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Abstract
A new Northwest Pacific climatology and persistence (CLIPER) model is derived with historical tropical cyclone tracks during the satellite and aircraft reconnaissance era (1970–95). The new CLIPER extends the forecasts from 3 to 5 days and exhibits smaller forecast biases than the previous CLIPER, although forecast errors are comparable. The new model is based on more accurate historical tropical cyclone track data, and a simpler derivation of the regression equations, than is the old model. Nonlinear systems analysis shows that the predictability timescale in which the average errors increase by a factor e is just over 15 h, which is about the same as that calculated by similar methods near Australia and in the North Atlantic. This suggests that 5-day tropical cyclone track forecasts may be beneficial, assuming small initial errors; therefore, a CLIPER model extended to 5 days is needed as a baseline to measure the forecast skill.
Abstract
A new Northwest Pacific climatology and persistence (CLIPER) model is derived with historical tropical cyclone tracks during the satellite and aircraft reconnaissance era (1970–95). The new CLIPER extends the forecasts from 3 to 5 days and exhibits smaller forecast biases than the previous CLIPER, although forecast errors are comparable. The new model is based on more accurate historical tropical cyclone track data, and a simpler derivation of the regression equations, than is the old model. Nonlinear systems analysis shows that the predictability timescale in which the average errors increase by a factor e is just over 15 h, which is about the same as that calculated by similar methods near Australia and in the North Atlantic. This suggests that 5-day tropical cyclone track forecasts may be beneficial, assuming small initial errors; therefore, a CLIPER model extended to 5 days is needed as a baseline to measure the forecast skill.
Abstract
The National Hurricane Center (NHC) has been forecasting gale force wind radii for many years, and more recently (starting in 2004) began routine postanalysis or “best tracking” of the maximum radial extent of gale [34 knots (kt; 1 kt = 0.514 m s−1)] force winds in compass quadrants surrounding the tropical cyclone (wind radii). At approximately the same time, a statistical wind radii forecast, based solely on climatology and persistence, was implemented so that wind radii forecasts could be evaluated for skill. If the best-track gale radii are used as ground truth (even accounting for random errors in the analyses), the skill of the NHC forecasts appears to be improving at 2- and 3-day lead times, suggesting that the guidance has also improved. In this paper several NWP models are evaluated for their skill, an equally weighted average or “consensus” of the model forecasts is constructed, and finally the consensus skill is evaluated. The results are similar to what is found with tropical cyclone track and intensity in that the consensus skill is comparable to or better than that of the individual models. Furthermore, the consensus skill is high enough to be of potential use as forecast guidance or as a proxy for official gale force wind radii forecasts at the longer lead times.
Abstract
The National Hurricane Center (NHC) has been forecasting gale force wind radii for many years, and more recently (starting in 2004) began routine postanalysis or “best tracking” of the maximum radial extent of gale [34 knots (kt; 1 kt = 0.514 m s−1)] force winds in compass quadrants surrounding the tropical cyclone (wind radii). At approximately the same time, a statistical wind radii forecast, based solely on climatology and persistence, was implemented so that wind radii forecasts could be evaluated for skill. If the best-track gale radii are used as ground truth (even accounting for random errors in the analyses), the skill of the NHC forecasts appears to be improving at 2- and 3-day lead times, suggesting that the guidance has also improved. In this paper several NWP models are evaluated for their skill, an equally weighted average or “consensus” of the model forecasts is constructed, and finally the consensus skill is evaluated. The results are similar to what is found with tropical cyclone track and intensity in that the consensus skill is comparable to or better than that of the individual models. Furthermore, the consensus skill is high enough to be of potential use as forecast guidance or as a proxy for official gale force wind radii forecasts at the longer lead times.
