Deep Learning Reconstruction of Tropical Cyclogenesis in the Western North Pacific from Climate Reanalysis Dataset

Abstract

This study presents a deep learning (DL) architecture based on residual convolutional neural networks (ResNet) to reconstruct the climatology of tropical cyclogenesis (TCG) in the Western North Pacific (WNP) basin from climate reanalysis datasets. Using different TCG data labeling strategies and data enrichment windows for the NASA Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA2) dataset during the 1980-2020 period, we demonstrate that ResNet can reasonably reproduce the overall TCG climatology in the WNP, capturing both its seasonality and spatial distribution. Our sensitivity analyses and optimizations show that this TCG reconstruction depends on both the type of TCG climatology that one wishes to reconstruct and the strategies used to label TCG data. Of interest, analyses of different input features reveal that DL-based reconstruction of TCG climatology needs only a subset of channels rather than all available data, which is consistent with previous modeling and observational studies of TCG. These results not only enhance our understanding of the TCG process but also provide a promising pathway for predicting or downscaling TCG climatology based on large-scale environments from global model forecasts or climate output. Overall, our study demonstrates that DL can offer an effective approach for studying TC climatology beyond the traditional physical-based simulations and vortex-tracking algorithms used in current climate model analyses.