Browse
Abstract
The Japan Meteorological Agency (JMA) Typhoon Ensemble Prediction System (TEPS) and its performance are described. In February 2008, JMA started an operation of TEPS that was designed for providing skillful tropical cyclone (TC) track predictions in both deterministic and probabilistic ways. TEPS consists of 1 nonperturbed prediction and 10 perturbed predictions based on the lower-resolution version (TL319L60) of the JMA Global Spectral Model (GSM; TL959L60) and a global analysis for JMA/GSM. A singular vector method is employed to create initial perturbations. Focusing on TCs in the western North Pacific Ocean and the South China Sea (0°–60°N, 100°E–180°), TEPS runs 4 times a day, initiated at 0000, 0600, 1200, and 1800 UTC with a prediction range of 132 h. The verifications of TEPS during the quasi-operational period from May to December 2007 indicate that the ensemble mean track predictions statistically have better performance as compared with the control (nonperturbed) predictions: the error reduction in the 5-day predictions is 40 km on average. Moreover, it is found that the ensemble spread of tracks is an indicator of position error, indicating that TEPS will be useful in presenting confidence information on TC track predictions. For 2008 when TEPS was in operational use, however, it was also found that the ensemble mean was significantly worse than the deterministic model (JMA/GSM) out to 84 h.
Abstract
The Japan Meteorological Agency (JMA) Typhoon Ensemble Prediction System (TEPS) and its performance are described. In February 2008, JMA started an operation of TEPS that was designed for providing skillful tropical cyclone (TC) track predictions in both deterministic and probabilistic ways. TEPS consists of 1 nonperturbed prediction and 10 perturbed predictions based on the lower-resolution version (TL319L60) of the JMA Global Spectral Model (GSM; TL959L60) and a global analysis for JMA/GSM. A singular vector method is employed to create initial perturbations. Focusing on TCs in the western North Pacific Ocean and the South China Sea (0°–60°N, 100°E–180°), TEPS runs 4 times a day, initiated at 0000, 0600, 1200, and 1800 UTC with a prediction range of 132 h. The verifications of TEPS during the quasi-operational period from May to December 2007 indicate that the ensemble mean track predictions statistically have better performance as compared with the control (nonperturbed) predictions: the error reduction in the 5-day predictions is 40 km on average. Moreover, it is found that the ensemble spread of tracks is an indicator of position error, indicating that TEPS will be useful in presenting confidence information on TC track predictions. For 2008 when TEPS was in operational use, however, it was also found that the ensemble mean was significantly worse than the deterministic model (JMA/GSM) out to 84 h.
Abstract
This study explores the assimilation of Doppler radar radial velocity observations for cloud-resolving hurricane analysis, initialization, and prediction with an ensemble Kalman filter (EnKF). The case studied is Hurricane Humberto (2007), the first landfalling hurricane in the United States since the end of the 2005 hurricane season and the most rapidly intensifying near-landfall storm in U.S. history. The storm caused extensive damage along the southeast Texas coast but was poorly predicted by operational models and forecasters. It is found that the EnKF analysis, after assimilating radial velocity observations from three Weather Surveillance Radars-1988 Doppler (WSR-88Ds) along the Gulf coast, closely represents the best-track position and intensity of Humberto. Deterministic forecasts initialized from the EnKF analysis, despite displaying considerable variability with different lead times, are also capable of predicting the rapid formation and intensification of the hurricane. These forecasts are also superior to simulations without radar data assimilation or with a three-dimensional variational scheme assimilating the same radar observations. Moreover, nearly all members from the ensemble forecasts initialized with EnKF analysis perturbations predict rapid formation and intensification of the storm. However, the large ensemble spread of peak intensity, which ranges from a tropical storm to a category 2 hurricane, echoes limited predictability in deterministic forecasts of the storm and the potential of using ensembles for probabilistic forecasts of hurricanes.
Abstract
This study explores the assimilation of Doppler radar radial velocity observations for cloud-resolving hurricane analysis, initialization, and prediction with an ensemble Kalman filter (EnKF). The case studied is Hurricane Humberto (2007), the first landfalling hurricane in the United States since the end of the 2005 hurricane season and the most rapidly intensifying near-landfall storm in U.S. history. The storm caused extensive damage along the southeast Texas coast but was poorly predicted by operational models and forecasters. It is found that the EnKF analysis, after assimilating radial velocity observations from three Weather Surveillance Radars-1988 Doppler (WSR-88Ds) along the Gulf coast, closely represents the best-track position and intensity of Humberto. Deterministic forecasts initialized from the EnKF analysis, despite displaying considerable variability with different lead times, are also capable of predicting the rapid formation and intensification of the hurricane. These forecasts are also superior to simulations without radar data assimilation or with a three-dimensional variational scheme assimilating the same radar observations. Moreover, nearly all members from the ensemble forecasts initialized with EnKF analysis perturbations predict rapid formation and intensification of the storm. However, the large ensemble spread of peak intensity, which ranges from a tropical storm to a category 2 hurricane, echoes limited predictability in deterministic forecasts of the storm and the potential of using ensembles for probabilistic forecasts of hurricanes.
Abstract
The impacts of special Geostationary Operational Environmental Satellite (GOES) rapid-scan (RS) wind observations on numerical model 24–120-h track forecasts of Hurricane Katrina are examined in a series of data assimilation and forecast experiments. The RS wind vectors are derived from geostationary satellites by tracking cloud motions through successive 5-min images. In these experiments, RS wind observations are added over the area 15°–60°N, 60°–110°W, and they supplement the observations used in operational forecasts. The inclusion of RS wind observations reduces errors in numerical forecasts of the Katrina landfall position at 1200 UTC 29 August 2005 by an average of 12% compared to control cases that include “targeted” dropsonde observations in the Katrina environment. The largest average improvements are made to the 84- to 120-h Katrina track forecasts, rather than to the short-range track forecasts. These results suggest that RS wind observations can potentially be used in future cases to improve track forecasts of tropical cyclones.
Abstract
The impacts of special Geostationary Operational Environmental Satellite (GOES) rapid-scan (RS) wind observations on numerical model 24–120-h track forecasts of Hurricane Katrina are examined in a series of data assimilation and forecast experiments. The RS wind vectors are derived from geostationary satellites by tracking cloud motions through successive 5-min images. In these experiments, RS wind observations are added over the area 15°–60°N, 60°–110°W, and they supplement the observations used in operational forecasts. The inclusion of RS wind observations reduces errors in numerical forecasts of the Katrina landfall position at 1200 UTC 29 August 2005 by an average of 12% compared to control cases that include “targeted” dropsonde observations in the Katrina environment. The largest average improvements are made to the 84- to 120-h Katrina track forecasts, rather than to the short-range track forecasts. These results suggest that RS wind observations can potentially be used in future cases to improve track forecasts of tropical cyclones.
Abstract
Conditional nonlinear optimal perturbation (CNOP), which is a natural extension of the linear singular vector into the nonlinear regime, is proposed in this study for the determination of sensitive areas in adaptive observations for tropical cyclone prediction. Three tropical cyclone cases, Mindulle (2004), Meari (2004), and Matsa (2005), are investigated. Using the metrics of kinetic and dry energies, CNOPs and the first singular vectors (FSVs) are obtained over a 24-h optimization interval. Their spatial structures, their energies, and their nonlinear evolutions as well as the induced humidity changes are compared. A series of sensitivity experiments are designed to find out what benefit can be obtained by reductions of CNOP-type errors versus FSV-type errors. It is found that the structures of CNOPs may differ much from those of FSVs depending on the constraint, metric, and the basic state. The CNOP-type errors have larger impact on the forecasts in the verification area as well as the tropical cyclones than the FSV-types errors. The results of sensitivity experiments indicate that reductions of CNOP-type errors in the initial states provide more benefits than reductions of FSV-type errors. These results suggest that it is worthwhile to use CNOP as a method to identify the sensitive areas in adaptive observation for tropical cyclone prediction.
