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- Author or Editor: John A. Knaff x
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Abstract
A method is developed to adjust the Kaplan and DeMaria tropical cyclone inland wind decay model for storms that move over narrow landmasses. The basic assumption that the wind speed decay rate after landfall is proportional to the wind speed is modified to include a factor equal to the fraction of the storm circulation that is over land. The storm circulation is defined as a circular area with a fixed radius. Application of the modified model to Atlantic Ocean cases from 1967 to 2003 showed that a circulation radius of 110 km minimizes the bias in the total sample of landfalling cases and reduces the mean absolute error of the predicted maximum winds by about 12%. This radius is about 2 times the radius of maximum wind of a typical Atlantic tropical cyclone. The modified decay model was applied to the Statistical Hurricane Intensity Prediction Scheme (SHIPS), which uses the Kaplan and DeMaria decay model to adjust the intensity for the portion of the predicted track that is over land. The modified decay model reduced the intensity forecast errors by up to 8% relative to the original decay model for cases from 2001 to 2004 in which the storm was within 500 km from land.
Abstract
A method is developed to adjust the Kaplan and DeMaria tropical cyclone inland wind decay model for storms that move over narrow landmasses. The basic assumption that the wind speed decay rate after landfall is proportional to the wind speed is modified to include a factor equal to the fraction of the storm circulation that is over land. The storm circulation is defined as a circular area with a fixed radius. Application of the modified model to Atlantic Ocean cases from 1967 to 2003 showed that a circulation radius of 110 km minimizes the bias in the total sample of landfalling cases and reduces the mean absolute error of the predicted maximum winds by about 12%. This radius is about 2 times the radius of maximum wind of a typical Atlantic tropical cyclone. The modified decay model was applied to the Statistical Hurricane Intensity Prediction Scheme (SHIPS), which uses the Kaplan and DeMaria decay model to adjust the intensity for the portion of the predicted track that is over land. The modified decay model reduced the intensity forecast errors by up to 8% relative to the original decay model for cases from 2001 to 2004 in which the storm was within 500 km from land.
Abstract
Horizontal winds at 850 hPa from tropical cyclones retrieved using the nonlinear balance equation, where the mass field was determined from Advanced Microwave Sounding Unit (AMSU) temperature soundings, are compared with the surface wind fields derived from NASA's Quick Scatterometer (QuikSCAT) and Hurricane Research Division H*Wind analyses. It was found that the AMSU-derived wind speeds at 850 hPa have linear relations with the surface wind speeds from QuikSCAT or H*Wind. There are also characteristic biases of wind direction between AMSU and QuikSCAT or H*Wind. Using this information to adjust the speed and correct for the directional bias, a new algorithm was developed for estimation of the tropical cyclone surface wind field from the AMSU-derived 850-hPa winds. The algorithm was evaluated in two independent cases from Hurricanes Floyd (1999) and Michelle (2001), which were observed simultaneously by AMSU, QuikSCAT, and H*Wind. In this evaluation the AMSU adjustment algorithm for wind speed worked well. Results also showed that the bias correction algorithm for wind direction has room for improvement.
Abstract
Horizontal winds at 850 hPa from tropical cyclones retrieved using the nonlinear balance equation, where the mass field was determined from Advanced Microwave Sounding Unit (AMSU) temperature soundings, are compared with the surface wind fields derived from NASA's Quick Scatterometer (QuikSCAT) and Hurricane Research Division H*Wind analyses. It was found that the AMSU-derived wind speeds at 850 hPa have linear relations with the surface wind speeds from QuikSCAT or H*Wind. There are also characteristic biases of wind direction between AMSU and QuikSCAT or H*Wind. Using this information to adjust the speed and correct for the directional bias, a new algorithm was developed for estimation of the tropical cyclone surface wind field from the AMSU-derived 850-hPa winds. The algorithm was evaluated in two independent cases from Hurricanes Floyd (1999) and Michelle (2001), which were observed simultaneously by AMSU, QuikSCAT, and H*Wind. In this evaluation the AMSU adjustment algorithm for wind speed worked well. Results also showed that the bias correction algorithm for wind direction has room for improvement.
Abstract
Previous work, in which Advanced Microwave Sounding Unit (AMSU) data from the Atlantic Ocean and east Pacific Ocean basins during 1999–2001 were used to provide objective estimates of 1-min maximum sustained surface winds, minimum sea level pressure, and the radii of 34-, 50-, and 64-kt (1 kt ≡ 0.5144 m s−1) winds in the northeast, southeast, southwest, and northwest quadrants of tropical cyclones, is updated to reflect larger datasets, improved statistical analysis techniques, and improved estimation through dependent variable transforms. A multiple regression approach, which utilizes best-subset predictor selection and cross validation, is employed to develop the estimation models, where the dependent data (i.e., maximum sustained winds, minimum pressure, wind radii) are from the extended best track and the independent data consist of AMSU-derived parameters that give information about retrieved pressure, winds, temperature, moisture, and satellite resolution. The developmental regression models result in mean absolute errors (MAE) of 10.8 kt and 7.8 hPa for estimating maximum winds and minimum pressure, respectively. The MAE for the 34-, 50-, and 64-kt azimuthally averaged wind radii are 16.9, 13.3, and 6.8 n mi (1 n mi ≡ 1852 m), respectively.
