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Abstract
This study attempted to forecast the morning fog expansion (MFE), commonly referred to as the “sea of clouds,” utilizing an artificial intelligence (AI) algorithm. The radiation fog phenomenon that contributes to the sea of clouds is caused by various weather conditions. Hence, the MFE was predicted using datasets from public meteorological observations and a mesoscale numerical model (MSM). In this study, a machine learning technique, the gradient-boosting method, was adopted as the AI algorithm. The Miyoshi basin in Japan, renowned for its MFE, was selected as the experimental region. Training models were developed using datasets from October to December 2018–21. Subsequently, these models were applied to forecast MFE in 2022. The model employing the upper-atmospheric prediction data from the MSM demonstrated the highest robustness and accuracy among the proposed models. For untrained data in the fog season during 2022, the model was confirmed to be sufficiently reliable for forecasting MFE, with a high hit rate of 0.935, a low Brier score of 0.119, and a high area under the curve (AUC) of 0.944. Furthermore, the analysis of the importance of the features elucidated that the meteorological factors, such as synoptic-scale weak wind, temperatures close to the dewpoint temperature, and the absence of middle-level cloud cover at midnight, strongly contribute to the MFE. Therefore, the incorporation of upper-level meteorological elements improves the forecast accuracy for MFE.
Significance Statement
An AI-driven forecasting model for predicting morning fog expansion (MFE), sea of clouds, which often affects local livelihoods, was constructed. Fog forecasting machine learning techniques were utilized in the Japanese region famous for the morning fog. This study revealed that more accurate forecasting models incorporate numerically predicted weather elements sourced from the public routine system rather than real-time observed weather elements. Notably, the upper-level wind speed reflecting synoptic-scale dynamics, surface dewpoint depression, and middle-level cloud cover play significant roles in governing MFE. Therefore, incorporating upper-level meteorological elements into the features to machine learning is crucial for improving the forecasting accuracy of MFE.
Abstract
This study attempted to forecast the morning fog expansion (MFE), commonly referred to as the “sea of clouds,” utilizing an artificial intelligence (AI) algorithm. The radiation fog phenomenon that contributes to the sea of clouds is caused by various weather conditions. Hence, the MFE was predicted using datasets from public meteorological observations and a mesoscale numerical model (MSM). In this study, a machine learning technique, the gradient-boosting method, was adopted as the AI algorithm. The Miyoshi basin in Japan, renowned for its MFE, was selected as the experimental region. Training models were developed using datasets from October to December 2018–21. Subsequently, these models were applied to forecast MFE in 2022. The model employing the upper-atmospheric prediction data from the MSM demonstrated the highest robustness and accuracy among the proposed models. For untrained data in the fog season during 2022, the model was confirmed to be sufficiently reliable for forecasting MFE, with a high hit rate of 0.935, a low Brier score of 0.119, and a high area under the curve (AUC) of 0.944. Furthermore, the analysis of the importance of the features elucidated that the meteorological factors, such as synoptic-scale weak wind, temperatures close to the dewpoint temperature, and the absence of middle-level cloud cover at midnight, strongly contribute to the MFE. Therefore, the incorporation of upper-level meteorological elements improves the forecast accuracy for MFE.
Significance Statement
An AI-driven forecasting model for predicting morning fog expansion (MFE), sea of clouds, which often affects local livelihoods, was constructed. Fog forecasting machine learning techniques were utilized in the Japanese region famous for the morning fog. This study revealed that more accurate forecasting models incorporate numerically predicted weather elements sourced from the public routine system rather than real-time observed weather elements. Notably, the upper-level wind speed reflecting synoptic-scale dynamics, surface dewpoint depression, and middle-level cloud cover play significant roles in governing MFE. Therefore, incorporating upper-level meteorological elements into the features to machine learning is crucial for improving the forecasting accuracy of MFE.
