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The potential economic benefit associated with the use of an ensemble of forecasts versus an equivalent or higher-resolution control forecast is discussed. Neither forecast systems are postprocessed, except a simple calibration that is applied to make them reliable. A simple decision-making model is used where all potential users of weather forecasts are characterized by the ratio between the cost of their action to prevent weather-related damages, and the loss that they incur in case they do not protect their operations. It is shown that the ensemble forecast system can be used by a much wider range of users. Furthermore, for many, and for beyond 4-day lead time for all users, the ensemble provides greater potential economic benefit than a control forecast, even if the latter is run at higher horizontal resolution. It is argued that the added benefits derive from 1) the fact that the ensemble provides a more detailed forecast probability distribution, allowing the users to tailor their weather forecast–related actions to their particular cost–loss situation, and 2) the ensemble's ability to differentiate between high-and low-predictability cases. While single forecasts can statistically be supplemented by more detailed probability distributions, it is not clear whether with more sophisticated postprocessing they can identify more and less predictable forecast cases as successfully as ensembles do.
The potential economic benefit associated with the use of an ensemble of forecasts versus an equivalent or higher-resolution control forecast is discussed. Neither forecast systems are postprocessed, except a simple calibration that is applied to make them reliable. A simple decision-making model is used where all potential users of weather forecasts are characterized by the ratio between the cost of their action to prevent weather-related damages, and the loss that they incur in case they do not protect their operations. It is shown that the ensemble forecast system can be used by a much wider range of users. Furthermore, for many, and for beyond 4-day lead time for all users, the ensemble provides greater potential economic benefit than a control forecast, even if the latter is run at higher horizontal resolution. It is argued that the added benefits derive from 1) the fact that the ensemble provides a more detailed forecast probability distribution, allowing the users to tailor their weather forecast–related actions to their particular cost–loss situation, and 2) the ensemble's ability to differentiate between high-and low-predictability cases. While single forecasts can statistically be supplemented by more detailed probability distributions, it is not clear whether with more sophisticated postprocessing they can identify more and less predictable forecast cases as successfully as ensembles do.
Abstract
Precipitation forecasts from five global numerical weather prediction (NWP) models are verified against rain gauge observations using the new stable equitable error in probability space (SEEPS) score. It is based on a 3 × 3 contingency table and measures the ability of a forecast to discriminate between “dry,” “light precipitation,” and “heavy precipitation.” In SEEPS, the threshold defining the boundary between the light and heavy categories varies systematically with precipitation climate. Results obtained for SEEPS are compared to those of more well-known scores, and are broken down with regard to individual contributions from the contingency table. It is found that differences in skill between the models are consistent for different scores, but are small compared to seasonal and geographical variations, which themselves can be largely ascribed to the varying prevalence of deep convection. Differences between the tropics and extratropics are quite pronounced. SEEPS scores at forecast day 1 in the tropics are similar to those at day 6 in the extratropics. It is found that the model ranking is robust with respect to choices in the score computation. The issue of observation representativeness is addressed using a “quasi-perfect model” approach. Results suggest that just under one-half of the current forecast error at day 1 in the extratropics can be attributed to the fact that gridbox values are verified against point observations.
Abstract
Precipitation forecasts from five global numerical weather prediction (NWP) models are verified against rain gauge observations using the new stable equitable error in probability space (SEEPS) score. It is based on a 3 × 3 contingency table and measures the ability of a forecast to discriminate between “dry,” “light precipitation,” and “heavy precipitation.” In SEEPS, the threshold defining the boundary between the light and heavy categories varies systematically with precipitation climate. Results obtained for SEEPS are compared to those of more well-known scores, and are broken down with regard to individual contributions from the contingency table. It is found that differences in skill between the models are consistent for different scores, but are small compared to seasonal and geographical variations, which themselves can be largely ascribed to the varying prevalence of deep convection. Differences between the tropics and extratropics are quite pronounced. SEEPS scores at forecast day 1 in the tropics are similar to those at day 6 in the extratropics. It is found that the model ranking is robust with respect to choices in the score computation. The issue of observation representativeness is addressed using a “quasi-perfect model” approach. Results suggest that just under one-half of the current forecast error at day 1 in the extratropics can be attributed to the fact that gridbox values are verified against point observations.
