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Abstract
Flash flooding is the most damaging and deadly type of flooding event in the continental United States (CONUS), and one of the deadliest hazards worldwide. The Weather Prediction Center’s (WPC) Excessive Rainfall Outlook (ERO) is used to highlight regions at risk of receiving excessive rainfall that can lead to flash flooding. While EROs have been validated by the WPC across several metrics, an analysis of flash floods that were not forecast by EROs, which we define as missed flash floods, has not been performed, nor have damages associated with missed flash floods been examined. Using EROs, flash flood data from the Unified Flooding Verification System (UFVS), and flash flood damage data from the National Centers for Environmental Information (NCEI) Storm Events Database, this research investigates the characteristics of missed flash floods. We find that missed damages occur most frequently in summer and least frequently in winter, but that only 9.2% of flash flood damages and 23.2% of damaging events are missed by the ERO. Missed damaging flash flood events occur frequently in the Southwest US. In this region, missed damages and missed events are primarily attributed to the North American monsoon (NAM). We also investigate forecast flash floods by ERO risk category. High Risks incur the most damages despite having the fewest number of damaging events, indicating that the ERO is able to distinguish higher impact events. The ERO predicts damaging flash floods well, although the Southwest and urban areas broadly could be investigated further to ensure that the potential of damaging flash floods are accurately forecast.
Abstract
Flash flooding is the most damaging and deadly type of flooding event in the continental United States (CONUS), and one of the deadliest hazards worldwide. The Weather Prediction Center’s (WPC) Excessive Rainfall Outlook (ERO) is used to highlight regions at risk of receiving excessive rainfall that can lead to flash flooding. While EROs have been validated by the WPC across several metrics, an analysis of flash floods that were not forecast by EROs, which we define as missed flash floods, has not been performed, nor have damages associated with missed flash floods been examined. Using EROs, flash flood data from the Unified Flooding Verification System (UFVS), and flash flood damage data from the National Centers for Environmental Information (NCEI) Storm Events Database, this research investigates the characteristics of missed flash floods. We find that missed damages occur most frequently in summer and least frequently in winter, but that only 9.2% of flash flood damages and 23.2% of damaging events are missed by the ERO. Missed damaging flash flood events occur frequently in the Southwest US. In this region, missed damages and missed events are primarily attributed to the North American monsoon (NAM). We also investigate forecast flash floods by ERO risk category. High Risks incur the most damages despite having the fewest number of damaging events, indicating that the ERO is able to distinguish higher impact events. The ERO predicts damaging flash floods well, although the Southwest and urban areas broadly could be investigated further to ensure that the potential of damaging flash floods are accurately forecast.
Abstract
The extended-range forecast with a lead time of 10–30 days is the gap between weather (<10 days) and climate (>30 days) predictions. Improving the forecast skill of extreme weather events at the extended range is crucial for risk management of disastrous events. In this study, three deep learning (DL) models based on the methods of convolutional neural networks and gate recurrent units are constructed to predict the rainfall anomalies and associated extreme events in East China at lead times of 1–6 pentads. All DL models show skillful prediction of the temporal variation of rainfall anomalies (in terms of temporal correlation coefficient skill) over most regions in East China beyond 4 pentads, outperforming the dynamical models from the China Meteorological Administration (CMA) and the European Centre for Medium-Range Weather Forecasts (ECMWF). The spatial distribution of the rainfall anomalies is also better predicted by the DL models than the dynamical models; and the DL models show higher pattern correlation coefficients than the dynamical models at lead times of 3–6 pentads. The higher skill of DL models in predicting the rainfall anomalies will help to improve the accuracy of extreme-event predictions. The Heidke skill scores of the extreme rainfall event forecast performed by the DL models are also superior to those of the dynamical models at a lead time beyond about 4 pentads. Heat map analysis for the DL models shows that the predictability sources are mainly the large-scale factors modulating the East Asian monsoon rainfall.
