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Abstract
East African countries benefit economically from the largest freshwater lake in Africa: Lake Victoria (LV). Around 30 million people live along its coastline, and 5.4 million people subsist on its fishing industry. However, more than 1000 fishermen die annually by high-wave conditions often produced by severe convective wind phenomena, which marks this lake one of the deadliest places in the world for hazardous weather impacts. The World Meteorological Organization launched the 3-yr High Impact Weather Lake System (HIGHWAY) project, with the main objective to reduce loss of lives and economic goods in the lake basin and improve the resilience of the local communities. The project conducted a field campaign in 2019 aiming to provide forecasters with high-resolution observations and to study the storm life cycle over the lake basin. The research discussed here used the S-band polarimetric Tanzania radar from the field campaign to investigate the diurnal cycle of the convective mode over the lake. We classified the lake storms occurring during the two wet seasons into six different convective modes and present their diurnal evolution, organization, and main radar-based attributes, thereby extending the knowledge of convection on the lake. The result is the creation of a “convection catalog for Lake Victoria,” using the operational forecast lake sectors, and defining the exact times for the different timeslots resulting from the HIGHWAY project for the marine forecast. This will inform methods to improve the marine operational forecasts for Lake Victoria, and to provide the basis for new standard operation procedures (SOP) for severe weather surveillance and warning.
Significance Statement
In this work we use new radar data over Lake Victoria, Africa, to study convective mode organization and its diurnal cycle over the lake. This work is of particular importance due to the numerous hazardous weather events and related accidents on the lake, including capsized boats, plane crashes, floods, and hailstorms on the shore settlements, that are responsible for a high annual fatality toll. Results of our analyses provide updated information for operational marine forecasts using relevant time segments and sectors of the lake to improve nowcasting operations in Lake Victoria.
Abstract
East African countries benefit economically from the largest freshwater lake in Africa: Lake Victoria (LV). Around 30 million people live along its coastline, and 5.4 million people subsist on its fishing industry. However, more than 1000 fishermen die annually by high-wave conditions often produced by severe convective wind phenomena, which marks this lake one of the deadliest places in the world for hazardous weather impacts. The World Meteorological Organization launched the 3-yr High Impact Weather Lake System (HIGHWAY) project, with the main objective to reduce loss of lives and economic goods in the lake basin and improve the resilience of the local communities. The project conducted a field campaign in 2019 aiming to provide forecasters with high-resolution observations and to study the storm life cycle over the lake basin. The research discussed here used the S-band polarimetric Tanzania radar from the field campaign to investigate the diurnal cycle of the convective mode over the lake. We classified the lake storms occurring during the two wet seasons into six different convective modes and present their diurnal evolution, organization, and main radar-based attributes, thereby extending the knowledge of convection on the lake. The result is the creation of a “convection catalog for Lake Victoria,” using the operational forecast lake sectors, and defining the exact times for the different timeslots resulting from the HIGHWAY project for the marine forecast. This will inform methods to improve the marine operational forecasts for Lake Victoria, and to provide the basis for new standard operation procedures (SOP) for severe weather surveillance and warning.
Significance Statement
In this work we use new radar data over Lake Victoria, Africa, to study convective mode organization and its diurnal cycle over the lake. This work is of particular importance due to the numerous hazardous weather events and related accidents on the lake, including capsized boats, plane crashes, floods, and hailstorms on the shore settlements, that are responsible for a high annual fatality toll. Results of our analyses provide updated information for operational marine forecasts using relevant time segments and sectors of the lake to improve nowcasting operations in Lake Victoria.
Abstract
Spatial patterns of tropical cyclone tornadoes (TCTs), and their relationship to patterns of mesoscale predictors within U.S. landfalling tropical cyclones (LTCs) are investigated using multicase composites from 27 years of reanalysis data (1995–2021). For 72 cases of LTCs with wide-ranging TC intensities at landfall, daytime TCT frequency maxima are found in the northeast, right-front, and downshear-right quadrants when their composites are constructed in ground-relative, TC-heading relative, and environmental shear relative coordinates, respectively. TCT maxima are located near maxima of 10-m–700-hPa bulk wind difference (BWD), which are enhanced by the TC circulation. This proxy for bulk vertical shear in roughly the lowest 3 km is among the best predictors of maximum TCT frequency. Relative to other times, the position of maximum TCT frequency during the afternoon shifts ∼100 km outward from the LTC center toward larger MLCAPE values. Composites containing the strongest LTCs have the strongest maximum 10-m–700-hPa and 10-m–500-hPa BWDs (∼20 m s−1) with nearby maximum frequencies of TCTs. Corresponding composites containing weaker LTCs but still many TCTs, had bulk vertical shear values that were ∼20% smaller (∼16 m s−1). Additional composites of cases having similarly weak average LTC strength at landfall, but few or no TCTs, had both maximum bulk vertical shears that were an additional ∼20% lower (∼12 m s−1) and smaller MLCAPE. TCT environments occurring well inland are distinguished from others by having stronger westerly shear and a west–east-oriented baroclinic zone (i.e., north–south temperature gradient) that enhances mesoscale ascent and deep convection on the LTC’s east side.
