Tropical Cyclone Intensity Research Articles

The Rise of Data-Driven Weather Forecasting: A First Statistical Assessment of Machine Learning–Based Weather Forecasts in an Operational-Like Context

Abstract Data-driven modeling based on machine learning (ML) is showing enormous potential for weather forecasting. Rapid progress has been made with impressive results for some applications. The uptake of ML methods could be a game changer for the incremental progress in traditional numerical weather prediction (NWP) known as the “quiet revolution” of weather forecasting. The computational cost of running a forecast with standard NWP systems greatly hinders the improvements that can be made by increasing model resolution and ensemble sizes. An emerging new generation of ML models, developed using high-quality reanalysis datasets like ERA5 for training, allows forecasts that require much lower computational costs and that are highly competitive in terms of accuracy. Here, we compare for the first time ML-generated forecasts with standard NWP-based forecasts in an operational-like context, initialized from the same initial conditions. Focusing on deterministic forecasts, we apply common forecast verification tools to assess to what extent a data-driven forecast produced with one of the recently developed ML models (PanguWeather) matches the quality and attributes of a forecast from one of the leading global NWP systems (the ECMWF IFS). The results are very promising, with comparable accuracy for both global metrics and extreme events, when verified against both the operational IFS analysis and synoptic observations. Overly smooth forecasts, increasing bias with forecast lead time, and poor performance in predicting tropical cyclone intensity are identified as current drawbacks of ML-based forecasts. A new NWP paradigm is emerging relying on inference from ML models and state-of-the-art analysis and reanalysis datasets for forecast initialization and model training.

The Effects of Upper-Ocean Sea Temperatures and Salinity on the Intensity Change of Tropical Cyclones over the Western North Pacific and the South China Sea: An Observational Study

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 highly densely populated cities, especially over the Pearl River Delta (PRD) region. Intense TCs maintaining their strength or the RI of TCs close to the coastal region can present substantial forecasting challenges and have significant potential impacts on the coastal population. This study investigates the effect of sea-surface and sub-surface temperatures and salinity on the intensification of five TCs, namely Super Typhoon Hato in 2017, Super Typhoon Mangkhut in 2018, and Typhoon Talim, Super Typhoon Saola, and Severe Typhoon Koinu in 2023, which have significantly affected the South China coastal region and triggered high TC warning signals in Hong Kong in the past few years. This analysis utilised the Hong Kong Observatory’s TC best-track and intensity data, along with sea temperature and salinity profiles generated using the China Ocean ReAnalysis version 2 (CORA2) product from the National Marine Data and Information Service of China. It was found that high sea-surface temperatures (SST) of 30 °C or above for a depth of about 20 m, low sea-surface salinity (SSS) levels of 33.8 psu or below for a depth of at least 20 m, and strong salinity stratification of at least 0.6 psu per 100 m depth might offer useful hints for predicting the RI of TCs over the western North Pacific and the South China Sea (SCS) in operational forecasting, while noting other contributing environmental factors and synoptic flow patterns conducive to RI. This study represents the first documentation of sub-surface salinity’s impact on some intense TCs traversing the SCS during 2017–2023 based on an observational study. Our aim is to supplement operational techniques for forecasting RI with some quantitative guidance based on upper-level ocean observations of temperatures and salinity, on top of well-known but more rapidly changing dynamical factors like low-level convergence, weak vertical wind shear, and upper-level divergent outflow, as forecasted with numerical weather prediction models. This study will also encourage further research to refine the analysis of quantitative contributions from different RI factors and the identification of essential features for developing AI models as one way to improve the forecasting of TC RI before the TC makes landfall near the PRD, with due consideration given to the effect of freshwater river discharge from the Pearl River.

Improving Tropical Cyclone Precipitation Forecasting With Deep Learning and Satellite Image Sequencing

AbstractPrecipitation forecasting in tropical cyclones (TC) is vital for warning systems and disaster management. Artificial intelligence (AI)‐based methods show promise in this domain. Here, we investigate two aspects of AI forecasting for TC precipitation: modeling satellite image sequencing and analyzing predictability. To the former, using the Global Precipitation Measurement, we establish a high‐accuracy regional and intensity forecasting method. Through an analysis of precipitation patterns and intensities, we have demonstrated the effectiveness, reliability, and robustness of forecasting TC precipitation. To the latter, we conduct predictability research, which covers different intensity categories and landfall versus non‐landfall TC precipitation. The conclusions are: (a) TC precipitation varies regionally with predictability differences among intensity categories; (b) Forecasting landfalling TC precipitation is less challenging than non‐landfalling, considering TC intensity and paths. The proposed method also demonstrates strong forecasting capabilities in handling extreme and accumulated precipitation within 0–120 min, achieving an accuracy rate of 87%.