Abstract
Synoptic-scale vortices known as monsoon low pressure systems (LPSs) frequently produce intense precipitation and hydrological disasters in South Asia, so accurately forecasting LPS genesis is crucial for improving disaster preparedness and response. However, the accuracy of LPS genesis forecasts by numerical weather prediction models has remained unknown. Here, we evaluate the performance of two global ensemble models—the U.S. Global Ensemble Forecast System (GEFS) and the Ensemble Prediction System of the European Centre for Medium-Range Weather Forecasts (ECMWF)—in predicting LPS genesis during the years 2021–22. The GEFS successfully predicted about half the observed LPS genesis events 1–2 days in advance; the ECMWF model captured an additional 10% of observed genesis events. Both models had a false alarm ratio (FAR) of around 50% for 1–2-day lead times. In both ensembles, the control run typically exhibited a higher probability of detection (POD) of observed events and a lower FAR compared to the perturbed ensemble members. However, a consensus forecast, in which genesis is predicted when at least 20% of ensemble members forecast LPS formation, had POD values surpassing those of the control run for all lead times. Moreover, probabilistic predictions of genesis over the Bay of Bengal, where most LPSs form, were skillful, with the fraction of ensemble members predicting LPS formation over a 5-day lead time approximating the observed frequency of genesis, without any adjustment or bias correction.
Abstract
Synoptic-scale vortices known as monsoon low pressure systems (LPSs) frequently produce intense precipitation and hydrological disasters in South Asia, so accurately forecasting LPS genesis is crucial for improving disaster preparedness and response. However, the accuracy of LPS genesis forecasts by numerical weather prediction models has remained unknown. Here, we evaluate the performance of two global ensemble models—the U.S. Global Ensemble Forecast System (GEFS) and the Ensemble Prediction System of the European Centre for Medium-Range Weather Forecasts (ECMWF)—in predicting LPS genesis during the years 2021–22. The GEFS successfully predicted about half the observed LPS genesis events 1–2 days in advance; the ECMWF model captured an additional 10% of observed genesis events. Both models had a false alarm ratio (FAR) of around 50% for 1–2-day lead times. In both ensembles, the control run typically exhibited a higher probability of detection (POD) of observed events and a lower FAR compared to the perturbed ensemble members. However, a consensus forecast, in which genesis is predicted when at least 20% of ensemble members forecast LPS formation, had POD values surpassing those of the control run for all lead times. Moreover, probabilistic predictions of genesis over the Bay of Bengal, where most LPSs form, were skillful, with the fraction of ensemble members predicting LPS formation over a 5-day lead time approximating the observed frequency of genesis, without any adjustment or bias correction.
Abstract
The St. Lawrence River Valley experiences a variety of precipitation types (p-types) during the cold season, such as rain, freezing rain, ice pellets, and snow. These varied precipitation types exert considerable impacts on aviation, road transportation, power generation and distribution, and winter recreation and are shaped by diverse multiscale processes that interact with the region’s complex topography. This study utilizes ERA5 reanalysis data, surface cyclone climatology, and hourly station observations from Montréal, Québec, and Burlington, Vermont, during October–April 2000–18 to investigate the spectrum of synoptic-scale weather regimes that induce cold-season precipitation across the St. Lawrence River Valley. In particular, k-means clustering and self-organizing maps (SOMs) are used to classify cyclone tracks passing near the St. Lawrence River Valley, and their accompanying thermodynamic profiles, into a set of event types that include a U.S. East Coast track, a central U.S. track, and two Canadian clipper tracks. Composite analyses are subsequently performed to reveal the synoptic-scale environments and the characteristic p-types that most frequently accompany each event type. Global Ensemble Forecast System version 12 (GEFSv12) reforecasts are then used to examine the relative predictability of cyclone characteristics and the local thermodynamic profile associated with each event type at 0–5-day forecast lead times. The analysis suggests that forecasted cyclones near the St. Lawrence River Valley develop too quickly and are located left-of-track relative to the reanalysis on average, which has implications for forecasts of the local thermodynamic profile and p-type across the region when the temperature is near 0°C.
Significance Statement
Diverse precipitation types are observed when near-surface temperatures approach 0°C during the cold season, especially across the St. Lawrence River Valley in southern Québec. This study classifies cold-season precipitation events impacting the St. Lawrence River Valley based on the track of storm systems across the region and quantifies the average meteorological characteristics and predictability of each track. Our analysis reveals that forecasted low pressure systems develop too quickly and are left of their observed track 0–5 days prior to an event on average, which has implications for forecasted temperatures and the type of precipitation observed across the region. Our results can inform future operational forecasts of cold-season precipitation events by providing a storm-focused perspective on forecast errors during these impactful events.