Abstract
Spatial patterns of tropical cyclone tornadoes (TCTs), and their relationship to patterns of mesoscale predictors within U.S. landfalling tropical cyclones (LTCs) are investigated using multicase composites from 27 years of reanalysis data (1995–2021). For 72 cases of LTCs with wide-ranging TC intensities at landfall, daytime TCT frequency maxima are found in the northeast, right-front, and downshear-right quadrants when their composites are constructed in ground-relative, TC-heading relative, and environmental shear relative coordinates, respectively. TCT maxima are located near maxima of 10-m–700-hPa bulk wind difference (BWD), which are enhanced by the TC circulation. This proxy for bulk vertical shear in roughly the lowest 3 km is among the best predictors of maximum TCT frequency. Relative to other times, the position of maximum TCT frequency during the afternoon shifts ∼100 km outward from the LTC center toward larger MLCAPE values. Composites containing the strongest LTCs have the strongest maximum 10-m–700-hPa and 10-m–500-hPa BWDs (∼20 m s−1) with nearby maximum frequencies of TCTs. Corresponding composites containing weaker LTCs but still many TCTs, had bulk vertical shear values that were ∼20% smaller (∼16 m s−1). Additional composites of cases having similarly weak average LTC strength at landfall, but few or no TCTs, had both maximum bulk vertical shears that were an additional ∼20% lower (∼12 m s−1) and smaller MLCAPE. TCT environments occurring well inland are distinguished from others by having stronger westerly shear and a west–east-oriented baroclinic zone (i.e., north–south temperature gradient) that enhances mesoscale ascent and deep convection on the LTC’s east side.
Abstract
The primary source of guidance used by the Storm Surge Unit (SSU) at the National Hurricane Center (NHC) for issuing storm surge watches and warnings is the Probabilistic Tropical Storm Surge model (P-Surge). P-Surge is an ensemble of Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model forecasts that is generated based on historical error distributions from NHC official forecasts. A probabilistic framework is used for operational storm surge forecasting to account for uncertainty related to the tropical cyclone track and wind forcing. Previous studies have shown that the size of a storm’s wind field is an important factor that can affect storm surge. A simple radius of maximum wind (RMW) prediction scheme was developed to forecast RMW based on NHC forecast parameters. Verification results indicate this scheme is an improvement over the RMW forecasts used by previous versions of P-Surge. To test the impact of the updated RMW forecasts in P-Surge, retrospective cases were selected from 25 storms from 2008 to 2020 that had an adequate number of observations. Evaluation of P-Surge forecasts using these improved RMW forecasts shows that the probability of detection is higher for most probability of exceedance thresholds. In addition, the forecast reliability is improved, and there is an increase in the number of high probability forecasts for extreme events at longer lead times. The improved RMW forecasts were recently incorporated into the operational version of P-Surge (v2.9), and serve as an important step toward extending the lead time of skillful and reliable storm surge forecasts.
Abstract
The primary source of guidance used by the Storm Surge Unit (SSU) at the National Hurricane Center (NHC) for issuing storm surge watches and warnings is the Probabilistic Tropical Storm Surge model (P-Surge). P-Surge is an ensemble of Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model forecasts that is generated based on historical error distributions from NHC official forecasts. A probabilistic framework is used for operational storm surge forecasting to account for uncertainty related to the tropical cyclone track and wind forcing. Previous studies have shown that the size of a storm’s wind field is an important factor that can affect storm surge. A simple radius of maximum wind (RMW) prediction scheme was developed to forecast RMW based on NHC forecast parameters. Verification results indicate this scheme is an improvement over the RMW forecasts used by previous versions of P-Surge. To test the impact of the updated RMW forecasts in P-Surge, retrospective cases were selected from 25 storms from 2008 to 2020 that had an adequate number of observations. Evaluation of P-Surge forecasts using these improved RMW forecasts shows that the probability of detection is higher for most probability of exceedance thresholds. In addition, the forecast reliability is improved, and there is an increase in the number of high probability forecasts for extreme events at longer lead times. The improved RMW forecasts were recently incorporated into the operational version of P-Surge (v2.9), and serve as an important step toward extending the lead time of skillful and reliable storm surge forecasts.
