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Using multi‐satellite observations, the cloud dynamic and microphysical characteristics were revealed during the rapid intensification (RI) of super‐typhoon Nanmadol (2022). As the storm intensifies, the eyewall contracts, the upper‐level divergence strengthens, and the cirrus cloud increases, leading to stronger upper‐level radial outflow and the vertical updraft. Meanwhile, it is found that there exists a dynamically attractive area in the outer rainbands, where particles grow effectively and form “a small amount of large particles” around 300 km from the eye. A theory of cloud dynamics‐microphysics interaction, called “tunnel theory,” is further proposed to explain the generation, accumulation, and concentrated downflow of large particles in the outer rainbands during RI. Results suggest the unique feature of particle distribution in the outer rainbands could be a potential indicator for RI. Plain Language Summary The rapid intensification (RI) of tropical cyclones (TCs) becomes more frequent in recent years, but the TC RI forecasts still remain challenging. Better understanding of the physical processes associated with RI of TCs would essentially improve its forecasting capability. The cloud dynamical and microphysical processes, especially their interactions that respond to RI are not well explored. In this study, the cloud macro and micro characteristics associated with RI of a super‐typhoon Nanmadol (2022) over the western Pacific are investigated using multiple satellites observations. The storm underwent RI during 15–16 September 2022, and it has wreaked havoc on Japan's most cities as it moved across the Japanese island afterward with a track length of about 1,120 km. It is found inside Nanmadol as well as other typhoons that a few large particles tend to occur in the outer rainbands during RI, due to the interaction of cloud dynamical and microphysical processes. Such unique feature of particle distribution in the outer rainbands could be a potential indicator for RI, and should also be paid attention to in model forecasting of typhoon precipitation. Key Points First satellite‐based observational study on the interaction of cloud dynamics and microphysics during typhoon rapid intensification (RI) The eyewall contracts, the upper‐level divergence strengthens, and the convection column increases, providing kinetic energy for typhoon RI A “tunnel theory” is proposed for the generation, accumulation, and downflow of large particles in the outer rainbands during typhoon RI

The premise has been validated that a tropical cyclone (TC, typhoon, hurricane), one of the most powerful large-scale formations systematically arising in the atmosphere, is an element of the ocean–atmosphere–ionosphere–magnetosphere system. The TC plays a crucial role with regard to a global-scale mass and energy exchange in this system. The study of this system encompasses a broad spectrum of physical phenomena occurring and processes operating within the system components, as well as the mechanisms of their interactions. The problem under discussion pertains to interdisciplinary science. Its scope ranges from different Earth sciences to geospace sciences, which comprise the physics of the ocean, meteorology, the physics of the Earth’s atmospheric and space environment, etc. Observations of the ionospheric response to the impact of a number of unique typhoons made using multifrequency multiple path oblique incidence ionospheric sounding have confirmed the definitive role that the internal gravity waves and infrasound play in producing atmospheric–ionospheric disturbances. It has been demonstrated that these disturbances are capable of significantly affecting the characteristics of high-frequency radio waves.

An accurate center localization in near real‐time is critical for tropical cyclone (TC) monitoring and forecasting. This study presents a robust algorithm for localizing typhoon centers using the Chinese geostationary (GEO) meteorological satellite. The results using the Advanced Geostationary Radiation Imager (AGRI) onboard Fengyun‐4A (FY‐4A) satellite data, achieving a mean absolute error (MAE) of 29.4 km across various typhoon intensities in the Western North Pacific, superior to other baseline methods. By harnessing the multi‐spectral imagery from the FY‐4A and incorporating an attention mechanism, it significantly boosts the deep learning convolutional neural network's ability to identify typhoon cloud features and their centers, even during their initial and weakest stages, which is laudable because these are the most difficult for center fixing even for human analysts. Remarkably, it requires just a single moment satellite imagery to locate the center of typhoon, enabling automated updates of the typhoon centers in near real‐time applications. Plain Language Summary Understanding and observing the exact location of the typhoon's center are crucial for monitoring and forecasting the storm path and intensity. Geostationary (GEO) satellites have become the primary means of continuously observing typhoon centers with Advanced Dvorak Technique (ADT). Nevertheless, locating the center from GEO observations more quickly and accurately is still desired for operational applications. For such purpose, a new algorithm that utilizes just a single moment images from a geostationary satellite to locate the center of typhoon rapidly and accurately has been developed. This method represents a significant improvement, capable of locating the center of typhoon with a mean absolute error of only 29.4 km, outperforming existing other baseline methods. Its sophisticated design allows it to discern intricate details of cloud features from multi‐spectral satellite imagery, thus providing valuable insights particularly during the early stages of a typhoon's development. Key Points A new and robust typhoon center localization algorithm using geostationary satellite imagery Smaller differences in typhoon center across all the intensities are found between the new locating algorithm and the best track results This methodology can be applied to advanced geostationary imager data for near real‐time TC monitoring

