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Abstract
Historically, the Gulf of Mexico has been considered a primary source of water vapor that influences the weather for much of the United States east of the Rocky Mountains. Although severe thunderstorms and tornadoes occur most frequently during the spring and summer months, the periodic transport of Gulf moisture inland ahead of traveling baroclinic waves can result in significant severe-weather episodes during the cool season.
To gain insight into the short-range skill in forecasting surface synoptic patterns associated with moisture return from the Gulf, operational numerical weather prediction models from the National Meteorological Center were examined. Sea level pressure fields from the Limited-Area Fine-Mesh Model (LFM), Nested Grid Model (NGM), and the aviation (AVN) run of the Global Spectral Model, valid 48 h after initial data time, were evaluated for three cool-season cases that preceded severe local storm outbreaks. The NGM and AVN provided useful guidance in forecasting the onset of return flow along the Gulf coast. There was a slight tendency for these models to be slightly slow in the development of return flow. In contrast the LFM typically overforecasts the occurrence of return flow and tends to “open the Gulf” from west to east too quickly.
Although the low-level synoptic pattern may be forecast correctly, the overall prediction process is hampered by a data void over the Gulf. It is hypothesized that when the return-flow moisture is located over the Gulf, model forecasts of stability and the resultant operational severe local storm forecasts are less skillful compared to situations when the moisture has spread inland already. This hypothesis is tested by examining the performance of the initial second-day (day 2) severe thunderstorm outlook issued by the National Severe Storms Forecast Center during the Gulf of Mexico Experiment (GUFMEX) in early 1988.
It has been found that characteristically different air masses were present along the Gulf coast prior to the issuance of outlooks that accurately predicted the occurrence of severe thunderstorms versus outlooks that did not verify well. Unstable air masses with ample low-level moisture were in place along the coast prior to the issuance of the “good” day 2 outlooks, whereas relatively dry, stable air masses were present before the issuance of “false-alarm” outlooks. In the latter cases, large errors in the NGM 48-h lifted-index predictions were located north of the Gulf coast.
Abstract
Historically, the Gulf of Mexico has been considered a primary source of water vapor that influences the weather for much of the United States east of the Rocky Mountains. Although severe thunderstorms and tornadoes occur most frequently during the spring and summer months, the periodic transport of Gulf moisture inland ahead of traveling baroclinic waves can result in significant severe-weather episodes during the cool season.
To gain insight into the short-range skill in forecasting surface synoptic patterns associated with moisture return from the Gulf, operational numerical weather prediction models from the National Meteorological Center were examined. Sea level pressure fields from the Limited-Area Fine-Mesh Model (LFM), Nested Grid Model (NGM), and the aviation (AVN) run of the Global Spectral Model, valid 48 h after initial data time, were evaluated for three cool-season cases that preceded severe local storm outbreaks. The NGM and AVN provided useful guidance in forecasting the onset of return flow along the Gulf coast. There was a slight tendency for these models to be slightly slow in the development of return flow. In contrast the LFM typically overforecasts the occurrence of return flow and tends to “open the Gulf” from west to east too quickly.
Although the low-level synoptic pattern may be forecast correctly, the overall prediction process is hampered by a data void over the Gulf. It is hypothesized that when the return-flow moisture is located over the Gulf, model forecasts of stability and the resultant operational severe local storm forecasts are less skillful compared to situations when the moisture has spread inland already. This hypothesis is tested by examining the performance of the initial second-day (day 2) severe thunderstorm outlook issued by the National Severe Storms Forecast Center during the Gulf of Mexico Experiment (GUFMEX) in early 1988.
It has been found that characteristically different air masses were present along the Gulf coast prior to the issuance of outlooks that accurately predicted the occurrence of severe thunderstorms versus outlooks that did not verify well. Unstable air masses with ample low-level moisture were in place along the coast prior to the issuance of the “good” day 2 outlooks, whereas relatively dry, stable air masses were present before the issuance of “false-alarm” outlooks. In the latter cases, large errors in the NGM 48-h lifted-index predictions were located north of the Gulf coast.
