Browse
Abstract
A modification to the mixing length formulation in a planetary boundary-layer (PBL) scheme is introduced to improve the intensity forecast of tropical cyclones (TCs) in a basin-scale Hurricane Analysis and Forecast System (HAFS) for the real-time experiment in 2021. The 2020 basin-scale HAFS with the physics suite of the NCEP operational global forecast system performs well in terms of the reduced root mean square (RMS) errors in track and intensity except for the mean intensity bias, compared with NCEP operational hurricane models. To address the large intensity bias issue, the vertical mixing length near the surface used in the PBL scheme is increased to follow the similarity theory, consistent with that used in the surface layer scheme. Test results show that the RMS error and bias in intensity are further reduced without the degradation of the track forecast. An idealized one-dimensional TC PBL model is used to understand the model response to the modification, indicating that the radial wind is strengthened to dynamically balance the enhanced downward momentum mixing. This is also exhibited in the case study of a three-dimensional HAFS simulation, with the improved vertical distribution of the simulated wind speed in the eyewall area. Given the improvement, the modification has been implemented in one of the configurations of the first version of operational HAFS at NCEP. Finally, the adjustment of the parameterization of diffusion and mixing in TC simulations is discussed.
Abstract
A modification to the mixing length formulation in a planetary boundary-layer (PBL) scheme is introduced to improve the intensity forecast of tropical cyclones (TCs) in a basin-scale Hurricane Analysis and Forecast System (HAFS) for the real-time experiment in 2021. The 2020 basin-scale HAFS with the physics suite of the NCEP operational global forecast system performs well in terms of the reduced root mean square (RMS) errors in track and intensity except for the mean intensity bias, compared with NCEP operational hurricane models. To address the large intensity bias issue, the vertical mixing length near the surface used in the PBL scheme is increased to follow the similarity theory, consistent with that used in the surface layer scheme. Test results show that the RMS error and bias in intensity are further reduced without the degradation of the track forecast. An idealized one-dimensional TC PBL model is used to understand the model response to the modification, indicating that the radial wind is strengthened to dynamically balance the enhanced downward momentum mixing. This is also exhibited in the case study of a three-dimensional HAFS simulation, with the improved vertical distribution of the simulated wind speed in the eyewall area. Given the improvement, the modification has been implemented in one of the configurations of the first version of operational HAFS at NCEP. Finally, the adjustment of the parameterization of diffusion and mixing in TC simulations is discussed.
Abstract
Tornadoes produced by right-moving supercells (RMs) and quasi-linear convective systems (QLCSs) are compared across the contiguous United States for the period 2003–21, based on the maximum F/EF-scale rating per hour on a 40-km horizontal grid. The frequency of QLCS tornadoes has increased dramatically since 2003, while the frequency of RM tornadoes has decreased during that same period. The finding of prior work that the most common damage rating for QLCS tornadoes at night is EF1 persists in this larger, independent sample. A comparison of WSR-88D radar attributes between RM and QLCS tornadoes shows no appreciable differences between EF0 tornadoes produced by either convective mode. Differences become apparent for EF1–2 tornadoes, where rotational velocity is larger and velocity couplet diameter is smaller for RM tornadoes compared to QLCS tornadoes. The frequency of tornadic debris signatures (TDSs) in dual-polarization data is also larger for EF1–2 RM tornadoes when controlling for tornadoes sampled relatively close to the radar sites and in those occurring during daylight versus overnight. The weaker rotational velocities, broader velocity couplet diameters, and lower frequencies of TDSs both close to the radar and at night for QLCS EF1 tornadoes suggest that a combination of inadequate radar sampling and occasional misclassification of wind damage may be responsible for the irregularities in the historical record of QLCS tornado reports.
Significance Statement
A comparison of radar attributes between tornadoes with right-moving supercells and squall-line mesovortices suggests some irregularities in squall-line tornado records in the contiguous United States. The irregularities appear to be the result of both inadequate radar sampling for the relatively shallow squall-line tornadoes and occasional misclassification of wind damage with the lack of other corroborating evidence, especially overnight.