Current trend and future projection of tropical cyclone intensity over the Bay of Bengal

Current trend and future projection of tropical cyclone intensity over the Bay of Bengal

TC-SINDy: Improving physics-based deterministic tropical cyclone track and intensity model via data-driven sparse identification of Nonlinear Dynamics

Physics-based deterministic tropical cyclone (TC) simulation models play an important role in TC risk analysis, for their unique capability to represent physical principles and incorporate climate change impacts. However, establishing effective deterministic models can be challenging. In this paper, we present a simple yet powerful framework that distills physics-based TC models from historical TC data. By treating TC evolutions as dynamical systems, we adaptively discover the TC track and intensity governing equations from historical TC and environmental data via the Sparse Identification of Nonlinear Dynamics (SINDy) approach. Our proposed TC-SINDy approach is able to identify parsimonious, interpretable yet effective physics-based TC models. Compared to alternative physics-based deterministic models, our models match historical records much better in terms of the intensity evolution, prevailing tracks, and key parameter statistics across the coastlines of the Western North Pacific (WNP) basin and the North Atlantic (NA) basin. Our proposed models were finally coupled with a widely used parametric wind field, and the obtained probabilistic distribution of the surface wind also matched the historical histograms. Overall, the proposed method presents a general data-driven framework for discovering physics-based TC models, enabling reliable effective TC simulations while reserving the capacity to accommodate the changing climate.

A flexible tropical cyclone vortex initialization technique for GFDL SHiELD

Tropical cyclone (TC) intensity forecasting poses challenges due to complex dynamical processes and data inadequacies during model initialization. This paper describes efforts to improve TC intensity prediction in the Geophysical Fluid Dynamics Laboratory (GFDL) System for High-resolution prediction on Earth-to-Local Domains (SHiELD) model by implementing a Vortex Initialization (VI) technique. The GFDL SHiELD model, relying on the Global Forecast System (GFS) analysis for initialization, faces deficiencies in initial TC structure and intensity. The VI method involves adjusting the TC vortex inherited from the GFS analysis and merging it back into the environment at the observed location, enhancing the analyzed representation of storm structure. We made modifications to the VI package implemented in the operational Hurricane Analysis and Forecast System, including handling initial condition data, reducing input domain size, and improving storm intensity enhancement. Experiments using the T-SHiELD configuration demonstrate that using VI significantly improves the representation of initial TC intensity and size, enhancing TC predictions, particularly in storm intensity and outer wind forecasts within the first 48 h.

Estimating the Intensity of Tropical Cyclones from Spiral Signatures Acquired by Spaceborne SAR

Accurate estimates of tropical cyclone (TC) intensity are important for improving forecasts as well as studying ocean dynamics during such extreme events. Since most cyclone life occurs over the open ocean, remote sensing techniques play an important role in obtaining the necessary data. The possibility of using the configuration of spiral signatures of mature tropical cyclones (TCs) observed in synthetic aperture radar (SAR) images to estimate the maximum wind speed of a TC is considered. This study assessed the intensity of 14 TCs in the Atlantic and Pacific Oceans using radar images obtained by the Radarsat Hurricane Application Project. TC intensity was estimated using the hyperbolic-logarithmic approximation of TC spiral signatures (HLS approximation). Additionally, the edges of the spiral signatures were partially fitted using a logarithmic spiral to improve the reliability of the HLS approximation. For the first time, a physical model of changing the crossing angle of the logarithmic portion of the edges was proposed and tested on SAR images of the TC. HLS maximum wind speed estimates were compared with Best Track estimates. The results showed the closeness of both estimates with a correlation of 0.95 and a standard deviation of 2.9 m s−1. The results indicate the possibility of using the HLS approximation to estimate the intensity of mature TCs from SAR data.

Ocean internal tides suppress tropical cyclones in the South China Sea

Tropical Cyclones (TCs) are devastating natural disasters. Analyzing four decades of global TC data, here we find that among all global TC-active basins, the South China Sea (SCS) stands out as particularly difficult ocean for TCs to intensify, despite favorable atmosphere and ocean conditions. Over the SCS, TC intensification rate and its probability for a rapid intensification (intensification by ≥ 15.4 m s−1 day−1) are only 1/2 and 1/3, respectively, of those for the rest of the world ocean. Originating from complex interplays between astronomic tides and the SCS topography, gigantic ocean internal tides interact with TC-generated oceanic near-inertial waves and induce a strong ocean cooling effect, suppressing the TC intensification. Inclusion of this interaction between internal tides and TC in operational weather prediction systems is expected to improve forecast of TC intensity in the SCS and in other regions where strong internal tides are present.

