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Abstract
We developed a storm surge ensemble prediction system (EPS) for lagoons and transitional environments. Lagoons are often threatened by storm surge events with consequent risks for human life and economic losses. The uncertainties connected with a classic deterministic forecast are many, thus, an ensemble forecast system is required to properly consider them and inform the end-user community accordingly. The technological resources now available allow us to investigate the possibility of operational ensemble forecasting systems that will become increasingly essential for coastal management. We show the advantages and limitations of an EPS applied to a lagoon, using a very high-resolution unstructured grid finite element model and 45 EPS members. For five recent storm surge events, the EPS generally improves the forecast skill on the third forecast day compared to just one deterministic forecast, while they are similar in the first two days. A weighting system is implemented to compute an improved ensemble mean. The uncertainties regarding sea level due to meteorological forcing, river runoff, initial boundaries, and lateral boundaries are evaluated for a special case in the northern Adriatic Sea, and the different forecasts are used to compose the EPS members. We conclude that the largest uncertainty is in the initial and lateral boundary fields at different time and space scales, including the tidal components.
Significance Statement
Storm surges are extreme sea level events that may threaten densely populated coastal areas. The purpose of this work is to improve the extreme sea level forecast for transitional areas with the understanding of what are the most important forcing generating uncertainties and find a technique to reach a reliable sea level forecast. This is achieved by implementing an ensemble prediction system running 45 members for each event considered. Results show that initial and lateral boundary conditions provide most of the uncertainty, including the tidal components. The weighting system applied to find the ensemble mean improves the forecast skill on the third forecast day while it is comparable with the deterministic forecast in the first two days.
Abstract
We developed a storm surge ensemble prediction system (EPS) for lagoons and transitional environments. Lagoons are often threatened by storm surge events with consequent risks for human life and economic losses. The uncertainties connected with a classic deterministic forecast are many, thus, an ensemble forecast system is required to properly consider them and inform the end-user community accordingly. The technological resources now available allow us to investigate the possibility of operational ensemble forecasting systems that will become increasingly essential for coastal management. We show the advantages and limitations of an EPS applied to a lagoon, using a very high-resolution unstructured grid finite element model and 45 EPS members. For five recent storm surge events, the EPS generally improves the forecast skill on the third forecast day compared to just one deterministic forecast, while they are similar in the first two days. A weighting system is implemented to compute an improved ensemble mean. The uncertainties regarding sea level due to meteorological forcing, river runoff, initial boundaries, and lateral boundaries are evaluated for a special case in the northern Adriatic Sea, and the different forecasts are used to compose the EPS members. We conclude that the largest uncertainty is in the initial and lateral boundary fields at different time and space scales, including the tidal components.
Significance Statement
Storm surges are extreme sea level events that may threaten densely populated coastal areas. The purpose of this work is to improve the extreme sea level forecast for transitional areas with the understanding of what are the most important forcing generating uncertainties and find a technique to reach a reliable sea level forecast. This is achieved by implementing an ensemble prediction system running 45 members for each event considered. Results show that initial and lateral boundary conditions provide most of the uncertainty, including the tidal components. The weighting system applied to find the ensemble mean improves the forecast skill on the third forecast day while it is comparable with the deterministic forecast in the first two days.
Abstract
The assimilation of two surface-sensitive channels of the AMSU-A instruments on board the NOAA-15/NOAA-18/NOAA-19 and MetOp-A/MetOp-B satellites over land was achieved in the China Meteorological Administration Global Forecast System (CMA_GFS). The land surface emissivity was calculated by 1) the window channel retrieval method and 2) the Tool to Estimate Land Surface Emissivities at Microwave frequencies (TELSEM2). Quality controls for these satellite microwave observations over land were conducted. The predictors and regression coefficients used for oceanic satellite data were retained during the bias correction over land and found to perform well. Three batch experiments were implemented in CMA_GFS with 4D-Var: 1) assimilating only the default data, and adding the above data over land with land surface emissivity obtained from 2) TELSEM2 and 3) the window channel retrieval method. The results indicated that the window channel retrieval method can better reduce the departure between the observed and simulated brightness temperature. Over most land types, the positive impacts of this method exceed those of TELSEM2. Both TELSEM2 and the window channel retrieval method improve the humidity analysis near the ground, as well as the forecast capability globally, particularly in those regions where the land coverage is greater, such as in the Northern Hemisphere. The data utilization of the two surface-sensitive channels increase by 6% and 12%, respectively, and the additional data every 6 h can cover most land, where there was no surface-sensitive data assimilated before. This study marks the beginning of near-surface channel assimilation over land in CMA_GFS and represents a breakthrough in the assimilation of other surface-sensitive channels in other satellite instruments.
