Pub Date : 2024-01-05DOI: 10.15191/nwajom.2024.1201
Sean R. Ernst, J. Ripberger, Julie Krutz, Carol L. Silva, H. Jenkins‐Smith, Anna Wanless, David Nowicki, Kimberly E. Klockow-McClain, Kodi L. Berry, Holly B. Obermeier, Makenzie J. Krocak
Broadcast meteorologists are the primary source of weather information for the public, and thus are key to messaging the multiple weather hazards that can occur during simultaneous tornado and flash flood, or TORFF, events. Due in part to the challenge and cost needed to study broadcast coverage, there has been limited study into how broadcasters present these hazards to their viewers during TORFF events. To begin to address this knowledge gap, we developed the Coding Algorithm for Storm coverage Transcripts, or CAST. Bot, a simple algorithm that can efficiently and inexpensively compare the mentions of tornado and flash flood hazards made by meteorologists during on-air coverage. For this study, we used CAST.Bot to quickly analyze 39 segments of coverage from eight TORFF events. Findings suggest that broadcasters generally favor mentions of tornadoes more than flash flooding during TORFF events with many tornado warnings, with more balanced coverage identified during events with similar numbers of tornado and flash flood warnings. Additional study of two cases, 1) the El Reno/Oklahoma City, Oklahoma, tornado and flash flood on 31 May 2013, and 2) Hurricane Harvey in Houston, Texas, on 26 August 2017, suggests that TORFF event coverage on television is subject to differences across stations and the way that the tornado and flash flood hazards in a TORFF unfold. Future work should seek to better understand how changes in the focus of messaging for TORFF events can impact viewers decisions and identify how context can influence TORFF message content. Options for use of the CAST.Bot algorithm to aid broadcasters during multi-hazard event coverage are also discussed.
{"title":"A Tale of Two Hazards: Studying Broadcast Meteorologist Communication of Simultaneous Tornado and Flash Flood (TORFF) Events","authors":"Sean R. Ernst, J. Ripberger, Julie Krutz, Carol L. Silva, H. Jenkins‐Smith, Anna Wanless, David Nowicki, Kimberly E. Klockow-McClain, Kodi L. Berry, Holly B. Obermeier, Makenzie J. Krocak","doi":"10.15191/nwajom.2024.1201","DOIUrl":"https://doi.org/10.15191/nwajom.2024.1201","url":null,"abstract":"Broadcast meteorologists are the primary source of weather information for the public, and thus are key to messaging the multiple weather hazards that can occur during simultaneous tornado and flash flood, or TORFF, events. Due in part to the challenge and cost needed to study broadcast coverage, there has been limited study into how broadcasters present these hazards to their viewers during TORFF events. To begin to address this knowledge gap, we developed the Coding Algorithm for Storm coverage Transcripts, or CAST. Bot, a simple algorithm that can efficiently and inexpensively compare the mentions of tornado and flash flood hazards made by meteorologists during on-air coverage. For this study, we used CAST.Bot to quickly analyze 39 segments of coverage from eight TORFF events. Findings suggest that broadcasters generally favor mentions of tornadoes more than flash flooding during TORFF events with many tornado warnings, with more balanced coverage identified during events with similar numbers of tornado and flash flood warnings. Additional study of two cases, 1) the El Reno/Oklahoma City, Oklahoma, tornado and flash flood on 31 May 2013, and 2) Hurricane Harvey in Houston, Texas, on 26 August 2017, suggests that TORFF event coverage on television is subject to differences across stations and the way that the tornado and flash flood hazards in a TORFF unfold. Future work should seek to better understand how changes in the focus of messaging for TORFF events can impact viewers decisions and identify how context can influence TORFF message content. Options for use of the CAST.Bot algorithm to aid broadcasters during multi-hazard event coverage are also discussed.","PeriodicalId":44039,"journal":{"name":"Journal of Operational Meteorology","volume":"26 15","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139383734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-29DOI: 10.15191/nwajom.2023.1111
Cole Vaughn, Kathleen Sherman-Morris, Michael Brown
Weather information can now be accessed through a variety of different media. This study used a survey to determine if the weather app was the primary source for weather information in the United States and whether this was related to age and other personal characteristics. More than 75% of the sample reported using a weather app for general forecast information. In cases of severe weather, weather apps and websites were reported to be the top two primary sources. While younger demographics had more weather app users than older demographics, the weather app was still the most popular source among the older groups. The most popular apps were the pre-downloaded app on a phone, The Weather Channel’s app, and the AccuWeather app. Participants who chose to use an app other than the pre-downloaded one reported higher self-perceived knowledge about, and interest in, weather. In addition, 80% of respondents reported getting severe weather notifications on their phone. The study’s survey sample was heavily skewed toward a younger population and may not be generalizable to all socioeconomic demographics. Considering previous research, these results indicate a shift in the predominant forecast sources used by the public over the last 10–15 yr. Consequently, it has resulted in a widespread transfer of responsibility for interpreting and explaining the forecast. A majority of the public—untrained in meteorology—is now interpreting the forecast on their own without the help of a broadcast meteorologist as would be present in a television forecast, making the forecast open to misinterpretation and false expectation. This study calls for continued research to combat misinterpretation and to enhance weather apps and mobile notifications with more personalized information that can aid weather-related decision making to make weather apps a strong leader in forecast messaging.