Abstract
Tornadoes produced by right-moving supercells (RMs) and quasi-linear convective systems (QLCSs) are compared across the contiguous United States for the period 2003–21, based on the maximum F/EF-scale rating per hour on a 40-km horizontal grid. The frequency of QLCS tornadoes has increased dramatically since 2003, while the frequency of RM tornadoes has decreased during that same period. The finding of prior work that the most common damage rating for QLCS tornadoes at night is EF1 persists in this larger, independent sample. A comparison of WSR-88D radar attributes between RM and QLCS tornadoes shows no appreciable differences between EF0 tornadoes produced by either convective mode. Differences become apparent for EF1–2 tornadoes, where rotational velocity is larger and velocity couplet diameter is smaller for RM tornadoes compared to QLCS tornadoes. The frequency of tornadic debris signatures (TDSs) in dual-polarization data is also larger for EF1–2 RM tornadoes when controlling for tornadoes sampled relatively close to the radar sites and in those occurring during daylight versus overnight. The weaker rotational velocities, broader velocity couplet diameters, and lower frequencies of TDSs both close to the radar and at night for QLCS EF1 tornadoes suggest that a combination of inadequate radar sampling and occasional misclassification of wind damage may be responsible for the irregularities in the historical record of QLCS tornado reports.
Significance Statement
A comparison of radar attributes between tornadoes with right-moving supercells and squall-line mesovortices suggests some irregularities in squall-line tornado records in the contiguous United States. The irregularities appear to be the result of both inadequate radar sampling for the relatively shallow squall-line tornadoes and occasional misclassification of wind damage with the lack of other corroborating evidence, especially overnight.
Abstract
Based on the hourly merged precipitation product, the performance of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) in simulating the diurnal variations of precipitation during warm season over the western periphery of the Sichuan basin (SCB) has been evaluated, and the underlying physical causes associated with the wet biases have also been investigated. The results show that the IFS well reproduces the spatial distributions of precipitation amount, frequency, and intensity over the SCB, as well as their diurnal variations, but the simulated precipitation peaks earlier than the observation with notable wet biases over the western periphery of the SCB. In addition, the strong wet biases exhibit notable regional differences over the western periphery of the SCB. The simulated wet biases over the southwestern periphery of the SCB expand westward to higher altitudes along the windward slope, with the maximum wet biases occurring at night. The westward expansion of the simulated stronger upward motions results in a westward shift of precipitation. However, the simulated precipitation over the northwestern periphery of the SCB has small difference in terms of the location; hence, the overestimated precipitation is associated with the stronger atmospheric instability, resulting from the higher potential temperature and the larger specific humidity near the surface. The findings revealed in this study indicate that the ECMWF forecast shows distinct uncertainties over the different complex terrain, and thus offers a promising way forward for improvements of model physical processes.
Abstract
Based on the hourly merged precipitation product, the performance of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) in simulating the diurnal variations of precipitation during warm season over the western periphery of the Sichuan basin (SCB) has been evaluated, and the underlying physical causes associated with the wet biases have also been investigated. The results show that the IFS well reproduces the spatial distributions of precipitation amount, frequency, and intensity over the SCB, as well as their diurnal variations, but the simulated precipitation peaks earlier than the observation with notable wet biases over the western periphery of the SCB. In addition, the strong wet biases exhibit notable regional differences over the western periphery of the SCB. The simulated wet biases over the southwestern periphery of the SCB expand westward to higher altitudes along the windward slope, with the maximum wet biases occurring at night. The westward expansion of the simulated stronger upward motions results in a westward shift of precipitation. However, the simulated precipitation over the northwestern periphery of the SCB has small difference in terms of the location; hence, the overestimated precipitation is associated with the stronger atmospheric instability, resulting from the higher potential temperature and the larger specific humidity near the surface. The findings revealed in this study indicate that the ECMWF forecast shows distinct uncertainties over the different complex terrain, and thus offers a promising way forward for improvements of model physical processes.
