Chapter 12Bridging the Gap Between Operationsand Research to Improve Weather Predictionin Mountainous Regions

W. James Steenburgh, David M. Schultz, Bradley J. Snyder,and Michael P. Meyers

Abstract A gap between operational and research meteorologists has existedsince the infancy of weather forecasting and represents an obstacle to progressin meteorology. This gap is related to the profoundly different perspectives andprofessional expectations of operational and research meteorologists. For theknowledge, observations, tools, and models described in this book to reach theirfull potential, the mountain meteorology community must work more effectively tobridge this gap, as described in this chapter. Essential to this effort are advocateswho are capable of interacting, communicating, and commanding respect with boththe operational and research communities. As a result, the mountain meteorologycommunity should provide the attention and resources needed to ensure thatfuture advocates are created from the pool of young scientists and forecasters.The community should also ensure that knowledge and technological advancesfrom field programs and other research efforts are effectively transferred intooperations and, at least in North America, explore the development of an integratedresearch and forecast center to tackle challenges in mountain hydrometeorology

W.J. Steenburgh (�)Department of Atmospheric Sciences, University of Utah, Salt Lake City, UT, USAe-mail: jim.steenburgh@utah.edu

D.M. SchultzDivision 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. SnyderMeteorological Service of Canada, Vancouver, Canada

M.P. MeyersNOAA/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 ahealthy scientific and forecasting enterprise, these and other gap-bridging activitiesand incentives described in this chapter should benefit the entire mountain weathercommunity, its operational and research sectors, and, via improved forecasts, societyat large.

12.1 Introduction

“One of the greatest obstacles to the progress in meteorology is undoubtedly to befound in the wide gulf between the mathematical theory on one hand and the appliedscience weather-map analysis and forecasting, on the other.” As true now as whenRossby (1934) said it, the gap between operational and research meteorologistsprevents forecasters from extracting maximum benefit from today’s sophisticatedobservations, forecast tools, and numerical models, and inhibits researchers fromfully evaluating weaknesses in current scientific understanding and capabilities.History suggests that forecast improvements and scientific advances acceleratewhen operational and research meteorologists respect their unique perspectivesand 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 integratedcollaboration to address critical challenges for weather prediction in mountainousregions.

This chapter provides a roadmap for bridging the gap and accelerating progress inmountain meteorology by examining the differing perspectives of operational andresearch meteorologists, identifying ingredients for successful gap bridging, andproviding specific examples upon which to pattern future collaborative efforts. Werefer to a single gap, although in reality there may be multiple gaps in the chain frombasic research to operations. Although the operational and research communitieseach stand to benefit from increased collaboration, the primary reason to bridgethe gap is to produce better weather forecasts for the benefit of society. As such, agap 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 interactionsbetween operational and research meteorologists in the mountain meteorologycommunity.

12.2 Causes of the Gap

Winston Churchill said, “true genius resides in the capacity for evaluation ofuncertain, hazardous, and conflicting information.” If so, Churchill would con-sider forecasters the epitome of true genius. Every working day forecasters faceuncertainty. They never have all the data they want, are confronted with several

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computer models (and ensembles) that produce different forecasts, and haveimperfect knowledge of a chaotic atmosphere. Forecast deadlines and the urgentnature of severe weather demand that forecasters be comfortable making decisionsin the face of uncertainty, even when the data and forecast guidance cannot be fullyanalyzed and interpreted. The best forecasters develop schema to quickly organizethe wide array of observational data and model guidance, identify critical issues,and make good decisions (e.g., Doswell 2004). This ability to make decisions fromlimited evidence is known as forecaster intuition, a combination of experience,conceptual model application, and educated guesses. Such skills are most apparentin the engaged forecaster (e.g., Roebber et al. 2002; Pliske et al. 2004; Stuartet al. 2007). Weather forecasting is a scientific endeavor involving hypothesisformulation, hypothesis testing, and prediction (e.g., Roebber et al. 2004), but itis inherently less rigorous and more speculative than scientific research.

Although the best forecasters are comfortable dealing with incomplete infor-mation and evidence, conscientious scientists are not. Scientists must extensivelytest hypotheses and determine the generality of their conclusions. They demandthat conclusions be justified and consider statements based on limited evidence(i.e., the forecaster’s intuition) to be nothing more than speculation. Just as anOlympic alpine skier may not make the best Nordic skier, despite both disciplinesrequiring strength, cardiovascular fitness, and balance on skis, operational andresearch meteorologists, despite their common skills and knowledge in meteorology,are not necessarily interchangeable.

The gap between operations and research originates from these profoundlydifferent worldviews, which allow forecasters and researchers to succeed withintheir individual communities by meeting the expectations of their colleagues andorganizations, but make crossing boundaries difficult. Forecasting is challenging formost researchers, who are unable to fully ponder and investigate under the deadlineof getting the forecast out. In contrast, research and publication are challenging forforecasters—the intuition that works well for them in day-to-day operations cannotbe relied upon in formal publications to convince a critical audience. Those whoaccommodate and accept these two worldviews recognize that forecaster experienceand practice cannot always be justified with a citation or easily quantified throughcalculations, but that evidence and logic are essential for both weather forecastingand scientific research.