Abstract
The St. Lawrence River Valley experiences a variety of precipitation types (p-types) during the cold season, such as rain, freezing rain, ice pellets, and snow. These varied precipitation types exert considerable impacts on aviation, road transportation, power generation and distribution, and winter recreation and are shaped by diverse multiscale processes that interact with the region’s complex topography. This study utilizes ERA5 reanalysis data, surface cyclone climatology, and hourly station observations from Montréal, Québec, and Burlington, Vermont, during October–April 2000–18 to investigate the spectrum of synoptic-scale weather regimes that induce cold-season precipitation across the St. Lawrence River Valley. In particular, k-means clustering and self-organizing maps (SOMs) are used to classify cyclone tracks passing near the St. Lawrence River Valley, and their accompanying thermodynamic profiles, into a set of event types that include a U.S. East Coast track, a central U.S. track, and two Canadian clipper tracks. Composite analyses are subsequently performed to reveal the synoptic-scale environments and the characteristic p-types that most frequently accompany each event type. Global Ensemble Forecast System version 12 (GEFSv12) reforecasts are then used to examine the relative predictability of cyclone characteristics and the local thermodynamic profile associated with each event type at 0–5-day forecast lead times. The analysis suggests that forecasted cyclones near the St. Lawrence River Valley develop too quickly and are located left-of-track relative to the reanalysis on average, which has implications for forecasts of the local thermodynamic profile and p-type across the region when the temperature is near 0°C.
Significance Statement
Diverse precipitation types are observed when near-surface temperatures approach 0°C during the cold season, especially across the St. Lawrence River Valley in southern Québec. This study classifies cold-season precipitation events impacting the St. Lawrence River Valley based on the track of storm systems across the region and quantifies the average meteorological characteristics and predictability of each track. Our analysis reveals that forecasted low pressure systems develop too quickly and are left of their observed track 0–5 days prior to an event on average, which has implications for forecasted temperatures and the type of precipitation observed across the region. Our results can inform future operational forecasts of cold-season precipitation events by providing a storm-focused perspective on forecast errors during these impactful events.
Abstract
Data assimilation is an important approach to improve the prediction performance of near-surface wind and wind power. Based on the four-dimensional variational technique, this study proposes an approach to improve near-surface wind and wind power prediction by extracting and assimilating the principal components of cabin radar radial wind observations installed at wind turbines within a wind farm. The verification for a series of cases under strong and weak vertical wind shear conditions indicates that compared to the simulations without assimilation, the predicted ultra-short-term (0–4 h) mean absolute error of near-surface wind and single turbine wind power could be reduced by 0.09–1.17 m s−1 and 53–209 kW after the assimilation of radial wind directly and by 0.33–1.38 m s−1 and 62–239 kW after the assimilation of principal components. These illustrate that assimilating the principal components of radial wind is superior to assimilating radial wind directly and could obviously reduce prediction error. Further investigation suggests that extracting the principal components of radial wind has marginal influences on the density and distribution of observations, but could obviously reduce the fluctuation of the observations and the correlation among the observations. The prediction improvement by assimilating the principal components of radial wind is essentially due to the assimilation of low-frequency and low-correlation information involved in the observations.
Abstract
Data assimilation is an important approach to improve the prediction performance of near-surface wind and wind power. Based on the four-dimensional variational technique, this study proposes an approach to improve near-surface wind and wind power prediction by extracting and assimilating the principal components of cabin radar radial wind observations installed at wind turbines within a wind farm. The verification for a series of cases under strong and weak vertical wind shear conditions indicates that compared to the simulations without assimilation, the predicted ultra-short-term (0–4 h) mean absolute error of near-surface wind and single turbine wind power could be reduced by 0.09–1.17 m s−1 and 53–209 kW after the assimilation of radial wind directly and by 0.33–1.38 m s−1 and 62–239 kW after the assimilation of principal components. These illustrate that assimilating the principal components of radial wind is superior to assimilating radial wind directly and could obviously reduce prediction error. Further investigation suggests that extracting the principal components of radial wind has marginal influences on the density and distribution of observations, but could obviously reduce the fluctuation of the observations and the correlation among the observations. The prediction improvement by assimilating the principal components of radial wind is essentially due to the assimilation of low-frequency and low-correlation information involved in the observations.
