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Rebecca E. Morss

Atmospheric science information is a component of numerous public policy decisions. Moreover, many resources for atmospheric science are allocated by governments, in other words, through public policy decisions. Thus, all atmospheric scientists—those interested in helping address societal problems, and those interested primarily in advancing science—have a stake in public policy decisions. Yet atmospheric science and public policy are sufficiently different that atmospheric scientists often find it challenging to contribute effectively to public policy. To help reduce this gap, this article examines the area where atmospheric science, public policy research, and public policy decisions intersect. Focusing on how atmospheric science and public policy inform each other, the article discusses and illustrates a key concept in public policy—the importance of problem definition—using an atmospheric science policy issue of current interest: observing-system design for weather prediction. To help the atmospheric science community participate more effectively in societal decision making (on observing-system design and other topics), the article closes with three suggestions for atmospheric scientists considering policy issues.

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Rebecca E. Morss
and
Fuqing Zhang

After the 2005 hurricane season, several meteorology students at Texas A&M University became interested in understanding Hurricane Rita's forecasts and societal impacts in greater depth. In response to the students' interest, we developed a collaborative student research study at Texas A&M University associated with an undergraduate course in the spring semester of 2006. The study included both a meteorological and an interdisciplinary component, in which students performed an in-person survey of Texas Gulf Coast residents. Students were involved in multiple phases of the research, from the design to implementation to dissemination of results. This collaborative research model engaged and motivated the students, providing substantial educational benefits. The study and class linked the students' classroom knowledge to reality while generating new knowledge about the societal aspects of Hurricane Rita and other hurricanes. This paper reviews key aspects of the study and class, presenting a prototype integrated research-education model for others interested in incorporating active learning, collaborative inquiry, and interdisciplinary study into undergraduate classrooms. The model can be implemented at both colleges and research universities for a variety of topics of interest to students, teachers, the research community, and society.

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Rebecca E. Morss
and
David S. Battisti

Abstract

The Tropical Atmosphere Ocean (TAO) array of moored buoys in the tropical Pacific Ocean is a major source of data for understanding and predicting El Niño–Southern Oscillation (ENSO). Despite the importance of the TAO array, limited work has been performed where observations are most important for predicting ENSO effectively. To address this issue, this study performs a series of observing system simulation experiments (OSSEs) with a linearized intermediate coupled ENSO model, stochastically forced. ENSO forecasts are simulated for a variety of observing network configurations, and forecast skill averaged over many simulated ENSO events is compared.

The first part of this study examined the relative importance of sea surface temperature (SST) and subsurface ocean observations, requirements for spacing and meridional extent of observations, and important regions for observations in this system. Using these results as a starting point, this paper develops efficient observing networks for forecasting ENSO in this system, where efficient is defined as providing reasonably skillful forecasts for relatively few observations. First, efficient networks that provide SST and thermocline depth data at the same locations are developed and discussed. Second, efficient networks of only thermocline depth observations are addressed, assuming that many SST observations are available from another source (e.g., satellites). The dependence of the OSSE results on the duration of the simulated data record is also explored. The results suggest that several decades of data may be sufficient for evaluating the effects of observing networks on ENSO forecast skill, despite being insufficient for evaluating the long-term potential predictability of ENSO.

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Rebecca E. Morss
and
David S. Battisti

Abstract

The Tropical Atmosphere Ocean (TAO) array of moored buoys in the tropical Pacific Ocean is a major source of data for understanding and predicting the El Niño–Southern Oscillation (ENSO). Despite the importance of the TAO array, limited work has been performed to date on the number and locations of observations required to predict ENSO effectively. To address this issue, this study performs a series of observing system simulation experiments (OSSEs) with a linearized intermediate coupled ENSO model, stochastically forced. ENSO forecasts are simulated for a number of observing network configurations, and forecast skill averaged over 1000 years of simulated ENSO events is compared.