Abstract
Accurate subseasonal-to-seasonal (S2S) weather forecasts are crucial to making important decisions in many sectors. However, significant gaps exist between the needs of society and what forecasters can produce, especially at weekly and longer lead times. We hypothesize that by clustering atmospheric states into a number of predefined categories, the noise can be reduced and, consequently, medium-range forecasts can be improved. Self-organizing map (SOM)-based clustering was used on daily mean sea level pressure (MSLP) data from the North American Regional Reanalysis to categorize the synoptic-scale circulation for eastern North America from 1979 to 2016 into 28 discrete patterns. Then, using two goodness-of-fit metrics, the relative skill of four different forecasting methods over a 90-day lead time was studied: 1) a circulation pattern (CP) forecast, 2) raw forecast output from the Climate Forecast System (CFS) operated by the National Centers for Environmental Prediction (NCEP), 3) a simple climatology forecast, and 4) a simple persistence forecast. As expected, forecast skill of both the CP forecast and the raw CFS forecast generally decreased rapidly from the first day, coming to parity with the skill of climatology after 10–12 days when using correlation, and at 7–10 days when using the root-mean-square error (RMSE). Most importantly, this study found that the CP forecast was the most skillful forecast method over the 8–11-day lead time when using RMSE. On a spatial basis, the skill of the CP forecast and the raw CFS decreases latitudinally from north to south. This study thus demonstrates the potential utility of categorical or circulation pattern–based forecasting at 1–2-week lead times.
Abstract
Accurate subseasonal-to-seasonal (S2S) weather forecasts are crucial to making important decisions in many sectors. However, significant gaps exist between the needs of society and what forecasters can produce, especially at weekly and longer lead times. We hypothesize that by clustering atmospheric states into a number of predefined categories, the noise can be reduced and, consequently, medium-range forecasts can be improved. Self-organizing map (SOM)-based clustering was used on daily mean sea level pressure (MSLP) data from the North American Regional Reanalysis to categorize the synoptic-scale circulation for eastern North America from 1979 to 2016 into 28 discrete patterns. Then, using two goodness-of-fit metrics, the relative skill of four different forecasting methods over a 90-day lead time was studied: 1) a circulation pattern (CP) forecast, 2) raw forecast output from the Climate Forecast System (CFS) operated by the National Centers for Environmental Prediction (NCEP), 3) a simple climatology forecast, and 4) a simple persistence forecast. As expected, forecast skill of both the CP forecast and the raw CFS forecast generally decreased rapidly from the first day, coming to parity with the skill of climatology after 10–12 days when using correlation, and at 7–10 days when using the root-mean-square error (RMSE). Most importantly, this study found that the CP forecast was the most skillful forecast method over the 8–11-day lead time when using RMSE. On a spatial basis, the skill of the CP forecast and the raw CFS decreases latitudinally from north to south. This study thus demonstrates the potential utility of categorical or circulation pattern–based forecasting at 1–2-week lead times.
Abstract
A 5-yr climatology and composite study of precipitation bands associated with extratropical cyclones over the British Isles from April 2017 to March 2022 is constructed. A total of 249 single bands were manually identified from radar network mosaics in association with 167 cyclones identified from surface maps. More bands formed over water near the coast than over inland areas, and most had a meridional orientation. The average lengths of bands at the times of formation and maximum length were 290 and 460 km, respectively; only 20% of bands reached a maximum length exceeding 600 km. The number of bands decreased with increasing duration, with 31% of bands lasting for 2–3 h, with bands lasting more than 10 h uncommon. The bands were classified into six categories, with occluded-frontal bands (19 yr−1), warm-frontal bands (11 yr−1), and cold-frontal bands (10 yr−1) being the most frequent. Occluded-frontal and warm-frontal bands commonly occurred west of Scotland and in the east quadrant relative to their parent cyclones. In contrast, cold-frontal bands commonly occurred southwest of Great Britain and in the south quadrant relative to their parent cyclones. Composites for northwest–southeast occluded-frontal and warm-frontal bands west of Scotland, and southwest–northeast cold-frontal bands southwest of Great Britain, show the different synoptic environments that favor bands. The low-level jet transports moisture into the band and is similar to the location and scale of the composite bands, similar to that of an atmospheric river. These results are compared to previous studies on bands from the United States.