Abstract
Blowing snow is a hazard for motorists because it may rapidly reduce visibility. Numerical weather prediction models in the United States do not capture the movement of snow once it reaches the ground, but visibility reductions due to blowing snow can be diagnosed based on model-predicted land surface and environmental conditions that correlate with blowing snow occurrence. A recently developed diagnostic framework for forecasting blowing snow concentration and the associated visibility reduction is applied to High-Resolution Rapid Refresh (HRRR) and Rapid Refresh Forecast System (RRFS) model output including surface snow conditions to predict surface visibility reduction due to blowing snow. Twelve blowing snow events around Wyoming from 2018 to 2023 are examined. The analysis shows that visibility reductions due to blowing snow tend to be overpredicted, caused by the initial assumption of full driftability of the snowpack. This study refines the aging of the blowing snow reservoir with two methods. The first method estimates driftability based on time-varying snow density from the Rapid Update Cycle land surface model (RUC LSM) used in the HRRR and experimental RRFS models and is evaluated in a real-time context with the RRFS model. The second, complementary method diagnoses snowpack driftability using a process-based approach that requires data for recent snowfall, wind speed, and skin temperature. Compared to the full driftability assumption, this method shows limited improvements in forecasting skill. To improve model-based diagnosis of visibility reduction due to blowing snow, empirical work is needed to determine the relation between snowpack driftability and the recent history of snowfall and other weather conditions.
Significance Statement
Blowing snow presents a significant hazard to motorists and airport operations through sometimes very rapid and intense reductions in visibility, yet little predictive guidance exists for blowing snow. This study aims to improve the prediction of blowing snow occurrence and associated surface visibility reduction using diagnostics from an operational high-resolution weather model. One key challenge regards the question of driftability of the snowpack. This study evaluates two approaches to quantify driftability in terms of visibility reduction due to blowing snow and acknowledges that more measurements are needed to improve the representation of blowing snow physics in NWP models.
Abstract
Blowing snow is a hazard for motorists because it may rapidly reduce visibility. Numerical weather prediction models in the United States do not capture the movement of snow once it reaches the ground, but visibility reductions due to blowing snow can be diagnosed based on model-predicted land surface and environmental conditions that correlate with blowing snow occurrence. A recently developed diagnostic framework for forecasting blowing snow concentration and the associated visibility reduction is applied to High-Resolution Rapid Refresh (HRRR) and Rapid Refresh Forecast System (RRFS) model output including surface snow conditions to predict surface visibility reduction due to blowing snow. Twelve blowing snow events around Wyoming from 2018 to 2023 are examined. The analysis shows that visibility reductions due to blowing snow tend to be overpredicted, caused by the initial assumption of full driftability of the snowpack. This study refines the aging of the blowing snow reservoir with two methods. The first method estimates driftability based on time-varying snow density from the Rapid Update Cycle land surface model (RUC LSM) used in the HRRR and experimental RRFS models and is evaluated in a real-time context with the RRFS model. The second, complementary method diagnoses snowpack driftability using a process-based approach that requires data for recent snowfall, wind speed, and skin temperature. Compared to the full driftability assumption, this method shows limited improvements in forecasting skill. To improve model-based diagnosis of visibility reduction due to blowing snow, empirical work is needed to determine the relation between snowpack driftability and the recent history of snowfall and other weather conditions.
Significance Statement
Blowing snow presents a significant hazard to motorists and airport operations through sometimes very rapid and intense reductions in visibility, yet little predictive guidance exists for blowing snow. This study aims to improve the prediction of blowing snow occurrence and associated surface visibility reduction using diagnostics from an operational high-resolution weather model. One key challenge regards the question of driftability of the snowpack. This study evaluates two approaches to quantify driftability in terms of visibility reduction due to blowing snow and acknowledges that more measurements are needed to improve the representation of blowing snow physics in NWP models.
Abstract
Nowcasting hail size poses a major challenge in operational practice due to physical limitations of weather radar technology once hailstones are sufficiently large to enter the resonance scattering regime. Numerous radar-based hail size proxies have been derived in recent decades, but their performance is generally poor in identifying giant hail (≥ 10 cm). Using a novel thunderstorm updraft detection method, we examine the updraft characteristics of hailstorms in the U.S. Great Plains based on a NEXRAD dataset of 114 hail events between 2013 and 2023. We find that some radar-derived variables within the detected updraft are well suited for discriminating between small (1.0 - 3.0 cm) and severe (≥ 3.5 cm) hail, e.g. minimum co-polar cross-correlation coefficient in the mid-level updraft, whereas other radar metrics such as the area of reflectivity > 50 dBZ in the upper portion of the updraft suggest the presence of giant hail. However, the statistical distributions of each variable overlap for different hail sizes and there is no single metric which performs well across the entire hail size spectrum. Therefore, we trained a Random Forest model to nowcast hail size categories using a multitude of these radar metrics. The model shows promising performance for discriminating hail sizes > 5 cm but requires further refinement for smaller hail. We showcase the model’s capabilities for a set of hailstorms in the Great Plains.
