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Abstract
Prediction systems to enable Earth system predictability research on the subseasonal time scale have been developed with the Community Earth System Model, version 2 (CESM2) using two configurations that differ in their atmospheric components. One system uses the Community Atmosphere Model, version 6 (CAM6) with its top near 40 km, referred to as CESM2(CAM6). The other employs the Whole Atmosphere Community Climate Model, version 6 (WACCM6) whose top extends to ∼140 km, and it includes fully interactive tropospheric and stratospheric chemistry [CESM2(WACCM6)]. Both systems are utilized to carry out subseasonal reforecasts for the 1999–2020 period following the Subseasonal Experiment’s (SubX) protocol. Subseasonal prediction skill from both systems is compared to those of the National Oceanic and Atmospheric Administration CFSv2 and European Centre for Medium-Range Weather Forecasts (ECMWF) operational models. CESM2(CAM6) and CESM2(WACCM6) show very similar subseasonal prediction skill of 2-m temperature, precipitation, the Madden–Julian oscillation, and North Atlantic Oscillation to its previous version and to the NOAA CFSv2 model. Overall, skill of CESM2(CAM6) and CESM2(WACCM6) is a little lower than that of the ECMWF system. In addition to typical output provided by subseasonal prediction systems, CESM2 reforecasts provide comprehensive datasets for predictability research of multiple Earth system components, including three-dimensional output for many variables, and output specific to the mesosphere and lower-thermosphere (MLT) region from CESM2(WACCM6). It is shown that sudden stratosphere warming events, and the associated variability in the MLT, can be predicted ∼10 days in advance. Weekly real-time forecasts and reforecasts with CESM2(CAM6) and CESM2(WACCM6) are freely available.
Significance Statement
We describe here the design and prediction skill of two subseasonal prediction systems based on two configurations of the Community Earth System Model, version 2 (CESM2): CESM2 with the Community Atmosphere Model, version 6 [CESM2(CAM6)] and CESM 2 with Whole Atmosphere Community Climate Model, version 6 [CESM2(WACCM6)] as its atmospheric component. These two systems provide a foundation for community-model based subseasonal prediction research. The CESM2(WACCM6) system provides a novel capability to explore the predictability of the stratosphere, mesosphere, and lower thermosphere. Both CESM2(CAM6) and CESM2(WACCM6) demonstrate subseasonal surface prediction skill comparable to that of the NOAA CFSv2 model, and a little lower than that of the ECMWF forecasting system. CESM2 reforecasts provide a comprehensive dataset for predictability research of multiple aspects of the Earth system, including the whole atmosphere up to 140 km, land, and sea ice. Weekly real-time forecasts, reforecasts, and models are publicly available.
Abstract
Prediction systems to enable Earth system predictability research on the subseasonal time scale have been developed with the Community Earth System Model, version 2 (CESM2) using two configurations that differ in their atmospheric components. One system uses the Community Atmosphere Model, version 6 (CAM6) with its top near 40 km, referred to as CESM2(CAM6). The other employs the Whole Atmosphere Community Climate Model, version 6 (WACCM6) whose top extends to ∼140 km, and it includes fully interactive tropospheric and stratospheric chemistry [CESM2(WACCM6)]. Both systems are utilized to carry out subseasonal reforecasts for the 1999–2020 period following the Subseasonal Experiment’s (SubX) protocol. Subseasonal prediction skill from both systems is compared to those of the National Oceanic and Atmospheric Administration CFSv2 and European Centre for Medium-Range Weather Forecasts (ECMWF) operational models. CESM2(CAM6) and CESM2(WACCM6) show very similar subseasonal prediction skill of 2-m temperature, precipitation, the Madden–Julian oscillation, and North Atlantic Oscillation to its previous version and to the NOAA CFSv2 model. Overall, skill of CESM2(CAM6) and CESM2(WACCM6) is a little lower than that of the ECMWF system. In addition to typical output provided by subseasonal prediction systems, CESM2 reforecasts provide comprehensive datasets for predictability research of multiple Earth system components, including three-dimensional output for many variables, and output specific to the mesosphere and lower-thermosphere (MLT) region from CESM2(WACCM6). It is shown that sudden stratosphere warming events, and the associated variability in the MLT, can be predicted ∼10 days in advance. Weekly real-time forecasts and reforecasts with CESM2(CAM6) and CESM2(WACCM6) are freely available.
