in short-range forecasting, as illustrated by comparing day-1,
24-h quantitative precipitation forecasts produced by operational numerical weather
prediction models run by the National Centers for Environmental Prediction
(NCEP) with those produced by forecasters at the NWS Hydrometeorological
Prediction Center (HPC, Fig. 12.1). Over the past 15 years, even as model forecasts
have improved, HPC forecasters have maintained a consistent annual threat score
advantage of 0.05 over the NCEP models. Clearly, advances in numerical model
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skill do not necessarily result in a reduction in the value added by humans. Instead,
well-trained forecasters take advantage of these improvements and extend forecast
skill (e.g., Bosart 2003; Harper et al. 2007; Erkkilä 2007; Sills 2009).
On the other hand, forecasters that lack sufficient training, fail to keep up with
scientific progress, and rely excessively on numerical model guidance provide
little benefit to the forecast process, particularly in areas of complex terrain where
numerical weather prediction models inadequately resolve orographic processes and
fine-scale weather variability. Snellman (1977) coined the term “meteorological
cancer” to describe the insidious overreliance on information generated by computers at the expense of human interpretation and cognition. As stated forcefully
by Bosart (2003), forecasters who grow accustomed to letting MOS [model output
statistics] and the models do their thinking for them on a regular basis during the
course of their daily activities are at high risk of “going down in flames” when
the atmosphere is in an outlier mode. Clearly, the mountain weather forecasting
community can only reach its full potential if forecasters are both highly educated
and fully engaged in the forecast process. This goal can only be accomplished if the
operational and research communities work together.
To summarize, these perspectives highlight three critical challenges that individuals seeking to bridge the gap must address:
• Forecasters must be motivated, fully engaged, and encouraged to participate in
research and training on a regular basis. To a large extent, this motivation must be
internal, but also bureaucratic barriers preventing regular participation in research
and training can be removed.
• Researchers must recognize and be willing to communicate the forecast relevance of their research. This requires knowledge and respect of the operational
forecast environment, understanding of the role of humans in the forecast process, and the use of effective methods for transferring knowledge and techniques
from research to operations.
• Forecasters and their managers must recognize the value of a mathematical
and theoretical education and develop the necessary educational foundation to
pursue one, while researchers must embrace the potential for weather analysis
and forecasting to enable them to formulate and test scientific hypotheses using
real weather data.
12.4 Why Bridge the Gap?
Addressing these three challenges requires effort—often considerable effort. Why
should anyone take this hard road to bridge the gap, when remaining isolated
within one’s community can produce relatively easy personal success? There are
two reasons.
The first is the shared desire among forecasters and research scientists to have
a positive impact on our profession and society. The second is that the mountain
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weather community simply cannot reach its full potential without productive
collaboration between operational and research meteorologists. Forecasters must
be well educated and trained to extract maximum benefit from today’s observations
and forecast tools and to prevent avoidable forecast errors related to the misuse
or ignorance of scientific understanding (Doswell 1986, 2007; Bosart 2003).
Researchers can benefit from the regular analysis of evolving weather systems,
which, even given the inherent limitations of the operational data stream, enables
improved formulation and testing of scientific hypotheses, stimulates the broadening
of research results, and prevents the overgeneralization of conclusions based on
comprehensively sampled but limited sample size field-program studies (Doswell
1986, 2007). Finally, increasingly tight budgets demand the rigorous and efficient
testing of research advances, including quantification of their impact on the forecast
cycle and decision making (e.g., Doswell and Brooks 1998). Such a cycle requires
increased collaboration between researchers, forecasters, and decision makers
through an end-to-end research approach (Morss et al. 2005).
12.5 Ingredients of Successful Gap Bridging
At the heart of successful gap-bridging efforts are forecasters and scientists
determined to be advocates for improved collaboration. At least two advocates
(preferably more) are generally needed (one from each side) and, if the collaboration
is between organizations, the advocates may occupy supervisory, managerial, or
administrative roles within their organizations. Without advocates on each side, no
way exists to make or enforce collaboration, especially when resources (personnel,
computer support, release time from forecasting for collaborative projects, etc.) are
needed. Advocates must be able to communicate with and command respect from
both the forecast and research communities. These individuals may be doctorallevel scientists if they are forecasters, perhaps with considerable research experience
before becoming a forecaster, or researchers who began their career as a forecaster
or are passionate about weather and forecasting. Advocates may also be hands-on
managers who have the ability to change reward structures for participants or reduce
bureaucratic barriers to collaboration.
In addition to an advocate or advocates, additional ingredients for successful
bridging of the gap are typically required. Although not all need be present, these
ingredients may include:
• Buy-in and commitment. Both the operational and research sides must embrace
each others’ different perspectives, learn to speak each others’ language, and
possess the staying power to see the work to completion. Not everyone in the
project or organization needs to be “on board,” but a critical mass is needed for
success, particularly in larger projects. Researchers must recognize that spending
time educating forecasters about new research approaches will be required
(e.g., Morss and Ralph 2007). Likewise, forecasters must be open to learning and
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Fig. 12.2 Attendees of the 2008 American Meteorological Society/Cooperative Program
for Operational Meteorology, Education and Training/Meteorological Service of Canada
(AMS/COMET/MSC) Mountain Weather Workshop: Bridging the Gap Between Research and
Forecasting, Whistler, BC, Canada, included research, operational, and student meteorologists.
Presentations and interactions at the workshop stimulated this book and chapter
applying new approaches. Setbacks during collaboration are common, so buy-in
is essential for long-term success.
• Stimulation at the grassroots level. The cliché “if the people lead, eventually
the leaders will follow” applies here. The most successful collaborations begin
or are stimulated at the bottom and are not simply imposed by management.
Grassroots collaborations can succeed with either the support or neglect of
management. (Active interference by management is likely to lead to failure.)
Forced collaborations might be successful in the short term, but total top-down
efforts are rarely successful (Doswell 2007, p. 16).
• Collocation. Collocation enables more regular and routine interaction of research
and forecast meteorologists. It facilitates communication and collaboration. The
collocation may be temporary (e.g., joint forecast production, residency courses,
Fig. 12.2), or permanent where forecast and research meteorologists are housed
in a common facility (and perhaps share some job responsibilities). Collocation
alone, however, does not guarantee success. As noted by Doswell (2007),
“organizational structure and proximity do not necessarily result in productive
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collaboration. People chose to collaborate, or not.” Given collocation, advocates
must still provide leadership, encouragement, and a framework for collaboration.
• Securing the time, resources, and personnel needed for meaningful collaboration. Discussion and interaction must be actively promoted, but talking alone
is not sufficient. Forecasters must gain release time from operational responsibilities to participate in research, education, and training on a regular basis.
