Abstract: To investigate characteristics of near-surface winds associated with explosive cyclones (ECs) over the mid-latitude oceans of the Northern Hemisphere, a multiscale data assimilation framework by National Oceanic and Atmospheric Administration (NOAA) is adopted, and a high-resolution sea surface wind dataset produced by Space-time Multigrid Analysis System (STMAS) during the period from 2019 to 2023 is utilized. This dataset integrates multi-source satellite-derived sea surface wind speed data with in-situ observations, such as those collected from oceanic buoys, thereby providing a more complete and accurate representation of wind conditions over the ocean surface. To evaluate the performance and advantages of this merged dataset (MERG), comprehensive comparisons are conducted with other widely used sea surface wind products, including the atmospheric reanalysis dataset from European Center for Medium-range Weather Forecasts (ECMWF) and Cross-calibrated Multi-platform (CCMP) ocean wind product, which are both frequently used in operational forecasting and climate research.
Results show that MERG outperforms these datasets in several key aspects related to EC-associated wind structures. First, due to its finer spatial resolution and advanced multiscale analysis capabilities, MERG is able to resolve more realistic and detailed spatial distributions of EC-induced near-surface strong winds, especially in complex regions such as marginal and coastal seas, where small-scale features are often underrepresented or even missed in coarser-resolution datasets. Second, MERG captures more extreme wind speeds and more rapid temporal changes with greater accuracy. Specifically, the maximum near-surface wind speed associated with ECs reaches 55.71 m·s^-1, and the largest observed 6-h wind speed change exceeds 11.58 m·s^-1, both of which highlight the system’s ability to effectively track fast-evolving hazardous wind events. Third, during mature and decaying stages of cyclone development, MERG demonstrates a superior capability in depicting strong winds driven by cold conveyor belts (CCBs), showing significantly greater spatial extent and intensity than those captured by the other datasets examined.
These advantages collectively suggest that MERG dataset provides a more accurate, high-resolution, and comprehensive characterization of EC-induced surface wind structures, and therefore holds great potential for improving our scientific understanding of hazardous marine wind events and enhancing early warning and disaster preparedness systems for oceanic storms.