Significance Statement
Surface-sensitive microwave channels are difficult to assimilate in NWP due to the lack of both direct measurement and appropriate modeling for instantaneous land surface emissivity. This paper discusses a method that improves the surface emissivity estimates, which has allowed the utilization of surface-sensitive microwave channels in CMA_GFS. Those capabilities have resulted in better data utilization, improved forecasts of temperature, geopotential height, and winds in the Northern Hemisphere at 3–7 days, and represent an incremental and important improvement to CMA_GFS.
Abstract
The assimilation of two surface-sensitive channels of the AMSU-A instruments on board the NOAA-15/NOAA-18/NOAA-19 and MetOp-A/MetOp-B satellites over land was achieved in the China Meteorological Administration Global Forecast System (CMA_GFS). The land surface emissivity was calculated by 1) the window channel retrieval method and 2) the Tool to Estimate Land Surface Emissivities at Microwave frequencies (TELSEM2). Quality controls for these satellite microwave observations over land were conducted. The predictors and regression coefficients used for oceanic satellite data were retained during the bias correction over land and found to perform well. Three batch experiments were implemented in CMA_GFS with 4D-Var: 1) assimilating only the default data, and adding the above data over land with land surface emissivity obtained from 2) TELSEM2 and 3) the window channel retrieval method. The results indicated that the window channel retrieval method can better reduce the departure between the observed and simulated brightness temperature. Over most land types, the positive impacts of this method exceed those of TELSEM2. Both TELSEM2 and the window channel retrieval method improve the humidity analysis near the ground, as well as the forecast capability globally, particularly in those regions where the land coverage is greater, such as in the Northern Hemisphere. The data utilization of the two surface-sensitive channels increase by 6% and 12%, respectively, and the additional data every 6 h can cover most land, where there was no surface-sensitive data assimilated before. This study marks the beginning of near-surface channel assimilation over land in CMA_GFS and represents a breakthrough in the assimilation of other surface-sensitive channels in other satellite instruments.
Significance Statement
Surface-sensitive microwave channels are difficult to assimilate in NWP due to the lack of both direct measurement and appropriate modeling for instantaneous land surface emissivity. This paper discusses a method that improves the surface emissivity estimates, which has allowed the utilization of surface-sensitive microwave channels in CMA_GFS. Those capabilities have resulted in better data utilization, improved forecasts of temperature, geopotential height, and winds in the Northern Hemisphere at 3–7 days, and represent an incremental and important improvement to CMA_GFS.
Abstract
This study evaluates the representation of tropical cyclone precipitation (TCP) in reforecasts from the Subseasonal to Seasonal (S2S) Prediction Project. The global distribution of precipitation in S2S models shows relevant biases in the multimodel mean ensemble that are characterized by wet biases in total precipitation and TCP, except for the Atlantic. The TCP biases can contribute more than 50% of the total precipitation biases in basins such as the southern Indian Ocean and South Pacific. The magnitude and spatial pattern of these biases exhibit little variation with lead time. The origins of TCP biases can be attributed to biases in the frequency of tropical cyclone occurrence. The S2S models simulate too few TCs in the Atlantic and western North Pacific and too many TCs in the Southern Hemisphere and eastern North Pacific. At the storm scale, the average peak precipitation near the storm center is lower in the models than observations due to a too high proportion of weak TCs. However, this bias is offset in some models by higher than observed precipitation rates at larger radii (300–500 km). An analysis of the mean TCP for each TC at each grid point reveals an overestimation of TCP rates, particularly in the near-equatorial Indian and western Pacific Oceans. These findings suggest that the simulation of TC occurrence and the storm-scale precipitation require better representation in order to reduce TCP biases and enhance the subseasonal prediction skill of mean and extreme total precipitation.
Abstract
This study evaluates the representation of tropical cyclone precipitation (TCP) in reforecasts from the Subseasonal to Seasonal (S2S) Prediction Project. The global distribution of precipitation in S2S models shows relevant biases in the multimodel mean ensemble that are characterized by wet biases in total precipitation and TCP, except for the Atlantic. The TCP biases can contribute more than 50% of the total precipitation biases in basins such as the southern Indian Ocean and South Pacific. The magnitude and spatial pattern of these biases exhibit little variation with lead time. The origins of TCP biases can be attributed to biases in the frequency of tropical cyclone occurrence. The S2S models simulate too few TCs in the Atlantic and western North Pacific and too many TCs in the Southern Hemisphere and eastern North Pacific. At the storm scale, the average peak precipitation near the storm center is lower in the models than observations due to a too high proportion of weak TCs. However, this bias is offset in some models by higher than observed precipitation rates at larger radii (300–500 km). An analysis of the mean TCP for each TC at each grid point reveals an overestimation of TCP rates, particularly in the near-equatorial Indian and western Pacific Oceans. These findings suggest that the simulation of TC occurrence and the storm-scale precipitation require better representation in order to reduce TCP biases and enhance the subseasonal prediction skill of mean and extreme total precipitation.
