Search Results
You are looking at 11 - 20 of 67 items for
- Author or Editor: Adam J. Clark x
- Refine by Access: All Content x
Abstract
Previous studies examining convection-allowing models (CAMs), as well as NOAA/Hazardous Weather Testbed Spring Forecasting Experiments (SFEs) have typically emphasized “day 1” (12–36 h) forecast guidance. These studies find a distinct advantage in CAMs relative to models that parameterize convection, especially for fields strongly tied to convection like precipitation. During the 2014 SFE, “day 2” (36–60 h) forecast products from a CAM ensemble provided by the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma were examined. Quantitative precipitation forecasts (QPFs) from the CAPS ensemble, known as the Storm Scale Ensemble Forecast (SSEF) system, are compared to NCEP’s operational Short Range Ensemble Forecast (SREF) system, which provides lateral boundary conditions for the SSEF, to see if the CAM ensemble outperforms the SREF through forecast hours 36–60. Equitable threat scores (ETSs) were computed for precipitation thresholds ranging from 0.10 to 0.75 in. for each SSEF and SREF member, as well as ensemble means, for 3-h accumulation periods. The ETS difference between the SSEF and SREF peaked during hours 36–42. Probabilistic forecasts were evaluated using the area under the receiver operating characteristic curve (ROC area). The SSEF had higher values of ROC area, especially at thresholds ≥ 0.50 in. Additionally, time–longitude diagrams of diurnally averaged rainfall were constructed for each SSEF/SREF ensemble member. Spatial correlation coefficients between forecasts and observations in time–longitude space indicated that the SSEF depicted the diurnal cycle much better than the SREF, which underforecasted precipitation with a peak that had a 3-h phase lag. A minority of SREF members performed well.
Abstract
Previous studies examining convection-allowing models (CAMs), as well as NOAA/Hazardous Weather Testbed Spring Forecasting Experiments (SFEs) have typically emphasized “day 1” (12–36 h) forecast guidance. These studies find a distinct advantage in CAMs relative to models that parameterize convection, especially for fields strongly tied to convection like precipitation. During the 2014 SFE, “day 2” (36–60 h) forecast products from a CAM ensemble provided by the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma were examined. Quantitative precipitation forecasts (QPFs) from the CAPS ensemble, known as the Storm Scale Ensemble Forecast (SSEF) system, are compared to NCEP’s operational Short Range Ensemble Forecast (SREF) system, which provides lateral boundary conditions for the SSEF, to see if the CAM ensemble outperforms the SREF through forecast hours 36–60. Equitable threat scores (ETSs) were computed for precipitation thresholds ranging from 0.10 to 0.75 in. for each SSEF and SREF member, as well as ensemble means, for 3-h accumulation periods. The ETS difference between the SSEF and SREF peaked during hours 36–42. Probabilistic forecasts were evaluated using the area under the receiver operating characteristic curve (ROC area). The SSEF had higher values of ROC area, especially at thresholds ≥ 0.50 in. Additionally, time–longitude diagrams of diurnally averaged rainfall were constructed for each SSEF/SREF ensemble member. Spatial correlation coefficients between forecasts and observations in time–longitude space indicated that the SSEF depicted the diurnal cycle much better than the SREF, which underforecasted precipitation with a peak that had a 3-h phase lag. A minority of SREF members performed well.
Abstract
Hourly maximum fields of simulated storm diagnostics from experimental versions of convection-permitting models (CPMs) provide valuable information regarding severe weather potential. While past studies have focused on predicting any type of severe weather, this study uses a CPM-based Weather Research and Forecasting (WRF) Model ensemble initialized daily at the National Severe Storms Laboratory (NSSL) to derive tornado probabilities using a combination of simulated storm diagnostics and environmental parameters. Daily probabilistic tornado forecasts are developed from the NSSL-WRF ensemble using updraft helicity (UH) as a tornado proxy. The UH fields are combined with simulated environmental fields such as lifted condensation level (LCL) height, most unstable and surface-based CAPE (MUCAPE and SBCAPE, respectively), and multifield severe weather parameters such as the significant tornado parameter (STP). Varying thresholds of 2–5-km updraft helicity were tested with differing values of σ in the Gaussian smoother that was used to derive forecast probabilities, as well as different environmental information, with the aim of maximizing both forecast skill and reliability. The addition of environmental information improved the reliability and the critical success index (CSI) while slightly degrading the area under the receiver operating characteristic (ROC) curve across all UH thresholds and σ values. The probabilities accurately reflected the location of tornado reports, and three case studies demonstrate value to forecasters. Based on initial tests, four sets of tornado probabilities were chosen for evaluation by participants in the 2015 National Oceanic and Atmospheric Administration’s Hazardous Weather Testbed Spring Forecasting Experiment from 4 May to 5 June 2015. Participants found the probabilities useful and noted an overforecasting tendency.