Abstract
Nowcasting hail size poses a major challenge in operational practice due to physical limitations of weather radar technology once hailstones are sufficiently large to enter the resonance scattering regime. Numerous radar-based hail size proxies have been derived in recent decades, but their performance is generally poor in identifying giant hail (≥ 10 cm). Using a novel thunderstorm updraft detection method, we examine the updraft characteristics of hailstorms in the U.S. Great Plains based on a NEXRAD dataset of 114 hail events between 2013 and 2023. We find that some radar-derived variables within the detected updraft are well suited for discriminating between small (1.0 - 3.0 cm) and severe (≥ 3.5 cm) hail, e.g. minimum co-polar cross-correlation coefficient in the mid-level updraft, whereas other radar metrics such as the area of reflectivity > 50 dBZ in the upper portion of the updraft suggest the presence of giant hail. However, the statistical distributions of each variable overlap for different hail sizes and there is no single metric which performs well across the entire hail size spectrum. Therefore, we trained a Random Forest model to nowcast hail size categories using a multitude of these radar metrics. The model shows promising performance for discriminating hail sizes > 5 cm but requires further refinement for smaller hail. We showcase the model’s capabilities for a set of hailstorms in the Great Plains.
Abstract
Multi-model ensemble forecasts have gained widespread use over the past decade. A yet unresolved issue is whether forecast skill benefits from the use of prior skill from each model in providing a weighted combination. Here we use the available seasonal ensemble forecasts of six models from the North American Multi-Model Ensemble (NMME) to study various aspects of prior skill-based weighting schemes and explore ways to merge multi-model forecasts. First, we post-process each NMME model through quantile mapping and a simple spread error adjustment. Then, using an equal weighted combination as the baseline forecast, we test merging the models together through skill-based weights by varying the prior skill metric and varying how the metrics are aggregated across the different subbasins and time of year. Results confirm prior work that the combined forecasts do outperform individual models. When evaluating prior skill, equal weighting generally performed as well or slightly better than all weighting schemes tried. The skill of the weighting scheme was not found to be strongly dependent on prior metric but did improve when aggregating all forecasted months and subbasins together to provide one overall weight to each model. Also, we found that including an offset to the prior metric that nudged the weights closer to equal weighting improves skill especially at longer leads where individual model skill is low. Results also show that the weighting schemes performed better than regression-based techniques including multiple linear regression and random forest.
Abstract
Multi-model ensemble forecasts have gained widespread use over the past decade. A yet unresolved issue is whether forecast skill benefits from the use of prior skill from each model in providing a weighted combination. Here we use the available seasonal ensemble forecasts of six models from the North American Multi-Model Ensemble (NMME) to study various aspects of prior skill-based weighting schemes and explore ways to merge multi-model forecasts. First, we post-process each NMME model through quantile mapping and a simple spread error adjustment. Then, using an equal weighted combination as the baseline forecast, we test merging the models together through skill-based weights by varying the prior skill metric and varying how the metrics are aggregated across the different subbasins and time of year. Results confirm prior work that the combined forecasts do outperform individual models. When evaluating prior skill, equal weighting generally performed as well or slightly better than all weighting schemes tried. The skill of the weighting scheme was not found to be strongly dependent on prior metric but did improve when aggregating all forecasted months and subbasins together to provide one overall weight to each model. Also, we found that including an offset to the prior metric that nudged the weights closer to equal weighting improves skill especially at longer leads where individual model skill is low. Results also show that the weighting schemes performed better than regression-based techniques including multiple linear regression and random forest.
Abstract
The Maryland Mesonet Project will construct a network of 75 surface observing stations with aims that include mitigating the statewide impact of severe convective storms and improving analyses of record. The spatial configuration of mesonet stations is expected to affect the utility newly provided observations will have via data assimilation, making it desirable to study the effects of mesonet configuration. Furthermore, the impact associated with any observing system configuration is constrained by errors inherent to the prediction systems used to generate forecasts, which may change with future advances in data assimilation methodology, physical parameterization schemes, and resource availability. To address such possibilities, we perform sets of observing system simulation experiments using a high-resolution regional modeling system to assess the expected impact of four candidate mesonet configurations. Experiments cover seven 18-hour case-study events featuring moist convective regimes associated with severe weather over the state of Maryland and are performed using two versions of our experimental modeling system: a ’standard-uncertainty’ configuration tuned to be representative of existing convective-allowing prediction systems, and a ’constrained-uncertainty’ configuration with reduced boundary condition and model error that reflects a possible trajectory for future prediction systems. We find that the assimilation of mesonet data produces definitive improvements to analysis fields below 1000 m that are mediated by modeling system uncertainty. Conversely, mesonet impact on forecast verification is inconclusive and strongly variable across verification metrics. The impact of mesonet configuration appears limited by a saturation effect that caps local analysis improvements past a minimal density of observing stations.