Abstract
Parametric models of tropical cyclone winds are widely used for risk assessment. Although tropical cyclones often present their worst wind risk to humanity during landfall, parametric models that represent land–sea differences are rare. This paper presents a parametric model with explicit representation of land–sea differences. Statistical models were developed over each surface of the frictional wind speed reduction from gradient level to 10 m, and of the surface inflow angle, based on about 1200 simulations with a three-dimensional dynamical boundary layer model. The wind profile of Willoughby et al. is used to represent the gradient flow, and a maximum likelihood scheme used to fit this profile to best track data. The mean RMS difference between the statistical and dynamical surface winds within 100 km of the storm center is 0.78 m s−1 and 4.26° over sea, and 1.04 m s−1 and 4.59° over land. During landfall, the use of a common gradient-level structure, but different surface roughnesses, provides dynamical consistency between the estimated winds over sea and land. A simple representation of internal boundary layers is applied near the coast. Analysis of the dynamical simulations revealed substantial consistency with observational studies of the tropical cyclone boundary layer, including that the azimuth of the surface wind maximum is on average 65° from the front of the storm, in the left-forward quadrant in the Southern Hemisphere. There was, however, substantial variability around this figure, with the maximum occurring in the opposite forward quadrant in storms that were intense, and/or had a relatively rapid decrease in wind speed outside of the radius of maximum winds.
Abstract
Parametric models of tropical cyclone winds are widely used for risk assessment. Although tropical cyclones often present their worst wind risk to humanity during landfall, parametric models that represent land–sea differences are rare. This paper presents a parametric model with explicit representation of land–sea differences. Statistical models were developed over each surface of the frictional wind speed reduction from gradient level to 10 m, and of the surface inflow angle, based on about 1200 simulations with a three-dimensional dynamical boundary layer model. The wind profile of Willoughby et al. is used to represent the gradient flow, and a maximum likelihood scheme used to fit this profile to best track data. The mean RMS difference between the statistical and dynamical surface winds within 100 km of the storm center is 0.78 m s−1 and 4.26° over sea, and 1.04 m s−1 and 4.59° over land. During landfall, the use of a common gradient-level structure, but different surface roughnesses, provides dynamical consistency between the estimated winds over sea and land. A simple representation of internal boundary layers is applied near the coast. Analysis of the dynamical simulations revealed substantial consistency with observational studies of the tropical cyclone boundary layer, including that the azimuth of the surface wind maximum is on average 65° from the front of the storm, in the left-forward quadrant in the Southern Hemisphere. There was, however, substantial variability around this figure, with the maximum occurring in the opposite forward quadrant in storms that were intense, and/or had a relatively rapid decrease in wind speed outside of the radius of maximum winds.
Abstract
The Hurricane Analysis and Forecasting System (HAFS) is the next-generation, FV3-based tropical cyclone (TC) forecasting system. Unlike operational implementations of NOAA’s Hurricane Weather Research and Forecast (HWRF) modeling system, current data assimilation (DA) capabilities in HAFS permit the uninterrupted basin-wide assimilation of measurements. This feature of HAFS opens a variety of new research directions for TC prediction, including new strategies for DA algorithm development and self-contained probabilistic forecasting. The present research focuses more narrowly on new opportunities HAFS brings for optimizing the use of satellite measurements for TC prediction. While satellite radiometers provide a wealth of information for characterizing temperature, moisture, and wind in TC environments, the provided measurements are often biased and contain unknown cross-channel error correlations. For mature global modeling systems, these statistics are estimated from information gathered during DA, namely, innovations collected over large spatial and temporal training periods. The estimated statistics, however, are imperfect owing to unknown error sources such as model process error, which are difficult to separate from observation error. As such, bias and uncertainty specifications that rely on information from external models are suboptimal—as is the current strategy for HWRF. In this paper, it will be demonstrated that bias estimation for satellite radiance observations is particularly sensitive to common design choices, such as using a bias model trained from the Global Data Assimilation System instead of within the native modeling system. Implications of this finding for TC prediction are examined over a 6-week period from 2020, which included the development and intensification of nine tropical cyclones.