Abstract
A six-member ensemble is developed in which the ensemble members only vary in their model physical process parameterization schemes. This approach is accomplished by mixing three different convective parameterization schemes with two different planetary boundary layer schemes within the nonhydrostatic Pennsylvania State University–National Center for Atmospheric Research Fifth-Generation Mesoscale Model (MM5). The initial and boundary conditions for each ensemble member are identical and are provided by the National Centers for Environmental Prediction Eta Model forecasts starting from 0000 UTC. Verification of the ensemble predictions against Eta Model analyses over 42 days indicates that, although this ensemble system is underdispersive and imperfect, the ensemble forecasts show some skill in predicting the probability of various severe-weather parameters exceeding selected threshold values. This model physics ensemble allows us to begin exploring the possible uses of ensemble forecasts for severe-weather events. Results from this six-member ensemble forecasting system of the 3 May 1999 tornado outbreak indicate that the ensemble provides a strong signal of two mesoscale-sized regions, one in Oklahoma and Kansas and the other in eastern Nebraska, that have the potential for supporting tornadic supercell thunderstorms. Several of the model forecasts also produce convection in these regions. Tornadic thunderstorm reports are found in both of these areas. This ensemble guidance does not provide any clues as to why the tornadoes in Oklahoma and Kansas were so severe, as compared with those in Nebraska, but it does provide hope that ensembles may be useful for short-range forecasting of severe weather.
Abstract
A six-member ensemble is developed in which the ensemble members only vary in their model physical process parameterization schemes. This approach is accomplished by mixing three different convective parameterization schemes with two different planetary boundary layer schemes within the nonhydrostatic Pennsylvania State University–National Center for Atmospheric Research Fifth-Generation Mesoscale Model (MM5). The initial and boundary conditions for each ensemble member are identical and are provided by the National Centers for Environmental Prediction Eta Model forecasts starting from 0000 UTC. Verification of the ensemble predictions against Eta Model analyses over 42 days indicates that, although this ensemble system is underdispersive and imperfect, the ensemble forecasts show some skill in predicting the probability of various severe-weather parameters exceeding selected threshold values. This model physics ensemble allows us to begin exploring the possible uses of ensemble forecasts for severe-weather events. Results from this six-member ensemble forecasting system of the 3 May 1999 tornado outbreak indicate that the ensemble provides a strong signal of two mesoscale-sized regions, one in Oklahoma and Kansas and the other in eastern Nebraska, that have the potential for supporting tornadic supercell thunderstorms. Several of the model forecasts also produce convection in these regions. Tornadic thunderstorm reports are found in both of these areas. This ensemble guidance does not provide any clues as to why the tornadoes in Oklahoma and Kansas were so severe, as compared with those in Nebraska, but it does provide hope that ensembles may be useful for short-range forecasting of severe weather.
Tornado watch and severe local storm outlook verification statistics reveal the trends in forecast skill at the National Severe Storms Forecast Center. The skill level of the outlook has been steadily increasing since 1973. The percentage of watches verifying has been gradually increasing since 1970. While the probability of detection for tornadoes has decreased slightly since 1974, this appears to be highly correlated with the number of outbreak tornadoes reported in a given year. During significant tornado days, a much higher degree of skill is exhibited for both outlooks and watches. Factors influencing the results are discussed, including the impact of snoptic scale operational numerical prediction models on the severe local storm forecasting process.
Tornado watch and severe local storm outlook verification statistics reveal the trends in forecast skill at the National Severe Storms Forecast Center. The skill level of the outlook has been steadily increasing since 1973. The percentage of watches verifying has been gradually increasing since 1970. While the probability of detection for tornadoes has decreased slightly since 1974, this appears to be highly correlated with the number of outbreak tornadoes reported in a given year. During significant tornado days, a much higher degree of skill is exhibited for both outlooks and watches. Factors influencing the results are discussed, including the impact of snoptic scale operational numerical prediction models on the severe local storm forecasting process.
Abstract
This study analyzes the low short-range predictability of the 3 May 2020 derecho using a 40-member convection-allowing Model for Prediction Across Scales (MPAS) ensemble. Elevated storms formed in south-central Kansas late at night and evolved into a progressive mesoscale convective system (MCS) during the morning while moving across southern Missouri and northern Arkansas, and affected western and middle Tennessee and southern Kentucky in the afternoon. The convective initiation (CI) in south-central Kansas, the organization of a dominant bow echo MCS, and the MCS maintenance over Tennessee were identified as the three main predictability issues. These issues were explored using three MPAS ensemble members, observations, and the Rapid Refresh analyses. The MPAS members were classified as successful or unsuccessful with regard to each predictability issue. CI in south-central Kansas was sensitive to the temperature and dewpoint profiles in low levels, which were associated with greater elevated thermodynamic instability and lower level of free convection in the successful member. The subsequent organization of a dominant bowing MCS was well predicted by the member that had more widespread convection in the early stages and no detrimental interaction with other simulated convective systems. Last, the inability of MPAS ensemble members to predict the MCS maintenance over western and middle Tennessee was linked to a dry bias in low levels and much lower thermodynamic instability ahead of the MCS compared to observations. This case demonstrates the challenges in operational forecasting of warm-season derecho-producing progressive MCSs, particularly when ensemble numerical weather prediction guidance solutions differ considerably.