{"title":"A Change in the Weather: Understanding Public Usage of Weather Apps","authors":"Cole Vaughn, Kathleen Sherman-Morris, Michael Brown","doi":"10.15191/nwajom.2023.1111","DOIUrl":"https://doi.org/10.15191/nwajom.2023.1111","url":null,"abstract":"Weather information can now be accessed through a variety of different media. This study used a survey to determine if the weather app was the primary source for weather information in the United States and whether this was related to age and other personal characteristics. More than 75% of the sample reported using a weather app for general forecast information. In cases of severe weather, weather apps and websites were reported to be the top two primary sources. While younger demographics had more weather app users than older demographics, the weather app was still the most popular source among the older groups. The most popular apps were the pre-downloaded app on a phone, The Weather Channel’s app, and the AccuWeather app. Participants who chose to use an app other than the pre-downloaded one reported higher self-perceived knowledge about, and interest in, weather. In addition, 80% of respondents reported getting severe weather notifications on their phone. The study’s survey sample was heavily skewed toward a younger population and may not be generalizable to all socioeconomic demographics. Considering previous research, these results indicate a shift in the predominant forecast sources used by the public over the last 10–15 yr. Consequently, it has resulted in a widespread transfer of responsibility for interpreting and explaining the forecast. A majority of the public—untrained in meteorology—is now interpreting the forecast on their own without the help of a broadcast meteorologist as would be present in a television forecast, making the forecast open to misinterpretation and false expectation. This study calls for continued research to combat misinterpretation and to enhance weather apps and mobile notifications with more personalized information that can aid weather-related decision making to make weather apps a strong leader in forecast messaging.","PeriodicalId":44039,"journal":{"name":"Journal of Operational Meteorology","volume":"8 3","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139147602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-20DOI: 10.15191/nwajom.2023.1110
Thomas A. Jones, J. Mecikalski
Forecasting convection initiation (CI) has advanced greatly during the past decade through the use of high-resolution satellite observations and model output. One of the primary CI products used in forecast operations is based on GOES-16 visible and infrared imagery along with GLM lightning flash detections to determine the location of growing ice-containing cumulus clouds that are the precursor to developing thunderstorms. Another approach to CI forecasting that has recently become available is high frequency output from numerical weather prediction (NWP) models such as the Warn-on-Forecast System (WoFS). NWP model simulated composite reflectivity forecasts are one method used to determine when and where severe thunderstorms might develop. However, waiting for high reflectivity (> 40 dBZ) to be created within the NWP model limits the potential lead time available to forecasters when using WoFS output to anticipate areas where convection might form.Also, forecast reflectivity alone does not always give an indication of whether or not the precipitation developed by the NWP model is convective in nature. To address these limitations, this work applies a CI forecasting methodology developed for GOES satellite data on synthetic satellite imagery produced from WoFS output. Forecast cloud objects are tracked over a 10-min interval and CI forecasting parameters are applied to determine whether or not these cloud objects will continue to develop into organized thunderstorms.