Abstract
A technique used widely to forecast the potential for QLCS mesovortices is known as the “Three Ingredients Method” (3IM). The 3IM states that mesovortices are favored where 1) the QLCS cold pool and ambient low-level shear are said to be nearly balanced or slightly shear dominant, 2) where the component of the 0–3-km wind shear normal to the convective line is ≥30 kt (1 kt ≈ 0.51 m s−1), and 3) where a rear-inflow jet or enhanced outflow causes a surge or bow along the convective line. Despite its widespread use in operational settings, this method has received little evaluation in formal literature. To evaluate the 3IM, radiosonde observations are compared to radar-observed QLCS properties. The distance between the gust front and high reflectivity in the leading convective line (the “U-to-R distance”), the presence of rear-inflow surges, and mesovortices (MVs) were each assessed across 1820 line segments within 50 observed QLCSs. Although 0–3-km line-normal wind shear is statistically different between MV-genesis and null segments, values are ≤30 kt for 44% of MV-genesis segments. The 0–6-km line-normal wind shear also shows strong discrimination between MV-genesis and null segments and displays the best linear relationship of the U-to-R distance (a measure of system balance) among layers tested, although the scatter and overlap in distributions suggest that many factors can impact MV genesis (as expected). Overall, most MVs occur where the U-to-R distance lies between −5 and 5 km in the presence of a rear-inflow surge, along with positive 0–1-km wind shear, 0–3-km wind shear > 10 kt, and 0–6-km wind shear > 20 kt (all line-normal).
Significance Statement
Near the leading edge of thunderstorm lines, areas of rotation that can produce tornadoes and strong winds (“mesovortices”) often develop rapidly. Despite advances in understanding mesovortices, few operational guidelines exist to anticipate their genesis. One popular method used to forecast mesovortices—the “Three Ingredients Method”—is evaluated in this study. Our work confirms the importance of two of the ingredients—a surge of outflow winds and thunderstorms that stay nearly atop the leading edge of the outflow. However, we find that many mesovortices occur below the threshold of low-level wind shear ascribed by the forecast method. Refinements to the method are suggested, including the favorable distance between the leading edge of the outflow and thunderstorm updrafts and lower bounds of wind shear over multiple layers, below which mesovortices may be unlikely.
Abstract
A technique used widely to forecast the potential for QLCS mesovortices is known as the “Three Ingredients Method” (3IM). The 3IM states that mesovortices are favored where 1) the QLCS cold pool and ambient low-level shear are said to be nearly balanced or slightly shear dominant, 2) where the component of the 0–3-km wind shear normal to the convective line is ≥30 kt (1 kt ≈ 0.51 m s−1), and 3) where a rear-inflow jet or enhanced outflow causes a surge or bow along the convective line. Despite its widespread use in operational settings, this method has received little evaluation in formal literature. To evaluate the 3IM, radiosonde observations are compared to radar-observed QLCS properties. The distance between the gust front and high reflectivity in the leading convective line (the “U-to-R distance”), the presence of rear-inflow surges, and mesovortices (MVs) were each assessed across 1820 line segments within 50 observed QLCSs. Although 0–3-km line-normal wind shear is statistically different between MV-genesis and null segments, values are ≤30 kt for 44% of MV-genesis segments. The 0–6-km line-normal wind shear also shows strong discrimination between MV-genesis and null segments and displays the best linear relationship of the U-to-R distance (a measure of system balance) among layers tested, although the scatter and overlap in distributions suggest that many factors can impact MV genesis (as expected). Overall, most MVs occur where the U-to-R distance lies between −5 and 5 km in the presence of a rear-inflow surge, along with positive 0–1-km wind shear, 0–3-km wind shear > 10 kt, and 0–6-km wind shear > 20 kt (all line-normal).