Abstract
This study analyzes the low short-range predictability of the 3 May 2020 derecho using a 40-member convection-allowing Model for Prediction Across Scales (MPAS) ensemble. Elevated storms formed in south-central Kansas late at night and evolved into a progressive mesoscale convective system (MCS) during the morning while moving across southern Missouri and northern Arkansas, and affected western and middle Tennessee and southern Kentucky in the afternoon. The convective initiation (CI) in south-central Kansas, the organization of a dominant bow echo MCS, and the MCS maintenance over Tennessee were identified as the three main predictability issues. These issues were explored using three MPAS ensemble members, observations, and the Rapid Refresh analyses. The MPAS members were classified as successful or unsuccessful with regard to each predictability issue. CI in south-central Kansas was sensitive to the temperature and dewpoint profiles in low levels, which were associated with greater elevated thermodynamic instability and lower level of free convection in the successful member. The subsequent organization of a dominant bowing MCS was well predicted by the member that had more widespread convection in the early stages and no detrimental interaction with other simulated convective systems. Last, the inability of MPAS ensemble members to predict the MCS maintenance over western and middle Tennessee was linked to a dry bias in low levels and much lower thermodynamic instability ahead of the MCS compared to observations. This case demonstrates the challenges in operational forecasting of warm-season derecho-producing progressive MCSs, particularly when ensemble numerical weather prediction guidance solutions differ considerably.
Abstract
Proximity sounding studies typically seek to optimize several trade-offs that involve somewhat arbitrary definitions of how to define a “proximity sounding.” More restrictive proximity criteria, which presumably produce results that are more characteristic of the near-storm environment, typically result in smaller sample sizes that can reduce the statistical significance of the results. Conversely, the use of broad proximity criteria will typically increase the sample size and the apparent robustness of the statistical analysis, but the sounding data may not necessarily be representative of near-storm environments, given the presence of mesoscale variability in the atmosphere. Previous investigations have used a wide range of spatial and temporal proximity criteria to analyze severe storm environments. However, the sensitivity of storm environment climatologies to the proximity definition has not yet been rigorously examined.
In this study, a very large set (∼1200) of proximity soundings associated with significant tornado reports is used to generate distributions of several parameters typically used to characterize severe weather environments. Statistical tests are used to assess the sensitivity of the parameter distributions to the proximity criteria. The results indicate that while soundings collected too far in space and time from significant tornadoes tend to be more representative of the larger-scale environment than of the storm environment, soundings collected too close to the tornado also tend to be less representative due to the convective feedback process. The storm environment itself is thus optimally sampled at an intermediate spatiotemporal range referred to here as the Goldilocks zone. Implications of these results for future proximity sounding studies are discussed.
Abstract
Proximity sounding studies typically seek to optimize several trade-offs that involve somewhat arbitrary definitions of how to define a “proximity sounding.” More restrictive proximity criteria, which presumably produce results that are more characteristic of the near-storm environment, typically result in smaller sample sizes that can reduce the statistical significance of the results. Conversely, the use of broad proximity criteria will typically increase the sample size and the apparent robustness of the statistical analysis, but the sounding data may not necessarily be representative of near-storm environments, given the presence of mesoscale variability in the atmosphere. Previous investigations have used a wide range of spatial and temporal proximity criteria to analyze severe storm environments. However, the sensitivity of storm environment climatologies to the proximity definition has not yet been rigorously examined.
In this study, a very large set (∼1200) of proximity soundings associated with significant tornado reports is used to generate distributions of several parameters typically used to characterize severe weather environments. Statistical tests are used to assess the sensitivity of the parameter distributions to the proximity criteria. The results indicate that while soundings collected too far in space and time from significant tornadoes tend to be more representative of the larger-scale environment than of the storm environment, soundings collected too close to the tornado also tend to be less representative due to the convective feedback process. The storm environment itself is thus optimally sampled at an intermediate spatiotemporal range referred to here as the Goldilocks zone. Implications of these results for future proximity sounding studies are discussed.
