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- Author or Editor: Michael P. Foster x
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Abstract
During the spring of 1995, an operational forecast experiment using a three-dimensional cloud model was carried out for the north Texas region. Gridpoint soundings were obtained from the daily operational numerical weather prediction models run at the National Centers for Environmental Prediction, and these soundings were then used to initialize a limited-domain cloud-resolving model in an attempt to predict convective storm type and morphology in a timely manner. The results indicate that this type of convective forecast may be useful in the operational environment, despite several limitations associated with this methodology. One interesting result from the experiment is that while the gridpoint soundings obtained from the NCEP models generally overforecast instability and vertical wind shear, the resulting convective storm evolution and morphology in the cloud model was often similar to that of the observed storms. Therefore the “overforecast” of mesoscale environment’s instability and vertical wind shear still resulted in a thunderstorm-scale forecast that provided useful information to operational forecasters.
Abstract
During the spring of 1995, an operational forecast experiment using a three-dimensional cloud model was carried out for the north Texas region. Gridpoint soundings were obtained from the daily operational numerical weather prediction models run at the National Centers for Environmental Prediction, and these soundings were then used to initialize a limited-domain cloud-resolving model in an attempt to predict convective storm type and morphology in a timely manner. The results indicate that this type of convective forecast may be useful in the operational environment, despite several limitations associated with this methodology. One interesting result from the experiment is that while the gridpoint soundings obtained from the NCEP models generally overforecast instability and vertical wind shear, the resulting convective storm evolution and morphology in the cloud model was often similar to that of the observed storms. Therefore the “overforecast” of mesoscale environment’s instability and vertical wind shear still resulted in a thunderstorm-scale forecast that provided useful information to operational forecasters.
Abstract
Two experimental forecasting projects, each called Mesoscale Applications Project (MAP), were conducted jointly by the National Severe Storms Laboratory and the National Weather Service Forecast Office at Norman, Oklahoma, during 1988 and 1989. This paper focuses primarily on the verification of the MAP'88 and MAP'89 experimental forecasts, and combines the results with those from a similar experiment run in 1987, to examine the evolution of forecast skill over that three-year period.
Results suggest that the severe-weather outlooks issued on a given experiment day exhibited good skill, with the skill being fairly stable over the three-year period studied. For outlooks issued the day before, the skill was notably higher in 1987 than in the subsequent years. Convective-mode forecasts ranged from poor to moderate skill levels, and did not change significantly from results obtained in 1987. Areal lightning forecasts were attempted in 1988 and 1989, with skill increasing more or less as the valid area increased, that area being defined as a circle ranging from 10 to 40 km of Norman. Advance outlooks for lightning, issued the day before the anticipated event, showed little or no skill. Some discussion of the possible reasons for the observed forecasting skill and its trends is presented. Several aspects of forecasting experiments in general are discussed also, based on experience during the MAP experiments.
Abstract
Two experimental forecasting projects, each called Mesoscale Applications Project (MAP), were conducted jointly by the National Severe Storms Laboratory and the National Weather Service Forecast Office at Norman, Oklahoma, during 1988 and 1989. This paper focuses primarily on the verification of the MAP'88 and MAP'89 experimental forecasts, and combines the results with those from a similar experiment run in 1987, to examine the evolution of forecast skill over that three-year period.
Results suggest that the severe-weather outlooks issued on a given experiment day exhibited good skill, with the skill being fairly stable over the three-year period studied. For outlooks issued the day before, the skill was notably higher in 1987 than in the subsequent years. Convective-mode forecasts ranged from poor to moderate skill levels, and did not change significantly from results obtained in 1987. Areal lightning forecasts were attempted in 1988 and 1989, with skill increasing more or less as the valid area increased, that area being defined as a circle ranging from 10 to 40 km of Norman. Advance outlooks for lightning, issued the day before the anticipated event, showed little or no skill. Some discussion of the possible reasons for the observed forecasting skill and its trends is presented. Several aspects of forecasting experiments in general are discussed also, based on experience during the MAP experiments.
Abstract
Supercell thunderstorm forecasting and detection is discussed, in light of the disastrous weather events that often accompany supercells. The emphasis is placed on using a scientific approach to evaluate supercell potential and to recognize their presence rather than the more empirical methodologies (e.g., “rules of thumb”) that have been used in the past. Operational forecasters in the National Weather Service (NWS) can employ conceptual models of the supercell, and of the meteorological environments that produce supercells, to make operational decisions scientifically.
The presence of a mesocyclone is common to all supercells, but operational recognition of supercells is clouded by the various radar and visual characteristics they exhibit. The notion of a supercell spectrum is introduced in an effort to guide improved operational detection of supercells. An important part of recognition is the anticipation of what potential exists for supercells in the prestorm environment. Current scientific understanding suggests that cyclonic updraft rotation originates from streamwise vorticity (in the storm's reference frame) within its environment. A discussion of how storm-relative helicity can be used to evaluate supercell potential is given. An actual supercell event is employed to illustrate the usefulness of conceptual model visualization when issuing statements and warnings for supercell storms. Finally, supercell detection strategies using the advanced datasets from the modernized and restructured NWS are described.
Abstract
Supercell thunderstorm forecasting and detection is discussed, in light of the disastrous weather events that often accompany supercells. The emphasis is placed on using a scientific approach to evaluate supercell potential and to recognize their presence rather than the more empirical methodologies (e.g., “rules of thumb”) that have been used in the past. Operational forecasters in the National Weather Service (NWS) can employ conceptual models of the supercell, and of the meteorological environments that produce supercells, to make operational decisions scientifically.
