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Abstract
It is challenging to communicate uncertainty for high-impact weather events to the public and decision-makers. As a result, there is an increased emphasis and training within the National Weather Service (NWS) for “impact-based decision support.” A Collaborative Science, Technology, And Research (CSTAR) project led by Stony Brook University (SBU) in collaboration with the Alan Alda Center for Communicating Science, several NWS forecast offices, and NWS operational centers held two workshops at SBU on effective forecast communication of probabilistic information for high-impact weather. Trainers in two 1.5-day workshops helped 15–20 forecasters learn to distill their messages, engage audiences, and more effectively communicate risk and uncertainty to decision-makers, media, and the general public. The novel aspect of the first workshop focused on using improvisational techniques to connect with audiences along with exercises to improve communication skills using short, clear, conversational statements. The same forecasters participated in the second workshop, which focused on matching messages to intended audiences and stakeholder interaction. Using a recent high-impact weather event, representatives in emergency management, TV media, departments of transportation, and emergency services provided feedback on the forecaster oral presentations (2–3 min) and a visual slide. This article describes our innovative workshop approach, illustrates some of the techniques used, and highlights participant feedback.
Abstract
It is challenging to communicate uncertainty for high-impact weather events to the public and decision-makers. As a result, there is an increased emphasis and training within the National Weather Service (NWS) for “impact-based decision support.” A Collaborative Science, Technology, And Research (CSTAR) project led by Stony Brook University (SBU) in collaboration with the Alan Alda Center for Communicating Science, several NWS forecast offices, and NWS operational centers held two workshops at SBU on effective forecast communication of probabilistic information for high-impact weather. Trainers in two 1.5-day workshops helped 15–20 forecasters learn to distill their messages, engage audiences, and more effectively communicate risk and uncertainty to decision-makers, media, and the general public. The novel aspect of the first workshop focused on using improvisational techniques to connect with audiences along with exercises to improve communication skills using short, clear, conversational statements. The same forecasters participated in the second workshop, which focused on matching messages to intended audiences and stakeholder interaction. Using a recent high-impact weather event, representatives in emergency management, TV media, departments of transportation, and emergency services provided feedback on the forecaster oral presentations (2–3 min) and a visual slide. This article describes our innovative workshop approach, illustrates some of the techniques used, and highlights participant feedback.
Abstract
Environmental education is key in solving environmental problems and for producing a future workforce capable of solving issues of climate change. Over the last two decades, the Coastal Roots Program at Louisiana State University (LSU) has reached more than 26,676 K–12 students in Louisiana to teach them environmental science and has brought them to restoration sites to plant 194,336 school-grown trees and grasses. The codirectors of Coastal Roots are continually searching for opportunities to enrich the experience of teachers and students in connecting school subjects, Coastal Roots, and stewardship. In school year 2018/19, students in five local schools entered a pilot program to learn how tree-ring science informs environmental science broadly. During their scheduled restoration planting trips, students were asked to collect the following tree data: tree cores, tree height, tree diameter, tree species, and global positioning system location points. Data were given to scientists at LSU for preliminary analysis, and graphical representation of the data were shown to the students for their interpretation. Results from this program indicate that bringing students into the field and teaching them a new scientific skill improved their understanding of environmental science and their role in coastal restoration, and tree-ring data showed significant correlations to various climate parameters in Louisiana. Additionally, we find that bringing this knowledge to teachers allows the knowledge to spread for multiple generations of students. Here we present tree-ring data from this project, lessons learned during the pilot program, advantages to student-based citizen science, and recommendations for similar programs.
