Consensus Estimates of Tropical Cyclone Intensity using Integrated Multispectral (IR and MW) Satellite Observations

Several existing or promising satellite-based methods to estimate tropical cyclone (TC) intensity are available to forecasters today. Some of these, such as the Dvorak Technique (DT), have been utilized operationally for more than 30 years. Others, such as those based on microwave (MW) data, are just emerging as new, more capable, meteorological satellite instruments become operational. Each of the methods, by themselves, represents or promises significant contributions to TC intensity analysis. However, each technique (or instrument that it is based on) also has its limitations.

An effort is underway at CIMSS to build an integrated algorithm that is fully automated, objective, and utilizes a multispectral approach. This system builds on the latest science advances in existing (and emerging) methods. The components include: Geostationary satellite IR/VIS/WV image interpretation methods (including both the DT and Advanced Objective Dvorak Technique), and polar-orbiting satellite observations such as those from the Advanced Microwave Sounding Unit and the Special Sensor Microwave Imager. As a first step, each of these tools or methods is individually characterized in terms of accuracy and consistency in estimating TC intensity. Most importantly, this “method benchmarking” includes a thorough analysis of the error distribution and characteristic behavior in specific situations. Future versions will address data refresh and real-time availability issues, and the potential employment of time-averaging, blending, or morphing techniques. Reconnaissance ground truth reports of TC intensity are used to validate each technique and develop situation-dependent confidence indicators.

The integrated approach yields the current intensity estimate of a TC from a unified satellite perspective by employing a weighted consensus of the independent estimates. The algorithm outputs consensus intensity parameters and trends, and the graphical output also includes estimates from the individual components of the system. The algorithm was tested during the 2005 Atlantic hurricane season, and several examples of its performance will be shown. Preliminary analysis shows the integrated consensus approach can improve on the individual existing methods.