Similarly, researchers must be patient because the progress of working with shift
forecasters may take longer than a typical research project. Because scientists are
more likely to have experience with applying for grants and funding, most of the
onus is upon them to supply students and personnel to help with the research,
although some operational avenues may be available.
• Clearly defined priorities and goals. Key scientific issues are not necessarily key
operational issues, so compromises and adjustments must be made to ensure that
they are “win–win” for both operations and research. Identifying goals for the
operational community and mapping them onto the capabilities of the researchers
is one way to propose achievable goals and articulate collaboration priorities.
• Establishing incentives. Because the institutional reward systems for researchers
and forecasters are different, each community must recognize that bridging
the gap may require nontraditional incentives for the individuals involved.
Oftentimes the advocate is a senior person for whom the traditional reward
system is no longer a motivator—the reward is in seeing the success of the
interaction between researchers and forecasters and the fruits of their labor.
12.6 Examples of Successful Gap Bridging
How do these ingredients work in practice to produce a fruitful collaboration?
Here we provide examples of successful gap bridging both inside and outside the
mountain weather community. Examples from the latter are used in areas where the
mountain meteorology community can learn from the efforts in other meteorological
subdisciplines (e.g., severe convective weather, tropical meteorology).
12.6.1 Advocates for Collaboration
One powerful example of what advocates can accomplish is provided by Dr. Cliff
Mass, professor of atmospheric sciences at the University of Washington, and Dr.
Brad Colman, Science and Operations Officer (since promoted to MeteorologistIn-Charge) at the NWS Forecast Office in Seattle, who have forged a two-decade
relationship to advance local weather knowledge and prediction. In addition to
passion, these two individuals possess the key characteristics identified above as
necessary for bridging the gap. Dr. Mass is a research scientist with a passion
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for weather forecasting and an ability to communicate to operational forecasters,
whereas Dr. Colman is a doctoral-level forecaster who commands the respect of the
research community.
Efforts led by these two advocates have: (1) established a mesoscale surface
observing network, (2) created local high-resolution and regional-scale ensemble
modeling systems, and (3) contributed to the successful execution of the COAST
and IMPROVE field programs examining front–mountain interactions and orographic precipitation processes over the Olympic, Coast, and Cascade Mountains
(e.g., Bond et al. 1997; Mass et al. 2003; Stoelinga et al. 2003). Mass et al.
(2003) describe how this interaction has stimulated research at the University of
Washington, including efforts to improve model parameterization and ensemble
prediction techniques. In turn, NWS forecasters (and ultimately the general public)
have benefitted from the transfer of knowledge and forecast tools into operations.
For example, the powerful “Hanukkah Eve Wind Storm” (14–15 Dec 2006), which
killed 15 people in western Washington and left an estimated 4.08 million people
without power, was forecast with remarkable specificity, urgency, and lead time by
the NWS Forecast Office in Seattle (Washington State Military Department 2007,
pp. 10–11). Such forecasts, which minimized loss of life and enabled a timely emergency response and recovery, were enabled by knowledge of windstorms produced
by landfalling cyclones spawned by Mass’ research group (e.g., Steenburgh and
Mass 1996), advances in local numerical forecast modeling (e.g., Mass et al. 2003),
and well-educated and fully engaged forecasters.
There are two major lessons to be learned from this example. The first is
the power of bringing two talented and motivated advocates together from the
operational and research communities. In this case, the late Dr. Tom Potter, former
director of NWS Western Region, helped stimulate the partnership by encouraging
Dr. Colman to move to Seattle, become the science and operations officer, and
improve training and science within the ranks of the NWS. The second is the
benefit provided to each respective community when the gap is bridged. The
research program built at the University of Washington has benefited from drawing
motivation from applied forecasting problems (Mass et al. 2003), whereas NWS
forecasters benefit from new knowledge and state-of-the art weather analysis and
forecast tools.
12.6.2 Buy in and Commitment Stimulated by a Common
Research and Forecast Problem
12.6.2.1 Olympic Winter Games
Over a 17-day period every 4 years, the Olympic Winter Games require extremely
precise mountain weather forecasts at high temporal and spatial resolution (Horel
et al. 2002a). Significant and sometimes hazardous weather has impacted nearly
every Olympic Winter Games, and nearly all outdoor competitions are sensitive
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Fig. 12.3 The Olympic Winter Games provide a uniting goal and an exceptional opportunity for
bridging as exhibited by the closer interactions between research and operational meteorologists
(pictured) prior to and during Vancouver 2010
to subtle variations in otherwise “garden variety weather.” Knowledge and skill
predicting all of the phenomena described in this book (and more), each of which
occurs in a region with unique climatological and topographic characteristics, are
needed for a successful Games.
The Olympic Winter Games provide a uniting goal for operational and research
meteorologists, with the “Olympic Spirit” stimulating buy-in and commitment
(Fig. 12.3). During the 2002 Olympic Games in Salt Lake, for example, three
disparate groups were brought together—the University of Utah to develop and
provide a regional mesonet and modeling system, the NWS to provide public
forecasts and warnings to protect lives and property, and a team of 13 private sector
meteorologists to provide detailed forecasts for the outdoor sports venues (Horel
et al. 2002a).
These three groups collaborated to ensure forecast-relevant research and development and to provide the necessary education and training to take advantage of
new knowledge and forecast tools. For example, the mesoscale modeling system
developed for the games was used by forecasters for 3 years prior to the Games. This
long-term experience with the modeling system enabled the forecasters to determine
that the direct model output was insufficient to provide the detailed forecasts
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required at outdoor venues and other locations. As a result, the University of Utah
developed model output statistics (MOS) for 18 sites in the Olympic region (see
Chap. 11 for details on the use of MOS). This mesoscale-model-based MOS was
relatively straightforward to develop, provided forecasts at multiple sites at several
outdoor venues, including three along the men’s downhill, and proved extremely
beneficial for the forecast effort during the Olympics (Hart et al. 2004).
Similar gap-bridging activities have occurred during other Olympic Games.
Sydney 2000 and Beijing 2008 included World Weather Research Program Forecast
Demonstration Projects in which researchers shared a work area with forecasters to
test leading-edge nowcasting tools (e.g., Keenan et al. 2003; May et al. 2004; Ebert
et al. 2004; Wilson et al. 2004; Joe et al. 2010; Mailhot et al. 2010). Testing and
evaluation of a multimodel superensemble and high-resolution limited-area models
occurred during Torino 2006 (Cane and Milelli 2006; Stauffer et al. 2007), with the
superensemble used subsequently for operational weather prediction over the Piedmont region of Italy (Cane and Milelli 2008). During the 2010 Vancouver Games, a
team of researchers participated in the Science and Nowcasting of Olympic Weather
for Vancouver 2010 (SNOW-V10) in order to improve the understanding of and
ability to forecast and nowcast low cloud, visibility, precipitation (amount and
type), and winds in complex terrain (Joe et al. 2010). The project involved the
deployment of an enhanced network of instruments at specific venues, as well as
the production of prototype forecast products. Researchers collaborated extensively
with forecasters on the instrument siting and assisted in pre-Games training.