12.3 Perceptions of the Gap

The gap between operational and research meteorologists has been recognized sinceat least the early twentieth century (e.g., Rossby 1934; Bergeron 1959) and remainsconsiderable even today (Doswell et al. 1981; Ramage 1993; Doswell 2007). Snyderand Loney (2007) reported that operational meteorologists in the MeteorologicalService of Canada (MSC) and the National Weather Service (NWS) of the UnitedStates believe that there are three major contributors to the current gap.

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• Limited exposure of researchers to the operational forecast environment. Manyof today’s young research scientists have limited (if any) experience withsynoptic weather analysis and have never worked a forecast shift, so they havelittle exposure to the unique demands of operational weather forecasting, asargued by Doswell (1986). This lack of exposure to operations is ironic becausemany of their scientific forebears began their careers as weather forecasters.For example, analysis and forecasting was essential to the scientific advances ofthe Bergen School (Friedman 1989, 1999), and many distinguished atmosphericscientists of the mid to late twentieth century served as weather observers orforecasters in World War II [e.g., Heinz Lettau (Besser 2008), Edward Lorenz(Palmer 2008), Sverre Petterssen (Bundgaard 1979; Petterssen 2001), RichardReed (Reed 2003), Frederick Sanders (Sanders 2008), Joanne Simpson (Lewis1995)]. Nearly all universities emphasize atmospheric dynamics and physics atthe expense of applications and forecasting. When taught, weather analysis andforecasting is often decoupled from dynamics and physics, when, in practice,forecasting (and research) requires a broad view that integrates across theseatmospheric 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 meteorologicalliterature, identify scientific articles relevant to their job, and participate inresearch (e.g., Doswell et al. 1981; Doswell 1986). Many forecast organizationshave positions designed to address this need (e.g., the NWS Science andOperations Officer), but administrative obstacles and other work demandsfrequently limit the time available for scientific research and forecaster training.As a result, many forecasters are unable to keep up with recent progress in theatmospheric sciences or maintain the mathematical and theoretical knowledgeneeded to communicate effectively with the research community. Exacerbatingthis problem are bureaucratic rules that prevent or limit forecaster participationin research and training projects, a lack of release time from forecast shiftsfor research and training, and limited rewards and funding for forecasterparticipation 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 Schultz2009). Because of contrasting cultures and professional expectations, forecastersand researchers prefer different modes of communication. Researchers typicallydisseminate research results in journal articles and conference presentations.Such approaches appeal to scientists and university faculty, but are not favoredby students and forecasters who prefer concrete examples and active participation(Roebber 2005; Stuart et al. 2007). In fact, only 40% of the US students whoresponded to an American Meteorological Society (AMS) member survey saidthat they “read printed research literature on a daily or weekly basis” (Stanitskiand Charlevoix 2008). Further, forecasters consider reading journal articlesand attending scientific conferences amongst the least effective training modesand consider collaborative forecasting with experts (i.e., “double banking”),weather-event simulators, and residency training courses with close and regular

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Fig. 12.1 Annual threat scores for 24-h, 1-in. (2.54-cm), day 1 quantitative precipitation forecastsproduced 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 (Snyderand Loney 2007). Such responses are not unexpected as forecasters would muchrather learn in ways that directly relate to their job rather than through waysthat researchers learn best, and many training programs may not contain enoughoperationally 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 meteorologycommunity to extract maximum societal benefit from today’s sophisticated obser-vations and forecast tools. Although concerns that numerical weather predictionand automation will eliminate humans from the forecast process have been raisedsince at least the 1950s (e.g., Harper 2008, pp. 229–231; 2009), well-trained andfully engaged forecasters continue to play “a clear role in the forecast process bycontributing a wealth of knowledge, tools, and techniques that cannot be duplicatedby computers or [numerical weather prediction]” (McCarthy et al. 2007). Thisis particularly true in short-range forecasting, as illustrated by comparing day-1,24-h quantitative precipitation forecasts produced by operational numerical weatherprediction models run by the National Centers for Environmental Prediction(NCEP) with those produced by forecasters at the NWS HydrometeorologicalPrediction Center (HPC, Fig. 12.1). Over the past 15 years, even as model forecastshave improved, HPC forecasters have maintained a consistent annual threat scoreadvantage 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 forecastskill (e.g., Bosart 2003; Harper et al. 2007; Erkkila 2007; Sills 2009).

On the other hand, forecasters that lack sufficient training, fail to keep up withscientific progress, and rely excessively on numerical model guidance providelittle benefit to the forecast process, particularly in areas of complex terrain wherenumerical weather prediction models inadequately resolve orographic processes andfine-scale weather variability. Snellman (1977) coined the term “meteorologicalcancer” to describe the insidious overreliance on information generated by com-puters at the expense of human interpretation and cognition. As stated forcefullyby Bosart (2003), forecasters who grow accustomed to letting MOS [model outputstatistics] and the models do their thinking for them on a regular basis during thecourse of their daily activities are at high risk of “going down in flames” whenthe atmosphere is in an outlier mode. Clearly, the mountain weather forecastingcommunity can only reach its full potential if forecasters are both highly educatedand fully engaged in the forecast process. This goal can only be accomplished if theoperational and research communities work together.

To summarize, these perspectives highlight three critical challenges that individ-uals seeking to bridge the gap must address:

• Forecasters must be motivated, fully engaged, and encouraged to participate inresearch and training on a regular basis. To a large extent, this motivation must beinternal, but also bureaucratic barriers preventing regular participation in researchand training can be removed.