Significance Statement
We describe here the design and prediction skill of two subseasonal prediction systems based on two configurations of the Community Earth System Model, version 2 (CESM2): CESM2 with the Community Atmosphere Model, version 6 [CESM2(CAM6)] and CESM 2 with Whole Atmosphere Community Climate Model, version 6 [CESM2(WACCM6)] as its atmospheric component. These two systems provide a foundation for community-model based subseasonal prediction research. The CESM2(WACCM6) system provides a novel capability to explore the predictability of the stratosphere, mesosphere, and lower thermosphere. Both CESM2(CAM6) and CESM2(WACCM6) demonstrate subseasonal surface prediction skill comparable to that of the NOAA CFSv2 model, and a little lower than that of the ECMWF forecasting system. CESM2 reforecasts provide a comprehensive dataset for predictability research of multiple aspects of the Earth system, including the whole atmosphere up to 140 km, land, and sea ice. Weekly real-time forecasts, reforecasts, and models are publicly available.
Abstract
Tornado motion changes occurring with major internal rear-flank momentum surges are examined in three significant tornado-producing supercells. The analysis primarily uses fixed-site Doppler radar data, but also utilizes in situ and videographic observations when available. In the cases examined, the peak lowest-level remotely sensed or in situ rear-flank surge wind speeds ranged from 48 to at least 63 m s−1. Contemporaneous with major surges impacting the tornadoes and their parent low-level mesocyclones, longer-duration tornado heading changes were leftward and ranged from 30° to 55°. In all cases, the tornado speed increased substantially upon surge impact, with tornado speeds approximately doubling in two of the events. A storm-relative change in the hook echo orientation accompanied the major surges and provided a signal that a marked leftward heading change for an ongoing tornado was under way. Concurrent with the surge interaction, the hook echo tip and associated low-level mesocyclone turned leftward while also moving in a storm-relative downshear direction. The major rear-flank internal surges influenced tornado motion such that a generally favorable storm updraft-relative position was maintained. In all cases, the tornado lasted well beyond (≥21 min) the time of the surge-associated left turn with no evident marked loss of intensity until well down-track of the turn. The local momentum balance between outflow and inflow that bounds the tornado or its parent circulation, especially the directionality evolution of the bounding momentum, is the most apparent explanation for tornado down-track or off-track accelerations in the featured events.
Abstract
Tornado motion changes occurring with major internal rear-flank momentum surges are examined in three significant tornado-producing supercells. The analysis primarily uses fixed-site Doppler radar data, but also utilizes in situ and videographic observations when available. In the cases examined, the peak lowest-level remotely sensed or in situ rear-flank surge wind speeds ranged from 48 to at least 63 m s−1. Contemporaneous with major surges impacting the tornadoes and their parent low-level mesocyclones, longer-duration tornado heading changes were leftward and ranged from 30° to 55°. In all cases, the tornado speed increased substantially upon surge impact, with tornado speeds approximately doubling in two of the events. A storm-relative change in the hook echo orientation accompanied the major surges and provided a signal that a marked leftward heading change for an ongoing tornado was under way. Concurrent with the surge interaction, the hook echo tip and associated low-level mesocyclone turned leftward while also moving in a storm-relative downshear direction. The major rear-flank internal surges influenced tornado motion such that a generally favorable storm updraft-relative position was maintained. In all cases, the tornado lasted well beyond (≥21 min) the time of the surge-associated left turn with no evident marked loss of intensity until well down-track of the turn. The local momentum balance between outflow and inflow that bounds the tornado or its parent circulation, especially the directionality evolution of the bounding momentum, is the most apparent explanation for tornado down-track or off-track accelerations in the featured events.
Abstract
Recently, with the accumulation of remote sensing data, the traditional tropical cyclone (TC) track prediction methods (e.g., dynamic methods and statistical methods) have limitations in prediction efficiency and accuracy when dealing with a large amount of data. However, deep learning methods begin to show their advantages to capture the complex spatiotemporal features in high-dimensional data. The task of TC track prediction based on remote sensing images can be formulated as a spatiotemporal sequence-to-sequence problem. Therefore, a novel encoding-to-forecasting model with convolutional long short-term memory (ConvLSTM) and spatial attention network (SAN-EFSModel) was proposed to predict TC tracks in this paper, which can fully extract the long-term spatial and temporal features. The proposed model was evaluated on the real remote sensing images in the western North Pacific Ocean to forecast 24-h TC tracks. Compared with ECMWF-HRES model and NCEP-GFS model, the proposed method has a better prediction accuracy in the testing set with an average position error about 30 km less. Compared with the deep learning methods, the proposed method also has the best performance.
