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

Abstract

The statistical properties of analysis and forecast errors from commonly used ensemble perturbation methodologies are explored. A quasigeostrophic channel model is used, coupled with a 3D-variational data assimilation scheme. A perfect model is assumed.

Three perturbation methodologies are considered. The breeding and singular-vector (SV) methods approximate the strategies currently used at operational centers in the United States and Europe, respectively. The perturbed observation (PO) methodology approximates a random sample from the analysis probability density function (pdf) and is similar to the method performed at the Canadian Meteorological Centre. Initial conditions for the PO ensemble are analyses from independent, parallel data assimilation cycles. Each assimilation cycle utilizes observations perturbed by random noise whose statistics are consistent with observational error covariances. Each member’s assimilation/forecast cycle is also started from a distinct initial condition.

Relative to breeding and SV, the PO method here produced analyses and forecasts with desirable statistical characteristics. These include consistent rank histogram uniformity for all variables at all lead times, high spread/skill correlations, and calibrated, reduced-error probabilistic forecasts. It achieved these improvements primarily because 1) the ensemble mean of the PO initial conditions was more accurate than the mean of the bred or singular-vector ensembles, which were centered on a less-skilful control initial condition—much of the improvement was lost when PO initial conditions were recentered on the control analysis; and 2) by construction, the perturbed observation ensemble initial conditions permitted realistic variations in spread from day to day, while bred and singular-vector perturbations did not. These results suggest that in the absence of model error, an ensemble of initial conditions performs better when the initialization method is designed to produce random samples from the analysis pdf. The perturbed observation method did this much more satisfactorily than either the breeding or singular-vector methods.

The ability of the perturbed observation ensemble to sample randomly from the analysis pdf also suggests that such an ensemble can provide useful information on forecast covariances and hence improve future data assimilation techniques.

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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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Rebecca E. Morss
,
Kerry A. Emanuel
, and
Chris Snyder

Abstract

Adaptive sampling uses information about individual atmospheric situations to identify regions where additional observations are likely to improve weather forecasts of interest. The observation network could be adapted for a wide range of forecasting goals, and it could be adapted either by allocating existing observations differently or by adding observations from programmable platforms to the existing network. In this study, observing strategies are explored in a simulated idealized system with a three-dimensional quasigeostrophic model and a realistic data assimilation scheme. Using simple error norms, idealized adaptive observations are compared to nonadaptive observations for a range of observation densities.

The results presented show that in this simulated system, the influence of both adaptive and nonadaptive observations depends strongly on the observation density. For sparse observation networks, the simple adaptive strategies tested are beneficial: adaptive observations can, on average, reduce analysis and forecast errors more than the same number of nonadaptive observations, and they can reduce errors by a given amount using fewer observational resources. In contrast, for dense observation networks it is much more difficult to benefit from adapting observations, at least for the data assimilation method used here. The results suggest that the adaptive strategies tested are most effective when the observations are adapted regularly and frequently, giving the data assimilation system as many opportunities as possible to reduce errors as they evolve. They also indicate that ensemble-based estimates of initial condition errors may be useful for adaptive observations. Further study is needed to understand the extent to which the results from this idealized study apply to more complex, more realistic systems.

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Hyun Mee Kim
,
Michael C. Morgan
, and
Rebecca E. Morss

Abstract

The structure and evolution of analysis error and adjoint-based sensitivities [potential enstrophy initial singular vectors (SVs) and gradient sensitivities of the forecast error to initial conditions] are compared following a cyclone development in a three-dimensional quasigeostrophic channel model. The results show that the projection of the evolved SV onto the forecast error increases during the evolution.

Based on the similarities of the evolved SV to the forecast error, use of the evolved SV is suggested as an adaptive observation strategy. The use of the evolved SV strategy for adaptive observations is evaluated by performing observation system simulation experiments using a three-dimensional variational data assimilation scheme under the perfect model assumption. Adaptive strategies using the actual forecast error, gradient sensitivity, and initial SV are also tested. The observation system simulation experiments are implemented for five simulated synoptic cases with two different observation spacings and three different configurations of adaptive observation location densities (sparse, dense, and mixed), and the impact of the adaptive strategies is compared with that of the nonadaptive, fixed observations.

The impact of adaptive strategies varies with the observation density. For a small number of observations, several of the adaptive strategies tested reduce forecast error more than the nonadaptive strategy. For a large number of observations, it is more difficult to reduce forecast errors using adaptive observations. The evolved SV strategy performs as well as or better than the adjoint-based strategies for both observation densities. The impact of using the evolved SVs rather than the adjoint-based sensitivities for adaptive observation purposes is larger in the situation of a large number of observation stations for which the forecast error reduction by adjoint- based adaptive strategies is difficult.

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

Abstract

Weather forecasts are inherently uncertain, and meteorologists have information about weather forecast uncertainty that is not readily available to most forecast users. Yet effectively communicating forecast uncertainty to nonmeteorologists remains challenging. Improving forecast uncertainty communication requires research-based knowledge that can inform decisions on what uncertainty information to communicate, when, and how to do so. To help build such knowledge, this article explores the public’s perspectives on everyday weather forecast uncertainty and uncertainty information using results from a nationwide survey. By contributing to the fundamental understanding of laypeople’s views on forecast uncertainty, the findings can inform both uncertainty communication and related research.