Significance Statement
Tropical cyclone–focused numerical weather prediction is difficult due to complex nonlinear physical processes and a lack of in situ observations over open ocean. Prediction systems rely heavily on satellite radiance measurements, which have high spatial–temporal resolution over the entire domain but require bias correction. Estimation of observation bias requires long training periods and large spatial domain coverage, which is typically not permitted outside of global models. However, bias specification is strongly model dependent, as bias correction methods cannot easily separate model and observation bias. In this study, we perform satellite radiance bias specification internally for an experimental version of the NOAA Hurricane Analysis and Forecast System and demonstrate major implications for tropical cyclone prediction.
Abstract
The Hurricane Analysis and Forecasting System (HAFS) is the next-generation, FV3-based tropical cyclone (TC) forecasting system. Unlike operational implementations of NOAA’s Hurricane Weather Research and Forecast (HWRF) modeling system, current data assimilation (DA) capabilities in HAFS permit the uninterrupted basin-wide assimilation of measurements. This feature of HAFS opens a variety of new research directions for TC prediction, including new strategies for DA algorithm development and self-contained probabilistic forecasting. The present research focuses more narrowly on new opportunities HAFS brings for optimizing the use of satellite measurements for TC prediction. While satellite radiometers provide a wealth of information for characterizing temperature, moisture, and wind in TC environments, the provided measurements are often biased and contain unknown cross-channel error correlations. For mature global modeling systems, these statistics are estimated from information gathered during DA, namely, innovations collected over large spatial and temporal training periods. The estimated statistics, however, are imperfect owing to unknown error sources such as model process error, which are difficult to separate from observation error. As such, bias and uncertainty specifications that rely on information from external models are suboptimal—as is the current strategy for HWRF. In this paper, it will be demonstrated that bias estimation for satellite radiance observations is particularly sensitive to common design choices, such as using a bias model trained from the Global Data Assimilation System instead of within the native modeling system. Implications of this finding for TC prediction are examined over a 6-week period from 2020, which included the development and intensification of nine tropical cyclones.
Significance Statement
Tropical cyclone–focused numerical weather prediction is difficult due to complex nonlinear physical processes and a lack of in situ observations over open ocean. Prediction systems rely heavily on satellite radiance measurements, which have high spatial–temporal resolution over the entire domain but require bias correction. Estimation of observation bias requires long training periods and large spatial domain coverage, which is typically not permitted outside of global models. However, bias specification is strongly model dependent, as bias correction methods cannot easily separate model and observation bias. In this study, we perform satellite radiance bias specification internally for an experimental version of the NOAA Hurricane Analysis and Forecast System and demonstrate major implications for tropical cyclone prediction.
Abstract
We used object-oriented verification and self-organizing maps (SOMs) to identify patterns in environmental parameters correlating with mesoscale snowband predictive skill by the High-Resolution Ensemble Forecast (HREF) system between 2017 and 2022. First, HREF snowband forecasts for 305 banding events were verified based on similarities between forecast and observed feature properties. HREF members performed comparably, demonstrating large positional errors, but the non-time-lagged High-Resolution Rapid Refresh member demonstrated the greatest overall skill. Observed banding events were clustered by 500-hPa geopotential height anomalies, mean sea level pressure, vertical velocity, frontogenesis, and saturation equivalent potential vorticity from the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 using SOMs. Clusters reaffirmed the presence of midlevel frontogenesis, ascent, and reduced stability in most banding cases, and the predominant synoptic environments conducive to band development. Clusters were compared to determine whether patterns in the variables were correlated with predictive skill. Strength of upward motion was correlated with skill, with the strongest upward motion cases verifying 10% better than the weakest upward motion cases due to smaller positional error. Additionally, events with a single region of strong upward motion verified better than events with disorganized, but comparably intense, upward motion. The magnitude of frontogenesis was uncorrelated with skill, but events with more upright frontogenesis collocated with the band centroid were better predicted than events with shallower slopes and low-level frontogenesis displaced toward warmer air. The skill variance associated with different vertical motion magnitudes could assist forecasters in modulating forecast confidence, while the most common types of errors documented here may be beneficial to model developers in refining HREF member snowfall forecasts.
Significance Statement
High-resolution numerical weather prediction (NWP) models generally have limited predictive skill for mesoscale snowband forecasts. Even so, some snowbands are forecast by NWP models with much greater skill than others. In this work, we apply artificial intelligence to group snowband events based on atmospheric conditions and then determine whether different groups are easier or harder for models to predict. Identification of these groups could help forecasters know when to trust or be skeptical of NWP output and help developers improve snowband formation processes in NWP models.
