Thursday, 19 April 2018
Champions DEFGH (Sawgrass Marriott)
Observations from field campaigns such as ONR TCI and NOAA IFEX have provided unprecedented observations to improve the prediction and understanding of hurricane rapid intensification. Our early work showed that assimilating these observations using the newly developed GSI-based, continuously cycled, dual-resolution hybrid 3DEnVar DA (data assimilation) system for HWRF (Lu and Wang et al., 2017) improve different aspects of the analyses for hurricane Patricia (2015). Specifically, the TCI HDSS dropsonde data improved the 3-dimensional analyses of both the inner core and outflow regions of the storm. CIMSS AMV data can help modify the upper level environmental flow 100 km away from the storm center, complementary to the TCI data. TDR improved the 3-dimensional inner core structures of the storm especially in the middle and lower levels. Flight level and SFMR data improved the 3-dimensional inner core structure of Patricia but have little impact on the upper level. These observations are complementary to each other and altogether providing a better description of the storm structures through DA. However, significant spin-down (Vmax drops greater than 10m/s for the first 6 hours) has been found and jeopardized the realistic DA impacts on intensity forecasts. Our recent work suggested that the spin-down issue can be attributed to the inferior model physics that can not maintain the realistic DA analyses and a modified turbulent mixing parameterization scheme significantly alleviated the spin down issue (Lu and Wang 2017).
Therefore, in this study, we revisited the impacts of these field campaign observations with improved model physics on the prediction of rapid intensification as well as the structure evolution of Patricia. The inner-core observations like TCI dropsondes and TDR observations are found to improve both the analyzed and forecasted storm structures the most compared to other individual observations. Further experiments show that the peak intensity forecasts can be improved significantly by increasing model resolution. With a higher model resolution, TCI out of all individual observations shows the best Vmax forecast. Overall, combining all the observations showed the best analyses and forecasts compared to assimilating individual type of observations.
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner