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Abstract
The extent to which the tropical cyclone (TC) intensity forecast error of IVCN and S5YY, consensus models routinely used by forecasters at the National Hurricane Center and the Joint Typhoon Warning Center, respectively, can be predicted is determined. A number of predictors of consensus intensity forecast error, which must be quantities that are available prior to the official forecast deadline, were examined for the Atlantic and eastern North Pacific basins for 2008–11 and the western North Pacific basin for 2012. Leading predictors were found to be forecast TC intensity and intensity change, initial intensity and latitude of the TC, and consensus model spread, defined to be the average of the absolute intensity differences between the member forecasts and the consensus forecast. Using stepwise linear regression and the full pool of predictors, regression models were found for each forecast length to predict the IVCN and S5YY TC intensity forecast errors. Using the regression models, intervals were determined centered on the IVCN and S5YY forecasts that contained the verifying TC intensity about 67% of the time. Based on the size of these intervals, a forecaster can determine the confidence that can be placed upon the IVCN or S5YY forecasts. Independent data testing yielded results only slightly degraded from those of dependent data testing, highlighting the capability of these methods in practical forecasting applications.
Abstract
The extent to which the tropical cyclone (TC) intensity forecast error of IVCN and S5YY, consensus models routinely used by forecasters at the National Hurricane Center and the Joint Typhoon Warning Center, respectively, can be predicted is determined. A number of predictors of consensus intensity forecast error, which must be quantities that are available prior to the official forecast deadline, were examined for the Atlantic and eastern North Pacific basins for 2008–11 and the western North Pacific basin for 2012. Leading predictors were found to be forecast TC intensity and intensity change, initial intensity and latitude of the TC, and consensus model spread, defined to be the average of the absolute intensity differences between the member forecasts and the consensus forecast. Using stepwise linear regression and the full pool of predictors, regression models were found for each forecast length to predict the IVCN and S5YY TC intensity forecast errors. Using the regression models, intervals were determined centered on the IVCN and S5YY forecasts that contained the verifying TC intensity about 67% of the time. Based on the size of these intervals, a forecaster can determine the confidence that can be placed upon the IVCN or S5YY forecasts. Independent data testing yielded results only slightly degraded from those of dependent data testing, highlighting the capability of these methods in practical forecasting applications.
Abstract
The tropical cyclone (TC) track forecasting skill of operational numerical weather prediction (NWP) models and their consensus is examined for the western North Pacific from 1992 to 2002. The TC track forecasting skill of the operational NWP models is steadily improving. For the western North Pacific, the typical 72-h model forecast error has decreased from roughly 600 km to roughly 400 km over the past ten years and is now comparable to the typical 48-h model forecast error of 10 years ago. In this study the performance of consensus aids that are formed whenever the TC track forecasts from at least two models from a specified pool of operational NWP models are available is examined. The 72-h consensus forecast error has decreased from about 550 km to roughly 310 km over the past ten years and is now better than the 48-h consensus forecast error of 10 years ago. For 2002, the 72-h forecast errors for a consensus computed from a specified pool of two, five, seven, and eight models were 357, 342, 329, and 309 km, respectively. The consensus forecast availability is defined as the percent of the time that consensus forecasts were available to the forecaster when he/she was required to make a TC forecast. While the addition of models to the consensus has a modest impact on forecast skill, it has a more marked impact on consensus forecast availability. The forecast availabilities for 72-h consensus forecasts computed from a pool of two, five, seven, and eight models were 84%, 89%, 92%, and 97%, respectively.