Significance Statement
Improving the forecast skill for extreme weather events at the extended range (10–30 days in advance), particularly over populated regions such as East China, is crucial for risk management. This study aims to develop skillful models of the rainfall anomalies and associated extreme heavy rainfall events using deep learning techniques. The models constructed here benefit from the capability of deep learning to identify the predictability sources of rainfall variability, and outperform the current operational models, including the ECMWF and the CMA models, at forecast lead times beyond 3–4 pentads. These results reveal the promising application prospect of deep learning techniques in the extended-range forecast.
Abstract
The extended-range forecast with a lead time of 10–30 days is the gap between weather (<10 days) and climate (>30 days) predictions. Improving the forecast skill of extreme weather events at the extended range is crucial for risk management of disastrous events. In this study, three deep learning (DL) models based on the methods of convolutional neural networks and gate recurrent units are constructed to predict the rainfall anomalies and associated extreme events in East China at lead times of 1–6 pentads. All DL models show skillful prediction of the temporal variation of rainfall anomalies (in terms of temporal correlation coefficient skill) over most regions in East China beyond 4 pentads, outperforming the dynamical models from the China Meteorological Administration (CMA) and the European Centre for Medium-Range Weather Forecasts (ECMWF). The spatial distribution of the rainfall anomalies is also better predicted by the DL models than the dynamical models; and the DL models show higher pattern correlation coefficients than the dynamical models at lead times of 3–6 pentads. The higher skill of DL models in predicting the rainfall anomalies will help to improve the accuracy of extreme-event predictions. The Heidke skill scores of the extreme rainfall event forecast performed by the DL models are also superior to those of the dynamical models at a lead time beyond about 4 pentads. Heat map analysis for the DL models shows that the predictability sources are mainly the large-scale factors modulating the East Asian monsoon rainfall.
Significance Statement
Improving the forecast skill for extreme weather events at the extended range (10–30 days in advance), particularly over populated regions such as East China, is crucial for risk management. This study aims to develop skillful models of the rainfall anomalies and associated extreme heavy rainfall events using deep learning techniques. The models constructed here benefit from the capability of deep learning to identify the predictability sources of rainfall variability, and outperform the current operational models, including the ECMWF and the CMA models, at forecast lead times beyond 3–4 pentads. These results reveal the promising application prospect of deep learning techniques in the extended-range forecast.
Abstract
A new probabilistic tornado detection algorithm was developed to potentially replace the operational tornado detection algorithm (TDA) for the WSR-88D radar network. The tornado probability algorithm (TORP) uses a random forest machine learning technique to estimate a probability of tornado occurrence based on single-radar data, and is trained on 166 145 data points derived from 0.5°-tilt radar data and storm reports from 2011 to 2016, of which 10.4% are tornadic. A variety of performance evaluation metrics show a generally good model performance for discriminating between tornadic and nontornadic points. When using a 50% probability threshold to decide whether the model is predicting a tornado or not, the probability of detection and false alarm ratio are 57% and 50%, respectively, showing high skill by several metrics and vastly outperforming the TDA. The model weaknesses include false alarms associated with poor-quality radial velocity data and greatly reduced performance when used in the western United States. Overall, TORP can provide real-time guidance for tornado warning decisions, which can increase forecaster confidence and encourage swift decision-making. It has the ability to condense a multitude of radar data into a concise object-based information readout that can be displayed in visualization software used by the National Weather Service, core partners, and researchers.
Significance Statement
This study describes the tornado probability algorithm (TORP) and its performance. Operational forecasters can use TORP as real-time guidance when issuing tornado warnings, causing increased confidence in warning decisions, which in turn can extend tornado warning lead times.
Abstract
A new probabilistic tornado detection algorithm was developed to potentially replace the operational tornado detection algorithm (TDA) for the WSR-88D radar network. The tornado probability algorithm (TORP) uses a random forest machine learning technique to estimate a probability of tornado occurrence based on single-radar data, and is trained on 166 145 data points derived from 0.5°-tilt radar data and storm reports from 2011 to 2016, of which 10.4% are tornadic. A variety of performance evaluation metrics show a generally good model performance for discriminating between tornadic and nontornadic points. When using a 50% probability threshold to decide whether the model is predicting a tornado or not, the probability of detection and false alarm ratio are 57% and 50%, respectively, showing high skill by several metrics and vastly outperforming the TDA. The model weaknesses include false alarms associated with poor-quality radial velocity data and greatly reduced performance when used in the western United States. Overall, TORP can provide real-time guidance for tornado warning decisions, which can increase forecaster confidence and encourage swift decision-making. It has the ability to condense a multitude of radar data into a concise object-based information readout that can be displayed in visualization software used by the National Weather Service, core partners, and researchers.