Abstract
Environmental education is key in solving environmental problems and for producing a future workforce capable of solving issues of climate change. Over the last two decades, the Coastal Roots Program at Louisiana State University (LSU) has reached more than 26,676 K–12 students in Louisiana to teach them environmental science and has brought them to restoration sites to plant 194,336 school-grown trees and grasses. The codirectors of Coastal Roots are continually searching for opportunities to enrich the experience of teachers and students in connecting school subjects, Coastal Roots, and stewardship. In school year 2018/19, students in five local schools entered a pilot program to learn how tree-ring science informs environmental science broadly. During their scheduled restoration planting trips, students were asked to collect the following tree data: tree cores, tree height, tree diameter, tree species, and global positioning system location points. Data were given to scientists at LSU for preliminary analysis, and graphical representation of the data were shown to the students for their interpretation. Results from this program indicate that bringing students into the field and teaching them a new scientific skill improved their understanding of environmental science and their role in coastal restoration, and tree-ring data showed significant correlations to various climate parameters in Louisiana. Additionally, we find that bringing this knowledge to teachers allows the knowledge to spread for multiple generations of students. Here we present tree-ring data from this project, lessons learned during the pilot program, advantages to student-based citizen science, and recommendations for similar programs.
Abstract
Today, the vast majority of meteorological data are collected in open, rural environments to comply with the standards set by the World Meteorological Organization. However, these traditional networks lack local information that would be of immense value, for example, for studying urban microclimates, evaluating climate adaptation measures, or improving high-resolution numerical weather predictions. Therefore, an urgent need exists for reliable meteorological data in other environments (e.g., cities, lakes, forests) to complement these conventional networks. At present, however, high-accuracy initiatives tend to be limited in space and/or time as a result of the substantial budgetary requirements faced by research teams and operational services. We present a novel approach for addressing the existing observational gaps based on an intense collaboration with high schools. This methodology resulted in the establishment of a regionwide climate monitoring network of 59 accurate weather stations in a wide variety of locations across northern Belgium. The project is also of large societal relevance as it bridges the gap between the youth and atmospheric science. To guarantee a sustainable and mutually valuable collaboration, the schools and their students are involved at all stages, ranging from proposing measurement locations, building the weather stations, and even data analysis. We illustrate how the approach received overwhelming enthusiasm from high schools and students and resulted in a high-accuracy monitoring network with unique locations offering novel insights.
Abstract
Today, the vast majority of meteorological data are collected in open, rural environments to comply with the standards set by the World Meteorological Organization. However, these traditional networks lack local information that would be of immense value, for example, for studying urban microclimates, evaluating climate adaptation measures, or improving high-resolution numerical weather predictions. Therefore, an urgent need exists for reliable meteorological data in other environments (e.g., cities, lakes, forests) to complement these conventional networks. At present, however, high-accuracy initiatives tend to be limited in space and/or time as a result of the substantial budgetary requirements faced by research teams and operational services. We present a novel approach for addressing the existing observational gaps based on an intense collaboration with high schools. This methodology resulted in the establishment of a regionwide climate monitoring network of 59 accurate weather stations in a wide variety of locations across northern Belgium. The project is also of large societal relevance as it bridges the gap between the youth and atmospheric science. To guarantee a sustainable and mutually valuable collaboration, the schools and their students are involved at all stages, ranging from proposing measurement locations, building the weather stations, and even data analysis. We illustrate how the approach received overwhelming enthusiasm from high schools and students and resulted in a high-accuracy monitoring network with unique locations offering novel insights.
Abstract
According to recent Census data, the Hispanic or Latino population represents nearly 1 in 5 Americans today, where 71.1% of these individuals speak Spanish at home. Despite increased efforts among the weather enterprise, establishing effective risk communication strategies for Spanish-speaking populations has been an uphill battle. No frameworks exist for translating weather information into the Spanish language, nor are there collective solutions that address this problem within the weather world. The objective of this article is threefold. First, the current translation issue in Spanish is highlighted. Through research conducted at the NOAA/NWS Storm Prediction Center, situations are revealed where regional varieties of Spanish contributed to inconsistent risk messaging across the bilingual weather community. Second, existing resources are featured so that interested readers are aware of ongoing efforts to translate weather information into Spanish. Organizations within the weather service, like the NWS Multimedia Assistance in Spanish Team and the NWS Spanish Outreach Team, are highlighted for their pioneer work on Spanish weather communication. Last, a framework for translation standardization in the atmospheric sciences is introduced, along with future initiatives that are being sought by NWS and AMS to enhance Spanish hazardous weather communication.