For the Vancouver 2010 Olympic Winter Games, a comprehensive training
strategy was also devised based in part on the survey findings described in
Sect. 12.3 (Snyder et al. 2006). One aspect of this training involved creating a
mountain weather residency course in cooperation with the University Corporation
for Atmospheric Research Cooperative Program for Operational Meteorology,
Education and Training (COMET) program. Subject matter experts, many of
whom have contributed to chapters in this compilation (e.g., Chaps. 2, 6, and 7),
delivered lectures on the latest knowledge in mountain meteorology and applied this
knowledge through case studies and forecast labs. As a testament to the quality of
instruction and the focus on operationally relevant research findings, course ratings
were some of the highest ever given to a COMET course. In another effort to
facilitate interaction between the operational and research teams, a meteorologist
was assigned the dual-role of venue forecaster and applied research meteorologist
for Olympic forecast product development. Buy-in and commitment were not an
issue during the Games but adding a person who could speak the others’ language
solidified the gap-bridging effort
12.6.2.2 MAP D-PHASE
The Mesoscale Alpine Programme Demonstration of Probabilistic Hydrological
and Atmospheric Simulation of Flood Events (MAP D-PHASE) is a project to
demonstrate and evaluate potential improvements in the operational forecasting of
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flood events in the European Alps (Zappa et al. 2008; Rotach et al. 2009a, b).
MAP D-PHASE seeks to demonstrate forecast advances derived from the Mesoscale
Alpine Programme (MAP), which involves substantial collaboration between the
research and operational sectors (Volkert and Gutermann 2007). Participants include
17 countries, 18 operational centers, and 7 research institutions (Arpagaus et al.
2009). MAP D-PHASE has many goals including: (1) assessing high-resolution
deterministic and probabilistic hydrological and atmospheric modeling systems, (2)
delivering advanced flood warnings and background information for end users, (3)
developing nowcasting tools, (4) improving radar observations of precipitation over
complex terrain, and (5) improving decision making by civil protection authorities.
As such, MAP D-PHASE involves not only bridging the gap between research and
operations, but also between meteorologists and policy makers, end-users, and other
scientific disciplines.
Integral to MAP D-PHASE is the testing and evaluation of an end-to-end forecast
system based on new methods of assessing forecast uncertainty, specifically,
ensembles of hydrologic forecasts created using ensembles of weather forecasts.
The hydrological forecasts are produced for 43 catchments, and warnings are issued
if a deterministic forecast or a third of the ensemble members exceed one of three
criteria based on flood return periods. MAP D-PHASE also incorporates a Webbased visualization platform containing all MAP D-PHASE graphical information
(e.g., nowcasting products, warning maps, validation products). Although this
common framework requires compromise by the participants, all warnings are based
on the same thresholds and procedures, allowing different regions and models to be
fairly and uniformly intercompared.
Although the results of MAP D-PHASE are only beginning to be revealed, the
effort required to integrate data, model ensembles, and warnings into a common
framework suggests an immense level of cooperation and planning between research, operations, and end users. Rotach et al. (2009a) suggest that the use of
common formats, warning levels, and routines amongst different forecast models
is essential for program success. MAP D-PHASE was designed from the beginning
to address a research and operational challenge, ensuring that both research and
operational centers would have vested interests in seeing success. It sought the input
and participation of end users. In short, MAP D-PHASE is perhaps the best single
example from the mountain meteorology community of a large-scale collaboration
between research and operations.
12.6.3 Stimulating and Funding Grass-Roots Efforts:
CSTAR and COMET
Research requires resources, even efforts that begin at the grassroots. The NWS
has developed two successful programs to support these grassroots efforts: (1) the
Collaborative Science, Technology, and Applied Research (CSTAR) program and
(2) the COMET program.
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The CSTAR program provides funding to university scientists to support highly
collaborative applied research activities with the NWS. CSTAR partnerships are collaborative efforts requiring a buy-in from researchers and forecasters, consequently
providing a foundation for the ongoing infusion of science and technology into the
forecast office. The importance of CSTAR funding is clearly evident in the examples
noted earlier as it has helped support collaborative activities between the University
of Washington and the NWS Forecast Office in Seattle (Sect. 12.6.1) and between
the University of Utah, NWS, and private forecasters for the 2002 Olympic Winter
Games (Sect. 12.6.2.1). The COMET program addresses meteorological education
and training, including mountain-related topics, through distance learning, residence
classes, and an outreach program that facilitates the transfer of research results to
operations and provides funding for the academic and operational communities to
participate in collaborative research. A similar European effort for education and
training project is called Eumetcal (http://www.eumetcal.org).
CSTAR and COMET provide the funding needed to bring together researchers
and forecasters who otherwise would not typically interact. These two programs
force the two groups into a middle ground where both an operational and research
focus is achieved, accomplishing the mandates of both groups. Sometimes, it
requires the researchers, forecasters, or both to go beyond their comfort level. One
example is the COMET partnership between the NWS Forecast Office in Grand
Junction and researchers from the Desert Research Institute Storm Peak Laboratory
and Colorado State University. These three groups worked from 2002 to 2006 to
investigate orographic precipitation over the Park Range of north-central Colorado
(Wetzel et al. 2004). To minimize any potential reluctance by the forecasters,
the researchers visited the Grand Junction NWS forecast office to describe their
research. The session included a short seminar followed by one-on-one interaction
between the forecasters and the researchers, which enhanced the personal connection between the two groups. The operational staff also demonstrated for the
researchers the forecast system and methodology. Some of the forecasters then
visited the Storm Peak Laboratory, enabling direct communication between the
groups in a laboratory setting. These interactions increased buy-in and commitment
of both groups.
Because of the communication and team-building efforts of the researchers, the
forecasters were committed to success during and following the field study. One of
the project objectives was to increase meteorological understanding of the physical
controls on precipitation over this relatively data-sparse mountainous region in a
way that would directly benefit operational forecasting. Because of the availability
of supplemental data during the field study, the forecasters gained invaluable
insights into the local orographic forcing over the Park Range, knowledge that
they have expanded upon after the project conclusion. For example, the forecasters
developed a better understanding of the predominance of heavy snowfall during
moist, post-frontal flow and the sensitivity of quantitative snowfall forecasts to the
snow-to-liquid ratio. The forecasters learned that snow-to-liquid ratios of 30:1 are
not uncommon in this region and that an underestimation of this ratio, as happened
in the past, has a significant negative impact on the prediction of snowfall amount
12 Bridging the Gap Between Operations and Research. . .
707
and winter storm potential. The impact of improved understanding was evident
during the 2009 winter season when 24 winter storms in the Park Range region
were forecast with a 100% probability of detection at a lead time of 32 h.
