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- Author or Editor: Clark Evans x
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Abstract
Previous studies have suggested that the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model is unable, in its default configuration, to adequately resolve the capping inversions that are commonly found in the warm-season, thunderstorm-supporting environments of the central United States. Since capping inversions typically form in environments of synoptic-scale subsidence, this study tests the hypothesis that this degradation results, in part, from implicit numerical damping of shorter-wavelength features associated with the model-default third-order-accurate vertical advection finite-differencing scheme. To aid in testing this hypothesis, two short-range, deterministic, convection-allowing model forecasts, one using the default third-order-accurate vertical advection finite-differencing scheme and another using a fourth-order-accurate differencing scheme (which lacks implicit damping but is numerically dispersive), are conducted for 25 days during the 2017 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. Model-derived vertical profiles at lead times of 11 and 23 h are validated against available rawinsonde observations released in regions located in the Storm Prediction Center’s 0600 UTC day 1 convection outlook’s “general thunderstorm” forecast area. The fourth-order-accurate vertical advection finite-differencing scheme is shown to not result in statistically significant improvements to model-forecast capping inversions or, more generally, the vertical thermodynamic profile in the lower troposphere. Instead, the fourth-order-accurate differencing scheme primarily impacts the representation of longer-wavelength features already reasonably well resolved by the model. The analysis does, however, provide quantitative evidence over a large sample that, on average, the WRF-ARW model forecasts capping inversions that are too weak, with negative buoyancy spread out over too deep of a vertical layer, compared to observations.
Abstract
Previous studies have suggested that the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model is unable, in its default configuration, to adequately resolve the capping inversions that are commonly found in the warm-season, thunderstorm-supporting environments of the central United States. Since capping inversions typically form in environments of synoptic-scale subsidence, this study tests the hypothesis that this degradation results, in part, from implicit numerical damping of shorter-wavelength features associated with the model-default third-order-accurate vertical advection finite-differencing scheme. To aid in testing this hypothesis, two short-range, deterministic, convection-allowing model forecasts, one using the default third-order-accurate vertical advection finite-differencing scheme and another using a fourth-order-accurate differencing scheme (which lacks implicit damping but is numerically dispersive), are conducted for 25 days during the 2017 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. Model-derived vertical profiles at lead times of 11 and 23 h are validated against available rawinsonde observations released in regions located in the Storm Prediction Center’s 0600 UTC day 1 convection outlook’s “general thunderstorm” forecast area. The fourth-order-accurate vertical advection finite-differencing scheme is shown to not result in statistically significant improvements to model-forecast capping inversions or, more generally, the vertical thermodynamic profile in the lower troposphere. Instead, the fourth-order-accurate differencing scheme primarily impacts the representation of longer-wavelength features already reasonably well resolved by the model. The analysis does, however, provide quantitative evidence over a large sample that, on average, the WRF-ARW model forecasts capping inversions that are too weak, with negative buoyancy spread out over too deep of a vertical layer, compared to observations.
Abstract
Herein, an analysis of a 3-km explicit convective simulation of an unusually intense bow echo and associated mesoscale vortex that were responsible for producing an extensive swath of high winds across Kansas, southern Missouri, and southern Illinois on 8 May 2009 is presented. The simulation was able to reproduce many of the key attributes of the observed system, including an intense [~100 kt (51.4 m s−1) at 850 hPa], 10-km-deep, 100-km-wide warm-core mesovortex and associated surface mesolow associated with a tropical storm–like reflectivity eye. A detailed analysis suggests that the simulated convection develops north of a weak east–west lower-tropospheric baroclinic zone, at the nose of an intensifying low-level jet. The system organizes into a north–south-oriented bow echo as it moves eastward along the preexisting baroclinic zone in an environment of large convective available potential energy (CAPE) and strong tropospheric vertical wind shear. Once the system moves east of the low-level jet and into an environment of weaker CAPE and weaker vertical wind shear, it begins an occlusion-like phase, producing a pronounced comma-shaped reflectivity echo with an intense warm-core mesovortex at the head of the comma. During this phase, a deep strip of cyclonic vertical vorticity located on the backside of the bow echo consolidates into a single vortex core. A notable weakening of the low-level convectively generated cold pool also occurs during this phase, perhaps drawing parallels to theories of tropical cyclogenesis wherein cold convective downdrafts must be substantially mitigated for subsequent system intensification.