{"title":"Convection Initiation Forecasting Using Synthetic Satellite Imagery from the Warn-on-Forecast System","authors":"Thomas A. Jones, J. Mecikalski","doi":"10.15191/nwajom.2023.1110","DOIUrl":"https://doi.org/10.15191/nwajom.2023.1110","url":null,"abstract":"Forecasting convection initiation (CI) has advanced greatly during the past decade through the use of high-resolution satellite observations and model output. One of the primary CI products used in forecast operations is based on GOES-16 visible and infrared imagery along with GLM lightning flash detections to determine the location of growing ice-containing cumulus clouds that are the precursor to developing thunderstorms.\u0000Another approach to CI forecasting that has recently become available is high frequency output from numerical weather prediction (NWP) models such as the Warn-on-Forecast System (WoFS). NWP model simulated composite reflectivity forecasts are one method used to determine when and where severe thunderstorms might develop. However, waiting for high reflectivity (> 40 dBZ) to be created within the NWP model limits the potential lead time available to forecasters when using WoFS output to anticipate areas where convection might form.Also, forecast reflectivity alone does not always give an indication of whether or not the precipitation developed by the NWP model is convective in nature. To address these limitations, this work applies a CI forecasting methodology developed for GOES satellite data on synthetic satellite imagery produced from WoFS output. Forecast cloud objects are tracked over a 10-min interval and CI forecasting parameters are applied to determine whether or not these cloud objects will continue to develop into organized thunderstorms.","PeriodicalId":44039,"journal":{"name":"Journal of Operational Meteorology","volume":"35 26","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138955748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-19DOI: 10.15191/nwajom.2023.1109
P. Skinner, Katie A. Wilson, B. Matilla, Brett Roberts, N. Yussouf, P. Burke, Pamela L. HeinseIman, Burkely T. Gallo, Thomas A. Jones, K. Knopfmeier, Montgomery Flora, Joshua Martin, Jorge E. Guerra, T. T. Lindley, Chad M. Gravelle, Stephen W. Bieda III
The Warn-on-Forecast System (WoFS) is a convection-allowing ensemble prediction system designed to primarily provide guidance on thunderstorm hazards from the meso-beta to storm-scale in space and from several hours to less than one hour in time. This article describes unique aspects of WoFS guidance product design and application to short-term severe weather forecasting. General probabilistic forecasting concepts for convection allowing ensembles, including the use of neighborhood, probability of exceedance, percentile, and paintball products, are reviewed, and the design of real-time WoFS guidance products is described. Recommendations for effectively using WoFS guidance for severe weather prediction include evaluation of the quality of WoFS storm-scale analyses, interrogating multiple probabilistic guidance products to efficiently span the envelope of guidance provided by ensemble members, and application of conceptual models of convective storm dynamics and interaction with the broader mesoscale environment. Part II of this study provides specific examples where WoFS guidance can provide useful or potentially misleading guidance on convective storm likelihood and evolution.
{"title":"Interpreting Warn-on-Forecast System Guidance, Part I: Review of Probabilistic Guidance Concepts, Product Design, and Best Practices","authors":"P. Skinner, Katie A. Wilson, B. Matilla, Brett Roberts, N. Yussouf, P. Burke, Pamela L. HeinseIman, Burkely T. Gallo, Thomas A. Jones, K. Knopfmeier, Montgomery Flora, Joshua Martin, Jorge E. Guerra, T. T. Lindley, Chad M. Gravelle, Stephen W. Bieda III","doi":"10.15191/nwajom.2023.1109","DOIUrl":"https://doi.org/10.15191/nwajom.2023.1109","url":null,"abstract":"The Warn-on-Forecast System (WoFS) is a convection-allowing ensemble prediction system designed to primarily provide guidance on thunderstorm hazards from the meso-beta to storm-scale in space and from several hours to less than one hour in time. This article describes unique aspects of WoFS guidance product design and application to short-term severe weather forecasting. General probabilistic forecasting concepts for convection allowing ensembles, including the use of neighborhood, probability of exceedance, percentile, and paintball products, are reviewed, and the design of real-time WoFS guidance products is described. Recommendations for effectively using WoFS guidance for severe weather prediction include evaluation of the quality of WoFS storm-scale analyses, interrogating multiple probabilistic guidance products to efficiently span the envelope of guidance provided by ensemble members, and application of conceptual models of convective storm dynamics and interaction with the broader mesoscale environment. Part II of this study provides specific examples where WoFS guidance can provide useful or potentially misleading guidance on convective storm likelihood and evolution.","PeriodicalId":44039,"journal":{"name":"Journal of Operational Meteorology","volume":" 6","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138961512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-25DOI: 10.15191/nwajom.2023.1108
Jillian R. Olson, Claire M. Doyle, Daphne S. LaDue, Alex N. Marmo