Significance Statement
Near the leading edge of thunderstorm lines, areas of rotation that can produce tornadoes and strong winds (“mesovortices”) often develop rapidly. Despite advances in understanding mesovortices, few operational guidelines exist to anticipate their genesis. One popular method used to forecast mesovortices—the “Three Ingredients Method”—is evaluated in this study. Our work confirms the importance of two of the ingredients—a surge of outflow winds and thunderstorms that stay nearly atop the leading edge of the outflow. However, we find that many mesovortices occur below the threshold of low-level wind shear ascribed by the forecast method. Refinements to the method are suggested, including the favorable distance between the leading edge of the outflow and thunderstorm updrafts and lower bounds of wind shear over multiple layers, below which mesovortices may be unlikely.
Abstract
Road surface temperatures are a critical factor in determining driving conditions, especially during winter storms. Road temperature observations across the United States are sparse and located mainly along major highways. A machine learning–based system for nowcasting the probability of subfreezing road surface temperatures was developed at NSSL to allow for widespread monitoring of road conditions in real time. In this article, these products were evaluated over two winter seasons. Strengths and weaknesses in the nowcast system were identified by stratifying the evaluation metrics into various subsets. These results show that the current system performed well in general, but significantly underpredicted the probability of subfreezing roads during frozen precipitation events. Machine learning experiments were performed to attempt to address these issues. Evaluations of these experiments indicate reduction in errors when precipitation phase was included as a predictor and precipitating cases were more substantially represented in the training data for the machine learning system.
Significance Statement
The purpose of this study is to better understand the strengths and weaknesses of a system that predicts the probability of subfreezing road surface temperatures. We found that the system performed well in general, but underpredicted the probabilities when frozen precipitation was predicted to reach the surface. These biases were substantially improved by modifying the system to increase its focus on situations with falling precipitation. The updated system should allow for improved monitoring and forecasting of potentially hazardous conditions during winter storms.
Abstract
Road surface temperatures are a critical factor in determining driving conditions, especially during winter storms. Road temperature observations across the United States are sparse and located mainly along major highways. A machine learning–based system for nowcasting the probability of subfreezing road surface temperatures was developed at NSSL to allow for widespread monitoring of road conditions in real time. In this article, these products were evaluated over two winter seasons. Strengths and weaknesses in the nowcast system were identified by stratifying the evaluation metrics into various subsets. These results show that the current system performed well in general, but significantly underpredicted the probability of subfreezing roads during frozen precipitation events. Machine learning experiments were performed to attempt to address these issues. Evaluations of these experiments indicate reduction in errors when precipitation phase was included as a predictor and precipitating cases were more substantially represented in the training data for the machine learning system.
Significance Statement
The purpose of this study is to better understand the strengths and weaknesses of a system that predicts the probability of subfreezing road surface temperatures. We found that the system performed well in general, but underpredicted the probabilities when frozen precipitation was predicted to reach the surface. These biases were substantially improved by modifying the system to increase its focus on situations with falling precipitation. The updated system should allow for improved monitoring and forecasting of potentially hazardous conditions during winter storms.
Abstract
Atmospheric River Reconnaissance has held field campaigns during cool seasons since 2016. These campaigns have provided thousands of dropsonde data profiles, which are assimilated into multiple global operational numerical weather prediction models. Data denial experiments, conducted by running a parallel set of forecasts that exclude the dropsonde information, allow testing of the impact of the dropsonde data on model analyses and the subsequent forecasts. Here, we investigate the differences in skill between the control forecasts (with dropsonde data assimilated) and denial forecasts (without dropsonde data assimilated) in terms of both precipitation and integrated vapor transport (IVT) at multiple thresholds. The differences are considered in the times and locations where there is a reasonable expectation of influence of an intensive observation period (IOP). Results for 2019 and 2020 from both the European Centre for Medium-Range Weather Forecasts (ECMWF) model and the National Centers for Environmental Prediction (NCEP) global model show improvements with the added information from the dropsondes. In particular, significant improvements in the control forecast IVT generally occur in both models, especially at higher values. Significant improvements in the control forecast precipitation also generally occur in both models, but the improvements vary depending on the lead time and metrics used.
Significance Statement
Atmospheric River Reconnaissance is a program that uses targeted aircraft flights over the northeast Pacific to take measurements of meteorological fields. These data are then ingested into global weather models with the intent of improving the initial conditions and resulting forecasts along the U.S. West Coast. The impacts of these observations on two global numerical weather models were investigated to determine their influence on the forecasts. The integrated vapor transport, a measure of both wind and humidity, saw significant improvements in both models with the additional observations. Precipitation forecasts were also improved, but with differing results between the two models.
