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- Author or Editor: Christopher S. Velden x
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Abstract
The evolution of upper-tropospheric thermal patterns associated with extratropical cyclone events is often not well represented by the conventional observational network, especially in marine situations. In this paper, a potential tool for qualitatively analyzing tropopause-level thermal structure and variations based on remotely sensed passive microwave data from satellites is examined. Specifically, warm anomalies associated with tropopause undulations in upper-tropospheric waves are captured in imagery from the 54.96-GHz channel of the Microwave Sounding Unit (MSU) onboard the current series of NOAA polar-orbiting satellites. Examples of this imagery during selected western North Atlantic cyclone events are presented, and the potential usefulness of these observations in analysis and forecasting is discussed.
Abstract
The evolution of upper-tropospheric thermal patterns associated with extratropical cyclone events is often not well represented by the conventional observational network, especially in marine situations. In this paper, a potential tool for qualitatively analyzing tropopause-level thermal structure and variations based on remotely sensed passive microwave data from satellites is examined. Specifically, warm anomalies associated with tropopause undulations in upper-tropospheric waves are captured in imagery from the 54.96-GHz channel of the Microwave Sounding Unit (MSU) onboard the current series of NOAA polar-orbiting satellites. Examples of this imagery during selected western North Atlantic cyclone events are presented, and the potential usefulness of these observations in analysis and forecasting is discussed.
Abstract
On 1 December 1987, an unusual midlatitude cyclone affected much of southeastern Australia. The storm was characterized by unforced rapid deepening to a near record low (locally) mean sea-level pressure, high winds, anomalously cold surface temperatures, and near-record rainfall in some areas. The storm resulted in extensive damage, including a massive livestock kill. Comparison with storm tracks over southern Australia from the past 20 years shows that the path of this storm was quite unusual for this time of year.
Utilizing a series of analyses prepared from an incremental limited area data assimilation system, it is shown that: 1) an amplifying upper-tropospheric wave influenced the initial development and path of the cyclone as it crossed the southern coast of Australia, 2) transverse circulations associated with two juxtaposed upper-level jet streaks embedded in the wave focussed upper-level divergence and midlevel ascent over the low during its rapid intensification phase, and 3) a distinct upper-tropospheric isentropic potential vorticity maximum was identified well upstream of the developing low, but with no evidence of an extrusion of this air penetrating and enhancing the low-level circulation as has been found in other cases of rapid cyclogenesis.
Given that inadequate operational numerical weather prediction (NWP) guidance was partially to blame for the underforecast of this event, the operational limited area NWP forecasts are presented and compared with forecasts based on the research analyses from the assimilation system. 11 is shown that improved forecasts of cyclone intensification and of precipitation result when the model is initialized with the assimilation analyses. Further improvements are obtained when the grid resolution of the forecast model is increased. With the operational implementation of the assimilation system into the Australian Bureau of Meteorology (BOM) in 1989, the improved guidance resulting from the assimilated analyses is currently available to forecasters in Australia.
Abstract
On 1 December 1987, an unusual midlatitude cyclone affected much of southeastern Australia. The storm was characterized by unforced rapid deepening to a near record low (locally) mean sea-level pressure, high winds, anomalously cold surface temperatures, and near-record rainfall in some areas. The storm resulted in extensive damage, including a massive livestock kill. Comparison with storm tracks over southern Australia from the past 20 years shows that the path of this storm was quite unusual for this time of year.
Utilizing a series of analyses prepared from an incremental limited area data assimilation system, it is shown that: 1) an amplifying upper-tropospheric wave influenced the initial development and path of the cyclone as it crossed the southern coast of Australia, 2) transverse circulations associated with two juxtaposed upper-level jet streaks embedded in the wave focussed upper-level divergence and midlevel ascent over the low during its rapid intensification phase, and 3) a distinct upper-tropospheric isentropic potential vorticity maximum was identified well upstream of the developing low, but with no evidence of an extrusion of this air penetrating and enhancing the low-level circulation as has been found in other cases of rapid cyclogenesis.
Given that inadequate operational numerical weather prediction (NWP) guidance was partially to blame for the underforecast of this event, the operational limited area NWP forecasts are presented and compared with forecasts based on the research analyses from the assimilation system. 11 is shown that improved forecasts of cyclone intensification and of precipitation result when the model is initialized with the assimilation analyses. Further improvements are obtained when the grid resolution of the forecast model is increased. With the operational implementation of the assimilation system into the Australian Bureau of Meteorology (BOM) in 1989, the improved guidance resulting from the assimilated analyses is currently available to forecasters in Australia.
Abstract
A simple barotropic model is employed to investigate relative impacts on tropical cyclone motion forecasts in the Australian region when wind analyses from different tropospheric levels or layers are used as the input to the model. The model is initialized with selected horizontal wind analyses from individual pressure levels, and vertical averages of several pressure levels (layer-means).
The 48-h mean forecast errors (MFE) from this model are analyzed for 300 tropical cyclone cases that cover a wide range of intensities. A significant reduction in the track forecast errors results when the depth of the vertically-averaged initial wind analysis depends upon the initial storm intensity. Mean forecast errors show that the traditionally-utilized 1000-100-hPa deep layer-mean (DLM) analysis is a good approximation of future motion only in cases of very intense tropical cyclones. Shallower, lower-tropospheric layer-means consistently outperform single-level analyses, and are best correlated with future motion in weak and moderate intensity cases.
These results suggest that barotropic track forecasting in the Australian region can be significantly improved if the depth of the vertically-averaged initial wind analysis is based upon the tropical cyclone intensity.
Abstract
A simple barotropic model is employed to investigate relative impacts on tropical cyclone motion forecasts in the Australian region when wind analyses from different tropospheric levels or layers are used as the input to the model. The model is initialized with selected horizontal wind analyses from individual pressure levels, and vertical averages of several pressure levels (layer-means).
The 48-h mean forecast errors (MFE) from this model are analyzed for 300 tropical cyclone cases that cover a wide range of intensities. A significant reduction in the track forecast errors results when the depth of the vertically-averaged initial wind analysis depends upon the initial storm intensity. Mean forecast errors show that the traditionally-utilized 1000-100-hPa deep layer-mean (DLM) analysis is a good approximation of future motion only in cases of very intense tropical cyclones. Shallower, lower-tropospheric layer-means consistently outperform single-level analyses, and are best correlated with future motion in weak and moderate intensity cases.
These results suggest that barotropic track forecasting in the Australian region can be significantly improved if the depth of the vertically-averaged initial wind analysis is based upon the tropical cyclone intensity.
ABSTRACT
A consensus-based algorithm for estimating the current intensity of global tropical cyclones (TCs) from meteorological satellites is described. The method objectively combines intensity estimates from infrared and microwave-based techniques to produce a consensus TC intensity estimate, which is more skillful than the individual members. The method, called Satellite Consensus (SATCON), can be run in near–real time and employs information sharing between member algorithms and a weighting strategy that relies on the situational precision of each member. An evaluation of the consensus algorithm’s performance in comparison with its individual members and other available operational estimates of TC intensity is presented. It is shown that SATCON can provide valuable objective intensity estimates for poststorm assessments, especially in the absence of other data such as provided by reconnaissance aircraft. It can also serve as a near-real-time estimator of TC intensity for forecasters, with the ability to quickly reconcile differences in objective intensity methods and thus decrease the uncertainty and amount of time spent on the intensity analysis. Near-real-time SATCON estimates are being provided to global operational TC forecast centers.
ABSTRACT
A consensus-based algorithm for estimating the current intensity of global tropical cyclones (TCs) from meteorological satellites is described. The method objectively combines intensity estimates from infrared and microwave-based techniques to produce a consensus TC intensity estimate, which is more skillful than the individual members. The method, called Satellite Consensus (SATCON), can be run in near–real time and employs information sharing between member algorithms and a weighting strategy that relies on the situational precision of each member. An evaluation of the consensus algorithm’s performance in comparison with its individual members and other available operational estimates of TC intensity is presented. It is shown that SATCON can provide valuable objective intensity estimates for poststorm assessments, especially in the absence of other data such as provided by reconnaissance aircraft. It can also serve as a near-real-time estimator of TC intensity for forecasters, with the ability to quickly reconcile differences in objective intensity methods and thus decrease the uncertainty and amount of time spent on the intensity analysis. Near-real-time SATCON estimates are being provided to global operational TC forecast centers.
Abstract
Tropical cyclones are becoming an increasing menace to society as populations grow in coastal regions. Forecasting the intensity of these often-temperamental weather systems can be a real challenge, especially if the true intensity at the forecast time is not well known. To address this issue, techniques to accurately estimate tropical cyclone intensity from satellites are a natural goal because in situ observations over the vast oceanic basins are scarce. The most widely utilized satellite-based method to estimate tropical cyclone intensity is the Dvorak technique, a partially subjective scheme that has been employed operationally at tropical forecast centers around the world for over 30 yr. With the recent advent of improved satellite sensors, the rapid advances in computing capacity, and accumulated experience with the behavioral characteristics of the Dvorak technique, the development of a fully automated, computer-based objective scheme to derive tropical cyclone intensity has become possible.
In this paper the advanced Dvorak technique is introduced, which, as its name implies, is a derivative of the original Dvorak technique. The advanced Dvorak technique builds on the basic conceptual model and empirically derived rules of the original Dvorak technique, but advances the science and applicability in an automated environment that does not require human intervention. The algorithm is the culmination of a body of research that includes the objective Dvorak technique (ODT) and advanced objective Dvorak technique (AODT) developed at the University of Wisconsin—Madison’s Cooperative Institute for Meteorological Satellite Studies. The ODT could only be applied to storms that possessed a minimum intensity of hurricane/typhoon strength. In addition, the ODT still required a storm center location to be manually selected by an analyst prior to algorithm execution. These issues were the primary motivations for the continued advancement of the algorithm (AODT). While these two objective schemes had as their primary goal to simply achieve the basic functionality and performance of the Dvorak technique in a computer-driven environment, the advanced Dvorak technique exceeds the boundaries of the original Dvorak technique through modifications based on rigorous statistical and empirical analysis. It is shown that the accuracy of the advanced Dvorak technique is statistically competitive with the original Dvorak technique, and can provide objective tropical cyclone intensity guidance for systems in all global basins.
Abstract
Tropical cyclones are becoming an increasing menace to society as populations grow in coastal regions. Forecasting the intensity of these often-temperamental weather systems can be a real challenge, especially if the true intensity at the forecast time is not well known. To address this issue, techniques to accurately estimate tropical cyclone intensity from satellites are a natural goal because in situ observations over the vast oceanic basins are scarce. The most widely utilized satellite-based method to estimate tropical cyclone intensity is the Dvorak technique, a partially subjective scheme that has been employed operationally at tropical forecast centers around the world for over 30 yr. With the recent advent of improved satellite sensors, the rapid advances in computing capacity, and accumulated experience with the behavioral characteristics of the Dvorak technique, the development of a fully automated, computer-based objective scheme to derive tropical cyclone intensity has become possible.
In this paper the advanced Dvorak technique is introduced, which, as its name implies, is a derivative of the original Dvorak technique. The advanced Dvorak technique builds on the basic conceptual model and empirically derived rules of the original Dvorak technique, but advances the science and applicability in an automated environment that does not require human intervention. The algorithm is the culmination of a body of research that includes the objective Dvorak technique (ODT) and advanced objective Dvorak technique (AODT) developed at the University of Wisconsin—Madison’s Cooperative Institute for Meteorological Satellite Studies. The ODT could only be applied to storms that possessed a minimum intensity of hurricane/typhoon strength. In addition, the ODT still required a storm center location to be manually selected by an analyst prior to algorithm execution. These issues were the primary motivations for the continued advancement of the algorithm (AODT). While these two objective schemes had as their primary goal to simply achieve the basic functionality and performance of the Dvorak technique in a computer-driven environment, the advanced Dvorak technique exceeds the boundaries of the original Dvorak technique through modifications based on rigorous statistical and empirical analysis. It is shown that the accuracy of the advanced Dvorak technique is statistically competitive with the original Dvorak technique, and can provide objective tropical cyclone intensity guidance for systems in all global basins.