Significance Statement
Precipitation bands are lines of heavy precipitation as seen on weather radar. Most studies of bands in extratropical cyclones have occurred in the United States. We examine 5 years of bands in extratropical cyclones over the British Isles to better understand their characteristics. Bands form in preferred geographic regions: offshore of the west coasts of Scotland, Wales, and southwest England. The most common bands are associated with occluded fronts (37% of all bands). The average scale of the bands is associated with the average scale of wind maxima 1–2 km above ground. These results provide a better understanding of the typical characteristics and conditions under which bands form and their geographical variability compared to the United States.
Abstract
A 5-yr climatology and composite study of precipitation bands associated with extratropical cyclones over the British Isles from April 2017 to March 2022 is constructed. A total of 249 single bands were manually identified from radar network mosaics in association with 167 cyclones identified from surface maps. More bands formed over water near the coast than over inland areas, and most had a meridional orientation. The average lengths of bands at the times of formation and maximum length were 290 and 460 km, respectively; only 20% of bands reached a maximum length exceeding 600 km. The number of bands decreased with increasing duration, with 31% of bands lasting for 2–3 h, with bands lasting more than 10 h uncommon. The bands were classified into six categories, with occluded-frontal bands (19 yr−1), warm-frontal bands (11 yr−1), and cold-frontal bands (10 yr−1) being the most frequent. Occluded-frontal and warm-frontal bands commonly occurred west of Scotland and in the east quadrant relative to their parent cyclones. In contrast, cold-frontal bands commonly occurred southwest of Great Britain and in the south quadrant relative to their parent cyclones. Composites for northwest–southeast occluded-frontal and warm-frontal bands west of Scotland, and southwest–northeast cold-frontal bands southwest of Great Britain, show the different synoptic environments that favor bands. The low-level jet transports moisture into the band and is similar to the location and scale of the composite bands, similar to that of an atmospheric river. These results are compared to previous studies on bands from the United States.
Significance Statement
Precipitation bands are lines of heavy precipitation as seen on weather radar. Most studies of bands in extratropical cyclones have occurred in the United States. We examine 5 years of bands in extratropical cyclones over the British Isles to better understand their characteristics. Bands form in preferred geographic regions: offshore of the west coasts of Scotland, Wales, and southwest England. The most common bands are associated with occluded fronts (37% of all bands). The average scale of the bands is associated with the average scale of wind maxima 1–2 km above ground. These results provide a better understanding of the typical characteristics and conditions under which bands form and their geographical variability compared to the United States.
Abstract
A severe derecho impacted the Midwestern United States on 10 August 2020, causing over $12 billion (U.S. dollars) in damage, and producing peak winds estimated at 63 m s−1, with the worst impacts in Iowa. The event was not forecast well by operational forecasters, nor even by operational and quasi-operational convection-allowing models. In the present study, nine simulations are performed using the Limited Area Model version of the Finite-Volume-Cubed-Sphere model (FV3-LAM) with three horizontal grid spacings and two physics suites. In addition, when a prototype of the Rapid Refresh Forecast System (RRFS) physics is used, sensitivity tests are performed to examine the impact of using the Grell–Freitas (GF) convective scheme. Several unusual results are obtained. With both the RRFS (not using GF) and Global Forecast System (GFS) physics suites, simulations using relatively coarse 13- and 25-km horizontal grid spacing do a much better job of showing an organized convective system in Iowa during the daylight hours of 10 August than the 3-km grid spacing runs. In addition, the RRFS run with 25-km grid spacing becomes much worse when the GF convective scheme is used. The 3-km RRFS run that does not use the GF scheme develops spurious nocturnal convection the night before the derecho, removing instability and preventing the derecho from being simulated at all. When GF is used, the spurious storms are removed and an excellent forecast is obtained with an intense bowing echo, exceptionally strong cold pool, and roughly 50 m s−1 surface wind gusts.