Abstract
Herein, an analysis of a 3-km explicit convective simulation of an unusually intense bow echo and associated mesoscale vortex that were responsible for producing an extensive swath of high winds across Kansas, southern Missouri, and southern Illinois on 8 May 2009 is presented. The simulation was able to reproduce many of the key attributes of the observed system, including an intense [~100 kt (51.4 m s−1) at 850 hPa], 10-km-deep, 100-km-wide warm-core mesovortex and associated surface mesolow associated with a tropical storm–like reflectivity eye. A detailed analysis suggests that the simulated convection develops north of a weak east–west lower-tropospheric baroclinic zone, at the nose of an intensifying low-level jet. The system organizes into a north–south-oriented bow echo as it moves eastward along the preexisting baroclinic zone in an environment of large convective available potential energy (CAPE) and strong tropospheric vertical wind shear. Once the system moves east of the low-level jet and into an environment of weaker CAPE and weaker vertical wind shear, it begins an occlusion-like phase, producing a pronounced comma-shaped reflectivity echo with an intense warm-core mesovortex at the head of the comma. During this phase, a deep strip of cyclonic vertical vorticity located on the backside of the bow echo consolidates into a single vortex core. A notable weakening of the low-level convectively generated cold pool also occurs during this phase, perhaps drawing parallels to theories of tropical cyclogenesis wherein cold convective downdrafts must be substantially mitigated for subsequent system intensification.
Abstract
The proliferation of ensemble forecast system output in recent years motivates this investigation into how operational forecasters utilize convection-permitting ensemble forecast system guidance in the forecast preparation process. A 16-member, convection-permitting ensemble forecast of the high-impact heavy precipitation resulting from Tropical Storm Fay (2008) is conducted and evaluated. The ensemble provides a skillful, albeit underdispersive and bimodal, forecast at all precipitation thresholds considered. A forecasting exercise is conducted to evaluate how forecasters utilize the ensemble forecast system guidance. Forecasters made two storm-total accumulated precipitation forecasts: one before and one after evaluating the ensemble guidance. Concurrently, forecasters were presented with questionnaires designed to gauge their thought processes in preparing each of their forecasts. Exercise participants felt that the high-resolution ensemble guidance added value and confidence to their forecasts, although it did not meaningfully reduce forecast uncertainty. Incorporation of the ensemble guidance into the forecast preparation process resulted in a modest mean improvement in forecast skill, with each forecast found to be skillful at all accumulated precipitation thresholds. Forecasters primarily utilized the ensemble guidance to identify a “most likely” forecast outcome from disparate deterministic guidance solutions and to help quantify the uncertainty associated with the forecast. Forecasters preferred ensemble guidance that enabled them to quickly understand the range of solutions provided by the ensemble, particularly over the entirety of the domain. Forecasters were generally aware of the diversity of solutions provided by the ensemble guidance; however, only a select few actively interrogated this information when revising their forecasts and each did so in different ways.
Abstract
The proliferation of ensemble forecast system output in recent years motivates this investigation into how operational forecasters utilize convection-permitting ensemble forecast system guidance in the forecast preparation process. A 16-member, convection-permitting ensemble forecast of the high-impact heavy precipitation resulting from Tropical Storm Fay (2008) is conducted and evaluated. The ensemble provides a skillful, albeit underdispersive and bimodal, forecast at all precipitation thresholds considered. A forecasting exercise is conducted to evaluate how forecasters utilize the ensemble forecast system guidance. Forecasters made two storm-total accumulated precipitation forecasts: one before and one after evaluating the ensemble guidance. Concurrently, forecasters were presented with questionnaires designed to gauge their thought processes in preparing each of their forecasts. Exercise participants felt that the high-resolution ensemble guidance added value and confidence to their forecasts, although it did not meaningfully reduce forecast uncertainty. Incorporation of the ensemble guidance into the forecast preparation process resulted in a modest mean improvement in forecast skill, with each forecast found to be skillful at all accumulated precipitation thresholds. Forecasters primarily utilized the ensemble guidance to identify a “most likely” forecast outcome from disparate deterministic guidance solutions and to help quantify the uncertainty associated with the forecast. Forecasters preferred ensemble guidance that enabled them to quickly understand the range of solutions provided by the ensemble, particularly over the entirety of the domain. Forecasters were generally aware of the diversity of solutions provided by the ensemble guidance; however, only a select few actively interrogated this information when revising their forecasts and each did so in different ways.