Abstract
Finescale single- and dual-Doppler observations are used to diagnose the three-dimensional structure of the wind field surrounding a tornado that occurred near the town of Orleans, Nebraska, on 22 May 2004. The evolution of the vorticity and divergence fields and other structures near the tornado are documented in the lowest kilometer. Changes in tornado intensity are compared to the position of the tornado relative to primary and secondary gust fronts. Circulation on scales of a few kilometers surrounding the tornado remains relatively constant during the analysis period, which spans the intensifying and mature periods of the tornado’s life cycle. Stretching of vertical vorticity and tilting of horizontal vorticity are diagnosed, but the latter is near or below the threshold of detectability in this analysis during the observation period in the analyzed domain. Low-level circulation within 500 m of the tornado increased several minutes before vortex-relative and ground-relative near-surface wind speeds in the tornado increased, raising the possibility that such trends in circulation may be useful in forecasting tornado intensification.
Abstract
Finescale single- and dual-Doppler observations are used to diagnose the three-dimensional structure of the wind field surrounding a tornado that occurred near the town of Orleans, Nebraska, on 22 May 2004. The evolution of the vorticity and divergence fields and other structures near the tornado are documented in the lowest kilometer. Changes in tornado intensity are compared to the position of the tornado relative to primary and secondary gust fronts. Circulation on scales of a few kilometers surrounding the tornado remains relatively constant during the analysis period, which spans the intensifying and mature periods of the tornado’s life cycle. Stretching of vertical vorticity and tilting of horizontal vorticity are diagnosed, but the latter is near or below the threshold of detectability in this analysis during the observation period in the analyzed domain. Low-level circulation within 500 m of the tornado increased several minutes before vortex-relative and ground-relative near-surface wind speeds in the tornado increased, raising the possibility that such trends in circulation may be useful in forecasting tornado intensification.
Abstract
High-resolution Doppler radar velocities and in situ surface observations collected in a tornadic supercell on 5 June 2009 during the second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2) are assimilated into a simulated convective storm using an ensemble Kalman filter (EnKF). A series of EnKF experiments using a 1-km horizontal model grid spacing demonstrates the sensitivity of the cold pool and kinematic structure of the storm to the assimilation of these observations and to different model microphysics parameterizations. An experiment is performed using a finer grid spacing (500 m) and the most optimal data assimilation and model configurations from the sensitivity tests to produce a realistically evolving storm. Analyses from this experiment are verified against dual-Doppler and in situ observations and are evaluated for their potential to confidently evaluate mesocyclone-scale processes in the storm using trajectory analysis and calculations of Lagrangian vorticity budgets. In Part II of this study, these analyses will be further evaluated to learn the roles that mesocyclone-scale processes play in tornado formation, maintenance, and decay. The coldness of the simulated low-level outflow is generally insensitive to the choice of certain microphysical parameterizations, likely owing to the vast quantity of kinematic and in situ thermodynamic observations assimilated. The three-dimensional EnKF wind fields and parcel trajectories resemble those retrieved from dual-Doppler observations within the storm, suggesting that realistic four-dimensional mesocyclone-scale processes are captured. However, potential errors are found in trajectories and Lagrangian three-dimensional vorticity budget calculations performed within the mesocyclone that may be due to the coarse (2 min) temporal resolution of the analyses. Therefore, caution must be exercised when interpreting trajectories in this area of the storm.
Abstract
High-resolution Doppler radar velocities and in situ surface observations collected in a tornadic supercell on 5 June 2009 during the second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2) are assimilated into a simulated convective storm using an ensemble Kalman filter (EnKF). A series of EnKF experiments using a 1-km horizontal model grid spacing demonstrates the sensitivity of the cold pool and kinematic structure of the storm to the assimilation of these observations and to different model microphysics parameterizations. An experiment is performed using a finer grid spacing (500 m) and the most optimal data assimilation and model configurations from the sensitivity tests to produce a realistically evolving storm. Analyses from this experiment are verified against dual-Doppler and in situ observations and are evaluated for their potential to confidently evaluate mesocyclone-scale processes in the storm using trajectory analysis and calculations of Lagrangian vorticity budgets. In Part II of this study, these analyses will be further evaluated to learn the roles that mesocyclone-scale processes play in tornado formation, maintenance, and decay. The coldness of the simulated low-level outflow is generally insensitive to the choice of certain microphysical parameterizations, likely owing to the vast quantity of kinematic and in situ thermodynamic observations assimilated. The three-dimensional EnKF wind fields and parcel trajectories resemble those retrieved from dual-Doppler observations within the storm, suggesting that realistic four-dimensional mesocyclone-scale processes are captured. However, potential errors are found in trajectories and Lagrangian three-dimensional vorticity budget calculations performed within the mesocyclone that may be due to the coarse (2 min) temporal resolution of the analyses. Therefore, caution must be exercised when interpreting trajectories in this area of the storm.
