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Abstract
A deep learning convolutional neural network model is used to explore the possibilities of estimating tropical cyclone (TC) intensity from satellite images in the 37- and 85–92-GHz bands. The model, called “DeepMicroNet,” has unique properties such as a probabilistic output, the ability to operate from partial scans, and resiliency to imprecise TC center fixes. The 85–92-GHz band is the more influential data source in the model, with 37 GHz adding a marginal benefit. Training the model on global best track intensities produces model estimates precise enough to replicate known best track intensity biases when compared to aircraft reconnaissance observations. Model root-mean-square error (RMSE) is 14.3 kt (1 kt ≈ 0.5144 m s−1) compared to two years of independent best track records, but this improves to an RMSE of 10.6 kt when compared to the higher-standard aircraft reconnaissance-aided best track dataset, and to 9.6 kt compared to the reconnaissance-aided best track when using the higher-resolution TRMM TMI and Aqua AMSR-E microwave observations only. A shortage of training and independent testing data for category 5 TCs leaves the results at this intensity range inconclusive. Based on this initial study, the application of deep learning to TC intensity analysis holds tremendous promise for further development with more advanced methodologies and expanded training datasets.
Abstract
A deep learning convolutional neural network model is used to explore the possibilities of estimating tropical cyclone (TC) intensity from satellite images in the 37- and 85–92-GHz bands. The model, called “DeepMicroNet,” has unique properties such as a probabilistic output, the ability to operate from partial scans, and resiliency to imprecise TC center fixes. The 85–92-GHz band is the more influential data source in the model, with 37 GHz adding a marginal benefit. Training the model on global best track intensities produces model estimates precise enough to replicate known best track intensity biases when compared to aircraft reconnaissance observations. Model root-mean-square error (RMSE) is 14.3 kt (1 kt ≈ 0.5144 m s−1) compared to two years of independent best track records, but this improves to an RMSE of 10.6 kt when compared to the higher-standard aircraft reconnaissance-aided best track dataset, and to 9.6 kt compared to the reconnaissance-aided best track when using the higher-resolution TRMM TMI and Aqua AMSR-E microwave observations only. A shortage of training and independent testing data for category 5 TCs leaves the results at this intensity range inconclusive. Based on this initial study, the application of deep learning to TC intensity analysis holds tremendous promise for further development with more advanced methodologies and expanded training datasets.
As part of the American Meteorological Society's 30th Conference on Hurricanes and Tropical Meteorology in Ponte Vedra Beach, Florida, in April 2012, an academic lineage (“family tree”) of that community was presented to document the history of contributors to the field on the anniversary. For every self-identified or colleague-identified tropical meteorology scientist, the year of the person's most senior degree, major professor or mentors of that degree, and institution of that degree were documented and graphically presented. This information was supplemented through mining of websites, libraries, news and journal articles, obituaries, and other various historical archives. This manuscript documents the genesis of the family tree, the overall history represented by it, some statistics represented by the current incarnation, colorful personal stories that have come forward during its development, and plans for its expansion to the broader meteorology community.
As part of the American Meteorological Society's 30th Conference on Hurricanes and Tropical Meteorology in Ponte Vedra Beach, Florida, in April 2012, an academic lineage (“family tree”) of that community was presented to document the history of contributors to the field on the anniversary. For every self-identified or colleague-identified tropical meteorology scientist, the year of the person's most senior degree, major professor or mentors of that degree, and institution of that degree were documented and graphically presented. This information was supplemented through mining of websites, libraries, news and journal articles, obituaries, and other various historical archives. This manuscript documents the genesis of the family tree, the overall history represented by it, some statistics represented by the current incarnation, colorful personal stories that have come forward during its development, and plans for its expansion to the broader meteorology community.
Abstract
A satellite-based investigation is performed of a class of tropical cyclones (TCs) that unexpectedly undergo rapid intensification (RI) in moderate vertical wind shear between 5 and 10 m s−1 calculated as 200–850-hPa shear. This study makes use of both infrared (IR; 11 μm) and water vapor (WV; 6.5 μm) geostationary satellite data, the Statistical Hurricane Prediction Intensity System (SHIPS), and model reanalyses to highlight commonalities of the six TCs. The commonalities serve as predictive guides for forecasters and common features that can be used to constrain and verify idealized modeling studies. Each of the TCs exhibits a convective cloud structure that is identified as a tilt-modulated convective asymmetry (TCA). These TCAs share similar shapes, upshear-relative positions, and IR cloud-top temperatures (below −70°C). They pulse over the core of the TC with a periodicity of between 4 and 8 h. Using WV satellite imagery, two additional features identified are asymmetric warming/drying upshear of the TC relative to downshear, as well as radially thin arc-shaped clouds on the upshear side. The WV brightness temperatures of these arcs are between −40° and −60°C. All of the TCs are sheared by upper-level anticyclones, which limits the strongest environmental winds to near the tropopause.