Ahead of severe weather, National Weather Service (NWS) core partners are responsible for making decisions that ensure the safety of their jurisdiction. NWS defines core partners as government and nongovernment officials who make weather related decisions; this study uses the term “end-user” to refer to those individuals within emergency management, fire departments, public works, and school systems. Owing to the complex science of severe weather forecasting, many events turn out to be null events and end-users learn to be selective in which forecasts warrant extra preparations. While end-users state they would rather be overprepared than under prepared, end-users face varying consequences in the wake of null events that could lead them to be more hesitant in the future. Our team conducted background and event-based interviews with emergency managers, fire departments, public works, and school officials in various states across the United States east of the Rocky Mountains. Event-based interviews were separated into sets according to their Storm Prediction Center (SPC) risk level and coded thematically; this study specifically focuses on how end-users perceived the threat of severe weather in the hours ahead of its forecasted occurrence. Analyses concluded that (i) end-users in the same SPC risk level perceived threats differently, (ii) end-users in the SPC Enhanced risk had the most variation, and (iii) threat perceptions were driven by forecast information, the end-users’ personal experiences, and environmental cues. As SPC risk level increased, end-users increasingly applied information from the three themes to adjust their situational awareness and build confidence before making potentially costly decisions. By understanding the impacts of null events and how end-users gauge threats, the NWS can better support end-users and use null events as an opportunity to build trust and partnership with their core partners.
{"title":"End-User Threat Perception: Building Confidence to Make Decisions Ahead of Severe Weather","authors":"Jillian R. Olson, Claire M. Doyle, Daphne S. LaDue, Alex N. Marmo","doi":"10.15191/nwajom.2023.1108","DOIUrl":"https://doi.org/10.15191/nwajom.2023.1108","url":null,"abstract":"Ahead of severe weather, National Weather Service (NWS) core partners are responsible for making decisions that ensure the safety of their jurisdiction. NWS defines core partners as government and nongovernment officials who make weather related decisions; this study uses the term “end-user” to refer to those individuals within emergency management, fire departments, public works, and school systems. Owing to the complex science of severe weather forecasting, many events turn out to be null events and end-users learn to be selective in which forecasts warrant extra preparations. While end-users state they would rather be overprepared than under prepared, end-users face varying consequences in the wake of null events that could lead them to be more hesitant in the future. Our team conducted background and event-based interviews with emergency managers, fire departments, public works, and school officials in various states across the United States east of the Rocky Mountains. Event-based interviews were separated into sets according to their Storm Prediction Center (SPC) risk level and coded thematically; this study specifically focuses on how end-users perceived the threat of severe weather in the hours ahead of its forecasted occurrence. Analyses concluded that (i) end-users in the same SPC risk level perceived threats differently, (ii) end-users in the SPC Enhanced risk had the most variation, and (iii) threat perceptions were driven by forecast information, the end-users’ personal experiences, and environmental cues. As SPC risk level increased, end-users increasingly applied information from the three themes to adjust their situational awareness and build confidence before making potentially costly decisions. By understanding the impacts of null events and how end-users gauge threats, the NWS can better support end-users and use null events as an opportunity to build trust and partnership with their core partners.","PeriodicalId":44039,"journal":{"name":"Journal of Operational Meteorology","volume":"1 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135112255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-07DOI: 10.15191/nwajom.2023.1107
Katie A. Wilson, N. Yussouf, P. Skinner, K. Knopfmeier, B. Matilla, P. Heinselman, Andrew Orrison, Richard Otto, Michael Erickson
This study examines use of experimental Warn-on-Forecast System (WoFS) guidance for short-term flash flood prediction at the NOAA Weather Prediction Center’s Meteorological Watch (Metwatch) desk. The WoFS guidance provides storm-scale ensemble forecasts for individual thunderstorms out to six hours and has previously shown great promise in its predictive skill for heavy rainfall events. Its operational utility was examined during 2019 and 2020 in a formal collaboration between Warn-on-Forecast scientists and Metwatch meteorologists. During that time, Metwatch meteorologists integrated real-time WoFS guidance into their Mesoscale Precipitation Discussion forecast processes and provided evaluations via a post-event survey. The survey queried impacts of WoFS guidance on their situational awareness, workload, and confidence, and Metwatch meteorologists also reported subjective assessments of model performance. Survey results highlighted the importance of viewing consistency in WoFS guidance across runs and agreement between WoFS guidance with conceptual models, other numerical weather prediction guidance, and observations. The use of WoFS tended to either maintain or slightly increase Metwatch meteorologists’ workload, while also increasing their confidence (notably for events perceived as better predicted). Of the different forecast attributes evaluated, Metwatch meteorologists reported convective mode as the attribute best predicted by WoFS. Use of WoFS guidance supported Mesoscale Precipitation Discussion decision making, including the placement and spatial extent of the product and the level of specificity provided about the related flash flood threat(s).