Significance Statement
Tropical cyclones have a great impact on human life and natural environment due to their high frequency of occurrence, heavy degree of harm, wide impact range, and long disaster chain. In this article, we propose an encoding-to-forecasting model with convolutional long short-term memory (ConvLSTM) and spatial attention network (SAN-EFSModel) to predict tropical cyclone tracks, in which the SAN module and the convolution module of ConvLSTM have the ability to extract spatial features, and the long short-term memory (LSTM) module of ConvLSTM can extract temporal features from remote sensing images and tropical cyclone tracks. The results show that the average position error (APE) of our proposed method has about 30% improvement in a 24-h forecast compared with prevailing numerical weather prediction models.
Abstract
Recently, with the accumulation of remote sensing data, the traditional tropical cyclone (TC) track prediction methods (e.g., dynamic methods and statistical methods) have limitations in prediction efficiency and accuracy when dealing with a large amount of data. However, deep learning methods begin to show their advantages to capture the complex spatiotemporal features in high-dimensional data. The task of TC track prediction based on remote sensing images can be formulated as a spatiotemporal sequence-to-sequence problem. Therefore, a novel encoding-to-forecasting model with convolutional long short-term memory (ConvLSTM) and spatial attention network (SAN-EFSModel) was proposed to predict TC tracks in this paper, which can fully extract the long-term spatial and temporal features. The proposed model was evaluated on the real remote sensing images in the western North Pacific Ocean to forecast 24-h TC tracks. Compared with ECMWF-HRES model and NCEP-GFS model, the proposed method has a better prediction accuracy in the testing set with an average position error about 30 km less. Compared with the deep learning methods, the proposed method also has the best performance.
Significance Statement
Tropical cyclones have a great impact on human life and natural environment due to their high frequency of occurrence, heavy degree of harm, wide impact range, and long disaster chain. In this article, we propose an encoding-to-forecasting model with convolutional long short-term memory (ConvLSTM) and spatial attention network (SAN-EFSModel) to predict tropical cyclone tracks, in which the SAN module and the convolution module of ConvLSTM have the ability to extract spatial features, and the long short-term memory (LSTM) module of ConvLSTM can extract temporal features from remote sensing images and tropical cyclone tracks. The results show that the average position error (APE) of our proposed method has about 30% improvement in a 24-h forecast compared with prevailing numerical weather prediction models.
Abstract
Providing timely warnings for severe and potentially tornadic convection is a critical component of the NWS mission, and owing to the associated large reflectivity gradients, sidelobe contamination is possible. This paper focuses on elevation sidelobe contamination appearing in the low-level inflow region of supercells. A qualitative conceptual model of the Weather Surveillance Radar-1988 Doppler (WSR-88D) antenna pattern interacting with supercells is introduced, along with Doppler power spectrum representations of the potential mix of returned power from the main lobe and the sidelobes. These tools inform the multiple ways elevation sidelobe contamination appears in the low levels, primarily below 3 km (10 kft) of radar data. The most common manifestation is somewhat noisy data similar to particulates or biota in clear air. Trained NWS forecasters are accustomed to mentally filtering out noisy clear-air returns as less important. Elevation sidelobe contamination can be mixed with the three-body scatter spike (TBSS) artifact, though the TBSS remains the more salient feature. The most consequential form is the apparent circulation, and when it is incorrectly interpreted as valid, contributes to the false alarm ratio (FAR) for NWS tornado warnings. Quantitative results on the effect of elevation sidelobe contamination on FAR are presented. Diagnostic techniques are emphasized, and with familiarization, can be used in real-time warning operations to identify the apparent circulation as either valid or an imposter. Identification of these contaminated velocity signatures offers a unique opportunity to reduce the NWS tornado warning FAR without also reducing the probability of detection (POD).
Significance Statement
The WSR-88D weather radars provide overall high-quality data for users. However, with some severe thunderstorms, an artifact called elevation sidelobe contamination can produce what looks like a rotation signature, but it may not be real. These ambiguous velocity signatures can contribute to tornado warnings based on rotation signatures that are false circulations. This paper specifically focuses on elevation sidelobe contamination due to its impact on tornado warning decisions. Diagnostic techniques, including several examples, are presented here to aid the reader in correctly identifying elevation sidelobe contamination and why it may occur. Correct identification of an apparent circulation as an imposter due to contamination is a unique opportunity to improve NWS tornado warning performance by reducing warning false alarms.
