Search Results
You are looking at 1 - 8 of 8 items for :
- Author or Editor: Johnny C. L. Chan x
- Weather and Forecasting x
- Refine by Access: All Content x
Abstract
Supertyphoon Abby (1983), although not one of the most destructive on record, received a great deal of attention from the typhoon forecasters in Guam. For a large part of Abby's lifetime, nearly all objectively predicted tracks were almost 90° to the left of the actual track of the cyclone. This study is an attempt to understand the reasons for the failure of the forecast models.
The intensity and size (horizontal extent) of the supertyphoon are hypothesized to be the main factors contributing to such a forecast failure. After intensifying to a maximum wind speed of 75 m s−1 (145 kt), Abby continued to grow, with the radius of 15 m s−1 (30 kt) winds extending beyond 600 km. Abby's circulation, which can be readily identified on synoptic charts, apparently affected the performance of the dynamical models. The “steering flow” vector as estimated from the operational analyses is found to be almost normal to the motion vector of Abby, which might provide a partial explanation of the forecasts by the objective methods.
These results suggest the need to analyze the performance of forecast models under different synoptic as well as storm-related factors. They also suggest the importance of studying the interaction between the tropical cyclone circulation and its environment.
Abstract
Supertyphoon Abby (1983), although not one of the most destructive on record, received a great deal of attention from the typhoon forecasters in Guam. For a large part of Abby's lifetime, nearly all objectively predicted tracks were almost 90° to the left of the actual track of the cyclone. This study is an attempt to understand the reasons for the failure of the forecast models.
The intensity and size (horizontal extent) of the supertyphoon are hypothesized to be the main factors contributing to such a forecast failure. After intensifying to a maximum wind speed of 75 m s−1 (145 kt), Abby continued to grow, with the radius of 15 m s−1 (30 kt) winds extending beyond 600 km. Abby's circulation, which can be readily identified on synoptic charts, apparently affected the performance of the dynamical models. The “steering flow” vector as estimated from the operational analyses is found to be almost normal to the motion vector of Abby, which might provide a partial explanation of the forecasts by the objective methods.
These results suggest the need to analyze the performance of forecast models under different synoptic as well as storm-related factors. They also suggest the importance of studying the interaction between the tropical cyclone circulation and its environment.
Abstract
In 1991, Typhoon Nat over the western North Pacific made four directional reversals due to its interactions with two other tropical cyclones (TCs), Luke and Mireille. This paper analyzes the performance of three global and two regional models in predicting the movement of Nat to determine the extent to which each of the models was capable of correctly simulating such binary interactions. The global models include those of the European Centre for Medium-Range Weather Forecasts (ECMWF) and the U.K. Meteorological Office (UKMO) and the U.S. Navy Operational Global Atmospheric Prediction System (NOGAPS). The regional models studied are the Typhoon Model (TYM) of the Japan Meteorological Agency and the One-Way Tropical Cyclone Model (OTCM) of the U.S. Navy.
It was found that in general the global models made better predictions than the regional ones, especially when the large-scale flow was well defined. During the interaction periods, the UKMO model and the TYM were the best. The ECMWF model was also quite good in capturing the latter part of the Nat-Mireille interaction when Mireille had a large circulation. Although NOGAPS had a bogus vortex in the model, it did not predict the interactions very well. The OTCM was the worst of the models, possibly because of the steering flow imposed onto the model vortex.
The main conclusions from this study are that a bogus vortex representative of the actual TC appears to be necessary for properly simulating the interaction between TCs. An increase in resolution may also help in this respect. However, imposing a persistence vector into a model to simulate steering may prove detrimental in predicting binary interactions.
Abstract
In 1991, Typhoon Nat over the western North Pacific made four directional reversals due to its interactions with two other tropical cyclones (TCs), Luke and Mireille. This paper analyzes the performance of three global and two regional models in predicting the movement of Nat to determine the extent to which each of the models was capable of correctly simulating such binary interactions. The global models include those of the European Centre for Medium-Range Weather Forecasts (ECMWF) and the U.K. Meteorological Office (UKMO) and the U.S. Navy Operational Global Atmospheric Prediction System (NOGAPS). The regional models studied are the Typhoon Model (TYM) of the Japan Meteorological Agency and the One-Way Tropical Cyclone Model (OTCM) of the U.S. Navy.