这项研究考察了NOAA天气预报中心气象观测台（Metwatch）对短期山洪预测的实验性预警系统（WoFS）指南的使用情况。WoFS指南为6小时内的个别雷暴提供了风暴级综合预报，此前在强降雨事件的预测技巧方面表现出了巨大的前景。在2019年和2020年期间，Warn on Forecast科学家和Metwatch气象学家的正式合作对其运行效用进行了检查。在此期间，Metwatch气象学家将实时WoFS指南纳入了他们的中尺度降水讨论预测过程，并通过事后调查提供了评估。该调查询问了WoFS指导对他们的态势感知、工作量和信心的影响，Metwatch气象学家也报告了对模型性能的主观评估。调查结果强调了WoFS指南在各次运行中观察一致性的重要性，以及WoFS指南与概念模型、其他数值天气预测指南和观测之间的一致性。WoFS的使用往往会维持或略微增加Metwatch气象学家的工作量，同时也会增加他们的信心（尤其是对于被认为预测更好的事件）。Metwatch气象学家报告称，在评估的不同预测属性中，对流模式是WoFS预测的最佳属性。WoFS指南的使用支持中尺度降水讨论决策，包括产品的位置和空间范围，以及相关山洪威胁的特异性水平。
{"title":"The NOAA Weather Prediction Center’s Use and Evaluation of Experimental Warn-on-Forecast System Guidance","authors":"Katie A. Wilson, N. Yussouf, P. Skinner, K. Knopfmeier, B. Matilla, P. Heinselman, Andrew Orrison, Richard Otto, Michael Erickson","doi":"10.15191/nwajom.2023.1107","DOIUrl":"https://doi.org/10.15191/nwajom.2023.1107","url":null,"abstract":"This study examines use of experimental Warn-on-Forecast System (WoFS) guidance for short-term flash flood prediction at the NOAA Weather Prediction Center’s Meteorological Watch (Metwatch) desk. The WoFS guidance provides storm-scale ensemble forecasts for individual thunderstorms out to six hours and has previously shown great promise in its predictive skill for heavy rainfall events. Its operational utility was examined during 2019 and 2020 in a formal collaboration between Warn-on-Forecast scientists and Metwatch meteorologists. During that time, Metwatch meteorologists integrated real-time WoFS guidance into their Mesoscale Precipitation Discussion forecast processes and provided evaluations via a post-event survey. The survey queried impacts of WoFS guidance on their situational awareness, workload, and confidence, and Metwatch meteorologists also reported subjective assessments of model performance. Survey results highlighted the importance of viewing consistency in WoFS guidance across runs and agreement between WoFS guidance with conceptual models, other numerical weather prediction guidance, and observations. The use of WoFS tended to either maintain or slightly increase Metwatch meteorologists’ workload, while also increasing their confidence (notably for events perceived as better predicted). Of the different forecast attributes evaluated, Metwatch meteorologists reported convective mode as the attribute best predicted by WoFS. Use of WoFS guidance supported Mesoscale Precipitation Discussion decision making, including the placement and spatial extent of the product and the level of specificity provided about the related flash flood threat(s).","PeriodicalId":44039,"journal":{"name":"Journal of Operational Meteorology","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49089110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-24DOI: 10.15191/nwajom.2023.1106
T. Lindley, A. B. Zwink, Ryan R. Barnes, G. Murdoch, B. Ancell, P. Burke, P. Skinner
Multiple high-impact wildfire episodes on the southern Great Plains in 2021/22 provided unique opportunities to demonstrate the emerging utility of Convection-allowing Models (CAMs) in fire-weather forecasting. This short contribution article will present preliminary analyses of the deterministic Texas Tech Real Time Weather Prediction System’s Red Flag Threat Index (RFTI) compared to wildfire activity observed via the Geostationary Operational Environmental Satellite-16 during four southern Great Plains wildfire outbreaks. Visual side-by-side comparisons of model-predicted RFTI and satellite-detected wildfires will be shown in static and animated displays that demonstrate the model’s prognostic signal in depicting fire-outbreak evolution. The data analyses are supplemented with preliminary information from state forestry agencies that provide context to predicted RFTI relative to size-based categorization of observed wildfires and human casualties. In addition, use of the National Severe Storm Laboratory’s Warn-on-Forecast System to provide short-term updates on the evolution of fire-effective atmospheric features that promote new fire ignition, problematic spread, and extreme fire behavior is also demonstrated. The examples presented here suggest that CAMs serve an important role in the mesoscale prediction of dangerous wildfire conditions. With this novel use of CAMs in fire meteorology, the authors advocate for expanded availability of fire weather-specific fields and parameters in high-resolution numerical weather prediction systems that would improve wildfire forecasts and associated impact-based decision support.