Abstract
We used object-oriented verification and self-organizing maps (SOMs) to identify patterns in environmental parameters correlating with mesoscale snowband predictive skill by the High-Resolution Ensemble Forecast (HREF) system between 2017 and 2022. First, HREF snowband forecasts for 305 banding events were verified based on similarities between forecast and observed feature properties. HREF members performed comparably, demonstrating large positional errors, but the non-time-lagged High-Resolution Rapid Refresh member demonstrated the greatest overall skill. Observed banding events were clustered by 500-hPa geopotential height anomalies, mean sea level pressure, vertical velocity, frontogenesis, and saturation equivalent potential vorticity from the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 using SOMs. Clusters reaffirmed the presence of midlevel frontogenesis, ascent, and reduced stability in most banding cases, and the predominant synoptic environments conducive to band development. Clusters were compared to determine whether patterns in the variables were correlated with predictive skill. Strength of upward motion was correlated with skill, with the strongest upward motion cases verifying 10% better than the weakest upward motion cases due to smaller positional error. Additionally, events with a single region of strong upward motion verified better than events with disorganized, but comparably intense, upward motion. The magnitude of frontogenesis was uncorrelated with skill, but events with more upright frontogenesis collocated with the band centroid were better predicted than events with shallower slopes and low-level frontogenesis displaced toward warmer air. The skill variance associated with different vertical motion magnitudes could assist forecasters in modulating forecast confidence, while the most common types of errors documented here may be beneficial to model developers in refining HREF member snowfall forecasts.
Significance Statement
High-resolution numerical weather prediction (NWP) models generally have limited predictive skill for mesoscale snowband forecasts. Even so, some snowbands are forecast by NWP models with much greater skill than others. In this work, we apply artificial intelligence to group snowband events based on atmospheric conditions and then determine whether different groups are easier or harder for models to predict. Identification of these groups could help forecasters know when to trust or be skeptical of NWP output and help developers improve snowband formation processes in NWP models.
Abstract
Forecasts produced by EPSs provide the potential state of the future atmosphere and quantify uncertainty. However, the raw ensemble forecasts from a single EPS are typically characterized by underdispersive predictions, especially for precipitation that follows a right-skewed gamma distribution. In this study, censored and shifted gamma distribution ensemble model output statistics (CSG-EMOS) is performed as one of the state-of-the-art methods for probabilistic precipitation postprocessing across China. Ensemble forecasts from multiple EPSs, including the European Centre for Medium-Range Weather Forecasts, the National Centers for Environmental Prediction, and the Met Office, are collected as raw ensembles. A conditional CSG EMOS (Cond-CSG-EMOS) model is further proposed to calibrate the ensemble forecasts for heavy-precipitation events, where the standard CSG-EMOS is insufficient. The precipitation samples from the training period are divided into two categories, light- and heavy-precipitation events, according to a given precipitation threshold and prior ensemble forecast. Then individual models are, respectively, optimized for adequate parameter estimation. The results demonstrate that the Cond-CSG-EMOS is superior to the raw EPSs and the standard CSG-EMOS, especially for the calibration of heavy-precipitation events. The spatial distribution of forecast skills shows that the Cond-CSG-EMOS outperforms the others over most of the study region, particularly in North and Central China. A sensitivity testing on the precipitation threshold shows that a higher threshold leads to better outcomes for the regions that have more heavy-precipitation events, i.e., South China. Our results indicate that the proposed Cond-CSG-EMOS model is a promising approach for the statistical postprocessing of ensemble precipitation forecasts.
Significance Statement
Heavy-precipitation events are of highly socioeconomic relevance. But it remains a great challenge to obtain high-quality probabilistic quantitative precipitation forecasting (PQPF) from the operational ensemble prediction systems (EPSs). Statistical postprocessing is commonly used to calibrate the systematic errors of the raw EPSs forecasts. However, the non-Gaussian nature of precipitation and the imbalance between the size of light- and heavy-precipitation samples add to the challenge. This study proposes a conditional postprocessing method to improve PQPF of heavy precipitation by performing calibration separately for light and heavy precipitation, in contrast to some previous studies. Our results indicate that the conditional model mitigates the underestimation of heavy precipitation, as well as with a better calibration for the light- and moderate-precipitation.