Abstract
The tropical cyclone (TC) track forecasting skill of operational numerical weather prediction (NWP) models and their consensus is examined for the western North Pacific from 1992 to 2002. The TC track forecasting skill of the operational NWP models is steadily improving. For the western North Pacific, the typical 72-h model forecast error has decreased from roughly 600 km to roughly 400 km over the past ten years and is now comparable to the typical 48-h model forecast error of 10 years ago. In this study the performance of consensus aids that are formed whenever the TC track forecasts from at least two models from a specified pool of operational NWP models are available is examined. The 72-h consensus forecast error has decreased from about 550 km to roughly 310 km over the past ten years and is now better than the 48-h consensus forecast error of 10 years ago. For 2002, the 72-h forecast errors for a consensus computed from a specified pool of two, five, seven, and eight models were 357, 342, 329, and 309 km, respectively. The consensus forecast availability is defined as the percent of the time that consensus forecasts were available to the forecaster when he/she was required to make a TC forecast. While the addition of models to the consensus has a modest impact on forecast skill, it has a more marked impact on consensus forecast availability. The forecast availabilities for 72-h consensus forecasts computed from a pool of two, five, seven, and eight models were 84%, 89%, 92%, and 97%, respectively.
Abstract
A method to predict an anisotropic expected forecast error distribution for consensus forecasts of tropical cyclone (TC) tracks is presented. The method builds upon the Goerss predicted consensus error (GPCE), which predicts the isotropic radius of the 70% isopleth of expected TC track error. Consensus TC track forecasts are computed as the mean of a collection of TC track forecasts from different models and are basin dependent. A novel aspect of GPCE is that it uses not only the uncertainty in the collection of constituent models to predict expected error, but also other features of the predicted storm, including initial intensity, forecast intensity, and storm speed. The new method, called GPCE along–across (GPCE-AX), takes a similar approach but separates the predicted error into across-track and along-track components. GPCE-AX has been applied to consensus TC track forecasts in the Atlantic (CONU/TVCN, where CONU is consensus version U and TVCN is the track variable consensus) and in the western North Pacific (consensus version W, CONW). The results for both basins indicate that GPCE-AX either outperforms or is equal in quality to GPCE in terms of reliability (the fraction of time verification is bound by the 70% uncertainty isopleths) and sharpness (the area bound by the 70% isopleths). GPCE-AX has been implemented at both the National Hurricane Center and at the Joint Typhoon Warning Center for real-time testing and evaluation.
Abstract
A method to predict an anisotropic expected forecast error distribution for consensus forecasts of tropical cyclone (TC) tracks is presented. The method builds upon the Goerss predicted consensus error (GPCE), which predicts the isotropic radius of the 70% isopleth of expected TC track error. Consensus TC track forecasts are computed as the mean of a collection of TC track forecasts from different models and are basin dependent. A novel aspect of GPCE is that it uses not only the uncertainty in the collection of constituent models to predict expected error, but also other features of the predicted storm, including initial intensity, forecast intensity, and storm speed. The new method, called GPCE along–across (GPCE-AX), takes a similar approach but separates the predicted error into across-track and along-track components. GPCE-AX has been applied to consensus TC track forecasts in the Atlantic (CONU/TVCN, where CONU is consensus version U and TVCN is the track variable consensus) and in the western North Pacific (consensus version W, CONW). The results for both basins indicate that GPCE-AX either outperforms or is equal in quality to GPCE in terms of reliability (the fraction of time verification is bound by the 70% uncertainty isopleths) and sharpness (the area bound by the 70% isopleths). GPCE-AX has been implemented at both the National Hurricane Center and at the Joint Typhoon Warning Center for real-time testing and evaluation.
Abstract
Experimental wind datasets were derived for two time periods (13–20 July and 24 August–10 September 1995) from GOES-8 observations processed at the University of Wisconsin Cooperative Institute for Meteorological Satellite Studies (UW CIMSS). The first dataset was focused on Tropical Storm Chantal, and the second dataset was focused on the multiple-storm environment that included Hurricanes Humberto, Iris, and Luis. Both datasets feature a processing and quality control strategy designed to optimize the quantity and content of geostationary satellite-derived winds in the vicinity of tropical cyclones. Specifically, the winds were extracted from high-density targets obtained from multispectral imagery, which included three water vapor bands (6.7, 7.0, and 7.3 μm), infrared, and visible. The Navy Operational Global Atmospheric Prediction System (NOGAPS) was used as the vehicle to determine the impact of these winds upon tropical cyclone track forecasts. During the 1995 Atlantic hurricane season the NOGAPS forecasts were found to be quite skillful, displaying relative improvement of tropical cyclone position error with respect to CLIPER (climate and persistence) of 20% at 24 h, 35% at 48 h, and 33% at 72 h. The NOGAPS data assimilation system was run with and without the high-density GOES-8 winds for the two aforementioned time periods. The assimilation of these winds resulted in significant improvements in the NOGAPS forecasts for Tropical Storm Chantal and Hurricane Iris and mixed results for Hurricanes Humberto and Luis. Overall, for all four cyclones, the NOGAPS forecasts made with the use of the UW CIMSS winds displayed relative improvement of forecast position error with respect to those made without the use of the UW CIMSS winds of 14% at 24 h, and 12% at both 48 and 72 h.
