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Abstract
Numerous changes to information technology and media consumption over the last 2 decades have facilitated a fundamental shift in how people receive information, including weather forecasts. Historically, weather information was generally vetted through experts (often broadcast meteorologists) to members of the public in a relatively top-down system. Now, with the availability of internet websites, phone applications, and social media, people have an increasingly diverse set of sources from which to get weather forecasts. In this piece, we present results from CONUS-wide surveys that show this diversification of weather sources for nonroutine weather events. While older respondents still tend to get weather information from television, younger respondents are increasingly reliant on less traditional sources, including phone notifications, internet websites, social media, and family and friends. This shift to nontraditional sources means that a more diverse set of actors will have the opportunity to provide weather information to users, which could impact the quality, reliability, and accessibility of the weather information in the future.
Abstract
Numerous changes to information technology and media consumption over the last 2 decades have facilitated a fundamental shift in how people receive information, including weather forecasts. Historically, weather information was generally vetted through experts (often broadcast meteorologists) to members of the public in a relatively top-down system. Now, with the availability of internet websites, phone applications, and social media, people have an increasingly diverse set of sources from which to get weather forecasts. In this piece, we present results from CONUS-wide surveys that show this diversification of weather sources for nonroutine weather events. While older respondents still tend to get weather information from television, younger respondents are increasingly reliant on less traditional sources, including phone notifications, internet websites, social media, and family and friends. This shift to nontraditional sources means that a more diverse set of actors will have the opportunity to provide weather information to users, which could impact the quality, reliability, and accessibility of the weather information in the future.
Abstract
Tabletop exercises examining weather-related hazards are not uncommon but are often built around somewhat generic scenarios that only touch on the meteorological communication environment at a very shallow level. A recent exercise in central Oklahoma sought to change that. A local emergency manager, personnel from a National Weather Service (NWS) forecast office, and a severe weather researcher with a background in exercise design and facilitation worked together to create and deliver a realistic severe weather simulation. Exercise participants were exposed to detailed forecast information via NWSChat—a dedicated communication tool used to connect NWS forecasters, emergency managers, and media members for real-time information sharing. NWS forecasters were able to both actively play in the exercise due to the use of NWSChat and observe how local decision-makers interpreted and utilized the impact-based decision support service (IDSS) graphics and short-term forecast updates. The collaborative approach of developing a detailed scenario with numerous real-world IDSS graphics, along with the use of NWSChat for real-time delivery, resulted in overwhelmingly positive feedback from the participants. The local emergency management office identified numerous areas for improvement in communicating real-time forecast information across their jurisdiction, along with gaps in current plans and resources. Meanwhile, the NWS forecast office had the opportunity to experiment with using the new NWSChat platform in a high-impact severe weather environment before a real-world event took place. Forecasters also gained insight into current IDSS graphic interpretation, noting areas for improved messaging to end users, such as adding storm motion to existing severe weather graphics.
Abstract
Tabletop exercises examining weather-related hazards are not uncommon but are often built around somewhat generic scenarios that only touch on the meteorological communication environment at a very shallow level. A recent exercise in central Oklahoma sought to change that. A local emergency manager, personnel from a National Weather Service (NWS) forecast office, and a severe weather researcher with a background in exercise design and facilitation worked together to create and deliver a realistic severe weather simulation. Exercise participants were exposed to detailed forecast information via NWSChat—a dedicated communication tool used to connect NWS forecasters, emergency managers, and media members for real-time information sharing. NWS forecasters were able to both actively play in the exercise due to the use of NWSChat and observe how local decision-makers interpreted and utilized the impact-based decision support service (IDSS) graphics and short-term forecast updates. The collaborative approach of developing a detailed scenario with numerous real-world IDSS graphics, along with the use of NWSChat for real-time delivery, resulted in overwhelmingly positive feedback from the participants. The local emergency management office identified numerous areas for improvement in communicating real-time forecast information across their jurisdiction, along with gaps in current plans and resources. Meanwhile, the NWS forecast office had the opportunity to experiment with using the new NWSChat platform in a high-impact severe weather environment before a real-world event took place. Forecasters also gained insight into current IDSS graphic interpretation, noting areas for improved messaging to end users, such as adding storm motion to existing severe weather graphics.
