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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
A simultaneous equations solution technique is developed to estimate the noise variance density spectra of three or more independent measurement systems observing the same input. Using dew-point data collected during a National Hail Research Experiment aircraft intercomparison flight, this technique is compared to a previously developed method and is found to be superior.
Abstract
A simultaneous equations solution technique is developed to estimate the noise variance density spectra of three or more independent measurement systems observing the same input. Using dew-point data collected during a National Hail Research Experiment aircraft intercomparison flight, this technique is compared to a previously developed method and is found to be superior.
An analysis scheme is developed to estimate drift or low frequency noise in ship measurement systems during GATE. The term “measurement system”. is used to encompass the collection of hand recorded as well as automatically recorded observations. The scheme is applied to ship systems measuring pressure, wet-bulb temperature, sea-surface temperature, dry-bulb temperature, wind speed, and wind direction. The drift analysis scheme is found to be quite successful in estimating the drift for the pressure and temperature measurement systems, but little success is found in the analysis of the wind data. In many cases the analysis scheme provides information about the error content of systems that would otherwise be unavailable and shows that drift corrections based solely on ship intercomparison results can, in some cases, lead to significant errors.
An analysis scheme is developed to estimate drift or low frequency noise in ship measurement systems during GATE. The term “measurement system”. is used to encompass the collection of hand recorded as well as automatically recorded observations. The scheme is applied to ship systems measuring pressure, wet-bulb temperature, sea-surface temperature, dry-bulb temperature, wind speed, and wind direction. The drift analysis scheme is found to be quite successful in estimating the drift for the pressure and temperature measurement systems, but little success is found in the analysis of the wind data. In many cases the analysis scheme provides information about the error content of systems that would otherwise be unavailable and shows that drift corrections based solely on ship intercomparison results can, in some cases, lead to significant errors.
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
In January of 1988, significant upgrades were made to the Navy Operational Global Atmospheric Prediction System (NOGAPS). Among these improvements was the implementation of a multivariate optimum interpolation analysis scheme. Since that time, this analysis scheme has been improved and expanded to satisfy almost all of the navy's atmospheric analysis requirements. In this paper, we describe the global database available for analysis, the techniques currently used to produce the NOGAPS analyzed fields, and the modification required for other applications.
Abstract
In January of 1988, significant upgrades were made to the Navy Operational Global Atmospheric Prediction System (NOGAPS). Among these improvements was the implementation of a multivariate optimum interpolation analysis scheme. Since that time, this analysis scheme has been improved and expanded to satisfy almost all of the navy's atmospheric analysis requirements. In this paper, we describe the global database available for analysis, the techniques currently used to produce the NOGAPS analyzed fields, and the modification required for other applications.
Abstract
A cross-spectral analysis method is developed to estimate the noise variance spectra of three or more independent measurement systems observing the same input. The noise is modeled using a first-order autoregressive process. Estimates of the two process parameters are used to determine confidence limits for system noise that can be placed on the observed data.
The method has been applied to dew-point data collected in aircraft intercomparison flights and a research flight in the National Hail Research Experiment. The six dew-point systems used were manufactured by the same company and operate by electrically cooling a metal mirror until a film of water vapor or frost is optically detected on the mirrored surface. The ratio of the signal to noise variance was found to vary between about 10:1 and 100:1 at the origin and decrease to zero between about 0.15 and 0.3 Hz, both properties dependent on atmospheric conditions and the structure of system noise. Since the data were collected once per second, appropriate filtering and decimation could be performed for archiving purposes with a 50% space savings and small loss of “information.” Ninety-five percent confidence limits on the filtered observed data with a cutoff at 0.2 Hz vary from ±0.5 to ±0.1°C, with the latter figure most representative of those computed.
Abstract
A cross-spectral analysis method is developed to estimate the noise variance spectra of three or more independent measurement systems observing the same input. The noise is modeled using a first-order autoregressive process. Estimates of the two process parameters are used to determine confidence limits for system noise that can be placed on the observed data.