Abstract
According to recent Census data, the Hispanic or Latino population represents nearly 1 in 5 Americans today, where 71.1% of these individuals speak Spanish at home. Despite increased efforts among the weather enterprise, establishing effective risk communication strategies for Spanish-speaking populations has been an uphill battle. No frameworks exist for translating weather information into the Spanish language, nor are there collective solutions that address this problem within the weather world. The objective of this article is threefold. First, the current translation issue in Spanish is highlighted. Through research conducted at the NOAA/NWS Storm Prediction Center, situations are revealed where regional varieties of Spanish contributed to inconsistent risk messaging across the bilingual weather community. Second, existing resources are featured so that interested readers are aware of ongoing efforts to translate weather information into Spanish. Organizations within the weather service, like the NWS Multimedia Assistance in Spanish Team and the NWS Spanish Outreach Team, are highlighted for their pioneer work on Spanish weather communication. Last, a framework for translation standardization in the atmospheric sciences is introduced, along with future initiatives that are being sought by NWS and AMS to enhance Spanish hazardous weather communication.
Abstract
Monitoring and modeling/predicting air pollution are crucial to understanding the links between emissions and air pollution levels, to supporting air quality management, and to reducing human exposure. Yet, current monitoring networks and modeling capabilities are unfortunately inadequate to understand the physical and chemical processes above ground and to support attribution of sources. We highlight the need for the development of an international stereoscopic monitoring strategy that can depict three-dimensional (3D) distribution of atmospheric composition to reduce the uncertainties and to advance diagnostic understanding and prediction of air pollution. There are three reasons for the implementation of stereoscopic monitoring: 1) current observation networks provide only partial view of air pollution, and this can lead to misleading air quality management actions; 2) satellite retrievals of air pollutants are widely used in air pollution studies, but too often users do not acknowledge that they have large uncertainties, which can be reduced with measurements of vertical profiles; and 3) air quality modeling and forecasting require 3D observational constraints. We call on researchers and policymakers to establish stereoscopic monitoring networks and share monitoring data to better characterize the formation of air pollution, optimize air quality management, and protect human health. Future directions for advancing monitoring and modeling/predicting air pollution are also discussed.
Abstract
Monitoring and modeling/predicting air pollution are crucial to understanding the links between emissions and air pollution levels, to supporting air quality management, and to reducing human exposure. Yet, current monitoring networks and modeling capabilities are unfortunately inadequate to understand the physical and chemical processes above ground and to support attribution of sources. We highlight the need for the development of an international stereoscopic monitoring strategy that can depict three-dimensional (3D) distribution of atmospheric composition to reduce the uncertainties and to advance diagnostic understanding and prediction of air pollution. There are three reasons for the implementation of stereoscopic monitoring: 1) current observation networks provide only partial view of air pollution, and this can lead to misleading air quality management actions; 2) satellite retrievals of air pollutants are widely used in air pollution studies, but too often users do not acknowledge that they have large uncertainties, which can be reduced with measurements of vertical profiles; and 3) air quality modeling and forecasting require 3D observational constraints. We call on researchers and policymakers to establish stereoscopic monitoring networks and share monitoring data to better characterize the formation of air pollution, optimize air quality management, and protect human health. Future directions for advancing monitoring and modeling/predicting air pollution are also discussed.
Abstract
MTrack is an automated algorithm that determines the center location (latitude and longitude) of a tropical cyclone from a scalar wind field derived from satellite observations. Accurate storm centers are useful for operational forecasting of tropical cyclones and for their reanalysis (e.g., research on storm surge modeling). Currently, storm center fixes have significantly larger errors for weak, disorganized storms. The MTrack algorithm presented here improves storm centers in some of those cases. It is also automated and, therefore, less subjective than manual fixes made by forecasters. The MTrack algorithm, which was originally designed to work with CYGNSS wind speed measurements, is applied to SMAP winds for the first time. The average difference between MTrack and Best Track storm center locations is 21, 36, and 46 km for major hurricanes, category 1–2 hurricanes, and tropical storms, respectively. MTrack is shown to operate successfully when a storm is only partially sampled by the observing satellite and when the eye of the storm is not resolved.
