Combining the logarithmic law with the Charnock relation yields a drag coefficient that is a function of wind speed with the Charnock coefficient as a parameter. It is found that the function is nearly linear within the typically measured range of the drag coefficient. The slope of the linear function is dominated by the Charnock coefficient. When the Charnock relation is extended to a wave age–dependent function, the drag coefficient remains a near-linear function of wind speed after invoking the 3/2 power law. The slope of the linear function is dominated by wave steepness.

Although the effect of sea surface temperature anomalies in the Pacific and Indian Oceans on precipitation over the Yangtze River Basin (YRB) is well known, the impact of air–sea sensible and latent heat fluxes (SHF and LHF) has not been well studied. Based on the statistical and diagnostic analyses of historical precipitation data over the YRB, SHF and LHF over the global ocean, as well as global atmospheric reanalysis data, the impacts of SHF and LHF in the selected ocean regions on YRB extreme precipitation were investigated. The spatial distributions of the correlation coefficients between the YRB extreme precipitation indices (YRB‐EPI) and SHF and LHF over the global ocean were analysed to identify critical air–sea interaction regions affecting YRB extreme precipitation. Results show that a significant positive correlation exists between YRB‐EPI and air–sea heat fluxes over the Northwest Indian Ocean, Southeast Indian Ocean, Southwest Indian Ocean and South China Sea regions. Negative correlations were found over Central Equatorial Pacific, while positive lagged correlations exist over East Pacific. Composite analyses of global wind, geo‐potential height and water vapour transport were also conducted for ten heaviest and ten lightest YRB precipitation years, respectively. In heavy YRB precipitation years, the atmospheric circulation pattern is generally characterized by a strengthened Western Pacific Subtropical High (WPSH) extending northwestward, a strengthened lower tropospheric convergence zone over the YRB, three distinct moisture transport paths from adjacent oceans to the YRB and low‐level mid‐latitude northerly wind anomalies. Whereas, in light YRB precipitation years, it is dominated by the southwesterly summer monsoon over the YRB, with a convergence zone displaced to the north of the YRB, a weak WPSH, and only two main paths of moisture transport. These distinctions between the heavy and light YRB precipitation years provide a physical basis for establishing a statistical prediction model for YRB extreme precipitation.

The effect of the isobathic curvature on the development and evolution of Gulf Stream frontal waves (meanders and eddies) in the vicinity of the Charleston Bump (a topographic rise on the upper slope off Charleston, South Carolina; referred to as CB hereinafter) is studied using the Hybrid-Coordinate Ocean Model (HYCOM). Baroclinic and barotropic energy transfers from the Gulf Stream to its meanders and eddies that appear as cold and warm anomalies are computed for four different cases. In case I, the curvature of the isobaths is artificially reduced and the CB is removed from the bathymetry. In this simulation, the simulated Gulf Stream meanders were barely noticeable in the study region. Energy transfer from the Gulf Stream to meanders and eddies was negligible. In case II, the curvature of the isobaths was the same as in case I, but a bump of the scale of the CB was added to the bathymetry. In this simulation, Gulf Stream meanders were amplified while passing over the CB. In case III, the CB was removed from the bathymetry as in case I, but the curvature of the isobaths was similar to the actual bathymetry, which was larger than that of cases I and II. In this simulation, large meanders were simulated, but the development of these meanders was not confined to the region of the CB. The total baroclinic and barotropic energy transfer rate in this case was an order of magnitude greater than in case II, suggesting that isobathic curvature was able to generate Gulf Stream meanders and eddies even without the presence of the CB. In case IV, actual bathymetry data, which contain both the CB and the isobathic curvature, were used. In this case, large-amplitude Gulf Stream meanders were simulated and there was also a tendency for the amplification of the meanders to be anchored downstream of the CB, consistent with observations. The results from this study suggest that the formation of the “Charleston Trough,” a Gulf Stream meander that appears as a low pressure or depressed water surface region downstream of the bump, is the result of the combined effect of the CB and the isobathic curvature in the region. The isobathic curvature plays a major role in enhancing the baroclinic and barotropic energy transfer rates, whereas the bump provided a localized mechanism to maximize the energy transfer rate downstream of the CB.

Accurately forecasting a tropical cyclone’s (TC) track and intensity remains one of the top priorities in weather forecasting. A dynamical downscaling approach based on the scale-selective data assimilation (SSDA) method is applied to demonstrate its effectiveness in TC track and intensity forecasting. The SSDA approach retains the merits of global models in representing large-scale environmental flows and regional models in describing small-scale characteristics. The regional model is driven from the model domain interior by assimilating large-scale flows from global models, as well as from the model lateral boundaries by the conventional sponge zone relaxation. By using Hurricane Felix (2007) as a demonstration case, it is shown that, by assimilating large-scale flows from the Global Forecast System (GFS) forecasts into the regional model, the SSDA experiments perform better than both the original GFS forecasts and the control experiments, in which the regional model is only driven by lateral boundary conditions. The overall mean track forecast error for the SSDA experiments is reduced by over 40% relative to the control experiments, and by about 30% relative to the GFS forecasts, respectively. In terms of TC intensity, benefiting from higher grid resolution that better represents regional and small-scale processes, both the control and SSDA runs outperform the GFS forecasts. The SSDA runs show approximately 14% less overall mean intensity forecast error than do the control runs. It should be noted that, for the Felix case, the advantage of SSDA becomes more evident for forecasts with a lead time longer than 48 h.

Recent studies indicate that the cross-polarization synthetic aperture radar (SAR) images have the ability of retrieving high wind speed on ocean surface without wind direction input. This study presents a new approach for tropical cyclone (TC) wind speed retrieval utilizing thermal-noise-removed Sentinel-1 dual-polarization (VV + VH) Extra-Wide Swath (EW) Mode products. Based on 20 images of 9 TCs observed in the 2016 and 2018 and SAR-collocated European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5) data and the National Oceanic and Atmospheric Administration (NOAA) Hurricane Research Division’s (HRD) Real-time Hurricane Wind Analysis System (H*Wind) data, a subswath-based geophysical model function (GMF) Sentinel-1 EW Mode Wind Speed Retrieval Model after Noise Removal (S1EW.NR) is developed and validated statistically. TC wind speed is retrieved by using the proposed GMF and the C-band model 5.N (CMOD5.N). The results show that the wind speeds retrieved by the S1EW.NR model are in good agreement with wind references up to 31 m s⁻¹. The correlation coefficient, bias, and standard deviation between the retrieval results and reference wind speeds are 0.74, −0.11, and 3.54 m s⁻¹, respectively. Comparison of the wind speeds retrieved from both channels suggests that the cross-polarized signal is more suitable for high–wind speed retrieval, indicating the promising capability of cross-polarization SAR for TC monitoring.

Long-term hurricane predictions have been of acute interest in order to protect the community from the loss of lives, and environmental damage. Such predictions help by providing an early warning guidance for any proper precaution and planning. In this paper, we present a machine learning model capable of making good preseason-prediction of Atlantic hurricane activity. The development of this model entails a judicious and non-linear fusion of various data modalities such as sea-level pressure (SLP), sea surface temperature (SST), and wind. A Convolutional Neural Network (CNN) was utilized as a feature extractor for each data modality. This is followed by a feature level fusion to achieve a proper inference. This highly non-linear model was further shown to have the potential to make skillful predictions up to 18 months in advance.