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- Author or Editor: Chad M. Gravelle x
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Abstract
In 2001, the National Weather Service (NWS) began a Lightning Safety Awareness Campaign to reduce lightning-related fatalities in the United States. Although fatalities have decreased 41% since the campaign began, lightning still poses a significant threat to public safety as the majority of victims have little or no warning of cloud-to-ground lightning. This suggests it would be valuable to message the threat of lightning before it occurs, especially to NWS core partners that have the responsibility to protect large numbers of people. During the summer of 2018, a subset of forecasters from the Jacksonville, Florida, NWS Weather Forecast Office investigated if messaging the threat of cloud-to-ground (CG) lightning in developing convection was possible. Based on previous CG lightning forecasting research, forecasters incorporated new high-resolution Geostationary Operational Environmental Satellite (GOES)-16 Day Cloud Phase Distinction red–green–blue (RGB) composite imagery with Multi-Radar Multi-Sensor isothermal reflectivity and total lightning data to determine if there was enough confidence to message the threat of CG lightning before it occurred. This paper will introduce the Day Cloud Phase Distinction RGB composite, show how it can add value for short-term lightning forecasting, and provide an operational example illustrating how fusing these datasets together may be able to provide confidence and extend the lead time when messaging the threat of cloud-to-ground lightning before it occurs.
Abstract
In 2001, the National Weather Service (NWS) began a Lightning Safety Awareness Campaign to reduce lightning-related fatalities in the United States. Although fatalities have decreased 41% since the campaign began, lightning still poses a significant threat to public safety as the majority of victims have little or no warning of cloud-to-ground lightning. This suggests it would be valuable to message the threat of lightning before it occurs, especially to NWS core partners that have the responsibility to protect large numbers of people. During the summer of 2018, a subset of forecasters from the Jacksonville, Florida, NWS Weather Forecast Office investigated if messaging the threat of cloud-to-ground (CG) lightning in developing convection was possible. Based on previous CG lightning forecasting research, forecasters incorporated new high-resolution Geostationary Operational Environmental Satellite (GOES)-16 Day Cloud Phase Distinction red–green–blue (RGB) composite imagery with Multi-Radar Multi-Sensor isothermal reflectivity and total lightning data to determine if there was enough confidence to message the threat of CG lightning before it occurred. This paper will introduce the Day Cloud Phase Distinction RGB composite, show how it can add value for short-term lightning forecasting, and provide an operational example illustrating how fusing these datasets together may be able to provide confidence and extend the lead time when messaging the threat of cloud-to-ground lightning before it occurs.
Abstract
Between February and April of 2015, the National Weather Service (NWS) Operations Proving Ground (OPG) facilitated an evaluation of the usefulness of 1-min satellite imagery for NWS operations in the Geostationary Operational Environmental Satellite-R (GOES-R) series era. The overarching goal of the evaluation was to provide quantitative and qualitative guidance to NWS management, including the regional NWS Scientific Services division chiefs, on how satellite imagery with a refresh rate of 1 min impacts NWS forecaster decision-making. During the simulations, forecasters evaluated 1- and 5-min satellite imagery while completing tasks ranging from aviation forecasting and wildfire decision support services to monitoring where convective initiation would occur and integrating the imagery into the convective warning decision-making process. Feedback was gathered to assess if the satellite imagery had influence on forecaster decision-making, if the satellite imagery provided them with more confidence in making those decisions, if forecasters could assimilate the data into operational practices, and if there were adverse impacts on forecaster workload. Forecasters overwhelmingly were of the opinion that 1-min satellite imagery improved their ability and increased their confidence to make effective forecast and warning decisions. The majority of participants expressed that they were able to internally assimilate the imagery with ease. However, feedback gathered when forecasters were asked how useful and easy the imagery was to use in convective warning operations was mixed. Some forecasters expressed difficulty integrating both satellite imagery and radar data while issuing convective warnings. Others felt that with ample training and experience the imagery would be invaluable in warning operations.
Abstract
Between February and April of 2015, the National Weather Service (NWS) Operations Proving Ground (OPG) facilitated an evaluation of the usefulness of 1-min satellite imagery for NWS operations in the Geostationary Operational Environmental Satellite-R (GOES-R) series era. The overarching goal of the evaluation was to provide quantitative and qualitative guidance to NWS management, including the regional NWS Scientific Services division chiefs, on how satellite imagery with a refresh rate of 1 min impacts NWS forecaster decision-making. During the simulations, forecasters evaluated 1- and 5-min satellite imagery while completing tasks ranging from aviation forecasting and wildfire decision support services to monitoring where convective initiation would occur and integrating the imagery into the convective warning decision-making process. Feedback was gathered to assess if the satellite imagery had influence on forecaster decision-making, if the satellite imagery provided them with more confidence in making those decisions, if forecasters could assimilate the data into operational practices, and if there were adverse impacts on forecaster workload. Forecasters overwhelmingly were of the opinion that 1-min satellite imagery improved their ability and increased their confidence to make effective forecast and warning decisions. The majority of participants expressed that they were able to internally assimilate the imagery with ease. However, feedback gathered when forecasters were asked how useful and easy the imagery was to use in convective warning operations was mixed. Some forecasters expressed difficulty integrating both satellite imagery and radar data while issuing convective warnings. Others felt that with ample training and experience the imagery would be invaluable in warning operations.