Significance Statement
This study describes the tornado probability algorithm (TORP) and its performance. Operational forecasters can use TORP as real-time guidance when issuing tornado warnings, causing increased confidence in warning decisions, which in turn can extend tornado warning lead times.
Abstract
A hybrid three-dimensional ensemble–variational (En3D-Var) data assimilation system has been developed to explore incorporating information from an 11-member regional ensemble prediction system, which is dynamically downscaled from a global ensemble system, into a 3-hourly cycling convective-scale data assimilation system over the western Maritime Continent. From the ensemble, there exists small-scale ensemble perturbation structures associated with positional differences of tropical convection, but these structures are well represented only after the downscaled ensemble forecast has evolved for at least 6 h due to spinup. There was also a robust moderate negative correlation between total specific humidity and potential temperature background errors, presumably because of incorrect vertical motion in the presence of clouds. Time shifting of the ensemble perturbations, by using those available from adjacent cycles, helped to ameliorate the sampling error prevalent in their raw autocovariances. Monthlong hybrid En3D-Var trials were conducted using different weights assigned to the ensemble-derived and climatological background error covariances. The forecast fits to radiosonde relative humidity and wind observations were generally improved with hybrid En3D-Var, but in all experiments, the fits to surface observations were degraded compared to the baseline 3D-Var configuration. Over the Singapore radar domain, there was a general improvement in the precipitation forecasts, especially when the weighting toward the climatological background error covariance was larger, and with the additional application of time-shifted ensemble perturbations. Future work involves consolidating the ensemble prediction and deterministic system, by centering the ensemble prediction system on the hybrid analysis, to better represent the analysis and forecast uncertainties.
Abstract
A hybrid three-dimensional ensemble–variational (En3D-Var) data assimilation system has been developed to explore incorporating information from an 11-member regional ensemble prediction system, which is dynamically downscaled from a global ensemble system, into a 3-hourly cycling convective-scale data assimilation system over the western Maritime Continent. From the ensemble, there exists small-scale ensemble perturbation structures associated with positional differences of tropical convection, but these structures are well represented only after the downscaled ensemble forecast has evolved for at least 6 h due to spinup. There was also a robust moderate negative correlation between total specific humidity and potential temperature background errors, presumably because of incorrect vertical motion in the presence of clouds. Time shifting of the ensemble perturbations, by using those available from adjacent cycles, helped to ameliorate the sampling error prevalent in their raw autocovariances. Monthlong hybrid En3D-Var trials were conducted using different weights assigned to the ensemble-derived and climatological background error covariances. The forecast fits to radiosonde relative humidity and wind observations were generally improved with hybrid En3D-Var, but in all experiments, the fits to surface observations were degraded compared to the baseline 3D-Var configuration. Over the Singapore radar domain, there was a general improvement in the precipitation forecasts, especially when the weighting toward the climatological background error covariance was larger, and with the additional application of time-shifted ensemble perturbations. Future work involves consolidating the ensemble prediction and deterministic system, by centering the ensemble prediction system on the hybrid analysis, to better represent the analysis and forecast uncertainties.
Abstract
Herein, 14 severe quasi-linear convective systems (QLCS) covering a wide range of geographical locations and environmental conditions are simulated for both 1- and 3-km horizontal grid resolutions, to further clarify their comparative capabilities in representing convective system features associated with severe weather production. Emphasis is placed on validating the simulated reflectivity structures, cold pool strength, mesoscale vortex characteristics, and surface wind strength. As to the overall reflectivity characteristics, the basic leading-line trailing stratiform structure was often better defined at 1 versus 3 km, but both resolutions were capable of producing bow echo and line echo wave pattern type features. Cold pool characteristics for both the 1- and 3-km simulations were also well replicated for the differing environments, with the 1-km cold pools slightly colder and often a bit larger. Both resolutions captured the larger mesoscale vortices, such as line-end or bookend vortices, but smaller, leading-line mesoscale updraft vortices, that often promote QLCS tornadogenesis, were largely absent in the 3-km simulations. Finally, while maximum surface winds were only marginally well predicted for both resolutions, the simulations were able to reasonably differentiate the relative contributions of the cold pool versus mesoscale vortices. The present results suggest that while many QLCS characteristics can be reasonably represented at a grid scale of 3 km, some of the more detailed structures, such as overall reflectivity characteristics and the smaller leading-line mesoscale vortices would likely benefit from the finer 1-km grid spacing.
