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Yuhang Zhu, Yineng Li, and Shiqiu Peng

Abstract

The track and accompanying sea wave forecasts of Typhoon Mangkhut (2018) by a real-time regional forecasting system are assessed in this study. The real-time regional forecasting system shows a good track forecast skill with a mean error of 69.9 km for the forecast period of 1–72 h. In particular, it predicted well the landfall location on the coastal island of South China with distance (time) biases of 76.89 km (3 h) averaging over all forecasting made during 1–72 h and only 3.55 km (1 h) for the forecasting initialized 27 h ahead of the landfall. The sea waves induced by Mangkhut (2018) were also predicted well by the wave model of the forecasting system with a mean error of 0.54 m and a mean correlation coefficient up to 0.94 for significant wave height. Results from sensitivity experiments show that the improvement of track forecasting skill for Mangkhut (2018) are mainly attributed to application of a scale-selective data assimilation scheme in the atmosphere model that helps to maintain a more realistic large-scale flow obtained from the GFS forecasts, whereas the air–sea coupling has slightly negative impact on the track forecast skill.

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Yu-Kun Qian, Shiqiu Peng, and Yineng Li

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Eulerian and Lagrangian statistics of the surface flows over the South China Sea (SCS) are investigated based on observations of satellite-tracked Lagrangian drifters of the Surface Velocity Program. Although the dataset contains the most recent observations up to 2011, the spatial and temporal distributions of the data are quite inhomogeneous. The Luzon Strait as well as the northern and western parts of the SCS are heavily sampled by drifters whereas the internal SCS is less sampled. The overall Eulerian mean circulation derived from all drifter observations resembles the wintertime circulations because of the seasonal sampling bias toward wintertime. It is found that the calculation of the diffusivity may be misled by the low-frequency temporal variabilities such as annual cycle, which could be removed using the Gauss–Markov decomposition. In the regions around Luzon Strait, typical values of diffusivity, time scale, and length scale are 3.7–11.9 × 107 cm2 s−1, 0.8–1.9 days, and 16.4–44.4 km, respectively. In the regions around Hainan Island and the western boundary of the SCS, the typical estimated diffusivity and length scale are slightly smaller, that is, 2.3–6.9 × 107 cm2 s−1 and 12.8–33.7 km while the time scale (0.8–2.0 days) is approximately the same as that in the regions around Luzon Strait. Although these regions are close to the coast, the “flux versus gradient” relationship is still valid in these coastal regions.

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Xiaowei Wang, Zhiyu Liu, and Shiqiu Peng

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Using a high-resolution regional ocean model, the impact of tidal mixing on water mass transformation and circulation in the South China Sea (SCS) is investigated through a set of numerical experiments with different configurations of tide-induced diapycnal diffusivity. The results show that including tidal mixing in both the Luzon Strait (LS) and SCS has significant impact on the LS transport and the intermediate–deep layer circulation in the SCS Basin. Analysis of the density field indicates that tidal mixing in both the LS and SCS are essential for sustaining a consistent density gradient and thus a persistent outward-directed baroclinic pressure gradient both between the western Pacific and LS and between the LS and SCS Basin, so as to maintain the strong deep-water transport through the LS. Further analysis of water mass properties suggests that tidal mixing in the deep SCS would strengthen the horizontal density gradient, intensify the basin-scale cyclonic circulation, induce more vigorous overturning, as well as generate the subbasin-scale eddies in the abyssal SCS. The results imply that tidal mixing in both the LS and SCS plays a key dynamic role in controlling water mass properties and deep circulation features in the SCS and thus need to be deliberately parameterized in ocean circulation models for this region.

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Shiqiu Peng, Yineng Li, and Lian Xie

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A three-dimensional ocean model and its adjoint model are used to adjust the drag coefficient in the calculation of wind stress for storm surge forecasting. A number of identical twin experiments (ITEs) with different error sources imposed are designed and performed. The results indicate that when the errors come from the wind speed, the drag coefficient is adjusted to an “optimal value” to compensate for the wind errors, resulting in significant improvements of the specific storm surge forecasting. In practice, the “true” drag coefficient is unknown and the wind field, which is usually calculated by an empirical parameter model or a numerical weather prediction model, may contain large errors. In addition, forecasting errors may also come from imperfect model physics and numerics, such as insufficient resolution and inaccurate physical parameterizations. The results demonstrate that storm surge forecasting errors can be reduced through data assimilation by adjusting the drag coefficient regardless of the error sources. Therefore, although data assimilation may not fix model imperfection, it is effective in improving storm surge forecasting by adjusting the wind stress drag coefficient using the adjoint technique.