The article uses empirical data from a nationwide survey of the U.S. public to investigate beliefs commonly held among meteorologists and to explore new topics. The results show that when given a deterministic temperature forecast, most respondents expected the temperature to fall within a range around the predicted value. In other words, most people inferred uncertainty into the deterministic forecast. People’s preferences for deterministic versus nondeterministic forecasts were examined in two situations; in both, a significant majority of respondents liked weather forecasts that expressed uncertainty, and many preferred such forecasts to single-valued forecasts. The article also discusses people’s confidence in different types of forecasts, their interpretations of the probability of precipitation forecasts, and their preferences for how forecast uncertainty is conveyed. Further empirical research is needed to study the article’s findings in other contexts and to continue exploring perception, interpretation, communication, and use of weather forecast uncertainty.

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Rebecca E. Morss
,
Jamie Vickery
,
Heather Lazrus
,
Julie Demuth
, and
Ann Bostrom

Abstract

As tropical cyclone threats evolve, broadcast meteorologists and emergency managers rely on timely forecast information to help them communicate risk with the public and protect public safety. This study aims to improve the usability and applicability of National Weather Service (NWS) forecast information in the context of these NWS core partners’ decisions during tropical cyclone threats. The research collected and analyzed data from in-depth interviews with broadcast meteorologists and emergency managers in three coastal U.S. states. These data were used to analyze broadcast meteorologists’ and emergency managers’ tropical cyclone decision and action timelines, their use of tropical cyclone information during different phases of threats, and gaps in forecast information for decision-making. Based on these findings, several opportunities for improving tropical cyclone risk communication were identified. Recommendations to address gaps in the NWS tropical cyclone product suite include designing improved ways to communicate storm-specific storm surge risk at greater than 48 h of lead time, expanding the use of concise highlights that help people quickly extract and understand key information, and improving product understandability and usability by more comprehensively integrating users’ perspectives into product research and development. Broader strategic recommendations include developing new approaches for informing broadcast meteorologists about major forecast updates, presenting forecast information in ways that enable locally relevant interpretation, and supporting human forecasters’ contributions to the effectiveness of NWS products and services. These findings and recommendations can help NOAA prioritize ways to modernize the current NWS tropical cyclone product suite as well as motivate research to enable longer-term high-priority improvements.

Significance Statement

Tropical cyclones pose significant risks to coastal and inland U.S. populations. This project aims to improve creation, communication, and use of tropical cyclone forecast and warning information by studying broadcast meteorologists’ and emergency managers’ information needs for decision-making during different phases of tropical cyclone threats. We identify several priority areas for improvement, including advancing longer-lead-time storm surge forecast communication, enhancing dissemination of forecast updates, and increasing use of concise text highlights. Additional findings include the importance of locally interpretable forecast information, the value of human forecasters in weather risk communication, and the need for iterative, user-informed forecast product development. These findings can help NOAA and the research community improve forecast communication and invest in research that facilitates continued improvements.

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Ann Bostrom
,
Rebecca E. Morss
,
Jeffrey K. Lazo
,
Julie L. Demuth
,
Heather Lazrus
, and
Rebecca Hudson

Abstract

The study reported here explores how to enhance the public value of hurricane forecast and warning information by examining the entire warning process. A mental models research approach is applied to address three risk management tasks critical to warnings for extreme weather events: 1) understanding the risk decision and action context for hurricane warnings, 2) understanding the commonalities and conflicts in interpretations of that context and associated risks, and 3) exploring the practical implications of these insights for hurricane risk communication and management. To understand the risk decision and action context, the study develops a decision-focused model of the hurricane forecast and warning system on the basis of results from individual mental models interviews with forecasters from the National Hurricane Center (n = 4) and the Miami–South Florida Weather Forecast Office (n = 4), media broadcasters (n = 5), and public officials (n = 6), as well as a group decision-modeling session with a subset of the forecasters. Comparisons across professionals reveal numerous shared perceptions, as well as some critical differences. Implications for improving extreme weather event forecast and warning systems and risk communication are threefold: 1) promote thinking about forecast and warning decisions as a system, with informal as well as formal elements; 2) evaluate, coordinate, and consider controlling the proliferation of forecast and warning information products; and 3) further examine the interpretation and representation of uncertainty within the hurricane forecast and warning system as well as for users.

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Michelle E. Saunders
,
Kevin D. Ash
,
Jennifer M. Collins
, and
Rebecca E. Morss

Abstract

A radar display is a tool that depicts meteorological data over space and time; therefore, an individual must think spatially and temporally in addition to drawing on their own meteorological knowledge and past weather experiences. We aimed to understand how the construal of situational risks and outcomes influences the perceived usefulness of a radar display and to explore how radar users interpret distance, time, and meteorological attributes using hypothetical scenarios in the Tampa Bay area (Florida). Ultimately, we wanted to understand how and why individuals use weather radar and to discover what makes it a useful tool. To do this, construal level theory and geospatial thinking guided the mixed methods used in this study to investigate four research objectives. Our findings show that radar is used most often by our participants to anticipate what will happen in the near future in their area. Participants described in their own words what they were viewing while using a radar display and reported what hazards they expected at the study location. Many participants associated the occurrence of lightning or strong winds with “red” and “orange” reflectivity values on a radar display. Participants provided valuable insight into what was and was not found useful about certain radar displays. We also found that most participants overestimated the amount of time they would have before precipitation would begin at their location. Overall, weather radar was found to be a very useful tool; however, judging spatial and temporal proximity became difficult when storm motion/direction was not easily identifiable.

Significance Statement

The purpose of this study is to understand how and why individuals use weather radar and to discover what makes radar a useful tool. We were particularly interested to explore how distance and time are thought about when using radar. We found that radar is generally a useful tool for decision-making except when a storm event was stationary. Participants use their personal experiences and knowledge of past weather events when they use a radar display. We also discovered that deciding how much time is available before rain occurs is often overestimated. These findings are helpful to understand how individuals use weather radar to make decisions that may help us to better understand protective action behavior.

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