• Researchers must recognize and be willing to communicate the forecast rele-vance of their research. This requires knowledge and respect of the operationalforecast environment, understanding of the role of humans in the forecast pro-cess, and the use of effective methods for transferring knowledge and techniquesfrom research to operations.

• Forecasters and their managers must recognize the value of a mathematicaland theoretical education and develop the necessary educational foundation topursue one, while researchers must embrace the potential for weather analysisand forecasting to enable them to formulate and test scientific hypotheses usingreal weather data.

12.4 Why Bridge the Gap?

Addressing these three challenges requires effort—often considerable effort. Whyshould anyone take this hard road to bridge the gap, when remaining isolatedwithin one’s community can produce relatively easy personal success? There aretwo reasons.

The first is the shared desire among forecasters and research scientists to havea 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 productivecollaboration between operational and research meteorologists. Forecasters mustbe well educated and trained to extract maximum benefit from today’s observationsand forecast tools and to prevent avoidable forecast errors related to the misuseor 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, enablesimproved formulation and testing of scientific hypotheses, stimulates the broadeningof research results, and prevents the overgeneralization of conclusions based oncomprehensively sampled but limited sample size field-program studies (Doswell1986, 2007). Finally, increasingly tight budgets demand the rigorous and efficienttesting of research advances, including quantification of their impact on the forecastcycle and decision making (e.g., Doswell and Brooks 1998). Such a cycle requiresincreased collaboration between researchers, forecasters, and decision makersthrough 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 scientistsdetermined to be advocates for improved collaboration. At least two advocates(preferably more) are generally needed (one from each side) and, if the collaborationis between organizations, the advocates may occupy supervisory, managerial, oradministrative roles within their organizations. Without advocates on each side, noway exists to make or enforce collaboration, especially when resources (personnel,computer support, release time from forecasting for collaborative projects, etc.) areneeded. Advocates must be able to communicate with and command respect fromboth the forecast and research communities. These individuals may be doctoral-level scientists if they are forecasters, perhaps with considerable research experiencebefore becoming a forecaster, or researchers who began their career as a forecasteror are passionate about weather and forecasting. Advocates may also be hands-onmanagers who have the ability to change reward structures for participants or reducebureaucratic barriers to collaboration.

In addition to an advocate or advocates, additional ingredients for successfulbridging of the gap are typically required. Although not all need be present, theseingredients may include:

• Buy-in and commitment. Both the operational and research sides must embraceeach others’ different perspectives, learn to speak each others’ language, andpossess the staying power to see the work to completion. Not everyone in theproject or organization needs to be “on board,” but a critical mass is needed forsuccess, particularly in larger projects. Researchers must recognize that spendingtime 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 Programfor Operational Meteorology, Education and Training/Meteorological Service of Canada(AMS/COMET/MSC) Mountain Weather Workshop: Bridging the Gap Between Research andForecasting, 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-inis essential for long-term success.

• Stimulation at the grassroots level. The cliche “if the people lead, eventuallythe leaders will follow” applies here. The most successful collaborations beginor are stimulated at the bottom and are not simply imposed by management.Grassroots collaborations can succeed with either the support or neglect ofmanagement. (Active interference by management is likely to lead to failure.)Forced collaborations might be successful in the short term, but total top-downefforts are rarely successful (Doswell 2007, p. 16).

• Collocation. Collocation enables more regular and routine interaction of researchand forecast meteorologists. It facilitates communication and collaboration. Thecollocation may be temporary (e.g., joint forecast production, residency courses,Fig. 12.2), or permanent where forecast and research meteorologists are housedin a common facility (and perhaps share some job responsibilities). Collocationalone, 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, advocatesmust still provide leadership, encouragement, and a framework for collaboration.

• Securing the time, resources, and personnel needed for meaningful collabora-tion. Discussion and interaction must be actively promoted, but talking aloneis not sufficient. Forecasters must gain release time from operational respon-sibilities to participate in research, education, and training on a regular basis.Similarly, researchers must be patient because the progress of working with shiftforecasters may take longer than a typical research project. Because scientists aremore likely to have experience with applying for grants and funding, most of theonus 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 keyoperational issues, so compromises and adjustments must be made to ensure thatthey are “win–win” for both operations and research. Identifying goals for theoperational community and mapping them onto the capabilities of the researchersis one way to propose achievable goals and articulate collaboration priorities.

• Establishing incentives. Because the institutional reward systems for researchersand forecasters are different, each community must recognize that bridgingthe gap may require nontraditional incentives for the individuals involved.Oftentimes the advocate is a senior person for whom the traditional rewardsystem is no longer a motivator—the reward is in seeing the success of theinteraction 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 themountain weather community. Examples from the latter are used in areas where themountain meteorology community can learn from the efforts in other meteorologicalsubdisciplines (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. CliffMass, professor of atmospheric sciences at the University of Washington, and Dr.Brad Colman, Science and Operations Officer (since promoted to Meteorologist-In-Charge) at the NWS Forecast Office in Seattle, who have forged a two-decaderelationship to advance local weather knowledge and prediction. In addition topassion, these two individuals possess the key characteristics identified above asnecessary 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 theresearch community.

