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Abstract
Recent NOAA Hazardous Weather Testbed Spring Forecasting Experiments have emphasized the sensitivity of forecast sensible weather fields to how boundary layer processes are represented in the Weather Research and Forecasting (WRF) Model. Thus, since 2010, the Center for Analysis and Prediction of Storms has configured at least three members of their WRF-based Storm-Scale Ensemble Forecast (SSEF) system specifically for examination of sensitivities to parameterizations of turbulent mixing, including the Mellor–Yamada–Janjić (MYJ); quasi-normal scale elimination (QNSE); Asymmetrical Convective Model, version 2 (ACM2); Yonsei University (YSU); and Mellor–Yamada–Nakanishi–Niino (MYNN) schemes (hereafter PBL members). In postexperiment analyses, significant differences in forecast boundary layer structure and evolution have been observed, and for preconvective environments MYNN was found to have a superior depiction of temperature and moisture profiles. This study evaluates the 24-h forecast dryline positions in the SSEF system PBL members during the period April–June 2010–12 and documents sensitivities of the vertical distribution of thermodynamic and kinematic variables in near-dryline environments. Main results include the following. Despite having superior temperature and moisture profiles, as indicated by a previous study, MYNN was one of the worst-performing PBL members, exhibiting large eastward errors in forecast dryline position. During April–June 2010–11, a dry bias in the North American Mesoscale Forecast System (NAM) initial conditions largely contributed to eastward dryline errors in all PBL members. An upgrade to the NAM and assimilation system in October 2011 apparently fixed the dry bias, reducing eastward errors. Large sensitivities of CAPE and low-level shear to the PBL schemes were found, which were largest between 1.0° and 3.0° to the east of drylines. Finally, modifications to YSU to decrease vertical mixing and mitigate its warm and dry bias greatly reduced eastward dryline errors.
Abstract
Recent NOAA Hazardous Weather Testbed Spring Forecasting Experiments have emphasized the sensitivity of forecast sensible weather fields to how boundary layer processes are represented in the Weather Research and Forecasting (WRF) Model. Thus, since 2010, the Center for Analysis and Prediction of Storms has configured at least three members of their WRF-based Storm-Scale Ensemble Forecast (SSEF) system specifically for examination of sensitivities to parameterizations of turbulent mixing, including the Mellor–Yamada–Janjić (MYJ); quasi-normal scale elimination (QNSE); Asymmetrical Convective Model, version 2 (ACM2); Yonsei University (YSU); and Mellor–Yamada–Nakanishi–Niino (MYNN) schemes (hereafter PBL members). In postexperiment analyses, significant differences in forecast boundary layer structure and evolution have been observed, and for preconvective environments MYNN was found to have a superior depiction of temperature and moisture profiles. This study evaluates the 24-h forecast dryline positions in the SSEF system PBL members during the period April–June 2010–12 and documents sensitivities of the vertical distribution of thermodynamic and kinematic variables in near-dryline environments. Main results include the following. Despite having superior temperature and moisture profiles, as indicated by a previous study, MYNN was one of the worst-performing PBL members, exhibiting large eastward errors in forecast dryline position. During April–June 2010–11, a dry bias in the North American Mesoscale Forecast System (NAM) initial conditions largely contributed to eastward dryline errors in all PBL members. An upgrade to the NAM and assimilation system in October 2011 apparently fixed the dry bias, reducing eastward errors. Large sensitivities of CAPE and low-level shear to the PBL schemes were found, which were largest between 1.0° and 3.0° to the east of drylines. Finally, modifications to YSU to decrease vertical mixing and mitigate its warm and dry bias greatly reduced eastward dryline errors.