By 2005, over 250 COMET-funded research projects had been completed, a
result of collaborations between over 70 universities and over 90 NWS forecast
offices (Waldstreicher 2005). Importantly, the research funded by COMET has led
to demonstrable improvements in forecasts, as Waldstreicher (2005) has shown.
For example, NWS Eastern Region offices with COMET projects aimed at better
understanding severe thunderstorms or tornadoes improved twice as much in
probability of detection for severe thunderstorm and tornado warnings as the region
as a whole. The rate of improvement was also strong for lead times for severe
thunderstorms and tornadoes (eight times), lead times for winter storm warnings
(two times), and lead times for flash flood warnings (four times). Furthermore,
an office with a long-term history of collaborative research, Raleigh, NC, also
demonstrated remarkable improvement in their metrics compared to the other offices
in the region. Consequently, Waldstreicher’s (2005) results provide quantitative
evidence showing the positive impact of research–operations collaborations on
forecast quality.
12.6.4 Collocation: The Research Support Desk
An example of the value of collocation for gap bridging is the so-called Research
Support Desk (RSD), which was implemented in some MSC forecast offices to
increase real-time interaction between forecasters and researchers (Sills and Taylor
2008). The RSD involves the collocation of a researcher with operational forecasters
in the weather center. Although the RSD is staffed only during busy seasons,
it has proven to be an effective knowledge transfer mechanism. Education and
training about new techniques and technologies is passed from the researcher to
the forecasters through direct real-time interaction and formal briefings. In turn, the
researcher is able to also evaluate experimental products and identify science needs
in an operational environment.
The first real challenge for this initiative was getting permission to have the RSD
located in the operational environment. Forecasters were concerned about increased
demands on their time, particularly during severe weather (Sills 2005). Once
established, however, the overwhelming majority of forecasters were comfortable
with the researcher in operations and 72% of forecasters responding to a survey
believed the RSD enhanced the learning environment. This experience exemplifies
some of the other key ingredients to bridging the gap (advocates for collaboration,
stimulation at the grassroots level). With motivated individuals and the political will,
the mountain meteorology community could benefit by applying this model in other
weather centers.
As part of the SNOW-V10 project, a RSD was put in place in Vancouver for
the 2010 Olympic Games. This desk fostered communication between researchers
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and forecasters and was especially helpful in exposing forecasters to a vast array
of new data. To facilitate collaboration between a larger pool of researchers and
forecasters, daily web-based briefings were also held over a 2-month period before
and during the Games.
12.6.5 Collocation: Forecasting and Research Teams
in Mountain Meteorology
Examples of collocated operational and research groups that concentrate on mountain weather applications exist in Europe (i.e., MeteoSwiss where the radar application and research group is collocated with the forecasters in Locarno) and Canada
(the Pacific and Yukon Region National Lab for Coastal and Mountain Meteorology), but no major center for mountain weather research and operations exists in
the United States. This is unfortunate, because collocation of motivated individuals
and groups has enabled dramatic advances in knowledge and forecasting in other
meteorological subdisciplines. Perhaps the earliest and best-known example is the
Bergen School of Meteorology (e.g., Friedman 1989, 1999). To address the needs
of farmers and fishermen in Norway, Vilhelm Bjerknes developed a system and
received funding to collect and analyze mesoscale observations of weather systems.
Study of these weather systems led to the formulation of the Norwegian cyclone
model. Bergen School members were trained physicists and mathematicians; their
emphasis was on understanding the relevant weather processes to produce the best
forecast. As such, theirs is arguably the first scientific effort to bridge the gap
between research and operations in atmospheric science.
In the 1950s, a dominant center for bridging the gap between research and
operations emerged in Chicago (e.g., Allen 2001). Gordon Dunn, an employee
of the Weather Bureau (predecessor of the NWS), convinced the Weather Bureau
to locate the Chicago Forecast Office at the Department of Meteorology at the
University of Chicago (Burpee 1989, pp. 576, 581–582). Dunn, concerned about
the gap between researchers and forecasters, arranged daily map discussions with
both groups participating. In such an environment, understanding of the jet stream
improved, field research programs on tropical and midlatitude convection began,
and convective-storm and tornado expert Ted Fujita prospered.
In 1959, Dunn also contributed to the collocation of the National Hurricane
Center (now the Tropical Prediction Center) and the National Hurricane Research
Project (later the National Hurricane Research Laboratory and then the Hurricane
Research Division of the NOAA/Atlantic Oceanographic and Meteorology Laboratory) (Burpee 1989, p. 580). The Joint Hurricane Testbed (Knabb et al. 2005) was
the eventual result of this interaction. One of the benefits of the cooperation and
collocation was the development of statistical hurricane track prediction models,
assimilation of data from reconnaissance aircraft, and operational targeting of
additional observations.
12 Bridging the Gap Between Operations and Research. . .
709
Collocation does not, however, guarantee collaboration. Doswell (2007) describes a situation at the National Severe Storms Forecasting Center (predecessor
to the Storm Prediction Center) where the initial collaboration between researchers
and forecasters developed into a schism where the researchers and forecasters
were placed on separate floors in the same building because of some of the
researchers’ disdain of having to do shift work. The schism in the severe weather
community would later be repaired by the collocation of the National Severe
Storms Laboratory and Storm Prediction Center in Norman, Oklahoma, in the late
1990s. This collocation has subsequently led to a flourish of collaborative research
and forecasting endeavors (e.g., Kain et al. 2003a, b, 2006, 2008) including the
development and application of convection-allowing models in operations (e.g.,
Kain et al. 2006, 2008), efforts to improve the interpretation of forecast-model
soundings (e.g., Baldwin et al. 2002), research into the use of ensembles for
convective-storm forecasting (e.g., Bright et al. 2004; Weiss et al. 2006, 2007), and
other research projects on convective storms (e.g., Craven et al. 2002; Banacos and
Schultz 2005; Coniglio et al. 2007). Because of these largely grassroots successes,
other individuals, groups, and companies—both domestic and international—have
collaborated with these two groups in Norman, now renamed the Hazardous
Weather Testbed, showing the power that an initial interaction can have for growing
the research–operations connection. Indeed, a small initial success can grow into a
sustained and much larger success, given the right ingredients.