Abstract
This study evaluates the influence of planetary boundary layer parameterization on short-range (0–15 h) convection initiation (CI) forecasts within convection-allowing ensembles that utilize subsynoptic-scale observations collected during the Mesoscale Predictability Experiment. Three cases, 19–20 May, 31 May–1 June, and 8–9 June 2013, are considered, each characterized by a different large-scale flow pattern. An object-based method is used to verify and analyze CI forecasts. Local mixing parameterizations have, relative to nonlocal mixing parameterizations, higher probabilities of detection but also higher false alarm ratios, such that the ensemble mean forecast skill only subtly varied between parameterizations considered. Temporal error distributions associated with matched events are approximately normal around a zero mean, suggesting little systematic timing bias. Spatial error distributions are skewed, with average mean (median) distance errors of approximately 44 km (28 km). Matched event cumulative distribution functions suggest limited forecast skill increases beyond temporal and spatial thresholds of 1 h and 100 km, respectively. Forecast skill variation is greatest between cases with smaller variation between PBL parameterizations or between individual ensemble members for a given case, implying greatest control on CI forecast skill by larger-scale features than PBL parameterization. In agreement with previous studies, local mixing parameterizations tend to produce simulated boundary layers that are too shallow, cool, and moist, while nonlocal mixing parameterizations tend to be deeper, warmer, and drier. Forecasts poorly resolve strong capping inversions across all parameterizations, which is hypothesized to result primarily from implicit numerical diffusion associated with the default finite-differencing formulation for vertical advection used herein.
Abstract
This study evaluates the influence of planetary boundary layer parameterization on short-range (0–15 h) convection initiation (CI) forecasts within convection-allowing ensembles that utilize subsynoptic-scale observations collected during the Mesoscale Predictability Experiment. Three cases, 19–20 May, 31 May–1 June, and 8–9 June 2013, are considered, each characterized by a different large-scale flow pattern. An object-based method is used to verify and analyze CI forecasts. Local mixing parameterizations have, relative to nonlocal mixing parameterizations, higher probabilities of detection but also higher false alarm ratios, such that the ensemble mean forecast skill only subtly varied between parameterizations considered. Temporal error distributions associated with matched events are approximately normal around a zero mean, suggesting little systematic timing bias. Spatial error distributions are skewed, with average mean (median) distance errors of approximately 44 km (28 km). Matched event cumulative distribution functions suggest limited forecast skill increases beyond temporal and spatial thresholds of 1 h and 100 km, respectively. Forecast skill variation is greatest between cases with smaller variation between PBL parameterizations or between individual ensemble members for a given case, implying greatest control on CI forecast skill by larger-scale features than PBL parameterization. In agreement with previous studies, local mixing parameterizations tend to produce simulated boundary layers that are too shallow, cool, and moist, while nonlocal mixing parameterizations tend to be deeper, warmer, and drier. Forecasts poorly resolve strong capping inversions across all parameterizations, which is hypothesized to result primarily from implicit numerical diffusion associated with the default finite-differencing formulation for vertical advection used herein.
Abstract
This study investigates the short-range (0–12 h) predictability of convection initiation (CI) using the Advanced Research Weather Research and Forecasting (WRF) Model (ARW) with a horizontal grid spacing of 429 m. A unique object-based method is used to evaluate model performance for 25 cases of CI across the west-central high plains of the United States from the 2010 convective season. In the aggregate, there exists a high probability of detection but, due to the significant overproduction of CI events by the model, high false alarm and bias ratios that lead to modestly skillful forecasts. Model CI objects that are matched with observed CI objects show, on average, an early bias of about 3 min and distance errors of around 38 km. The operational utility and inherent biases of such high-resolution simulations are discussed.
Abstract
This study investigates the short-range (0–12 h) predictability of convection initiation (CI) using the Advanced Research Weather Research and Forecasting (WRF) Model (ARW) with a horizontal grid spacing of 429 m. A unique object-based method is used to evaluate model performance for 25 cases of CI across the west-central high plains of the United States from the 2010 convective season. In the aggregate, there exists a high probability of detection but, due to the significant overproduction of CI events by the model, high false alarm and bias ratios that lead to modestly skillful forecasts. Model CI objects that are matched with observed CI objects show, on average, an early bias of about 3 min and distance errors of around 38 km. The operational utility and inherent biases of such high-resolution simulations are discussed.