Significance Statement
High-resolution model forecasts using 3-km grid spacing have proven to offer significant forecast guidance enhancements for severe convective weather. However, it is unclear whether additional enhancements can be obtained by decreasing grid spacings further to 1 km. Herein, we compare forecasts of severe quasi-linear convective systems (QLCS) simulated using 1- versus 3-km grids to document the potential value added of such increases in grid resolutions. It is shown that some significant improvements can be obtained in the representation of many QLCS features, especially as regards reflectivity structure and in the development of small, leading-line mesoscale vortices that can contribute to both severe surface wind and tornado production.
Abstract
Herein, 14 severe quasi-linear convective systems (QLCS) covering a wide range of geographical locations and environmental conditions are simulated for both 1- and 3-km horizontal grid resolutions, to further clarify their comparative capabilities in representing convective system features associated with severe weather production. Emphasis is placed on validating the simulated reflectivity structures, cold pool strength, mesoscale vortex characteristics, and surface wind strength. As to the overall reflectivity characteristics, the basic leading-line trailing stratiform structure was often better defined at 1 versus 3 km, but both resolutions were capable of producing bow echo and line echo wave pattern type features. Cold pool characteristics for both the 1- and 3-km simulations were also well replicated for the differing environments, with the 1-km cold pools slightly colder and often a bit larger. Both resolutions captured the larger mesoscale vortices, such as line-end or bookend vortices, but smaller, leading-line mesoscale updraft vortices, that often promote QLCS tornadogenesis, were largely absent in the 3-km simulations. Finally, while maximum surface winds were only marginally well predicted for both resolutions, the simulations were able to reasonably differentiate the relative contributions of the cold pool versus mesoscale vortices. The present results suggest that while many QLCS characteristics can be reasonably represented at a grid scale of 3 km, some of the more detailed structures, such as overall reflectivity characteristics and the smaller leading-line mesoscale vortices would likely benefit from the finer 1-km grid spacing.
Significance Statement
High-resolution model forecasts using 3-km grid spacing have proven to offer significant forecast guidance enhancements for severe convective weather. However, it is unclear whether additional enhancements can be obtained by decreasing grid spacings further to 1 km. Herein, we compare forecasts of severe quasi-linear convective systems (QLCS) simulated using 1- versus 3-km grids to document the potential value added of such increases in grid resolutions. It is shown that some significant improvements can be obtained in the representation of many QLCS features, especially as regards reflectivity structure and in the development of small, leading-line mesoscale vortices that can contribute to both severe surface wind and tornado production.
Abstract
A time–space shift method is developed for relocating model-predicted tornado vortices to radar-observed locations to improve the model initial conditions and subsequent predictions of tornadoes. The method consists of the following three steps. (i) Use the vortex center location estimated from radar observations to sample the best ensemble member from tornado-resolving ensemble predictions. Here, the best member is defined in terms of the predicted vortex center track that has a closest point, say at the time of t = t *, to the estimated vortex center at the initial time t 0 (when the tornado vortex signature is first detected in radar observations). (ii) Create a time-shifted field from the best ensemble member in which the field within a circular area of about 10-km radius around the vortex center is taken from t = t *, while the field outside this circular area is transformed smoothly via temporal interpolation to the best ensemble member at t 0. (iii) Create a time–space-shifted field in which the above time-shifted circular area is further shifted horizontally to co-center with the estimated vortex center at t 0, while the field outside this circular area is transformed smoothly via spatial interpolation to the non-shifted field at t 0 from the best ensemble member. The method is applied to the 20 May 2013 Oklahoma Newcastle–Moore tornado case, and is shown to be very effective in improving the tornado track and intensity predictions.