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 National Hurricane Center (NHC) has a long history of forecasting the radial extent of gale force or 34-knot (kt; where 1 kt = 0.51 m s−1) winds for tropical cyclones in their area of responsibility. These are referred to collectively as gale force wind radii forecasts. These forecasts are generated as part of the 6-hourly advisory messages made available to the public. In 2004, NHC began a routine of postanalysis or “best tracking” of gale force wind radii that continues to this day. At approximately the same time, a statistical wind radii forecast, based solely on climatology and persistence, was implemented so that NHC all-wind radii forecasts could be evaluated for skill. This statistical wind radii baseline forecast is also currently used in several applications as a substitute for or to augment NHC wind radii forecasts. This investigation examines the performance of NHC gale force wind radii forecasts in the North Atlantic over the last decade. Results presented within indicate that NHC’s gale force wind radii forecasts have increased in skill relative to the best tracks by several measures, and now significantly outperform statistical wind radii baseline forecasts. These results indicate that it may be time to reinvestigate whether applications that depend on wind radii forecast information can be improved through better use of NHC wind radii forecast information.
Abstract
The National Hurricane Center (NHC) has a long history of forecasting the radial extent of gale force or 34-knot (kt; where 1 kt = 0.51 m s−1) winds for tropical cyclones in their area of responsibility. These are referred to collectively as gale force wind radii forecasts. These forecasts are generated as part of the 6-hourly advisory messages made available to the public. In 2004, NHC began a routine of postanalysis or “best tracking” of gale force wind radii that continues to this day. At approximately the same time, a statistical wind radii forecast, based solely on climatology and persistence, was implemented so that NHC all-wind radii forecasts could be evaluated for skill. This statistical wind radii baseline forecast is also currently used in several applications as a substitute for or to augment NHC wind radii forecasts. This investigation examines the performance of NHC gale force wind radii forecasts in the North Atlantic over the last decade. Results presented within indicate that NHC’s gale force wind radii forecasts have increased in skill relative to the best tracks by several measures, and now significantly outperform statistical wind radii baseline forecasts. These results indicate that it may be time to reinvestigate whether applications that depend on wind radii forecast information can be improved through better use of NHC wind radii forecast information.
The Automated Tropical Cyclone Forecasting System (ATCF) is software intended to automate and optimize much of the tropical cyclone forecasting process. The system features global tracking capability, a suite of objective aids, and a user interface that allows simultaneous tracking of multiple tropical cyclones. The version discussed in this article, ATCF 3.2, runs on UNIX workstations. The Joint Typhoon Warning Center in Guam, the Naval Pacific Meteorology and Oceanography Center in Pearl Harbor, and the Naval Atlantic Meteorology and Oceanography Center in Norfolk successfully used ATCF 3.2 during the 1998 tropical cyclone season.
The Automated Tropical Cyclone Forecasting System (ATCF) is software intended to automate and optimize much of the tropical cyclone forecasting process. The system features global tracking capability, a suite of objective aids, and a user interface that allows simultaneous tracking of multiple tropical cyclones. The version discussed in this article, ATCF 3.2, runs on UNIX workstations. The Joint Typhoon Warning Center in Guam, the Naval Pacific Meteorology and Oceanography Center in Pearl Harbor, and the Naval Atlantic Meteorology and Oceanography Center in Norfolk successfully used ATCF 3.2 during the 1998 tropical cyclone season.