Abstract
Forecasts produced by EPSs provide the potential state of the future atmosphere and quantify uncertainty. However, the raw ensemble forecasts from a single EPS are typically characterized by underdispersive predictions, especially for precipitation that follows a right-skewed gamma distribution. In this study, censored and shifted gamma distribution ensemble model output statistics (CSG-EMOS) is performed as one of the state-of-the-art methods for probabilistic precipitation postprocessing across China. Ensemble forecasts from multiple EPSs, including the European Centre for Medium-Range Weather Forecasts, the National Centers for Environmental Prediction, and the Met Office, are collected as raw ensembles. A conditional CSG EMOS (Cond-CSG-EMOS) model is further proposed to calibrate the ensemble forecasts for heavy-precipitation events, where the standard CSG-EMOS is insufficient. The precipitation samples from the training period are divided into two categories, light- and heavy-precipitation events, according to a given precipitation threshold and prior ensemble forecast. Then individual models are, respectively, optimized for adequate parameter estimation. The results demonstrate that the Cond-CSG-EMOS is superior to the raw EPSs and the standard CSG-EMOS, especially for the calibration of heavy-precipitation events. The spatial distribution of forecast skills shows that the Cond-CSG-EMOS outperforms the others over most of the study region, particularly in North and Central China. A sensitivity testing on the precipitation threshold shows that a higher threshold leads to better outcomes for the regions that have more heavy-precipitation events, i.e., South China. Our results indicate that the proposed Cond-CSG-EMOS model is a promising approach for the statistical postprocessing of ensemble precipitation forecasts.
Significance Statement
Heavy-precipitation events are of highly socioeconomic relevance. But it remains a great challenge to obtain high-quality probabilistic quantitative precipitation forecasting (PQPF) from the operational ensemble prediction systems (EPSs). Statistical postprocessing is commonly used to calibrate the systematic errors of the raw EPSs forecasts. However, the non-Gaussian nature of precipitation and the imbalance between the size of light- and heavy-precipitation samples add to the challenge. This study proposes a conditional postprocessing method to improve PQPF of heavy precipitation by performing calibration separately for light and heavy precipitation, in contrast to some previous studies. Our results indicate that the conditional model mitigates the underestimation of heavy precipitation, as well as with a better calibration for the light- and moderate-precipitation.
Abstract
Mesoscale snowbands are impactful winter weather phenomena but are challenging to predict due to small-scale forcings and ingredients. Previous work has established that even deterministic convection-allowing models (CAMs) often struggle to represent these features with much precision and recommended the application of ingredients-based or probabilistic forecast strategies. Based on these recommendations, we develop and evaluate four different models for forecasting snowbands. The first model, referred to as the “HREF threshold probability” model, detects band development in High-Resolution Ensemble Forecast (HREF) system members’ 1000-m simulated reflectivities, then uses these detections to calculate a snowband probability. The second model is a random forest incorporating features explicitly linked to snowbands, such as the detection of bands in each HREF member and statistical summaries of simulated reflectivity and the categorical snow field. The third model is a random forest model incorporating snowband ingredients, such as midtropospheric frontogenesis, moist symmetric stability, and vertical velocity. Last, the fourth model combines the features of the explicit and implicit random forests. Binary band predictions based upon the HREF threshold probability model resulted in a critical success index 27% higher than the average HREF member. The explicit feature random forest model further improved performance by an additional 11%, with statistics of the reflectivity field holding the most predictive value. The implicit and combined random forests slightly underperformed the explicit random forest, perhaps due to a large number of noisy, correlated features. Ultimately, we demonstrate that simple probabilistic snowband forecasting strategies can yield substantial improvements over deterministic CAMs.
Significance Statement
Mesoscale snowbands have the potential for major societal impacts but are difficult to predict due to their small spatial scales. Previous work has shown that individual high-resolution numerical weather prediction (NWP) models struggle to predict whether or not a snowband will occur. In this work, we evaluate whether a probabilistic forecast strategy using high-resolution ensemble NWP output leads to improved snowband forecasts, and whether we can gain additional predictive skill by combining this output with artificial intelligence (AI) methods. AI can also help us understand the environmental factors associated with snowbands and compare environmental importance in forecasting to just using the model output snowfall forecasts explicitly.
