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- Author or Editor: Eric Uhlhorn x
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Abstract
In this study, a new multiscale intensity (MSI) metric for evaluating tropical cyclone (TC) intensity forecasts is presented. The metric consists of the resolvable and observable, low-wavenumber intensity represented by the sum of amplitudes of azimuthal wavenumbers 0 and 1 for wind speed within the TC vortex at the radius of maximum wind and a stochastic residual, all determined at 10-m elevation. The residual wind speed is defined as the difference between an estimate of maximum speed and the low-wavenumber intensity. The MSI metric is compared to the standard metric that includes only the maximum speed. Using stepped-frequency microwave radiometer wind speed observations from TC aircraft reconnaissance to estimate the low-wavenumber intensity and the National Hurricane Center’s best-track (BT) intensity for the maximum wind speed estimate, it is shown that the residual intensity is well represented as a stochastic quantity with small mean, standard deviation, and absolute norm values that are within the expected uncertainty of the BT estimates. The result strongly suggests that the practical predictability of TC intensity is determined by the observable and resolvable low-wavenumber intensity within the vortex. Verification of a set of high-resolution numerical forecasts using the MSI metric demonstrates that this metric provides more informative and more realistic estimates of the intensity forecast errors. It is also shown that the maximum speed metric allows for error compensation between the low-wavenumber and residual intensities, which could lead to forecast skill overestimation and inaccurate assessment of the impact of forecast system change on the skill.
Abstract
In this study, a new multiscale intensity (MSI) metric for evaluating tropical cyclone (TC) intensity forecasts is presented. The metric consists of the resolvable and observable, low-wavenumber intensity represented by the sum of amplitudes of azimuthal wavenumbers 0 and 1 for wind speed within the TC vortex at the radius of maximum wind and a stochastic residual, all determined at 10-m elevation. The residual wind speed is defined as the difference between an estimate of maximum speed and the low-wavenumber intensity. The MSI metric is compared to the standard metric that includes only the maximum speed. Using stepped-frequency microwave radiometer wind speed observations from TC aircraft reconnaissance to estimate the low-wavenumber intensity and the National Hurricane Center’s best-track (BT) intensity for the maximum wind speed estimate, it is shown that the residual intensity is well represented as a stochastic quantity with small mean, standard deviation, and absolute norm values that are within the expected uncertainty of the BT estimates. The result strongly suggests that the practical predictability of TC intensity is determined by the observable and resolvable low-wavenumber intensity within the vortex. Verification of a set of high-resolution numerical forecasts using the MSI metric demonstrates that this metric provides more informative and more realistic estimates of the intensity forecast errors. It is also shown that the maximum speed metric allows for error compensation between the low-wavenumber and residual intensities, which could lead to forecast skill overestimation and inaccurate assessment of the impact of forecast system change on the skill.
Abstract
Simultaneous observations by the lower fuselage (LF) radar, the tail (TA) radar, and the Stepped Frequency Microwave Radiometer (SFMR) on board the NOAA WP-3D aircraft are used to validate the rainfall rate estimates from microwave emission measurements of SFMR in tropical cyclones. Data collected in Hurricane Bonnie (1998) and Hurricane Humberto (2001) with a total of 820 paired samples are used in the comparisons. The SFMR 10-s path-integrated rain rates are found to have an overestimate in light rain and an underestimate in heavy rain relative to radar rainfall estimates. Examination of the existing SFMR algorithm shows that the coefficient should be changed in the attenuation—rain-rate relationship used in the inversion algorithm. After this correction, a linear regression result with a correlation coefficient of 0.8 and a slope close to 1 is obtained. But an overall high bias of 5 mm h−1 of the SFMR rainfall estimate relative to radar is also found. The error analysis shows that the bias is nearly independent of rain type, a result confirming Jorgensen and Willis’s conclusion that the drop size distributions between convective and stratiform rain in hurricanes are similar. It is also shown that the bias is a weak function of wind speed, as well as a weak inverse function of radial distance to the hurricane center. Temperature dependence has been ruled out as the main explanation. After doing sensitivity tests, the authors conclude that the bias results from a combination of two factors: an underestimate of the freezing-level height, and a downward increase of radar reflectivity in the high wind regions. If the true downward increase is 1–2 dBZ km−1, a 0.5-km underestimate of the freezing-level height could account for up to a 3–5 mm h−1 bias.
Abstract
Simultaneous observations by the lower fuselage (LF) radar, the tail (TA) radar, and the Stepped Frequency Microwave Radiometer (SFMR) on board the NOAA WP-3D aircraft are used to validate the rainfall rate estimates from microwave emission measurements of SFMR in tropical cyclones. Data collected in Hurricane Bonnie (1998) and Hurricane Humberto (2001) with a total of 820 paired samples are used in the comparisons. The SFMR 10-s path-integrated rain rates are found to have an overestimate in light rain and an underestimate in heavy rain relative to radar rainfall estimates. Examination of the existing SFMR algorithm shows that the coefficient should be changed in the attenuation—rain-rate relationship used in the inversion algorithm. After this correction, a linear regression result with a correlation coefficient of 0.8 and a slope close to 1 is obtained. But an overall high bias of 5 mm h−1 of the SFMR rainfall estimate relative to radar is also found. The error analysis shows that the bias is nearly independent of rain type, a result confirming Jorgensen and Willis’s conclusion that the drop size distributions between convective and stratiform rain in hurricanes are similar. It is also shown that the bias is a weak function of wind speed, as well as a weak inverse function of radial distance to the hurricane center. Temperature dependence has been ruled out as the main explanation. After doing sensitivity tests, the authors conclude that the bias results from a combination of two factors: an underestimate of the freezing-level height, and a downward increase of radar reflectivity in the high wind regions. If the true downward increase is 1–2 dBZ km−1, a 0.5-km underestimate of the freezing-level height could account for up to a 3–5 mm h−1 bias.