Abstract
A severe derecho impacted the Midwestern United States on 10 August 2020, causing over $12 billion (U.S. dollars) in damage, and producing peak winds estimated at 63 m s−1, with the worst impacts in Iowa. The event was not forecast well by operational forecasters, nor even by operational and quasi-operational convection-allowing models. In the present study, nine simulations are performed using the Limited Area Model version of the Finite-Volume-Cubed-Sphere model (FV3-LAM) with three horizontal grid spacings and two physics suites. In addition, when a prototype of the Rapid Refresh Forecast System (RRFS) physics is used, sensitivity tests are performed to examine the impact of using the Grell–Freitas (GF) convective scheme. Several unusual results are obtained. With both the RRFS (not using GF) and Global Forecast System (GFS) physics suites, simulations using relatively coarse 13- and 25-km horizontal grid spacing do a much better job of showing an organized convective system in Iowa during the daylight hours of 10 August than the 3-km grid spacing runs. In addition, the RRFS run with 25-km grid spacing becomes much worse when the GF convective scheme is used. The 3-km RRFS run that does not use the GF scheme develops spurious nocturnal convection the night before the derecho, removing instability and preventing the derecho from being simulated at all. When GF is used, the spurious storms are removed and an excellent forecast is obtained with an intense bowing echo, exceptionally strong cold pool, and roughly 50 m s−1 surface wind gusts.
Abstract
Eyewall replacement cycles (ERCs) in tropical cyclones (TCs) are generally associated with rapid changes in TC wind intensity and broadening of the TC wind field, both of which can create unique forecasting challenges. As part of the NOAA Joint Hurricane Testbed Project, a new model was developed to provide operational probabilistic guidance on ERC onset. The model is based on the time evolution of TC wind intensity and passive satellite microwave imagery and is named “M-PERC” for Microwave-Based Probability of Eyewall Replacement Cycle. The model was initially developed in the Atlantic basin but is found to be globally applicable and skillful. The development of M-PERC and its performance characteristics are described here, as well as a new intensity prediction model that extends previous work. Application of these models is expected to contribute to a reduction of TC intensity forecast error.
Abstract
Eyewall replacement cycles (ERCs) in tropical cyclones (TCs) are generally associated with rapid changes in TC wind intensity and broadening of the TC wind field, both of which can create unique forecasting challenges. As part of the NOAA Joint Hurricane Testbed Project, a new model was developed to provide operational probabilistic guidance on ERC onset. The model is based on the time evolution of TC wind intensity and passive satellite microwave imagery and is named “M-PERC” for Microwave-Based Probability of Eyewall Replacement Cycle. The model was initially developed in the Atlantic basin but is found to be globally applicable and skillful. The development of M-PERC and its performance characteristics are described here, as well as a new intensity prediction model that extends previous work. Application of these models is expected to contribute to a reduction of TC intensity forecast error.
Abstract
Accurate and reliable seasonal forecasts are important for water and energy supply management. Recognizing the important role of snow water equivalent (SWE) for water management, here we include the seasonal forecast of SWE in addition to precipitation (P) and 2-m temperature (T2m) over hydrologically defined regions of the western United States. A two-stage process is applied to seasonal predictions from two models (NCEP CFSv2 and ECMWF SEAS5) through 1) postprocessing to remove biases in the mean, variance, and ensemble spread and 2) further reducing the residual errors by linear regression using climate indices. The adjusted forecasts from the two models are combined to form a superensemble using weights based on their prior skill. The adjusted forecasts are consistently improved over raw model forecasts probabilistically for all variables and deterministically for SWE forecasts. Overall skill of the superensemble usually improves upon the skill of forecasts from individual models; however, the percentage of seasons and regions with increased skill was approximately the same as those with decreased skill relative to the top performing postprocessed individual model. Seasonal SWE has the highest prediction skill, followed by T2m, with P showing lower prediction skill. Persistence contributes strongly to the skill of SWE and moderately to the skill of T2m. Furthermore, a distinct seasonality in the skill is seen in SWE, with a higher skill from late spring through early summer.
Significance Statement
Here we test the postprocessing of seasonal forecasts from two state-of-the-art seasonal prediction models for traditionally forecasted elements of precipitation and temperature as well as snowpack, which is important for water management. A two-stage procedure is utilized, including ocean and atmospheric teleconnection indices that have been shown to impact seasonal weather across the western United States. First, we adjust model output based on the average error in historic runs and then relate the remaining error to these teleconnection indices. A final step combines each adjusted model based on its historic performance. Forecasts are shown to improve upon the original models when assessed probabilistically. The snowpack forecasts perform better than temperature and precipitation forecasts with the best performance from late winter through early summer. Persistence is found to contribute strongly to the skill of snowpack and moderately to the skill of temperature.