Abstract
On 2 June 1995, the large-scale environment of eastern New Mexico and western Texas was generally favorable for the occurrence of supercells because of the presence of strong deep shear and storm-relative helicity, as well as sufficient convective available potential energy (CAPE). Indeed, many supercells occurred, but the only storms to produce tornadoes were those supercells that crossed, or developed and persisted on the immediate cool side of a particular outflow boundary generated by earlier convection. Surface conditions, vertical vorticity, and horizontal vorticity near this boundary are documented using conventional and special observations from the VORTEX field program. It is shown that the boundary was locally rich in horizontal vorticity, had somewhat enhanced vertical vorticity, and enhanced CAPE. Theoretical arguments indicate that the observed horizontal vorticity (around 1 × 10−2 s−1), largely parallel to the boundary, can be readily produced with the type of buoyancy contrast observed. It is hypothesized that such local enhancement of horizontal vorticity often is required for the occurrence of significant (e.g., F2 or stronger) tornadoes, even in large-scale environments that appear conducive to tornado occurrence without the aid of local influences.
Abstract
On 2 June 1995, the large-scale environment of eastern New Mexico and western Texas was generally favorable for the occurrence of supercells because of the presence of strong deep shear and storm-relative helicity, as well as sufficient convective available potential energy (CAPE). Indeed, many supercells occurred, but the only storms to produce tornadoes were those supercells that crossed, or developed and persisted on the immediate cool side of a particular outflow boundary generated by earlier convection. Surface conditions, vertical vorticity, and horizontal vorticity near this boundary are documented using conventional and special observations from the VORTEX field program. It is shown that the boundary was locally rich in horizontal vorticity, had somewhat enhanced vertical vorticity, and enhanced CAPE. Theoretical arguments indicate that the observed horizontal vorticity (around 1 × 10−2 s−1), largely parallel to the boundary, can be readily produced with the type of buoyancy contrast observed. It is hypothesized that such local enhancement of horizontal vorticity often is required for the occurrence of significant (e.g., F2 or stronger) tornadoes, even in large-scale environments that appear conducive to tornado occurrence without the aid of local influences.
Abstract
The 3 May 1999 Oklahoma City storm is unique from a weather radar perspective because a long-track violent tornado passed within close range of several Doppler radars, because a detailed damage survey was conducted immediately after the event, and because high-quality visual observations of the tornado were available. The tornado passed relatively close (15–60 km) to two fixed-location Doppler radars: the National Weather Service Weather Surveillance Radar-1988 Doppler (WSR-88D) at Twin Lakes (KTLX) and the Radar Operations Center WSR-88D test radar at Norman (KCRI). Two mobile Doppler on Wheels (DOW) radars also observed the tornado at very close range (3–8 km). The data from DOW2 are nearly continuous for a substantial portion of the tornado's life and provide detailed information on the high-resolution velocity field in and around the tornado. These data permit good estimates of tornado rotational velocity and diameter. The data also allow comparison with tornado damage survey estimates of damage/intensity and with visual observations of tornado size. Further, one can compare the high-resolution DOW data to the lower-resolution WSR-88D observations to determine how much tornado information is lost because of the broader beamwidth (longer range) and longer gate length of the WSR-88Ds. Results of the study will be useful in determining how better to interpret future WSR-88D observations of near-range tornadoes, including any possible real-time estimation of tornado intensity and evolution.
Abstract
The 3 May 1999 Oklahoma City storm is unique from a weather radar perspective because a long-track violent tornado passed within close range of several Doppler radars, because a detailed damage survey was conducted immediately after the event, and because high-quality visual observations of the tornado were available. The tornado passed relatively close (15–60 km) to two fixed-location Doppler radars: the National Weather Service Weather Surveillance Radar-1988 Doppler (WSR-88D) at Twin Lakes (KTLX) and the Radar Operations Center WSR-88D test radar at Norman (KCRI). Two mobile Doppler on Wheels (DOW) radars also observed the tornado at very close range (3–8 km). The data from DOW2 are nearly continuous for a substantial portion of the tornado's life and provide detailed information on the high-resolution velocity field in and around the tornado. These data permit good estimates of tornado rotational velocity and diameter. The data also allow comparison with tornado damage survey estimates of damage/intensity and with visual observations of tornado size. Further, one can compare the high-resolution DOW data to the lower-resolution WSR-88D observations to determine how much tornado information is lost because of the broader beamwidth (longer range) and longer gate length of the WSR-88Ds. Results of the study will be useful in determining how better to interpret future WSR-88D observations of near-range tornadoes, including any possible real-time estimation of tornado intensity and evolution.