Abstract
It is known that both Dvorak technique and advanced Dvorak technique–derived intensity estimates for tropical cyclones during extratropical transition are less reliable because the empirical relationships between cloud patterns and cyclone intensity underlying each technique are primarily tropical in nature and thus less robust during extratropical transition. However, as direct observations of cyclone intensity during extratropical transition are rare, the precise extent to which such remotely sensed intensity estimates are in error is uncertain. To address this uncertainty and provide insight into how advanced Dvorak technique–derived intensity estimates during extratropical transition may be improved, the advanced Dvorak technique is applied to synthetic satellite imagery derived from 25 numerical simulations of Atlantic basin tropical cyclones—five cases, five microphysical parameterizations—during extratropical transition. From this, an internally consistent evaluation between model-derived and advanced Dvorak technique–derived cyclone intensity estimates is conducted. Intensity estimate error and bias peak at the beginning of extratropical transition and decline thereafter for maximum sustained surface wind. On average, synthetic advanced Dvorak technique–derived estimates of maximum sustained surface wind asymptote toward or remain near their weakest-possible values after extratropical transition begins. Minimum sea level pressure estimates exhibit minimal bias, although this result is sensitive to microphysical parameterization. Such sensitivity to microphysical parameterization, particularly with respect to cloud radiative properties, suggests that only qualitative insight regarding advanced Dvorak technique–derived intensity estimate error during extratropical transition may be obtained utilizing synthetic satellite imagery. Implications toward developing improved intensity estimates during extratropical transition are discussed.
Abstract
It is known that both Dvorak technique and advanced Dvorak technique–derived intensity estimates for tropical cyclones during extratropical transition are less reliable because the empirical relationships between cloud patterns and cyclone intensity underlying each technique are primarily tropical in nature and thus less robust during extratropical transition. However, as direct observations of cyclone intensity during extratropical transition are rare, the precise extent to which such remotely sensed intensity estimates are in error is uncertain. To address this uncertainty and provide insight into how advanced Dvorak technique–derived intensity estimates during extratropical transition may be improved, the advanced Dvorak technique is applied to synthetic satellite imagery derived from 25 numerical simulations of Atlantic basin tropical cyclones—five cases, five microphysical parameterizations—during extratropical transition. From this, an internally consistent evaluation between model-derived and advanced Dvorak technique–derived cyclone intensity estimates is conducted. Intensity estimate error and bias peak at the beginning of extratropical transition and decline thereafter for maximum sustained surface wind. On average, synthetic advanced Dvorak technique–derived estimates of maximum sustained surface wind asymptote toward or remain near their weakest-possible values after extratropical transition begins. Minimum sea level pressure estimates exhibit minimal bias, although this result is sensitive to microphysical parameterization. Such sensitivity to microphysical parameterization, particularly with respect to cloud radiative properties, suggests that only qualitative insight regarding advanced Dvorak technique–derived intensity estimate error during extratropical transition may be obtained utilizing synthetic satellite imagery. Implications toward developing improved intensity estimates during extratropical transition are discussed.
Abstract
This study evaluates forecast vertical thermodynamic profiles and derived thermodynamic parameters from two regional/convection-allowing model pairs, the North American Mesoscale Forecast System and the North American Mesoscale Nest model pair and the Rapid Refresh and High Resolution Rapid Refresh model pair, in warm-season, thunderstorm-supporting environments. Differences in bias and mean absolute error between the regional and convection-allowing models in each of the two pairs, while often statistically significant, are practically small for the variables, parameters, and vertical levels considered, such that the smaller-scale variability resolved by convection-allowing models does not degrade their forecast skill. Model biases shared by the regional and convection-allowing models in each pair are documented, particularly the substantial cool and moist biases in the planetary boundary layer arising from the Mellor–Yamada–Janjić planetary boundary layer parameterization used by the North American Mesoscale model and the Nest version as well as the middle-tropospheric moist bias shared by the Rapid Refresh and High Resolution Rapid Refresh models. Bias and mean absolute errors typically have larger magnitudes in the evening, when buoyancy is a significant contributor to turbulent vertical mixing, than in the morning. Vertical thermodynamic profile biases extend over a deep vertical layer in the western United States given strong sensible heating of the underlying surface. The results suggest that convection-allowing models can fulfill the use cases typically and historically met by regional models in operations at forecast entities such as the Storm Prediction Center, a fruitful finding given the proposed elimination of regional models with the Next-Generation Global Prediction System initiative.