Abstract
Typhoon passages across Taiwan can generate sudden surface warming in downslope regions. Special characteristics and mechanisms for 54 such warming events that were identified during the 1961–2007 period are examined. Preferred warming regions were identified in northwest Taiwan, where warming is generated by downslope flow from east or northeast winds in westward-moving typhoons, and in southeast Taiwan, where it is generated by downslope flow from west or northwest winds in northwestward-moving typhoons. In addition to the orographic effect, warmings occurred exclusively within nonprecipitation zones of typhoons. Most northwest (southeast) warmings occur during the day (night) with an average lifetime of 4 (5) h, which roughly corresponds to the average time a nonprecipitation zone remains over a station. During the period examined, three typhoons generated warming events in both northwest and southeast Taiwan, and only Typhoon Haitang (2005) generated warmings with comparable magnitudes (∼12-K increase) in both regions. For Typhoon Haitang as an example, diagnostic analyses with two different approaches reveal that the majority of the warming is contributed by downslope adiabatic warming, but the warming associated with the passage of a nonprecipitation zone is not negligible. Similar results were found when these two diagnostic approaches were applied to the other warming events. The diurnal mode of the atmospheric divergent circulation over East Asia–western North Pacific undergoes a clockwise rotation. The vorticity tendency generated by this diurnal divergent circulation through vortex stretching may modulate the arrival time of typhoons to cause daily (nighttime) warming in the northwest (southeast).
Abstract
Typhoon passages across Taiwan can generate sudden surface warming in downslope regions. Special characteristics and mechanisms for 54 such warming events that were identified during the 1961–2007 period are examined. Preferred warming regions were identified in northwest Taiwan, where warming is generated by downslope flow from east or northeast winds in westward-moving typhoons, and in southeast Taiwan, where it is generated by downslope flow from west or northwest winds in northwestward-moving typhoons. In addition to the orographic effect, warmings occurred exclusively within nonprecipitation zones of typhoons. Most northwest (southeast) warmings occur during the day (night) with an average lifetime of 4 (5) h, which roughly corresponds to the average time a nonprecipitation zone remains over a station. During the period examined, three typhoons generated warming events in both northwest and southeast Taiwan, and only Typhoon Haitang (2005) generated warmings with comparable magnitudes (∼12-K increase) in both regions. For Typhoon Haitang as an example, diagnostic analyses with two different approaches reveal that the majority of the warming is contributed by downslope adiabatic warming, but the warming associated with the passage of a nonprecipitation zone is not negligible. Similar results were found when these two diagnostic approaches were applied to the other warming events. The diurnal mode of the atmospheric divergent circulation over East Asia–western North Pacific undergoes a clockwise rotation. The vorticity tendency generated by this diurnal divergent circulation through vortex stretching may modulate the arrival time of typhoons to cause daily (nighttime) warming in the northwest (southeast).
Abstract
This paper develops a multivariate mosaic subgrid approach to represent subgrid variability in land surface models (LSMs). The k-means clustering is used to take an arbitrary number of input descriptors and objectively determine areas of similarity within a catchment or mesoscale model grid box. Two different classifications of hydrologic similarity are compared: an a priori classification, where clusters are based solely on known physiographic information, and an a posteriori classification, where clusters are defined based on high-resolution LSM simulations. Simulations from these clustering approaches are compared to high-resolution gridded simulations, as well as to three common mosaic approaches used in LSMs: the “lumped” approach (no subgrid variability), disaggregation by elevation bands, and disaggregation by vegetation types in two subcatchments. All watershed disaggregation methods are incorporated in the Noah Multi-Physics (Noah-MP) LSM and applied to snowmelt-dominated subcatchments within the Reynolds Creek watershed in Idaho. Results demonstrate that the a priori clustering method is able to capture the aggregate impact of finescale spatial variability with O(10) simulation points, which is practical for implementation into an LSM scheme for coupled predictions on continental–global scales. The multivariate a priori approach better represents snow cover and depth variability than the univariate mosaic approaches, critical in snowmelt-dominated areas. Catchment-averaged energy fluxes are generally within 10%–15% for the high-resolution and a priori simulations, while displaying more subgrid variability than the univariate mosaic methods. Examination of observed and simulated streamflow time series shows that the a priori method generally reproduces hydrograph characteristics better than the simple disaggregation approaches.