Abstract
Conditional nonlinear optimal perturbation (CNOP), which is a natural extension of the linear singular vector into the nonlinear regime, is proposed in this study for the determination of sensitive areas in adaptive observations for tropical cyclone prediction. Three tropical cyclone cases, Mindulle (2004), Meari (2004), and Matsa (2005), are investigated. Using the metrics of kinetic and dry energies, CNOPs and the first singular vectors (FSVs) are obtained over a 24-h optimization interval. Their spatial structures, their energies, and their nonlinear evolutions as well as the induced humidity changes are compared. A series of sensitivity experiments are designed to find out what benefit can be obtained by reductions of CNOP-type errors versus FSV-type errors. It is found that the structures of CNOPs may differ much from those of FSVs depending on the constraint, metric, and the basic state. The CNOP-type errors have larger impact on the forecasts in the verification area as well as the tropical cyclones than the FSV-types errors. The results of sensitivity experiments indicate that reductions of CNOP-type errors in the initial states provide more benefits than reductions of FSV-type errors. These results suggest that it is worthwhile to use CNOP as a method to identify the sensitive areas in adaptive observation for tropical cyclone prediction.
Abstract
Singular vectors (SVs) are used to study the sensitivity of 2-day forecasts of recurving tropical cyclones (TCs) in the western North Pacific to changes in the initial state. The SVs are calculated using the tangent and adjoint models of the Navy Operational Global Atmospheric Prediction System (NOGAPS) for 72 forecasts for 18 TCs in the western North Pacific during 2006. In addition to the linear SV calculation, nonlinear perturbation experiments are also performed in order to examine 1) the similarity between nonlinear and linear perturbation growth and 2) the downstream impacts over the North Pacific and North America that result from changes to the 2-day TC forecast. Both nonrecurving and recurving 2-day storm forecasts are sensitive to changes in the initial state in the near-storm environment (in an annulus approximately 500 km from the storm center). During recurvature, sensitivity develops to the northwest of the storm, usually associated with a trough moving in from the west. These upstream sensitivities can occur as far as 4000 km to the northwest of the storm, over the Asian mainland, which has implications for adaptive observations. Nonlinear perturbation experiments indicate that the linear calculations reflect case-to-case variability in actual nonlinear perturbation growth fairly well, especially when the growth is large. The nonlinear perturbations show that for recurving tropical cyclones, small initial perturbations optimized to change the 2-day TC forecast can grow and propagate downstream quickly, reaching North America in 5 days. The fastest 5-day perturbation growth is associated with recurving storm forecasts that occur when the baroclinic instability over the North Pacific is relatively large. These results suggest that nonlinear forecasts perturbed using TC SVs may have utility for predicting the downstream impact of TC forecast errors over the North Pacific and North America.
Abstract
Singular vectors (SVs) are used to study the sensitivity of 2-day forecasts of recurving tropical cyclones (TCs) in the western North Pacific to changes in the initial state. The SVs are calculated using the tangent and adjoint models of the Navy Operational Global Atmospheric Prediction System (NOGAPS) for 72 forecasts for 18 TCs in the western North Pacific during 2006. In addition to the linear SV calculation, nonlinear perturbation experiments are also performed in order to examine 1) the similarity between nonlinear and linear perturbation growth and 2) the downstream impacts over the North Pacific and North America that result from changes to the 2-day TC forecast. Both nonrecurving and recurving 2-day storm forecasts are sensitive to changes in the initial state in the near-storm environment (in an annulus approximately 500 km from the storm center). During recurvature, sensitivity develops to the northwest of the storm, usually associated with a trough moving in from the west. These upstream sensitivities can occur as far as 4000 km to the northwest of the storm, over the Asian mainland, which has implications for adaptive observations. Nonlinear perturbation experiments indicate that the linear calculations reflect case-to-case variability in actual nonlinear perturbation growth fairly well, especially when the growth is large. The nonlinear perturbations show that for recurving tropical cyclones, small initial perturbations optimized to change the 2-day TC forecast can grow and propagate downstream quickly, reaching North America in 5 days. The fastest 5-day perturbation growth is associated with recurving storm forecasts that occur when the baroclinic instability over the North Pacific is relatively large. These results suggest that nonlinear forecasts perturbed using TC SVs may have utility for predicting the downstream impact of TC forecast errors over the North Pacific and North America.