Abstract
In late 2017, the Rapid Intensification Prediction Aid (RIPA) was transitioned to operations at the Joint Typhoon Warning Center (JTWC). RIPA probabilistically predicts seven rapid intensification (RI) thresholds over three separate time periods: 25-, 30-, 35-, and 40-kt (1 kt ≈ 0.51 m s−1) increases in 24 h (RI25, RI30, RI35, RI40); 45- and 55-kt increases in 36 h (RI45 and RI55); and 70-kt increases in 48 h (RI70). RIPA’s probabilistic forecasts are also used to produce deterministic forecasts when probabilities exceed 40%, and the latter are included as members of the operational intensity consensus forecast aid. RIPA, initially designed for the western North Pacific, performed remarkably well in all JTWC areas of responsibility (AOR) and is now incorporated into JTWC’s ever improving suite of intensity forecast guidance. Even so, making real-time operational RIPA forecasts exposed some methodological weaknesses such as overprediction of RI for weak/disorganized systems (i.e., systems with maximum winds less than 35 kt), prediction of RI during landfall, input data reliability, and statistical inconsistencies. Modifications to the deterministic forecasts that address these issues are presented, and newly derived and more statistically consistent models are developed using data from all of JTWC’s AORs. The updated RIPA is tested as it would be run in operations and verified using a 2-yr (2018–19) independent sample. The performance from this test indicates the new RIPA—when compared to its predecessor—has improved probabilistic verification statistics, and similar deterministic skill while using fewer predictors to make forecasts.
Abstract
In late 2017, the Rapid Intensification Prediction Aid (RIPA) was transitioned to operations at the Joint Typhoon Warning Center (JTWC). RIPA probabilistically predicts seven rapid intensification (RI) thresholds over three separate time periods: 25-, 30-, 35-, and 40-kt (1 kt ≈ 0.51 m s−1) increases in 24 h (RI25, RI30, RI35, RI40); 45- and 55-kt increases in 36 h (RI45 and RI55); and 70-kt increases in 48 h (RI70). RIPA’s probabilistic forecasts are also used to produce deterministic forecasts when probabilities exceed 40%, and the latter are included as members of the operational intensity consensus forecast aid. RIPA, initially designed for the western North Pacific, performed remarkably well in all JTWC areas of responsibility (AOR) and is now incorporated into JTWC’s ever improving suite of intensity forecast guidance. Even so, making real-time operational RIPA forecasts exposed some methodological weaknesses such as overprediction of RI for weak/disorganized systems (i.e., systems with maximum winds less than 35 kt), prediction of RI during landfall, input data reliability, and statistical inconsistencies. Modifications to the deterministic forecasts that address these issues are presented, and newly derived and more statistically consistent models are developed using data from all of JTWC’s AORs. The updated RIPA is tested as it would be run in operations and verified using a 2-yr (2018–19) independent sample. The performance from this test indicates the new RIPA—when compared to its predecessor—has improved probabilistic verification statistics, and similar deterministic skill while using fewer predictors to make forecasts.
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
Forecasts of tropical cyclone (TC) surface wind structure have recently begun to show some skill, but the number of reliable forecast tools, mostly regional hurricane and select global models, remains limited. To provide additional wind structure guidance, this work presents the development of a statistical–dynamical method to predict tropical cyclone wind structure in terms of wind radii, which are defined as the maximum extent of the 34-, 50-, and 64-kt (1 kt = 0.514 m s−1) winds in geographical quadrants about the center of the storm. The basis for TC size variations is developed from an infrared satellite-based record of TC size, which is homogenously calculated from a global sample. The change in TC size is predicted using a statistical–dynamical approach where predictors are based on environmental diagnostics derived from global model forecasts and observed storm conditions. Once the TC size has been predicted, the forecast intensity and track are used along with a parametric wind model to estimate the resulting wind radii. To provide additional guidance for applications and users that require forecasts of central pressure, a wind–pressure relationship that is a function of TC motion, intensity, wind radii (i.e., size), and latitude is then applied to these forecasts. This forecast method compares well with similar wind structure forecasts made by global forecast and regional hurricane models and when these forecasts are used as a member of a simple consensus; its inclusion improves the forecast performance of the consensus.