Abstract
University students can learn about weather warnings and contribute to a database for the World Meteorological Organization (WMO) project on value chain approaches to evaluate the end-to-end warning chain. The project offers students a way to understand how information about high-impact weather is created, shared, and used within a complete warning system for a selected event. Their contributions are intended to inform researchers and practitioners on what has and what has not worked well in the warning process. The students use a structured questionnaire designed to collect information on observations, forecasting, hazards, impacts, warning communications, and responses.
Abstract
University students can learn about weather warnings and contribute to a database for the World Meteorological Organization (WMO) project on value chain approaches to evaluate the end-to-end warning chain. The project offers students a way to understand how information about high-impact weather is created, shared, and used within a complete warning system for a selected event. Their contributions are intended to inform researchers and practitioners on what has and what has not worked well in the warning process. The students use a structured questionnaire designed to collect information on observations, forecasting, hazards, impacts, warning communications, and responses.
Abstract
Color vision deficiency (CVD) is a decreased ability to discern between particular colors. Eight percent of genetic males and half a percent of genetic females have some form of CVD, with many in the radar community falling into this group. When presenting data on a two-dimensional plane, it is common to use colors to represent values via a colormap. Colormap choice in the radar community is influenced by the ability to highlight scientifically interesting features in data, institutional choices, and domain dominance of legacy colormaps. The problem with these current colormaps is that many do not project well for those with CVD (i.e., green next to red). In working with the CVD community to address this problem, multiple colormaps for moments such as equivalent reflectivity factor and Doppler velocity were created for users with forms of CVD such as deuteranomaly, protanomaly, protanopia, and deuteranopia using Python tools such as colorspacious and viscm. We show how these colormaps can improve interpretability for four cases: a mesoscale convective system, a pyrocumulonimbus storm, a wintertime midlatitude cyclone, and widespread storms with a large bird migration. These new radar equivalent reflectivity factor, Doppler velocity, and polarization colormaps are designed to highlight rain, frozen precipitation, nonmeteorological targets, and velocity-based items, are perceptually uniform, and are visually friendly for those with CVD.
Abstract
Color vision deficiency (CVD) is a decreased ability to discern between particular colors. Eight percent of genetic males and half a percent of genetic females have some form of CVD, with many in the radar community falling into this group. When presenting data on a two-dimensional plane, it is common to use colors to represent values via a colormap. Colormap choice in the radar community is influenced by the ability to highlight scientifically interesting features in data, institutional choices, and domain dominance of legacy colormaps. The problem with these current colormaps is that many do not project well for those with CVD (i.e., green next to red). In working with the CVD community to address this problem, multiple colormaps for moments such as equivalent reflectivity factor and Doppler velocity were created for users with forms of CVD such as deuteranomaly, protanomaly, protanopia, and deuteranopia using Python tools such as colorspacious and viscm. We show how these colormaps can improve interpretability for four cases: a mesoscale convective system, a pyrocumulonimbus storm, a wintertime midlatitude cyclone, and widespread storms with a large bird migration. These new radar equivalent reflectivity factor, Doppler velocity, and polarization colormaps are designed to highlight rain, frozen precipitation, nonmeteorological targets, and velocity-based items, are perceptually uniform, and are visually friendly for those with CVD.
Abstract
The Hawai‘i Climate Data Portal (HCDP) is designed to facilitate streamlined access to a wide variety of climate data and information for the State of Hawai‘i. Prior to the development of the HCDP, gridded climate products and point datasets were fragmented, outdated, not easily accessible, and not available in near–real time. To address these limitations, HCDP researchers developed the cyberinfrastructure necessary to 1) operationalize data acquisition and product production in a near-real-time environment and 2) make data and products easily accessible to a wide range of users. The HCDP hosts several high-resolution (250 m) gridded products including monthly rainfall and daily temperature (maximum, minimum, and mean), station data, and gridded future projections of rainfall and temperature. HCDP users can visualize both gridded and point data, create and download custom maps, and query station and gridded data for export with relative ease. The “virtual station” feature allows users to create a climate time series at any grid point. The primary objective of the HCDP is to promote sharing and access to data and information to streamline research activities, improve awareness, and promote the development of tools and resources that can help to build adaptive capacities. The HCDP products have the potential to serve a wide range of users including researchers, resource managers, city planners, engineers, teachers, students, civil society organizations, and the broader community.
