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Abstract
Florida annually leads the United States in lightning-caused fatalities. While many studies have examined the lightning frequency maximum near Cape Canaveral, relatively little attention has been paid to the western Florida peninsula, which features a similar warm-season lightning event density. Of particular concern are first cloud-to-ground (FCG) lightning events in developing thunderstorms, which are difficult to predict with sufficient lead time and can catch people off guard. This study performs an environmental analysis of warm-season (May–September) FCG events (2014–21) across the western Florida peninsula using high-resolution model analysis data, including a comparison to null (No CG) days. FCG events and No CG days are first identified from ground-based lightning data and partitioned into nine synoptic-scale flow regimes. Next, spatiotemporal distributions of FCG events are elucidated for the western Florida peninsula. An ingredients-based analysis shows that the convective environment one hour before FCG events during strong south-southeast flow features the largest amounts of moisture, but the smallest instability values and weak midtropospheric lapse rates, primarily due to warm advection and moisture transport from the Atlantic Ocean. Environments one hour before FCG events in all nine flow regimes feature markedly greater instability values, larger relative humidity values, and steeper midtropospheric lapse rates than do No CG days. Results emphasize that instability and moisture are the key ingredients for warm-season FCG events in the region. Convective parameter statistical distributions and composite soundings populate an online dashboard that can be used by regional forecasters to better predict FCG events and increase alert lead times.
Significance Statement
Florida annually leads the United States in lightning fatalities. Of particular concern are first cloud-to-ground (FCG) lightning events, which are difficult to forecast and can catch people off guard especially during outdoor recreational activities and labor. We investigate the environmental characteristics of warm-season FCG events across the western Florida peninsula. Among nine regional flow patterns, some are associated with a less moist and more unstable atmosphere one hour before an FCG event, while other regimes exhibit a more moist and less unstable atmosphere. However, regardless of flow pattern, FCG events consistently feature substantially greater instability and moisture than do null events. Key findings are displayed on an online dashboard, to better inform regional forecasters.
Abstract
Florida annually leads the United States in lightning-caused fatalities. While many studies have examined the lightning frequency maximum near Cape Canaveral, relatively little attention has been paid to the western Florida peninsula, which features a similar warm-season lightning event density. Of particular concern are first cloud-to-ground (FCG) lightning events in developing thunderstorms, which are difficult to predict with sufficient lead time and can catch people off guard. This study performs an environmental analysis of warm-season (May–September) FCG events (2014–21) across the western Florida peninsula using high-resolution model analysis data, including a comparison to null (No CG) days. FCG events and No CG days are first identified from ground-based lightning data and partitioned into nine synoptic-scale flow regimes. Next, spatiotemporal distributions of FCG events are elucidated for the western Florida peninsula. An ingredients-based analysis shows that the convective environment one hour before FCG events during strong south-southeast flow features the largest amounts of moisture, but the smallest instability values and weak midtropospheric lapse rates, primarily due to warm advection and moisture transport from the Atlantic Ocean. Environments one hour before FCG events in all nine flow regimes feature markedly greater instability values, larger relative humidity values, and steeper midtropospheric lapse rates than do No CG days. Results emphasize that instability and moisture are the key ingredients for warm-season FCG events in the region. Convective parameter statistical distributions and composite soundings populate an online dashboard that can be used by regional forecasters to better predict FCG events and increase alert lead times.
Significance Statement
Florida annually leads the United States in lightning fatalities. Of particular concern are first cloud-to-ground (FCG) lightning events, which are difficult to forecast and can catch people off guard especially during outdoor recreational activities and labor. We investigate the environmental characteristics of warm-season FCG events across the western Florida peninsula. Among nine regional flow patterns, some are associated with a less moist and more unstable atmosphere one hour before an FCG event, while other regimes exhibit a more moist and less unstable atmosphere. However, regardless of flow pattern, FCG events consistently feature substantially greater instability and moisture than do null events. Key findings are displayed on an online dashboard, to better inform regional forecasters.