It was found that in general the global models made better predictions than the regional ones, especially when the large-scale flow was well defined. During the interaction periods, the UKMO model and the TYM were the best. The ECMWF model was also quite good in capturing the latter part of the Nat-Mireille interaction when Mireille had a large circulation. Although NOGAPS had a bogus vortex in the model, it did not predict the interactions very well. The OTCM was the worst of the models, possibly because of the steering flow imposed onto the model vortex.
The main conclusions from this study are that a bogus vortex representative of the actual TC appears to be necessary for properly simulating the interaction between TCs. An increase in resolution may also help in this respect. However, imposing a persistence vector into a model to simulate steering may prove detrimental in predicting binary interactions.
Abstract
A simple method based on the cumulative number of tropical cyclones (TCs) up to a given month in the early season is proposed to update the seasonal prediction of the annual number of TCs in a given ocean basin. For the western North Pacific, if this number is below normal by July or August, it is very likely that the annual activity will also be below normal. The reverse (for relating above-normal number with above-normal annual activity) is also true although the probability is smaller than for the below-normal category. Similar results are found for TCs in the eastern North Pacific and the North Atlantic, with the latter having the smallest likelihood. These results change only slightly when the samples are separated into dependent and independent subsets.
Abstract
A simple method based on the cumulative number of tropical cyclones (TCs) up to a given month in the early season is proposed to update the seasonal prediction of the annual number of TCs in a given ocean basin. For the western North Pacific, if this number is below normal by July or August, it is very likely that the annual activity will also be below normal. The reverse (for relating above-normal number with above-normal annual activity) is also true although the probability is smaller than for the below-normal category. Similar results are found for TCs in the eastern North Pacific and the North Atlantic, with the latter having the smallest likelihood. These results change only slightly when the samples are separated into dependent and independent subsets.
Abstract
This study describes an improved statistical scheme for predicting the annual number of tropical cyclones (TCs) making landfall along the coast of south China using data from 1965 to 2005. Based on the factors affecting TC behavior inside the South China Sea (SCS), those responsible for TCs making landfall are identified. Equations are then developed using the coefficients of empirical orthogonal functions of these factors to predict, in April, the number of these TCs in the early (May–August) and late (September–December) seasons, and in June, the number in the period between July to December. The new scheme achieves a forecast skill of 51% over climatology, or an improvement of about 11% compared to previous studies, when predicting landfalling TC for the whole season, and it seems to be able to capture the decrease in their number in the recent years. Analyses of the flow patterns suggest that the conditions inside the SCS are apparently the major factor affecting the number of landfalling TCs. In years in which this number is above normal, conditions inside the SCS are favorable for TC genesis, and vice versa. The strength of the 500-hPa subtropical high also seems to be a factor in determining whether TCs from the western North Pacific (WNP) could enter the SCS and make landfall.
Abstract
This study describes an improved statistical scheme for predicting the annual number of tropical cyclones (TCs) making landfall along the coast of south China using data from 1965 to 2005. Based on the factors affecting TC behavior inside the South China Sea (SCS), those responsible for TCs making landfall are identified. Equations are then developed using the coefficients of empirical orthogonal functions of these factors to predict, in April, the number of these TCs in the early (May–August) and late (September–December) seasons, and in June, the number in the period between July to December. The new scheme achieves a forecast skill of 51% over climatology, or an improvement of about 11% compared to previous studies, when predicting landfalling TC for the whole season, and it seems to be able to capture the decrease in their number in the recent years. Analyses of the flow patterns suggest that the conditions inside the SCS are apparently the major factor affecting the number of landfalling TCs. In years in which this number is above normal, conditions inside the SCS are favorable for TC genesis, and vice versa. The strength of the 500-hPa subtropical high also seems to be a factor in determining whether TCs from the western North Pacific (WNP) could enter the SCS and make landfall.