Abstract
Targeted observation is one of the most important research and forecasting issues for improving tropical cyclone predictability. A new parameter [i.e., the adjoint-derived sensitivity steering vector (ADSSV)] has been proposed and adopted as one of the targeted observing strategies in the Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR). The ADSSV identifies the sensitive areas at the observing time to the steering flow at the verifying time through the adjoint calculation. In this study, the ADSSV is calculated from the nonlinear forecast model of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) and its adjoint to interpret the dynamical processes in the interaction between Typhoon Shanshan (2006) and the midlatitude trough. The ADSSV results imply that high-sensitivity regions affecting the motion of Typhoon Shanshan are located at the edge of the subtropical high and the 500-hPa midlatitude trough over northern central China. These ADSSV signals are in very good agreement with the quantitative evaluation based on the potential vorticity (PV) diagnosis. The vertical structure of the ADSSV is also shown for more physical insights into the typhoon–trough interaction. The maximum ADSSV occurs at 800–500 hPa to the southeast of Shanshan (associated with the subtropical high), while distinct ADSSV signals are located upstream of the storm center at about 500–300 hPa (associated with the mid- to upper-tropospheric midlatitude trough). Overall, it is demonstrated that the ADSSV features can well capture the signal of the large-scale trough feature affecting the motion of Shanshan, which can also be well validated from the PV analysis.
Abstract
Targeted observation is one of the most important research and forecasting issues for improving tropical cyclone predictability. A new parameter [i.e., the adjoint-derived sensitivity steering vector (ADSSV)] has been proposed and adopted as one of the targeted observing strategies in the Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR). The ADSSV identifies the sensitive areas at the observing time to the steering flow at the verifying time through the adjoint calculation. In this study, the ADSSV is calculated from the nonlinear forecast model of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) and its adjoint to interpret the dynamical processes in the interaction between Typhoon Shanshan (2006) and the midlatitude trough. The ADSSV results imply that high-sensitivity regions affecting the motion of Typhoon Shanshan are located at the edge of the subtropical high and the 500-hPa midlatitude trough over northern central China. These ADSSV signals are in very good agreement with the quantitative evaluation based on the potential vorticity (PV) diagnosis. The vertical structure of the ADSSV is also shown for more physical insights into the typhoon–trough interaction. The maximum ADSSV occurs at 800–500 hPa to the southeast of Shanshan (associated with the subtropical high), while distinct ADSSV signals are located upstream of the storm center at about 500–300 hPa (associated with the mid- to upper-tropospheric midlatitude trough). Overall, it is demonstrated that the ADSSV features can well capture the signal of the large-scale trough feature affecting the motion of Shanshan, which can also be well validated from the PV analysis.
Abstract
In this study, the structures and growth rates of singular vectors (SVs) for Typhoon Usagi were investigated using different moist physics and norms. The fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) and its tangent linear and adjoint models with a Lanczos algorithm were used to calculate SVs over a 36-h period. The moist physics used for linear (i.e., tangent linear and adjoint model) integrations is large-scale precipitation, and the norms used are dry and moist total energy (TE) norms. Overall, moist physics in linear integrations and a moist TE norm increase the growth rates of SVs and cause smaller horizontal structures and vertical distributions closer to the lower boundary. With a dry TE norm, the SV energy distributions show similar (dissimilar) large- (small-) scale horizontal SV structures for experiments, regardless of physics. The SVs with moist linear physics and a moist TE norm have maximum horizontal energy structures near the typhoon center. With a small weighting on the moisture term in the moist TE norm, both the remote and nearby influences on the TC are indicated by the horizontal SV energy distributions. The kinetic energy shows the largest contributions to the vertical SV TE distributions in most of the experiments, except for the largest moisture (potential energy) contributions to the SV TE at the final (initial) time in the moist TE norm (dry and weighted moist TE norms at uppermost levels). In contrast, the SV vorticity distributions show more consistent structures among experiments with different linear physics and norms, implying that, in terms of the rotational component of the wind field, the SVs are not sensitive to the choice of moist physics and norms. Given large-scale precipitation as the linear moist physics, the SV energy structures and growth rate with a moist TE norm show the largest difference when compared with those with other norms.