The presence of a mesocyclone is common to all supercells, but operational recognition of supercells is clouded by the various radar and visual characteristics they exhibit. The notion of a supercell spectrum is introduced in an effort to guide improved operational detection of supercells. An important part of recognition is the anticipation of what potential exists for supercells in the prestorm environment. Current scientific understanding suggests that cyclonic updraft rotation originates from streamwise vorticity (in the storm's reference frame) within its environment. A discussion of how storm-relative helicity can be used to evaluate supercell potential is given. An actual supercell event is employed to illustrate the usefulness of conceptual model visualization when issuing statements and warnings for supercell storms. Finally, supercell detection strategies using the advanced datasets from the modernized and restructured NWS are described.
Abstract
To test the utility and added value of polarimetric radar products in an operational environment, data from the Norman, Oklahoma (KOUN), polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D) were delivered to the National Weather Service Weather Forecast Office (WFO) in Norman as part of the Joint Polarization Experiment (JPOLE). KOUN polarimetric base data and algorithms were used at the WFO during the decision-making and forecasting processes for severe convection, flash floods, and winter storms. The delivery included conventional WSR-88D radar products, base polarimetric radar variables, a polarimetric hydrometeor classification algorithm, and experimental polarimetric quantitative precipitation estimation algorithms. The JPOLE data collection, delivery, and operational demonstration are described, with examples of several forecast and warning decision-making successes. Polarimetric data aided WFO forecasters during several periods of heavy rain, numerous large-hail-producing thunderstorms, tornadic and nontornadic supercell thunderstorms, and a major winter storm. Upcoming opportunities and challenges associated with the emergence of polarimetric radar data in the operational community are also described.
Abstract
To test the utility and added value of polarimetric radar products in an operational environment, data from the Norman, Oklahoma (KOUN), polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D) were delivered to the National Weather Service Weather Forecast Office (WFO) in Norman as part of the Joint Polarization Experiment (JPOLE). KOUN polarimetric base data and algorithms were used at the WFO during the decision-making and forecasting processes for severe convection, flash floods, and winter storms. The delivery included conventional WSR-88D radar products, base polarimetric radar variables, a polarimetric hydrometeor classification algorithm, and experimental polarimetric quantitative precipitation estimation algorithms. The JPOLE data collection, delivery, and operational demonstration are described, with examples of several forecast and warning decision-making successes. Polarimetric data aided WFO forecasters during several periods of heavy rain, numerous large-hail-producing thunderstorms, tornadic and nontornadic supercell thunderstorms, and a major winter storm. Upcoming opportunities and challenges associated with the emergence of polarimetric radar data in the operational community are also described.
Convective-Scale Warn-on-Forecast System
A Vision for 2020
The National Oceanic and Atmospheric Administration's (NOAA's) National Weather Service (NWS) issues warnings for severe thunderstorms, tornadoes, and flash floods because these phenomena are a threat to life and property. These warnings are presently based upon either visual confirmation of the phenomena or the observational detection of proxy signatures that are largely based upon radar observations. Convective-scale weather warnings are unique in the NWS, having little reliance on direct numerical forecast guidance. Because increasing severe thunderstorm, tornado, and flash-flood warning lead times are a key NOAA strategic mission goal designed to reduce the loss of life, injury, and economic costs of these high-impact weather phenomena, a new warning paradigm is needed in which numerical model forecasts play a larger role in convective-scale warnings. This new paradigm shifts the warning process from warn on detection to warn on forecast, and it has the potential to dramatically increase warning lead times.
A warn-on-forecast system is envisioned as a probabilistic convective-scale ensemble analysis and forecast system that assimilates in-storm observations into a high-resolution convection-resolving model ensemble. The building blocks needed for such a system are presently available, and initial research results clearly illustrate the value of radar observations to the production of accurate analyses of convective weather systems and improved forecasts. Although a number of scientific and cultural challenges still need to be overcome, the potential benefits are significant. A probabilistic convective-scale warn-on-forecast system is a vision worth pursuing.
The National Oceanic and Atmospheric Administration's (NOAA's) National Weather Service (NWS) issues warnings for severe thunderstorms, tornadoes, and flash floods because these phenomena are a threat to life and property. These warnings are presently based upon either visual confirmation of the phenomena or the observational detection of proxy signatures that are largely based upon radar observations. Convective-scale weather warnings are unique in the NWS, having little reliance on direct numerical forecast guidance. Because increasing severe thunderstorm, tornado, and flash-flood warning lead times are a key NOAA strategic mission goal designed to reduce the loss of life, injury, and economic costs of these high-impact weather phenomena, a new warning paradigm is needed in which numerical model forecasts play a larger role in convective-scale warnings. This new paradigm shifts the warning process from warn on detection to warn on forecast, and it has the potential to dramatically increase warning lead times.
A warn-on-forecast system is envisioned as a probabilistic convective-scale ensemble analysis and forecast system that assimilates in-storm observations into a high-resolution convection-resolving model ensemble. The building blocks needed for such a system are presently available, and initial research results clearly illustrate the value of radar observations to the production of accurate analyses of convective weather systems and improved forecasts. Although a number of scientific and cultural challenges still need to be overcome, the potential benefits are significant. A probabilistic convective-scale warn-on-forecast system is a vision worth pursuing.