Abstract
In the last few years, central Europe faced a number of severe, record-breaking heatwaves. Previous studies focused on predictability of heatwaves on medium-range to subseasonal time scales (5–30 days). However, also short-range (3-day) forecasts of maximum temperature (Tmax) can exhibit substantial errors even on larger spatial scales. This study investigates the causes of short-range forecast errors in Tmax over central Europe for the summers of 2015–20 using the 50-member ensemble of the operational ECMWF-IFS (ECMWF-ENS). The 3-day forecast errors, individually calculated for each ensemble member with respect to a 0–18-h control forecast, are fed into a multivariate linear regression model to study the relative importance of different error sources. Outside of heatwaves, errors in Tmax forecasts are predominantly caused by incorrectly predicted downwelling shortwave radiation, mainly due to errors in low cloud cover. During heatwaves, ECMWF-ENS exhibits a systematic underestimation of Tmax (−0.4 K), which is exacerbated under clear-sky and low wind conditions, and other error sources gain importance: the second most important error source is over- or underestimation of nocturnal temperatures in the residual layer. Additional Lagrangian trajectory analysis for the years 2018–20 (due to limited data availability) suggests a link to accumulating errors in near-surface diabatic heating of air masses associated with forecast errors in residence time over land and cloud cover. Regionally, other physical processes can be of dominant importance during heatwaves. Coastal regions are influenced by errors in near-surface wind whereas errors in soil moisture are more important in southeastern parts of central Europe.
Abstract
In the last few years, central Europe faced a number of severe, record-breaking heatwaves. Previous studies focused on predictability of heatwaves on medium-range to subseasonal time scales (5–30 days). However, also short-range (3-day) forecasts of maximum temperature (Tmax) can exhibit substantial errors even on larger spatial scales. This study investigates the causes of short-range forecast errors in Tmax over central Europe for the summers of 2015–20 using the 50-member ensemble of the operational ECMWF-IFS (ECMWF-ENS). The 3-day forecast errors, individually calculated for each ensemble member with respect to a 0–18-h control forecast, are fed into a multivariate linear regression model to study the relative importance of different error sources. Outside of heatwaves, errors in Tmax forecasts are predominantly caused by incorrectly predicted downwelling shortwave radiation, mainly due to errors in low cloud cover. During heatwaves, ECMWF-ENS exhibits a systematic underestimation of Tmax (−0.4 K), which is exacerbated under clear-sky and low wind conditions, and other error sources gain importance: the second most important error source is over- or underestimation of nocturnal temperatures in the residual layer. Additional Lagrangian trajectory analysis for the years 2018–20 (due to limited data availability) suggests a link to accumulating errors in near-surface diabatic heating of air masses associated with forecast errors in residence time over land and cloud cover. Regionally, other physical processes can be of dominant importance during heatwaves. Coastal regions are influenced by errors in near-surface wind whereas errors in soil moisture are more important in southeastern parts of central Europe.
Abstract
The Dynamical–Statistical–Analog Ensemble Forecast model for Landfalling Typhoon Precipitation (DSAEF_LTP) was developed as a supplementary method to numerical weather prediction (NWP). A successful strategy for improving the forecasting skill of the DSAEF_LTP model is to include as many relevant variables as possible in the generalized initial value (GIV) of this model. In this study, a new variable, TC translation speed, is incorporated into the DSAEF_LTP model, producing a new version of this model named DSAEF_LTP-4. Then, the best scheme of the model for South China is obtained by applying this model to the forecast of the accumulated rainfall of 13 landfalling tropical cyclones (LTCs) that occurred over South China during 2012–14. In addition, the forecast performance of the best scheme is estimated by forecast experiments with eight LTCs in 2015–16 over South China, and then compared to that of the other versions of the DSAEF_LTP model and three NWP models (i.e., ECMWF, GFS, and T639). Results show further the improved performance of the DSAEF_LTP-4 model in simulating precipitation of ≥250 and ≥100 mm. However, the forecast performance of DSAEF_LTP-4 is less satisfactory than DSAEF_LTP-2. This is mainly because of a large proportion of TCs with anomalous tracks and more sensitivity to the characteristics of experiment samples of DSAEF_LTP-4. Of significance is that the DSAEF_LTP model performs better than three NWP models for LTCs with typical tracks.
Significance Statement
The purpose of this study is to improve the performance of the Dynamical–Statistical–Analog Ensemble Forecast model for Landfalling Typhoon Precipitation (DSAEF_LTP) model by incorporating typhoon translation speed similarity. Compared with the dynamical models, which are more prone to misses, the DSAEF_LTP model is more prone to false alarms. The superiority of the DSAEF_LTP model shows especially in predicting the precipitation of TCs with typical tracks.