Abstract
Previous work, in which Advanced Microwave Sounding Unit (AMSU) data from the Atlantic Ocean and east Pacific Ocean basins during 1999–2001 were used to provide objective estimates of 1-min maximum sustained surface winds, minimum sea level pressure, and the radii of 34-, 50-, and 64-kt (1 kt ≡ 0.5144 m s−1) winds in the northeast, southeast, southwest, and northwest quadrants of tropical cyclones, is updated to reflect larger datasets, improved statistical analysis techniques, and improved estimation through dependent variable transforms. A multiple regression approach, which utilizes best-subset predictor selection and cross validation, is employed to develop the estimation models, where the dependent data (i.e., maximum sustained winds, minimum pressure, wind radii) are from the extended best track and the independent data consist of AMSU-derived parameters that give information about retrieved pressure, winds, temperature, moisture, and satellite resolution. The developmental regression models result in mean absolute errors (MAE) of 10.8 kt and 7.8 hPa for estimating maximum winds and minimum pressure, respectively. The MAE for the 34-, 50-, and 64-kt azimuthally averaged wind radii are 16.9, 13.3, and 6.8 n mi (1 n mi ≡ 1852 m), respectively.
Abstract
A new and improved method for estimating tropical-cyclone (TC) flight-level winds using globally and routinely available TC information and infrared (IR) satellite imagery is presented. The developmental dataset is composed of aircraft reconnaissance (1995–2012) that has been analyzed to a 1 km × 10° polar grid that extends outward 165 km from the TC center. The additional use of an azimuthally average tangential wind at 500 km, based on global model analyses, allows the estimation of winds at larger radii. Analyses are rotated to a direction-relative framework, normalized by dividing the wind field by the observed maximum, and then decomposed into azimuthal wavenumbers in terms of amplitudes and phases. Using a single-field principal component method, the amplitudes and phases of the wind field are then statistically related to principal components of motion-relative IR images and factors related to the climatological radius of maximum winds. The IR principal components allow the wind field to be related to the radial and azimuthal variability of the wind field. Results show that this method, when provided with the storm location, the estimated TC intensity, the TC motion vector, and a single IR image, is able to estimate the azimuthal wavenumber 0 and 1 components of the wind field. The resulting wind field reconstruction significantly improves on the method currently used for satellite-based operational TC wind field estimates. This application has several potential uses that are discussed within.
Abstract
A new and improved method for estimating tropical-cyclone (TC) flight-level winds using globally and routinely available TC information and infrared (IR) satellite imagery is presented. The developmental dataset is composed of aircraft reconnaissance (1995–2012) that has been analyzed to a 1 km × 10° polar grid that extends outward 165 km from the TC center. The additional use of an azimuthally average tangential wind at 500 km, based on global model analyses, allows the estimation of winds at larger radii. Analyses are rotated to a direction-relative framework, normalized by dividing the wind field by the observed maximum, and then decomposed into azimuthal wavenumbers in terms of amplitudes and phases. Using a single-field principal component method, the amplitudes and phases of the wind field are then statistically related to principal components of motion-relative IR images and factors related to the climatological radius of maximum winds. The IR principal components allow the wind field to be related to the radial and azimuthal variability of the wind field. Results show that this method, when provided with the storm location, the estimated TC intensity, the TC motion vector, and a single IR image, is able to estimate the azimuthal wavenumber 0 and 1 components of the wind field. The resulting wind field reconstruction significantly improves on the method currently used for satellite-based operational TC wind field estimates. This application has several potential uses that are discussed within.
Abstract
A method to estimate objectively the surface wind fields associated with tropical cyclones using only data from multiple satellite platforms and satellite-based wind retrieval techniques is described. The analyses are computed on a polar grid using a variational data-fitting method that allows for the application of variable data weights to input data. The combination of gross quality control and the weighted variational analysis also produces wind estimates that have generally smaller errors than do the raw input data. The resulting surface winds compare well to the NOAA Hurricane Research Division H*Wind aircraft reconnaissance–based surface wind analyses, and operationally important wind radii estimated from these wind fields are shown to be generally more accurate than those based on climatological data. Most important, the analysis system produces global tropical cyclone surface wind analyses and related products every 6 h—without aircraft reconnaissance data. Also, the analysis and products are available in time for consideration by forecasters at the Joint Typhoon Warning Center, the Central Pacific Hurricane Center, and the National Hurricane Center in preparing their forecasts and advisories. This Multiplatform Tropical Cyclone Surface Wind Analysis (MTCSWA) product is slated to become an operationally supported product at the National Environmental Satellite Data and Information Service (NESDIS). The input data, analysis method, products, and verification statistics associated with the MTCSWA are discussed within.