Abstract
Mesoscale snowbands are impactful winter weather phenomena but are challenging to predict due to small-scale forcings and ingredients. Previous work has established that even deterministic convection-allowing models (CAMs) often struggle to represent these features with much precision and recommended the application of ingredients-based or probabilistic forecast strategies. Based on these recommendations, we develop and evaluate four different models for forecasting snowbands. The first model, referred to as the “HREF threshold probability” model, detects band development in High-Resolution Ensemble Forecast (HREF) system members’ 1000-m simulated reflectivities, then uses these detections to calculate a snowband probability. The second model is a random forest incorporating features explicitly linked to snowbands, such as the detection of bands in each HREF member and statistical summaries of simulated reflectivity and the categorical snow field. The third model is a random forest model incorporating snowband ingredients, such as midtropospheric frontogenesis, moist symmetric stability, and vertical velocity. Last, the fourth model combines the features of the explicit and implicit random forests. Binary band predictions based upon the HREF threshold probability model resulted in a critical success index 27% higher than the average HREF member. The explicit feature random forest model further improved performance by an additional 11%, with statistics of the reflectivity field holding the most predictive value. The implicit and combined random forests slightly underperformed the explicit random forest, perhaps due to a large number of noisy, correlated features. Ultimately, we demonstrate that simple probabilistic snowband forecasting strategies can yield substantial improvements over deterministic CAMs.
Significance Statement
Mesoscale snowbands have the potential for major societal impacts but are difficult to predict due to their small spatial scales. Previous work has shown that individual high-resolution numerical weather prediction (NWP) models struggle to predict whether or not a snowband will occur. In this work, we evaluate whether a probabilistic forecast strategy using high-resolution ensemble NWP output leads to improved snowband forecasts, and whether we can gain additional predictive skill by combining this output with artificial intelligence (AI) methods. AI can also help us understand the environmental factors associated with snowbands and compare environmental importance in forecasting to just using the model output snowfall forecasts explicitly.
Abstract
The 48-h intensity forecasts for Hurricane Pamela (2021) from numerical weather prediction models, statistical–dynamical aids, and forecasters were a major forecast bust with Pamela making landfall as a minor rather than major hurricane. From the satellite presentation, Pamela exhibited a symmetric pattern referred to as central cold cover (CCC) in the subjective Dvorak intensity technique. Per the technique, the CCC pattern is accompanied by arrested development in intensity despite the seemingly favorable convective signature. To understand forecast uncertainty during occurrences, central cold cover frequency from 2011 to 2021 is documented. From these cases, composites of longwave infrared brightness temperatures from geostationary satellites for CCC cases are presented, and the surrounding tropical cyclone large-scale environment is quantified and compared with other tropical cyclones at similar latitudes and intensities. These composites show that central cold cover has a consistent presentation, but varies in the preceding hours for storms that eventually intensify or weaken. And, the synoptic-scale environment surrounding the tropical cyclone thermodynamically supports the vigorous deep convection associated with CCC. Finally, intensity forecast errors from numerical weather prediction models and statistical–dynamical aids are examined in comparison to similar tropical cyclones. This work shows that guidance struggles during CCC cases with intensity errors from these models being in the lowest percentiles of performance, particularly for 24- and 36-h forecasts.
Significance Statement
The appearance of symmetric cold clouds near the center of developing tropical cyclones is most often associated with future intensification. This simple relationship is widely used by statistical tropical cyclone intensity forecast models. Here, we reexamine and confirm that one subjectively determined nighttime cold cyclone cloud pattern termed the “central cold cover” pattern in Vern Dvorak’s seminal technique for estimating tropical cyclone intensity from infrared satellite images is indeed related to slow or arrested development, and represents a failure mode for these simple forecast models.
Abstract
The 48-h intensity forecasts for Hurricane Pamela (2021) from numerical weather prediction models, statistical–dynamical aids, and forecasters were a major forecast bust with Pamela making landfall as a minor rather than major hurricane. From the satellite presentation, Pamela exhibited a symmetric pattern referred to as central cold cover (CCC) in the subjective Dvorak intensity technique. Per the technique, the CCC pattern is accompanied by arrested development in intensity despite the seemingly favorable convective signature. To understand forecast uncertainty during occurrences, central cold cover frequency from 2011 to 2021 is documented. From these cases, composites of longwave infrared brightness temperatures from geostationary satellites for CCC cases are presented, and the surrounding tropical cyclone large-scale environment is quantified and compared with other tropical cyclones at similar latitudes and intensities. These composites show that central cold cover has a consistent presentation, but varies in the preceding hours for storms that eventually intensify or weaken. And, the synoptic-scale environment surrounding the tropical cyclone thermodynamically supports the vigorous deep convection associated with CCC. Finally, intensity forecast errors from numerical weather prediction models and statistical–dynamical aids are examined in comparison to similar tropical cyclones. This work shows that guidance struggles during CCC cases with intensity errors from these models being in the lowest percentiles of performance, particularly for 24- and 36-h forecasts.