Abstract
MTrack is an automated algorithm that determines the center location (latitude and longitude) of a tropical cyclone from a scalar wind field derived from satellite observations. Accurate storm centers are useful for operational forecasting of tropical cyclones and for their reanalysis (e.g., research on storm surge modeling). Currently, storm center fixes have significantly larger errors for weak, disorganized storms. The MTrack algorithm presented here improves storm centers in some of those cases. It is also automated and, therefore, less subjective than manual fixes made by forecasters. The MTrack algorithm, which was originally designed to work with CYGNSS wind speed measurements, is applied to SMAP winds for the first time. The average difference between MTrack and Best Track storm center locations is 21, 36, and 46 km for major hurricanes, category 1–2 hurricanes, and tropical storms, respectively. MTrack is shown to operate successfully when a storm is only partially sampled by the observing satellite and when the eye of the storm is not resolved.
Abstract
We report about a new third-party observation, namely, wind measurements derived from hot-air balloon (HAB) tracks. We first compare the HAB winds with wind measurements from a meteorological tower and a radio acoustic wind profiler, both situated at the topographically flat observatory near Cabauw, the Netherlands. To explore the potential of this new type of wind observation in other topographies, we present an intriguing HAB flight in Austria with a spectacular mountain–valley circulation. Subsequently, we compare the HAB data with a numerical weather prediction (NWP) model during 2011–13 and the standard deviation of the wind speed is 2.3 m s−1. Finally, we show results from a data assimilation feasibility experiment that reveals that HAB wind information can have a positive impact on a hindcasted NWP trajectory.
Abstract
We report about a new third-party observation, namely, wind measurements derived from hot-air balloon (HAB) tracks. We first compare the HAB winds with wind measurements from a meteorological tower and a radio acoustic wind profiler, both situated at the topographically flat observatory near Cabauw, the Netherlands. To explore the potential of this new type of wind observation in other topographies, we present an intriguing HAB flight in Austria with a spectacular mountain–valley circulation. Subsequently, we compare the HAB data with a numerical weather prediction (NWP) model during 2011–13 and the standard deviation of the wind speed is 2.3 m s−1. Finally, we show results from a data assimilation feasibility experiment that reveals that HAB wind information can have a positive impact on a hindcasted NWP trajectory.
Abstract
Over 50 years since Lorenz’s 1963 study and a follow-up presentation in 1972, the statement “weather is chaotic” has been well accepted. Such a view turns our attention from regularity associated with Laplace’s view of determinism to irregularity associated with chaos. In contrast to single-type chaotic solutions, recent studies using a generalized Lorenz model (GLM) have focused on the coexistence of chaotic and regular solutions that appear within the same model using the same modeling configurations but different initial conditions. The results, with attractor coexistence, suggest that the entirety of weather possesses a dual nature of chaos and order with distinct predictability. In this study, based on the GLM, we illustrate the following two mechanisms that may enable or modulate two kinds of attractor coexistence and, thus, contribute to distinct predictability: 1) the aggregated negative feedback of small-scale convective processes that can produce stable nontrivial equilibrium points and, thus, enable the appearance of stable steady-state solutions and their coexistence with chaotic or nonlinear oscillatory solutions, referred to as the first and second kinds of attractor coexistence; and 2) the modulation of large-scale time-varying forcing (heating) that can determine (or modulate) the alternative appearance of two kinds of attractor coexistence. Based on our results, we then discuss new opportunities and challenges in predictability research with the aim of improving predictions at extended-range time scales, as well as subseasonal to seasonal time scales.