Abstract
Hourly maximum fields of simulated storm diagnostics from experimental versions of convection-permitting models (CPMs) provide valuable information regarding severe weather potential. While past studies have focused on predicting any type of severe weather, this study uses a CPM-based Weather Research and Forecasting (WRF) Model ensemble initialized daily at the National Severe Storms Laboratory (NSSL) to derive tornado probabilities using a combination of simulated storm diagnostics and environmental parameters. Daily probabilistic tornado forecasts are developed from the NSSL-WRF ensemble using updraft helicity (UH) as a tornado proxy. The UH fields are combined with simulated environmental fields such as lifted condensation level (LCL) height, most unstable and surface-based CAPE (MUCAPE and SBCAPE, respectively), and multifield severe weather parameters such as the significant tornado parameter (STP). Varying thresholds of 2–5-km updraft helicity were tested with differing values of σ in the Gaussian smoother that was used to derive forecast probabilities, as well as different environmental information, with the aim of maximizing both forecast skill and reliability. The addition of environmental information improved the reliability and the critical success index (CSI) while slightly degrading the area under the receiver operating characteristic (ROC) curve across all UH thresholds and σ values. The probabilities accurately reflected the location of tornado reports, and three case studies demonstrate value to forecasters. Based on initial tests, four sets of tornado probabilities were chosen for evaluation by participants in the 2015 National Oceanic and Atmospheric Administration’s Hazardous Weather Testbed Spring Forecasting Experiment from 4 May to 5 June 2015. Participants found the probabilities useful and noted an overforecasting tendency.
Abstract
Planning and managing commercial airplane routes to avoid thunderstorms requires very skillful and frequently updated 0–8-h forecasts of convection. The National Oceanic and Atmospheric Administration’s High-Resolution Rapid Refresh (HRRR) model is well suited for this purpose, being initialized hourly and providing explicit forecasts of convection out to 15 h. However, because of difficulties with depicting convection at the time of model initialization and shortly thereafter (i.e., during model spinup), relatively simple extrapolation techniques, on average, perform better than the HRRR at 0–2-h lead times. Thus, recently developed nowcasting techniques blend extrapolation-based forecasts with numerical weather prediction (NWP)-based forecasts, heavily weighting the extrapolation forecasts at 0–2-h lead times and transitioning emphasis to the NWP-based forecasts at the later lead times. In this study, a new approach to applying different weights to blend extrapolation and model forecasts based on intensities and forecast times is applied and tested. An image-processing method of morphing between extrapolation and model forecasts to create nowcasts is described and the skill is compared to extrapolation forecasts and forecasts from the HRRR. The new approach is called salient cross dissolve (Sal CD), which is compared to a commonly used method called linear cross dissolve (Lin CD). Examinations of forecasts and observations of the maximum altitude of echo-top heights ≥18 dBZ and measurement of forecast skill using neighborhood-based methods shows that Sal CD significantly improves upon Lin CD, as well as the HRRR at 2–5-h lead times.
