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- Author or Editor: John E. Ten Hoeve x
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Abstract
As the cost and societal impacts of extreme weather, water, and climate events continue to rise across the United States, the National Weather Service (NWS) has adopted a strategic vision of a Weather-Ready Nation that aims to help all citizens be ready, responsive, and resilient to extreme weather, water, and climate events. To achieve this vision and to meet the NWS mission of saving lives and property and enhancing the national economy, the NWS must improve the accuracy and timeliness of forecasts and warnings, and must directly connect these forecasts and warnings to critical life- and property-saving decisions through the provision of impact-based decision support services (IDSS). While the NWS has been moving in this direction for years, the shift to delivering IDSS holistically requires an agency-wide transformation. This article discusses the elements driving the need for change at the NWS to build a Weather-Ready Nation; the foundational basis for IDSS; ongoing challenges to provide IDSS across federal, state, local, tribal, and territorial levels of government; the path toward evolving the NWS to deliver more effective IDSS; the importance of partnerships within the weather, water, and climate enterprise and with those responsible for public safety to achieve the Weather-Ready Nation vision; and initial supporting evidence and lessons learned from early efforts.
Abstract
As the cost and societal impacts of extreme weather, water, and climate events continue to rise across the United States, the National Weather Service (NWS) has adopted a strategic vision of a Weather-Ready Nation that aims to help all citizens be ready, responsive, and resilient to extreme weather, water, and climate events. To achieve this vision and to meet the NWS mission of saving lives and property and enhancing the national economy, the NWS must improve the accuracy and timeliness of forecasts and warnings, and must directly connect these forecasts and warnings to critical life- and property-saving decisions through the provision of impact-based decision support services (IDSS). While the NWS has been moving in this direction for years, the shift to delivering IDSS holistically requires an agency-wide transformation. This article discusses the elements driving the need for change at the NWS to build a Weather-Ready Nation; the foundational basis for IDSS; ongoing challenges to provide IDSS across federal, state, local, tribal, and territorial levels of government; the path toward evolving the NWS to deliver more effective IDSS; the importance of partnerships within the weather, water, and climate enterprise and with those responsible for public safety to achieve the Weather-Ready Nation vision; and initial supporting evidence and lessons learned from early efforts.
Abstract
Land use, vegetation, albedo, and soil-type data are combined in a global model that accounts for roofs and roads at near their actual resolution to quantify the effects of urban surface and white roofs on climate. In 2005, ~0.128% of the earthrsquo;s surface contained urban land cover, half of which was vegetated. Urban land cover was modeled over 20 years to increase gross global warming (warming before cooling due to aerosols and albedo change are accounted for) by 0.06–0.11 K and population-weighted warming by 0.16–0.31 K, based on two simulations under different conditions. As such, the urban heat island (UHI) effect may contribute to 2%–4% of gross global warming, although the uncertainty range is likely larger than the model range presented, and more verification is needed. This may be the first estimate of the UHI effect derived from a global model while considering both UHI local heating and large-scale feedbacks. Previous data estimates of the global UHI, which considered the effect of urban areas but did not treat feedbacks or isolate temperature change due to urban surfaces from other causes of urban temperature change, imply a smaller UHI effect but of similar order. White roofs change surface albedo and affect energy demand. A worldwide conversion to white roofs, accounting for their albedo effect only, was calculated to cool population-weighted temperatures by ~0.02 K but to warm the earth overall by ~0.07 K. White roof local cooling may also affect energy use, thus emissions, a factor not accounted for here. As such, conclusions here regarding white roofs apply only to the assumptions made.
Abstract
Land use, vegetation, albedo, and soil-type data are combined in a global model that accounts for roofs and roads at near their actual resolution to quantify the effects of urban surface and white roofs on climate. In 2005, ~0.128% of the earthrsquo;s surface contained urban land cover, half of which was vegetated. Urban land cover was modeled over 20 years to increase gross global warming (warming before cooling due to aerosols and albedo change are accounted for) by 0.06–0.11 K and population-weighted warming by 0.16–0.31 K, based on two simulations under different conditions. As such, the urban heat island (UHI) effect may contribute to 2%–4% of gross global warming, although the uncertainty range is likely larger than the model range presented, and more verification is needed. This may be the first estimate of the UHI effect derived from a global model while considering both UHI local heating and large-scale feedbacks. Previous data estimates of the global UHI, which considered the effect of urban areas but did not treat feedbacks or isolate temperature change due to urban surfaces from other causes of urban temperature change, imply a smaller UHI effect but of similar order. White roofs change surface albedo and affect energy demand. A worldwide conversion to white roofs, accounting for their albedo effect only, was calculated to cool population-weighted temperatures by ~0.02 K but to warm the earth overall by ~0.07 K. White roof local cooling may also affect energy use, thus emissions, a factor not accounted for here. As such, conclusions here regarding white roofs apply only to the assumptions made.