Abstract
No abstract available.
Abstract
No abstract available.
Abstract
Parameterized updraft mass flux, available as a unique predictive field from the Kain–Fritsch (KF) convective parameterization, is presented as a potentially valuable predictor of convective intensity. The KF scheme is described in some detail, focusing on a version that is currently being run semioperationally in an experimental version of the Eta Model. It is shown that updraft mass flux computed by this scheme is a function of the specific algorithm that it utilizes and is very sensitive to the thermodynamic characteristics of input soundings. These same characteristics appear to be related to the severity of convection, suggesting that updraft mass flux predicted by the KF scheme has value for predicting severe weather. This argument is supported by anecdotal evidence and a case study.
Abstract
Parameterized updraft mass flux, available as a unique predictive field from the Kain–Fritsch (KF) convective parameterization, is presented as a potentially valuable predictor of convective intensity. The KF scheme is described in some detail, focusing on a version that is currently being run semioperationally in an experimental version of the Eta Model. It is shown that updraft mass flux computed by this scheme is a function of the specific algorithm that it utilizes and is very sensitive to the thermodynamic characteristics of input soundings. These same characteristics appear to be related to the severity of convection, suggesting that updraft mass flux predicted by the KF scheme has value for predicting severe weather. This argument is supported by anecdotal evidence and a case study.
Abstract
From 15 July through 30 September of 2001, an ensemble cloud-scale model was run for the Storm Prediction Center on a daily basis. Each ensemble run consisted of 78 members whose initial conditions were derived from the 20-km Rapid Update Cycle Model, the 22-km operational Eta Model, and a locally run version of the 22-km Eta Model using the Kain–Fritsch convective parameterization. Each ensemble was run over a 160 km × 160 km region and was valid for the 9-h period from 1630 through 0130 UTC. The ensembles were used primarily to provide severe-weather guidance. To that end, model storms with lifetimes greater than 60 min and/or a sustained correlation of at least 0.5 between midlevel updrafts and positive vorticity (the supercell criterion) were considered to be severe-weather indicators. Heidke skill scores, along with the true skill statistic, are between 0.2 and 0.3 when long-lived storms or storms meeting the supercell criteria are used as severe-weather indicators. Equivalent skill scores result when modeled and observed storms are categorized by lifetime and supercell characteristics and compared with expertly interpreted radar data.
Abstract
From 15 July through 30 September of 2001, an ensemble cloud-scale model was run for the Storm Prediction Center on a daily basis. Each ensemble run consisted of 78 members whose initial conditions were derived from the 20-km Rapid Update Cycle Model, the 22-km operational Eta Model, and a locally run version of the 22-km Eta Model using the Kain–Fritsch convective parameterization. Each ensemble was run over a 160 km × 160 km region and was valid for the 9-h period from 1630 through 0130 UTC. The ensembles were used primarily to provide severe-weather guidance. To that end, model storms with lifetimes greater than 60 min and/or a sustained correlation of at least 0.5 between midlevel updrafts and positive vorticity (the supercell criterion) were considered to be severe-weather indicators. Heidke skill scores, along with the true skill statistic, are between 0.2 and 0.3 when long-lived storms or storms meeting the supercell criteria are used as severe-weather indicators. Equivalent skill scores result when modeled and observed storms are categorized by lifetime and supercell characteristics and compared with expertly interpreted radar data.
Abstract
This study analyzes the operational predictability of warm-season (May-August) progressive derechos. A subset of 47 derechos occurring between 2010 and 2022 were selected based on NCEI Storm Data and radar imagery. The Storm Prediction Center Convective Outlooks issued from five days before each derecho to the day of the event were used to determine if the severe weather and derecho predictability was low, moderate, or high, for each case. The cases were also categorized based on the synoptic-scale midlevel flow pattern. Composite maps of synoptic patterns associated with the derecho cases and distributions of severe weather parameters in the derecho inflow region were generated. About 72% of the 47 derechos selected for this study were associated with low predictability, and the remainder were categorized with moderate predictability. None of the cases had high predictability. Most derechos occurring under northwest and zonal flow regimes (80% and 85%, respectively) had low predictability. Composites of low- and moderate-predictability derechos indicate that derechos formed in the equatorward entrance region of the upper-level jet, where low-level warm advection exists in conjunction with an equivalent potential temperature maximum. A midlevel trough is located upstream of the derecho initiation point in many of the moderate-predictability composites, but is absent in the low-predictability composites, which indicated weaker synoptic-scale forcing for ascent in low-predictability cases. The distributions of severe weather parameters for low- and moderate-predictability derechos are similar, suggesting that these parameters alone are not particularly useful as a discriminator of derecho predictability.
