Improved Tropical Cyclone Intensity Forecasts by Assimilating Coastal Surface Currents in an Idealized Study

Abstract

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.

Development of a Convection‐Permitting Air‐Sea‐Coupled Ensemble Data Assimilation System for Tropical Cyclone Prediction

“…The WRF model state variables to be updated during the analysis stage are temperature, pressure perturbation, three‐dimensional Cartesian velocity, and mixing ratios of water vapor, rainwater, graupel, cloud water, and cloud ice, which are identical to that in Zhang et al (). The ROMS model state variables to be updated are horizontal currents, sea surface height, temperature, and salinity, which are the same as in Li and Toumi (). y in equation represents the observation vector, and H is the observation operator mapping the state variables onto the observations.…”

Nonlinear Impacts of Surface Exchange Coefficient Uncertainty on Tropical Cyclone Intensity and Air‐Sea Interactions

“…Lastly, the surface exchange coefficient-driven differences in ocean feedbacks, combined with the recent advances in coupled data assimilation (Chen & Zhang, 2019;Li & Toumi, 2018;Penny & Hamill, 2017;Sluka et al, 2016), also highlight a potential opportunity. It may be possible in the near future to utilize ocean observations-in addition to available atmospheric observations-to constrain the uncertainty in the surface exchange coefficients using a well-developed coupled ocean-atmosphere ensemble data assimilation system through parameter estimation (Aksoy et al, 2006a(Aksoy et al, , 2006bHu et al, 2010).…”

Promises in Air‐Sea Fully Coupled Data Assimilation for Future Hurricane Prediction

“…The recent article by Li and Toumi (2018) explored the potential for improving tropical cyclone intensity forecasts by assimilating synthetic coastal surface currents from simulated high-frequency (HF) radar observations. Although this is an idealized study using simulated observations initialized with a synthetic ZHANG AND EMANUEL 13,173…”

“…Since the HF radar can measure surface currents in all conditions, it is also possible that such high-resolution observations can be used to estimate the elusive, uncertain air-sea exchange coefficients, for example, through augmenting such uncertain flux coefficients as part of the state variables in a coupled-model ensemble Kalman filter system that can perform ensemble-based simultaneous state and parameter estimation. The likely strong, flow-dependent correlations between these flux coefficients, especially the drag and the surface ocean currents, will help to better estimate these coefficients and better initialize both the atmosphere and ocean circulations, as demonstrated in Li and Toumi (2018).…”

“…The recent article by Li and Toumi (2018) explored the potential for improving tropical cyclone intensity forecasts by assimilating synthetic coastal surface currents from simulated high-frequency (HF) radar observations. Although this is an idealized study using simulated observations initialized with a synthetic vortex in a quiescent environment, to the best of our knowledge, this is the first study that explores a new frontier for future hurricane prediction through ingesting remotely sensed oceanic current observations into a fully coupled analysis and prediction system.…”