Abstract
Planning and managing commercial airplane routes to avoid thunderstorms requires very skillful and frequently updated 0–8-h forecasts of convection. The National Oceanic and Atmospheric Administration’s High-Resolution Rapid Refresh (HRRR) model is well suited for this purpose, being initialized hourly and providing explicit forecasts of convection out to 15 h. However, because of difficulties with depicting convection at the time of model initialization and shortly thereafter (i.e., during model spinup), relatively simple extrapolation techniques, on average, perform better than the HRRR at 0–2-h lead times. Thus, recently developed nowcasting techniques blend extrapolation-based forecasts with numerical weather prediction (NWP)-based forecasts, heavily weighting the extrapolation forecasts at 0–2-h lead times and transitioning emphasis to the NWP-based forecasts at the later lead times. In this study, a new approach to applying different weights to blend extrapolation and model forecasts based on intensities and forecast times is applied and tested. An image-processing method of morphing between extrapolation and model forecasts to create nowcasts is described and the skill is compared to extrapolation forecasts and forecasts from the HRRR. The new approach is called salient cross dissolve (Sal CD), which is compared to a commonly used method called linear cross dissolve (Lin CD). Examinations of forecasts and observations of the maximum altitude of echo-top heights ≥18 dBZ and measurement of forecast skill using neighborhood-based methods shows that Sal CD significantly improves upon Lin CD, as well as the HRRR at 2–5-h lead times.
Abstract
This study compares next-day forecasts of storm motion from convection-allowing models with 1- and 4-km grid spacing. A tracking algorithm is used to determine the motion of discrete storms in both the model forecasts and an analysis of radar observations. The distributions of both the raw storm motions and the deviations of these motions from the environmental flow are examined to determine the overall biases of the 1- and 4-km forecasts and how they compare to the observed storm motions. The mean storm speeds for the 1-km forecasts are significantly closer to the observed mean than those for the 4-km forecasts when viewed relative to the environmental flow/shear, but mostly for the shorter-lived storms. For storm directions, the 1-km forecast storms move similarly to the 4-km forecast storms on average. However, for the raw storm motions and those relative to the 0–6-km shear, results suggest that the 1-km forecasts may alleviate some of a clockwise (rightward) bias of the 4-km forecasts, particularly for those that do not deviate strongly from the 0–6-km shear vector. This improvement in a clockwise bias also is seen for the longer-lived storms, but is not seen when viewing the storm motions relative to the 850–300-hPa mean wind or Bunkers motion vector. These results suggest that a reduction from 4- to 1-km grid spacing can potentially improve forecasts of storm motion, but further analysis of closer storm analogs are needed to confirm these results and to explore specific hypotheses for their differences.
Abstract
This study compares next-day forecasts of storm motion from convection-allowing models with 1- and 4-km grid spacing. A tracking algorithm is used to determine the motion of discrete storms in both the model forecasts and an analysis of radar observations. The distributions of both the raw storm motions and the deviations of these motions from the environmental flow are examined to determine the overall biases of the 1- and 4-km forecasts and how they compare to the observed storm motions. The mean storm speeds for the 1-km forecasts are significantly closer to the observed mean than those for the 4-km forecasts when viewed relative to the environmental flow/shear, but mostly for the shorter-lived storms. For storm directions, the 1-km forecast storms move similarly to the 4-km forecast storms on average. However, for the raw storm motions and those relative to the 0–6-km shear, results suggest that the 1-km forecasts may alleviate some of a clockwise (rightward) bias of the 4-km forecasts, particularly for those that do not deviate strongly from the 0–6-km shear vector. This improvement in a clockwise bias also is seen for the longer-lived storms, but is not seen when viewing the storm motions relative to the 850–300-hPa mean wind or Bunkers motion vector. These results suggest that a reduction from 4- to 1-km grid spacing can potentially improve forecasts of storm motion, but further analysis of closer storm analogs are needed to confirm these results and to explore specific hypotheses for their differences.
Abstract
A continuous-update-cycle storm-scale ensemble data assimilation (DA) and prediction system using the ARW model and DART software is used to generate retrospective 0–6-h ensemble forecasts of the 31 May 2013 tornado and flash flood event over central Oklahoma, with a focus on the prediction of heavy rainfall. Results indicate that the model-predicted probabilities of strong low-level mesocyclones correspond well with the locations of observed mesocyclones and with the observed damage track. The ensemble-mean quantitative precipitation forecast (QPF) from the radar DA experiments match NCEP’s stage IV analyses reasonably well in terms of location and amount of rainfall, particularly during the 0–3-h forecast period. In contrast, significant displacement errors and lower rainfall totals are evident in a control experiment that withholds radar data during the DA. The ensemble-derived probabilistic QPF (PQPF) from the radar DA experiment is more skillful than the PQPF from the no_radar experiment, based on visual inspection and probabilistic verification metrics. A novel object-based storm-tracking algorithm provides additional insight, suggesting that explicit assimilation and 1–2-h prediction of the dominant supercell is remarkably skillful in the radar experiment. The skill in both experiments is substantially higher during the 0–3-h forecast period than in the 3–6-h period. Furthermore, the difference in skill between the two forecasts decreases sharply during the latter period, indicating that the impact of radar DA is greatest during early forecast hours. Overall, the results demonstrate the potential for a frequently updated, high-resolution ensemble system to extend probabilistic low-level mesocyclone and flash flood forecast lead times and improve accuracy of convective precipitation nowcasting.