After tornado outbreaks or individual violent tornadoes occur in the central United States, media stories often attribute the location, number, or intensity of tornadoes to the “clash of air masses” between warm tropical air and cold polar air. This article argues that such a characterization of tornadogenesis is oversimplified, outdated, and incorrect. Airmass boundaries and associated temperature gradients can be important in tornadogenesis, but not in the ways envisioned on the synoptic scale with the clash-of-air-masses conceptual model. In fact, excessively strong horizontal temperature gradients (either on the synoptic scale or associated with a storm's own cool outflow) may be detrimental to tornadogenesis. Where adjacent air masses are relevant is through their vertical distribution that produces the requisite instability for the convective storm, but that instability is not directly related to the formation of tornadoes. Therefore, this article recommends that a greater effort be made to communicate accurately to the public the current scientific understanding of the conditions under which tornadoes are formed.
After tornado outbreaks or individual violent tornadoes occur in the central United States, media stories often attribute the location, number, or intensity of tornadoes to the “clash of air masses” between warm tropical air and cold polar air. This article argues that such a characterization of tornadogenesis is oversimplified, outdated, and incorrect. Airmass boundaries and associated temperature gradients can be important in tornadogenesis, but not in the ways envisioned on the synoptic scale with the clash-of-air-masses conceptual model. In fact, excessively strong horizontal temperature gradients (either on the synoptic scale or associated with a storm's own cool outflow) may be detrimental to tornadogenesis. Where adjacent air masses are relevant is through their vertical distribution that produces the requisite instability for the convective storm, but that instability is not directly related to the formation of tornadoes. Therefore, this article recommends that a greater effort be made to communicate accurately to the public the current scientific understanding of the conditions under which tornadoes are formed.
Abstract
Spatial variability of precipitation is analyzed to characterize to what extent precipitation observed at a single location is representative of precipitation over a larger area. Characterization of precipitation representativeness is made in probabilistic terms using a parametric approach, namely, by fitting a censored shifted gamma distribution to observation measurements. Parameters are estimated and analyzed for independent precipitation datasets, among which one is based on high-density gauge measurements. The results of this analysis serve as a basis for accounting for representativeness error in an ensemble verification process. Uncertainty associated with the scale mismatch between forecast and observation is accounted for by applying a perturbed-ensemble approach before the computation of scores. Verification results reveal a large impact of representativeness error on precipitation forecast reliability and skill estimates. The parametric model and estimated coefficients presented in this study could be used directly for forecast postprocessing to partly compensate for the limitation of any modeling system in terms of precipitation subgrid-scale variability.
Abstract
Spatial variability of precipitation is analyzed to characterize to what extent precipitation observed at a single location is representative of precipitation over a larger area. Characterization of precipitation representativeness is made in probabilistic terms using a parametric approach, namely, by fitting a censored shifted gamma distribution to observation measurements. Parameters are estimated and analyzed for independent precipitation datasets, among which one is based on high-density gauge measurements. The results of this analysis serve as a basis for accounting for representativeness error in an ensemble verification process. Uncertainty associated with the scale mismatch between forecast and observation is accounted for by applying a perturbed-ensemble approach before the computation of scores. Verification results reveal a large impact of representativeness error on precipitation forecast reliability and skill estimates. The parametric model and estimated coefficients presented in this study could be used directly for forecast postprocessing to partly compensate for the limitation of any modeling system in terms of precipitation subgrid-scale variability.
Abstract
In support of the Next Generation Global Prediction System (NGGPS) project, processes leading to convection initiation in the North American Mesoscale Forecast System, version 3 (NAMv3) are explored. Two severe weather outbreaks—occurring over the southeastern United States on 28 April 2014 and the central Great Plains on 6 May 2015—are forecast retrospectively using the NAMv3 CONUS (4 km) and Fire Weather (1.33 km) nests, each with 5-min output. Points of convection initiation are identified, and patterns leading to convection initiation in the model forecasts are determined. Results indicate that in the 30 min preceding convection initiation at a grid point, upward motion at low levels of the atmosphere enables a parcel to rise to its level of free convection, above which it is accelerated by the buoyancy force. A moist absolutely unstable layer (MAUL) typically is produced at the top of the updraft. However, when strong updrafts are collocated with large vertical gradients of potential temperature and moisture, noisy vertical profiles of temperature, moisture, and hydrometeor concentration develop beneath the rising MAUL. The noisy profiles found in this study are qualitatively similar to those that resulted in NAMv3 failures during simulations of Hurricane Joaquin in 2015. The CM1 cloud model is used to reproduce these noisy profiles, and results indicate that the noise can be mitigated by including explicit vertical diffusion in the model. Left unchecked, the noisy profiles are shown to impact convective storm features such as cold pools, precipitation, updraft helicity intensity and tracks, and the initiation of spurious convection.