Abstract
This paper develops a multivariate mosaic subgrid approach to represent subgrid variability in land surface models (LSMs). The k-means clustering is used to take an arbitrary number of input descriptors and objectively determine areas of similarity within a catchment or mesoscale model grid box. Two different classifications of hydrologic similarity are compared: an a priori classification, where clusters are based solely on known physiographic information, and an a posteriori classification, where clusters are defined based on high-resolution LSM simulations. Simulations from these clustering approaches are compared to high-resolution gridded simulations, as well as to three common mosaic approaches used in LSMs: the “lumped” approach (no subgrid variability), disaggregation by elevation bands, and disaggregation by vegetation types in two subcatchments. All watershed disaggregation methods are incorporated in the Noah Multi-Physics (Noah-MP) LSM and applied to snowmelt-dominated subcatchments within the Reynolds Creek watershed in Idaho. Results demonstrate that the a priori clustering method is able to capture the aggregate impact of finescale spatial variability with O(10) simulation points, which is practical for implementation into an LSM scheme for coupled predictions on continental–global scales. The multivariate a priori approach better represents snow cover and depth variability than the univariate mosaic approaches, critical in snowmelt-dominated areas. Catchment-averaged energy fluxes are generally within 10%–15% for the high-resolution and a priori simulations, while displaying more subgrid variability than the univariate mosaic methods. Examination of observed and simulated streamflow time series shows that the a priori method generally reproduces hydrograph characteristics better than the simple disaggregation approaches.
Abstract
An object-based probabilistic (OBPROB) forecasting framework is developed and applied, together with a more traditional neighborhood-based framework, to convection-permitting ensemble forecasts produced by the University of Oklahoma (OU) Multiscale data Assimilation and Predictability (MAP) laboratory during the 2017 and 2018 NOAA Hazardous Weather Testbed Spring Forecasting Experiments. Case studies from 2017 are used for parameter tuning and demonstration of methodology, while the 2018 ensemble forecasts are systematically verified. The 2017 case study demonstrates that the OBPROB forecast product can provide a unique tool to operational forecasters that includes convective-scale details such as storm mode and morphology, which are typically lost in neighborhood-based methods, while also providing quantitative ensemble probabilistic guidance about those details in a more easily interpretable format than the more commonly used paintball plots. The case study also demonstrates that objective verification metrics reveal different relative performance of the ensemble at different forecast lead times depending on the verification framework (i.e., object versus neighborhood) because of the different features emphasized by object- and neighborhood-based evaluations. Both frameworks are then used for a systematic evaluation of 26 forecasts from the spring of 2018. The OBPROB forecast verification as configured in this study shows less sensitivity to forecast lead time than the neighborhood forecasts. Both frameworks indicate a need for probabilistic calibration to improve ensemble reliability. However, lower ensemble discrimination for OBPROB than the neighborhood-based forecasts is also noted.
Abstract
An object-based probabilistic (OBPROB) forecasting framework is developed and applied, together with a more traditional neighborhood-based framework, to convection-permitting ensemble forecasts produced by the University of Oklahoma (OU) Multiscale data Assimilation and Predictability (MAP) laboratory during the 2017 and 2018 NOAA Hazardous Weather Testbed Spring Forecasting Experiments. Case studies from 2017 are used for parameter tuning and demonstration of methodology, while the 2018 ensemble forecasts are systematically verified. The 2017 case study demonstrates that the OBPROB forecast product can provide a unique tool to operational forecasters that includes convective-scale details such as storm mode and morphology, which are typically lost in neighborhood-based methods, while also providing quantitative ensemble probabilistic guidance about those details in a more easily interpretable format than the more commonly used paintball plots. The case study also demonstrates that objective verification metrics reveal different relative performance of the ensemble at different forecast lead times depending on the verification framework (i.e., object versus neighborhood) because of the different features emphasized by object- and neighborhood-based evaluations. Both frameworks are then used for a systematic evaluation of 26 forecasts from the spring of 2018. The OBPROB forecast verification as configured in this study shows less sensitivity to forecast lead time than the neighborhood forecasts. Both frameworks indicate a need for probabilistic calibration to improve ensemble reliability. However, lower ensemble discrimination for OBPROB than the neighborhood-based forecasts is also noted.
Abstract
An object-based probabilistic (OBPROB) forecasting framework is developed and applied, together with a more traditional neighborhood-based framework, to convection-permitting ensemble forecasts produced by the University of Oklahoma (OU) Multiscale data Assimilation and Predictability (MAP) laboratory during the 2017 and 2018 NOAA Hazardous Weather Testbed Spring Forecasting Experiments. Case studies from 2017 are used for parameter tuning and demonstration of methodology, while the 2018 ensemble forecasts are systematically verified. The 2017 case study demonstrates that the OBPROB forecast product can provide a unique tool to operational forecasters that includes convective-scale details such as storm mode and morphology, which are typically lost in neighborhood-based methods, while also providing quantitative ensemble probabilistic guidance about those details in a more easily interpretable format than the more commonly used paintball plots. The case study also demonstrates that objective verification metrics reveal different relative performance of the ensemble at different forecast lead times depending on the verification framework (i.e., object versus neighborhood) because of the different features emphasized by object- and neighborhood-based evaluations. Both frameworks are then used for a systematic evaluation of 26 forecasts from the spring of 2018. The OBPROB forecast verification as configured in this study shows less sensitivity to forecast lead time than the neighborhood forecasts. Both frameworks indicate a need for probabilistic calibration to improve ensemble reliability. However, lower ensemble discrimination for OBPROB than the neighborhood-based forecasts is also noted.