Abstract
NOAA’s National Weather Service (NWS) provides forecasts, warnings, and decision support to the public for the protection of life and property. The NWS Weather-Ready Nation model describes the process of applying weather information to achieve societal value. However, it is not clear how different racial and socioeconomic groups across the United States receive, understand, and act upon the weather information supplied under this model. There may be barriers that keep important, lifesaving information from the populations at the highest risk of severe weather impacts. This paper estimates the extent of racial and socioeconomic disparities in severe weather risk information reception, comprehension, response, and trust, as well as severe weather preparedness and risk perceptions in the United States. We use data from the University of Oklahoma’s Severe Weather and Society Survey, which is annually completed by a sample of 3000 U.S. adults (age 18+) that is designed to match the characteristics of the U.S. population. We pool data over four years (2017–20) to provide reliable severe weather risk prevalence statistics for adults by race, ethnicity, and socioeconomic characteristics. As a robustness check, we supplement this information with data from the annual FEMA National Household Survey. We find that racial and socioeconomic groups receive, understand, trust, and act upon severe weather information differently. These findings suggest that NWS and their partners should adjust their communication strategies to ensure all populations receive and understand actionable severe weather information.
Significance Statement
It is crucial that severe weather risk communication is received, appropriately interpreted, and trusted by all communities—especially the most vulnerable. Past research has not explained how different racial and socioeconomic groups receive, understand, and act upon NWS forecasts and warnings. This study finds that racial and socioeconomic groups receive, understand, trust, and act upon severe weather information differently. Risk communication strategies should be adjusted to eliminate barriers that keep important, lifesaving information from vulnerable populations.
Abstract
NOAA’s National Weather Service (NWS) provides forecasts, warnings, and decision support to the public for the protection of life and property. The NWS Weather-Ready Nation model describes the process of applying weather information to achieve societal value. However, it is not clear how different racial and socioeconomic groups across the United States receive, understand, and act upon the weather information supplied under this model. There may be barriers that keep important, lifesaving information from the populations at the highest risk of severe weather impacts. This paper estimates the extent of racial and socioeconomic disparities in severe weather risk information reception, comprehension, response, and trust, as well as severe weather preparedness and risk perceptions in the United States. We use data from the University of Oklahoma’s Severe Weather and Society Survey, which is annually completed by a sample of 3000 U.S. adults (age 18+) that is designed to match the characteristics of the U.S. population. We pool data over four years (2017–20) to provide reliable severe weather risk prevalence statistics for adults by race, ethnicity, and socioeconomic characteristics. As a robustness check, we supplement this information with data from the annual FEMA National Household Survey. We find that racial and socioeconomic groups receive, understand, trust, and act upon severe weather information differently. These findings suggest that NWS and their partners should adjust their communication strategies to ensure all populations receive and understand actionable severe weather information.
Significance Statement
It is crucial that severe weather risk communication is received, appropriately interpreted, and trusted by all communities—especially the most vulnerable. Past research has not explained how different racial and socioeconomic groups receive, understand, and act upon NWS forecasts and warnings. This study finds that racial and socioeconomic groups receive, understand, trust, and act upon severe weather information differently. Risk communication strategies should be adjusted to eliminate barriers that keep important, lifesaving information from vulnerable populations.
Abstract
Simulations using an idealized, atmospheric general circulation model (GCM) subjected to various thermal forcings are analyzed via a combination of probability density function (PDF) estimation and spectral analysis techniques. Seven different GCM runs are examined, each model run being characterized by different values in the strength of the tropical heating and high-latitude cooling. For each model run, it is shown that a linear stochastic model constructed in the phase space of the ten leading empirical orthogonal functions (EOFs) of the zonal-mean zonal flow provides an excellent statistical approximation to the simulated zonal flow variability, which includes zonal index fluctuations, and quasi-oscillatory, poleward, zonal-mean flow anomaly propagation. Statistically significant deviations from the above linear stochastic null hypothesis arise in the form of a few anomalously persistent, or statistically nonlinear, flow patterns, which occupy particular regions of the model’s phase space. Some of these nonlinear regimes occur during certain phases of the poleward propagation; however, such an association is, in general, weak. This indicates that the regimes and oscillations in the model may be governed by distinct dynamical mechanisms.
Abstract
Simulations using an idealized, atmospheric general circulation model (GCM) subjected to various thermal forcings are analyzed via a combination of probability density function (PDF) estimation and spectral analysis techniques. Seven different GCM runs are examined, each model run being characterized by different values in the strength of the tropical heating and high-latitude cooling. For each model run, it is shown that a linear stochastic model constructed in the phase space of the ten leading empirical orthogonal functions (EOFs) of the zonal-mean zonal flow provides an excellent statistical approximation to the simulated zonal flow variability, which includes zonal index fluctuations, and quasi-oscillatory, poleward, zonal-mean flow anomaly propagation. Statistically significant deviations from the above linear stochastic null hypothesis arise in the form of a few anomalously persistent, or statistically nonlinear, flow patterns, which occupy particular regions of the model’s phase space. Some of these nonlinear regimes occur during certain phases of the poleward propagation; however, such an association is, in general, weak. This indicates that the regimes and oscillations in the model may be governed by distinct dynamical mechanisms.