12.7 Fostering Improved Future Coordination
The successful gap-bridging examples described above raises hope for the future,
but individuals, agencies, and organizations must increase their efforts if the mountain weather community is to meet its full potential. Mass (2006) raises concerns
about a lack of coordination between the research and operational communities
in the United States, and he provides a substantial list of recommendations for
improvement, which we expand on here for the mountain weather community. In
particular, there are four critical areas in which we should focus our attention and
resources:
1. Creating tomorrow’s advocates. As noted earlier, the most important factor to
ensure the overall success of the mountain-meteorology enterprise are advocates
who work to bridge the gap between research and operations (and ultimately
decision makers; Morss et al. 2005). As such, greater emphasis must be placed
on educating, mentoring, and grooming young scientists and forecasters to bridge
the gap between mathematical theory and scientific forecasting as they move
through their careers (e.g., Doswell 1986). On the research side, universities
should provide curricula and opportunities for tomorrow’s scientific leaders
(i.e., current graduate students) to participate in scientific forecasting and gain
exposure to how theory is used in practice (as argued by Ramage 1978).
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It should not be acceptable for a student to earn an advanced degree without
exposure to weather analysis and forecasting. Such exposure can be achieved
through courses in synoptic–dynamic meteorology and forecasting, internships
at forecasting agencies, and collaborative research projects leading to advanced
degrees. Approaches that challenge the student to test, apply, and refine their
research in an operational environment should be strongly encouraged. On
the operational side, forecasting agencies must provide additional time and
incentives for forecasters to participate in research by creating more opportunities
for young forecasters to return to graduate school to pursue graduate degrees or
regular release time to participate in research projects and personal training. In
either case, it is essential that the forecaster work with research meteorologists
and conduct substantive research to gain exposure to the scientific method.
A forecaster with a doctoral education or substantive prior research experience
could serve as the scientific mentor in these efforts, but the participation of
research scientists from universities and government labs should be encouraged.
2. Prioritizing and providing support for the transfer of field-program advances
into operations. Field research in mountain meteorology continues to provide
valuable data for scientific analyses and publications. MAP, for example, has
spawned more than 220 scientific publications (Volkert and Gutermann 2007).
For the operational meteorological community, however, the challenge has
been transferring this wealth of knowledge to the forecast environment so that
theoretical concepts can be applied to forecasting in complex terrain (Snyder
et al. 2006). Historically, many mountain meteorology field programs have
contained no component or clear mechanism for transferring field-program
advances into operations (although many field-program proposals state that
forecast improvements are a likely broader impact of the research). Frequently,
operational meteorologists provide support for field-program operations, but are
less involved in the early field-program planning or subsequent analysis.
The MAP D-PHASE program, however, represents a recent major undertaking to transfer knowledge and achievements from MAP into the forecast
and decision-making process. Similarly, the Hydrometeorological Testbed in
the American Fork River Basin of northern California, a collaboration between
the Earth Systems Research Laboratory and California NWS offices (Ralph
et al. 2005; http://hmt.noaa.gov) explicitly integrates operational, research, and
decision-making activities. The strengths and weaknesses of these programs
should be identified and used to improve the impact of future field programs
on operations. Within the United States, greater support from NOAA to mine the
successes of National Science Foundation–sponsored field programs would also
help.
Outside of these major undertakings, Smith et al. (2001) created a list of 14
more ordinary, but quite specific, items to increase the transfer of knowledge
between forecasters and researchers. This list includes such items as “(ii) ask
forecasters to flag interesting/important observed cases, noting the nature of the
event to alert researchers to cases for possible study; (iii) ask researchers to
flag interesting new results in terms accessible to forecasters;” “(v) commission
12 Bridging the Gap Between Operations and Research. . .
711
researchers to write articles on phenomena and issues in language intelligible
to forecasters; (vi) encourage the World Meteorological Organization (WMO)
and National Weather Services to fund visits of researchers to forecast offices
for immersion in the culture/science of forecasting, not procedures; (vii) identify forecast offices as possible sites for study leave.” These and the other
recommendations do not require significant amounts of funding, if any, yet,
with enthusiastic advocates, could lead to greater and more positive interactions
between researchers and forecasters.
3. Leveraging the power of proximity through collocation. Despite the tremendous
impact that the collocation of motivated individuals and groups has had on
knowledge and forecasting advances in severe-convective weather and tropical
storms (e.g., Hazardous Weather Testbed, Joint Hurricane Testbed), no major
collocated center for mountain weather research and forecasting exists in the
United States. This situation contrasts with many countries in Europe, which perhaps due to smaller size and less diverse national forecast challenges, often have
weather services with stronger cooperation between the operational and research
communities. In North America, however, there are some strong collaborations
fostered by geographic proximity or shear will, such as collaborations between
the University of Washington and NWS Forecast Office in Seattle (Sect. 12.6a);
University of Utah, NWS Forecast Office in Salt Lake City, and NWS Western
Region Headquarters (Sect. 12.6b; Horel et al. 2002a, b); and the Earth Systems
Research Laboratory and California NWS forecast offices via the Western US
Hydrometeorological Testbed (Sect. 12.7; Ralph et al. 2005). Given the growing
financial losses imposed by flooding and wildfire events in the western United
States (Ross and Lott 2003), the time is ripe to evaluate the need for an integrated
research and forecast center concentrating on hydrometeorological and fire–
atmosphere prediction in areas of complex terrain.
4. Establishing incentives for collaboration. Although forced collaborations are
rarely effective, there are a number of ways to create opportunities for increased
collaboration. These include providing support for operational forecasters
to obtain continuing education or an advanced degree, increasing funding
(both number and size of awards) for successful collaborative programs like
CSTAR and COMET, providing salary for University faculty to take sabbaticals
in forecast offices, and providing rewards (e.g., promotion, pay, awards) to
individuals who engage or encourage successful collaboration. Such incentives
will help create the culture of collaboration needed for the mountain weather
enterprise to reach its full potential.
Acknowledgments We thank the participants in the 2008 AMS/COMET/MSC Mountain Weather
Workshop: Bridging the Gap between Research and Forecasting for 4 days of lectures and
discussion that stimulated this chapter, as well as our editors and chapter coauthors for their
contributions to this book. We also thank Katja Friedrich, John Lewis, Andrea Rossa, Mathias
Rotach, Andrew Russell, David Stensrud, Hans Volkert, and three anonymous reviewers for their
contributions to the manuscript. Participants in the panel discussion “Enhancing the Connectivity
between Research and Applications for the Benefit of Society” at the 2008 AMS Annual Meeting
also provided thoughts and ideas that influenced parts of this chapter. Contributing author
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Steenburgh acknowledges the support of the National Science Foundation and National Weather
Service. Contributing author Schultz acknowledges the support of Vaisala Oyj. Any opinions,
findings, and conclusions or recommendations expressed in this material are those of the authors
and do not necessarily reflect the views of the National Science Foundation, National Weather
Service, or Vaisala Oyj.