Abstract
Accurate and reliable seasonal forecasts are important for water and energy supply management. Recognizing the important role of snow water equivalent (SWE) for water management, here we include the seasonal forecast of SWE in addition to precipitation (P) and 2-m temperature (T2m) over hydrologically defined regions of the western United States. A two-stage process is applied to seasonal predictions from two models (NCEP CFSv2 and ECMWF SEAS5) through 1) postprocessing to remove biases in the mean, variance, and ensemble spread and 2) further reducing the residual errors by linear regression using climate indices. The adjusted forecasts from the two models are combined to form a superensemble using weights based on their prior skill. The adjusted forecasts are consistently improved over raw model forecasts probabilistically for all variables and deterministically for SWE forecasts. Overall skill of the superensemble usually improves upon the skill of forecasts from individual models; however, the percentage of seasons and regions with increased skill was approximately the same as those with decreased skill relative to the top performing postprocessed individual model. Seasonal SWE has the highest prediction skill, followed by T2m, with P showing lower prediction skill. Persistence contributes strongly to the skill of SWE and moderately to the skill of T2m. Furthermore, a distinct seasonality in the skill is seen in SWE, with a higher skill from late spring through early summer.
Significance Statement
Here we test the postprocessing of seasonal forecasts from two state-of-the-art seasonal prediction models for traditionally forecasted elements of precipitation and temperature as well as snowpack, which is important for water management. A two-stage procedure is utilized, including ocean and atmospheric teleconnection indices that have been shown to impact seasonal weather across the western United States. First, we adjust model output based on the average error in historic runs and then relate the remaining error to these teleconnection indices. A final step combines each adjusted model based on its historic performance. Forecasts are shown to improve upon the original models when assessed probabilistically. The snowpack forecasts perform better than temperature and precipitation forecasts with the best performance from late winter through early summer. Persistence is found to contribute strongly to the skill of snowpack and moderately to the skill of temperature.
Abstract
Improving estimates of tropical cyclone forecast uncertainty remains an important goal of the Hurricane Forecast Improvement Project (HFIP). Intensity forecast uncertainty near landfall is especially complicated because intensity forecasts depend on track forecasts. Ensembles can be difficult to interpret near land due to differences in both spatial and temporal resolution and differences in landfall timing (if at all) and location. The Monte Carlo Wind Speed Probability (WSP) model is a statistical ensemble based on the error characteristics of forecasts by the National Hurricane Center (NHC) and the spread of several track forecast models. The landfall distribution product (LDP) introduced in this paper was developed to use the statistical ensemble of forecasts from the WSP model to estimate both the track and intensity forecast uncertainty associated with potential landfalls. The LDP includes probabilistic intensity estimates as well as estimates of the most likely and reasonable strongest intensity at landfall. These products could communicate concise intensity uncertainty information to users at risk for tropical cyclone impacts. Demonstration on a retrospective dataset from 2010 to 2018 and evaluation of the LDP on the 2020–21 Atlantic hurricane seasons shows that the probability of landfall and the landfall intensity probabilities generated by the WSP model are reliable and potentially useful for preparedness decision-making. A case study of Hurricane Ida (2021) highlights how the LDP can be implemented to communicate landfall uncertainty to a broad range of users.
Significance Statement
With the new landfall distribution product (LDP), the National Hurricane Center can provide both track and intensity forecast uncertainty surrounding the landfall of hurricanes. The issuance of a reasonable worst case scenario for the strongest winds that could impact a region could amplify messaging to encourage people to take appropriate action prior to a landfall.
