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Satellite salinity data from the Soil Moisture and Ocean Salinity (SMOS) mission was recently enhanced, increasing the spatial extent near the coast that eluded earlier versions. In a pilot attempt we assimilate this data into a coastal ocean model (ROMS) using variational assimilation and, for the first time, investigate the impact on the simulation of a major river plume (the Congo River). Four experiments were undertaken consisting of a control (without data assimilation) and the assimilation of either sea surface height (SSH), SMOS and the combination of both, SMOS SSH. Several metrics specific to the plume were utilised, including the area of the plume, distance to the centre of mass, orientation and average salinity. The assimilation of SMOS and combined SMOS SSH consistently produced the best results in the plume analysis. Argo float salinity profiles provided independent verification of the forecast. The SMOS or SMOS SSH forecast produced the closest agreement for Argo profiles over the whole domain (outside and inside the plume) for three of four months analysed, improving over the control and a persistence baseline. The number of samples of Argo floats determined to be inside the plume were limited. Nevertheless, for the limited plume-detected floats the largest improvements were found for the SMOS or SMOS SSH forecast for two of the four months.

The inland effects of major tropical cyclones (TC) can be devastating (Coch, 2020). An increase of inland risk is implied by recent trends of intensification over the ocean (Bhatia et al., 2019;Wang et al., 2020), coastal migration (Wang & Toumi, 2021), and slowing land decay (Li & Chakraborty, 2020). A combination of factors are thought to control the decay of TCs after landfall including the reduction of surface moisture fluxes and increased frictional dissipation from the rougher land (Chen & Chavas, 2020). A simple empirical exponential model of post-landfall decay (Kaplan & DeMaria, 1995) has been widely used operationally within the Statistical Hurricane Intensity Prediction Scheme (SHIPS) (DeMaria et al., 2005), typhoon prediction scheme of Knaff et al. (2005) and the Southern Hemisphere Statistical Typhoon Intensity Prediction Scheme (SH STIPS) (Knaff & Sampson, 2009). In stochastic risk modeling, the exponential model is also used (Bloemendaal et al., 2020). In regional studies, the exponential model has been applied to case studies for New England, USA (Kaplan & DeMaria, 2001), India (Bhowmik et al., 2005), and Southern China (Wong et al., 2008) landfalls. In a recent trend analysis, the exponential model was used to infer a recent slowing of land decay of hurricanes (Li & Chakraborty, 2020). Several refinements have been considered by including an adjustment to the initial landfall wind speed, consideration of islands (DeMaria et al., 2006), and a pressure filling variant (Vickery, 2005).Despite the extensive use of the exponential model, an empirical model can only provide limited understanding of the decay problem. Furthermore, theoretical studies of the spin-down of geophysical vortexes by Greenspan and Howard (1963) and Eliassen (1971) have demonstrated that the exponential decay of the tangential winds is strictly only valid for a laminar boundary layer. Therefore the assumption of a simple exponential decay is questionable for TC environments with shear-driven turbulent flow (Montgomery et al., 2001). An alternative for a turbulent flow regime would be in the form first theorized by Eliassen (1971) and expanded in Eliassen and Lystad (1977), predicting an algebraic temporal decay. This theory was later validated by Montgomery et al. ( 2001) for modeled hurricane strength vortexes over the

A new ocean evaluation metric, the crossover time, is defined as the time it takes for a numerical model to equal the performance of persistence. As an example, the average crossover time calculated using the Lagrangian separation distance (the distance between simulated trajectories and observed drifters) for the global MERCATOR ocean model analysis is found to be about 6 days. Conversely, the model forecast has an average crossover time longer than 6 days, suggesting limited skill in Lagrangian predictability by the current generation of global ocean models. The crossover time of the velocity error is less than 3 days, which is similar to the average decorrelation time of the observed drifters. The crossover time is a useful measure to quantify future ocean model improvements.

The forecast of tropical cyclone (TC) intensity is a significant challenge. In this study, we showcase the impact of strongly coupled data assimilation with hypothetical ocean currents on analyses and forecasts of Typhoon Hato (2017). Several observation simulation system experiments were undertaken with a regional coupled ocean-atmosphere model. We assimilated combinations of (or individually) a hypothetical coastal current HF radar network, a dense array of drifter floats and minimum sea-level pressure. During the assimilation, instant updates of many important atmospheric variables (winds and pressure) are achieved from the assimilation of ocean current observations using the cross-domain error covariance, significantly improving the track and intensity analysis of Typhoon Hato. As compared to a control experiment (with no assimilation), the error of minimum pressure decreased by up to 13 hPa (4 hPa / 57 % on average). The maximum wind speed error decreased by up to 18 knots (5 knots / 41 % on average). By contrast, weakly coupled implementations cannot match these reductions (10% on average). Although traditional atmospheric observations were not assimilated, such improvements indicate there is considerable potential in assimilating ocean currents from coastal HF radar, and surface drifters within a strongly coupled framework for intense landfalling TCs.

<p>The forecast of tropical cyclone (TC) intensity is a significant challenge.  In this study, we showcase the impact of strongly coupled data assimilation with hypothetical ocean currents on analyses and forecasts of Typhoon Hato (2017). </p><p>Several observation simulation system experiments were undertaken with a regional coupled ocean-atmosphere model. We assimilated combinations of (or individually) a hypothetical coastal current HF radar network, a dense array of drifter floats and minimum sea-level pressure. During the assimilation, instant updates of many important atmospheric variables (winds and pressure) are achieved from the assimilation of ocean current observations using the cross-domain error covariance, significantly improving the track and intensity analysis of Typhoon Hato. As compared to a control experiment (with no assimilation), the error of minimum pressure decreased by up to 13 hPa (4 hPa / 57 % on average). The maximum wind speed error decreased by up to 18 knots (5 knots / 41 % on average). </p><p>By contrast, weakly coupled implementations cannot match these reductions (10% on average). Although traditional atmospheric observations were not assimilated, such improvements indicate there is considerable potential in assimilating ocean currents from coastal HF radar, and surface drifters within a strongly coupled framework for intense landfalling TCs.</p>

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