The experiments demonstrate that an OSSE framework can be used with a linear, stochastically forced ENSO model to provide useful information about requirements for ENSO prediction. To the extent that the simplified model dynamics represent ENSO dynamics accurately, the experiments also suggest which types of observations in which regions are most important for ENSO prediction. The results indicate that, using this model and experimental setup, subsurface ocean observations are relatively unimportant for ENSO prediction when good information about sea surface temperature (SST) is available; adding subsurface observations primarily improves forecasts initialized in late summer. For short lead-time (1–2 month) forecasts, observations within approximately 3° of the equator are most important for skillful forecasts, while for longer lead-time forecasts, forecast skill is increased by including information at higher latitudes. For forecasts longer than a few months, the most important region for observations is the eastern equatorial Pacific, south of the equator; a secondary region of importance is the western equatorial Pacific. These regions correspond to those where the leading singular vector for the ENSO model has a large amplitude. In a continuation of this study, these results will be used to develop efficient observing networks for forecasting ENSO in this system.

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Rebecca E. Morss
and
F. Martin Ralph

Abstract

Winter storms making landfall in western North America can generate heavy precipitation and other significant weather, leading to floods, landslides, and other hazards that cause significant damage and loss of life. To help alleviate these negative impacts, the California Land-falling Jets (CALJET) and Pacific Land-falling Jets (PACJET) experiments took extra meteorological observations in the coastal region to investigate key research questions and aid operational West Coast 0–48-h weather forecasting. This article presents results from a study of how information provided by CALJET and PACJET was used by National Weather Service (NWS) forecasters and forecast users. The primary study methodology was analysis of qualitative data collected from observations of forecasters and from interviews with NWS personnel, CALJET–PACJET researchers, and forecast users. The article begins by documenting and discussing the many types of information that NWS forecasters combine to generate forecasts. Within this context, the article describes how forecasters used CALJET–PACJET observations to fill in key observational gaps. It then discusses researcher–forecaster interactions and examines how weather forecast information is used in emergency management decision making. The results elucidate the important role that forecasters play in integrating meteorological information and translating forecasts for users. More generally, the article illustrates how CALJET and PACJET benefited forecasts and society in real time, and it can inform future efforts to improve human-generated weather forecasts and future studies of the use and value of meteorological information.

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Rebecca E. Morss
,
Chris Snyder
, and
Richard Rotunno

Abstract

Results from homogeneous, isotropic turbulence suggest that predictability behavior is linked to the slope of a flow’s kinetic energy spectrum. Such a link has potential implications for the predictability behavior of atmospheric models. This article investigates these topics in an intermediate context: a multilevel quasigeostrophic model with a jet and temperature perturbations at the upper surface (a surrogate tropopause). Spectra and perturbation growth behavior are examined at three model resolutions. The results augment previous studies of spectra and predictability in quasigeostrophic models, and they provide insight that can help interpret results from more complex models. At the highest resolution tested, the slope of the kinetic energy spectrum is approximately at the upper surface but −3 or steeper at all but the uppermost interior model levels. Consistent with this, the model’s predictability behavior exhibits key features expected for flow with a shallower than −3 slope. At the highest resolution, upper-surface perturbation spectra peak below the energy-containing scales, and the error growth rate decreases as small scales saturate. In addition, as model resolution is increased and smaller scales are resolved, the peak of the upper-surface perturbation spectra shifts to smaller scales and the error growth rate increases. The implications for potential predictive improvements are not as severe, however, as in the standard picture of flows exhibiting a finite predictability limit. At the highest resolution, the model also exhibits periods of much faster-than-average perturbation growth that are associated with faster growth at smaller scales, suggesting predictability behavior that varies with time.

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Rebecca E. Morss
and
William H. Hooke

In many respects, the prospects for U.S. meteorological research have never been brighter. Knowledge is advancing rapidly, as are supporting observing and information technologies. The accuracy, timeliness, and information content of forecasts are improving year by year. As a result, new and growing markets eagerly await the products of weather research, and opportunities for commercialization abound. Furthermore, no end to the progress of knowledge is in sight; there is plenty of interesting research left to do.

Other trends, however, give cause for concern. In particular, the growing value of weather services and science is straining long-established public–private and international partnerships, vital to our field. Closer to home, the meteorological community can see nascent signs of some of the same commercialization-related difficulties that now challenge biotechnology.

In fact, the biotechnology community's experience with commercialization of research teaches valuable lessons. Attention to these issues now, and appropriate early action, may help the meteorological community benefit from commercialization while avoiding similar pitfalls. This would not only serve our field well, it would also ensure that society continues to benefit from meteorological research advances in the decades to come.