Abstract
Providing timely warnings for severe and potentially tornadic convection is a critical component of the NWS mission, and owing to the associated large reflectivity gradients, sidelobe contamination is possible. This paper focuses on elevation sidelobe contamination appearing in the low-level inflow region of supercells. A qualitative conceptual model of the Weather Surveillance Radar-1988 Doppler (WSR-88D) antenna pattern interacting with supercells is introduced, along with Doppler power spectrum representations of the potential mix of returned power from the main lobe and the sidelobes. These tools inform the multiple ways elevation sidelobe contamination appears in the low levels, primarily below 3 km (10 kft) of radar data. The most common manifestation is somewhat noisy data similar to particulates or biota in clear air. Trained NWS forecasters are accustomed to mentally filtering out noisy clear-air returns as less important. Elevation sidelobe contamination can be mixed with the three-body scatter spike (TBSS) artifact, though the TBSS remains the more salient feature. The most consequential form is the apparent circulation, and when it is incorrectly interpreted as valid, contributes to the false alarm ratio (FAR) for NWS tornado warnings. Quantitative results on the effect of elevation sidelobe contamination on FAR are presented. Diagnostic techniques are emphasized, and with familiarization, can be used in real-time warning operations to identify the apparent circulation as either valid or an imposter. Identification of these contaminated velocity signatures offers a unique opportunity to reduce the NWS tornado warning FAR without also reducing the probability of detection (POD).
Significance Statement
The WSR-88D weather radars provide overall high-quality data for users. However, with some severe thunderstorms, an artifact called elevation sidelobe contamination can produce what looks like a rotation signature, but it may not be real. These ambiguous velocity signatures can contribute to tornado warnings based on rotation signatures that are false circulations. This paper specifically focuses on elevation sidelobe contamination due to its impact on tornado warning decisions. Diagnostic techniques, including several examples, are presented here to aid the reader in correctly identifying elevation sidelobe contamination and why it may occur. Correct identification of an apparent circulation as an imposter due to contamination is a unique opportunity to improve NWS tornado warning performance by reducing warning false alarms.
Abstract
An evaluation of an operational wildfire air quality model (WFAQM) has been performed. Evaluation metrics were chosen through an analysis of interviews and a survey of professionals who use WFAQM forecasts as part of their daily responsibilities. The survey revealed that professional users generally focus on whether forecast air quality will exceed thresholds that trigger local air quality advisories (e.g., an event), their analysis scale is their region of responsibility, they are interested in short-term (≈24 h) guidance, missing an event is worse than issuing a false alarm, and there are two types of users—one that takes the forecast at face value, and the other that uses it as one of several information sources. Guided by these findings, model performance of Environment and Climate Change Canada’s current operational WFAQM (FireWork) was assessed over western Canada during three (2016–18) summer (May–September) wildfire seasons. Evaluation was performed at the geographic scale at which individual forecasts are issued (the forecast region) using gridded particulate matter 2.5 (PM2.5) fields developed from a machine learning–based downscaling of satellite and meteorological data. For the “at face value” user group, model performance was measured using the Peirce skill score. For the “as information source” user group, model performance was measured using the divergence skill score. For this metric, forecasts were first converted to event probabilities using binomial regression. We find when forecasts are taken at face value, FireWork cannot outperform a nearest-neighbor-based persistence model. However, when forecasts are considered as an information source, FireWork is superior to the persistence-based model.
Abstract
An evaluation of an operational wildfire air quality model (WFAQM) has been performed. Evaluation metrics were chosen through an analysis of interviews and a survey of professionals who use WFAQM forecasts as part of their daily responsibilities. The survey revealed that professional users generally focus on whether forecast air quality will exceed thresholds that trigger local air quality advisories (e.g., an event), their analysis scale is their region of responsibility, they are interested in short-term (≈24 h) guidance, missing an event is worse than issuing a false alarm, and there are two types of users—one that takes the forecast at face value, and the other that uses it as one of several information sources. Guided by these findings, model performance of Environment and Climate Change Canada’s current operational WFAQM (FireWork) was assessed over western Canada during three (2016–18) summer (May–September) wildfire seasons. Evaluation was performed at the geographic scale at which individual forecasts are issued (the forecast region) using gridded particulate matter 2.5 (PM2.5) fields developed from a machine learning–based downscaling of satellite and meteorological data. For the “at face value” user group, model performance was measured using the Peirce skill score. For the “as information source” user group, model performance was measured using the divergence skill score. For this metric, forecasts were first converted to event probabilities using binomial regression. We find when forecasts are taken at face value, FireWork cannot outperform a nearest-neighbor-based persistence model. However, when forecasts are considered as an information source, FireWork is superior to the persistence-based model.
