TC-Zarr: an Analysis-Ready Storage Approach for Tropical Cyclone Data

Research on tropical cyclones (TCs) requires the joint analysis of geostationary satellite imagery and meteorological reanalyses. However, these datasets are typically stored in heterogeneous formats across separate archives, making integrated retrieval and computation inefficient. As a result, researchers must manually download, parse, and coregister multiple sources, leading to tedious and error-prone workflows. To address this challenge, we propose TC-Zarr, an analysis-ready storage framework that fuses geostationary imagery and reanalysis fields into a unified multidimensional Zarr data cube indexed by time, latitude, longitude and variable. TC-Zarr employs a Guided, Mass-conserving Resolution Fusion (GMRF) algorithm to align high-resolution satellite imagery with lower-resolution reanalysis data on a common target grid. The aligned datasets are stored in the Zarr cube, enabling efficient retrieval and seamless joint analysis. In a case study of Super Typhoon Doksuri (2023), TC-Zarr integrates Himawari-8 imagery with ERA5 reanalyses and achieves significant improvements in data access efficiency compared with conventional file-based workflows.