Abstract
The Dynamical–Statistical–Analog Ensemble Forecast model for Landfalling Typhoon Precipitation (DSAEF_LTP) was developed as a supplementary method to numerical weather prediction (NWP). A successful strategy for improving the forecasting skill of the DSAEF_LTP model is to include as many relevant variables as possible in the generalized initial value (GIV) of this model. In this study, a new variable, TC translation speed, is incorporated into the DSAEF_LTP model, producing a new version of this model named DSAEF_LTP-4. Then, the best scheme of the model for South China is obtained by applying this model to the forecast of the accumulated rainfall of 13 landfalling tropical cyclones (LTCs) that occurred over South China during 2012–14. In addition, the forecast performance of the best scheme is estimated by forecast experiments with eight LTCs in 2015–16 over South China, and then compared to that of the other versions of the DSAEF_LTP model and three NWP models (i.e., ECMWF, GFS, and T639). Results show further the improved performance of the DSAEF_LTP-4 model in simulating precipitation of ≥250 and ≥100 mm. However, the forecast performance of DSAEF_LTP-4 is less satisfactory than DSAEF_LTP-2. This is mainly because of a large proportion of TCs with anomalous tracks and more sensitivity to the characteristics of experiment samples of DSAEF_LTP-4. Of significance is that the DSAEF_LTP model performs better than three NWP models for LTCs with typical tracks.
Significance Statement
The purpose of this study is to improve the performance of the Dynamical–Statistical–Analog Ensemble Forecast model for Landfalling Typhoon Precipitation (DSAEF_LTP) model by incorporating typhoon translation speed similarity. Compared with the dynamical models, which are more prone to misses, the DSAEF_LTP model is more prone to false alarms. The superiority of the DSAEF_LTP model shows especially in predicting the precipitation of TCs with typical tracks.
Abstract
Model improvement efforts involve an evaluation of changes in model skill in response to changes in model physics and parameterization. When using wind measurements from various remote sensors to determine model forecast accuracy, it is important to understand the effects of measurement-uncertainty differences among the sensors resulting from differences in the methods of measurement, the vertical and temporal resolution of the measurements, and the spatial variability of these differences. Here we quantify instrument measurement variability in 80-m wind speed during WFIP2 and its impact on the calculated errors and the change in error from one model version to another. The model versions tested involved updates in model physics from HRRRv1 to HRRRv4, and reductions in grid interval from 3 km to 750 m. Model errors were found to be 2–3 m s−1. Differences in errors as determined by various instruments at each site amounted to about 10% of this value, or 0.2–0.3 m s−1. Changes in model skill due to physics or grid-resolution updates also differed depending on the instrument used to determine the errors; most of the instrument-to-instrument differences were ∼0.1 m s−1, but some reached 0.3 m s−1. All instruments at a given site mostly showed consistency in the sign of the change in error. In two examples, though, the sign changed, illustrating a consequence of differences in measurements: errors determined using one instrument may show improvement in model skill, whereas errors determined using another instrument may indicate degradation. This possibility underscores the importance of having accurate measurements to determine the model error.
Significance Statement
To evaluate model forecast accuracy using remote sensing instruments, it is important to understand the effects of measurement uncertainties due to differences in the methods of measurement and data processing techniques, the vertical and temporal resolution of the measurements, and the spatial variability of these differences. In this study, three types of collocated remote sensing systems are used to quantify the impact of measurement variability on the magnitude of calculated errors and the change in error from one model version to another. The model versions tested involved updates in model physics from HRRRv1 to HRRRv4, and reductions in grid interval from 3 km to 750 m.
