Practical Uncertainties in the Limited Predictability of the Record-Breaking Intensification of Hurricane Patricia (2015)

Abstract

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.

Tropical Imaginaries and Climate Crisis: Embracing Relational Climate Discourses

“…However, Patricia changed her trajectory and intensified at an unprecedented speed, becoming, in approximately 24 hours, the most powerful hurricane ever recorded in terms of maximum sustained wind velocity (325 km/h), breaking several global records in the western hemisphere (Rogers et. al., 2017;Nystrom & Zhang, 2019). Following a completely contingent behavior, while still over the sea hurricane Patricia surprisingly weakened, also very quickly, thus reaching a only a scant rural land area in the state of Jalisco, Mexico, leaving seven dead and approximately 325 million dollars in damage .…”

Understanding Error Distributions of Hurricane Intensity Forecasts during Rapid Intensity Changes

“…Errors associated with RW occur in environments similar to RI with the largest error magnitudes for higher RH. The largest intensity forecast errors occur at low shear values with warm SST suggesting that additional factors and processes need to be considered to forecast RI and RW, such as inner-core dynamics (Van Sang et al 2008;Aberson et al 2015;Nystrom and Zhang 2019).…”

“…The inner-core dynamics have been shown to be important in understanding Hurricane Patricia's (2017) both record-breaking RI and over-ocean RW (Doyle et al 2017;Rogers et al 2017;Martinez et al 2019). Nystrom and Zhang (2019) found that assimilating inner-core radial velocities in Hurricane Patricia resulted in better forecasts of RI and a 40% reduction in forecast errors. In addition to intensity guidance from dynamical models, the development and implementation of the Statistical Hurricane Intensity Prediction Scheme (SHIPS) rapid intensification index (RII) has played a significant role in RI prediction and is a key operational forecast tool at NHC (Rozoff and Kossin 2011;Kaplan et al 2015;Cangialosi et al 2020).…”

Nonlinear Impacts of Surface Exchange Coefficient Uncertainty on Tropical Cyclone Intensity and Air‐Sea Interactions

“…While the significant advancements in global numerical weather prediction over recent decades (Bauer et al, 2015) have helped to significantly improve TC track forecasts (Alley et al, 2019; Cangialosi, 2019), TC intensity forecasts have improved at a much slower rate (Cangialosi, 2019). The smaller realized improvements in TC intensity prediction are the result of a combination of factors including insufficient model resolution (e.g.,Davis et al, 2008; Jin et al, 2014; Nystrom & Zhang, 2019), errors in initial conditions (e.g.,Brown & Hakim, 2013; Emanuel & Zhang, 2016; Hakim, 2013; Nystrom et al, 2018; Torn, 2016; Van Sang et al, 2008; Zhang & Sippel, 2009), and errors in model physics (e.g.,Braun & Tao, 2000; Bu et al, 2014; Green & Zhang, 2013, 2014; Judt et al, 2015; Melhauser et al, 2017).…”

“…Bell et al (2012) attempted to estimate the surface exchange coefficients and suggested that the current uncertainty may be larger than 40%. Additionally, Nystrom and Zhang (2019) suggested that the impacts from uncertainty in the surface drag coefficient can be as influential as initial condition uncertainty in limiting the intensity predictability of a strong TC.…”