{"title":"Preliminary Use of Convection-allowing Models in Fire Weather","authors":"T. Lindley, A. B. Zwink, Ryan R. Barnes, G. Murdoch, B. Ancell, P. Burke, P. Skinner","doi":"10.15191/nwajom.2023.1106","DOIUrl":"https://doi.org/10.15191/nwajom.2023.1106","url":null,"abstract":"Multiple high-impact wildfire episodes on the southern Great Plains in 2021/22 provided unique opportunities to demonstrate the emerging utility of Convection-allowing Models (CAMs) in fire-weather forecasting. This short contribution article will present preliminary analyses of the deterministic Texas Tech Real Time Weather Prediction System’s Red Flag Threat Index (RFTI) compared to wildfire activity observed via the Geostationary Operational Environmental Satellite-16 during four southern Great Plains wildfire outbreaks. Visual side-by-side comparisons of model-predicted RFTI and satellite-detected wildfires will be shown in static and animated displays that demonstrate the model’s prognostic signal in depicting fire-outbreak evolution. The data analyses are supplemented with preliminary information from state forestry agencies that provide context to predicted RFTI relative to size-based categorization of observed wildfires and human casualties. In addition, use of the National Severe Storm Laboratory’s Warn-on-Forecast System to provide short-term updates on the evolution of fire-effective atmospheric features that promote new fire ignition, problematic spread, and extreme fire behavior is also demonstrated. The examples presented here suggest that CAMs serve an important role in the mesoscale prediction of dangerous wildfire conditions. With this novel use of CAMs in fire meteorology, the authors advocate for expanded availability of fire weather-specific fields and parameters in high-resolution numerical weather prediction systems that would improve wildfire forecasts and associated impact-based decision support.","PeriodicalId":44039,"journal":{"name":"Journal of Operational Meteorology","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48638150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-22DOI: 10.15191/nwajom.2023.1105
Michael F. Sessa, R. Trapp
Analyses of Doppler radar data and environmental parameters for 300 tornado cases are used to propose an alternative framework for tornado intensity prediction during pretornadic stages of ongoing storms, conditional on tornadogenesis. This framework is founded on the robust relationship (R² = 0.69) between pretornadic mesocyclone width and the EF rating of the subsequent tornado. In contrast, the linear relationship between pretornadic mesocyclone intensity and EF scale is much weaker (R² = 0.29). Environmental information for each case was additionally used to explore relationships between environmental parameters and tornado intensity. Such relationships depend in part on how the tornado-intensity categories are distributed [i.e., nonsignificant (EF0–1) versus significant (EF2–5), or weak (EF0–1) versus strong (EF2–3) versus violent (EF4–5)]. Low-level shear parameters discriminate the environments of significant tornadoes from nonsignificant tornadoes, but not the environments of violent tornadoes from strong tornadoes. The converse is true for thermodynamic parameters. Operational implementation of this framework for thepurposes of impact-based warnings will require real-time, automated quantification of mesocyclone width in addition to intensity and other attributes. The information gained from the pretornadic analysis demonstrated in this study would allow an operational forecaster to be aware of—and communicate—information about potential tornado intensity in warning text to the public before a tornado develops to better protect life and property. Currently, these relationships are being utilized in machine learning models for binary prediction of non-significant versus significant tornado intensity where skill is being demonstrated.