Abstract
Model improvement efforts involve an evaluation of changes in model skill in response to changes in model physics and parameterization. When using wind measurements from various remote sensors to determine model forecast accuracy, it is important to understand the effects of measurement-uncertainty differences among the sensors resulting from differences in the methods of measurement, the vertical and temporal resolution of the measurements, and the spatial variability of these differences. Here we quantify instrument measurement variability in 80-m wind speed during WFIP2 and its impact on the calculated errors and the change in error from one model version to another. The model versions tested involved updates in model physics from HRRRv1 to HRRRv4, and reductions in grid interval from 3 km to 750 m. Model errors were found to be 2–3 m s−1. Differences in errors as determined by various instruments at each site amounted to about 10% of this value, or 0.2–0.3 m s−1. Changes in model skill due to physics or grid-resolution updates also differed depending on the instrument used to determine the errors; most of the instrument-to-instrument differences were ∼0.1 m s−1, but some reached 0.3 m s−1. All instruments at a given site mostly showed consistency in the sign of the change in error. In two examples, though, the sign changed, illustrating a consequence of differences in measurements: errors determined using one instrument may show improvement in model skill, whereas errors determined using another instrument may indicate degradation. This possibility underscores the importance of having accurate measurements to determine the model error.
Significance Statement
To evaluate model forecast accuracy using remote sensing instruments, it is important to understand the effects of measurement uncertainties due to differences in the methods of measurement and data processing techniques, the vertical and temporal resolution of the measurements, and the spatial variability of these differences. In this study, three types of collocated remote sensing systems are used to quantify the impact of measurement variability on the magnitude of calculated errors and the change in error from one model version to another. The model versions tested involved updates in model physics from HRRRv1 to HRRRv4, and reductions in grid interval from 3 km to 750 m.
Abstract
Parameterizing boundary layer turbulence is a critical component of numerical weather prediction and the representation of turbulent mixing of momentum, heat, and other tracers. The components that make up a boundary layer scheme can vary considerably, with each scheme having a combination of processes that are physically represented along with tuning parameters that optimize performance. Isolating a component of a PBL scheme to examine its impact is essential for understanding the evolution of boundary layer profiles and their impact on the mean structure. In this study we conduct three experiments with the scale-aware TKE eddy-diffusivity mass-flux (sa-TKE-EDMF) scheme: 1) releasing the upper limit constraints placed on mixing lengths, 2) incrementally adjusting the tuning coefficient related to wind shear in the modified Bougeault and Lacarrere (BouLac) mixing length formulation, and 3) replacing the current mixing length formulations with those used in the MYNN scheme. A diagnostic approach is adopted to characterize the bulk representation of turbulence within the residual layer and boundary layer in order to understand the importance of different terms in the TKE budget as well as to assess how the balance of terms changes between mixing length formulations. Although our study does not seek to determine the best formulation, it was found that strong imbalances led to considerably different profile structures both in terms of the resolved and subgrid fields. Experiments where this balance was preserved showed a minor impact on the mean structure regardless of the turbulence generated. Overall, it was found that changes to mixing length formulations and/or constraints had stronger impacts during the day while remaining partially insensitive during the evening.
Abstract
Parameterizing boundary layer turbulence is a critical component of numerical weather prediction and the representation of turbulent mixing of momentum, heat, and other tracers. The components that make up a boundary layer scheme can vary considerably, with each scheme having a combination of processes that are physically represented along with tuning parameters that optimize performance. Isolating a component of a PBL scheme to examine its impact is essential for understanding the evolution of boundary layer profiles and their impact on the mean structure. In this study we conduct three experiments with the scale-aware TKE eddy-diffusivity mass-flux (sa-TKE-EDMF) scheme: 1) releasing the upper limit constraints placed on mixing lengths, 2) incrementally adjusting the tuning coefficient related to wind shear in the modified Bougeault and Lacarrere (BouLac) mixing length formulation, and 3) replacing the current mixing length formulations with those used in the MYNN scheme. A diagnostic approach is adopted to characterize the bulk representation of turbulence within the residual layer and boundary layer in order to understand the importance of different terms in the TKE budget as well as to assess how the balance of terms changes between mixing length formulations. Although our study does not seek to determine the best formulation, it was found that strong imbalances led to considerably different profile structures both in terms of the resolved and subgrid fields. Experiments where this balance was preserved showed a minor impact on the mean structure regardless of the turbulence generated. Overall, it was found that changes to mixing length formulations and/or constraints had stronger impacts during the day while remaining partially insensitive during the evening.