Abstract
The Hawai‘i Climate Data Portal (HCDP) is designed to facilitate streamlined access to a wide variety of climate data and information for the State of Hawai‘i. Prior to the development of the HCDP, gridded climate products and point datasets were fragmented, outdated, not easily accessible, and not available in near–real time. To address these limitations, HCDP researchers developed the cyberinfrastructure necessary to 1) operationalize data acquisition and product production in a near-real-time environment and 2) make data and products easily accessible to a wide range of users. The HCDP hosts several high-resolution (250 m) gridded products including monthly rainfall and daily temperature (maximum, minimum, and mean), station data, and gridded future projections of rainfall and temperature. HCDP users can visualize both gridded and point data, create and download custom maps, and query station and gridded data for export with relative ease. The “virtual station” feature allows users to create a climate time series at any grid point. The primary objective of the HCDP is to promote sharing and access to data and information to streamline research activities, improve awareness, and promote the development of tools and resources that can help to build adaptive capacities. The HCDP products have the potential to serve a wide range of users including researchers, resource managers, city planners, engineers, teachers, students, civil society organizations, and the broader community.
Abstract
The Polar Prediction Project (PPP), one of the flagship programs of the World Meteorological Organization’s (WMO) World Weather Research Programme (WWRP), has come to an end after a decade of intensive and coordinated international observing, modeling, verification, user engagement, and education activities. While PPP facilitated many advancements in modeling and forecasting, critical investment is now required to turn prediction science into salient environmental services for the polar regions. In this commentary, the members of the Societal and Economic Research and Applications task team of PPP, a group of social scientists and service delivery specialists, identify a number of insights and lessons that are critical for the implementation of the follow-up program Polar Coupled Analysis and Prediction for Services (PCAPS). We argue that in order to raise the societal value of polar environmental services, we need to better understand the diversity of highly specific user contexts; to tailor the actionability of weather, water, ice, and climate (WWIC) service development in the polar regions through inclusive transdisciplinary approaches to coproduction; to assess the societal impact of improved environmental services in the polar regions; and to invest and provide dedicated funding for involving the social sciences in research and tailoring processes across all the polar regions.
Abstract
The Polar Prediction Project (PPP), one of the flagship programs of the World Meteorological Organization’s (WMO) World Weather Research Programme (WWRP), has come to an end after a decade of intensive and coordinated international observing, modeling, verification, user engagement, and education activities. While PPP facilitated many advancements in modeling and forecasting, critical investment is now required to turn prediction science into salient environmental services for the polar regions. In this commentary, the members of the Societal and Economic Research and Applications task team of PPP, a group of social scientists and service delivery specialists, identify a number of insights and lessons that are critical for the implementation of the follow-up program Polar Coupled Analysis and Prediction for Services (PCAPS). We argue that in order to raise the societal value of polar environmental services, we need to better understand the diversity of highly specific user contexts; to tailor the actionability of weather, water, ice, and climate (WWIC) service development in the polar regions through inclusive transdisciplinary approaches to coproduction; to assess the societal impact of improved environmental services in the polar regions; and to invest and provide dedicated funding for involving the social sciences in research and tailoring processes across all the polar regions.
Abstract
Climate change presents huge challenges to the already-complex decisions faced by U.S. agricultural producers, as seasonal weather patterns increasingly deviate from historical tendencies. Under USDA funding, a transdisciplinary team of researchers, extension experts, educators, and stakeholders is developing a climate decision support Dashboard for Agricultural Water use and Nutrient management (DAWN) to provide Corn Belt farmers with better predictive information. DAWN’s goal is to provide credible, usable information to support decisions by creating infrastructure to make subseasonal-to-seasonal forecasts accessible. DAWN uses an integrated approach to 1) engage stakeholders to coproduce a decision support and information delivery system; 2) build a coupled modeling system to represent and transfer holistic systems knowledge into effective tools; 3) produce reliable forecasts to help stakeholders optimize crop productivity and environmental quality; and 4) integrate research and extension into experiential, transdisciplinary education. This article presents DAWN’s framework for integrating climate–agriculture research, extension, and education to bridge science and service. We also present key challenges to the creation and delivery of decision support, specifically in infrastructure development, coproduction and trust building with stakeholders, product design, effective communication, and moving tools toward use.