Significance Statement
The time–space shift method developed in this paper can smoothly relocate tornado vortices in model-predicted fields to match radar-observed locations. The method is found to be very effective in improving not only model initial condition but also the subsequent tornado track and intensity predictions. The method is also not sensitive to small errors in radar-estimated vortex center location at the initial time. The method should be useful for future real-time or even operational applications although further tests and improvements are needed (and are planned).
Abstract
A time–space shift method is developed for relocating model-predicted tornado vortices to radar-observed locations to improve the model initial conditions and subsequent predictions of tornadoes. The method consists of the following three steps. (i) Use the vortex center location estimated from radar observations to sample the best ensemble member from tornado-resolving ensemble predictions. Here, the best member is defined in terms of the predicted vortex center track that has a closest point, say at the time of t = t *, to the estimated vortex center at the initial time t 0 (when the tornado vortex signature is first detected in radar observations). (ii) Create a time-shifted field from the best ensemble member in which the field within a circular area of about 10-km radius around the vortex center is taken from t = t *, while the field outside this circular area is transformed smoothly via temporal interpolation to the best ensemble member at t 0. (iii) Create a time–space-shifted field in which the above time-shifted circular area is further shifted horizontally to co-center with the estimated vortex center at t 0, while the field outside this circular area is transformed smoothly via spatial interpolation to the non-shifted field at t 0 from the best ensemble member. The method is applied to the 20 May 2013 Oklahoma Newcastle–Moore tornado case, and is shown to be very effective in improving the tornado track and intensity predictions.
Significance Statement
The time–space shift method developed in this paper can smoothly relocate tornado vortices in model-predicted fields to match radar-observed locations. The method is found to be very effective in improving not only model initial condition but also the subsequent tornado track and intensity predictions. The method is also not sensitive to small errors in radar-estimated vortex center location at the initial time. The method should be useful for future real-time or even operational applications although further tests and improvements are needed (and are planned).
Abstract
Hail forecasts produced by the CAM-HAILCAST pseudo-Lagrangian hail size forecasting model were evaluated during the 2019, 2020, and 2021 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiments (SFEs). As part of this evaluation, HWT SFE participants were polled about their definition of a “good” hail forecast. Participants were presented with two different verification methods conducted over three different spatiotemporal scales, and were then asked to subjectively evaluate the hail forecast as well as the different verification methods themselves. Results recommended use of multiple verification methods tailored to the type of forecast expected by the end-user interpreting and applying the forecast. The hail forecasts evaluated during this period included an implementation of CAM-HAILCAST in the Limited Area Model of the Unified Forecast System with the Finite Volume 3 (FV3) dynamical core. Evaluation of FV3-HAILCAST over both 1- and 24-h periods found continued improvement from 2019 to 2021. The improvement was largely a result of wide intervariability among FV3 ensemble members with different microphysics parameterizations in 2019 lessening significantly during 2020 and 2021. Overprediction throughout the diurnal cycle also lessened by 2021. A combination of both upscaling neighborhood verification and an object-based technique that only retained matched convective objects was necessary to understand the improvement, agreeing with the HWT SFE participants’ recommendations for multiple verification methods.
Significance Statement
“Good” forecasts of hail can be determined in multiple ways and must depend on both the performance of the guidance and the perspective of the end-user. This work looks at different verification strategies to capture the performance of the CAM-HAILCAST hail forecasting model across three years of the Spring Forecasting Experiment (SFE) in different parent models. Verification strategies were informed by SFE participant input via a survey. Skill variability among models decreased in SFE 2021 relative to prior SFEs. The FV3 model in 2021, compared to 2019, provided improved forecasts of both convective distribution and 38-mm (1.5 in.) hail size, as well as less overforecasting of convection from 1900 to 2300 UTC.
