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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 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 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, 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 Mary H. Hayden

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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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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 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, Kathleen A. Miller, and Maxine S. Vasil

Abstract

Observations of the current state of the atmosphere are a major input to production of modern weather forecasts. As a result, investments in observations are a major component of public expenditures related to weather forecasting. Consequently, from both a meteorological and societal perspective, it is desirable to select an appropriate level of public investment in observations. Although the meteorological community has discussed optimal investment in observations for more than three decades, it still lacks a practical, systematic framework for analyzing this issue. This paper presents the basic elements of such a framework, using an economic approach. The framework is then demonstrated using an example for radiosonde observations and numerical weather forecasts. In presenting and demonstrating the framework, the paper also identifies gaps in existing knowledge that must be addressed before a more complete economic evaluation of investment in observations can be implemented.

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Thomas M. Hamill, Chris Snyder, and Rebecca E. Morss

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A perfect model Monte Carlo experiment was conducted to explore the characteristics of analysis error in a quasigeostrophic model. An ensemble of cycled analyses was created, with each member of the ensemble receiving different observations and starting from different forecast states. Observations were created by adding random error (consistent with observational error statistics) to vertical profiles extracted from truth run data. Assimilation of new observations was performed every 12 h using a three-dimensional variational analysis scheme. Three observation densities were examined, a low-density network (one observation ∼ every 202 grid points), a moderate-density network (one observation ∼ every 102 grid points), and a high-density network (∼ every 52 grid points). Error characteristics were diagnosed primarily from a subset of 16 analysis times taken every 10 days from a long time series, with the first sample taken after a 50-day spinup. The goal of this paper is to understand the spatial, temporal, and some dynamical characteristics of analysis errors.

Results suggest a nonlinear relationship between observational data density and analysis error; there was a much greater reduction in error from the low- to moderate-density networks than from moderate to high density. Errors in the analysis reflected both structured errors created by the chaotic dynamics as well as random observational errors. The correction of the background toward the observations reduced the error but also randomized the prior dynamical structure of the errors, though there was a dependence of error structure on observational data density. Generally, the more observations, the more homogeneous the errors were in time and space and the less the analysis errors projected onto the leading backward Lyapunov vectors. Analyses provided more information at higher wavenumbers as data density increased. Errors were largest in the upper troposphere and smallest in the mid- to lower troposphere. Relatively small ensembles were effective in capturing a large percentage of the analysis-error variance, though more members were needed to capture a specified fraction of the variance as observation density increased.

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