References
Allen, D. R., 2001: The genesis of meteorology at the University of Chicago. Bull. Amer. Meteor.
Soc., 82, 1905–1909.
Arpagaus, M., and Coauthors, 2009: MAP D-PHASE: Demonstrating forecast capabilities for
flood events in the Alpine region. Veröffentlichung der MeteoSchweiz, 78, 75 pp. [Available
online at http://www.meteoschweiz.admin.ch/web/de/forschung/publikationen/meteoschweiz
publikationen/veroeffentlichungen.html.]
Baldwin, M. E., J. S. Kain, and M. P. Kay, 2002: Properties of the convection scheme in NCEP’s
Eta Model that affect forecast sounding interpretation. Wea. Forecasting, 17, 1063–1079.
Banacos, P. C., and D. M. Schultz, 2005: The use of moisture flux convergence in forecasting
convective initiation: Historical and operational perspectives. Wea. Forecasting, 20, 351–366.
Bergeron, T., 1959: Weather forecasting: Methods in scientific weather analysis: An outline in the
history of ideas and hints at a program. The Atmosphere and the Sea in Motion, B. Bolin, Ed.,
The Rockefeller Institute Press, 440–474.
Besser, B., 2008: Development of meteorology and geophysics at the University of Graz.
Proceedings of the First European History of Physics Conference, Graz, Austria, Sep 18–21
2006, P. M. Schuster and D. Weaire, Eds., Living Edition Publishers, 159–170. [Available
online at http://www.livingedition.at/en/titles/science/proceedings.]
Board on Atmospheric Sciences and Climate, 2000: From Research to Operations in Weather
Satellites and Numerical Weather Prediction: Crossing the Valley of Death. National Academy
Press, 96 pp. [Available online at http://www.nap.edu/catalog.php?record id=9948.]
Bond, N. A. and Coauthors, 1997: The Coastal Observation and Simulation with Topography
(COAST) Experiment. Bull. Amer. Meteor. Soc., 78, 1941–1955.
Bosart, L. F., 2003: Whither the weather analysis and forecasting process? Wea. Forecasting, 18,
520–529.
Bright, D. R., S. J. Weiss, J. J. Levit, M. S. Wandishin, J. S. Kain, and D. J. Stensrud, 2004:
Evaluation of short-range ensemble forecasts during the 2003 SPC/NSSL Spring Program.
Preprints, 22nd Conference on Severe Local Storms, Hyannis, MA, Amer. Meteor. Soc., CDROM, P15.5.
Bundgaard, R. C., 1979: Sverre Petterssen, weather forecaster. Bull. Amer. Meteor. Soc., 60,
182–195.
Burpee, R. W., 1989: Gordon E. Dunn: Preeminent forecaster of midlatitude storms and tropical
cyclones. Wea. Forecasting, 4, 573–584.
Cane, D., and M. Milelli, 2006: Weather forecasts obtained with a multimodel superensemble
technique in a complex orography region. Met. Zeitschrift, 15, 207–214.
Cane, D., and M. Milelli, 2008: Comparison of COSMO models and multimodel superensemble
outputs in Piemonte. COSMO Newsletter No. 9, 69–79. [Available online at http://www.
cosmo-model.org/content/model/documentation/newsLetters/newsLetter09/cnl9-13.pdf.]
Coniglio, M. C., H. E. Brooks, S. J. Weiss, and S. F. Corfidi, 2007: Forecasting the maintenance of
quasi-linear mesoscale convective systems. Wea. Forecasting, 22, 556–570.
Craven, J. P., R. E. Jewell, and H. E. Brooks, 2002: Comparison between observed convective
cloud-base heights and lifting condensation level for two different lifted parcels. Wea.
Forecasting, 17, 885–890.
12 Bridging the Gap Between Operations and Research. . .
713
Doswell, C. A. III, 1986: The human element in weather forecasting. Nat. Wea. Dig., 11 (2), 6–17.
Doswell, C. A. III, 2004: Weather forecasting by humans—Heuristics and decision making. Wea.
Forecasting, 19, 1115–1126.
Doswell, C. A. III, 2007: Historical overview of severe convective storms research. Electr. J. Severe
Storms Meteor., 2(1), 1–25.
Doswell, C. A. III, and H. E. Brooks, 1998: Budget cutting and the value of weather services. Wea.
Forecasting, 13, 206–212.
Doswell, C. A. III, L. R. Lemon, and R. A. Maddox, 1981: Forecaster training—A review and
analysis. Bull. Amer. Meteor. Soc., 62, 983–988.
Ebert, E., L. J. Wilson, B. G. Brown, P. Nurmi, H. E. Brooks, J. Bally, and M. Jaeneke, 2004:
Verification of nowcasts from the WWRP Sydney 2000 Forecast Demonstration Project. Wea.
Forecasting, 19, 73–96.
Erkkilä, T., 2007: About the nature of the forecaster profession and the human contribution to very
short range forecasts. The European Forecaster, 14, 6–11. [Available online at http://www.
euroforecaster.org/latenews/newsletter.html.]
Friedman, R. M., 1989: Appropriating the Weather: Vilhelm Bjerknes and the Construction of a
Modern Meteorology. Cornell Univ. Press, 251 pp.
Friedman, R. M., 1999: Constituting the polar front, 1919–1920. The Life Cycles of Extratropical
Cyclones. M. A. Shapiro and S. Grønås, Eds., Amer. Meteor. Soc., 29–40.
Harper, K. C., 2008: Weather by the Numbers: The Genesis of Modern Meteorology. MIT Press,
308 pp.
Harper, K. C., 2009: Will meteorologists lose their jobs? NWP and automation fears in the Fifties.
Presidential History Symposium, 89th American Meteorological Society Annual Meeting, P2.3.
[Available online at http://ams.confex.com/ams/89annual/techprogram/session 22297.htm.]
Harper, K., L. W. Uccellini, E. Kalnay, K. Carey, and L. Morone, 2007: 50th anniversary of
operational numerical weather prediction. Bull. Amer. Meteor. Soc., 88, 639–650.
Hart, K. A., W. J. Steenburgh, D. J. Onton, and A. J. Siffert, 2004: An evaluation of mesoscalemodel-based Model Output Statistics (MOS) during the 2002 Olympic and Paralympic Winter
Games. Wea. Forecasting, 19, 200–218.
Horel, J., T. Potter, L. Dunn, W. J. Steenburgh, M. Eubank, M. Splitt, and D. J. Onton, 2002a:
Weather support for the 2002 Winter Olympic and Paralympic Games. Bull. Amer. Meteor.