Significance Statement
The appearance of symmetric cold clouds near the center of developing tropical cyclones is most often associated with future intensification. This simple relationship is widely used by statistical tropical cyclone intensity forecast models. Here, we reexamine and confirm that one subjectively determined nighttime cold cyclone cloud pattern termed the “central cold cover” pattern in Vern Dvorak’s seminal technique for estimating tropical cyclone intensity from infrared satellite images is indeed related to slow or arrested development, and represents a failure mode for these simple forecast models.
Abstract
On 13 November 2019, seven commercial aircraft of China Eastern Airlines encountered nine severe-or-greater clear-air turbulence (CAT) events over central and eastern China within 12 h (0000–1200 UTC). These events mainly occurred at altitudes between 6.0 and 6.7 km. A high-resolution nested numerical simulation is carried out using the Weather Research and Forecasting (WRF) Model to investigate the generation mechanism of these CAT events, with a horizontal resolution of 1 km over the inner domain. In addition, seven CAT diagnostics with outstanding performances are employed for the mechanism analysis. The WRF Model can reasonably reproduce both synoptic-scale systems (Siberian high and upper-level jet stream) and local vertical structures (temperature, dewpoint temperature, and wind field). The simulation indicates that an upper-level front–jet system with a remarkable meridional temperature gradient intensifies over central and eastern China, with the maximum wind speed increasing from 59.0 to 67.3 m s−1. The intensification of the front–jet system induces the tropopause folding, and nine localized CAT events occur in the region with large vertical wind shear (VWS) (1.55 × 10−2–2.53 × 10−2 s−1) and small Richardson numbers (Ri) (0.42–0.85) below the cyclonic side of the jet stream. Diagnostic analysis reveals that Kelvin–Helmholtz instability plays an important role in CAT generation, while convective and inertial instability is not directly associated with CAT generation in this study. A typical flight case with continuous CAT events also suggests that large VWS (greater than 1.3 × 10−2 s−1) accompanied with small Ri (less than 1) favors CAT generation in a front–jet system environment.
Significance Statement
A high-resolution nested numerical simulation is carried out using the Weather Research and Forecasting (WRF) Model to investigate the generation mechanism of nine severe-or-greater clear-air turbulence (CAT) events over central and eastern China. Intensification of a front–jet system induces tropopause folding, and nine CAT events occur in the region with large vertical wind shear (greater than 1.55 × 10−2 s−1) and small Richardson numbers (less than 0.85) below the cyclonic side of the jet stream. Kelvin–Helmholtz instability plays an important role in the CAT generation, rather than convective and inertial instability.
Abstract
On 13 November 2019, seven commercial aircraft of China Eastern Airlines encountered nine severe-or-greater clear-air turbulence (CAT) events over central and eastern China within 12 h (0000–1200 UTC). These events mainly occurred at altitudes between 6.0 and 6.7 km. A high-resolution nested numerical simulation is carried out using the Weather Research and Forecasting (WRF) Model to investigate the generation mechanism of these CAT events, with a horizontal resolution of 1 km over the inner domain. In addition, seven CAT diagnostics with outstanding performances are employed for the mechanism analysis. The WRF Model can reasonably reproduce both synoptic-scale systems (Siberian high and upper-level jet stream) and local vertical structures (temperature, dewpoint temperature, and wind field). The simulation indicates that an upper-level front–jet system with a remarkable meridional temperature gradient intensifies over central and eastern China, with the maximum wind speed increasing from 59.0 to 67.3 m s−1. The intensification of the front–jet system induces the tropopause folding, and nine localized CAT events occur in the region with large vertical wind shear (VWS) (1.55 × 10−2–2.53 × 10−2 s−1) and small Richardson numbers (Ri) (0.42–0.85) below the cyclonic side of the jet stream. Diagnostic analysis reveals that Kelvin–Helmholtz instability plays an important role in CAT generation, while convective and inertial instability is not directly associated with CAT generation in this study. A typical flight case with continuous CAT events also suggests that large VWS (greater than 1.3 × 10−2 s−1) accompanied with small Ri (less than 1) favors CAT generation in a front–jet system environment.
Significance Statement
A high-resolution nested numerical simulation is carried out using the Weather Research and Forecasting (WRF) Model to investigate the generation mechanism of nine severe-or-greater clear-air turbulence (CAT) events over central and eastern China. Intensification of a front–jet system induces tropopause folding, and nine CAT events occur in the region with large vertical wind shear (greater than 1.55 × 10−2 s−1) and small Richardson numbers (less than 0.85) below the cyclonic side of the jet stream. Kelvin–Helmholtz instability plays an important role in the CAT generation, rather than convective and inertial instability.