The method has been applied to dew-point data collected in aircraft intercomparison flights and a research flight in the National Hail Research Experiment. The six dew-point systems used were manufactured by the same company and operate by electrically cooling a metal mirror until a film of water vapor or frost is optically detected on the mirrored surface. The ratio of the signal to noise variance was found to vary between about 10:1 and 100:1 at the origin and decrease to zero between about 0.15 and 0.3 Hz, both properties dependent on atmospheric conditions and the structure of system noise. Since the data were collected once per second, appropriate filtering and decimation could be performed for archiving purposes with a 50% space savings and small loss of “information.” Ninety-five percent confidence limits on the filtered observed data with a cutoff at 0.2 Hz vary from ±0.5 to ±0.1°C, with the latter figure most representative of those computed.
Abstract
The effect of ship heating on dry-bulb temperature measurements made during GATE is investigated. It is found that measurements taken on a bow boom are less affected than those taken on the ship's bridge. It is also seen that the ship heating effect must be accounted for before meaningful estimates of the error content of dry-bulb temperature measurement systems can be determined.
Abstract
The effect of ship heating on dry-bulb temperature measurements made during GATE is investigated. It is found that measurements taken on a bow boom are less affected than those taken on the ship's bridge. It is also seen that the ship heating effect must be accounted for before meaningful estimates of the error content of dry-bulb temperature measurement systems can be determined.
Abstract
In June 1990, the assimilation of synthetic tropical cyclone observations into the Navy Operational Global Atmospheric Prediction System (NOGAPS) was initiated at Fleet Numerical Oceanography Center (FNOC). These observations are derived directly from the information contained in the tropical cyclone warnings issued by the Joint Typhoon Warning Center (JTWC) and the National Hurricane Center. This paper describes these synthetic observations, the evolution of their use at FNOC, and the details of their assimilation into NOGAPS. The results of a comprehensive evaluation of the 1991 NOGAPS tropical cyclone forecast performance in the western North Pacific are presented. NOGAPS analysis and forecast position errors were determined for all tropical circulations of tropical storm strength or greater. It was found that, after the assimilation of synthetic observations, the NOGAPS spectral forecast model consistently maintained the tropical circulations as evidenced by detection percentages of 96%, 90% and 87% for 24-, 48-, and 72-h forecasts, respectively. The average forecast position errors were 188, 299, and 434 km for the respective forecasts. The respective errors for the One-Way Influence Tropical Cyclone Model (OTCM), one of JTWC's primary track-forecasting aids, were 215, 364, and 529 km. In homogeneous comparisons the percent improvement of the NOGAPS 48- and 72-h forecasts was 14% and 12% over the OTCM and 31% and 33% over JTWC's operational Climatology–Persistence Model.
Abstract
In June 1990, the assimilation of synthetic tropical cyclone observations into the Navy Operational Global Atmospheric Prediction System (NOGAPS) was initiated at Fleet Numerical Oceanography Center (FNOC). These observations are derived directly from the information contained in the tropical cyclone warnings issued by the Joint Typhoon Warning Center (JTWC) and the National Hurricane Center. This paper describes these synthetic observations, the evolution of their use at FNOC, and the details of their assimilation into NOGAPS. The results of a comprehensive evaluation of the 1991 NOGAPS tropical cyclone forecast performance in the western North Pacific are presented. NOGAPS analysis and forecast position errors were determined for all tropical circulations of tropical storm strength or greater. It was found that, after the assimilation of synthetic observations, the NOGAPS spectral forecast model consistently maintained the tropical circulations as evidenced by detection percentages of 96%, 90% and 87% for 24-, 48-, and 72-h forecasts, respectively. The average forecast position errors were 188, 299, and 434 km for the respective forecasts. The respective errors for the One-Way Influence Tropical Cyclone Model (OTCM), one of JTWC's primary track-forecasting aids, were 215, 364, and 529 km. In homogeneous comparisons the percent improvement of the NOGAPS 48- and 72-h forecasts was 14% and 12% over the OTCM and 31% and 33% over JTWC's operational Climatology–Persistence Model.
