Hurricane Harvey brought catastrophic destruction and historical flooding to the Gulf Coast region in late August 2017. Guided by numerical weather prediction models, operational forecasters at NOAA provided outstanding forecasts of Harvey’s future path and potential for record flooding days in advance. These forecasts were valuable to the public and emergency managers in protecting lives and property. The current study shows the potential for further improving Harvey’s analysis and prediction through advanced ensemble assimilation of high-spatiotemporal all-sky infrared radiances from the newly launched, next-generation geostationary weather satellite, GOES-16. Although findings from this single-event study should be further evaluated, the results highlight the potential improvement in hurricane prediction that is possible via sustained investment in advanced observing systems, such as those from weather satellites, comprehensive data assimilation methodologies that can more effectively ingest existing and future observations, higher-resolution weather prediction models with more accurate numerics and physics, and high-performance computing facilities that can perform advanced analysis and forecasting in a timely manner.

Real-time ensemble forecasts from the Pennsylvania State University (PSU) WRF EnKF system (APSU) for Hurricane Joaquin (2015) are examined in this study. The ensemble forecasts, from early in Joaquin’s life cycle, displayed large track spread, with nearly half of the ensemble members tracking Joaquin toward the U.S. East Coast and the other half tracking Joaquin out to sea. The ensemble forecasts also displayed large intensity spread, with many of the members developing into major hurricanes and other ensemble members not intensifying at all. Initial condition differences from the regions greater than (less than) 300 km were isolated by effectively removing initial condition differences in desired regions through relaxing each ensemble member to GFS (APSU) initial conditions. The regions of initial condition errors contributing to the track spread were examined, and the dominant source of track errors arose from the region greater than 300 km from the tropical cyclone center. Further examination of the track divergence revealed that the region between 600 and 900 km from the initial position of Joaquin was found to be the largest source of initial condition errors that contributed to this divergence. Small differences in the low-level steering flow, originating from perturbations between 600 and 900 km from the initial position, appear to have resulted in the bifurcation of the forecast tracks of Joaquin. The initial condition errors north of the initial position of Joaquin were also shown to contribute most significantly to the track divergence. The region inside of 300 km, specifically, the initial intensity of Joaquin, was the dominant source of initial condition errors contributing to the intensity spread.

The dynamics of an asymmetric rainband complex leading into secondary eyewall formation (SEF) are examined in a simulation of Hurricane Matthew (2016), with particular focus on the tangential wind field evolution. Prior to SEF, the storm experiences an axisymmetric broadening of the tangential wind field as a stationary rainband complex in the downshear quadrants intensifies. The axisymmetric acceleration pattern that causes this broadening is an inward-descending structure of positive acceleration nearly 100 km wide in radial extent and maximizes in the low-levels near 50 km radius. Vertical advection from convective updrafts in the downshear-right quadrant largely contributes to the low-level acceleration maximum, while the broader inward-descending pattern is due to horizontal advection within stratiform precipitation in the downshear-left quadrant. This broad slantwise pattern of positive acceleration is due to a mesoscale descending inflow (MDI) that is driven by midlevel cooling within the stratiform regions and draws absolute angular momentum inward. The MDI is further revealed by examining the irrotational component of the radial velocity, which shows the MDI extending downwind into the upshear-left quadrant. Here, the MDI connects with the boundary layer, where new convective updrafts are triggered along its inner edge; these new upshear-left updrafts are found to be important to the subsequent axisymmetrization of the low-level tangential wind maximum within the incipient secondary eyewall.

Tropical cyclones (TCs; see Appendix A for a list of acronyms) are among the most devastating natural disasters in the tropics and mid-latitudes. They make for a triple-threat of wind damage, surge inundation, and inland/freshwater flooding, the last of which is a leading cause of fatalities in the United States from TCs (Rappaport, 2014). Accurate predictions of TCs are valuable to society because they facilitate targeted and efficient preparations for mitigating the loss of life and property.While forecasts of TC track and intensity have been continually improving over recent decades (e.g., Cangialosi et al., 2020;DeMaria et al., 2014), one important remaining challenge is accurate prediction of hazardous TC precipitation (Kidder et al., 2005). Hazardous TC precipitation events are difficult to predict because such events often result from hard-to-predict TC rain bands (e.g., Hurricane Harvey (2017); Blake & Zelinsky, 2018) and

Hurricane Patricia (2015) was a record-breaking tropical cyclone that was difficult to forecast in real time by both operational numerical weather prediction models and operational forecasters. The current study examines the potential for improving intensity prediction for extreme cases like Hurricane Patricia. We find that Patricia’s intensity predictability is potentially limited by both initial conditions, related to the data assimilation, and model errors. First, convection-permitting assimilation of airborne Doppler radar radial velocity observations with an ensemble Kalman filter (EnKF) demonstrates notable intensity forecast improvements over assimilation of conventional observations alone. Second, decreasing the model horizontal grid spacing to 1 km and reducing the surface drag coefficient at high wind speed in the parameterization of the sea surface–atmosphere exchanges is also shown to notably improve intensity forecasts. The practical predictability of Patricia, its peak intensity, rapid intensification, and the underlying dynamics are further investigated through a high-resolution 60-member ensemble initialized with realistic initial condition uncertainties represented by the EnKF posterior analysis perturbations. Most of the ensemble members are able to predict the peak intensity of Patricia, but with greater uncertainty in the timing and rate of intensification; some members fail to reach the ultimate peak intensity before making landfall. Ensemble sensitivity analysis shows that initial differences in the region beyond the radius of maximum wind contributes the most to the differences between ensemble members in Patricia’s intensification. Ensemble members with stronger initial primary and secondary circulations beyond the radius of maximum wind intensify earlier, are able to maintain the intensification process for longer, and thus reach a greater and earlier peak intensity.