Accurate estimation of Tropical Cyclone Heat Potential (TCHP) is critical for operational forecasting of tropical cyclone (TC) intensification in the Northern Indian Ocean (NIO). This study validates TCHP estimates derived from three ocean general circulation models, the Regional Ocean Modeling System (ROMS), the Hybrid Coordinate Ocean Model (HYCOM), and the Global Ocean Data Assimilation System (GODAS), using in-situ observations from eight OMNI buoys and 1,899 ARGO profiling floats across the Arabian Sea and Bay of Bengal during January 2023–December 2024. A three-stage hierarchical validation framework employing temporal and spatial matching protocols was implemented. Results indicate that assimilative models (GODAS, HYCOM) consistently outperform the non-assimilative regional model (ROMS) in reproducing observed TCHP with lower root mean square error (RMSE: 7.9–8.7 kJ/cm2 vs. 11.6–14.9 kJ/cm2), higher correlation coefficients (r: 0.86–0.89 vs. 0.67–0.76), and reduced systematic bias across both basins. The assimilative frameworks of HYCOM and INCOIS-GODAS, together with the hybrid-coordinate system of HYCOM, enable a better representation of thermocline dynamics and seasonal heat content variability, particularly in the complex stratification regime of the Bay of Bengal. However, ROMS shows good skill in representing the spatial and temporal variability of TCHP, with only marginal differences likely associated with the absence of direct data assimilation within the model domain. Overall, all three modelling systems show good agreement with observations, while data assimilation and advanced vertical coordinate schemes enhance accuracy and consistency, highlighting their use for operational TCHP analysis and forecasting, and their contribution to tropical cyclone intensity prediction, early warning systems, and tropical cyclone-related studies in the NIO.
This study assesses how horizontal resolution affects the simulation of Super Typhoon Lekima (2019) in the Global-Regional Integrated forecast SysTem (GRIST). Three global variable-resolution experiments, with finest grid spacings of 1 km, 3.5 km, and 10 km, are evaluated against the China Meteorological Administration-Shanghai Typhoon Institute (CMA-STI) best track and environmental diagnostics based on the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5). All three experiments reproduce the general northwestward motion, but differences among the simulated tracks become more pronounced during the pre-landfall turning stage. The 1 km experiment yields the smallest track errors. At 36 h, the track errors are approximately 38, 63, and 92 km in the 1 km, 3.5 km, and 10 km experiments, respectively, increasing to approximately 140, 188, and 248 km at 72 h. At 0000 UTC on 8 August, the 500–850 hPa layer-mean steering direction in the 1 km experiment is closest to ERA5, whereas the 3.5 km and 10 km experiments exhibit stronger westward components. The intensity response is metric dependent. The 1 km experiment most closely reproduces the peak near-surface wind magnitude, whereas the 3.5 km experiment most closely reproduces the observed minimum pressure. All three simulations attain their intensity extrema earlier than the best track. Inner-core diagnostics show the strongest peak midlevel ascent in the 1 km experiment. However, the timing and mature-stage variability of the radius of maximum wind (RMW) do not vary monotonically with grid spacing. The radial alignment between the RMW and peak upward motion is also nonmonotonic. In addition, the 1 km experiment retains more kinetic energy at mesoscale and smaller wavelengths and delays spectral decay relative to the coarser experiments. Overall, the 1 km experiment produces the smallest track errors, most closely reproduces the peak-wind magnitude, and retains the most mesoscale kinetic energy. Its effects on other intensity and inner-core structural metrics are more complex and nonmonotonic.
This study employed wind profile radar data from inland stations in Fujian to analyze changes in characteristic quantities near the time of extreme winds for typhoons that made landfall or affected Fujian during 2012-2019, using 265 station-hours of extreme wind samples. A retrospective analysis of Typhoon Mekkhala (2020) was conducted as validation. A low-level strong wind belt (LLSW) was present within 3 h prior to and at the time of over 95% of extreme wind station-hours, with maximum wind speeds below 2 km (Vmax) ranging from 8 to 36 m/s in over 95% of observations and predominant directions of NE, SE, and NW. During the 3 h preceding event onset, Vmax and low-level wind intensity index I progressively increased while the LLSW base H12 descended, peaking at onset. As extreme wind approached, 500–850 hPa vertical wind shear remained relatively small. Mann-Whitney U tests confirmed that Vmax, LLSW base, low-level wind intensity index, and vertical wind shear differed significantly between pre-gale and non-gale samples (p<0.001). Subsequent ROC analysis revealed that parameters related to LLSW had the highest discriminative power (AUC: 0.883–0.923). Using median thresholds for 1 h lead time (Vmax: 20.4 m/s; H12: 0.79 km; I: 30×10-3 s-1; vertical wind shear: 2.1×10-3 s-1), Vmax achieved the highest POD (50.2%) with a low FAR (3.0%), while index I showed the highest AUC (0.923) but a lower POD (43.4%). These quantified thresholds and process insights derived from wind profiler data can provide objective guidance for short-term forecasting of inland typhoon gales in operational settings.