Abstract
Improving estimates of tropical cyclone forecast uncertainty remains an important goal of the Hurricane Forecast Improvement Project (HFIP). Intensity forecast uncertainty near landfall is especially complicated because intensity forecasts depend on track forecasts. Ensembles can be difficult to interpret near land due to differences in both spatial and temporal resolution and differences in landfall timing (if at all) and location. The Monte Carlo Wind Speed Probability (WSP) model is a statistical ensemble based on the error characteristics of forecasts by the National Hurricane Center (NHC) and the spread of several track forecast models. The landfall distribution product (LDP) introduced in this paper was developed to use the statistical ensemble of forecasts from the WSP model to estimate both the track and intensity forecast uncertainty associated with potential landfalls. The LDP includes probabilistic intensity estimates as well as estimates of the most likely and reasonable strongest intensity at landfall. These products could communicate concise intensity uncertainty information to users at risk for tropical cyclone impacts. Demonstration on a retrospective dataset from 2010 to 2018 and evaluation of the LDP on the 2020–21 Atlantic hurricane seasons shows that the probability of landfall and the landfall intensity probabilities generated by the WSP model are reliable and potentially useful for preparedness decision-making. A case study of Hurricane Ida (2021) highlights how the LDP can be implemented to communicate landfall uncertainty to a broad range of users.
Significance Statement
With the new landfall distribution product (LDP), the National Hurricane Center can provide both track and intensity forecast uncertainty surrounding the landfall of hurricanes. The issuance of a reasonable worst case scenario for the strongest winds that could impact a region could amplify messaging to encourage people to take appropriate action prior to a landfall.
Abstract
Strong downslope windstorms can cause extensive property damage and extreme wildfire spread, so their accurate prediction is important. Although some early studies suggested high predictability for downslope windstorms, more recent analyses have found limited predictability for such winds. Nevertheless, there is a theoretical basis for expecting higher downslope wind predictability in cases with a mean-state critical level, and this is supported by one previous effort to forecast actual events. To more thoroughly investigate downslope windstorm predictability, we compare archived simulations from the NCAR ensemble, a 10-member mesoscale ensemble run at 3-km horizontal grid spacing over the entire contiguous United States, to observed events at 15 stations in the western United States susceptible to strong downslope winds. We assess predictability in three contexts: the average ensemble spread, which provides an estimate of potential predictability; a forecast evaluation based upon binary-decision criteria, which is representative of operational hazard warnings; and a probabilistic forecast evaluation using the continuous ranked probability score (CRPS), which is a measure of an ensemble’s ability to generate the proper probability distribution for the events under consideration. We do find better predictive skill for the mean-state critical-level regime in comparison to other downslope windstorm–generating mechanisms. Our downslope windstorm warning performance, calculated using binary-decision criteria from the bias-corrected ensemble forecasts, performed slightly worse for no-critical-level events, and slightly better for critical-level events, than National Weather Service high-wind warnings aggregated over all types of high-wind events throughout the United States and annually averaged for each year between 2008 and 2019.
Abstract
Strong downslope windstorms can cause extensive property damage and extreme wildfire spread, so their accurate prediction is important. Although some early studies suggested high predictability for downslope windstorms, more recent analyses have found limited predictability for such winds. Nevertheless, there is a theoretical basis for expecting higher downslope wind predictability in cases with a mean-state critical level, and this is supported by one previous effort to forecast actual events. To more thoroughly investigate downslope windstorm predictability, we compare archived simulations from the NCAR ensemble, a 10-member mesoscale ensemble run at 3-km horizontal grid spacing over the entire contiguous United States, to observed events at 15 stations in the western United States susceptible to strong downslope winds. We assess predictability in three contexts: the average ensemble spread, which provides an estimate of potential predictability; a forecast evaluation based upon binary-decision criteria, which is representative of operational hazard warnings; and a probabilistic forecast evaluation using the continuous ranked probability score (CRPS), which is a measure of an ensemble’s ability to generate the proper probability distribution for the events under consideration. We do find better predictive skill for the mean-state critical-level regime in comparison to other downslope windstorm–generating mechanisms. Our downslope windstorm warning performance, calculated using binary-decision criteria from the bias-corrected ensemble forecasts, performed slightly worse for no-critical-level events, and slightly better for critical-level events, than National Weather Service high-wind warnings aggregated over all types of high-wind events throughout the United States and annually averaged for each year between 2008 and 2019.