Improving typhoon precipitation forecast with convection‐permitting models remains challenging. This study investigates the influence of cumulus parameterizations and turbulence models, including the Reconstruction and Nonlinear Anisotropy (RNA) turbulence scheme, on precipitation prediction in multiple typhoon cases. Incorporating the cumulus and RNA schemes increases domain‐averaged precipitation, improves recall scores, and lowers relative error across various precipitation thresholds, which is substantial in three out of four studied typhoon cases. Applying appropriate cumulus parameterization schemes alone also contributes to enhancing heavy precipitation forecasts. In Typhoon Hato, the RNA and Grell‐3 schemes demonstrated a doubled recall rate for extreme rainfall compared to simulations without any cumulus scheme. The improved forecasting ability is attributed to the RNA's capacity to model dissipation and backscatter. The RNA scheme can dynamically reinforce typhoon circulation with upgradient momentum transport in the lower troposphere and enhance the buoyancy by favorable heat flux distribution, which is conducive to developing heavy precipitation. Plain Language Summary Enhancing the forecast accuracy of typhoon‐induced rainfall prediction with numerical weather prediction models is still challenging. This study focused on the impact of cumulus convection schemes and a new turbulence scheme named the Reconstruction and Nonlinear Anisotropy (RNA) scheme on the precipitation forecast performance when typhoons hit. We found that the convection and the RNA schemes help predict more rain on average and make our predictions more accurate, especially regarding heavy rainfall. Still, it also leads to an overestimation of the precipitation. In addition, applying the cumulus and RNA scheme is beneficial in keeping the typhoon structure and intensity at a lower sea level pressure. This improvement in generating intense convections is due to the optimized configuration of the dissipation and backscattering caused by the subgrid‐scale turbulence. Key Points Applying the cumulus and RNA schemes improves the ability to catch heavy rainfall with higher recall scores Employing the cumulus and RNA schemes can help maintain the compact structure and strength of typhoons Considering the subgrid‐scale turbulence can optimize the dissipation and backscatter configuration to enhance deep convection

Tropical cyclones drive episodic phytoplankton blooms in nutrient‐depleted oceans, but the roles of storm intensity, size, and translation speed remain unclear. Using satellite‐derived chlorophyll a (Chl a) and typhoon data (2002–2023) from the oligotrophic Northwestern Pacific, we show that typhoon size (R34, radius of 34 kt winds) and translation speed, rather than intensity, dominate Chl a responses. Chl a bursts were concentrated within 2° of the typhoon center and peaked 1–6 days after passage. The strongest Chl a responses occurred with slow, large typhoons, while fast, small typhoons induced the weakest response. When translation speeds were below 4 m/s, both R34 and speed influenced the Chl a response; above 4 m/s, R34 was the primary driver. These results redefine the relative contributions of typhoon parameters to marine productivity, emphasizing size as a critical yet overlooked factor.

This study employed the new generation Taiwan global forecast system (TGFS) to focus on its performance in forecasting the tracks of western North Pacific typhoons during 2022–2023. TGFS demonstrated better forecasting performance in typhoon track compared to central weather administration (CWA) GFS. For forecasts with large track errors by TGFS at the 120th h, it was found that most of them originated during the early stages of typhoon development when the typhoons were of mild intensity. The tracks deviated predominantly towards the northeast and occasionally towards the southwest, which were speculated to be due to inadequate environmental steering guidance resulting from the failure to capture synoptic environmental features. The tracks could be corrected by replacing the original new simplified Arakawa–Schubert (NSAS) scheme with the new Tiedtke (NTDK) scheme to change the synoptic environmental field, not only for Typhoon Khanun, which occurred in the typhoon season of 2023, but also for Typhoon Bolaven, which occurred after the typhoon season, in October 2023, under atypical circulation characteristics over the western Pacific. The diagnosis of vorticity budget primarily analyzed the periods where divergence in typhoon tracks between control (CTRL) and NTDK experiments occurred. The different synoptic environmental fields in the NTDK experiment affected the wavenumber-1 vorticity distribution in the horizontal advection term, thereby enhancing the accuracy of typhoon translation velocity forecasts. This preliminary study suggests that utilizing the NTDK scheme might improve the forecasting skill of TGFS for typhoon tracks. To gain a more comprehensive understanding of the impact of NTDK on typhoon tracks, further examination for more typhoons is still in need.

