Data from: TC-GEN: Data-driven tropical cyclone downscaling using machine learning-based high-resolution weather model

Synthetic downscaling of tropical cyclones (TCs) is critically important
to estimate the long-term hazard of rare high-impact storm events.
Existing downscaling approaches rely on statistical or
statistical-deterministic models that are capable of generating large
samples of synthetic storms with characteristics similar to observed
storms. However, these models do not capture the complex two-way
interactions between a storm and its environment. In addition, these
approaches either necessitate a separate TC size model to simulate storm
size or involve post-processing to capture the asymmetries in the
simulated surface wind. In this study, we present an innovative
data-driven approach for TC synthetic downscaling. Using a machine
learning-based high-resolution global weather model (ML-GWM), our approach
can simulate the full life cycle of a storm with asymmetric surface wind
that accounts for the two-way interactions between the storm and its
environment. This approach consists of multiple components: a data-driven
model for generating synthetic TC seeds, a blending method that seamlessly
integrates storm seeds into the surrounding while maintaining the seed
structure, and a model based on a recurrent neural network to correct for
biases in storm intensity. Compared to observations and synthetic storms
simulated using existing statistical-deterministic and statistical
downscaling approaches, our method shows the ability to effectively
capture many aspects of TC statistics, including track density, landfall
frequency, landfall intensity, and outermost wind extent. Leveraging the
computational efficiency of ML-GWM, our approach shows substantial
potential for TC regional hazard and risk assessment.