Abstract
To examine the utility of smartphone pressure observations (SPOs), a climatology of mesoscale pressure features was developed to evaluate whether SPOs could better resolve mesoscale phenomena than existing surface pressure networks (MADIS). A comparison between MADIS and smartphone pressure analyses was performed by tracking and characterizing bandpass-filtered, mesoscale pressure features. Over the year 2018, nearly 3000 pressure features were tracked across the central and eastern United States. Pressure features identified by smartphone observations lasted, on average, 25 min longer, traveled 25 km farther, and exhibited larger amplitudes than features observed by MADIS. An examination of smartphone pressure features tracks by season and location found that almost all pressure features propagated eastward. With over 87% of observed pressure features associated with convection, the climatology of surface pressure features largely reflects the geographic and seasonal variation of mesoscale convection. Phase relationships between pressure features and other surface variables were consistent with those expected for mesohighs and wake lows. These results suggest that SPOs could enhance convective analyses and forecasts compared to existing surface networks like MADIS by better resolving mesoscale structures and features, such as wake lows and mesohighs.
Significance Statement
While smartphone pressure networks provide unprecedented observation coverage and density, it was unclear whether they can add value to existing surface pressure networks. This study addresses this question by developing a yearlong record of mesoscale pressure features over the eastern and central United States. Analysis of this record revealed that smartphone analyses better resolved mesoscale pressure features, especially across the central United States where existing surface pressure networks are sparser. Nearly all observed pressure features were observed near precipitation, with five in six associated with convection. Relationships between mesoscale pressure features and other surface state variables were consistent with those expected for mesohighs and wake lows.
Abstract
To examine the utility of smartphone pressure observations (SPOs), a climatology of mesoscale pressure features was developed to evaluate whether SPOs could better resolve mesoscale phenomena than existing surface pressure networks (MADIS). A comparison between MADIS and smartphone pressure analyses was performed by tracking and characterizing bandpass-filtered, mesoscale pressure features. Over the year 2018, nearly 3000 pressure features were tracked across the central and eastern United States. Pressure features identified by smartphone observations lasted, on average, 25 min longer, traveled 25 km farther, and exhibited larger amplitudes than features observed by MADIS. An examination of smartphone pressure features tracks by season and location found that almost all pressure features propagated eastward. With over 87% of observed pressure features associated with convection, the climatology of surface pressure features largely reflects the geographic and seasonal variation of mesoscale convection. Phase relationships between pressure features and other surface variables were consistent with those expected for mesohighs and wake lows. These results suggest that SPOs could enhance convective analyses and forecasts compared to existing surface networks like MADIS by better resolving mesoscale structures and features, such as wake lows and mesohighs.
Significance Statement
While smartphone pressure networks provide unprecedented observation coverage and density, it was unclear whether they can add value to existing surface pressure networks. This study addresses this question by developing a yearlong record of mesoscale pressure features over the eastern and central United States. Analysis of this record revealed that smartphone analyses better resolved mesoscale pressure features, especially across the central United States where existing surface pressure networks are sparser. Nearly all observed pressure features were observed near precipitation, with five in six associated with convection. Relationships between mesoscale pressure features and other surface state variables were consistent with those expected for mesohighs and wake lows.
Abstract
The importance of discriminating between environments supportive of supercell thunderstorms and those that are not supportive is widely recognized due to significant hazards associated with supercell storms. Previous research has led to forecast indices such as the energy helicity index and the supercell composite parameter to aid supercell forecasts. In this study three machine learning models are developed to identify environments supportive of supercells: a support vector machine, an artificial neural network, and an ensemble of gradient boosted trees. These models are trained and tested using a sample of over 1000 Rapid Update Cycle version 2 (RUC-2) model soundings from near-storm environments of both supercell and nonsupercell storms. Results show that all three machine learning models outperform classifications using either the energy helicity index or supercell composite parameter by a statistically significant margin. Using several model interpretability methods, it is concluded that generally speaking the relationships learned by the machine learning models are physically reasonable. These findings further illustrate the potential utility of machine learning–based forecast tools for severe storm forecasting.