Abstract
Observational data collection is extremely hazardous in supercell storm environments, which makes for a scarcity of data used for evaluating the storm-scale guidance from convection allowing models (CAMs) like the National Oceanic and Atmospheric Administration (NOAA) Warn-on-Forecast System (WoFS). The Targeted Observations with UAS and Radar of Supercells (TORUS) 2019 field mission provided a rare opportunity to not only collect these observations, but to do so with advanced technology: vertically pointing Doppler lidar. One standing question for WoFS is how the system forecasts the feedback between supercells and their near-storm environment. The lidar can observe vertical profiles of wind over time, creating unique datasets to compare to WoFS kinematic predictions in rapidly evolving severe weather environments. Mobile radiosonde data are also presented to provide a thermodynamic comparison. The five lidar deployments (three of which observed tornadic supercells) analyzed show WoFS accurately predicted general kinematic trends in the inflow environment; however, the predicted feedback between the supercell and its environment, which resulted in enhanced inflow and larger storm-relative helicity (SRH), were muted relative to observations. The radiosonde observations reveal an overprediction of CAPE in WoFS forecasts, both in the near and far field, with an inverse relationship between the CAPE errors and distance from the storm.
Significance Statement
It is difficult to evaluate the accuracy of weather prediction model forecasts of severe thunderstorms because observations are rarely available near the storms. However, the TORUS 2019 field experiment collected multiple specialized observations in the near-storm environment of supercells, which are compared to the same near-storm environments predicted by the National Oceanic and Atmospheric Administration (NOAA) Warn-on-Forecast System (WoFS) to gauge its performance. Unique to this study is the use of mobile Doppler lidar observations in the evaluation; lidar can retrieve the horizontal winds in the few kilometers above ground on time scales of a few minutes. Using lidar and radiosonde observations in the near-storm environment of three tornadic supercells, we find that WoFS generally predicts the expected trends in the evolution of the near-storm wind profile, but the response is muted compared to observations. We also find an inverse relationship of errors in instability to distance from the storm. These results can aid model developers in refining model physics to better predict severe storms.
Abstract
Observational data collection is extremely hazardous in supercell storm environments, which makes for a scarcity of data used for evaluating the storm-scale guidance from convection allowing models (CAMs) like the National Oceanic and Atmospheric Administration (NOAA) Warn-on-Forecast System (WoFS). The Targeted Observations with UAS and Radar of Supercells (TORUS) 2019 field mission provided a rare opportunity to not only collect these observations, but to do so with advanced technology: vertically pointing Doppler lidar. One standing question for WoFS is how the system forecasts the feedback between supercells and their near-storm environment. The lidar can observe vertical profiles of wind over time, creating unique datasets to compare to WoFS kinematic predictions in rapidly evolving severe weather environments. Mobile radiosonde data are also presented to provide a thermodynamic comparison. The five lidar deployments (three of which observed tornadic supercells) analyzed show WoFS accurately predicted general kinematic trends in the inflow environment; however, the predicted feedback between the supercell and its environment, which resulted in enhanced inflow and larger storm-relative helicity (SRH), were muted relative to observations. The radiosonde observations reveal an overprediction of CAPE in WoFS forecasts, both in the near and far field, with an inverse relationship between the CAPE errors and distance from the storm.
Significance Statement
It is difficult to evaluate the accuracy of weather prediction model forecasts of severe thunderstorms because observations are rarely available near the storms. However, the TORUS 2019 field experiment collected multiple specialized observations in the near-storm environment of supercells, which are compared to the same near-storm environments predicted by the National Oceanic and Atmospheric Administration (NOAA) Warn-on-Forecast System (WoFS) to gauge its performance. Unique to this study is the use of mobile Doppler lidar observations in the evaluation; lidar can retrieve the horizontal winds in the few kilometers above ground on time scales of a few minutes. Using lidar and radiosonde observations in the near-storm environment of three tornadic supercells, we find that WoFS generally predicts the expected trends in the evolution of the near-storm wind profile, but the response is muted compared to observations. We also find an inverse relationship of errors in instability to distance from the storm. These results can aid model developers in refining model physics to better predict severe storms.