Abstract
Hail forecasts produced by the CAM-HAILCAST pseudo-Lagrangian hail size forecasting model were evaluated during the 2019, 2020, and 2021 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiments (SFEs). As part of this evaluation, HWT SFE participants were polled about their definition of a “good” hail forecast. Participants were presented with two different verification methods conducted over three different spatiotemporal scales, and were then asked to subjectively evaluate the hail forecast as well as the different verification methods themselves. Results recommended use of multiple verification methods tailored to the type of forecast expected by the end-user interpreting and applying the forecast. The hail forecasts evaluated during this period included an implementation of CAM-HAILCAST in the Limited Area Model of the Unified Forecast System with the Finite Volume 3 (FV3) dynamical core. Evaluation of FV3-HAILCAST over both 1- and 24-h periods found continued improvement from 2019 to 2021. The improvement was largely a result of wide intervariability among FV3 ensemble members with different microphysics parameterizations in 2019 lessening significantly during 2020 and 2021. Overprediction throughout the diurnal cycle also lessened by 2021. A combination of both upscaling neighborhood verification and an object-based technique that only retained matched convective objects was necessary to understand the improvement, agreeing with the HWT SFE participants’ recommendations for multiple verification methods.
Significance Statement
“Good” forecasts of hail can be determined in multiple ways and must depend on both the performance of the guidance and the perspective of the end-user. This work looks at different verification strategies to capture the performance of the CAM-HAILCAST hail forecasting model across three years of the Spring Forecasting Experiment (SFE) in different parent models. Verification strategies were informed by SFE participant input via a survey. Skill variability among models decreased in SFE 2021 relative to prior SFEs. The FV3 model in 2021, compared to 2019, provided improved forecasts of both convective distribution and 38-mm (1.5 in.) hail size, as well as less overforecasting of convection from 1900 to 2300 UTC.
Abstract
Tropical cyclones are extreme events with enormous and devastating consequences to life, property, and our economies. As a result, large-scale efforts have been devoted to improving tropical cyclone forecasts with lead times ranging from a few days to months. More recently, subseasonal forecasts (e.g., 2–6-week lead time) of tropical cyclones have received greater attention. Here, we study whether bias-corrected, subseasonal tropical cyclone reforecasts of the GEFS and ECMWF models are skillful in the Atlantic basin. We focus on the peak hurricane season, July–November, using the reforecast years 2000–19. Model reforecasts of accumulated cyclone energy (ACE) are produced, and validated, for lead times of 1–2 and 3–4 weeks. Week-1–2 forecasts are substantially more skillful than a 31-day moving-window climatology, while week-3–4 forecasts still exhibit positive skill throughout much of the hurricane season. Furthermore, the skill of the combination of the two models is found to be an improvement with respect to either individual model. In addition to the GEFS and ECMWF model reforecasts, we develop a statistical modeling framework that solely relies on daily sea surface temperatures. The reforecasts of ACE from this statistical model are capable of producing better skill than the GEFS or ECMWF model, individually, and it can be leveraged to further enhance the model combination reforecast skill for the 3–4-week lead time.
Abstract
Tropical cyclones are extreme events with enormous and devastating consequences to life, property, and our economies. As a result, large-scale efforts have been devoted to improving tropical cyclone forecasts with lead times ranging from a few days to months. More recently, subseasonal forecasts (e.g., 2–6-week lead time) of tropical cyclones have received greater attention. Here, we study whether bias-corrected, subseasonal tropical cyclone reforecasts of the GEFS and ECMWF models are skillful in the Atlantic basin. We focus on the peak hurricane season, July–November, using the reforecast years 2000–19. Model reforecasts of accumulated cyclone energy (ACE) are produced, and validated, for lead times of 1–2 and 3–4 weeks. Week-1–2 forecasts are substantially more skillful than a 31-day moving-window climatology, while week-3–4 forecasts still exhibit positive skill throughout much of the hurricane season. Furthermore, the skill of the combination of the two models is found to be an improvement with respect to either individual model. In addition to the GEFS and ECMWF model reforecasts, we develop a statistical modeling framework that solely relies on daily sea surface temperatures. The reforecasts of ACE from this statistical model are capable of producing better skill than the GEFS or ECMWF model, individually, and it can be leveraged to further enhance the model combination reforecast skill for the 3–4-week lead time.