Abstract
Atmospheric River Reconnaissance has held field campaigns during cool seasons since 2016. These campaigns have provided thousands of dropsonde data profiles, which are assimilated into multiple global operational numerical weather prediction models. Data denial experiments, conducted by running a parallel set of forecasts that exclude the dropsonde information, allow testing of the impact of the dropsonde data on model analyses and the subsequent forecasts. Here, we investigate the differences in skill between the control forecasts (with dropsonde data assimilated) and denial forecasts (without dropsonde data assimilated) in terms of both precipitation and integrated vapor transport (IVT) at multiple thresholds. The differences are considered in the times and locations where there is a reasonable expectation of influence of an intensive observation period (IOP). Results for 2019 and 2020 from both the European Centre for Medium-Range Weather Forecasts (ECMWF) model and the National Centers for Environmental Prediction (NCEP) global model show improvements with the added information from the dropsondes. In particular, significant improvements in the control forecast IVT generally occur in both models, especially at higher values. Significant improvements in the control forecast precipitation also generally occur in both models, but the improvements vary depending on the lead time and metrics used.
Significance Statement
Atmospheric River Reconnaissance is a program that uses targeted aircraft flights over the northeast Pacific to take measurements of meteorological fields. These data are then ingested into global weather models with the intent of improving the initial conditions and resulting forecasts along the U.S. West Coast. The impacts of these observations on two global numerical weather models were investigated to determine their influence on the forecasts. The integrated vapor transport, a measure of both wind and humidity, saw significant improvements in both models with the additional observations. Precipitation forecasts were also improved, but with differing results between the two models.
Abstract
This study investigates regional, seasonal biases in convection-allowing model forecasts of near-surface temperature and dewpoint in areas of particular importance to forecasts of severe local storms. One method compares model forecasts with objective analyses of observed conditions in the inflow sectors of reported tornadoes. A second method captures a broader sample of environments, comparing model forecasts with surface observations under certain warm-sector criteria. Both methods reveal a cold bias across all models tested in Southeast U.S. cool-season warm sectors. This is an operationally important bias given the thermodynamic sensitivity of instability-limited severe weather that is common in the Southeast cool season. There is not a clear bias across models in the Great Plains warm season, but instead more varied behavior with differing model physics.
Significance Statement
The severity of thunderstorms and the types of hazards they produce depend in part on the low-level temperature and moisture in the near-storm environment. It is important for numerical forecast models to accurately represent these fields in forecasts of severe weather events. We show that the most widely used short-term, high-resolution forecast models have a consistent cold bias of about 1 K (up to 2 K in certain cases) in storm environments in the southeastern U.S. cool season. Human forecasters must recognize and adjust for this bias, and future model development should aim to improve it.
Abstract
This study investigates regional, seasonal biases in convection-allowing model forecasts of near-surface temperature and dewpoint in areas of particular importance to forecasts of severe local storms. One method compares model forecasts with objective analyses of observed conditions in the inflow sectors of reported tornadoes. A second method captures a broader sample of environments, comparing model forecasts with surface observations under certain warm-sector criteria. Both methods reveal a cold bias across all models tested in Southeast U.S. cool-season warm sectors. This is an operationally important bias given the thermodynamic sensitivity of instability-limited severe weather that is common in the Southeast cool season. There is not a clear bias across models in the Great Plains warm season, but instead more varied behavior with differing model physics.
Significance Statement
The severity of thunderstorms and the types of hazards they produce depend in part on the low-level temperature and moisture in the near-storm environment. It is important for numerical forecast models to accurately represent these fields in forecasts of severe weather events. We show that the most widely used short-term, high-resolution forecast models have a consistent cold bias of about 1 K (up to 2 K in certain cases) in storm environments in the southeastern U.S. cool season. Human forecasters must recognize and adjust for this bias, and future model development should aim to improve it.