Abstract
An object-based probabilistic (OBPROB) forecasting framework is developed and applied, together with a more traditional neighborhood-based framework, to convection-permitting ensemble forecasts produced by the University of Oklahoma (OU) Multiscale data Assimilation and Predictability (MAP) laboratory during the 2017 and 2018 NOAA Hazardous Weather Testbed Spring Forecasting Experiments. Case studies from 2017 are used for parameter tuning and demonstration of methodology, while the 2018 ensemble forecasts are systematically verified. The 2017 case study demonstrates that the OBPROB forecast product can provide a unique tool to operational forecasters that includes convective-scale details such as storm mode and morphology, which are typically lost in neighborhood-based methods, while also providing quantitative ensemble probabilistic guidance about those details in a more easily interpretable format than the more commonly used paintball plots. The case study also demonstrates that objective verification metrics reveal different relative performance of the ensemble at different forecast lead times depending on the verification framework (i.e., object versus neighborhood) because of the different features emphasized by object- and neighborhood-based evaluations. Both frameworks are then used for a systematic evaluation of 26 forecasts from the spring of 2018. The OBPROB forecast verification as configured in this study shows less sensitivity to forecast lead time than the neighborhood forecasts. Both frameworks indicate a need for probabilistic calibration to improve ensemble reliability. However, lower ensemble discrimination for OBPROB than the neighborhood-based forecasts is also noted.
Abstract
Most ensembles suffer from underdispersion and systematic biases. One way to correct for these shortcomings is via machine learning (ML), which is advantageous due to its ability to identify and correct nonlinear biases. This study uses a single random forest (RF) to calibrate next-day (i.e., 12–36-h lead time) probabilistic precipitation forecasts over the contiguous United States (CONUS) from the Short-Range Ensemble Forecast System (SREF) with 16-km grid spacing and the High-Resolution Ensemble Forecast version 2 (HREFv2) with 3-km grid spacing. Random forest forecast probabilities (RFFPs) from each ensemble are compared against raw ensemble probabilities over 496 days from April 2017 to November 2018 using 16-fold cross validation. RFFPs are also compared against spatially smoothed ensemble probabilities since the raw SREF and HREFv2 probabilities are overconfident and undersample the true forecast probability density function. Probabilistic precipitation forecasts are evaluated at four precipitation thresholds ranging from 0.1 to 3 in. In general, RFFPs are found to have better forecast reliability and resolution, fewer spatial biases, and significantly greater Brier skill scores and areas under the relative operating characteristic curve compared to corresponding raw and spatially smoothed ensemble probabilities. The RFFPs perform best at the lower thresholds, which have a greater observed climatological frequency. Additionally, the RF-based postprocessing technique benefits the SREF more than the HREFv2, likely because the raw SREF forecasts contain more systematic biases than those from the raw HREFv2. It is concluded that the RFFPs provide a convenient, skillful summary of calibrated ensemble output and are computationally feasible to implement in real time. Advantages and disadvantages of ML-based postprocessing techniques are discussed.