Abstract
This study analyzes the operational predictability of warm-season (May-August) progressive derechos. A subset of 47 derechos occurring between 2010 and 2022 were selected based on NCEI Storm Data and radar imagery. The Storm Prediction Center Convective Outlooks issued from five days before each derecho to the day of the event were used to determine if the severe weather and derecho predictability was low, moderate, or high, for each case. The cases were also categorized based on the synoptic-scale midlevel flow pattern. Composite maps of synoptic patterns associated with the derecho cases and distributions of severe weather parameters in the derecho inflow region were generated. About 72% of the 47 derechos selected for this study were associated with low predictability, and the remainder were categorized with moderate predictability. None of the cases had high predictability. Most derechos occurring under northwest and zonal flow regimes (80% and 85%, respectively) had low predictability. Composites of low- and moderate-predictability derechos indicate that derechos formed in the equatorward entrance region of the upper-level jet, where low-level warm advection exists in conjunction with an equivalent potential temperature maximum. A midlevel trough is located upstream of the derecho initiation point in many of the moderate-predictability composites, but is absent in the low-predictability composites, which indicated weaker synoptic-scale forcing for ascent in low-predictability cases. The distributions of severe weather parameters for low- and moderate-predictability derechos are similar, suggesting that these parameters alone are not particularly useful as a discriminator of derecho predictability.
Abstract
Since the early 2000s, growing computing resources for numerical weather prediction (NWP) and scientific advances enabled development and testing of experimental, real-time deterministic convection-allowing models (CAMs). By the late 2000s, continued advancements spurred development of CAM ensemble forecast systems, through which a broad range of successful forecasting applications have been demonstrated. This work has prepared the National Weather Service (NWS) for practical usage of the High Resolution Ensemble Forecast (HREF) system, which was implemented operationally in November 2017. Historically, methods for postprocessing and visualizing products from regional and global ensemble prediction systems (e.g., ensemble means and spaghetti plots) have been applied to fields that provide information on mesoscale to synoptic-scale processes. However, much of the value from CAMs is derived from the explicit simulation of deep convection and associated storm-attribute fields like updraft helicity and simulated reflectivity. Thus, fully exploiting CAM ensembles for forecasting applications has required the development of fundamentally new data extraction, postprocessing, and visualization strategies. In the process, challenges imposed by the immense data volume inherent to these systems required new approaches when considering diverse factors like forecaster interpretation and computational expense. In this article, we review the current state of postprocessing and visualization for CAM ensembles, with a particular focus on forecast applications for severe convective hazards that have been evaluated within NOAA’s Hazardous Weather Testbed. The HREF web viewer implemented at the NWS Storm Prediction Center (SPC) is presented as a prototype for deploying these techniques in real time on a flexible and widely accessible platform.
Abstract
Since the early 2000s, growing computing resources for numerical weather prediction (NWP) and scientific advances enabled development and testing of experimental, real-time deterministic convection-allowing models (CAMs). By the late 2000s, continued advancements spurred development of CAM ensemble forecast systems, through which a broad range of successful forecasting applications have been demonstrated. This work has prepared the National Weather Service (NWS) for practical usage of the High Resolution Ensemble Forecast (HREF) system, which was implemented operationally in November 2017. Historically, methods for postprocessing and visualizing products from regional and global ensemble prediction systems (e.g., ensemble means and spaghetti plots) have been applied to fields that provide information on mesoscale to synoptic-scale processes. However, much of the value from CAMs is derived from the explicit simulation of deep convection and associated storm-attribute fields like updraft helicity and simulated reflectivity. Thus, fully exploiting CAM ensembles for forecasting applications has required the development of fundamentally new data extraction, postprocessing, and visualization strategies. In the process, challenges imposed by the immense data volume inherent to these systems required new approaches when considering diverse factors like forecaster interpretation and computational expense. In this article, we review the current state of postprocessing and visualization for CAM ensembles, with a particular focus on forecast applications for severe convective hazards that have been evaluated within NOAA’s Hazardous Weather Testbed. The HREF web viewer implemented at the NWS Storm Prediction Center (SPC) is presented as a prototype for deploying these techniques in real time on a flexible and widely accessible platform.