Abstract
The Maryland Mesonet Project will construct a network of 75 surface observing stations with aims that include mitigating the statewide impact of severe convective storms and improving analyses of record. The spatial configuration of mesonet stations is expected to affect the utility newly provided observations will have via data assimilation, making it desirable to study the effects of mesonet configuration. Furthermore, the impact associated with any observing system configuration is constrained by errors inherent to the prediction systems used to generate forecasts, which may change with future advances in data assimilation methodology, physical parameterization schemes, and resource availability. To address such possibilities, we perform sets of observing system simulation experiments using a high-resolution regional modeling system to assess the expected impact of four candidate mesonet configurations. Experiments cover seven 18-hour case-study events featuring moist convective regimes associated with severe weather over the state of Maryland and are performed using two versions of our experimental modeling system: a ’standard-uncertainty’ configuration tuned to be representative of existing convective-allowing prediction systems, and a ’constrained-uncertainty’ configuration with reduced boundary condition and model error that reflects a possible trajectory for future prediction systems. We find that the assimilation of mesonet data produces definitive improvements to analysis fields below 1000 m that are mediated by modeling system uncertainty. Conversely, mesonet impact on forecast verification is inconclusive and strongly variable across verification metrics. The impact of mesonet configuration appears limited by a saturation effect that caps local analysis improvements past a minimal density of observing stations.
Abstract
A sample of 889 left-moving (LM) supercells of varying rotational strength across the United States was examined to determine if improvements could be made in predicting their motion using an existing hodograph-based technique. This technique was previously applied to a sample of only 30 LM supercells, and it was assumed that the same off-hodograph deviation from the mean wind for right-moving (RM) supercells was appropriate for LM supercells. However, our larger sample herein reveals the average deviation for LM supercells is less than the assumed 7.5 m s−1 based on a subset of 207 observed proximity soundings. At the same time, the 0–6-km mean-wind layer is still optimal for the advective component of storm motion (consistent with that for RM supercells). Applying the same methods to a subset of 678 model-derived RUC/RAP proximity soundings generally confirms these results, but with slightly smaller deviations. These findings support decreasing the deviation parameter to 5.0 m s−1 for predicting LM supercell motion (at least for the United States).
The sample of LM supercells additionally was subdivided based on strength and duration, and then reevaluated using the observed proximity soundings. The predicted motion of moderate-strength mesoanticyclones had the least error, whereas the strong category had the largest errors by about 1 m s−1. Similarly, mesoanticyclones lasting 60–120 min had the least error in predicted motion. These two findings also are consistent with the results when using the RUC/RAP proximity soundings.
Abstract
A sample of 889 left-moving (LM) supercells of varying rotational strength across the United States was examined to determine if improvements could be made in predicting their motion using an existing hodograph-based technique. This technique was previously applied to a sample of only 30 LM supercells, and it was assumed that the same off-hodograph deviation from the mean wind for right-moving (RM) supercells was appropriate for LM supercells. However, our larger sample herein reveals the average deviation for LM supercells is less than the assumed 7.5 m s−1 based on a subset of 207 observed proximity soundings. At the same time, the 0–6-km mean-wind layer is still optimal for the advective component of storm motion (consistent with that for RM supercells). Applying the same methods to a subset of 678 model-derived RUC/RAP proximity soundings generally confirms these results, but with slightly smaller deviations. These findings support decreasing the deviation parameter to 5.0 m s−1 for predicting LM supercell motion (at least for the United States).
The sample of LM supercells additionally was subdivided based on strength and duration, and then reevaluated using the observed proximity soundings. The predicted motion of moderate-strength mesoanticyclones had the least error, whereas the strong category had the largest errors by about 1 m s−1. Similarly, mesoanticyclones lasting 60–120 min had the least error in predicted motion. These two findings also are consistent with the results when using the RUC/RAP proximity soundings.