Abstract
Most ensembles suffer from underdispersion and systematic biases. One way to correct for these shortcomings is via machine learning (ML), which is advantageous due to its ability to identify and correct nonlinear biases. This study uses a single random forest (RF) to calibrate next-day (i.e., 12–36-h lead time) probabilistic precipitation forecasts over the contiguous United States (CONUS) from the Short-Range Ensemble Forecast System (SREF) with 16-km grid spacing and the High-Resolution Ensemble Forecast version 2 (HREFv2) with 3-km grid spacing. Random forest forecast probabilities (RFFPs) from each ensemble are compared against raw ensemble probabilities over 496 days from April 2017 to November 2018 using 16-fold cross validation. RFFPs are also compared against spatially smoothed ensemble probabilities since the raw SREF and HREFv2 probabilities are overconfident and undersample the true forecast probability density function. Probabilistic precipitation forecasts are evaluated at four precipitation thresholds ranging from 0.1 to 3 in. In general, RFFPs are found to have better forecast reliability and resolution, fewer spatial biases, and significantly greater Brier skill scores and areas under the relative operating characteristic curve compared to corresponding raw and spatially smoothed ensemble probabilities. The RFFPs perform best at the lower thresholds, which have a greater observed climatological frequency. Additionally, the RF-based postprocessing technique benefits the SREF more than the HREFv2, likely because the raw SREF forecasts contain more systematic biases than those from the raw HREFv2. It is concluded that the RFFPs provide a convenient, skillful summary of calibrated ensemble output and are computationally feasible to implement in real time. Advantages and disadvantages of ML-based postprocessing techniques are discussed.
Abstract
The life cycle of the Southeast Asian–western North Pacific monsoon circulation is established by the northward migrations of the monsoon trough and the western Pacific subtropical anticyclone, and is reflected by the intraseasonal variations of monsoon westerlies and trade easterlies in the form of an east–west seesaw oscillation. In this paper, an effort is made to disclose the influence of this monsoon circulation on tropical cyclone tracks during its different phases using composite charts of large-scale circulation for certain types of tracks.
A majority of straight-moving (recurving) tropical cyclones appear during weak (strong) monsoon westerlies and strong (weak) trade easterlies. The monsoon conditions associated with straight-moving tropical cyclones are linked to the intensified subtropical anticyclone, while that associated with recurving tropical cyclones is coupled with the deepened monsoon trough. The relationship between genesis locations and track characteristics is evolved from the intraseasonal variation of the monsoon circulation reflected by the east–west oscillation of monsoon westerlies and trade easterlies. Composite circulation differences between the flows associated with the two types of tropical cyclone tracks show a vertically uniform short wave train along the North Pacific rim, as portrayed by the Pacific–Japan oscillation. During the extreme phases of the monsoon life cycle, the anomalous circulation pattern east of Taiwan resembles this anomalous short wave train.
A vorticity budget analysis of the strong monsoon condition reveals a vorticity tendency dipole with a positive zone to the north and a negative zone to the south of the deepened monsoon trough. This meridional juxtaposition of vorticity tendency propagates the monsoon trough northward. The interaction of a tropical cyclone with the monsoon trough intensifies the north–south juxtaposition of the vorticity tendency and deflects the tropical cyclone northward. In contrast, during weak monsoon conditions, the interaction between a tropical cyclone and the subtropical high results in a northwestward motion steered by the intensified trade easterlies. The accurate prediction of the monsoon trough and the subtropical anticyclone variations coupled with the monsoon life cycle may help to improve the forecasting of tropical cyclone tracks.
Abstract
The life cycle of the Southeast Asian–western North Pacific monsoon circulation is established by the northward migrations of the monsoon trough and the western Pacific subtropical anticyclone, and is reflected by the intraseasonal variations of monsoon westerlies and trade easterlies in the form of an east–west seesaw oscillation. In this paper, an effort is made to disclose the influence of this monsoon circulation on tropical cyclone tracks during its different phases using composite charts of large-scale circulation for certain types of tracks.
A majority of straight-moving (recurving) tropical cyclones appear during weak (strong) monsoon westerlies and strong (weak) trade easterlies. The monsoon conditions associated with straight-moving tropical cyclones are linked to the intensified subtropical anticyclone, while that associated with recurving tropical cyclones is coupled with the deepened monsoon trough. The relationship between genesis locations and track characteristics is evolved from the intraseasonal variation of the monsoon circulation reflected by the east–west oscillation of monsoon westerlies and trade easterlies. Composite circulation differences between the flows associated with the two types of tropical cyclone tracks show a vertically uniform short wave train along the North Pacific rim, as portrayed by the Pacific–Japan oscillation. During the extreme phases of the monsoon life cycle, the anomalous circulation pattern east of Taiwan resembles this anomalous short wave train.