Abstract
The advanced Dvorak technique (ADT) is used operationally by tropical cyclone forecast centers worldwide to help estimate the intensity of tropical cyclones (TCs) from operational geostationary meteorological satellites. New enhancements to the objective ADT have been implemented by the algorithm development team to further expand its capabilities and precision. The advancements include the following: 1) finer tuning to aircraft-based TC intensity estimates in an expanded development sample, 2) the incorporation of satellite-based microwave information into the intensity estimation scheme, 3) more sophisticated automated TC center-fixing routines, 4) adjustments to the intensity estimates for subtropical systems and TCs undergoing extratropical transition, and 5) addition of a surface wind radii estimation routine. The goals of these upgrades and others are to provide TC analysts/forecasters with an expanded objective guidance tool to more accurately estimate the intensity of TCs and those storms forming from, or converting into, hybrid/nontropical systems. The 2018 TC season is used to illustrate the performance characteristics of the upgraded ADT.
Abstract
The advanced Dvorak technique (ADT) is used operationally by tropical cyclone forecast centers worldwide to help estimate the intensity of tropical cyclones (TCs) from operational geostationary meteorological satellites. New enhancements to the objective ADT have been implemented by the algorithm development team to further expand its capabilities and precision. The advancements include the following: 1) finer tuning to aircraft-based TC intensity estimates in an expanded development sample, 2) the incorporation of satellite-based microwave information into the intensity estimation scheme, 3) more sophisticated automated TC center-fixing routines, 4) adjustments to the intensity estimates for subtropical systems and TCs undergoing extratropical transition, and 5) addition of a surface wind radii estimation routine. The goals of these upgrades and others are to provide TC analysts/forecasters with an expanded objective guidance tool to more accurately estimate the intensity of TCs and those storms forming from, or converting into, hybrid/nontropical systems. The 2018 TC season is used to illustrate the performance characteristics of the upgraded ADT.
Abstract
A technique to identify and quantify intense convection in tropical cyclones (TCs) using bispectral, geostationary satellite imagery is explored. This technique involves differencing the water vapor (WV) and infrared window (IRW) channel brightness temperature values, which are available on all current operational geostationary weather satellites. Both the derived IRW minus WV (IRWV) imagery and the raw data values can be used in a variety of methods to provide TC forecasters with important information about current and future intensity trends, a component within the operational TC forecasting arena that has shown little improvement during the past few decades.
In this paper several possible uses for this bispectral technique, both qualitative and quantitative, are explored and outlined. Qualitative monitoring of intense convection can be used as a proxy for passive microwave (MW) imager data obtained from polar-orbiting satellite platforms when not available. In addition, the derived imagery may aid in the TC storm center identification process, both manually and objectively, especially in difficult situations where the IRW imagery alone cannot be used such as when the storm circulation center and/or eye features are obscured by a cirrus canopy. Quantitative methods discussed involve the predictive quality of the IRWV data in terms of TC intensity changes, primarily during TC intensification. Strong correlations exist between storm intensity changes and IRWV values at varying 6-h forecast interval periods, peaking between the 12- and 24-h time periods. Implications for the use of the IRWV data on such objective satellite intensity estimate algorithms as the University of Wisconsin—Madison (UW) Cooperative Institute for Meteorological Satellite Studies (CIMSS) advanced Dvorak technique (ADT) are also discussed.
Abstract
A technique to identify and quantify intense convection in tropical cyclones (TCs) using bispectral, geostationary satellite imagery is explored. This technique involves differencing the water vapor (WV) and infrared window (IRW) channel brightness temperature values, which are available on all current operational geostationary weather satellites. Both the derived IRW minus WV (IRWV) imagery and the raw data values can be used in a variety of methods to provide TC forecasters with important information about current and future intensity trends, a component within the operational TC forecasting arena that has shown little improvement during the past few decades.