Abstract
A detailed evaluation of the performance of the United Kingdom Meteorological Office Global Model (UKMO) in predicting the movement of 15 tropical cyclones (TCs) that occurred over the western North Pacific during 1987 is presented. The evaluation is based on the methodology used by Chan et al. That is, in addition to the usual mean forecast error, the following error measures are employed: systematic zonal and meridional errors, cross-track, and along-track error components relative to the climatology and persistence (CLIPER) track, and the M score. To identify further the strengths and weaknesses of the model, the forecasts are also evaluated according to four storm-related parameters: latitude, longitude, intensity, and 12-h intensity change.
The analyses for the entire sample show that the skill of the UKMO generally increases with forecast intervals, as in the case of other numerical prediction models. However, the short-term forecasts are worse than those of CLIPER, and a substantial error exists in the initial position of the tropical cyclone in the model. The UKMO also has a tendency to overpredict recurvature for westward-moving TCs, and acceleration for recurved TCs.
From the analyses of the subsamples stratified based on the storm-related parameters, the UKMO is found to have the best performance for TCs north of approximately 20°N and east of approximately 140°E. Tropical cyclones in the lower latitudes or in close proximity to large landmasses are usually poorly predicted by the model. The more intense the cyclone, the better the UKMO forecast. However, the model makes good predictions for TCs that are weakening.
Abstract
A detailed evaluation of the performance of the United Kingdom Meteorological Office Global Model (UKMO) in predicting the movement of 15 tropical cyclones (TCs) that occurred over the western North Pacific during 1987 is presented. The evaluation is based on the methodology used by Chan et al. That is, in addition to the usual mean forecast error, the following error measures are employed: systematic zonal and meridional errors, cross-track, and along-track error components relative to the climatology and persistence (CLIPER) track, and the M score. To identify further the strengths and weaknesses of the model, the forecasts are also evaluated according to four storm-related parameters: latitude, longitude, intensity, and 12-h intensity change.
The analyses for the entire sample show that the skill of the UKMO generally increases with forecast intervals, as in the case of other numerical prediction models. However, the short-term forecasts are worse than those of CLIPER, and a substantial error exists in the initial position of the tropical cyclone in the model. The UKMO also has a tendency to overpredict recurvature for westward-moving TCs, and acceleration for recurved TCs.
From the analyses of the subsamples stratified based on the storm-related parameters, the UKMO is found to have the best performance for TCs north of approximately 20°N and east of approximately 140°E. Tropical cyclones in the lower latitudes or in close proximity to large landmasses are usually poorly predicted by the model. The more intense the cyclone, the better the UKMO forecast. However, the model makes good predictions for TCs that are weakening.
Abstract
This paper presents the development of operational statistical forecasts of seasonal tropical cyclone (TC) activity over the western North Pacific (WNP) and the South China Sea (SCS) based on 30 yr of data (1965–94). Predictors include monthly values of indices representing (a) the El Niño–Southern Oscillation phenomenon, and (b) the environmental conditions over East Asia and the WNP for the months from April of the previous year to March of the current year. Trends and short-term oscillations of the TC activity are also incorporated.
The prediction equations are derived from the predictors of individual parameters using the Projection Pursuit Regression technique, which is a statistical method that reduces high-dimensional data to a lower-dimensional subspace before the regression is performed. This technique is found to provide explanations of certain nonlinear variations of the predictands. The predictions from individual parameters are then tested using the jackknife technique. Those predictions that have correlations (with the observed) significant at the 95% level or higher are retained. The values of the correlation coefficients are then used as weights in combining the predictions to form a single forecast of each predictand. The forecasts obtained this way are found to be superior to those from individual parameters.
The combined forecast equations are then used to predict the TC activity over the WNP and the SCS for 1997. The prediction is for a slightly above-normal activity for the entire WNP but slightly below normal for the SCS. The former is found to be correct and the latter has the right trend although the activity over the SCS was far below normal, probably as a result of the El Niño of 1997.
Abstract
This paper presents the development of operational statistical forecasts of seasonal tropical cyclone (TC) activity over the western North Pacific (WNP) and the South China Sea (SCS) based on 30 yr of data (1965–94). Predictors include monthly values of indices representing (a) the El Niño–Southern Oscillation phenomenon, and (b) the environmental conditions over East Asia and the WNP for the months from April of the previous year to March of the current year. Trends and short-term oscillations of the TC activity are also incorporated.