Abstract
A satellite-based investigation is performed of a class of tropical cyclones (TCs) that unexpectedly undergo rapid intensification (RI) in moderate vertical wind shear between 5 and 10 m s−1 calculated as 200–850-hPa shear. This study makes use of both infrared (IR; 11 μm) and water vapor (WV; 6.5 μm) geostationary satellite data, the Statistical Hurricane Prediction Intensity System (SHIPS), and model reanalyses to highlight commonalities of the six TCs. The commonalities serve as predictive guides for forecasters and common features that can be used to constrain and verify idealized modeling studies. Each of the TCs exhibits a convective cloud structure that is identified as a tilt-modulated convective asymmetry (TCA). These TCAs share similar shapes, upshear-relative positions, and IR cloud-top temperatures (below −70°C). They pulse over the core of the TC with a periodicity of between 4 and 8 h. Using WV satellite imagery, two additional features identified are asymmetric warming/drying upshear of the TC relative to downshear, as well as radially thin arc-shaped clouds on the upshear side. The WV brightness temperatures of these arcs are between −40° and −60°C. All of the TCs are sheared by upper-level anticyclones, which limits the strongest environmental winds to near the tropopause.
Abstract
The National Hurricane Center (NHC) has stated that guidance on tropical cyclone (TC) genesis is an operational forecast improvement need, particularly since numerical weather prediction models produce TC-like features and operationally required forecast lead times recently have increased. Using previously defined criteria for TC genesis in global models, this study bias corrects TC genesis forecasts from global models using multiple logistic regression. The derived regression equations provide 48- and 120-h probabilistic genesis forecasts for each TC genesis event that occurs in the Environment Canada Global Environmental Multiscale Model (CMC), the NCEP Global Forecast System (GFS), and the Met Office's global model (UKMET). Results show select global model output variables are good discriminators between successful and unsuccessful TC genesis forecasts. Independent verification of the regression-based probabilistic genesis forecasts during 2014 and 2015 are presented. Brier scores and reliability diagrams indicate that the forecasts generally are well calibrated and can be used as guidance for NHC’s Tropical Weather Outlook product. The regression-based TC genesis forecasts are available in real time online.
Abstract
The National Hurricane Center (NHC) has stated that guidance on tropical cyclone (TC) genesis is an operational forecast improvement need, particularly since numerical weather prediction models produce TC-like features and operationally required forecast lead times recently have increased. Using previously defined criteria for TC genesis in global models, this study bias corrects TC genesis forecasts from global models using multiple logistic regression. The derived regression equations provide 48- and 120-h probabilistic genesis forecasts for each TC genesis event that occurs in the Environment Canada Global Environmental Multiscale Model (CMC), the NCEP Global Forecast System (GFS), and the Met Office's global model (UKMET). Results show select global model output variables are good discriminators between successful and unsuccessful TC genesis forecasts. Independent verification of the regression-based probabilistic genesis forecasts during 2014 and 2015 are presented. Brier scores and reliability diagrams indicate that the forecasts generally are well calibrated and can be used as guidance for NHC’s Tropical Weather Outlook product. The regression-based TC genesis forecasts are available in real time online.
Abstract
We investigate a class of tropical cyclones (TCs) that undergo rapid intensification (RI) in moderate vertical wind shear through analysis of a series of idealized model simulations. Two key findings derived from observational analysis are that the average 200–850-hPa shear value is 7.5 m s−1 and that the TCs displayed coherent cloud structures, deemed tilt-modulated convective asymmetries (TCA), which feature pulses of deep convection with periods of between 4 and 8 h. Additionally, all of the TCs are embedded in an environment that is characterized by shear associated with anticyclones, a factor that limits depth of the strongest environmental winds in the vertical. The idealized TC develops in the presence of relatively shallow environmental wind shear of an anticyclone. An analysis of the TC tilt in the vertical demonstrates that the source of the observed 4–8-h periodicity of the TCAs can be explained by smaller-scale nutations of the tilt on the longer, slower upshear precession. When the environmental wind shear occurs over a deeper layer similar to that of a trough, the TC does not develop. The TCAs are characterized as collections of updrafts that are buoyant throughout the depth of the TC since they rise into a cold anomaly caused by the tilting vortex. At 90 h into the simulation, RI occurs, and the tilt nutations (and hence the TCAs) cease to occur.