Abstract
Experimental wind datasets were derived for two time periods (13–20 July and 24 August–10 September 1995) from GOES-8 observations processed at the University of Wisconsin Cooperative Institute for Meteorological Satellite Studies (UW CIMSS). The first dataset was focused on Tropical Storm Chantal, and the second dataset was focused on the multiple-storm environment that included Hurricanes Humberto, Iris, and Luis. Both datasets feature a processing and quality control strategy designed to optimize the quantity and content of geostationary satellite-derived winds in the vicinity of tropical cyclones. Specifically, the winds were extracted from high-density targets obtained from multispectral imagery, which included three water vapor bands (6.7, 7.0, and 7.3 μm), infrared, and visible. The Navy Operational Global Atmospheric Prediction System (NOGAPS) was used as the vehicle to determine the impact of these winds upon tropical cyclone track forecasts. During the 1995 Atlantic hurricane season the NOGAPS forecasts were found to be quite skillful, displaying relative improvement of tropical cyclone position error with respect to CLIPER (climate and persistence) of 20% at 24 h, 35% at 48 h, and 33% at 72 h. The NOGAPS data assimilation system was run with and without the high-density GOES-8 winds for the two aforementioned time periods. The assimilation of these winds resulted in significant improvements in the NOGAPS forecasts for Tropical Storm Chantal and Hurricane Iris and mixed results for Hurricanes Humberto and Luis. Overall, for all four cyclones, the NOGAPS forecasts made with the use of the UW CIMSS winds displayed relative improvement of forecast position error with respect to those made without the use of the UW CIMSS winds of 14% at 24 h, and 12% at both 48 and 72 h.
Abstract
We have compared analysis increments produced by the optimal interpolation scheme and initialization increments produced by the nonlinear normal-mode initialization scheme in the U.S. Navy Operational Global Atmospheric Prediction System. Results indicate that analysis increments of height in the tropics are partially removed by the subsequent initialization. Similar results are obtained for the field of horizontal velocity divergence within the extratropics as well as tropics. Consequently, for some fields in some areas, the initialized analyses are primarily defined by the model-produced background field, irrespective of the availability of observations or model error estimates.
Abstract
We have compared analysis increments produced by the optimal interpolation scheme and initialization increments produced by the nonlinear normal-mode initialization scheme in the U.S. Navy Operational Global Atmospheric Prediction System. Results indicate that analysis increments of height in the tropics are partially removed by the subsequent initialization. Similar results are obtained for the field of horizontal velocity divergence within the extratropics as well as tropics. Consequently, for some fields in some areas, the initialized analyses are primarily defined by the model-produced background field, irrespective of the availability of observations or model error estimates.
Abstract
The Weber barotropic model (WBAR) was originally developed using predefined 850–200-hPa analyses and forecasts from the NCEP Global Forecasting System. The WBAR tropical cyclone (TC) track forecast performance was found to be competitive with that of more complex numerical weather prediction models in the North Atlantic. As a result, WBAR was revised to incorporate the Navy Operational Global Atmospheric Prediction System (NOGAPS) analyses and forecasts for use at the Joint Typhoon Warning Center (JTWC). The model was also modified to analyze its own storm-dependent deep-layer mean fields from standard NOGAPS pressure levels. Since its operational installation at the JTWC in May 2003, WBAR TC track forecast performance has been competitive with the performance of other more complex NWP models in the western North Pacific. Its TC track forecast performance combined with its high availability rate (93%–95%) has warranted its inclusion in the JTWC operational consensus. The impact of WBAR on consensus TC track forecast performance has been positive and WBAR has added to the consensus forecast availability (i.e., having at least two models to provide a consensus forecast).