Abstract
A method to estimate objectively the surface wind fields associated with tropical cyclones using only data from multiple satellite platforms and satellite-based wind retrieval techniques is described. The analyses are computed on a polar grid using a variational data-fitting method that allows for the application of variable data weights to input data. The combination of gross quality control and the weighted variational analysis also produces wind estimates that have generally smaller errors than do the raw input data. The resulting surface winds compare well to the NOAA Hurricane Research Division H*Wind aircraft reconnaissance–based surface wind analyses, and operationally important wind radii estimated from these wind fields are shown to be generally more accurate than those based on climatological data. Most important, the analysis system produces global tropical cyclone surface wind analyses and related products every 6 h—without aircraft reconnaissance data. Also, the analysis and products are available in time for consideration by forecasters at the Joint Typhoon Warning Center, the Central Pacific Hurricane Center, and the National Hurricane Center in preparing their forecasts and advisories. This Multiplatform Tropical Cyclone Surface Wind Analysis (MTCSWA) product is slated to become an operationally supported product at the National Environmental Satellite Data and Information Service (NESDIS). The input data, analysis method, products, and verification statistics associated with the MTCSWA are discussed within.
Abstract
Advanced Microwave Sounding Unit (AMSU) data are used to provide objective estimates of 1-min maximum sustained surface winds, minimum sea level pressure, and the radii of 34-, 50-, and 64-kt (1 kt ≡ 0.5144 m s−1) winds in the northeast, southeast, southwest, and northwest quadrants of tropical cyclones. The algorithms are derived from AMSU temperature, pressure, and wind retrievals from all tropical cyclones in the Atlantic and east Pacific basins during 1999–2001. National Hurricane Center best-track intensity and operational radii estimates are used as dependent variables in a multiple-regression approach. The intensity algorithms are evaluated for the developmental sample using a jackknife procedure and independent cases from the 2002 hurricane season. Jackknife results for the maximum winds and minimum sea level pressure estimates are mean absolute errors (MAE) of 11.0 kt and 6.7 hPa, respectively, and rmse of 14.1 kt and 9.3 hPa, respectively. For cases with corresponding reconnaissance data, the MAE are 10.7 kt and 6.1 hPa, and the rmse are 14.9 kt and 9.2 hPa. The independent cases for 2002 have errors that are only slightly larger than those from the developmental sample. Results from the jackknife evaluation of the 34-, 50-, and 64-kt radii show mean errors of 30, 24, and 14 n mi, respectively. The results for the independent sample from 2002 are generally comparable to the developmental sample, except for the 64-kt wind radii, which have larger errors. The radii errors for the 2002 sample with aircraft reconnaissance data available are all comparable to the errors from the jackknife sample, including the 64-kt radii.
Abstract
Advanced Microwave Sounding Unit (AMSU) data are used to provide objective estimates of 1-min maximum sustained surface winds, minimum sea level pressure, and the radii of 34-, 50-, and 64-kt (1 kt ≡ 0.5144 m s−1) winds in the northeast, southeast, southwest, and northwest quadrants of tropical cyclones. The algorithms are derived from AMSU temperature, pressure, and wind retrievals from all tropical cyclones in the Atlantic and east Pacific basins during 1999–2001. National Hurricane Center best-track intensity and operational radii estimates are used as dependent variables in a multiple-regression approach. The intensity algorithms are evaluated for the developmental sample using a jackknife procedure and independent cases from the 2002 hurricane season. Jackknife results for the maximum winds and minimum sea level pressure estimates are mean absolute errors (MAE) of 11.0 kt and 6.7 hPa, respectively, and rmse of 14.1 kt and 9.3 hPa, respectively. For cases with corresponding reconnaissance data, the MAE are 10.7 kt and 6.1 hPa, and the rmse are 14.9 kt and 9.2 hPa. The independent cases for 2002 have errors that are only slightly larger than those from the developmental sample. Results from the jackknife evaluation of the 34-, 50-, and 64-kt radii show mean errors of 30, 24, and 14 n mi, respectively. The results for the independent sample from 2002 are generally comparable to the developmental sample, except for the 64-kt wind radii, which have larger errors. The radii errors for the 2002 sample with aircraft reconnaissance data available are all comparable to the errors from the jackknife sample, including the 64-kt radii.
