Passive microwave enhanced statistical hurricane intensity prediction scheme

The formulation and testing of an enhanced Statistical Hurricane Intensity Prediction Scheme (SHIPS) using new predictors derived from passive microwave imagery is presented. Passive microwave imagery is acquired for tropical cyclones in the Atlantic, Eastern North Pacific, Western North Pacific, and Southern Hemisphere between 1995 and 2003. Predictors relating to the inner core (within 100 km of center) precipitation and convective characteristics of tropical cyclones are derived. These predictors are combined with the climatological and environmental predictors used by SHIPS in a simple linear regression model with change in tropical cyclone intensity as the predictand. Separate linear regression models are produced for forecast intervals of 12, 24, 36, 48, 60, and 72 h from the time of a microwave sensor overpass.; Analysis of the resulting models indicates that microwave predictors, which provide an intensification signal to the model when above average precipitation and convective signatures are present, have comparable importance to vertical wind shear and sea surface temperature related predictors. The addition of the microwave predictors produces a 2 to 8% improvement in performance for tropical cyclone intensity forecasts out to 72 h when compared to an environmental-only model trained from the same sample. Improvement is also observed when compared against the current operational version of SHIPS. The improvement is greatest for substantially intensifying or weakening tropical cyclones.; More advanced microwave predictors derived using principle component analysis and classification techniques further increased skill for all basins with the exception of the Southern Hemisphere. However, further improvement over more basic microwave predictors was limited to less than 2%. The case studies reveal that environmental and inner core precipitation characteristics are interrelated and are both important to intensity change forecasting. Often, no one meteorological signal produces the correct forecast, but a combination of many is required.