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- Author or Editor: James S. Goerss x
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Abstract
The extent to which the tropical cyclone (TC) track forecast error of a consensus model (CONU) routinely used by the forecasters at the National Hurricane Center can be predicted is determined. A number of predictors of consensus forecast error, which must be quantities that are available prior to the official forecast deadline, were examined for the Atlantic basin in 2001–03. Leading predictors were found to be consensus model spread, defined to be the average distance of the member forecasts from the consensus forecast, and initial and forecast TC intensity. Using stepwise linear regression and the full pool of predictors, regression models were found for each forecast length to predict the CONU TC track forecast error. The percent variance of CONU TC track forecast error that could be explained by these regression models ranged from just over 15% at 48 h to nearly 50% at 120 h. Using the regression models, predicted radii were determined and were used to draw circular areas around the CONU forecasts that contained the verifying TC position 73%–76% of the time. Based on the size of these circular areas, a forecaster can determine the confidence that can be placed upon the CONU 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) track forecast error of a consensus model (CONU) routinely used by the forecasters at the National Hurricane Center can be predicted is determined. A number of predictors of consensus forecast error, which must be quantities that are available prior to the official forecast deadline, were examined for the Atlantic basin in 2001–03. Leading predictors were found to be consensus model spread, defined to be the average distance of the member forecasts from the consensus forecast, and initial and forecast TC intensity. Using stepwise linear regression and the full pool of predictors, regression models were found for each forecast length to predict the CONU TC track forecast error. The percent variance of CONU TC track forecast error that could be explained by these regression models ranged from just over 15% at 48 h to nearly 50% at 120 h. Using the regression models, predicted radii were determined and were used to draw circular areas around the CONU forecasts that contained the verifying TC position 73%–76% of the time. Based on the size of these circular areas, a forecaster can determine the confidence that can be placed upon the CONU 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 relative independence of the tropical cyclone track forecasts produced by regional and global numerical weather prediction models suggests that a simple ensemble average or consensus forecast derived from a combination of these models may be more accurate, on average, than the forecasts of the individual models. Forecast errors of a simple ensemble average of three models for the 1995–96 Atlantic hurricane seasons, and either three global models or two regional models for the western North Pacific during 1997, were compared with errors of the individual models. For the Atlantic, the mean errors for the joint ensemble were 120 km at 24 h, 194 km at 48 h, and 266 km at 72 h, which represent improvements of 16%, 20%, and 23% with respect to the best of the individual models. The joint ensemble also resulted in reduction in the standard deviation of the forecast error. The 95th percentile of forecast error for the ensemble was reduced 19%, 14%, and 23% with respect to the best of the individual models. The spread of the ensemble forecast was found to possess some potential for use by forecasters as a measure of confidence in the ensemble forecast. Similar results were found for the western North Pacific.
Abstract
The relative independence of the tropical cyclone track forecasts produced by regional and global numerical weather prediction models suggests that a simple ensemble average or consensus forecast derived from a combination of these models may be more accurate, on average, than the forecasts of the individual models. Forecast errors of a simple ensemble average of three models for the 1995–96 Atlantic hurricane seasons, and either three global models or two regional models for the western North Pacific during 1997, were compared with errors of the individual models. For the Atlantic, the mean errors for the joint ensemble were 120 km at 24 h, 194 km at 48 h, and 266 km at 72 h, which represent improvements of 16%, 20%, and 23% with respect to the best of the individual models. The joint ensemble also resulted in reduction in the standard deviation of the forecast error. The 95th percentile of forecast error for the ensemble was reduced 19%, 14%, and 23% with respect to the best of the individual models. The spread of the ensemble forecast was found to possess some potential for use by forecasters as a measure of confidence in the ensemble forecast. Similar results were found for the western North Pacific.
Abstract
The tropical cyclone (TC) track forecasts of the Navy Operational Global Atmospheric Prediction System (NOGAPS) were evaluated for a number of data assimilation experiments conducted using observational data from two periods: 4 July–31 October 2005 and 1 August–30 September 2006. The experiments were designed to illustrate the impact of different types of satellite observations on the NOGAPS TC track forecasts. The satellite observations assimilated in these experiments consisted of feature-track winds from geostationary and polar-orbiting satellites, Special Sensor Microwave Imager (SSM/I) total column precipitable water and wind speeds, Advanced Microwave Sounding Unit-A (AMSU-A) radiances, and Quick Scatterometer (QuikSCAT) and European Remote Sensing Satellite-2 (ERS-2) scatterometer winds. There were some differences between the results from basin to basin and from year to year, but the combined results for the 2005 and 2006 test periods for the North Pacific and Atlantic Ocean basins indicated that the assimilation of the feature-track winds from the geostationary satellites had the most impact, ranging from 7% to 24% improvement in NOGAPS TC track forecasts. This impact was statistically significant at all forecast lengths. The impact of the assimilation of SSM/I precipitable water was consistently positive and statistically significant at all forecast lengths. The improvements resulting from the assimilation of AMSU-A radiances were also consistently positive and significant at most forecast lengths. There were no significant improvements/degradations from the assimilation of the other satellite observation types [e.g., Moderate Resolution Imaging Spectroradiometer (MODIS) winds, SSM/I wind speeds, and scatterometer winds]. The assimilation of all satellite observations resulted in a gain in skill of roughly 12 h for the NOGAPS 48- and 72-h TC track forecasts and a gain in skill of roughly 24 h for the 96- and 120-h forecasts. The percent improvement in these forecasts ranged from almost 20% at 24 h to over 40% at 120 h.