Abstract
In support of the Next Generation Global Prediction System (NGGPS) project, processes leading to convection initiation in the North American Mesoscale Forecast System, version 3 (NAMv3) are explored. Two severe weather outbreaks—occurring over the southeastern United States on 28 April 2014 and the central Great Plains on 6 May 2015—are forecast retrospectively using the NAMv3 CONUS (4 km) and Fire Weather (1.33 km) nests, each with 5-min output. Points of convection initiation are identified, and patterns leading to convection initiation in the model forecasts are determined. Results indicate that in the 30 min preceding convection initiation at a grid point, upward motion at low levels of the atmosphere enables a parcel to rise to its level of free convection, above which it is accelerated by the buoyancy force. A moist absolutely unstable layer (MAUL) typically is produced at the top of the updraft. However, when strong updrafts are collocated with large vertical gradients of potential temperature and moisture, noisy vertical profiles of temperature, moisture, and hydrometeor concentration develop beneath the rising MAUL. The noisy profiles found in this study are qualitatively similar to those that resulted in NAMv3 failures during simulations of Hurricane Joaquin in 2015. The CM1 cloud model is used to reproduce these noisy profiles, and results indicate that the noise can be mitigated by including explicit vertical diffusion in the model. Left unchecked, the noisy profiles are shown to impact convective storm features such as cold pools, precipitation, updraft helicity intensity and tracks, and the initiation of spurious convection.
Abstract
A key aim of observational campaigns is to sample atmosphere–ocean phenomena to improve understanding of these phenomena, and in turn, numerical weather prediction. In early 2018 and 2019, the Atmospheric River Reconnaissance (AR Recon) campaign released dropsondes and radiosondes into atmospheric rivers (ARs) over the northeast Pacific Ocean to collect unique observations of temperature, winds, and moisture in ARs. These narrow regions of water vapor transport in the atmosphere—like rivers in the sky—can be associated with extreme precipitation and flooding events in the midlatitudes. This study uses the dropsonde observations collected during the AR Recon campaign and the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) to evaluate forecasts of ARs. Results show that ECMWF IFS forecasts 1) were colder than observations by up to 0.6 K throughout the troposphere; 2) have a dry bias in the lower troposphere, which along with weaker winds below 950 hPa, resulted in weaker horizontal water vapor fluxes in the 950–1000-hPa layer; and 3) exhibit an underdispersiveness in the water vapor flux that largely arises from model representativeness errors associated with dropsondes. Four U.S. West Coast radiosonde sites confirm the IFS cold bias throughout winter. These issues are likely to affect the model’s hydrological cycle and hence precipitation forecasts.
Abstract
A key aim of observational campaigns is to sample atmosphere–ocean phenomena to improve understanding of these phenomena, and in turn, numerical weather prediction. In early 2018 and 2019, the Atmospheric River Reconnaissance (AR Recon) campaign released dropsondes and radiosondes into atmospheric rivers (ARs) over the northeast Pacific Ocean to collect unique observations of temperature, winds, and moisture in ARs. These narrow regions of water vapor transport in the atmosphere—like rivers in the sky—can be associated with extreme precipitation and flooding events in the midlatitudes. This study uses the dropsonde observations collected during the AR Recon campaign and the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) to evaluate forecasts of ARs. Results show that ECMWF IFS forecasts 1) were colder than observations by up to 0.6 K throughout the troposphere; 2) have a dry bias in the lower troposphere, which along with weaker winds below 950 hPa, resulted in weaker horizontal water vapor fluxes in the 950–1000-hPa layer; and 3) exhibit an underdispersiveness in the water vapor flux that largely arises from model representativeness errors associated with dropsondes. Four U.S. West Coast radiosonde sites confirm the IFS cold bias throughout winter. These issues are likely to affect the model’s hydrological cycle and hence precipitation forecasts.
