29 Results Found

Prediction of tropical cyclone (TC) intensity is one of the ground challenges in weather forecasting, and rapid intensification (RI) is a key part of that prediction. Most of the current RI studies are based on a selected variable (feature) set, whic...

Rapid Intensification (RI) in Tropical Cyclone (TC) development is one of the most difficult and still challenging tasks in weather forecasting. In addition to the dynamical numerical simulations, commonly used techniques for RI (as well as TC intens...

Currently, most tropical cyclone (TC) rapid intensification (RI) prediction studies are conducted based on a subset of the SHIPS database using a relatively simple model structure. However, variables (features) in the SHIPS database are built upon hu...

From a traditional point of view, the growth of a tropical cyclone (TC) requires that the vertical wind shear (VWS) should be weak. However, Typhoon Rammasun (2014) underwent a rapid intensification (RI) even in the presence of a strong VWS backgroun...

Reliable forecasting of tropical cyclone (TC) intensity—particularly rapid intensification (RI) events—remains a major challenge in meteorology, largely due to the inherent difficulty of accurately quantifying predictive uncertainty. Trad...

Rapid intensification (RI) and eyewall replacement cycles (ERCs) frequently occur in intense tropical cyclones (TCs), often causing rapid, significant changes in intensity and structure. In some TCs, RI and ERCs can occur concurrently or within a sho...

Regarded as one of the most dangerous types of natural disaster, tropical cyclones threaten the life and health of human beings and often cause enormous economic loss. However, intensity forecasting of tropical cyclones, especially rapid intensificat...

In winter, the northwestern Pacific (NWP) is affected by two atmospheric intraseasonal oscillations (ISOs), the Madden–Julian oscillation (MJO) and the quasi-biweekly oscillation (QBWO). Using observational data and global reanalysis products,...

Tropical cyclone rapid intensification (RI) prediction still remains a big international challenge in numerical weather prediction. Hurricane Matthew (2016) underwent extreme and non-classic RI, intensifying from a Category 1 storm to a Category 5 hu...

The hypothesis that a warm ocean feature (WOF) such as a warm eddy may cause a passing typhoon to undergo rapid intensification (RI), that is, the storm’s maximum 1-min wind speed at 10-m height increases by more than 15.4 m/s in 1 day, is of interes...

A high-resolution Weather Research and Forecasting (WRF) model is used to simulate inner-core thermodynamic (such as moist static energy) and dynamic secondary circulation structure evolutions associated with the rapid intensification (RI) of Super T...

A warm ocean feature (WOF) is a blob of the ocean’s surface where the sea-surface temperature (SST) is anomalously warmer than its adjacent ambient SST. Examples are warm coastal seas in summer, western boundary currents, and warm eddies. Several stu...

Although there has been substantial improvement to numerical weather prediction models, accurate predictions of tropical cyclone rapid intensification (RI) remain elusive. The processes that govern RI, such as convection, may be inherently less predi...

Although some recent progress has been made in operational tropical cyclone (TC) intensity forecasting, the prediction of rapid intensification (RI) remains a challenging problem. To document RI forecast progress, deterministic and probabilistic oper...

As a category-3 typhoon, Hato (2017) experienced the notable rapid intensification (RI) over the hot sea surface before its landfall. The RI process and the influences of local sea surface temperature (SST) patterns on the evolution of Hato were well...

Using ensemble forecast experiments generated by the weather research and forecasting model, the forecast uncertainties of intensity and its rapid intensification (RI) induced by the uncertainty occurring in the boundary layer are investigated for Ty...

This study evaluates the ability of three machine learning methods—decision tree classifier (DTC), random forest classifier (RFC), and XGBoost classifier (XGBC)—to classify and predict tropical cyclone (TC) rapid intensification (RI) and...

The prediction of rapid intensification (RI) in tropical cyclones (TCs) is a challenging problem. In this study, the RI process and factors contributing to it are compared for two TCs: an axis-symmetric case (Hurricane Irma, 2017) and an asymmetric c...

The ECMWF‘s ensemble (ECEPS) predictions are documented for the lifecycles of six tropical cyclones (TCs) that formed during a long-lasting Rossby wave breaking event in the western North Pacific. All six TC tracks started between 20° N and...

In recent decades, the scientific ability to project tropical cyclone (TC) intensities and tracks has improved. Hurricanes undergoing the process of rapid intensification (RI) have created new barriers in formulating predictions of TC paths and peak...

The trend detection of the sudden change of typhoon intensity has always been a difficult issue in typhoon forecast. Artificial intelligence (AI) can implicitly extract the complex features in the images through learning a large number of samples, an...

Hurricane Otto (2016) was characterised by remarkable meteorological features of relevance for the scientific community and society. Scientifically, among the most important attributes of Otto is that it underwent a rapid intensification (RI) process...

With increasing air and sea temperatures, the thermodynamic environments over the oceans are becoming more favourable for the development of intense tropical cyclones (TCs) with rapid intensification (RI). The South China coastal region consists of h...

Super Typhoon Mujigae (2015) was simulated using the WRF-ARW model version 4.1 with the WSM3, WSM5, WSM6, and WSM7 microphysics schemes, which include 3, 5, 6, and 7 hydrometeor classes, respectively. This study investigated the species number of hyd...

This study utilizes an extremely high spatial resolution GOES-16 atmospheric motion vector (AMV) dataset processed at 15 min intervals in a modified version of our original dynamic initialization technique to analyze and forecast a rapid intensificat...

Four-dimensional COAMPS Dynamic Initialization (FCDI) analyses that include high-temporal- and high-spatial-resolution GOES-16 Atmospheric Motion Vector (AMV) datasets are utilized to understand and predict why pre-Bonnie (2022), designated as a Pote...

This study utilized the WRF model to investigate the track evolution and rapid intensification (RI) of Typhoon Doksuri (2023) as it moved across the Luzon Strait and through the South China Sea (SCS). The simulation results indicate that Doksuri has...

Typhoon Lekima occurred in early August 2019 and moved northwestward toward Taiwan. During offshore passage, the typhoon underwent rapid intensification, with a northward deflected track, moving closer to northeastern Taiwan. A global model, MPAS, at...

Cyclone Mocha, classified as an Extremely Severe Cyclonic Storm (ESCS), followed an unusual northeastward trajectory while exhibiting a well-defined eyewall structure. It experienced rapid intensification (RI) before making landfall along the Myanmar...