Significance Statement
Supercell thunderstorms are a type of thunderstorm that are important to forecast because they produce more tornadoes, hail, and wind gusts compared to other types of thunderstorms. This study uses machine learning to create models that predict if a supercell thunderstorm or nonsupercell thunderstorm is favored for a given environment. These models outperform current methods of assessing if a storm that forms will be a supercell. Using these models as guidance forecasters can better understand and predict if atmospheric conditions are favorable for the development of supercell thunderstorms. Improving forecasts of supercell thunderstorms using machine learning methods like those used in this study has the potential to limit the economic and societal impacts of these storms.
Abstract
The importance of discriminating between environments supportive of supercell thunderstorms and those that are not supportive is widely recognized due to significant hazards associated with supercell storms. Previous research has led to forecast indices such as the energy helicity index and the supercell composite parameter to aid supercell forecasts. In this study three machine learning models are developed to identify environments supportive of supercells: a support vector machine, an artificial neural network, and an ensemble of gradient boosted trees. These models are trained and tested using a sample of over 1000 Rapid Update Cycle version 2 (RUC-2) model soundings from near-storm environments of both supercell and nonsupercell storms. Results show that all three machine learning models outperform classifications using either the energy helicity index or supercell composite parameter by a statistically significant margin. Using several model interpretability methods, it is concluded that generally speaking the relationships learned by the machine learning models are physically reasonable. These findings further illustrate the potential utility of machine learning–based forecast tools for severe storm forecasting.
Significance Statement
Supercell thunderstorms are a type of thunderstorm that are important to forecast because they produce more tornadoes, hail, and wind gusts compared to other types of thunderstorms. This study uses machine learning to create models that predict if a supercell thunderstorm or nonsupercell thunderstorm is favored for a given environment. These models outperform current methods of assessing if a storm that forms will be a supercell. Using these models as guidance forecasters can better understand and predict if atmospheric conditions are favorable for the development of supercell thunderstorms. Improving forecasts of supercell thunderstorms using machine learning methods like those used in this study has the potential to limit the economic and societal impacts of these storms.
Abstract
Wind gusts, and in particular intense gusts, are societally relevant but extremely challenging to forecast. This study systematically assesses the skill enhancement that can be achieved using artificial neural networks (ANNs) for forecasting of wind gust occurrence and magnitude. Geophysical predictors from the ERA5 reanalysis are used in conjunction with an autoregressive term in regression and ANN models with different predictors, and varying model complexity. Models are derived and assessed for the warm (April–September) and cold (October–March) seasons for three high passenger volume airports in the United States. Model uncertainty is assessed by deriving models for 1000 different randomly selected training (70%) and testing (30%) subsets. Gust prediction fidelity in independent test samples is critically dependent on inclusion of an autoregressive term. Gust occurrence probabilities derived using five-layer ANNs exhibit consistently higher fidelity than those from regression models and shallower ANNs. Inclusion of the autoregressive term and increasing the number of hidden layers in ANNs from 1 to 5 also improve the model performance for gust magnitudes (lower RMSE, increased correlation, and model standard deviations that more closely approximate observed values). Deeper ANNs (e.g., 20 hidden layers) exhibit higher skill in forecasting strong (17–25.7 m s−1) and damaging (≥25.7 m s−1) wind gusts. However, such deep networks exhibit evidence of overfitting and still substantially underestimate (by 50%) the frequency of strong and damaging wind gusts at the three airports considered herein.
Significance Statement
Improved short-term forecasting of wind gusts will enhance aviation safety and logistics and may offer other societal benefits. Here we present a rigorous investigation of the relative skill of models of wind gust occurrence and magnitude that employ different statistical methods. It is shown that artificial neural networks (ANNs) offer considerable skill enhancement over regression methods, particularly for strong and damaging wind gusts. For wind gust magnitudes in particular, application of deeper learning networks (e.g., five or more hidden layers) offers tangible improvements in forecast accuracy. However, deeper networks are vulnerable to overfitting and exhibit substantial variability with the specific training and testing data subset used. Also, even deep ANNs reproduce only half of strong and damaging wind gusts. These results indicate the need for future work to elucidate the dynamical mechanisms of intense wind gusts and advance solutions to their prediction.