Abstract
The Weber barotropic model (WBAR) was originally developed using predefined 850–200-hPa analyses and forecasts from the NCEP Global Forecasting System. The WBAR tropical cyclone (TC) track forecast performance was found to be competitive with that of more complex numerical weather prediction models in the North Atlantic. As a result, WBAR was revised to incorporate the Navy Operational Global Atmospheric Prediction System (NOGAPS) analyses and forecasts for use at the Joint Typhoon Warning Center (JTWC). The model was also modified to analyze its own storm-dependent deep-layer mean fields from standard NOGAPS pressure levels. Since its operational installation at the JTWC in May 2003, WBAR TC track forecast performance has been competitive with the performance of other more complex NWP models in the western North Pacific. Its TC track forecast performance combined with its high availability rate (93%–95%) has warranted its inclusion in the JTWC operational consensus. The impact of WBAR on consensus TC track forecast performance has been positive and WBAR has added to the consensus forecast availability (i.e., having at least two models to provide a consensus forecast).
Abstract
The meteorological quality and operational utility of the Navy Operational Global Atmospheric Prediction System (NOGAPS) in forecasting tropical cyclones is evaluated and it is shown that the model can provide useful predictions of motion and formation on a real-time basis in the western North Pacific. The evaluation was conducted during the 1990 operational testing of a procedure to improve the initial analysis or specification of tropical cyclones (TCs) in NOGAPS by the U.S. Navy Fleet Numerical Oceanography Center (FNOC). The NOGAPS TC analysis procedure generates synthetic TC observations based on operational vortex data (e.g., location and maximum surface wind speed) and then adds the observations to the observational data base with flags to force their assimilation. Results from the first year of testing were favorable, despite intermittent application of the procedure.
The meteorological characteristics of the NOGAPS tropical cyclone predictions were evaluated by examining the formation of low-level cyclone circulation systems in the tropics and vortex structure in the NOGAPS analysis and verifying 72-h forecasts. Analyzed circulations were found in the vicinity of developing TCs for nearly all cyclones during the operational test period. This finding implies that the model is “primed” for assimilating the synthetic observations and may be accurately simulating the large-scale environments favorable to TC formation. The analyzed TC circulations had greater than observed horizontal extent due to coarse grid spacing (δx∼ 160 km) in the global model; however, the vortices, in general, were vertically stacked and maintained during the forecast by realistic amounts of thermodynamic forcing from the cumulus parameterization. Despite the large size of the NOGAPS TC vortices, the track forecasts were not overly biased with regard to track or speed. The operational utility of the NOGAPS track forecasts was analyzed through a comparison with the real-time runs of a baseline climatology persistence aid and with the best dynamical model used by the Joint Typhoon Warning Center, Guam. To ensure a realistic comparison of the forecasts and to improve the appearance of the global model tracks, a postprocessing adjustment procedure was employed that accounts for the observed initial motion and position. The adjusted NOGAPS track forecasts showed equitable skill to the baseline aid and the dynamical model. In fact, NOGAPS successfully predicted unusual equatorward turns for several straight-running cyclones. Overall, the adjusted NOGAPS track forecasts were judged to be competitive with other aids used by the operational forecasters at JTWC and it is suggested that global models may make important contributions to improving TC forecasting in the future.
Abstract
The meteorological quality and operational utility of the Navy Operational Global Atmospheric Prediction System (NOGAPS) in forecasting tropical cyclones is evaluated and it is shown that the model can provide useful predictions of motion and formation on a real-time basis in the western North Pacific. The evaluation was conducted during the 1990 operational testing of a procedure to improve the initial analysis or specification of tropical cyclones (TCs) in NOGAPS by the U.S. Navy Fleet Numerical Oceanography Center (FNOC). The NOGAPS TC analysis procedure generates synthetic TC observations based on operational vortex data (e.g., location and maximum surface wind speed) and then adds the observations to the observational data base with flags to force their assimilation. Results from the first year of testing were favorable, despite intermittent application of the procedure.
