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This study explores the impact on hurricane data assimilation and forecasts from the use of dropsondes and remotely sensed moisture profiles from the airborne Lidar Atmospheric Sensing Experiment (LASE) system. It is shown here that the use of these additional datasets, more than those from the conventional world weather watch, has a positive impact on hurricane predictions. The forecast tracks and intensity from the experiments show a marked improvement compared to the control experiment in which such datasets were excluded. A study of the moisture budget in these hurricanes showed enhanced evaporation and precipitation over the storm area. This resulted in these datasets making a large impact on the estimate of mass convergence and moisture fluxes, which were much smaller in the control runs. Overall this study points to the importance of high vertical resolution humidity datasets for improved model results. It is noted that the forecast impact from the moisture-profiling datasets for some of the storms is even larger than the impact from the use of dropwindsonde-based winds.

Impact of CAMEX-4 Datasets for Hurricane Forecasts Using a Global Model