Abstract
Hodographs are valuable sources of pattern recognition in severe convective storm forecasting. Certain shapes are known to discriminate between single cell, multicell, and supercell storm organization. Various derived quantities such as storm-relative helicity (SRH) have been found to predict tornado potential and intensity. Over the years, collective research has established a conceptual model for tornadic hodographs (large and “looping,” with high SRH). However, considerably less attention has been given to constructing a similar conceptual model for hodographs of severe hail. This study explores how hodograph shape may differentiate between the environments of severe hail and tornadoes. While supercells are routinely assumed to carry the potential to produce all hazards, this is not always the case, and we explore why. The Storm Prediction Center (SPC) storm mode dataset is used to assess the environments of 8958 tornadoes and 7256 severe hail reports, produced by right- and left-moving supercells. Composite hodographs and indices to quantify wind shear are assessed for each hazard, and clear differences are found between the kinematic environments of hail-producing and tornadic supercells. The sensitivity of the hodograph to common thermodynamic variables was also examined, with buoyancy and moisture found to influence the shape associated with the hazards. The results suggest that differentiating between tornadic and hail-producing storms may be possible using properties of the hodograph alone. While anticipating hail size does not appear possible using only the hodograph, anticipating tornado intensity appears readily so. When coupled with buoyancy profiles, the hodograph may assist in differentiating between both hail size and tornado intensity.
Abstract
Hodographs are valuable sources of pattern recognition in severe convective storm forecasting. Certain shapes are known to discriminate between single cell, multicell, and supercell storm organization. Various derived quantities such as storm-relative helicity (SRH) have been found to predict tornado potential and intensity. Over the years, collective research has established a conceptual model for tornadic hodographs (large and “looping,” with high SRH). However, considerably less attention has been given to constructing a similar conceptual model for hodographs of severe hail. This study explores how hodograph shape may differentiate between the environments of severe hail and tornadoes. While supercells are routinely assumed to carry the potential to produce all hazards, this is not always the case, and we explore why. The Storm Prediction Center (SPC) storm mode dataset is used to assess the environments of 8958 tornadoes and 7256 severe hail reports, produced by right- and left-moving supercells. Composite hodographs and indices to quantify wind shear are assessed for each hazard, and clear differences are found between the kinematic environments of hail-producing and tornadic supercells. The sensitivity of the hodograph to common thermodynamic variables was also examined, with buoyancy and moisture found to influence the shape associated with the hazards. The results suggest that differentiating between tornadic and hail-producing storms may be possible using properties of the hodograph alone. While anticipating hail size does not appear possible using only the hodograph, anticipating tornado intensity appears readily so. When coupled with buoyancy profiles, the hodograph may assist in differentiating between both hail size and tornado intensity.
Abstract
Severe weather probabilities are derived from the Warn-on-Forecast System (WoFS) run by NOAA’s National Severe Storms Laboratory (NSSL) during spring 2018 using the random forest (RF) machine learning algorithm. Recent work has shown this method generates skillful and reliable forecasts when applied to convection-allowing model ensembles for the “Day 1” time range (i.e., 12–36-h lead times), but it has been tested in only one other study for lead times relevant to WoFS (e.g., 0–6 h). Thus, in this paper, various sets of WoFS predictors, which include both environment and storm-based fields, are input into a RF algorithm and trained using the occurrence of severe weather reports within 39 km of a point to produce severe weather probabilities at 0–3-h lead times. We analyze the skill and reliability of these forecasts, sensitivity to different sets of predictors, and avenues for further improvements. The RF algorithm produced very skillful and reliable severe weather probabilities and significantly outperformed baseline probabilities calculated by finding the best performing updraft helicity (UH) threshold and smoothing parameter. Experiments where different sets of predictors were used to derive RF probabilities revealed 1) storm attribute fields contributed significantly more skill than environmental fields, 2) 2–5 km AGL UH and maximum updraft speed were the best performing storm attribute fields, 3) the most skillful ensemble summary metric was a smoothed mean, and 4) the most skillful forecasts were obtained when smoothed UH from individual ensemble members were used as predictors.