The meteorological characteristics of the NOGAPS tropical cyclone predictions were evaluated by examining the formation of low-level cyclone circulation systems in the tropics and vortex structure in the NOGAPS analysis and verifying 72-h forecasts. Analyzed circulations were found in the vicinity of developing TCs for nearly all cyclones during the operational test period. This finding implies that the model is “primed” for assimilating the synthetic observations and may be accurately simulating the large-scale environments favorable to TC formation. The analyzed TC circulations had greater than observed horizontal extent due to coarse grid spacing (δx∼ 160 km) in the global model; however, the vortices, in general, were vertically stacked and maintained during the forecast by realistic amounts of thermodynamic forcing from the cumulus parameterization. Despite the large size of the NOGAPS TC vortices, the track forecasts were not overly biased with regard to track or speed. The operational utility of the NOGAPS track forecasts was analyzed through a comparison with the real-time runs of a baseline climatology persistence aid and with the best dynamical model used by the Joint Typhoon Warning Center, Guam. To ensure a realistic comparison of the forecasts and to improve the appearance of the global model tracks, a postprocessing adjustment procedure was employed that accounts for the observed initial motion and position. The adjusted NOGAPS track forecasts showed equitable skill to the baseline aid and the dynamical model. In fact, NOGAPS successfully predicted unusual equatorward turns for several straight-running cyclones. Overall, the adjusted NOGAPS track forecasts were judged to be competitive with other aids used by the operational forecasters at JTWC and it is suggested that global models may make important contributions to improving TC forecasting in the future.
The coverage and quality of remotely sensed upper-tropospheric moisture parameters have improved considerably with the deployment of a new generation of operational geostationary meteorological satellites: GOES-8/9 and GMS-5. The GOES-8/9 water vapor imaging capabilities have increased as a result of improved radiometric sensitivity and higher spatial resolution. The addition of a water vapor sensing channel on the latest GMS permits nearly global viewing of upper-tropospheric water vapor (when joined with GOES and Meteosat) and enhances the commonality of geostationary meteorological satellite observing capabilities. Upper-tropospheric motions derived from sequential water vapor imagery provided by these satellites can be objectively extracted by automated techniques. Wind fields can be deduced in both cloudy and cloud-free environments. In addition to the spatially coherent nature of these vector fields, the GOES-8/9 multispectral water vapor sensing capabilities allow for determination of wind fields over multiple tropospheric layers in cloud-free environments. This article provides an update on the latest efforts to extract water vapor motion displacements over meteorological scales ranging from subsynoptic to global. The potential applications of these data to impact operations, numerical assimilation and prediction, and research studies are discussed.
The coverage and quality of remotely sensed upper-tropospheric moisture parameters have improved considerably with the deployment of a new generation of operational geostationary meteorological satellites: GOES-8/9 and GMS-5. The GOES-8/9 water vapor imaging capabilities have increased as a result of improved radiometric sensitivity and higher spatial resolution. The addition of a water vapor sensing channel on the latest GMS permits nearly global viewing of upper-tropospheric water vapor (when joined with GOES and Meteosat) and enhances the commonality of geostationary meteorological satellite observing capabilities. Upper-tropospheric motions derived from sequential water vapor imagery provided by these satellites can be objectively extracted by automated techniques. Wind fields can be deduced in both cloudy and cloud-free environments. In addition to the spatially coherent nature of these vector fields, the GOES-8/9 multispectral water vapor sensing capabilities allow for determination of wind fields over multiple tropospheric layers in cloud-free environments. This article provides an update on the latest efforts to extract water vapor motion displacements over meteorological scales ranging from subsynoptic to global. The potential applications of these data to impact operations, numerical assimilation and prediction, and research studies are discussed.