Abstract
In this study, the structures and growth rates of singular vectors (SVs) for Typhoon Usagi were investigated using different moist physics and norms. The fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) and its tangent linear and adjoint models with a Lanczos algorithm were used to calculate SVs over a 36-h period. The moist physics used for linear (i.e., tangent linear and adjoint model) integrations is large-scale precipitation, and the norms used are dry and moist total energy (TE) norms. Overall, moist physics in linear integrations and a moist TE norm increase the growth rates of SVs and cause smaller horizontal structures and vertical distributions closer to the lower boundary. With a dry TE norm, the SV energy distributions show similar (dissimilar) large- (small-) scale horizontal SV structures for experiments, regardless of physics. The SVs with moist linear physics and a moist TE norm have maximum horizontal energy structures near the typhoon center. With a small weighting on the moisture term in the moist TE norm, both the remote and nearby influences on the TC are indicated by the horizontal SV energy distributions. The kinetic energy shows the largest contributions to the vertical SV TE distributions in most of the experiments, except for the largest moisture (potential energy) contributions to the SV TE at the final (initial) time in the moist TE norm (dry and weighted moist TE norms at uppermost levels). In contrast, the SV vorticity distributions show more consistent structures among experiments with different linear physics and norms, implying that, in terms of the rotational component of the wind field, the SVs are not sensitive to the choice of moist physics and norms. Given large-scale precipitation as the linear moist physics, the SV energy structures and growth rate with a moist TE norm show the largest difference when compared with those with other norms.
Abstract
In this study, the structure and evolution of total energy singular vectors (SVs) of Typhoon Usagi (2007) are evaluated using the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) and its tangent linear and adjoint models with a Lanczos algorithm. Horizontal structures of the initial SVs following the tropical cyclone (TC) evolution suggest that, relatively far from the region of TC recurvature, SVs near the TC center have larger magnitudes than those in the midlatitude trough. The SVs in the midlatitude trough region become dominant as the TC passes by the region of recurvature. Increasing magnitude of the SVs over the midlatitude trough regions is associated with the extratropical transition of the TC. While the SV sensitivities near the TC center are mostly associated with warming in the midtroposphere and inflow toward the TC along the edge of the subtropical high, the SV sensitivities in the midlatitude are located under the upper trough with upshear-tilted structures and associated with strong baroclinicity and frontogenesis in the lower troposphere. Given the results in this study, sensitive regions for adaptive observations of TCs may be different following the TC development stage. Far from the TC recurvature, sensitive regions near TC center may be important. Closer to the TC recurvature, effects of the midlatitude trough become dominant and the vertical structures of the SVs in the midlatitude are basically similar to those of extratropical cyclones.
Abstract
In this study, the structure and evolution of total energy singular vectors (SVs) of Typhoon Usagi (2007) are evaluated using the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) and its tangent linear and adjoint models with a Lanczos algorithm. Horizontal structures of the initial SVs following the tropical cyclone (TC) evolution suggest that, relatively far from the region of TC recurvature, SVs near the TC center have larger magnitudes than those in the midlatitude trough. The SVs in the midlatitude trough region become dominant as the TC passes by the region of recurvature. Increasing magnitude of the SVs over the midlatitude trough regions is associated with the extratropical transition of the TC. While the SV sensitivities near the TC center are mostly associated with warming in the midtroposphere and inflow toward the TC along the edge of the subtropical high, the SV sensitivities in the midlatitude are located under the upper trough with upshear-tilted structures and associated with strong baroclinicity and frontogenesis in the lower troposphere. Given the results in this study, sensitive regions for adaptive observations of TCs may be different following the TC development stage. Far from the TC recurvature, sensitive regions near TC center may be important. Closer to the TC recurvature, effects of the midlatitude trough become dominant and the vertical structures of the SVs in the midlatitude are basically similar to those of extratropical cyclones.