Abstract
Fog is a phenomenon that exerts significant impacts on transportation, aviation, air quality, agriculture, and even water resources. While data-driven machine learning algorithms have shown promising performance in capturing nonlinear fog events at point locations, their applicability to different areas and time periods is questionable. This study addresses this issue by examining five decision-tree-based classifiers in a South Korean region, where diverse fog formation mechanisms are at play. The five machine learning algorithms were trained at point locations and tested with other point locations for time periods independent of the training processes. Using the ensemble classifiers and high-resolution atmospheric reanalysis data, we also attempted to establish fog occurrence maps in a regional area. Results showed that machine learning models trained on the local datasets exhibited superior performance in mountainous areas, where radiative cooling predominantly contributes to fog formation, compared to inland and coastal regions. As the fog generation mechanisms diversified, the tree-based ensemble models appeared to encounter challenges in delineating their decision boundaries. When they were trained with the reanalysis data, their predictive skills were significantly decreased, resulting in high false alarm rates. This prompted the need for postprocessing techniques to rectify overestimated fog frequency. While postprocessing may ameliorate overestimation, caution is needed to interpret the resultant fog frequency estimates, especially in regions with more diverse fog generation mechanisms. The spatial upscaling of machine learning–based fog prediction models poses challenges owing to the intricate interplay of various fog formation mechanisms, data imbalances, and potential inaccuracies in reanalysis data.
Abstract
Fog is a phenomenon that exerts significant impacts on transportation, aviation, air quality, agriculture, and even water resources. While data-driven machine learning algorithms have shown promising performance in capturing nonlinear fog events at point locations, their applicability to different areas and time periods is questionable. This study addresses this issue by examining five decision-tree-based classifiers in a South Korean region, where diverse fog formation mechanisms are at play. The five machine learning algorithms were trained at point locations and tested with other point locations for time periods independent of the training processes. Using the ensemble classifiers and high-resolution atmospheric reanalysis data, we also attempted to establish fog occurrence maps in a regional area. Results showed that machine learning models trained on the local datasets exhibited superior performance in mountainous areas, where radiative cooling predominantly contributes to fog formation, compared to inland and coastal regions. As the fog generation mechanisms diversified, the tree-based ensemble models appeared to encounter challenges in delineating their decision boundaries. When they were trained with the reanalysis data, their predictive skills were significantly decreased, resulting in high false alarm rates. This prompted the need for postprocessing techniques to rectify overestimated fog frequency. While postprocessing may ameliorate overestimation, caution is needed to interpret the resultant fog frequency estimates, especially in regions with more diverse fog generation mechanisms. The spatial upscaling of machine learning–based fog prediction models poses challenges owing to the intricate interplay of various fog formation mechanisms, data imbalances, and potential inaccuracies in reanalysis data.
Abstract
Multiscale valid time shifting (VTS) was explored for a real-time convection-allowing ensemble (CAE) data assimilation (DA) system featuring hourly assimilation of conventional in situ and radar reflectivity observations, developed by the Multiscale Data Assimilation and Predictability Laboratory. VTS triples the base ensemble size using two subensembles containing member forecast output before and after the analysis time. Three configurations were tested with 108-member VTS-expanded ensembles: VTS for individual mesoscale conventional DA (ConVTS) or storm-scale radar DA (RadVTS) and VTS integrated to both DA components (BothVTS). Systematic verification demonstrated that BothVTS matched the DA spread and accuracy of the best-performing individual component VTS. The 10-member forecasts showed BothVTS performs similarly to ConVTS, with RadVTS having better skill in 1-h precipitation at forecast hours 1–6, while Both/ConVTS had better skill at later hours 7–15. An objective splitting of cases by 2-m temperature cold bias revealed RadVTS was more skillful than Both/ConVTS out to hour 10 for cold-biased cases, while BothVTS performed best at most hours for less-biased cases. A sensitivity experiment demonstrated improved performance of BothVTS when reducing the underlying model cold bias. Diagnostics revealed enhanced spurious convection of BothVTS for cold-biased cases was tied to larger analysis increments in temperature than moisture, resulting in erroneously high convective instability. This study is the first to examine the benefits of a multiscale VTS implementation, showing that BothVTS can be utilized to improve the overall performance of a multiscale CAE system. Further, these results underscore the need to limit biases within a DA and forecast system to best take advantage of VTS analysis benefits.