In this paper several possible uses for this bispectral technique, both qualitative and quantitative, are explored and outlined. Qualitative monitoring of intense convection can be used as a proxy for passive microwave (MW) imager data obtained from polar-orbiting satellite platforms when not available. In addition, the derived imagery may aid in the TC storm center identification process, both manually and objectively, especially in difficult situations where the IRW imagery alone cannot be used such as when the storm circulation center and/or eye features are obscured by a cirrus canopy. Quantitative methods discussed involve the predictive quality of the IRWV data in terms of TC intensity changes, primarily during TC intensification. Strong correlations exist between storm intensity changes and IRWV values at varying 6-h forecast interval periods, peaking between the 12- and 24-h time periods. Implications for the use of the IRWV data on such objective satellite intensity estimate algorithms as the University of Wisconsin—Madison (UW) Cooperative Institute for Meteorological Satellite Studies (CIMSS) advanced Dvorak technique (ADT) are also discussed.
Abstract
Vertical wind shear is well known in the tropical cyclone (TC) forecasting community as an important environmental influence on storm structure and intensity change. The traditional way to define deep-tropospheric vertical wind shear in most prior research studies, and in operational forecast applications, is to simply use the vector difference of the 200- and 850-hPa wind fields based on global model analyses. However, is this rather basic approach to approximate vertical wind shear adequate for most TC applications? In this study, the traditional approach is compared to a different methodology for generating fields of vertical wind shear as produced by the University of Wisconsin Cooperative Institute for Meteorological Satellite Studies (CIMSS). The CIMSS fields are derived with heavy analysis weight given to available high-density satellite-derived winds. The resultant isobaric analyses are then used to create two mass-weighted layer-mean wind fields, one upper and one lower tropospheric, which are then differenced to produce the deep-tropospheric vertical wind shear field. The principal novelty of this approach is that it does not rely simply on the analyzed winds at two discrete levels, but instead attempts to account for some of the variable vertical wind structure in the calculation. It will be shown how the resultant vertical wind shear fields derived by the two approaches can diverge significantly in certain situations; the results also suggest that in many cases it is superior in depicting the wind structure's impact on TCs than the simple two-level differential that serves as the common contemporary vertical wind shear approximation.
Abstract
Vertical wind shear is well known in the tropical cyclone (TC) forecasting community as an important environmental influence on storm structure and intensity change. The traditional way to define deep-tropospheric vertical wind shear in most prior research studies, and in operational forecast applications, is to simply use the vector difference of the 200- and 850-hPa wind fields based on global model analyses. However, is this rather basic approach to approximate vertical wind shear adequate for most TC applications? In this study, the traditional approach is compared to a different methodology for generating fields of vertical wind shear as produced by the University of Wisconsin Cooperative Institute for Meteorological Satellite Studies (CIMSS). The CIMSS fields are derived with heavy analysis weight given to available high-density satellite-derived winds. The resultant isobaric analyses are then used to create two mass-weighted layer-mean wind fields, one upper and one lower tropospheric, which are then differenced to produce the deep-tropospheric vertical wind shear field. The principal novelty of this approach is that it does not rely simply on the analyzed winds at two discrete levels, but instead attempts to account for some of the variable vertical wind structure in the calculation. It will be shown how the resultant vertical wind shear fields derived by the two approaches can diverge significantly in certain situations; the results also suggest that in many cases it is superior in depicting the wind structure's impact on TCs than the simple two-level differential that serves as the common contemporary vertical wind shear approximation.
Abstract
While qualitative information from meteorological satellites has long been recognized as critical for monitoring tropical cyclone activity, quantitative data are required to improve the objective analysis and numerical weather prediction of these events. In this paper, results are presented that show that the inclusion of high-density, multispectral, satellite-derived information into the analysis of tropical cyclone environmental wind fields can effectively reduce the error of objective track forecasts. Two independent analysis and barotropic track-forecast systems are utilized in order to examine the consistency of the results. Both systems yield a 10%–23% reduction in middle- to long-range track-forecast errors with the inclusion of the satellite wind observations.