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Rebecca E. Morss
and
Mary H. Hayden

Abstract

Hurricane Ike made landfall near Galveston, Texas, on 13 September 2008 as a large category 2 storm that generated significant storm surge and flooding. This article presents findings from an empirical case study of Texas coastal residents’ perceptions of hurricane risk, protective decision making, and opinions of hurricane forecasts related to Hurricane Ike. The results are based on data from interviews with 49 residents affected by Hurricane Ike, conducted approximately five weeks after landfall. While most interviewees were aware that Ike was potentially dangerous, many were surprised by how much coastal flooding the hurricane caused and the resulting damage. For many—even long-time residents—Ike was a learning experience. As the hurricane approached, interviewees and their households made complex, evolving preparation and evacuation decisions. Although evacuation orders were an important consideration for some interviewees, many obtained information about Ike frequently from multiple sources to evaluate their own risk and make protective decisions. Given the storm surge and damage Ike caused, a number of interviewees believed that Ike’s classification on the Saffir–Simpson scale did not adequately communicate the risk Ike posed. The “certain death” statement issued by the National Weather Service helped convince several interviewees to evacuate. However, others had strong negative opinions of the statement that may negatively influence their interpretation of and response to future warnings. As these findings indicate, empirical studies of how intended audiences obtain, interpret, and use hurricane forecasts and warnings provide valuable knowledge that can help design more effective ways to convey hurricane risk.

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300 Billion Served

Sources, Perceptions, Uses, and Values of Weather Forecasts

Jeffrey K. Lazo
,
Rebecca E. Morss
, and
Julie L. Demuth

Understanding the public's sources, perceptions, uses, and values of weather forecasts is integral to providing those forecasts in the most societally beneficial manner. To begin developing this knowledge, we conducted a nationwide survey with more than 1,500 respondents to assess 1) where, when, and how often they obtain weather forecasts; 2) how they perceive forecasts; 3) how they use forecasts; and 4) the value they place on current forecast information. Our results indicate that the average U.S. adult obtains forecasts 115 times per month, which totals to more than 300 billion forecasts per year by the U.S. public. Overall, we find that respondents are highly satisfied with forecasts and have decreasing confidence in forecasts as lead time increases. Respondents indicated that they use forecasts across a range of decision-making contexts. Moreover, nearly three-quarters stated that they usually or always use forecasts simply to know what the weather will be like. Using a simplified valuation approach, we estimate the value of current weather forecast information to be approximately $286 per U.S. household per year, or $31.5 billion total per year value to U.S. households. This compares favorably with total U.S. public and private sector meteorology costs of $5.1 billion a year. To better support the provision of societally beneficial weather information, we advocate for well-designed periodic evaluations of the public's sources, perceptions, uses, and values of weather forecasts. These should include investigations of other important topics such as interpretations of hazardous weather warnings and presentation of uncertainty information.

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Julie L. Demuth
,
Jeffrey K. Lazo
, and
Rebecca E. Morss

Abstract

Past research has shown that individuals vary in their attitudes and behaviors regarding weather forecast information. To deepen knowledge about these variations, this article explores 1) patterns in people’s sources, uses, and perceptions of everyday weather forecasts; and 2) relationships among people’s sources, uses, and perceptions of forecasts, their personal characteristics, and their experiences with weather and weather forecasts. It does so by performing factor and regression analysis on data from a nationwide survey of the U.S. public, combined with other data. Forecast uses factored into planning for leisure activities and for work/school-related activities, while knowing what the weather will be like and planning how to dress remained separate. Forecast parameters factored into importance of precipitation parameters and of temperature-related parameters, suggesting that these represent conceptually different constructs. Regression analysis showed that the primary drivers for how often people obtain forecasts are what they use forecasts for and their perceived importance of and confidence in forecast information. People’s forecast uses are explained in large part by their frequency of obtaining forecasts and their perceived importance of temperature-related and precipitation forecast information. This suggests that that individuals’ frequency of obtaining forecasts, forecast use, and importance of forecast parameters are closely interrelated. Sociodemographic characteristics and, to a lesser extent, weather-related experience also influence some aspects of people’s forecast sources, uses, and perceptions. These findings continue to build understanding of variations among weather forecast users, which can help weather information providers improve communication of forecasts to better meet users’ needs.

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