Abstract
A continuous-update-cycle storm-scale ensemble data assimilation (DA) and prediction system using the ARW model and DART software is used to generate retrospective 0–6-h ensemble forecasts of the 31 May 2013 tornado and flash flood event over central Oklahoma, with a focus on the prediction of heavy rainfall. Results indicate that the model-predicted probabilities of strong low-level mesocyclones correspond well with the locations of observed mesocyclones and with the observed damage track. The ensemble-mean quantitative precipitation forecast (QPF) from the radar DA experiments match NCEP’s stage IV analyses reasonably well in terms of location and amount of rainfall, particularly during the 0–3-h forecast period. In contrast, significant displacement errors and lower rainfall totals are evident in a control experiment that withholds radar data during the DA. The ensemble-derived probabilistic QPF (PQPF) from the radar DA experiment is more skillful than the PQPF from the no_radar experiment, based on visual inspection and probabilistic verification metrics. A novel object-based storm-tracking algorithm provides additional insight, suggesting that explicit assimilation and 1–2-h prediction of the dominant supercell is remarkably skillful in the radar experiment. The skill in both experiments is substantially higher during the 0–3-h forecast period than in the 3–6-h period. Furthermore, the difference in skill between the two forecasts decreases sharply during the latter period, indicating that the impact of radar DA is greatest during early forecast hours. Overall, the results demonstrate the potential for a frequently updated, high-resolution ensemble system to extend probabilistic low-level mesocyclone and flash flood forecast lead times and improve accuracy of convective precipitation nowcasting.
Abstract
Spread and skill of mixed- and single-physics convection-allowing ensemble forecasts that share the same set of perturbed initial and lateral boundary conditions are investigated at a variety of spatial scales. Forecast spread is assessed for 2-m temperature, 2-m dewpoint, 500-hPa geopotential height, and hourly accumulated precipitation both before and after a bias-correction procedure is applied. Time series indicate that the mixed-physics ensemble forecasts generally have greater variance than comparable single-physics forecasts. While the differences tend to be small, they are greatest at the smallest spatial scales and when the ensembles are not calibrated for bias. Although differences between the mixed- and single-physics ensemble variances are smaller for the larger spatial scales, variance ratios suggest that the mixed-physics ensemble generates more spread relative to the single-physics ensemble at larger spatial scales. Forecast skill is evaluated for 2-m temperature, dewpoint temperature, and bias-corrected 6-h accumulated precipitation. The mixed-physics ensemble generally has lower 2-m temperature and dewpoint root-mean-square error (RMSE) compared to the single-physics ensemble. However, little difference in skill or reliability is found between the mixed- and single-physics bias-corrected precipitation forecasts. Overall, given that mixed- and single-physics ensembles have similar spread and skill, developers may prefer to implement single- as opposed to mixed-physics convection-allowing ensembles in future operational systems, while accounting for model error using stochastic methods.
Abstract
Spread and skill of mixed- and single-physics convection-allowing ensemble forecasts that share the same set of perturbed initial and lateral boundary conditions are investigated at a variety of spatial scales. Forecast spread is assessed for 2-m temperature, 2-m dewpoint, 500-hPa geopotential height, and hourly accumulated precipitation both before and after a bias-correction procedure is applied. Time series indicate that the mixed-physics ensemble forecasts generally have greater variance than comparable single-physics forecasts. While the differences tend to be small, they are greatest at the smallest spatial scales and when the ensembles are not calibrated for bias. Although differences between the mixed- and single-physics ensemble variances are smaller for the larger spatial scales, variance ratios suggest that the mixed-physics ensemble generates more spread relative to the single-physics ensemble at larger spatial scales. Forecast skill is evaluated for 2-m temperature, dewpoint temperature, and bias-corrected 6-h accumulated precipitation. The mixed-physics ensemble generally has lower 2-m temperature and dewpoint root-mean-square error (RMSE) compared to the single-physics ensemble. However, little difference in skill or reliability is found between the mixed- and single-physics bias-corrected precipitation forecasts. Overall, given that mixed- and single-physics ensembles have similar spread and skill, developers may prefer to implement single- as opposed to mixed-physics convection-allowing ensembles in future operational systems, while accounting for model error using stochastic methods.