Abstract
With the launch of the Geostationary Operational Environmental Satellite–R (GOES-R) series in 2016, there will be continuity of observations for the current GOES system operating over the Western Hemisphere. The GOES-R Proving Ground was established in 2008 to help prepare satellite user communities for the enhanced capabilities of GOES-R, including new instruments, imagery, and products that will have increased spectral, spatial, and temporal resolution. This is accomplished through demonstration and evaluation of proxy products that use current GOES data, higher-resolution data provided by polar-orbiting satellites, and model-derived synthetic satellite imagery. The GOES-R demonstration products presented here, made available to forecasters in near–real time (within 20 min) via the GOES-R Proving Ground, include the 0–9-h NearCast model, 0–1-h convective initiation probabilities, convective cloud-top cooling, overshooting top detection, and a pseudo–Geostationary Lightning Mapper total lightning tendency diagnostic. These products are designed to assist in identifying areas of increasing convective instability, pre-radar echo cumulus cloud growth preceding thunderstorm formation, storm updraft intensity, and potential storm severity derived from lightning trends. In turn, they provide the warning forecaster with improved situational awareness and short-term predictive information that enhance their ability to monitor atmospheric conditions preceding and associated with the development of deep convection, a time period that typically occurs between the issuance of National Weather Service (NWS) Storm Prediction Center convective watches and convective storm warnings issued by NWS forecast offices. This paper will focus on how this GOES-R satellite convective toolkit could have been used by warning forecasters to enhance near-storm environment analysis and the warning-decision-making process prior to and during the 20 May 2013 Moore, Oklahoma, tornado event.
Abstract
With the launch of the Geostationary Operational Environmental Satellite–R (GOES-R) series in 2016, there will be continuity of observations for the current GOES system operating over the Western Hemisphere. The GOES-R Proving Ground was established in 2008 to help prepare satellite user communities for the enhanced capabilities of GOES-R, including new instruments, imagery, and products that will have increased spectral, spatial, and temporal resolution. This is accomplished through demonstration and evaluation of proxy products that use current GOES data, higher-resolution data provided by polar-orbiting satellites, and model-derived synthetic satellite imagery. The GOES-R demonstration products presented here, made available to forecasters in near–real time (within 20 min) via the GOES-R Proving Ground, include the 0–9-h NearCast model, 0–1-h convective initiation probabilities, convective cloud-top cooling, overshooting top detection, and a pseudo–Geostationary Lightning Mapper total lightning tendency diagnostic. These products are designed to assist in identifying areas of increasing convective instability, pre-radar echo cumulus cloud growth preceding thunderstorm formation, storm updraft intensity, and potential storm severity derived from lightning trends. In turn, they provide the warning forecaster with improved situational awareness and short-term predictive information that enhance their ability to monitor atmospheric conditions preceding and associated with the development of deep convection, a time period that typically occurs between the issuance of National Weather Service (NWS) Storm Prediction Center convective watches and convective storm warnings issued by NWS forecast offices. This paper will focus on how this GOES-R satellite convective toolkit could have been used by warning forecasters to enhance near-storm environment analysis and the warning-decision-making process prior to and during the 20 May 2013 Moore, Oklahoma, tornado event.
Abstract
The empirical Probability of Severe (ProbSevere) model, developed by the National Oceanic and Atmospheric Administration (NOAA) and the Cooperative Institute for Meteorological Satellite Studies (CIMSS), automatically extracts information related to thunderstorm development from several data sources to produce timely, short-term, statistical forecasts of thunderstorm intensity. More specifically, ProbSevere utilizes short-term numerical weather prediction guidance (NWP), geostationary satellite, ground-based radar, and ground-based lightning data to determine the probability that convective storm cells will produce severe weather up to 90 min in the future. ProbSevere guidance, which updates approximately every 2 min, is available to National Weather Service (NWS) Weather Forecast Offices with very short latency. This paper focuses on the integration of ground-based lightning detection data into ProbSevere. In addition, a thorough validation analysis is presented. The validation analysis demonstrates that ProbSevere has slightly less skill compared to NWS severe weather warnings, but can offer greater lead time to initial hazards. Feedback from NWS users has been highly favorable, with most forecasters responding that ProbSevere increases confidence and lead time in numerous warning situations.
Abstract
The empirical Probability of Severe (ProbSevere) model, developed by the National Oceanic and Atmospheric Administration (NOAA) and the Cooperative Institute for Meteorological Satellite Studies (CIMSS), automatically extracts information related to thunderstorm development from several data sources to produce timely, short-term, statistical forecasts of thunderstorm intensity. More specifically, ProbSevere utilizes short-term numerical weather prediction guidance (NWP), geostationary satellite, ground-based radar, and ground-based lightning data to determine the probability that convective storm cells will produce severe weather up to 90 min in the future. ProbSevere guidance, which updates approximately every 2 min, is available to National Weather Service (NWS) Weather Forecast Offices with very short latency. This paper focuses on the integration of ground-based lightning detection data into ProbSevere. In addition, a thorough validation analysis is presented. The validation analysis demonstrates that ProbSevere has slightly less skill compared to NWS severe weather warnings, but can offer greater lead time to initial hazards. Feedback from NWS users has been highly favorable, with most forecasters responding that ProbSevere increases confidence and lead time in numerous warning situations.