Abstract
Wind gusts, and in particular intense gusts, are societally relevant but extremely challenging to forecast. This study systematically assesses the skill enhancement that can be achieved using artificial neural networks (ANNs) for forecasting of wind gust occurrence and magnitude. Geophysical predictors from the ERA5 reanalysis are used in conjunction with an autoregressive term in regression and ANN models with different predictors, and varying model complexity. Models are derived and assessed for the warm (April–September) and cold (October–March) seasons for three high passenger volume airports in the United States. Model uncertainty is assessed by deriving models for 1000 different randomly selected training (70%) and testing (30%) subsets. Gust prediction fidelity in independent test samples is critically dependent on inclusion of an autoregressive term. Gust occurrence probabilities derived using five-layer ANNs exhibit consistently higher fidelity than those from regression models and shallower ANNs. Inclusion of the autoregressive term and increasing the number of hidden layers in ANNs from 1 to 5 also improve the model performance for gust magnitudes (lower RMSE, increased correlation, and model standard deviations that more closely approximate observed values). Deeper ANNs (e.g., 20 hidden layers) exhibit higher skill in forecasting strong (17–25.7 m s−1) and damaging (≥25.7 m s−1) wind gusts. However, such deep networks exhibit evidence of overfitting and still substantially underestimate (by 50%) the frequency of strong and damaging wind gusts at the three airports considered herein.
Significance Statement
Improved short-term forecasting of wind gusts will enhance aviation safety and logistics and may offer other societal benefits. Here we present a rigorous investigation of the relative skill of models of wind gust occurrence and magnitude that employ different statistical methods. It is shown that artificial neural networks (ANNs) offer considerable skill enhancement over regression methods, particularly for strong and damaging wind gusts. For wind gust magnitudes in particular, application of deeper learning networks (e.g., five or more hidden layers) offers tangible improvements in forecast accuracy. However, deeper networks are vulnerable to overfitting and exhibit substantial variability with the specific training and testing data subset used. Also, even deep ANNs reproduce only half of strong and damaging wind gusts. These results indicate the need for future work to elucidate the dynamical mechanisms of intense wind gusts and advance solutions to their prediction.
Abstract
The evaluation and usefulness of lightning prediction for the Indian subcontinent are demonstrated. Implementation of the lightning parameterizations based on storm parameters, in the Weather Research and Forecasting (WRF) Model, with different microphysics schemes are carried out. With the availability of observed lightning measurements over Maharashtra from the lightning detection network (LDN), lightning cases have been identified during the pre-monsoon season of 2016–18. Lightning parameterization based on cloud top height defined by a reflectivity threshold factor of 20 dBZ is chosen. Initial analysis is carried out for 16 lightning events with four microphysical schemes for the usefulness in lightning prediction. Objective analysis is carried out and quantitative model performance (skill scores) is assessed based on observed data. The skills are evaluated for 10- and 50-km2 boxes from the 1-km domain. There is good POD of 0.86, 0.82, 0.85, and 0.84, and false alarm ratio (FAR) of 0.28, 0.25, 0.29, and 0.26 from WSM6, Thompson, Morrison, and WDM6, respectively. There is an overestimation in lightning flash with a spatial and temporal shift. The fractional skill score is evaluated as a function of spatial scale with neighborhoods from 25 to 250 km. These high skill scores and high degree of correlation between observations and model simulation gives us confidence to use the system for real-time operational forecast over India. The skill for 2019 and 2020 pre-monsoon are calculated to address the predictability of operational lightning prediction over India.
Significance Statement
A high-resolution model, namely, the Weather Research and Forecasting (WRF) Model, with multiple microphysics parameterization schemes and lightning parameterization is used here. The objective analysis is carried out for the lightning cases over India and the quantitative performance is assessed. The results highlight that there is fairly good probability of detection (POD) of 0.86, 0.82, 0.85, and 0.84 and false alarm ratio (FAR) of 0.28, 0.25, 0.29, and 0.26 from four different microphysical schemes (WSM6, Thompson, Morrison, and WDM6, respectively). These high skill scores and high degree of correlation between observations and model simulation gives us confidence to use the system for real-time operational forecast. The validation of lightning forecast system deployed over India for five pre-monsoon months in real time is carried out, which gives POD of 0.90, FAR of 0.64, hit rate of 0.57, and POFD of 0.50 for the whole Indian region.