Efforts led by these two advocates have: (1) established a mesoscale surfaceobserving network, (2) created local high-resolution and regional-scale ensemblemodeling systems, and (3) contributed to the successful execution of the COASTand IMPROVE field programs examining front–mountain interactions and oro-graphic 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 ofWashington, including efforts to improve model parameterization and ensembleprediction 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), whichkilled 15 people in western Washington and left an estimated 4.08 million peoplewithout power, was forecast with remarkable specificity, urgency, and lead time bythe NWS Forecast Office in Seattle (Washington State Military Department 2007,pp. 10–11). Such forecasts, which minimized loss of life and enabled a timely emer-gency response and recovery, were enabled by knowledge of windstorms producedby landfalling cyclones spawned by Mass’ research group (e.g., Steenburgh andMass 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 isthe power of bringing two talented and motivated advocates together from theoperational and research communities. In this case, the late Dr. Tom Potter, formerdirector of NWS Western Region, helped stimulate the partnership by encouragingDr. Colman to move to Seattle, become the science and operations officer, andimprove training and science within the ranks of the NWS. The second is thebenefit provided to each respective community when the gap is bridged. Theresearch program built at the University of Washington has benefited from drawingmotivation from applied forecasting problems (Mass et al. 2003), whereas NWSforecasters benefit from new knowledge and state-of-the art weather analysis andforecast tools.

12.6.2 Buy in and Commitment Stimulated by a CommonResearch and Forecast Problem

12.6.2.1 Olympic Winter Games

Over a 17-day period every 4 years, the Olympic Winter Games require extremelyprecise mountain weather forecasts at high temporal and spatial resolution (Horelet al. 2002a). Significant and sometimes hazardous weather has impacted nearlyevery 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 forbridging 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 skillpredicting all of the phenomena described in this book (and more), each of whichoccurs in a region with unique climatological and topographic characteristics, areneeded for a successful Games.

The Olympic Winter Games provide a uniting goal for operational and researchmeteorologists, with the “Olympic Spirit” stimulating buy-in and commitment(Fig. 12.3). During the 2002 Olympic Games in Salt Lake, for example, threedisparate groups were brought together—the University of Utah to develop andprovide a regional mesonet and modeling system, the NWS to provide publicforecasts and warnings to protect lives and property, and a team of 13 private sectormeteorologists to provide detailed forecasts for the outdoor sports venues (Horelet al. 2002a).

These three groups collaborated to ensure forecast-relevant research and devel-opment and to provide the necessary education and training to take advantage ofnew knowledge and forecast tools. For example, the mesoscale modeling systemdeveloped for the games was used by forecasters for 3 years prior to the Games. Thislong-term experience with the modeling system enabled the forecasters to determinethat 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 Utahdeveloped model output statistics (MOS) for 18 sites in the Olympic region (seeChap. 11 for details on the use of MOS). This mesoscale-model-based MOS wasrelatively straightforward to develop, provided forecasts at multiple sites at severaloutdoor venues, including three along the men’s downhill, and proved extremelybeneficial 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 ForecastDemonstration Projects in which researchers shared a work area with forecasters totest leading-edge nowcasting tools (e.g., Keenan et al. 2003; May et al. 2004; Ebertet al. 2004; Wilson et al. 2004; Joe et al. 2010; Mailhot et al. 2010). Testing andevaluation of a multimodel superensemble and high-resolution limited-area modelsoccurred during Torino 2006 (Cane and Milelli 2006; Stauffer et al. 2007), with thesuperensemble used subsequently for operational weather prediction over the Pied-mont region of Italy (Cane and Milelli 2008). During the 2010 Vancouver Games, ateam of researchers participated in the Science and Nowcasting of Olympic Weatherfor Vancouver 2010 (SNOW-V10) in order to improve the understanding of andability to forecast and nowcast low cloud, visibility, precipitation (amount andtype), and winds in complex terrain (Joe et al. 2010). The project involved thedeployment of an enhanced network of instruments at specific venues, as well asthe production of prototype forecast products. Researchers collaborated extensivelywith forecasters on the instrument siting and assisted in pre-Games training.

For the Vancouver 2010 Olympic Winter Games, a comprehensive trainingstrategy was also devised based in part on the survey findings described inSect. 12.3 (Snyder et al. 2006). One aspect of this training involved creating amountain weather residency course in cooperation with the University Corporationfor Atmospheric Research Cooperative Program for Operational Meteorology,Education and Training (COMET) program. Subject matter experts, many ofwhom 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 thisknowledge through case studies and forecast labs. As a testament to the quality ofinstruction and the focus on operationally relevant research findings, course ratingswere some of the highest ever given to a COMET course. In another effort tofacilitate interaction between the operational and research teams, a meteorologistwas assigned the dual-role of venue forecaster and applied research meteorologistfor Olympic forecast product development. Buy-in and commitment were not anissue during the Games but adding a person who could speak the others’ languagesolidified the gap-bridging effort

12.6.2.2 MAP D-PHASE

The Mesoscale Alpine Programme Demonstration of Probabilistic Hydrologicaland Atmospheric Simulation of Flood Events (MAP D-PHASE) is a project todemonstrate 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 MesoscaleAlpine Programme (MAP), which involves substantial collaboration between theresearch and operational sectors (Volkert and Gutermann 2007). Participants include17 countries, 18 operational centers, and 7 research institutions (Arpagaus et al.2009). MAP D-PHASE has many goals including: (1) assessing high-resolutiondeterministic 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 overcomplex terrain, and (5) improving decision making by civil protection authorities.As such, MAP D-PHASE involves not only bridging the gap between research andoperations, but also between meteorologists and policy makers, end-users, and otherscientific disciplines.