Abstract
The physical processes that determine the time scale of zonal-mean-flow variability are examined with an idealized numerical model that has a zonally symmetric lower boundary. In the part of the parameter space where the time-mean zonal flow is characterized by a single (double) jet, the dominant form of zonal-mean-flow variability is the zonal index (poleward propagation), and the time-mean potential vorticity gradient is found to be strong and sharp (weak and broad). The e-folding time scale of the zonal index is found to be close to 55 days, much longer than the observed 10-day time scale. The e-folding time scale of the poleward propagation is about 40 days. The long e-folding time scales for the zonal index are found to be consistent with an unrealistically strong and persistent eddy–zonal-mean-flow feedback. A calculation of the refractive index indicates that the background flow supports eddies that are trapped within midlatitudes, undergoing relatively little meridional propagation.
Additional model runs are performed with an idealized mountain to investigate whether zonal asymmetry can disrupt the eddy feedback. For single-jet states, the time scale is reduced to about 30 days if the mountain height is 4 km or less. The reduction in the time scale occurs because the stationary eddies excited by the mountain alter the background flow in a manner that leads to the replacement of zonal-index events by shorter-time-scale poleward propagation. With a 5-km mountain, the time scale reverts and increases to 105 days. This threshold behavior is again attributed to a sharpening of the background zonal jet, which arises from an extremely strong stationary wave momentum flux convergence. In contrast, for double-jet states, the time scale changes only slightly and the poleward propagation is maintained in all mountain runs.
Abstract
The physical processes that determine the time scale of zonal-mean-flow variability are examined with an idealized numerical model that has a zonally symmetric lower boundary. In the part of the parameter space where the time-mean zonal flow is characterized by a single (double) jet, the dominant form of zonal-mean-flow variability is the zonal index (poleward propagation), and the time-mean potential vorticity gradient is found to be strong and sharp (weak and broad). The e-folding time scale of the zonal index is found to be close to 55 days, much longer than the observed 10-day time scale. The e-folding time scale of the poleward propagation is about 40 days. The long e-folding time scales for the zonal index are found to be consistent with an unrealistically strong and persistent eddy–zonal-mean-flow feedback. A calculation of the refractive index indicates that the background flow supports eddies that are trapped within midlatitudes, undergoing relatively little meridional propagation.
Additional model runs are performed with an idealized mountain to investigate whether zonal asymmetry can disrupt the eddy feedback. For single-jet states, the time scale is reduced to about 30 days if the mountain height is 4 km or less. The reduction in the time scale occurs because the stationary eddies excited by the mountain alter the background flow in a manner that leads to the replacement of zonal-index events by shorter-time-scale poleward propagation. With a 5-km mountain, the time scale reverts and increases to 105 days. This threshold behavior is again attributed to a sharpening of the background zonal jet, which arises from an extremely strong stationary wave momentum flux convergence. In contrast, for double-jet states, the time scale changes only slightly and the poleward propagation is maintained in all mountain runs.
Abstract
Promising new opportunities to apply artificial intelligence (AI) to the Earth and environmental sciences are identified, informed by an overview of current efforts in the community. Community input was collected at the first National Oceanic and Atmospheric Administration (NOAA) workshop on “Leveraging AI in the Exploitation of Satellite Earth Observations and Numerical Weather Prediction” held in April 2019. This workshop brought together over 400 scientists, program managers, and leaders from the public, academic, and private sectors in order to enable experts involved in the development and adaptation of AI tools and applications to meet and exchange experiences with NOAA experts. Paths are described to actualize the potential of AI to better exploit the massive volumes of environmental data from satellite and in situ sources that are critical for numerical weather prediction (NWP) and other Earth and environmental science applications. The main lessons communicated from community input via active workshop discussions and polling are reported. Finally, recommendations are presented for both scientists and decision-makers to address some of the challenges facing the adoption of AI across all Earth science.
Abstract
Promising new opportunities to apply artificial intelligence (AI) to the Earth and environmental sciences are identified, informed by an overview of current efforts in the community. Community input was collected at the first National Oceanic and Atmospheric Administration (NOAA) workshop on “Leveraging AI in the Exploitation of Satellite Earth Observations and Numerical Weather Prediction” held in April 2019. This workshop brought together over 400 scientists, program managers, and leaders from the public, academic, and private sectors in order to enable experts involved in the development and adaptation of AI tools and applications to meet and exchange experiences with NOAA experts. Paths are described to actualize the potential of AI to better exploit the massive volumes of environmental data from satellite and in situ sources that are critical for numerical weather prediction (NWP) and other Earth and environmental science applications. The main lessons communicated from community input via active workshop discussions and polling are reported. Finally, recommendations are presented for both scientists and decision-makers to address some of the challenges facing the adoption of AI across all Earth science.