A vorticity budget analysis of the strong monsoon condition reveals a vorticity tendency dipole with a positive zone to the north and a negative zone to the south of the deepened monsoon trough. This meridional juxtaposition of vorticity tendency propagates the monsoon trough northward. The interaction of a tropical cyclone with the monsoon trough intensifies the north–south juxtaposition of the vorticity tendency and deflects the tropical cyclone northward. In contrast, during weak monsoon conditions, the interaction between a tropical cyclone and the subtropical high results in a northwestward motion steered by the intensified trade easterlies. The accurate prediction of the monsoon trough and the subtropical anticyclone variations coupled with the monsoon life cycle may help to improve the forecasting of tropical cyclone tracks.
Abstract
A three-dimensional (in space and time) object identification algorithm is applied to high-resolution forecasts of hourly maximum updraft helicity (UH)—a diagnostic that identifies simulated rotating storms—with the goal of diagnosing the relationship between forecast UH objects and observed tornado pathlengths. UH objects are contiguous swaths of UH exceeding a specified threshold. Including time allows tracks to span multiple hours and entire life cycles of simulated rotating storms. The object algorithm is applied to 3 yr of 36-h forecasts initialized daily from a 4-km grid-spacing version of the Weather Research and Forecasting Model (WRF) run in real time at the National Severe Storms Laboratory (NSSL), and forecasts from the Storm Scale Ensemble Forecast (SSEF) system run by the Center for Analysis and Prediction of Storms for the 2010 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. Methods for visualizing UH object attributes are presented, and the relationship between pathlengths of UH objects and tornadoes for corresponding 18- or 24-h periods is examined. For deterministic NSSL-WRF UH forecasts, the relationship of UH pathlengths to tornadoes was much stronger during spring (March–May) than in summer (June–August). Filtering UH track segments produced by high-based and/or elevated storms improved the UH–tornado pathlength correlations. The best ensemble results were obtained after filtering high-based and/or elevated UH track segments for the 20 cases in April–May 2010, during which correlation coefficients were as high as 0.91. The results indicate that forecast UH pathlengths during spring could be a very skillful predictor for the severity of tornado outbreaks as measured by total pathlength.
Abstract
A three-dimensional (in space and time) object identification algorithm is applied to high-resolution forecasts of hourly maximum updraft helicity (UH)—a diagnostic that identifies simulated rotating storms—with the goal of diagnosing the relationship between forecast UH objects and observed tornado pathlengths. UH objects are contiguous swaths of UH exceeding a specified threshold. Including time allows tracks to span multiple hours and entire life cycles of simulated rotating storms. The object algorithm is applied to 3 yr of 36-h forecasts initialized daily from a 4-km grid-spacing version of the Weather Research and Forecasting Model (WRF) run in real time at the National Severe Storms Laboratory (NSSL), and forecasts from the Storm Scale Ensemble Forecast (SSEF) system run by the Center for Analysis and Prediction of Storms for the 2010 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. Methods for visualizing UH object attributes are presented, and the relationship between pathlengths of UH objects and tornadoes for corresponding 18- or 24-h periods is examined. For deterministic NSSL-WRF UH forecasts, the relationship of UH pathlengths to tornadoes was much stronger during spring (March–May) than in summer (June–August). Filtering UH track segments produced by high-based and/or elevated storms improved the UH–tornado pathlength correlations. The best ensemble results were obtained after filtering high-based and/or elevated UH track segments for the 20 cases in April–May 2010, during which correlation coefficients were as high as 0.91. The results indicate that forecast UH pathlengths during spring could be a very skillful predictor for the severity of tornado outbreaks as measured by total pathlength.
Abstract
A new prognostic, spatially and temporally dependent variable is introduced to the Weather Research and Forecasting Model (WRF). This variable is called the potential electrical energy (Ep ). It was used to predict the dynamic contribution of the grid-scale-resolved microphysical and vertical velocity fields to the production of cloud-to-ground and intracloud lightning in convection-allowing forecasts. The source of Ep is assumed to be the noninductive charge separation process involving collisions of graupel and ice particles in the presence of supercooled liquid water. The Ep dissipates when it exceeds preassigned threshold values and lightning is generated. An analysis of four case studies is presented and analyzed. On the 4-km simulation grid, a single cloud-to-ground lightning event was forecast with about equal values of probability of detection (POD) and false alarm ratio (FAR). However, when lighting was integrated onto 12-km and then 36-km grid overlays, there was a large improvement in the forecast skill, and as many as 10 cloud-to-ground lighting events were well forecast on the 36-km grid. The impact of initial conditions on forecast accuracy is briefly discussed, including an evaluation of the scheme in wintertime, when lightning activity is weaker. The dynamic algorithm forecasts are also contrasted with statistical lightning forecasts and differences are noted. The scheme is being used operationally with the Rapid Refresh (13 km) data; the skill scores in these operational runs were very good in clearly defined convective situations.