Abstract
While qualitative information from meteorological satellites has long been recognized as critical for monitoring tropical cyclone activity, quantitative data are required to improve the objective analysis and numerical weather prediction of these events. In this paper, results are presented that show that the inclusion of high-density, multispectral, satellite-derived information into the analysis of tropical cyclone environmental wind fields can effectively reduce the error of objective track forecasts. Two independent analysis and barotropic track-forecast systems are utilized in order to examine the consistency of the results. Both systems yield a 10%–23% reduction in middle- to long-range track-forecast errors with the inclusion of the satellite wind observations.
Abstract
This study develops a probabilistic model based on a convolutional neural network to predict rapid intensification (RI) in both North Atlantic and eastern North Pacific tropical cyclones (TCs). Coined “I-RI,” an advantage of using a convolutional neural network to predict RI is that it is designed to learn from spatial fields, like two-dimensional satellite imagery, as well as scalar features. The resulting model RI probability output is validated against two operational RI guidances—an empirical and a deterministic method—to assess skill at predicting RI over 12-, 24-, 36-, 48-, and 72-h lead times. Results indicate that in North Atlantic TCs, AI-RI is more skillful at predicting RI over 12- and 24-h lead times compared to both operational RI guidances. In eastern North Pacific TCs, AI-RI is more skillful than the empirical operational RI guidance at most RI thresholds, but less skillful than the deterministic RI guidance at all thresholds. For TCs north of 15°N, where the deterministic skill was lower, AI-RI was more skillful than the deterministic operational guidance for over half of the RI thresholds. It is also found that AI-RI struggles to reach the higher RI probabilities produced by both of the operational RI guidances in both basins. This work demonstrates that the two-dimensional structures within the satellite imagery of TCs and the evolution of these structures identified using the difference in satellite images, captured by a convolutional neural network, yield better 12–24-h indicators of RI than existing scalar assessments of satellite brightness temperature.
Significance Statement
The purpose of this study is to develop a method to predict tropical cyclone rapid intensification using artificial intelligence. The developed model uses a convolutional neural network, which can identify features in satellite imagery that are indicative of rapid intensification. The results suggest that, compared with current operational rapid intensification models, a convolutional neural network approach is generally more skillful at predicting rapid intensification.
Abstract
This study develops a probabilistic model based on a convolutional neural network to predict rapid intensification (RI) in both North Atlantic and eastern North Pacific tropical cyclones (TCs). Coined “I-RI,” an advantage of using a convolutional neural network to predict RI is that it is designed to learn from spatial fields, like two-dimensional satellite imagery, as well as scalar features. The resulting model RI probability output is validated against two operational RI guidances—an empirical and a deterministic method—to assess skill at predicting RI over 12-, 24-, 36-, 48-, and 72-h lead times. Results indicate that in North Atlantic TCs, AI-RI is more skillful at predicting RI over 12- and 24-h lead times compared to both operational RI guidances. In eastern North Pacific TCs, AI-RI is more skillful than the empirical operational RI guidance at most RI thresholds, but less skillful than the deterministic RI guidance at all thresholds. For TCs north of 15°N, where the deterministic skill was lower, AI-RI was more skillful than the deterministic operational guidance for over half of the RI thresholds. It is also found that AI-RI struggles to reach the higher RI probabilities produced by both of the operational RI guidances in both basins. This work demonstrates that the two-dimensional structures within the satellite imagery of TCs and the evolution of these structures identified using the difference in satellite images, captured by a convolutional neural network, yield better 12–24-h indicators of RI than existing scalar assessments of satellite brightness temperature.
Significance Statement
The purpose of this study is to develop a method to predict tropical cyclone rapid intensification using artificial intelligence. The developed model uses a convolutional neural network, which can identify features in satellite imagery that are indicative of rapid intensification. The results suggest that, compared with current operational rapid intensification models, a convolutional neural network approach is generally more skillful at predicting rapid intensification.