Abstract
Forecasts of tropical cyclone (TC) surface wind structure have recently begun to show some skill, but the number of reliable forecast tools, mostly regional hurricane and select global models, remains limited. To provide additional wind structure guidance, this work presents the development of a statistical–dynamical method to predict tropical cyclone wind structure in terms of wind radii, which are defined as the maximum extent of the 34-, 50-, and 64-kt (1 kt = 0.514 m s−1) winds in geographical quadrants about the center of the storm. The basis for TC size variations is developed from an infrared satellite-based record of TC size, which is homogenously calculated from a global sample. The change in TC size is predicted using a statistical–dynamical approach where predictors are based on environmental diagnostics derived from global model forecasts and observed storm conditions. Once the TC size has been predicted, the forecast intensity and track are used along with a parametric wind model to estimate the resulting wind radii. To provide additional guidance for applications and users that require forecasts of central pressure, a wind–pressure relationship that is a function of TC motion, intensity, wind radii (i.e., size), and latitude is then applied to these forecasts. This forecast method compares well with similar wind structure forecasts made by global forecast and regional hurricane models and when these forecasts are used as a member of a simple consensus; its inclusion improves the forecast performance of the consensus.
Abstract
This note describes an updated tropical cyclone vortex climatology for the western North Pacific version of the operational wind radii climatology and persistence (i.e., CLIPER) model. The update addresses known shortcomings of the existing formulation, namely, that the wind radii used to develop the original model were too small and symmetric. The underlying formulation of the CLIPER model has not changed, but the larger and more realistic vortex climatology produces improved forecast biases. Other applications that make use of the vortex climatology and CLIPER model forecasts should also benefit from the bias improvements.
Abstract
This note describes an updated tropical cyclone vortex climatology for the western North Pacific version of the operational wind radii climatology and persistence (i.e., CLIPER) model. The update addresses known shortcomings of the existing formulation, namely, that the wind radii used to develop the original model were too small and symmetric. The underlying formulation of the CLIPER model has not changed, but the larger and more realistic vortex climatology produces improved forecast biases. Other applications that make use of the vortex climatology and CLIPER model forecasts should also benefit from the bias improvements.
Abstract
This work describes tropical cyclone rapid intensification forecast aids designed for the western North Pacific tropical cyclone basin and for use at the Joint Typhoon Warning Center. Two statistical methods, linear discriminant analysis and logistic regression, are used to create probabilistic forecasts for seven intensification thresholds including 25-, 30-, 35-, and 40-kt changes in 24 h, 45- and 55-kt in 36 h, and 70-kt in 48 h (1 kt = 0.514 m s−1). These forecast probabilities are further used to create an equally weighted probability consensus that is then used to trigger deterministic forecasts equal to the intensification thresholds once the probability in the consensus reaches 40%. These deterministic forecasts are incorporated into an operational intensity consensus forecast as additional members, resulting in an improved intensity consensus for these important and difficult to predict cases. Development of these methods is based on the 2000–15 typhoon seasons, and independent performance is assessed using the 2016 and 2017 typhoon seasons. In many cases, the probabilities have skill relative to climatology and adding the rapid intensification deterministic aids to the operational intensity consensus significantly reduces the negative forecast biases.
Abstract
This work describes tropical cyclone rapid intensification forecast aids designed for the western North Pacific tropical cyclone basin and for use at the Joint Typhoon Warning Center. Two statistical methods, linear discriminant analysis and logistic regression, are used to create probabilistic forecasts for seven intensification thresholds including 25-, 30-, 35-, and 40-kt changes in 24 h, 45- and 55-kt in 36 h, and 70-kt in 48 h (1 kt = 0.514 m s−1). These forecast probabilities are further used to create an equally weighted probability consensus that is then used to trigger deterministic forecasts equal to the intensification thresholds once the probability in the consensus reaches 40%. These deterministic forecasts are incorporated into an operational intensity consensus forecast as additional members, resulting in an improved intensity consensus for these important and difficult to predict cases. Development of these methods is based on the 2000–15 typhoon seasons, and independent performance is assessed using the 2016 and 2017 typhoon seasons. In many cases, the probabilities have skill relative to climatology and adding the rapid intensification deterministic aids to the operational intensity consensus significantly reduces the negative forecast biases.