Abstract
The evaluation and usefulness of lightning prediction for the Indian subcontinent are demonstrated. Implementation of the lightning parameterizations based on storm parameters, in the Weather Research and Forecasting (WRF) Model, with different microphysics schemes are carried out. With the availability of observed lightning measurements over Maharashtra from the lightning detection network (LDN), lightning cases have been identified during the pre-monsoon season of 2016–18. Lightning parameterization based on cloud top height defined by a reflectivity threshold factor of 20 dBZ is chosen. Initial analysis is carried out for 16 lightning events with four microphysical schemes for the usefulness in lightning prediction. Objective analysis is carried out and quantitative model performance (skill scores) is assessed based on observed data. The skills are evaluated for 10- and 50-km2 boxes from the 1-km domain. There is good POD of 0.86, 0.82, 0.85, and 0.84, and false alarm ratio (FAR) of 0.28, 0.25, 0.29, and 0.26 from WSM6, Thompson, Morrison, and WDM6, respectively. There is an overestimation in lightning flash with a spatial and temporal shift. The fractional skill score is evaluated as a function of spatial scale with neighborhoods from 25 to 250 km. These high skill scores and high degree of correlation between observations and model simulation gives us confidence to use the system for real-time operational forecast over India. The skill for 2019 and 2020 pre-monsoon are calculated to address the predictability of operational lightning prediction over India.
Significance Statement
A high-resolution model, namely, the Weather Research and Forecasting (WRF) Model, with multiple microphysics parameterization schemes and lightning parameterization is used here. The objective analysis is carried out for the lightning cases over India and the quantitative performance is assessed. The results highlight that there is fairly good probability of detection (POD) of 0.86, 0.82, 0.85, and 0.84 and false alarm ratio (FAR) of 0.28, 0.25, 0.29, and 0.26 from four different microphysical schemes (WSM6, Thompson, Morrison, and WDM6, respectively). These high skill scores and high degree of correlation between observations and model simulation gives us confidence to use the system for real-time operational forecast. The validation of lightning forecast system deployed over India for five pre-monsoon months in real time is carried out, which gives POD of 0.90, FAR of 0.64, hit rate of 0.57, and POFD of 0.50 for the whole Indian region.
Abstract
This study explored how forecasters can best use the two main forms of operational convection-allowing model guidance: the High-Resolution Ensemble Forecast (HREF) system and the hourly High-Resolution Rapid Refresh (HRRR). The former represents a wider range of possible outcomes, but the latter updates much more frequently and incorporates newer observations. HREF and time-lagged High-Resolution Rapid Refresh (HRRR-TL) probabilistic forecasts of reflectivity and updraft helicity, as well as two methods of combining HREF and HRRR into hourly updating blended guidance, were evaluated for the 2021 Spring Forecasting Experiment (SFE) period. In both objective skill and the subjective ratings of SFE participants, the 1200 UTC HREF proved difficult to outperform over this sample of events, even when incorporating HRRR initializations as late as 1800 UTC. It was usually better to use either of the experimental blending techniques than to simply discard the older HREF in favor of newer HRRR solutions. The greater model diversity and dispersion of solutions within the HREF is likely primarily responsible for this result. A possible bias in diurnal convection initiation timing and coverage in the newly upgraded HRRRv4 was also investigated, including on subdomains targeted to weakly forced diurnal initiation, and was found to have little or no systematic effect on HRRRv4’s operational utility.
Abstract
This study explored how forecasters can best use the two main forms of operational convection-allowing model guidance: the High-Resolution Ensemble Forecast (HREF) system and the hourly High-Resolution Rapid Refresh (HRRR). The former represents a wider range of possible outcomes, but the latter updates much more frequently and incorporates newer observations. HREF and time-lagged High-Resolution Rapid Refresh (HRRR-TL) probabilistic forecasts of reflectivity and updraft helicity, as well as two methods of combining HREF and HRRR into hourly updating blended guidance, were evaluated for the 2021 Spring Forecasting Experiment (SFE) period. In both objective skill and the subjective ratings of SFE participants, the 1200 UTC HREF proved difficult to outperform over this sample of events, even when incorporating HRRR initializations as late as 1800 UTC. It was usually better to use either of the experimental blending techniques than to simply discard the older HREF in favor of newer HRRR solutions. The greater model diversity and dispersion of solutions within the HREF is likely primarily responsible for this result. A possible bias in diurnal convection initiation timing and coverage in the newly upgraded HRRRv4 was also investigated, including on subdomains targeted to weakly forced diurnal initiation, and was found to have little or no systematic effect on HRRRv4’s operational utility.