Abstract
A majority of tropical cyclones in the North Atlantic develop from African easterly waves (AEWs), which originate along both the southern and northern flanks of the midtropospheric African easterly jet (AEWS and AEWn, respectively). The purpose of this note is to identify the contribution of AEWSs and AEWns to North Atlantic tropical cyclones that develop from AEWs. Applying a manual backtracking approach to identify the genesis locations of AEWS, it was found that the population ratio of tropical cyclones formed from AEWSs to those formed from AEWns is 1:1.2. Because the population ratio of AEWSs to AEWns is 1:2.5, the conversion rate of the former AEWS to tropical cyclones is twice as effective as the latter waves. In addition, it was found that AEWns travel farther and take longer to transform into tropical cyclones than AEWSs, which is likely because the AEWns are drier and shallower than AEWSs. An analysis of various terms in the moisture and vorticity budgets reveals that the monsoon trough over West Africa provides moisture and enhances low-level vorticity for both AEWns and AEWSs as they move off the West African coast. The monsoon trough appears to be of particular importance in supplying AEWns with enough moisture so that they have similar properties to AEWSs after they have traveled a considerable westward distance across the tropical Atlantic.
Abstract
A majority of tropical cyclones in the North Atlantic develop from African easterly waves (AEWs), which originate along both the southern and northern flanks of the midtropospheric African easterly jet (AEWS and AEWn, respectively). The purpose of this note is to identify the contribution of AEWSs and AEWns to North Atlantic tropical cyclones that develop from AEWs. Applying a manual backtracking approach to identify the genesis locations of AEWS, it was found that the population ratio of tropical cyclones formed from AEWSs to those formed from AEWns is 1:1.2. Because the population ratio of AEWSs to AEWns is 1:2.5, the conversion rate of the former AEWS to tropical cyclones is twice as effective as the latter waves. In addition, it was found that AEWns travel farther and take longer to transform into tropical cyclones than AEWSs, which is likely because the AEWns are drier and shallower than AEWSs. An analysis of various terms in the moisture and vorticity budgets reveals that the monsoon trough over West Africa provides moisture and enhances low-level vorticity for both AEWns and AEWSs as they move off the West African coast. The monsoon trough appears to be of particular importance in supplying AEWns with enough moisture so that they have similar properties to AEWSs after they have traveled a considerable westward distance across the tropical Atlantic.
Abstract
Extracting explicit severe weather forecast guidance from convection-allowing ensembles (CAEs) is challenging since CAEs cannot directly simulate individual severe weather hazards. Currently, CAE-based severe weather probabilities must be inferred from one or more storm-related variables, which may require extensive calibration and/or contain limited information. Machine learning (ML) offers a way to obtain severe weather forecast probabilities from CAEs by relating CAE forecast variables to observed severe weather reports. This paper develops and verifies a random forest (RF)-based ML method for creating day 1 (1200–1200 UTC) severe weather hazard probabilities and categorical outlooks based on 0000 UTC Storm-Scale Ensemble of Opportunity (SSEO) forecast data and observed Storm Prediction Center (SPC) storm reports. RF forecast probabilities are compared against severe weather forecasts from calibrated SSEO 2–5-km updraft helicity (UH) forecasts and SPC convective outlooks issued at 0600 UTC. Continuous RF probabilities routinely have the highest Brier skill scores (BSSs), regardless of whether the forecasts are evaluated over the full domain or regional/seasonal subsets. Even when RF probabilities are truncated at the probability levels issued by the SPC, the RF forecasts often have BSSs better than or comparable to corresponding UH and SPC forecasts. Relative to the UH and SPC forecasts, the RF approach performs best for severe wind and hail prediction during the spring and summer (i.e., March–August). Overall, it is concluded that the RF method presented here provides skillful, reliable CAE-derived severe weather probabilities that may be useful to severe weather forecasters and decision-makers.
