Long-Term Typhoon Trajectory Prediction: A Physics-Conditioned Approach
  Without Reanalysis Data
Young-Jae Park Minseok Seo Doyi Kim Hyeri Kim Sanghoon Choi Beomkyu Choi Jeongwon Ryu Sohee Son Hae-Gon Jeon Yeji Choi
In the face of escalating climate changes, typhoon intensities and their
ensuing damage have surged. Accurate trajectory prediction is crucial for
effective damage control. Traditional physics-based models, while
comprehensive, are computationally intensive and rely heavily on the expertise
of forecasters. Contemporary data-driven methods often rely on reanalysis data,
which can be considered to be the closest to the true representation of weather
conditions. However, reanalysis data is not produced in real-time and requires
time for adjustment because prediction models are calibrated with observational
data. This reanalysis data, such as ERA5, falls short in challenging real-world
situations. Optimal preparedness necessitates predictions at least 72 hours in
advance, beyond the capabilities of standard physics models. In response to
these constraints, we present an approach that harnesses real-time Unified
Model (UM) data, sidestepping the limitations of reanalysis data. Our model
provides predictions at 6-hour intervals for up to 72 hours in advance and
outperforms both state-of-the-art data-driven methods and numerical weather
prediction models. In line with our efforts to mitigate adversities inflicted
by \rthree{typhoons}, we release our preprocessed \textit{PHYSICS TRACK}
dataset, which includes ERA5 reanalysis data, typhoon best-track, and UM
forecast data.