Abstract
The tropical cyclone (TC) track forecasts of the Navy Operational Global Atmospheric Prediction System (NOGAPS) were evaluated for a number of data assimilation experiments conducted using observational data from two periods: 4 July–31 October 2005 and 1 August–30 September 2006. The experiments were designed to illustrate the impact of different types of satellite observations on the NOGAPS TC track forecasts. The satellite observations assimilated in these experiments consisted of feature-track winds from geostationary and polar-orbiting satellites, Special Sensor Microwave Imager (SSM/I) total column precipitable water and wind speeds, Advanced Microwave Sounding Unit-A (AMSU-A) radiances, and Quick Scatterometer (QuikSCAT) and European Remote Sensing Satellite-2 (ERS-2) scatterometer winds. There were some differences between the results from basin to basin and from year to year, but the combined results for the 2005 and 2006 test periods for the North Pacific and Atlantic Ocean basins indicated that the assimilation of the feature-track winds from the geostationary satellites had the most impact, ranging from 7% to 24% improvement in NOGAPS TC track forecasts. This impact was statistically significant at all forecast lengths. The impact of the assimilation of SSM/I precipitable water was consistently positive and statistically significant at all forecast lengths. The improvements resulting from the assimilation of AMSU-A radiances were also consistently positive and significant at most forecast lengths. There were no significant improvements/degradations from the assimilation of the other satellite observation types [e.g., Moderate Resolution Imaging Spectroradiometer (MODIS) winds, SSM/I wind speeds, and scatterometer winds]. The assimilation of all satellite observations resulted in a gain in skill of roughly 12 h for the NOGAPS 48- and 72-h TC track forecasts and a gain in skill of roughly 24 h for the 96- and 120-h forecasts. The percent improvement in these forecasts ranged from almost 20% at 24 h to over 40% at 120 h.
Abstract
Two assimilation schemes are described in which continuous indirect insertion of satellite-derived temperatures is performed, using a global primitive equation forecast model. Both schemes employ a relatively simple indirect insertion technique but utilize different methods (Noise Freezing Methods I and II) to control the noise induced within the forecast model by data insertion. Using data collected in February 1976 during NASA Data Systems Test 6, the effectiveness of these schemes is compared with that of a third scheme in which satellite-derived temperatures are assimilated using the same techniques that most operational forecast centers employ. After a 36 h start-up period, 48 h forecasts were produced using each assimilation scheme and root-mean-square errors computed for the differences between the forecast fields and upper-air observations of geopotential height. The forecasts of geopotential height made using the noise freezing methods are found to show substantial improvements over those made using conventional techniques. The forecasts produced using Noise Freezing Methods I and II are comparable with each other, and show average percent improvements over conventional forecasts ranging from 5 to 10% at 850 mb and from 10 to 15% at both 500 and 300 mb. Improvements of nearly 25% are observed for individual forecasts.
Abstract
Two assimilation schemes are described in which continuous indirect insertion of satellite-derived temperatures is performed, using a global primitive equation forecast model. Both schemes employ a relatively simple indirect insertion technique but utilize different methods (Noise Freezing Methods I and II) to control the noise induced within the forecast model by data insertion. Using data collected in February 1976 during NASA Data Systems Test 6, the effectiveness of these schemes is compared with that of a third scheme in which satellite-derived temperatures are assimilated using the same techniques that most operational forecast centers employ. After a 36 h start-up period, 48 h forecasts were produced using each assimilation scheme and root-mean-square errors computed for the differences between the forecast fields and upper-air observations of geopotential height. The forecasts of geopotential height made using the noise freezing methods are found to show substantial improvements over those made using conventional techniques. The forecasts produced using Noise Freezing Methods I and II are comparable with each other, and show average percent improvements over conventional forecasts ranging from 5 to 10% at 850 mb and from 10 to 15% at both 500 and 300 mb. Improvements of nearly 25% are observed for individual forecasts.
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 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.