Abstract
Extracting explicit severe weather forecast guidance from convection-allowing ensembles (CAEs) is challenging since CAEs cannot directly simulate individual severe weather hazards. Currently, CAE-based severe weather probabilities must be inferred from one or more storm-related variables, which may require extensive calibration and/or contain limited information. Machine learning (ML) offers a way to obtain severe weather forecast probabilities from CAEs by relating CAE forecast variables to observed severe weather reports. This paper develops and verifies a random forest (RF)-based ML method for creating day 1 (1200–1200 UTC) severe weather hazard probabilities and categorical outlooks based on 0000 UTC Storm-Scale Ensemble of Opportunity (SSEO) forecast data and observed Storm Prediction Center (SPC) storm reports. RF forecast probabilities are compared against severe weather forecasts from calibrated SSEO 2–5-km updraft helicity (UH) forecasts and SPC convective outlooks issued at 0600 UTC. Continuous RF probabilities routinely have the highest Brier skill scores (BSSs), regardless of whether the forecasts are evaluated over the full domain or regional/seasonal subsets. Even when RF probabilities are truncated at the probability levels issued by the SPC, the RF forecasts often have BSSs better than or comparable to corresponding UH and SPC forecasts. Relative to the UH and SPC forecasts, the RF approach performs best for severe wind and hail prediction during the spring and summer (i.e., March–August). Overall, it is concluded that the RF method presented here provides skillful, reliable CAE-derived severe weather probabilities that may be useful to severe weather forecasters and decision-makers.
Abstract
Recent research has shown that random forests (RFs) can create skillful probabilistic severe weather hazard forecasts from numerical weather prediction (NWP) ensemble data. However, it remains unclear how RFs use NWP data and how predictors should be generated from NWP ensembles. This paper compares two methods for creating RFs for next-day severe weather prediction using simulated forecast data from the convection-allowing High-Resolution Ensemble Forecast System, version 2.1 (HREFv2.1). The first method uses predictors from individual ensemble members (IM) at the point of prediction, while the second uses ensemble mean (EM) predictors at multiple spatial points. IM and EM RFs are trained with all predictors as well as predictor subsets, and the Python module tree interpreter (TI) is used to assess RF variable importance and the relationships learned by the RFs. Results show that EM RFs have better objective skill compared to similarly configured IM RFs for all hazards, presumably because EM predictors contain less noise. In both IM and EM RFs, storm variables are found to be most important, followed by index and environment variables. Interestingly, RFs created from storm and index variables tend to produce forecasts with greater or equal skill than those from the all-predictor RFs. TI analysis shows that the RFs emphasize different predictors for different hazards in a way that makes physical sense. Further, TI shows that RFs create calibrated hazard probabilities based on complex, multivariate relationships that go well beyond thresholding 2–5-km updraft helicity.
Abstract
Recent research has shown that random forests (RFs) can create skillful probabilistic severe weather hazard forecasts from numerical weather prediction (NWP) ensemble data. However, it remains unclear how RFs use NWP data and how predictors should be generated from NWP ensembles. This paper compares two methods for creating RFs for next-day severe weather prediction using simulated forecast data from the convection-allowing High-Resolution Ensemble Forecast System, version 2.1 (HREFv2.1). The first method uses predictors from individual ensemble members (IM) at the point of prediction, while the second uses ensemble mean (EM) predictors at multiple spatial points. IM and EM RFs are trained with all predictors as well as predictor subsets, and the Python module tree interpreter (TI) is used to assess RF variable importance and the relationships learned by the RFs. Results show that EM RFs have better objective skill compared to similarly configured IM RFs for all hazards, presumably because EM predictors contain less noise. In both IM and EM RFs, storm variables are found to be most important, followed by index and environment variables. Interestingly, RFs created from storm and index variables tend to produce forecasts with greater or equal skill than those from the all-predictor RFs. TI analysis shows that the RFs emphasize different predictors for different hazards in a way that makes physical sense. Further, TI shows that RFs create calibrated hazard probabilities based on complex, multivariate relationships that go well beyond thresholding 2–5-km updraft helicity.