{"title":"Environmental and Radar-Derived Predictors of Tornado\u0000Intensity within Ongoing Convective Storms","authors":"Michael F. Sessa, R. Trapp","doi":"10.15191/nwajom.2023.1105","DOIUrl":"https://doi.org/10.15191/nwajom.2023.1105","url":null,"abstract":"Analyses of Doppler radar data and environmental parameters for 300 tornado cases are used to propose an alternative framework for tornado intensity prediction during pretornadic stages of ongoing storms, conditional on tornadogenesis. This framework is founded on the robust relationship (R² = 0.69) between pretornadic mesocyclone width and the EF rating of the subsequent tornado. In contrast, the linear relationship between pretornadic mesocyclone intensity and EF scale is much weaker (R² = 0.29). Environmental information for each case was additionally used to explore relationships between environmental parameters and tornado intensity. Such relationships depend in part on how the tornado-intensity categories are distributed [i.e., nonsignificant (EF0–1) versus significant (EF2–5), or weak (EF0–1) versus strong (EF2–3) versus violent (EF4–5)]. Low-level shear parameters discriminate the environments of significant tornadoes from nonsignificant tornadoes, but not the environments of violent tornadoes from strong tornadoes. The converse is true for thermodynamic parameters. Operational implementation of this framework for thepurposes of impact-based warnings will require real-time, automated quantification of mesocyclone width in addition to intensity and other attributes. The information gained from the pretornadic analysis demonstrated in this study would allow an operational forecaster to be aware of—and communicate—information about potential tornado intensity in warning text to the public before a tornado develops to better protect life and property. Currently, these relationships are being utilized in machine learning models for binary prediction of non-significant versus significant tornado intensity where skill is being demonstrated.","PeriodicalId":44039,"journal":{"name":"Journal of Operational Meteorology","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47921166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-06DOI: 10.15191/nwajom.2023.1104
F. Mosher, C. Herbster, S. Miller, Mike Zuranski, Paul Sirvatka, Richard Khors, D. Hoese, Timothy L. Schmit, James P. Nelson, Robert Haley
The human eye is sensitive to three primary bands of light—centered on the red, green, and blue parts of the visible spectrum. The human eye is not very sensitive to variations in shades of gray—being able to distinguish only approximately 25 different gradations of gray in satellite images. However, by using the three different color sensors, the eye has the potential to distinguish up to a million different values of color. Hence, color is a powerful tool for distinguishing various objects of interest with subtle intensity variations. The Geostationary Operational Environmental Satellites-R (GOES-R) series of geostationary satellites do not have a green channel. However, a synthetic green channel can be constructed from the blue, red, and nearinfrared “veggie” channels for the use in a true-color visible image. Since the launch of the GOES-16 satellite, several different groups have developed color visible algorithms that are available on public websites. The purpose of this paper is to help explain the similarities and differences of true-color GOES images that are on the web and in other locations.