Integral to MAP D-PHASE is the testing and evaluation of an end-to-end forecastsystem 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 issuedif a deterministic forecast or a third of the ensemble members exceed one of threecriteria based on flood return periods. MAP D-PHASE also incorporates a Web-based visualization platform containing all MAP D-PHASE graphical information(e.g., nowcasting products, warning maps, validation products). Although thiscommon framework requires compromise by the participants, all warnings are basedon the same thresholds and procedures, allowing different regions and models to befairly and uniformly intercompared.

Although the results of MAP D-PHASE are only beginning to be revealed, theeffort required to integrate data, model ensembles, and warnings into a commonframework suggests an immense level of cooperation and planning between re-search, operations, and end users. Rotach et al. (2009a) suggest that the use ofcommon formats, warning levels, and routines amongst different forecast modelsis essential for program success. MAP D-PHASE was designed from the beginningto address a research and operational challenge, ensuring that both research andoperational centers would have vested interests in seeing success. It sought the inputand participation of end users. In short, MAP D-PHASE is perhaps the best singleexample from the mountain meteorology community of a large-scale collaborationbetween 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 NWShas developed two successful programs to support these grassroots efforts: (1) theCollaborative 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 highlycollaborative applied research activities with the NWS. CSTAR partnerships are col-laborative efforts requiring a buy-in from researchers and forecasters, consequentlyproviding a foundation for the ongoing infusion of science and technology into theforecast office. The importance of CSTAR funding is clearly evident in the examplesnoted earlier as it has helped support collaborative activities between the Universityof Washington and the NWS Forecast Office in Seattle (Sect. 12.6.1) and betweenthe University of Utah, NWS, and private forecasters for the 2002 Olympic WinterGames (Sect. 12.6.2.1). The COMET program addresses meteorological educationand training, including mountain-related topics, through distance learning, residenceclasses, and an outreach program that facilitates the transfer of research results tooperations and provides funding for the academic and operational communities toparticipate in collaborative research. A similar European effort for education andtraining project is called Eumetcal (http://www.eumetcal.org).

CSTAR and COMET provide the funding needed to bring together researchersand forecasters who otherwise would not typically interact. These two programsforce the two groups into a middle ground where both an operational and researchfocus is achieved, accomplishing the mandates of both groups. Sometimes, itrequires the researchers, forecasters, or both to go beyond their comfort level. Oneexample is the COMET partnership between the NWS Forecast Office in GrandJunction and researchers from the Desert Research Institute Storm Peak Laboratoryand Colorado State University. These three groups worked from 2002 to 2006 toinvestigate 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 theirresearch. The session included a short seminar followed by one-on-one interactionbetween the forecasters and the researchers, which enhanced the personal con-nection between the two groups. The operational staff also demonstrated for theresearchers the forecast system and methodology. Some of the forecasters thenvisited the Storm Peak Laboratory, enabling direct communication between thegroups in a laboratory setting. These interactions increased buy-in and commitmentof both groups.

Because of the communication and team-building efforts of the researchers, theforecasters were committed to success during and following the field study. One ofthe project objectives was to increase meteorological understanding of the physicalcontrols on precipitation over this relatively data-sparse mountainous region in away that would directly benefit operational forecasting. Because of the availabilityof supplemental data during the field study, the forecasters gained invaluableinsights into the local orographic forcing over the Park Range, knowledge thatthey have expanded upon after the project conclusion. For example, the forecastersdeveloped a better understanding of the predominance of heavy snowfall duringmoist, post-frontal flow and the sensitivity of quantitative snowfall forecasts to thesnow-to-liquid ratio. The forecasters learned that snow-to-liquid ratios of 30:1 arenot uncommon in this region and that an underestimation of this ratio, as happenedin the past, has a significant negative impact on the prediction of snowfall amount

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and winter storm potential. The impact of improved understanding was evidentduring the 2009 winter season when 24 winter storms in the Park Range regionwere 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, aresult of collaborations between over 70 universities and over 90 NWS forecastoffices (Waldstreicher 2005). Importantly, the research funded by COMET has ledto demonstrable improvements in forecasts, as Waldstreicher (2005) has shown.For example, NWS Eastern Region offices with COMET projects aimed at betterunderstanding severe thunderstorms or tornadoes improved twice as much inprobability of detection for severe thunderstorm and tornado warnings as the regionas a whole. The rate of improvement was also strong for lead times for severethunderstorms 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, alsodemonstrated remarkable improvement in their metrics compared to the other officesin the region. Consequently, Waldstreicher’s (2005) results provide quantitativeevidence showing the positive impact of research–operations collaborations onforecast quality.

12.6.4 Collocation: The Research Support Desk

An example of the value of collocation for gap bridging is the so-called ResearchSupport Desk (RSD), which was implemented in some MSC forecast offices toincrease real-time interaction between forecasters and researchers (Sills and Taylor2008). The RSD involves the collocation of a researcher with operational forecastersin the weather center. Although the RSD is staffed only during busy seasons,it has proven to be an effective knowledge transfer mechanism. Education andtraining about new techniques and technologies is passed from the researcher tothe forecasters through direct real-time interaction and formal briefings. In turn, theresearcher is able to also evaluate experimental products and identify science needsin an operational environment.