Abstract
We investigate a class of tropical cyclones (TCs) that undergo rapid intensification (RI) in moderate vertical wind shear through analysis of a series of idealized model simulations. Two key findings derived from observational analysis are that the average 200–850-hPa shear value is 7.5 m s−1 and that the TCs displayed coherent cloud structures, deemed tilt-modulated convective asymmetries (TCA), which feature pulses of deep convection with periods of between 4 and 8 h. Additionally, all of the TCs are embedded in an environment that is characterized by shear associated with anticyclones, a factor that limits depth of the strongest environmental winds in the vertical. The idealized TC develops in the presence of relatively shallow environmental wind shear of an anticyclone. An analysis of the TC tilt in the vertical demonstrates that the source of the observed 4–8-h periodicity of the TCAs can be explained by smaller-scale nutations of the tilt on the longer, slower upshear precession. When the environmental wind shear occurs over a deeper layer similar to that of a trough, the TC does not develop. The TCAs are characterized as collections of updrafts that are buoyant throughout the depth of the TC since they rise into a cold anomaly caused by the tilting vortex. At 90 h into the simulation, RI occurs, and the tilt nutations (and hence the TCAs) cease to occur.
Abstract
The U.S. Navy is keenly interested in analyses and predictions of waves at sea due to their effects on important tasks such as shipping, base preparedness, and disaster relief. U.S. Tropical Cyclone (TC) Forecast Centers routinely disseminate wind probabilities consistent with official TC forecasts worldwide, but do not do the same for wave forecasts. These probabilities are especially important at longer leads where TC forecast accuracy diminishes. This work describes global wave probabilities consistent with both the official TC forecasts and their wind probabilities. Real-time runs for 84 TCs between May 2018 and March 2019, with probabilities generated for 12- and 18-ft significant wave heights are used to calculate verification statistics. This results in 347, 319, 261, 214, 155, and 112 verification cases at lead times of 1, 2, 3, 4, and 5 days where each verification case consists of a 20° × 20° latitude–longitude grid around the verifying TC position. When compared with wave probabilities generated solely by a global numerical weather prediction model, the wind probability–based algorithm demonstrates improved consistency with official forecasts and provides additional benefits. Those benefits include an improved capability to discriminate between 12- and 18-ft significant wave events and nonevents. The verification statistics also shows that the wind probability–based algorithm has a consistent high bias. How these biases can be reduced in future efforts is also discussed.
Abstract
The U.S. Navy is keenly interested in analyses and predictions of waves at sea due to their effects on important tasks such as shipping, base preparedness, and disaster relief. U.S. Tropical Cyclone (TC) Forecast Centers routinely disseminate wind probabilities consistent with official TC forecasts worldwide, but do not do the same for wave forecasts. These probabilities are especially important at longer leads where TC forecast accuracy diminishes. This work describes global wave probabilities consistent with both the official TC forecasts and their wind probabilities. Real-time runs for 84 TCs between May 2018 and March 2019, with probabilities generated for 12- and 18-ft significant wave heights are used to calculate verification statistics. This results in 347, 319, 261, 214, 155, and 112 verification cases at lead times of 1, 2, 3, 4, and 5 days where each verification case consists of a 20° × 20° latitude–longitude grid around the verifying TC position. When compared with wave probabilities generated solely by a global numerical weather prediction model, the wind probability–based algorithm demonstrates improved consistency with official forecasts and provides additional benefits. Those benefits include an improved capability to discriminate between 12- and 18-ft significant wave events and nonevents. The verification statistics also shows that the wind probability–based algorithm has a consistent high bias. How these biases can be reduced in future efforts is also discussed.
Abstract
While there are a variety of modes for tropical cyclone (TC) development, there have been relatively few efforts to systematically catalog both nondeveloping and developing cases. This paper introduces an operationally derived climatology of tropical disturbances that were analyzed using the Dvorak technique at the National Hurricane Center (NHC) and the Central Pacific Hurricane Center from 2001 to 2011. Using these Dvorak intensity estimates, the likelihood of genesis is calculated as a historical baseline for TC prediction. Despite the limited period of record, the climatology of Dvorak analyses of incipient tropical systems has a spatial distribution that compares well with previous climatologies. The North Atlantic basin shows substantial regional variability in Dvorak classification frequency. In contrast, tropical disturbances in the combined eastern and central North Pacific basins (which split at 125°W into an eastern region and a central region) have a single broad frequency maximum and limited meridional extent. When applied to forecasting, several important features are discovered. Dvorak fixes are sometimes unavailable for disturbances that develop into TCs, especially at longer lead times. However, when probabilities of genesis are calculated by a Dvorak current intensity (CI) number, the likelihood stratifies well by basin and intensity. Tropical disturbances that are analyzed as being stronger (a higher Dvorak CI number) achieve genesis more often. Further, all else being equal, genesis rates are highest in the eastern Pacific, followed by the Atlantic. Out-of-sample verification of predictive skill shows comparable results to that of the NHC, with potential to inform forecasts and provide the first disturbance-centric baseline for tropical cyclogenesis potential.