Abstract
Over 50 years since Lorenz’s 1963 study and a follow-up presentation in 1972, the statement “weather is chaotic” has been well accepted. Such a view turns our attention from regularity associated with Laplace’s view of determinism to irregularity associated with chaos. In contrast to single-type chaotic solutions, recent studies using a generalized Lorenz model (GLM) have focused on the coexistence of chaotic and regular solutions that appear within the same model using the same modeling configurations but different initial conditions. The results, with attractor coexistence, suggest that the entirety of weather possesses a dual nature of chaos and order with distinct predictability. In this study, based on the GLM, we illustrate the following two mechanisms that may enable or modulate two kinds of attractor coexistence and, thus, contribute to distinct predictability: 1) the aggregated negative feedback of small-scale convective processes that can produce stable nontrivial equilibrium points and, thus, enable the appearance of stable steady-state solutions and their coexistence with chaotic or nonlinear oscillatory solutions, referred to as the first and second kinds of attractor coexistence; and 2) the modulation of large-scale time-varying forcing (heating) that can determine (or modulate) the alternative appearance of two kinds of attractor coexistence. Based on our results, we then discuss new opportunities and challenges in predictability research with the aim of improving predictions at extended-range time scales, as well as subseasonal to seasonal time scales.
ABSTRACT
We introduce a simple method for detecting changes, both transient and persistent, in reanalysis and merged satellite products due to both natural climate variability and changes to the data sources/analyses used as input. This note demonstrates this Histogram Anomaly Time Series (HATS) method using tropical ocean daily precipitation from MERRA-2 and from GPCP One-Degree Daily (1DD) precipitation estimates. Rather than averaging over space or time, we create a time series display of histograms for each increment of data (such as a day or month). Regional masks such as land–ocean can be used to isolate particular domains. While the histograms reveal subtle structures in the time series, we can amplify the signal by computing the histogram’s anomalies from its climatological seasonal cycle. The qualitative analysis provided by this scheme can then form the basis for more quantitative analyses of specific features, both real and analysis induced. As an example, in the tropical oceans the analysis clearly identifies changes in the time series of both reanalysis and observations that may be related to changing inputs.
ABSTRACT
We introduce a simple method for detecting changes, both transient and persistent, in reanalysis and merged satellite products due to both natural climate variability and changes to the data sources/analyses used as input. This note demonstrates this Histogram Anomaly Time Series (HATS) method using tropical ocean daily precipitation from MERRA-2 and from GPCP One-Degree Daily (1DD) precipitation estimates. Rather than averaging over space or time, we create a time series display of histograms for each increment of data (such as a day or month). Regional masks such as land–ocean can be used to isolate particular domains. While the histograms reveal subtle structures in the time series, we can amplify the signal by computing the histogram’s anomalies from its climatological seasonal cycle. The qualitative analysis provided by this scheme can then form the basis for more quantitative analyses of specific features, both real and analysis induced. As an example, in the tropical oceans the analysis clearly identifies changes in the time series of both reanalysis and observations that may be related to changing inputs.
Abstract
Hurricane Harvey and other recent weather extremes stimulated extensive public discourse about the role of anthropogenic climate change in amplifying, or otherwise modifying, such events. In tandem, the scientific community has made considerable progress on statistical “climate attribution.” However, explaining these statistical methods to the public has posed challenges. Using appropriately designed “spinner boards,” we find that even members of the general public who do not understand the difference between weather and climate are readily able to understand basic concepts of attribution and explain those concepts to others. This includes both understanding and explaining the way in which the probability of an extreme weather event may increase as a result of climate change and explaining how the intensity of hurricanes can be increased. If properly developed and used by TV weather forecasters and news reporters, this method holds the potential to significantly improve public understanding of climate attribution.
Abstract
Hurricane Harvey and other recent weather extremes stimulated extensive public discourse about the role of anthropogenic climate change in amplifying, or otherwise modifying, such events. In tandem, the scientific community has made considerable progress on statistical “climate attribution.” However, explaining these statistical methods to the public has posed challenges. Using appropriately designed “spinner boards,” we find that even members of the general public who do not understand the difference between weather and climate are readily able to understand basic concepts of attribution and explain those concepts to others. This includes both understanding and explaining the way in which the probability of an extreme weather event may increase as a result of climate change and explaining how the intensity of hurricanes can be increased. If properly developed and used by TV weather forecasters and news reporters, this method holds the potential to significantly improve public understanding of climate attribution.