Remote sensing provides indispensable observations for monitoring tropical cyclone (TC) surface winds, yet traditional numerical or parametric models often underestimate structural details and remain under-sampled. Leveraging more than 300 Sentinel-1 (S-1) synthetic aperture radar (SAR)-derived CyclObs wind products and the International Best Track Archive for Climate Stewardship (IBTrACS) best-track data from more than 200 TCs (2018–2024), we trained eXtreme Gradient Boosting (XGBoost) and Transformer models to reconstruct two-dimensional TC wind fields, and evaluated them on 100 independent TCs (2024–2025) against measurements from microwave radiometers onboard Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer-2 (AMSR-2) and Stepped-Frequency Microwave Radiometers (SFMR) measurements. The Transformer achieves the highest point-wise accuracy, with root-mean-square errors (RMSEs) of 2.85 m/s against SMAP/AMSR-2 and 3.06 m/s against SFMR, as well as superior correlation and structural similarity compared with XGBoost and the Generalized Asymmetric Holland Model (GAHM). However, its along-track reconstructions exhibit pronounced small-scale oscillations absent in the physically smooth GAHM, revealing that numerical closeness to observations does not guarantee physical authenticity. This purely data-driven approach lacks dynamical constraints and risks unphysical artifacts between tracks, thereby demanding independent two-dimensional validation. Structural validation against IBTrACS shows excellent correlations (r ≥ 0.90) for 34-kt (R34), 50-kt (R50), and 64-kt (R64), with RMSE decreasing from 42.71 to 11.03 km and biases converging toward zero, confirming faithful reproduction of inner-core gradients despite slight smoothing of the outer rainbands. Across 2–10 km resolutions, RMSE remains nearly constant (∼2.35 m/s) and maximum wind speed shows no systematic change, indicating that finer grids only sharpen asymmetry without improving error metrics, likely due to overfitting or interpolation rather than real oceanic signals. Performance degrades sharply above 50 m/s (sparse extreme-wind data) and for translation speeds larger than 11 m/s (inadequate learning of motion-induced asymmetry), yet these limitations are purely statistical, lacking mechanistic insight. Thus, while the Transformer outperforms conventional models and is viable for routine operational TC warning applications, its application to extreme wind speeds (>50 m/s) or fast-moving storms (>11 m/s) is inadvisable without rigorous physical validation, ideally coupled with dynamical models and bias-corrected training data.
This study integrates data on tropical cyclones, sea surface temperature (SST), and fishing activity derived from Automatic Identification System (AIS) records from 2013 to 2024. Using hotspot analysis, empirical orthogonal function (EOF) decomposition, and wavelet coherence analysis, it investigates the interactions among tropical cyclones, SST, and fishing activity in the South China Sea from both spatial and temporal perspectives. The results indicate that fishing hotspots in the South China Sea are primarily distributed along the western coastal regions, particularly concentrated in the Pearl River Estuary, the Beibu Gulf, and the central-western parts of the basin. Among these areas, the Pearl River Estuary is most strongly affected by tropical cyclones, whereas the Beibu Gulf and the central-western South China Sea experience relatively weaker impacts. In terms of spatial interactions, higher SST in the eastern South China Sea during summer and autumn provides substantial heat flux and moisture that facilitate the formation and intensification of tropical cyclones. As these cyclones move toward the western coastal regions, they enhance upwelling processes, leading to the formation of seasonal upwelling fishing grounds and consequently promoting the aggregation of fishing hotspots. Meanwhile, the upwelling effect also contributes to localized cooling in the western coastal waters. From a temporal perspective, the interactions among tropical cyclones, SST, and fishing effort are likely governed by an air–sea coupling mechanism jointly driven by the summer monsoon and the El Niño–Southern Oscillation (ENSO). Tropical cyclones, primarily modulated by the summer monsoon, enhance upwelling effects and primary productivity in the short term, thereby increasing fishing effort, whereas both tropical cyclones and fishing effort exhibit a lagged response to SST variability associated with the combined effects of ENSO and the summer monsoon. These findings provide a scientific basis for fisheries production and management in the South China Sea.
