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High-frequency (HF) radars can provide high-resolution and frequent ocean surface currents observations during tropical cyclone (TC) landfall. We describe the first assimilation of such potential observations using idealized twin experiments with and without these observations. The data assimilation system consists of the Ensemble Adjustment Kalman Filter and a coupled ocean-atmosphere model. In this system, synthetic HF radar-observed coastal currents are assimilated, and the 24-, 48- and 72-hr forecast performances are examined for TCs with various intensities, sizes, and translation speeds. Assimilating coastal surface currents improves the intensity forecast. The errors of the maximum wind speed reduce by 2.7 (33%) and 1.9 m/s (60%) in the 72-hr forecast and 2.8 (40%) and 1.4 m/s (62%) in the 48-hr forecast, for Category 4 and 2 cyclones, respectively. These improvements are similar to the current operational TC forecast errors, so that assimilating HF radar observations could be a substantial benefit.

Plain Language Summary Tropical cyclones (TC) cause great loss but the forecast of the intensity has not improved significantly for a long time. High-frequency radar systems measure the coastal (up to 200 km from the coast) surface currents with high resolution, even during TC landfall. In this study, we assimilate the synthetic observations in an idealized coupled tropical cyclone forecast system. By using strongly coupled data assimilation, the ocean observations update the atmospheric state and produce significant improvements. For instance, the errors of the maximum wind speed reduce by 2.7 (33%) and 1.9 m/s (60%) in 72-hr forecast and 2.8 (40%) and 1.4 m/s (62%) in 48-hr forecast, for Category 4 and 2 cyclones, respectively. These improvements are similar to the magnitude of the current operational TC forecast error (7 and 6 m/s for 72- and 48-hr forecast), so that assimilating the radar observations could be a substantial benefit.