Abstract
While there are a variety of modes for tropical cyclone (TC) development, there have been relatively few efforts to systematically catalog both nondeveloping and developing cases. This paper introduces an operationally derived climatology of tropical disturbances that were analyzed using the Dvorak technique at the National Hurricane Center (NHC) and the Central Pacific Hurricane Center from 2001 to 2011. Using these Dvorak intensity estimates, the likelihood of genesis is calculated as a historical baseline for TC prediction. Despite the limited period of record, the climatology of Dvorak analyses of incipient tropical systems has a spatial distribution that compares well with previous climatologies. The North Atlantic basin shows substantial regional variability in Dvorak classification frequency. In contrast, tropical disturbances in the combined eastern and central North Pacific basins (which split at 125°W into an eastern region and a central region) have a single broad frequency maximum and limited meridional extent. When applied to forecasting, several important features are discovered. Dvorak fixes are sometimes unavailable for disturbances that develop into TCs, especially at longer lead times. However, when probabilities of genesis are calculated by a Dvorak current intensity (CI) number, the likelihood stratifies well by basin and intensity. Tropical disturbances that are analyzed as being stronger (a higher Dvorak CI number) achieve genesis more often. Further, all else being equal, genesis rates are highest in the eastern Pacific, followed by the Atlantic. Out-of-sample verification of predictive skill shows comparable results to that of the NHC, with potential to inform forecasts and provide the first disturbance-centric baseline for tropical cyclogenesis potential.
Abstract
Accurately forecasting tropical cyclone (TC) genesis is an important operational need, especially since the National Hurricane Center’s Tropical Weather Outlook product has been extended from 2 to 5 days. A previous study by the coauthors verified North Atlantic TC genesis forecasts from five global models out to 4 days during 2004–11. This study expands on the previous research by 1) verifying TC genesis forecasts over both the Atlantic and eastern North Pacific basins, 2) extending the forecast window to 5 days, and 3) updating the analysis period through 2014. Verification statistics are presented and compared between the two basins. Probability of detection and critical success indices generally are greater over the eastern North Pacific basin compared to the North Atlantic. There is a trade-off between models that exhibit a greater probability of detection and a greater false alarm ratio, and models that exhibit a smaller false alarm ratio and a smaller probability of detection. Results also reveal that the models preferentially miss TCs over the North Atlantic (eastern North Pacific) that have a relatively small radius of the outer closed isobar (radius of maximum wind) at the forecast genesis time. Overall, global models have become a more reliable source of TC genesis guidance during the past few years compared to the early years in the dataset.
Abstract
Accurately forecasting tropical cyclone (TC) genesis is an important operational need, especially since the National Hurricane Center’s Tropical Weather Outlook product has been extended from 2 to 5 days. A previous study by the coauthors verified North Atlantic TC genesis forecasts from five global models out to 4 days during 2004–11. This study expands on the previous research by 1) verifying TC genesis forecasts over both the Atlantic and eastern North Pacific basins, 2) extending the forecast window to 5 days, and 3) updating the analysis period through 2014. Verification statistics are presented and compared between the two basins. Probability of detection and critical success indices generally are greater over the eastern North Pacific basin compared to the North Atlantic. There is a trade-off between models that exhibit a greater probability of detection and a greater false alarm ratio, and models that exhibit a smaller false alarm ratio and a smaller probability of detection. Results also reveal that the models preferentially miss TCs over the North Atlantic (eastern North Pacific) that have a relatively small radius of the outer closed isobar (radius of maximum wind) at the forecast genesis time. Overall, global models have become a more reliable source of TC genesis guidance during the past few years compared to the early years in the dataset.