Offshore wind turbines are very sensitive to wind effects, and wind information about tropical cyclone (TCs) lays the foundation for their wind-resistant design and anti-TC operation, especially in TC-prone areas. While the statistical characteristics of TCs have drawn continuous attention, the specific features of some typical TC events, which are of practical importance for the daily operation of marine turbines, receive less attention in the wind engineering community. Super typhoons Mangkhut and Saola are two of the strongest TCs that have ever impacted south China. Notably, although Saola was reported to be more intense than Mangkhut, it resulted in much less severe impact and damage. This article presents a comparison study of these two TCs based on comprehensive usage of field records. Results suggest that both Mangkhut and Saola exhibited a concentric eyewall structure during development, but Saola completed the eyewall replacement before landfall, whilst Mangkhut failed to do so. Consequently, Saola evolved into a more intense and compact storm. In contrast, Mangkhut decayed consistently but still exerted an extensive impact over a wider area. Consistent with these features, the wind characteristics of Mangkhut and Saola also demonstrated noteworthy discrepancies. These findings provide useful insights for operation and maintenance strategies of coastal and offshore wind turbines.

A couple waded down the aisle of a flooded church in Malolos, Philippines, on July 22, amid heavy rain and deadly flooding brought by Typhoon Whipa.

Typhoon-induced wind and wave can interrupt the operation and even threaten the safety of moving vehicles and bridges. However, the typhoon-induced maximum wind speed and maximum wave height are not coincident in time. Hence, it is a critical issue to include the time lag in the hazard model of wind and wave conditions for bridges. This paper adopts the concept of the pair-copula decomposed model to develop a trivariate joint probability model of typhoon-induced maximum wind speed, maximum wave height and their time lag. Pingtan Strait, where a sea-crossing bridge is being built, is taken as the example site. Considering the long-term measured wind and wave conditions under typhoons are not available, 58 tropical cyclones from 1990 to 2018 that influenced the example site are selected. The typhoon-induced maximum wind speed, wave height and their time lag at the example site are simulated using the validated SWAN + ADCIRC coupled numerical model. The trivariate joint probability modeling of wind, wave and time lag was carried out based on the simulated data. The trivariate environmental surfaces with the 50-year and 100-year return periods were finally obtained by the inverse first-order reliability method. The results show that more than 50% of typhoons have the maximum wind lagged behind the maximum wave at the example site. The higher typhoon-induced maximum wind speed and wave height tend to occur simultaneously. Two-parameter Weibull distribution is suitable to fit the distribution of the maximum wind speed and wave height, and the GEV distribution is ideal for the distribution of time lag. According to the trivariate environmental surface, neglecting the time lag might slightly overestimate the demand of the wind and wave loads. This study is of particular interest to the researchers and engineers in developing the metocean conditions.

This paper proposes a new post-processing method for model data in order to improve typhoon track and rainfall forecasts. The model data used in the article include low-resolution ensemble forecasts and high-resolution forecasts. The entire improvement method contains the following three steps. The first step is to correct the typhoon track forecast: three ensemble member optimization methods are applied to the low-resolution ensemble forecasts, and then the best optimization method is selected with the principle of the smallest average distance error. The results of rainfall forecasts show that the corrected rainfall forecast performs better than the original forecasts. The second step is to derive the high-resolution probability rainfall forecast: the neighborhood method is applied to the deterministic high-resolution rainfall forecast. The last step is to correct the typhoon rainfall forecast: the low- and high-resolution forecasts are blended using the probability-matching method with two different schemes. The results show that the forecasts of the two schemes perform better than the original forecast under all rainfall thresholds and all forecast lead times. In terms of bias score, a rain forecast from one scheme corrects the rainfall deviation from observation better for light and moderate rainfall, whereas a rain forecast from another scheme corrects the rainfall deviation better for heavy and torrential rainfall. The better performance of corrected rain forecasts in the case of Typhoon Lekima and Rumbia over eastern China is demonstrated.