Abstract
Climate change presents huge challenges to the already-complex decisions faced by U.S. agricultural producers, as seasonal weather patterns increasingly deviate from historical tendencies. Under USDA funding, a transdisciplinary team of researchers, extension experts, educators, and stakeholders is developing a climate decision support Dashboard for Agricultural Water use and Nutrient management (DAWN) to provide Corn Belt farmers with better predictive information. DAWN’s goal is to provide credible, usable information to support decisions by creating infrastructure to make subseasonal-to-seasonal forecasts accessible. DAWN uses an integrated approach to 1) engage stakeholders to coproduce a decision support and information delivery system; 2) build a coupled modeling system to represent and transfer holistic systems knowledge into effective tools; 3) produce reliable forecasts to help stakeholders optimize crop productivity and environmental quality; and 4) integrate research and extension into experiential, transdisciplinary education. This article presents DAWN’s framework for integrating climate–agriculture research, extension, and education to bridge science and service. We also present key challenges to the creation and delivery of decision support, specifically in infrastructure development, coproduction and trust building with stakeholders, product design, effective communication, and moving tools toward use.
Abstract
The size, duration, impact, and cost of wildland fire is increasing over the last several decades. A recent Interagency Council for Advancing Meteorological Services (ICAMS)-sponsored workshop focused on the scientific questions and challenges associated with subseasonal-to-seasonal wildfire outlooks. Opinions from this workshop, including recommended cross-agency motivation and activities, are provided.
Abstract
The size, duration, impact, and cost of wildland fire is increasing over the last several decades. A recent Interagency Council for Advancing Meteorological Services (ICAMS)-sponsored workshop focused on the scientific questions and challenges associated with subseasonal-to-seasonal wildfire outlooks. Opinions from this workshop, including recommended cross-agency motivation and activities, are provided.
Abstract
The Deep Numerical Analysis for Climate (DNA-Climate) is a pilot project to develop an Earth system model on a kilometer-scale horizontal mesh. The acronym “DNA” is based on the analogies between the hierarchical structures of atmospheric phenomena and living organisms. The multiscale structure of clouds and circulations may be analogous to the multiscale structure of cells and organs organized according to the blueprint, deoxyribonucleic acid (DNA). Whereas global cloud-resolving models (CRMs) can produce better solutions on shorter time scales that are decisively governed by the initial conditions, global climate models (GCMs) may generate reliable solutions on longer time scales that are largely determined to balance energy inputs and outputs. Our challenge is to build a physically valid model that consistently bridges the shorter- and longer-time-scale solutions in the intermediate time scales. Research topics of DNA-Climate are configured in consideration of the structural similarity between the climate modeling and the technique of matched asymptotic expansions in mathematics. The central question is whether a single modeling framework using only either global CRM or GCM will work adequately at all time scales of climate, or whether a multiscale modeling framework combining several models, of which each is only valid for limited time scales, will be needed. A multiscale modeling is an attractive framework for advancing climate modeling and would be an intriguing topic to be studied in parallel with global CRMs and GCMs.
Abstract
The Deep Numerical Analysis for Climate (DNA-Climate) is a pilot project to develop an Earth system model on a kilometer-scale horizontal mesh. The acronym “DNA” is based on the analogies between the hierarchical structures of atmospheric phenomena and living organisms. The multiscale structure of clouds and circulations may be analogous to the multiscale structure of cells and organs organized according to the blueprint, deoxyribonucleic acid (DNA). Whereas global cloud-resolving models (CRMs) can produce better solutions on shorter time scales that are decisively governed by the initial conditions, global climate models (GCMs) may generate reliable solutions on longer time scales that are largely determined to balance energy inputs and outputs. Our challenge is to build a physically valid model that consistently bridges the shorter- and longer-time-scale solutions in the intermediate time scales. Research topics of DNA-Climate are configured in consideration of the structural similarity between the climate modeling and the technique of matched asymptotic expansions in mathematics. The central question is whether a single modeling framework using only either global CRM or GCM will work adequately at all time scales of climate, or whether a multiscale modeling framework combining several models, of which each is only valid for limited time scales, will be needed. A multiscale modeling is an attractive framework for advancing climate modeling and would be an intriguing topic to be studied in parallel with global CRMs and GCMs.