{"title":"True-Color Imagery from GOES—A Synopsis of\u0000Past and Present","authors":"F. Mosher, C. Herbster, S. Miller, Mike Zuranski, Paul Sirvatka, Richard Khors, D. Hoese, Timothy L. Schmit, James P. Nelson, Robert Haley","doi":"10.15191/nwajom.2023.1104","DOIUrl":"https://doi.org/10.15191/nwajom.2023.1104","url":null,"abstract":"The human eye is sensitive to three primary bands of light—centered on the red, green, and blue parts of the visible spectrum. The human eye is not very sensitive to variations in shades of gray—being able to distinguish only approximately 25 different gradations of gray in satellite images. However, by using the three different color sensors, the eye has the potential to distinguish up to a million different values of color. Hence, color is a powerful tool for distinguishing various objects of interest with subtle intensity variations. \u0000The Geostationary Operational Environmental Satellites-R (GOES-R) series of geostationary satellites do not have a green channel. However, a synthetic green channel can be constructed from the blue, red, and nearinfrared “veggie” channels for the use in a true-color visible image. Since the launch of the GOES-16 satellite, several different groups have developed color visible algorithms that are available on public websites. The purpose of this paper is to help explain the similarities and differences of true-color GOES images that are on the web and in other locations.","PeriodicalId":44039,"journal":{"name":"Journal of Operational Meteorology","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48670540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-21DOI: 10.15191/nwajom.2023.1103
Christopher Wright, Dean Berkowitz, Julia Liu, Lauren Mock, Brandy Nisbet-Wilcox, K. Ross, T. Toth, K. Weber
Wildfire smoke can be transported far from its origin, adversely impacting human health. The height of the atmospheric mixing layer, the near-surface layer of the troposphere in which turbulent convection leads to vertical mixing, is called the mixing height. Mixing height is a critical input in the smoke dispersion and air quality models used by agencies that monitor wildfires and air pollution. These models, coupled with forecaster expertise, are also used to determine if it is safe to execute a prescribed burn. In this paper, we derive mixing heights from two satellite datasets in order to assess mixing height forecasts produced by the National Weather Service (NWS) Fire Weather Program. Namely, we use Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) Vertical Feature Masks (VFM) and vertical water vapor profiles from the Moderate Resolution Imaging Spectroradiometer (MODIS). Our comparison indicates that NWS forecasts tend to underestimate CALIOP mixing heights with a median relative error of –13% and a mean relative error of –3.34%. Although MODIS and NWS mixing heights showed some agreement below 3 km, the lower vertical resolution of the MODIS estimates hindered a full comparison. We examine the discrepancies among mixing heights over wildfire smoke plumes determined by these methods and discuss biases and limitations. This work provides insight into potential bias patterns present in current mixing height forecasts and provides directions for future improvements in both NWS mixing height forecasts and satellite-based measurements of mixing height.
{"title":"Evaluating Mixing Height Estimations in the Western United States Using Satellite Observations","authors":"Christopher Wright, Dean Berkowitz, Julia Liu, Lauren Mock, Brandy Nisbet-Wilcox, K. Ross, T. Toth, K. Weber","doi":"10.15191/nwajom.2023.1103","DOIUrl":"https://doi.org/10.15191/nwajom.2023.1103","url":null,"abstract":"Wildfire smoke can be transported far from its origin, adversely impacting human health. The height of the atmospheric mixing layer, the near-surface layer of the troposphere in which turbulent convection leads to vertical mixing, is called the mixing height. Mixing height is a critical input in the smoke dispersion and air quality models used by agencies that monitor wildfires and air pollution. These models, coupled with forecaster expertise, are also used to determine if it is safe to execute a prescribed burn. In this paper, we derive mixing heights from two satellite datasets in order to assess mixing height forecasts produced by the National Weather Service (NWS) Fire Weather Program. Namely, we use Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) Vertical Feature Masks (VFM) and vertical water vapor profiles from the Moderate Resolution Imaging Spectroradiometer (MODIS). Our comparison indicates that NWS forecasts tend to underestimate CALIOP mixing heights with a median relative error of –13% and a mean relative error of –3.34%. Although MODIS and NWS mixing heights showed some agreement below 3 km, the lower vertical resolution of the MODIS estimates hindered a full comparison. We examine the discrepancies among mixing heights over wildfire smoke plumes determined by these methods and discuss biases and limitations. This work provides insight into potential bias patterns present in current mixing height forecasts and provides directions for future improvements in both NWS mixing height forecasts and satellite-based measurements of mixing height.","PeriodicalId":44039,"journal":{"name":"Journal of Operational Meteorology","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45970867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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