Abstract
Multiscale valid time shifting (VTS) was explored for a real-time convection-allowing ensemble (CAE) data assimilation (DA) system featuring hourly assimilation of conventional in situ and radar reflectivity observations, developed by the Multiscale Data Assimilation and Predictability Laboratory. VTS triples the base ensemble size using two subensembles containing member forecast output before and after the analysis time. Three configurations were tested with 108-member VTS-expanded ensembles: VTS for individual mesoscale conventional DA (ConVTS) or storm-scale radar DA (RadVTS) and VTS integrated to both DA components (BothVTS). Systematic verification demonstrated that BothVTS matched the DA spread and accuracy of the best-performing individual component VTS. The 10-member forecasts showed BothVTS performs similarly to ConVTS, with RadVTS having better skill in 1-h precipitation at forecast hours 1–6, while Both/ConVTS had better skill at later hours 7–15. An objective splitting of cases by 2-m temperature cold bias revealed RadVTS was more skillful than Both/ConVTS out to hour 10 for cold-biased cases, while BothVTS performed best at most hours for less-biased cases. A sensitivity experiment demonstrated improved performance of BothVTS when reducing the underlying model cold bias. Diagnostics revealed enhanced spurious convection of BothVTS for cold-biased cases was tied to larger analysis increments in temperature than moisture, resulting in erroneously high convective instability. This study is the first to examine the benefits of a multiscale VTS implementation, showing that BothVTS can be utilized to improve the overall performance of a multiscale CAE system. Further, these results underscore the need to limit biases within a DA and forecast system to best take advantage of VTS analysis benefits.
Abstract
Rogue waves are stochastic, individual ocean surface waves that are disproportionately large compared to the background sea state. They present considerable risk to mariners and offshore structures especially when encountered in large seas. Current rogue wave forecasts are based on nonlinear processes quantified by the Benjamin Feir index (BFI). However, there is increasing evidence that the BFI has limited predictive power in the real ocean and that rogue waves are largely generated by bandwidth-controlled linear superposition. Recent studies have shown that the bandwidth parameter crest–trough correlation r shows the highest univariate correlation with rogue wave probability. We corroborate this result and demonstrate that r has the highest predictive power for rogue wave probability from the analysis of open ocean and coastal buoys in the northeast Pacific. This work further demonstrates that crest–trough correlation can be forecast by a regional WAVEWATCH III wave model with moderate accuracy. This result leads to the proposal of a novel empirical rogue wave risk assessment probability forecast based on r. Semilogarithmic fits between r and rogue wave probability were applied to generate the rogue wave probability forecast. A sample rogue wave probability forecast is presented for a large storm 21–22 October 2021.
Significance Statement
Rogue waves pose a considerable threat to ships and offshore structures. The rare and unexpected nature of rogue wave makes predicting them an ongoing and challenging goal. Recent work based on an extensive dataset of waves has suggested that the wave parameter called the crest–trough correlation shows the highest correlation with rogue wave probability. Our work demonstrates that crest–trough correlation can be reasonably well forecast by standard wave models. This suggests that current operational wave models can support rogue wave prediction models based on crest–trough correlation for improved rogue wave risk evaluation.
Abstract
Rogue waves are stochastic, individual ocean surface waves that are disproportionately large compared to the background sea state. They present considerable risk to mariners and offshore structures especially when encountered in large seas. Current rogue wave forecasts are based on nonlinear processes quantified by the Benjamin Feir index (BFI). However, there is increasing evidence that the BFI has limited predictive power in the real ocean and that rogue waves are largely generated by bandwidth-controlled linear superposition. Recent studies have shown that the bandwidth parameter crest–trough correlation r shows the highest univariate correlation with rogue wave probability. We corroborate this result and demonstrate that r has the highest predictive power for rogue wave probability from the analysis of open ocean and coastal buoys in the northeast Pacific. This work further demonstrates that crest–trough correlation can be forecast by a regional WAVEWATCH III wave model with moderate accuracy. This result leads to the proposal of a novel empirical rogue wave risk assessment probability forecast based on r. Semilogarithmic fits between r and rogue wave probability were applied to generate the rogue wave probability forecast. A sample rogue wave probability forecast is presented for a large storm 21–22 October 2021.
Significance Statement
Rogue waves pose a considerable threat to ships and offshore structures. The rare and unexpected nature of rogue wave makes predicting them an ongoing and challenging goal. Recent work based on an extensive dataset of waves has suggested that the wave parameter called the crest–trough correlation shows the highest correlation with rogue wave probability. Our work demonstrates that crest–trough correlation can be reasonably well forecast by standard wave models. This suggests that current operational wave models can support rogue wave prediction models based on crest–trough correlation for improved rogue wave risk evaluation.
