The coverage and quality of remotely sensed upper-tropospheric moisture parameters have improved considerably with the deployment of a new generation of operational geostationary meteorological satellites: GOES-8/9 and GMS-5. The GOES-8/9 water vapor imaging capabilities have increased as a result of improved radiometric sensitivity and higher spatial resolution. The addition of a water vapor sensing channel on the latest GMS permits nearly global viewing of upper-tropospheric water vapor (when joined with GOES and Meteosat) and enhances the commonality of geostationary meteorological satellite observing capabilities. Upper-tropospheric motions derived from sequential water vapor imagery provided by these satellites can be objectively extracted by automated techniques. Wind fields can be deduced in both cloudy and cloud-free environments. In addition to the spatially coherent nature of these vector fields, the GOES-8/9 multispectral water vapor sensing capabilities allow for determination of wind fields over multiple tropospheric layers in cloud-free environments. This article provides an update on the latest efforts to extract water vapor motion displacements over meteorological scales ranging from subsynoptic to global. The potential applications of these data to impact operations, numerical assimilation and prediction, and research studies are discussed.
The relative independence of the tropical cyclone track forecasts produced by regional and global numerical weather prediction models suggests that a simple ensemble average or consensus forecast derived from a combination of these models may be more accurate, on average, than the forecasts of the individual models. Forecast errors of a simple ensemble average of three models for the 1995-96 Atlantic hurricane seasons, and either three global models or two regional models for the western North Pacific during 1997, were compared with errors of the individual models. For the Atlantic, the mean errors for the joint ensemble were 120 km at 24 h, 194 km at 48 h, and 266 km at 72 h, which represent improvements of 16%, 20%, and 23% with respect to the best of the individual models. The joint ensemble also resulted in reduction in the standard deviation of the forecast error. The 95th percentile of forecast error for the ensemble was reduced 19%, 14%, and 23% with respect to the best of the individual models. The spread of the ensemble forecast was found to possess some potential for use by forecasters as a measure of confidence in the ensemble forecast. Similar results were found for the western North Pacific.
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