Abstract
The performance of the U.S. Navy global atmospheric ensemble prediction system is examined with a focus on tropical winds and tropical cyclone tracks. Ensembles are run at a triangular truncation of T119, T159, and T239, with 33, 17, and 9 ensemble members, respectively, to evaluate the impact of resolution versus the number of ensemble member tradeoffs on ensemble performance. Results indicate that the T159 and T239 ensemble mean tropical cyclone track errors are significantly smaller than those of the T119 ensemble out to 4 days. For ensemble forecasts of upper- and lower-tropospheric tropical winds, increasing resolution has only a small impact on ensemble mean root-mean-square error for wind speed, but does improve Brier scores for 10-m wind speed at the 5 m s−1 threshold. In addition to the resolution tests, modifications to the ensemble transform initial perturbation methodology and inclusion of stochastic kinetic energy backscatter are also evaluated. Stochastic kinetic energy backscatter substantially increases the ensemble spread and improves Brier scores in the tropics, but for the most part does not significantly reduce ensemble mean tropical cyclone track error.
Abstract
The performance of the U.S. Navy global atmospheric ensemble prediction system is examined with a focus on tropical winds and tropical cyclone tracks. Ensembles are run at a triangular truncation of T119, T159, and T239, with 33, 17, and 9 ensemble members, respectively, to evaluate the impact of resolution versus the number of ensemble member tradeoffs on ensemble performance. Results indicate that the T159 and T239 ensemble mean tropical cyclone track errors are significantly smaller than those of the T119 ensemble out to 4 days. For ensemble forecasts of upper- and lower-tropospheric tropical winds, increasing resolution has only a small impact on ensemble mean root-mean-square error for wind speed, but does improve Brier scores for 10-m wind speed at the 5 m s−1 threshold. In addition to the resolution tests, modifications to the ensemble transform initial perturbation methodology and inclusion of stochastic kinetic energy backscatter are also evaluated. Stochastic kinetic energy backscatter substantially increases the ensemble spread and improves Brier scores in the tropics, but for the most part does not significantly reduce ensemble mean tropical cyclone track error.
Abstract
In 2016, the Joint Typhoon Warning Center extended forecasts of gale-force and other wind radii to 5 days. That effort and a thrust to perform postseason analysis of gale-force wind radii for the “best tracks” (the quality controlled and documented tropical cyclone track and intensity estimates released after the season) have prompted requirements for new guidance to address the challenges of both. At the same time, operational tools to estimate and predict wind radii continue to evolve, now forming a quality suite of gale-force wind radii analysis and forecasting tools. This work provides an update to real-time estimates of gale-force wind radii (a mean/consensus of gale-force individual wind radii estimates) that includes objective scatterometer-derived estimates. The work also addresses operational gale-force wind radii forecasting in that it provides an update to a gale-force wind radii forecast consensus, which now includes gale-force wind radii forecast error estimates to accompany the gale-force wind radii forecasts. The gale-force wind radii forecast error estimates are computed using predictors readily available in real time (e.g., consensus spread, initial size, and forecast intensity) so that operational reliability and timeliness can be ensured. These updates were all implemented in operations at the Joint Typhoon Warning Center by January 2018, and more updates should be expected in the coming years as new and improved guidance becomes available.
Abstract
In 2016, the Joint Typhoon Warning Center extended forecasts of gale-force and other wind radii to 5 days. That effort and a thrust to perform postseason analysis of gale-force wind radii for the “best tracks” (the quality controlled and documented tropical cyclone track and intensity estimates released after the season) have prompted requirements for new guidance to address the challenges of both. At the same time, operational tools to estimate and predict wind radii continue to evolve, now forming a quality suite of gale-force wind radii analysis and forecasting tools. This work provides an update to real-time estimates of gale-force wind radii (a mean/consensus of gale-force individual wind radii estimates) that includes objective scatterometer-derived estimates. The work also addresses operational gale-force wind radii forecasting in that it provides an update to a gale-force wind radii forecast consensus, which now includes gale-force wind radii forecast error estimates to accompany the gale-force wind radii forecasts. The gale-force wind radii forecast error estimates are computed using predictors readily available in real time (e.g., consensus spread, initial size, and forecast intensity) so that operational reliability and timeliness can be ensured. These updates were all implemented in operations at the Joint Typhoon Warning Center by January 2018, and more updates should be expected in the coming years as new and improved guidance becomes available.
