  Anthropogenic influences have been linked to tropical cyclone (TC) poleward
migration, TC extreme precipitation, and an increased proportion of major
hurricanes [1, 2, 3, 4]. Understanding past TC trends and variability is
critical for projecting future TC impacts on human society considering the
changing climate [5]. However, past trends of TC structure/energy remain
uncertain due to limited observations; subjective-analyzed and
spatiotemporal-heterogeneous "best-track" datasets lead to reduced confidence
in the assessed TC repose to climate change [6, 7]. Here, we use deep learning
to reconstruct past "observations" and yield an objective global TC wind
profile dataset during 1981 to 2020, facilitating a comprehensive examination
of TC structure/energy. By training with uniquely labeled data integrating best
tracks and numerical model analysis of 2004 to 2018 TCs, our model converts
multichannel satellite imagery to a 0-750-km wind profile of axisymmetric
surface winds. The model performance is verified to be sufficient for climate
studies by comparing it to independent satellite-radar surface winds. Based on
the new homogenized dataset, the major TC proportion has increased by ~13% in
the past four decades. Moreover, the proportion of extremely high-energy TCs
has increased by ~25%, along with an increasing trend (> one standard deviation
of the 40-y variability) of the mean total energy of high-energy TCs. Although
the warming ocean favors TC intensification, the TC track migration to higher
latitudes and altered environments further affect TC structure/energy. This new
deep learning method/dataset reveals novel trends regarding TC structure
extremes and may help verify simulations/studies regarding TCs in the changing
climate.