Soc., 83, 227–240.
Horel, J., M. Splitt, L. Dunn, J. Pechmann, B. White, C. Ciliberti, S. Lazarus, J. Slemmer, D. Zaff,
and J. Burks, 2002b: Mesowest: Cooperative mesonets in the western United States. Bull. Amer.
Meteor. Soc., 83, 211–225.
Joe, P., and Coauthors, 2010: Weather services, science advances, and the Vancouver 2010 Olympic
and Paralympic Winter Games. Bull. Amer. Meteor. Soc., 91, 31–36.
Kain, J. S., M. E. Baldwin, P. R. Janish, S. J. Weiss, M. P. Kay, and G. W. Carbin, 2003a: Subjective
verification of numerical models as a component of a broader interaction between research and
operations. Wea. Forecasting, 18, 847–860.
Kain, J. S., P. R. Janish, S. J. Weiss, M. E. Baldwin, R. S. Schneider, and H. E. Brooks, 2003b:
Collaboration between forecasters and research scientists at the NSSL and SPC: The Spring
Program. Bull. Amer. Meteor. Soc., 84, 1797–1806.
Kain, J. S., S. J. Weiss, J. J. Levit, M. E. Baldwin, and D. R. Bright, 2006: Examination of
convection-allowing configurations of the WRF model for the prediction of severe convective
weather: The SPC/NSSL Spring Program 2004. Wea. Forecasting, 21, 167–181.
Kain, J. S., S. J. Weiss, D. R. Bright, M. E. Baldwin, J. J. Levit, G. W. Carbin, C. S. Schwartz,
M. L. Weisman, K. K. Droegemeier, D. B. Weber, and K. W. Thomas, 2008: Some practical
considerations regarding horizontal resolution in the first generation of operational convectionallowing NWP. Wea. Forecasting, 23, 931–952.
Keenan, T., and Coauthors, 2003: The Sydney 2000 World Weather Research Programme Forecast
Demonstration Project: Overview and current status. Bull. Amer. Meteor. Soc., 84, 1041–1054.
714
W.J. Steenburgh et al.
Knabb, R. D., J. G. Jiing, C. W. Landsea, and W. R. Seguin, 2005: The Joint Hurricane Testbed
(JHT): Progress and future plans. Preprints, Ninth Symposium on Integrated Observing and
Assimilation Systems for the Atmosphere, Oceans, and Land Surface, San Diego, CA, Amer.
Meteor. Soc., 2.2. [Available online at http://ams.confex.com/ams/Annual2005/techprogram/
paper 84938.htm.]
Lewis, J. M., 1995: WAVES forecasters in World War II (with a brief survey of other women
meteorologists in World War II). Bull. Amer. Meteor. Soc., 76, 2187–2202.
Mailhot, J., and Coauthors, 2010: Environment Canada’s experimental numerical weather prediction systems for the Vancouver 2010 Olympic and Paralympic Games. Bull. Amer. Meteor. Soc.,
in press.
Mass, C., 2006: The uncoordinated giant: Why U.S. weather research and prediction are not
achieving their potential. Bull. Amer. Meteor. Soc., 87, 573–584.
Mass, C. F., and Coauthors, 2003: Regional environmental prediction over the Pacific Northwest.
Bull. Amer. Meteor. Soc., 84, 1353–1366.
May, P. T., and Coauthors, 2004: The Sydney 2000 Olympic Games Forecast Demonstration
Project: Forecasting, observing network infrastructure, and data processing issues. Wea.
Forecasting, 19, 115–130.
McCarthy, P. J., D. Ball, and W. Purcell, 2007: Project Phoenix—Optimizing the machine-person
mix in high-impact weather forecasting. Preprints, 22nd Conference on Weather Analysis and
Forecasting/18th Conference on Numerical Weather Prediction, Park City, UT, Amer. Meteor.
Soc., P6A.5. [Available online at http://ams.confex.com/ams/22WAF18NWP/techprogram/
paper 122657.htm.]
Morss, R. E., and F. M. Ralph, 2007: Use of information by National Weather Service forecasters
and emergency managers during CALJET and PACJET-2001. Wea. Forecasting, 22, 539–555.
Morss, R. E., O. V. Wilhelmi, M. W. Downton, and E. Gruntfest, 2005: Flood risk, uncertainty,
and scientific information for decision making: Lessons from an interdisciplinary project. Bull.
Amer. Meteor. Soc., 86, 1593–1601.
Palmer, T., 2008: Edward Norton Lorenz. Physics Today, 61, 81–82.
Petterssen, S., 2001: Weathering the Storm: Sverre Petterssen, the D-Day Forecast, and the Rise of
Modern Meteorology, J. R. Fleming, Ed., Amer. Meteor. Soc., 329 pp.
Pliske, R. M., B. Crandall, and G. Klein, 2004: Competence in weather forecasting. Psychological
Investigations of Competence in Decision Making, K. Smith, J. Shanteau, and P. Johnson, Eds.,
Cambridge University Press, 40–68.
Ralph, F. M., and Coauthors, 2005: Improving short-term (0–48 h) cool-season quantitative
precipitation forecasting: Recommendations from a USWRP workshop. Bull. Amer. Meteor.
Soc., 86, 1619–1632.
Ramage, C. S., 1978: Further outlook—Hazy. Bull. Amer. Meteor. Soc., 59, 18–21.
Ramage, C. S., 1993: Forecasting in meteorology. Bull. Amer. Meteor. Soc., 74, 1863–1871.
Rauhala, J., and D. M. Schultz, 2009: Severe thunderstorm and tornado warnings in Europe. Atmos.
Res., 93, 369–380,
Reed, R. J., 2003: A short account of my education, career choice, and research motivation. A Half
Century of Progress in Meteorology: A Tribute to Richard Reed. R. H. Johnson and R. A. Houze
Jr., Eds., Amer. Meteor. Soc., 1–12.
Roebber, P. J., 2005: Bridging the gap between theory and applications: An inquiry into
atmospheric science teaching. Bull. Amer. Meteor. Soc., 86, 507–517.
Roebber, P. J., D. M. Schultz, and R. Romero, 2002: Synoptic regulation of the 3 May 1999 tornado
outbreak. Wea. Forecasting, 17, 399–429.
Roebber, P. J., D. M. Schultz, B. A. Colle, and D. J. Stensrud, 2004: Toward improved prediction:
High-resolution and ensemble modeling systems in operations. Wea. Forecasting, 19, 936–949.
Ross, T., and N. Lott, 2003: A climatology of 1980–2003 extreme weather and climate events.