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Yu-Kun Qian, Shiqiu Peng, and Chang-Xia Liang

Abstract

The present study reconciles theoretical differences between the Lagrangian diffusivity and effective diffusivity in a transformed spatial coordinate based on the contours of a quasi-conservative tracer. In the transformed coordinate, any adiabatic stirring effect, such as shear-induced dispersion, is naturally isolated from diabatic cross-contour motions. Therefore, Lagrangian particle motions in the transformed coordinate obey a transformed zeroth-order stochastic (i.e., random walk) model with the diffusivity replaced by the effective diffusivity. Such a stochastic model becomes the theoretical foundation on which both diffusivities are exactly unified. In the absence of small-scale diffusion, particles do not disperse at all in the transformed contour coordinate. Besides, the corresponding Lagrangian autocorrelation becomes a delta function and is thus free from pronounced overshoot and negative lobe at short time lags that may be induced by either Rossby waves or mesoscale eddies; that is, particles decorrelate immediately and Lagrangian diffusivity is already asymptotic no matter how small the time lag is. The resulting instantaneous Lagrangian spreading rate is thus conceptually identical to the effective diffusivity that only measures the instantaneous irreversible mixing. In these regards, the present study provides a new look at particle dispersion in contour-based coordinates.

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Shiqiu Peng, Lian Xie, Bin Liu, and Fredrick Semazzi

Abstract

A method referred to as scale-selective data assimilation (SSDA) is designed to inject the large-scale components of the atmospheric circulation from a global model into a regional model to improve regional climate simulations and predictions. The SSDA is implemented through the following procedure: 1) using a low-pass filter to extract the large-scale components of the atmospheric circulation from global analysis or model forecasts; 2) applying the filter to extract the regional-scale and the large-scale components of the atmospheric circulation from the regional model simulations or forecasts; 3) assimilating the large-scale circulation obtained from the global model into the corresponding component simulated by the regional model using the method of three-dimensional variational data assimilation (3DVAR) while maintaining the small-scale components from the regional model during the assimilation cycle; 4) combining the small-scale and the assimilated large-scale components as the adjusted forecasts by the regional climate model and allowing the two components to mutually adjust outside the data assimilation cycle. A case study of summer 2005 seasonal climate hindcasting for the regions of the Atlantic and the eastern United States indicates that the large-scale components from the Global Forecast System (GFS) analysis can be effectively assimilated into the regional model using the scale-selective data assimilation method devised in this study, resulting in an improvement in the overall results from the regional climate model.

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Yu-Kun Qian, Shiqiu Peng, Chang-Xia Liang, and Rick Lumpkin

Abstract

Eddy–mean flow decomposition is crucial to the estimation of Lagrangian diffusivity based on drifter data. Previous studies have shown that inhomogeneous mean flow induces shear dispersion that increases the estimated diffusivity with time. In the present study, the influences of nonstationary mean flows on the estimation of Lagrangian diffusivity, especially the asymptotic behavior, are investigated using a first-order stochastic model, with both idealized and satellite-based oceanic mean flows. Results from both experiments show that, in addition to inhomogeneity, nonstationarity of mean flows that contain slowly varying signals, such as a seasonal cycle, also cause large biases in the estimates of diffusivity within a time lag of 2 months if a traditional binning method is used. Therefore, when assessing Lagrangian diffusivity over regions where a seasonal cycle is significant [e.g., the Indian Ocean (IO) dominated by monsoon winds], inhomogeneity and nonstationarity of the mean flow should be simultaneously taken into account in eddy–mean flow decomposition. A temporally and spatially continuous fit through the Gauss–Markov (GM) estimator turns out to be very efficient in isolating the effects of inhomogeneity and nonstationarity of the mean flow, resulting in estimates that are closest to the true diffusivity, especially in regions where strong seasonal cycles exist such as the eastern coast of Somalia and the equatorial IO.