Abstract
This study evaluates forecast vertical thermodynamic profiles and derived thermodynamic parameters from two regional/convection-allowing model pairs, the North American Mesoscale Forecast System and the North American Mesoscale Nest model pair and the Rapid Refresh and High Resolution Rapid Refresh model pair, in warm-season, thunderstorm-supporting environments. Differences in bias and mean absolute error between the regional and convection-allowing models in each of the two pairs, while often statistically significant, are practically small for the variables, parameters, and vertical levels considered, such that the smaller-scale variability resolved by convection-allowing models does not degrade their forecast skill. Model biases shared by the regional and convection-allowing models in each pair are documented, particularly the substantial cool and moist biases in the planetary boundary layer arising from the Mellor–Yamada–Janjić planetary boundary layer parameterization used by the North American Mesoscale model and the Nest version as well as the middle-tropospheric moist bias shared by the Rapid Refresh and High Resolution Rapid Refresh models. Bias and mean absolute errors typically have larger magnitudes in the evening, when buoyancy is a significant contributor to turbulent vertical mixing, than in the morning. Vertical thermodynamic profile biases extend over a deep vertical layer in the western United States given strong sensible heating of the underlying surface. The results suggest that convection-allowing models can fulfill the use cases typically and historically met by regional models in operations at forecast entities such as the Storm Prediction Center, a fruitful finding given the proposed elimination of regional models with the Next-Generation Global Prediction System initiative.
Abstract
This study introduces a novel method for comparing vertical thermodynamic profiles, focusing on the atmospheric boundary layer, across a wide range of meteorological conditions. This method is developed using observed temperature and dewpoint temperature data from 31 153 soundings taken at 0000 UTC and 32 308 soundings taken at 1200 UTC between May 2019 and March 2020. Temperature and dewpoint temperature vertical profiles are first interpolated onto a height above ground level (AGL) coordinate, after which the temperature of the dry adiabat defined by the surface-based parcel’s temperature is subtracted from each quantity at all altitudes. This allows for common sounding features, such as turbulent mixed layers and inversions, to be similarly depicted regardless of temperature and dewpoint temperature differences resulting from altitude, latitude, or seasonality. The soundings that result from applying this method to the observed sounding collection described above are then clustered to identify distinct boundary layer structures in the data. Specifically, separately at 0000 and 1200 UTC, a k-means clustering analysis is conducted in the phase space of the leading two empirical orthogonal functions of the sounding data. As compared to clustering based on the original vertical profiles, which results in clusters that are dominated by seasonal and latitudinal differences, clusters derived from transformed data are less latitudinally and seasonally stratified and better represent boundary layer features such as turbulent mixed layers and pseudoadiabatic profiles. The sounding-comparison method thus provides an objective means of categorizing vertical thermodynamic profiles with wide-ranging applications, as demonstrated by using the method to verify short-range Global Forecast System model forecasts.
Abstract
This study introduces a novel method for comparing vertical thermodynamic profiles, focusing on the atmospheric boundary layer, across a wide range of meteorological conditions. This method is developed using observed temperature and dewpoint temperature data from 31 153 soundings taken at 0000 UTC and 32 308 soundings taken at 1200 UTC between May 2019 and March 2020. Temperature and dewpoint temperature vertical profiles are first interpolated onto a height above ground level (AGL) coordinate, after which the temperature of the dry adiabat defined by the surface-based parcel’s temperature is subtracted from each quantity at all altitudes. This allows for common sounding features, such as turbulent mixed layers and inversions, to be similarly depicted regardless of temperature and dewpoint temperature differences resulting from altitude, latitude, or seasonality. The soundings that result from applying this method to the observed sounding collection described above are then clustered to identify distinct boundary layer structures in the data. Specifically, separately at 0000 and 1200 UTC, a k-means clustering analysis is conducted in the phase space of the leading two empirical orthogonal functions of the sounding data. As compared to clustering based on the original vertical profiles, which results in clusters that are dominated by seasonal and latitudinal differences, clusters derived from transformed data are less latitudinally and seasonally stratified and better represent boundary layer features such as turbulent mixed layers and pseudoadiabatic profiles. The sounding-comparison method thus provides an objective means of categorizing vertical thermodynamic profiles with wide-ranging applications, as demonstrated by using the method to verify short-range Global Forecast System model forecasts.