The first real challenge for this initiative was getting permission to have the RSDlocated in the operational environment. Forecasters were concerned about increaseddemands on their time, particularly during severe weather (Sills 2005). Onceestablished, however, the overwhelming majority of forecasters were comfortablewith the researcher in operations and 72% of forecasters responding to a surveybelieved the RSD enhanced the learning environment. This experience exemplifiessome 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 otherweather centers.

As part of the SNOW-V10 project, a RSD was put in place in Vancouver forthe 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 arrayof new data. To facilitate collaboration between a larger pool of researchers andforecasters, daily web-based briefings were also held over a 2-month period beforeand during the Games.

12.6.5 Collocation: Forecasting and Research Teamsin Mountain Meteorology

Examples of collocated operational and research groups that concentrate on moun-tain weather applications exist in Europe (i.e., MeteoSwiss where the radar applica-tion and research group is collocated with the forecasters in Locarno) and Canada(the Pacific and Yukon Region National Lab for Coastal and Mountain Meteorol-ogy), but no major center for mountain weather research and operations exists inthe United States. This is unfortunate, because collocation of motivated individualsand groups has enabled dramatic advances in knowledge and forecasting in othermeteorological subdisciplines. Perhaps the earliest and best-known example is theBergen School of Meteorology (e.g., Friedman 1989, 1999). To address the needsof farmers and fishermen in Norway, Vilhelm Bjerknes developed a system andreceived funding to collect and analyze mesoscale observations of weather systems.Study of these weather systems led to the formulation of the Norwegian cyclonemodel. Bergen School members were trained physicists and mathematicians; theiremphasis was on understanding the relevant weather processes to produce the bestforecast. As such, theirs is arguably the first scientific effort to bridge the gapbetween research and operations in atmospheric science.

In the 1950s, a dominant center for bridging the gap between research andoperations emerged in Chicago (e.g., Allen 2001). Gordon Dunn, an employeeof the Weather Bureau (predecessor of the NWS), convinced the Weather Bureauto locate the Chicago Forecast Office at the Department of Meteorology at theUniversity of Chicago (Burpee 1989, pp. 576, 581–582). Dunn, concerned aboutthe gap between researchers and forecasters, arranged daily map discussions withboth groups participating. In such an environment, understanding of the jet streamimproved, 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 HurricaneCenter (now the Tropical Prediction Center) and the National Hurricane ResearchProject (later the National Hurricane Research Laboratory and then the HurricaneResearch Division of the NOAA/Atlantic Oceanographic and Meteorology Labora-tory) (Burpee 1989, p. 580). The Joint Hurricane Testbed (Knabb et al. 2005) wasthe eventual result of this interaction. One of the benefits of the cooperation andcollocation was the development of statistical hurricane track prediction models,assimilation of data from reconnaissance aircraft, and operational targeting ofadditional observations.

12 Bridging the Gap Between Operations and Research. . . 709

Collocation does not, however, guarantee collaboration. Doswell (2007) de-scribes a situation at the National Severe Storms Forecasting Center (predecessorto the Storm Prediction Center) where the initial collaboration between researchersand forecasters developed into a schism where the researchers and forecasterswere placed on separate floors in the same building because of some of theresearchers’ disdain of having to do shift work. The schism in the severe weathercommunity would later be repaired by the collocation of the National SevereStorms Laboratory and Storm Prediction Center in Norman, Oklahoma, in the late1990s. This collocation has subsequently led to a flourish of collaborative researchand forecasting endeavors (e.g., Kain et al. 2003a, b, 2006, 2008) including thedevelopment and application of convection-allowing models in operations (e.g.,Kain et al. 2006, 2008), efforts to improve the interpretation of forecast-modelsoundings (e.g., Baldwin et al. 2002), research into the use of ensembles forconvective-storm forecasting (e.g., Bright et al. 2004; Weiss et al. 2006, 2007), andother research projects on convective storms (e.g., Craven et al. 2002; Banacos andSchultz 2005; Coniglio et al. 2007). Because of these largely grassroots successes,other individuals, groups, and companies—both domestic and international—havecollaborated with these two groups in Norman, now renamed the HazardousWeather Testbed, showing the power that an initial interaction can have for growingthe research–operations connection. Indeed, a small initial success can grow into asustained 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 moun-tain weather community is to meet its full potential. Mass (2006) raises concernsabout a lack of coordination between the research and operational communitiesin the United States, and he provides a substantial list of recommendations forimprovement, which we expand on here for the mountain weather community. Inparticular, there are four critical areas in which we should focus our attention andresources:

1. Creating tomorrow’s advocates. As noted earlier, the most important factor toensure the overall success of the mountain-meteorology enterprise are advocateswho work to bridge the gap between research and operations (and ultimatelydecision makers; Morss et al. 2005). As such, greater emphasis must be placedon educating, mentoring, and grooming young scientists and forecasters to bridgethe gap between mathematical theory and scientific forecasting as they movethrough their careers (e.g., Doswell 1986). On the research side, universitiesshould provide curricula and opportunities for tomorrow’s scientific leaders(i.e., current graduate students) to participate in scientific forecasting and gainexposure 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 withoutexposure to weather analysis and forecasting. Such exposure can be achievedthrough courses in synoptic–dynamic meteorology and forecasting, internshipsat forecasting agencies, and collaborative research projects leading to advanceddegrees. Approaches that challenge the student to test, apply, and refine theirresearch in an operational environment should be strongly encouraged. Onthe operational side, forecasting agencies must provide additional time andincentives for forecasters to participate in research by creating more opportunitiesfor young forecasters to return to graduate school to pursue graduate degrees orregular release time to participate in research projects and personal training. Ineither case, it is essential that the forecaster work with research meteorologistsand conduct substantive research to gain exposure to the scientific method.A forecaster with a doctoral education or substantive prior research experiencecould serve as the scientific mentor in these efforts, but the participation ofresearch scientists from universities and government labs should be encouraged.

2. Prioritizing and providing support for the transfer of field-program advancesinto operations. Field research in mountain meteorology continues to providevaluable data for scientific analyses and publications. MAP, for example, hasspawned more than 220 scientific publications (Volkert and Gutermann 2007).For the operational meteorological community, however, the challenge hasbeen transferring this wealth of knowledge to the forecast environment so thattheoretical concepts can be applied to forecasting in complex terrain (Snyderet al. 2006). Historically, many mountain meteorology field programs havecontained no component or clear mechanism for transferring field-programadvances into operations (although many field-program proposals state thatforecast improvements are a likely broader impact of the research). Frequently,operational meteorologists provide support for field-program operations, but areless involved in the early field-program planning or subsequent analysis.

The MAP D-PHASE program, however, represents a recent major under-taking to transfer knowledge and achievements from MAP into the forecastand decision-making process. Similarly, the Hydrometeorological Testbed inthe American Fork River Basin of northern California, a collaboration betweenthe Earth Systems Research Laboratory and California NWS offices (Ralphet al. 2005; http://hmt.noaa.gov) explicitly integrates operational, research, anddecision-making activities. The strengths and weaknesses of these programsshould be identified and used to improve the impact of future field programson operations. Within the United States, greater support from NOAA to mine thesuccesses of National Science Foundation–sponsored field programs would alsohelp.

Outside of these major undertakings, Smith et al. (2001) created a list of 14more ordinary, but quite specific, items to increase the transfer of knowledgebetween forecasters and researchers. This list includes such items as “(ii) askforecasters to flag interesting/important observed cases, noting the nature of theevent to alert researchers to cases for possible study; (iii) ask researchers toflag 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 intelligibleto forecasters; (vi) encourage the World Meteorological Organization (WMO)and National Weather Services to fund visits of researchers to forecast officesfor immersion in the culture/science of forecasting, not procedures; (vii) iden-tify forecast offices as possible sites for study leave.” These and the otherrecommendations do not require significant amounts of funding, if any, yet,with enthusiastic advocates, could lead to greater and more positive interactionsbetween researchers and forecasters.

3. Leveraging the power of proximity through collocation. Despite the tremendousimpact that the collocation of motivated individuals and groups has had onknowledge and forecasting advances in severe-convective weather and tropicalstorms (e.g., Hazardous Weather Testbed, Joint Hurricane Testbed), no majorcollocated center for mountain weather research and forecasting exists in theUnited States. This situation contrasts with many countries in Europe, which per-haps due to smaller size and less diverse national forecast challenges, often haveweather services with stronger cooperation between the operational and researchcommunities. In North America, however, there are some strong collaborationsfostered by geographic proximity or shear will, such as collaborations betweenthe University of Washington and NWS Forecast Office in Seattle (Sect. 12.6a);University of Utah, NWS Forecast Office in Salt Lake City, and NWS WesternRegion Headquarters (Sect. 12.6b; Horel et al. 2002a, b); and the Earth SystemsResearch Laboratory and California NWS forecast offices via the Western USHydrometeorological Testbed (Sect. 12.7; Ralph et al. 2005). Given the growingfinancial losses imposed by flooding and wildfire events in the western UnitedStates (Ross and Lott 2003), the time is ripe to evaluate the need for an integratedresearch and forecast center concentrating on hydrometeorological and fire–atmosphere prediction in areas of complex terrain.

4. Establishing incentives for collaboration. Although forced collaborations arerarely effective, there are a number of ways to create opportunities for increasedcollaboration. These include providing support for operational forecastersto obtain continuing education or an advanced degree, increasing funding(both number and size of awards) for successful collaborative programs likeCSTAR and COMET, providing salary for University faculty to take sabbaticalsin forecast offices, and providing rewards (e.g., promotion, pay, awards) toindividuals who engage or encourage successful collaboration. Such incentiveswill help create the culture of collaboration needed for the mountain weatherenterprise to reach its full potential.