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Yu-Kun Qian, Chang-Xia Liang, Shiqiu Peng, Shumin Chen, and Sihua Wang

Abstract

A horizontal map of the upper-level forcing index (ULFI) is constructed to show the possible influence of upper-level large-scale environmental flow on the intensity change of tropical cyclones (TCs). The ULFI includes three commonly used diagnostics, that is, 200-hPa eddy flux convergences of both relative (REFC) and planetary angular momentum (PEFC), as well as axisymmetric absolute vorticity as a denominator that rescales the strength of the eddy forcings similar to the outflow-layer inertial stability. A simple procedure is adopted to convert these storm-relative components and the ULFI into Eulerian horizontal maps. Applications of this index map to three selected TC cases clearly demonstrate the process of upper-level TC–environment interaction: when a TC moves into a region of high (low) index, significant upper-level asymmetric forcing is exerted on the TC, leading to the strengthening (weakening) of the TC’s axisymmetric outflow and then possibly its intensity. As such, the horizontal map of ULFI not only provides a quantitative way of estimating the strength of upper-level asymmetric forcing at each grid point, but also serves as an indicator showing where the possible intensity change of a TC would occur under the influence of upper-level environmental flow. The index is thus recommended to be used in future studies of TC–environment interaction.

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Shiqiu Peng, Yu-Kun Qian, Rick Lumpkin, Yan Du, Dongxiao Wang, and Ping Li

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Using the 1985–2013 record of near-surface currents from satellite-tracked drifters, the pseudo-Eulerian statistics of the near-surface circulation in the Indian Ocean (IO) are analyzed. It is found that the distributions of the current velocities and mean kinetic energy (MKE) in the IO are extremely inhomogeneous in space and nonstationary in time. The most energetic regions with climatologic mean velocity over 50 cm s−1 and MKE over 500 cm2 s−2 are found off the eastern coast of Somalia (with maxima of over 100 cm s−1 and 1500 cm2 s−2) and the equatorial IO, associated with the strong, annually reversing Somalia Current and the twice-a-year eastward equatorial jets. High eddy kinetic energy (EKE) is found in regions of the equatorial IO, western boundary currents, and Agulhas Return Current, with a maximum of over 3000 cm2 s−2 off the eastern coast of Somalia. The lowest EKE (<500 cm2 s−2) occurs in the south subtropical gyre between 30° and 40°S and the central-eastern Arabian Sea. Annual and semiannual variability is a significant fraction of the total EKE off the eastern coast of Somalia and in the central-eastern equatorial IO. In general, both the MKE and EKE estimated in the present study are qualitatively in agreement with, but quantitatively larger than, estimates from previous studies. These pseudo-Eulerian MKE and EKE fields, based on the most extensive drifter dataset to date, are the most precise in situ estimates to date and can be used to validate satellite and numerical results.

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Shiqiu Peng, Yu-Kun Qian, Rick Lumpkin, Ping Li, Dongxiao Wang, and Yan Du

Abstract

Lagrangian statistics of the surface circulation in the Indian Ocean (IO) are investigated using drifter observations during 1985–2013. The methodology isolates the influence of low-frequency variations and horizontal shear of mean flow. The estimated Lagrangian statistics are spatially inhomogeneous and anisotropic over the IO basin, with values of ~6–85 × 107 cm2 s−1 for diffusivity, ~2–7 days for integral time scale, and ~33–223 km for length scale. Large diffusivities (>20 × 107 cm2 s−1) occur in the central-eastern equatorial IO and the eastern African coast. Small diffusivities (~6–8 × 107 cm2 s−1) appear in the subtropical gyre of the southern IO and the southeastern Arabian Sea. The equatorial IO has the largest zonal diffusivity (~85 × 107 cm2 s−1), corresponding to the largest time scale (~7 days) and length scale (~223 km), while the eastern coast of Somalia has the largest meridional diffusivity (~31 × 107 cm2 s−1). The minor component of the Lagrangian length scale is approximately equal to the first baroclinic Rossby radius (R 1) at midlatitudes (R 1 ~ 30–50 km), while the major component equals R 1 in the equatorial region (R 1 > 80 km). The periods of the energetic eddy-containing bands in the IO in Lagrangian spectra range from several days to a couple of months, where anticyclones dominate. A significant result is that the drifter-derived diffusivities asymptote to constant values in relatively short time lags (~10 days) for some subregions of the IO if they are correctly calculated. This is an important contribution to the ongoing debate regarding drifter-based diffusivity estimates with relatively short Lagrangian velocity time series versus tracer-based estimates.

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