Abstract
Tropical cyclone (TC) forecasts rely heavily on output from global numerical models. While considerable research has investigated the skill of various models with respect to track and intensity, few studies have considered how well global models forecast TC genesis in the North Atlantic basin. This paper analyzes TC genesis forecasts from five global models [Environment Canada's Global Environment Multiscale Model (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF) global model, the Global Forecast System (GFS), the Navy Operational Global Atmospheric Prediction System (NOGAPS), and the Met Office global model (UKMET)] over several seasons in the North Atlantic basin. Identifying TCs in the model is based on a combination of methods used previously in the literature and newly defined objective criteria. All model-indicated TCs are classified as a hit, false alarm, early genesis, or late genesis event. Missed events also are considered. Results show that the models' ability to predict TC genesis varies in time and space. Conditional probabilities when a model predicts genesis and more traditional performance metrics (e.g., critical success index) are calculated. The models are ranked among each other, and results show that the best-performing model varies from year to year. A spatial analysis of each model identifies preferred regions for genesis, and a temporal analysis indicates that model performance expectedly decreases as forecast hour (lead time) increases. Consensus forecasts show that the probability of genesis noticeably increases when multiple models predict the same genesis event. Overall, this study provides a climatology of objectively identified TC genesis forecasts in global models. The resulting verification statistics can be used operationally to help refine deterministic and probabilistic TC genesis forecasts and potentially improve the models examined.
Abstract
Tropical cyclone (TC) forecasts rely heavily on output from global numerical models. While considerable research has investigated the skill of various models with respect to track and intensity, few studies have considered how well global models forecast TC genesis in the North Atlantic basin. This paper analyzes TC genesis forecasts from five global models [Environment Canada's Global Environment Multiscale Model (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF) global model, the Global Forecast System (GFS), the Navy Operational Global Atmospheric Prediction System (NOGAPS), and the Met Office global model (UKMET)] over several seasons in the North Atlantic basin. Identifying TCs in the model is based on a combination of methods used previously in the literature and newly defined objective criteria. All model-indicated TCs are classified as a hit, false alarm, early genesis, or late genesis event. Missed events also are considered. Results show that the models' ability to predict TC genesis varies in time and space. Conditional probabilities when a model predicts genesis and more traditional performance metrics (e.g., critical success index) are calculated. The models are ranked among each other, and results show that the best-performing model varies from year to year. A spatial analysis of each model identifies preferred regions for genesis, and a temporal analysis indicates that model performance expectedly decreases as forecast hour (lead time) increases. Consensus forecasts show that the probability of genesis noticeably increases when multiple models predict the same genesis event. Overall, this study provides a climatology of objectively identified TC genesis forecasts in global models. The resulting verification statistics can be used operationally to help refine deterministic and probabilistic TC genesis forecasts and potentially improve the models examined.
Abstract
Interactions between the upper-level outflow of a sheared, rapidly intensifying tropical cyclone (TC) and the background environmental flow in an idealized model are presented. The most important finding is that the divergent outflow from convection localized by the tilt of the vortex serves to divert the background environmental flow around the TC, thus reducing the local vertical wind shear. We show that this effect can be understood from basic theoretical arguments related to Bernoulli flow around an obstacle. In the simulation discussed, the environmental flow diversion by the outflow is limited to 2 km below the tropopause in the 12–14-km (250–150 hPa) layer. Synthetic water vapor satellite imagery confirms the presence of upshear arcs in the cloud field, matching satellite observations. These arcs, which exist in the same layer as the outflow, are caused by slow-moving wave features and serve as visual markers of the outflow–environment interface. The blocking effect where the outflow and the environmental winds meet creates a dynamic high pressure whose pressure gradient extends nearly 1000 km upwind, thus causing the environmental winds to slow down, to converge, and to sink. We discuss these results with respect to the first part of this three-part study, and apply them to another atypical rapid intensification hurricane: Matthew (2016).
Abstract
Interactions between the upper-level outflow of a sheared, rapidly intensifying tropical cyclone (TC) and the background environmental flow in an idealized model are presented. The most important finding is that the divergent outflow from convection localized by the tilt of the vortex serves to divert the background environmental flow around the TC, thus reducing the local vertical wind shear. We show that this effect can be understood from basic theoretical arguments related to Bernoulli flow around an obstacle. In the simulation discussed, the environmental flow diversion by the outflow is limited to 2 km below the tropopause in the 12–14-km (250–150 hPa) layer. Synthetic water vapor satellite imagery confirms the presence of upshear arcs in the cloud field, matching satellite observations. These arcs, which exist in the same layer as the outflow, are caused by slow-moving wave features and serve as visual markers of the outflow–environment interface. The blocking effect where the outflow and the environmental winds meet creates a dynamic high pressure whose pressure gradient extends nearly 1000 km upwind, thus causing the environmental winds to slow down, to converge, and to sink. We discuss these results with respect to the first part of this three-part study, and apply them to another atypical rapid intensification hurricane: Matthew (2016).