National Climatic Data Center Technical Report No. 2003-01. [Available online at http://ols.
nndc.noaa.gov/plolstore/plsql/olstore.prodspecific?prodnum=C00580-PUB-A0001.]
Rossby, C.-G., 1934: Comments on meteorological research. J. Aeronaut. Sci., 1, 32–34.
12 Bridging the Gap Between Operations and Research. . .
715
Rotach, M. W., and Coauthors, 2009a: MAP D-PHASE: Real-time demonstration of weather
forecast quality in the Alpine region. Bull. Amer. Meteor. Soc., 90, 1321–1336.
Rotach, M. W., and Coauthors, 2009b: Supplement to MAP D-PHASE: Real-time demonstration of
weather forecast quality in the Alpine region: Additional applications of the D-PHASE datasets.
Bull. Amer. Meteor. Soc., 90, S28–S32.
Sanders, F., 2008: A career with fronts: Real ones and bogus ones. Synoptic–Dynamic Meteorology and Weather Analysis and Forecasting. A Tribute to Fred Sanders, L. F. Bosart and
H. B. Bluestein, Eds., Amer. Meteor. Soc., 421–422.
Sills, D. M. L., 2005: The Research Support Desk Initiative at the Ontario Storm Prediction Centre.
Meteorological Research Branch Technical Note #-2005-001, Environment Canada. 30 pp.
Sills, D. M. L., 2009: On the MSC forecasters forums and the future role of the human forecaster.
Bull. Amer. Meteor. Soc., 90, 619–627.
Sills, D. M. L., and N. M. Taylor, 2008: The Research Support Desk (RSD) initiative at
Environment Canada: Linking severe weather researchers and forecasters in a real-time
operational setting. Preprints, 24th AMS Conference on Severe Local Storms, Savannah, GA,
Amer. Meteor. Soc., Paper 9A.1. [Available online at http://ams.confex.com/ams/pdfpapers/
142033.pdf.]
Smith, R. K., G. Garden, J. Molinari, and R. K. Morton, 2001: Proceedings of an International
Workshop on the Dynamics and Forecasting of Tropical Weather Systems. Bull. Amer. Meteor.
Soc., 82, 2825–2829.
Snellman, L. W., 1977: Operational forecasting using automated guidance. Bull. Amer. Meteor.
Soc., 58, 1036–1044.
Snyder, B. J., C. Doyle, D. A. Wesley, J. D. Cummine, and M. Meyers, 2006: The first
MSC/COMET mountain weather course. Preprints, 12th Conf. on Mountain Meteorology,
Santa Fe, NM, Amer. Meteor. Soc., P16.2.
Snyder, B. J., and M. Loney, 2007. Survey of Forecaster Training, 2006 Results. Meteorological
Service of Canada. Unpublished.
Stanitski, D. M., and D. J. Charlevoix, 2008: AMS membership survey results: Who are the student
members of the AMS? Bull. Amer. Meteor. Soc., 89, 892–895.
Stauffer, D. R., G. K. Hunter, A. Deng, J. R. Zielonka, K. Tinklepaugh, P. Hayes, and C. Kiley,
2007: On the role of atmospheric data assimilation and model resolution on model forecast
accuracy for the Torino Winter Olympics. Preprints, 22nd Conference on Weather Analysis and
Forecasting/18th Conference on Numerical Weather Prediction, Park City, UT, Amer. Meteor.
Soc., P11A.6. [Available online at http://ams.confex.com/ams/22WAF18NWP/techprogram/
paper 124791.htm.]
Steenburgh, W. J., and C. F. Mass, 1996: Interaction of an intense extratropical cyclone with coastal
orography. Mon. Wea. Rev., 124, 1329–1352.
Stoelinga, M. T., and Coauthors, 2003: Improvement of Microphysical Parameterization through
Observational Verification Experiment. Bull. Amer. Meteor. Soc., 84, 1807–1826.
Stuart, N. A., P. S. Market, B. Telfeyan, G. M. Lackmann, K. Carey, H. E. Brooks, D. Nietfeld,
B. C. Motta, and K. Reeves, 2006: The future of humans in an increasingly automated forecast
process. Bull. Amer. Meteor. Soc., 87, 1497–1502.
Stuart, N. A., D. M. Schultz, and G. Klein, 2007: Maintaining the role of humans in the forecast
process: Analyzing the psyche of expert forecasters. Bull. Amer. Meteor. Soc., 88, 1893–1898.
Volkert, H., and T. Gutermann, 2007: Inter-domain cooperation for mesoscale atmospheric
laboratories: The Mesoscale Alpine Program as a rich study case. Quart. J. Roy. Meteor. Soc.,
133, 949–967.
Waldstreicher, J. S., 2005: Assessing the impact of collaborative research projects on NWS warning
performance. Bull. Amer. Meteor. Soc., 86, 193–203.
Washington State Military Department, 2007: Windstorm Response after Action Report: A
Statewide Report to the Governor. 77 pp. [Available online at http://www.emd.wa.gov/
publications/documents/FINAL AAR 040407.pdf.]
716
W.J. Steenburgh et al.
Weiss, S. J., D. R. Bright, J. S. Kain, J. J. Levit, M. E. Pyle, Z. I. Janjic, B. S. Ferrier, and J. Du,
2006: Complementary use of short-range ensemble and 4.5 km WRF-NMM model guidance
for severe weather forecasting at the Storm Prediction Center. Preprints, 23rd Conference on
Severe Local Storms, St. Louis, MO, Amer. Meteor. Soc., CD-ROM 8.5.
Weiss, S. J., J. S. Kain, D. R. Bright, J. J. Levit, G. W. Carbin, M. E. Pyle, Z. I. Janjic, B. S.
Ferrier, J. Du, M. L. Weisman, and M. Xue, 2007: The NOAA Hazardous Weather Testbed:
Collaborative testing of ensemble and convection-allowing WRF models and subsequent
transfer to operations at the Storm Prediction Center. Preprints, 22nd Conference on Weather
Analysis and Forecasting/18th Conference on Numerical Weather Prediction, Park City, UT,
Amer. Meteor. Soc., CD-ROM, 6B.4.
Wetzel, M., and Coauthors, 2004: Mesoscale snowfall prediction and verification in mountainous
terrain. Wea. Forecasting, 19, 806–828.
Wilson, J. W., E. Ebert, T. Saxen, C. Pierce, M. Sleigh, A. Seed, R. Roberts and C. Mueller, 2004:
Sydney 2000 Forecast Demonstration Project: Convective storm nowcasting. Wea. Forecasting,
19, 131–150.
Zappa, M., and Coauthors, 2008: MAP D-PHASE: Real-time demonstration of hydrological
ensemble prediction systems. Atmos. Sci. Lett., 9, 80–87.