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Updated TC Intensity Algorithm - Physical Retrievals with New Predictors

This update enhances the Tropical Cyclone intensity estimation algorithm by incorporating larger datasets, new predictors, and statistical corrections. The distribution of cases and error statistics for intensity estimates are extensively cross-validated, improving accuracy.

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Updated TC Intensity Algorithm - Physical Retrievals with New Predictors

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  1. CIRA/NESDIS AMSU TC Intensity and Structure Algorithm Update • Physical retrievals (no-changes) • Statistical Corrections Revised (Demuth et al 2005) • Larger Datasets 1999 – 2004, All basins • New predictors • Extensively cross validated

  2. Distribution of tropical cyclone cases by basin for the intensity estimation datasets. Shown is the dataset for MSW, with n=2637 cases. For MSLP, with n=2624 cases, only the Atlantic and east Pacific distributions are different, with 31.9% and 25.2%, respectively.

  3. Cross-validated error statistics for the estimates of MSW and MSLP, stratified by tropical cyclone intensity. The statistics for the hurricane-strength storms are shown all together, and divided by the categorical intensity based on the Saffir-Simpson scale.

  4. Comparison of variance explained (R2) and error statistics (MAE and rmse) for azimuthally averaged 34-, 50-, and 64-kt wind radii between revised algorithms (new model) and D04 algorithms (old model).

  5. SAB’s evaluations • 2005 reconnaissance pressure observations vs. AMSU intensity estimates • Through Vince (10/14/2005) Provided by Greg Gallina (NOAA/NESDIS/SAB)

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