Acknowledgments We thank the participants in the 2008 AMS/COMET/MSC Mountain WeatherWorkshop: Bridging the Gap between Research and Forecasting for 4 days of lectures anddiscussion that stimulated this chapter, as well as our editors and chapter coauthors for theircontributions to this book. We also thank Katja Friedrich, John Lewis, Andrea Rossa, MathiasRotach, Andrew Russell, David Stensrud, Hans Volkert, and three anonymous reviewers for theircontributions to the manuscript. Participants in the panel discussion “Enhancing the Connectivitybetween Research and Applications for the Benefit of Society” at the 2008 AMS Annual Meetingalso 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 WeatherService. Contributing author Schultz acknowledges the support of Vaisala Oyj. Any opinions,findings, and conclusions or recommendations expressed in this material are those of the authorsand do not necessarily reflect the views of the National Science Foundation, National WeatherService, 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 forflood events in the Alpine region. Veroffentlichung der MeteoSchweiz, 78, 75 pp. [Availableonline at http://www.meteoschweiz.admin.ch/web/de/forschung/publikationen/meteoschweizpublikationen/veroeffentlichungen.html.]

Baldwin, M. E., J. S. Kain, and M. P. Kay, 2002: Properties of the convection scheme in NCEP’sEta 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 forecastingconvective initiation: Historical and operational perspectives. Wea. Forecasting, 20, 351–366.

Bergeron, T., 1959: Weather forecasting: Methods in scientific weather analysis: An outline in thehistory 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 18–212006, P. M. Schuster and D. Weaire, Eds., Living Edition Publishers, 159–170. [Availableonline at http://www.livingedition.at/en/titles/science/proceedings.]

Board on Atmospheric Sciences and Climate, 2000: From Research to Operations in WeatherSatellites and Numerical Weather Prediction: Crossing the Valley of Death. National AcademyPress, 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., CD-ROM, 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 tropicalcyclones. Wea. Forecasting, 4, 573–584.

Cane, D., and M. Milelli, 2006: Weather forecasts obtained with a multimodel superensembletechnique in a complex orography region. Met. Zeitschrift, 15, 207–214.

Cane, D., and M. Milelli, 2008: Comparison of COSMO models and multimodel superensembleoutputs 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 ofquasi-linear mesoscale convective systems. Wea. Forecasting, 22, 556–570.

Craven, J. P., R. E. Jewell, and H. E. Brooks, 2002: Comparison between observed convectivecloud-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.

Erkkila, T., 2007: About the nature of the forecaster profession and the human contribution to veryshort 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 aModern Meteorology. Cornell Univ. Press, 251 pp.

Friedman, R. M., 1999: Constituting the polar front, 1919–1920. The Life Cycles of ExtratropicalCyclones. M. A. Shapiro and S. Grønas, 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 ofoperational 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 mesoscale-model-based Model Output Statistics (MOS) during the 2002 Olympic and Paralympic WinterGames. 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 Olympicand 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: Subjectiveverification of numerical models as a component of a broader interaction between research andoperations. 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 SpringProgram. 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 ofconvection-allowing configurations of the WRF model for the prediction of severe convectiveweather: 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 practicalconsiderations regarding horizontal resolution in the first generation of operational convection-allowing NWP. Wea. Forecasting, 23, 931–952.

Keenan, T., and Coauthors, 2003: The Sydney 2000 World Weather Research Programme ForecastDemonstration 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 andAssimilation 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 womenmeteorologists in World War II). Bull. Amer. Meteor. Soc., 76, 2187–2202.

Mailhot, J., and Coauthors, 2010: Environment Canada’s experimental numerical weather predic-tion 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 notachieving 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 DemonstrationProject: 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-personmix in high-impact weather forecasting. Preprints, 22nd Conference on Weather Analysis andForecasting/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 forecastersand 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 quantitativeprecipitation 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. HouzeJr., Eds., Amer. Meteor. Soc., 1–12.

Roebber, P. J., 2005: Bridging the gap between theory and applications: An inquiry intoatmospheric 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 tornadooutbreak. 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 weatherforecast 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 ofweather 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 Meteo-rology and Weather Analysis and Forecasting. A Tribute to Fred Sanders, L. F. Bosart andH. 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 atEnvironment Canada: Linking severe weather researchers and forecasters in a real-timeoperational 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 InternationalWorkshop 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 firstMSC/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. MeteorologicalService of Canada. Unpublished.

Stanitski, D. M., and D. J. Charlevoix, 2008: AMS membership survey results: Who are the studentmembers 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 forecastaccuracy for the Torino Winter Olympics. Preprints, 22nd Conference on Weather Analysis andForecasting/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 coastalorography. Mon. Wea. Rev., 124, 1329–1352.

Stoelinga, M. T., and Coauthors, 2003: Improvement of Microphysical Parameterization throughObservational 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 forecastprocess. Bull. Amer. Meteor. Soc., 87, 1497–1502.

Stuart, N. A., D. M. Schultz, and G. Klein, 2007: Maintaining the role of humans in the forecastprocess: Analyzing the psyche of expert forecasters. Bull. Amer. Meteor. Soc., 88, 1893–1898.

Volkert, H., and T. Gutermann, 2007: Inter-domain cooperation for mesoscale atmosphericlaboratories: 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 warningperformance. Bull. Amer. Meteor. Soc., 86, 193–203.

Washington State Military Department, 2007: Windstorm Response after Action Report: AStatewide 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 guidancefor severe weather forecasting at the Storm Prediction Center. Preprints, 23rd Conference onSevere 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 subsequenttransfer to operations at the Storm Prediction Center. Preprints, 22nd Conference on WeatherAnalysis 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 mountainousterrain. 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 hydrologicalensemble prediction systems. Atmos. Sci. Lett., 9, 80–87.