The prediction equations are derived from the predictors of individual parameters using the Projection Pursuit Regression technique, which is a statistical method that reduces high-dimensional data to a lower-dimensional subspace before the regression is performed. This technique is found to provide explanations of certain nonlinear variations of the predictands. The predictions from individual parameters are then tested using the jackknife technique. Those predictions that have correlations (with the observed) significant at the 95% level or higher are retained. The values of the correlation coefficients are then used as weights in combining the predictions to form a single forecast of each predictand. The forecasts obtained this way are found to be superior to those from individual parameters.
The combined forecast equations are then used to predict the TC activity over the WNP and the SCS for 1997. The prediction is for a slightly above-normal activity for the entire WNP but slightly below normal for the SCS. The former is found to be correct and the latter has the right trend although the activity over the SCS was far below normal, probably as a result of the El Niño of 1997.
Abstract
The fifth-generation Pennsylvania State University–National Center for Atmospheric Research nonhydrostatic Mesoscale Model is employed to evaluate the impact of the Geostationary Meteorological Satellite-5 water vapor and infrared atmospheric motion vectors (AMVs), incorporated with the four-dimensional variational (4DVAR) data assimilation technique, on tropical cyclone (TC) track predictions. Twenty-two cases from eight different TCs over the western North Pacific in 2002 have been examined. The 4DVAR assimilation of these satellite-derived wind observations leads to appreciable improvements in the track forecasts, with average reductions in track error of ∼5% at 12 h, 12% at 24 h, 10% at 36 h, and 7% at 48 h. Preliminary results suggest that the improvement depends on the quantity of the AMV data available for assimilation.
Abstract
The fifth-generation Pennsylvania State University–National Center for Atmospheric Research nonhydrostatic Mesoscale Model is employed to evaluate the impact of the Geostationary Meteorological Satellite-5 water vapor and infrared atmospheric motion vectors (AMVs), incorporated with the four-dimensional variational (4DVAR) data assimilation technique, on tropical cyclone (TC) track predictions. Twenty-two cases from eight different TCs over the western North Pacific in 2002 have been examined. The 4DVAR assimilation of these satellite-derived wind observations leads to appreciable improvements in the track forecasts, with average reductions in track error of ∼5% at 12 h, 12% at 24 h, 10% at 36 h, and 7% at 48 h. Preliminary results suggest that the improvement depends on the quantity of the AMV data available for assimilation.
Abstract
A recent scheme to predict tropical cyclone (TC) activity over the western North Pacific partially failed in 1997 and 1998, during which a warm and a cold event of the El Niño–Southern Oscillation (ENSO) occurred, respectively. This paper presents results of two approaches to improve on such predictions. The first is to include new predictors that are related to ENSO based on some recent research, and the second is to provide an updated prediction by incorporating monthly values of predictors in April and May of the current year.
The results suggest that new predictors related to ENSO can indeed be identified, which include temporal changes in the Southern Oscillation index, strength of the Australian monsoon, and intensity of the subtropical high in the South Pacific. These predictors, together with those selected from the original prediction scheme, are combined to form a modified scheme that in general gives better forecasts of TC activity. The updated scheme that includes April and May predictors further improves the accuracy of the predictions. Real-time predictions from both schemes for the year 2000, which were made in April and June, are found to be largely accurate. Both schemes show better skill compared with the original one.
Abstract
A recent scheme to predict tropical cyclone (TC) activity over the western North Pacific partially failed in 1997 and 1998, during which a warm and a cold event of the El Niño–Southern Oscillation (ENSO) occurred, respectively. This paper presents results of two approaches to improve on such predictions. The first is to include new predictors that are related to ENSO based on some recent research, and the second is to provide an updated prediction by incorporating monthly values of predictors in April and May of the current year.
The results suggest that new predictors related to ENSO can indeed be identified, which include temporal changes in the Southern Oscillation index, strength of the Australian monsoon, and intensity of the subtropical high in the South Pacific. These predictors, together with those selected from the original prediction scheme, are combined to form a modified scheme that in general gives better forecasts of TC activity. The updated scheme that includes April and May predictors further improves the accuracy of the predictions. Real-time predictions from both schemes for the year 2000, which were made in April and June, are found to be largely accurate. Both schemes show better skill compared with the original one.