Abstract
A new prognostic, spatially and temporally dependent variable is introduced to the Weather Research and Forecasting Model (WRF). This variable is called the potential electrical energy (Ep ). It was used to predict the dynamic contribution of the grid-scale-resolved microphysical and vertical velocity fields to the production of cloud-to-ground and intracloud lightning in convection-allowing forecasts. The source of Ep is assumed to be the noninductive charge separation process involving collisions of graupel and ice particles in the presence of supercooled liquid water. The Ep dissipates when it exceeds preassigned threshold values and lightning is generated. An analysis of four case studies is presented and analyzed. On the 4-km simulation grid, a single cloud-to-ground lightning event was forecast with about equal values of probability of detection (POD) and false alarm ratio (FAR). However, when lighting was integrated onto 12-km and then 36-km grid overlays, there was a large improvement in the forecast skill, and as many as 10 cloud-to-ground lighting events were well forecast on the 36-km grid. The impact of initial conditions on forecast accuracy is briefly discussed, including an evaluation of the scheme in wintertime, when lightning activity is weaker. The dynamic algorithm forecasts are also contrasted with statistical lightning forecasts and differences are noted. The scheme is being used operationally with the Rapid Refresh (13 km) data; the skill scores in these operational runs were very good in clearly defined convective situations.
Abstract
Despite an increased understanding of the environments that favor tornado formation, a high false-alarm rate for tornado warnings still exists, suggesting that tornado formation could be a volatile process that is largely internal to each storm. To assess this, an ensemble of 30 supercell simulations was constructed based on small variations to the nontornadic and tornadic environmental profiles composited from the second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2). All simulations produce distinct supercells despite occurring in similar environments. Both the tornadic and nontornadic ensemble members possess ample subtornadic surface vertical vorticity; the determinative factor is whether this vorticity can be converged and stretched by the low-level updraft. Each of the 15 members in the tornadic VORTEX2 ensemble produces a long-track, intense tornado. Although there are notable differences in the precipitation and near-surface buoyancy fields, each storm features strong dynamic lifting of surface air with vertical vorticity. This lifting is due to a steady low-level mesocyclone, which is linked to the ingestion of predominately streamwise environmental vorticity. In contrast, each nontornadic VORTEX2 simulation features a supercell with a disorganized low-level mesocyclone, due to crosswise vorticity in the lowest few hundred meters in the nontornadic environment. This generally leads to insufficient dynamic lifting and stretching to accomplish tornadogenesis. Even so, 40% of the nontornadic VORTEX2 ensemble members become weakly tornadic. This implies that chaotic within-storm details can still play a role and, occasionally, lead to marginally tornadic vortices in suboptimal storms.
Abstract
Despite an increased understanding of the environments that favor tornado formation, a high false-alarm rate for tornado warnings still exists, suggesting that tornado formation could be a volatile process that is largely internal to each storm. To assess this, an ensemble of 30 supercell simulations was constructed based on small variations to the nontornadic and tornadic environmental profiles composited from the second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2). All simulations produce distinct supercells despite occurring in similar environments. Both the tornadic and nontornadic ensemble members possess ample subtornadic surface vertical vorticity; the determinative factor is whether this vorticity can be converged and stretched by the low-level updraft. Each of the 15 members in the tornadic VORTEX2 ensemble produces a long-track, intense tornado. Although there are notable differences in the precipitation and near-surface buoyancy fields, each storm features strong dynamic lifting of surface air with vertical vorticity. This lifting is due to a steady low-level mesocyclone, which is linked to the ingestion of predominately streamwise environmental vorticity. In contrast, each nontornadic VORTEX2 simulation features a supercell with a disorganized low-level mesocyclone, due to crosswise vorticity in the lowest few hundred meters in the nontornadic environment. This generally leads to insufficient dynamic lifting and stretching to accomplish tornadogenesis. Even so, 40% of the nontornadic VORTEX2 ensemble members become weakly tornadic. This implies that chaotic within-storm details can still play a role and, occasionally, lead to marginally tornadic vortices in suboptimal storms.