Abstract
Severe weather probabilities are derived from the Warn-on-Forecast System (WoFS) run by NOAA’s National Severe Storms Laboratory (NSSL) during spring 2018 using the random forest (RF) machine learning algorithm. Recent work has shown this method generates skillful and reliable forecasts when applied to convection-allowing model ensembles for the “Day 1” time range (i.e., 12–36-h lead times), but it has been tested in only one other study for lead times relevant to WoFS (e.g., 0–6 h). Thus, in this paper, various sets of WoFS predictors, which include both environment and storm-based fields, are input into a RF algorithm and trained using the occurrence of severe weather reports within 39 km of a point to produce severe weather probabilities at 0–3-h lead times. We analyze the skill and reliability of these forecasts, sensitivity to different sets of predictors, and avenues for further improvements. The RF algorithm produced very skillful and reliable severe weather probabilities and significantly outperformed baseline probabilities calculated by finding the best performing updraft helicity (UH) threshold and smoothing parameter. Experiments where different sets of predictors were used to derive RF probabilities revealed 1) storm attribute fields contributed significantly more skill than environmental fields, 2) 2–5 km AGL UH and maximum updraft speed were the best performing storm attribute fields, 3) the most skillful ensemble summary metric was a smoothed mean, and 4) the most skillful forecasts were obtained when smoothed UH from individual ensemble members were used as predictors.
Abstract
The development of sea surface temperature (SST) anomalies over the northeast Pacific and their impacts on lower-tropospheric air temperatures over the Pacific Northwest are examined. Northeast Pacific SST anomalies are influenced by the synoptic-scale flow, with high pressure and weak surface winds associated with developing warm SST anomalies, while large pressure gradients and strong surface winds result in SST declines. SST over the northeast Pacific correlates significantly with surface air temperatures over the Pacific Northwest, with correlations increasing when high-frequency variability is filtered out. The correlations between unfiltered time series of SST and surface air temperature are largest for a zero-day lag and are strongest near the coast, contrasting with weaker correlations over the Columbia basin east of the Cascade Mountains. SST correlations with minimum surface air temperature are largest during the warm season, and maximum temperature correlations are highest in March; both have low correlations during autumn. Model simulations of periods with warm and cold northeast Pacific SST anomalies possess lower-tropospheric air temperature warming or cooling over the coastal zone, with SST influence weakening east of the Cascade crest. Eastern Pacific SST anomalies influence sea level pressure and lower-tropospheric heights, with warm SST anomalies resulting in simulated lowered pressure near the surface and increased heights aloft. The relationship between northeast Pacific SST and surface air temperature over land evince complex feedbacks: SST temperature anomalies can be advected inland from the Pacific, the SST anomalies can influence the synoptic-scale flow that affects the SST anomalies, and the synoptic-scale anomalies that produce the SST anomalies can directly influence temperatures over land.
Significance Statement
Understanding the connection between northeast Pacific sea surface temperatures and low-level air temperatures over land is valuable for both subseasonal prediction and for examining the fidelity of model physics.
Abstract
The development of sea surface temperature (SST) anomalies over the northeast Pacific and their impacts on lower-tropospheric air temperatures over the Pacific Northwest are examined. Northeast Pacific SST anomalies are influenced by the synoptic-scale flow, with high pressure and weak surface winds associated with developing warm SST anomalies, while large pressure gradients and strong surface winds result in SST declines. SST over the northeast Pacific correlates significantly with surface air temperatures over the Pacific Northwest, with correlations increasing when high-frequency variability is filtered out. The correlations between unfiltered time series of SST and surface air temperature are largest for a zero-day lag and are strongest near the coast, contrasting with weaker correlations over the Columbia basin east of the Cascade Mountains. SST correlations with minimum surface air temperature are largest during the warm season, and maximum temperature correlations are highest in March; both have low correlations during autumn. Model simulations of periods with warm and cold northeast Pacific SST anomalies possess lower-tropospheric air temperature warming or cooling over the coastal zone, with SST influence weakening east of the Cascade crest. Eastern Pacific SST anomalies influence sea level pressure and lower-tropospheric heights, with warm SST anomalies resulting in simulated lowered pressure near the surface and increased heights aloft. The relationship between northeast Pacific SST and surface air temperature over land evince complex feedbacks: SST temperature anomalies can be advected inland from the Pacific, the SST anomalies can influence the synoptic-scale flow that affects the SST anomalies, and the synoptic-scale anomalies that produce the SST anomalies can directly influence temperatures over land.
Significance Statement
Understanding the connection between northeast Pacific sea surface temperatures and low-level air temperatures over land is valuable for both subseasonal prediction and for examining the fidelity of model physics.