4500-year paleohurricane record from the Western Gulf of Mexico, Coastal Central TX, USA

Texas receives the second-highest number of tropical cyclone (TC) landfalls per year in the United States. At present, long-term TC projections from climate models remain uncertain due to the short and biased nature of Atlantic TC observations. Sediment archives of past storms can help extend the observational record of TC strikes over the past few millennia. When a TC makes landfall along the central Texas coast, coastal downwelling channels and storm currents transport and deposit coarse sediment to a zone of rapid accumulation along the shelf, known as the Texas Mud Blanket (TMB). This “backwash” process results in expansive storm deposits along the shelf, making this region ideal for paleotempestological reconstructions. Here, we present two sediment cores, located approximately 6 km southeast of Matagorda Island (TX), that collectively yield a ∼4500-year paleohurricane record. 210Pb and 137Cs are utilized in conjunction with radiocarbon ages to produce high-resolution Bayesian age models. One-centimeter interval grain size analyses are used to identify TC deposits. Two-centimeter interval X-Ray Fluorescence (XRF) is used as an additional measure to verify depositional mechanisms in this shelf environment. We define an intense paleohurricane event threshold through statistical analysis of mean grain size data. The sediment-derived TC record is correlated to Palmer Drought Severity Index (PDSI) data from Paleo Hydrodynamics Data Assimilation (PHYDA) to bolster our interpretation of the TC record, revealing a coupled relationship between PDSI and TCs since ∼300 yr BP. Over the ∼4500-year period, 24 intense TCs were recorded in the sediment record, yielding a long-term annual landfall probability of ∼0.53%. Additionally, comparisons between other TC records within the Atlantic establish a relationship between enhanced TC activity in the Western Gulf of Mexico (GOM) and TCs formed in the Caribbean Sea.

Footprint of the air-sea momentum transfer saturation observed by ocean wave buoy network in extreme tropical cyclones

Tropical cyclones are one of the most destructive natural phenomena, causing tremendous coastal disasters worldwide. The maximum intensity of tropical cyclones is determined by momentum and heat transfer at the air-sea interface. Momentum transfer corresponds to the momentum loss of tropical cyclones and, consequently, to the underlying ocean's momentum gain causing extreme ocean waves and storm surges. Air-side observations of wind profiles and ocean-side observations of ocean subsurface currents show a slowdown of momentum transfer under high tropical cyclone wind speeds in the previous studies. However, there is still disagreement regarding the slowdown owing to lack of data. Here, we show momentum transfer under high wind speed conditions by observing ocean waves. Although ocean wave observations are highly spatially limited, we deployed a fleet of drifting ocean wave buoys covering the active area of tropical cyclones in the Western North Pacific. The buoy fleet captured extreme waves near the eye of the strongest category 5 tropical cyclone, indicating an ocean wave footprint of the momentum transfer saturation above surface wind speeds of 25 m/s. Our approach from ocean surface wave observation is a unique contribution to determination on air-sea momentum transfer slowdown under extreme wind speeds, which can compensate for the knowledge from conventional air and ocean-side observations. This finding advances tropical cyclones, extreme ocean waves, and storm surge modeling.

Impact Of Global Warming on Tropical Cyclones in The Northwest Pacific Ocean

Tropical cyclones are meteorological phenomena that can affect mid to low latitudes around the world. This Paper systematically summarizes and reviews the main characteristics, potential influencing factors, and influencing mechanisms of tropical cyclones in Northwestern Pacific, and summarizes and analyzes their changing trends in the context of global change. Global warming will affect the generation and development of tropical cyclones from multiple dimensions, with the most intuitive being the surface temperature of seawater. Through the analysis of global surface temperature and tropical cyclones in the Northwest Pacific, it is shown that as global climate warms, the frequency of tropical cyclones in the Northwest Pacific decreases, the extreme central pressure decreases, and the extreme maximum central wind speed increases, there is no significant trend in the average minimum pressure and average maximum wind speed of tropical cyclones. In other words, the extreme intensity of tropical cyclones strengthens; In the 2010s, this relationship was particularly prominent.

Enhancing tropical cyclone intensity forecasting with explainable deep learning integrating satellite observations and numerical model outputs

Enhancing tropical cyclone intensity forecasting with explainable deep learning integrating satellite observations and numerical model outputs

Modelling additive extremile regression by iteratively penalized least asymmetric weighted squares and gradient descent boosting

Quantile regression has emerged as one of the standard tools for regression analysis that enables a proper assessment of the complete conditional distribution of responses. This article considers a valuable alternative class to quantiles, called extremiles. Extremiles bear much better than quantiles the burden of representing an alert risk measure to the magnitude of infrequent catastrophic losses. Additive regression model has concentrated on making the regression structure more flexible by including nonlinear effects of continuous covariates and interaction effects. As a consequence, additive extremile regression based on minimizing an asymmetrically weighted sum of squared residuals is introduced. Different estimation procedures are presented including iteratively penalized least asymmetric weighted squares and gradient descent boosting. The properties of these procedures are investigated in a simulation study and an analysis of tropical cyclone intensity of the North Atlantic which reveals the function of variable selection by modelling additive extremile regression simultaneously.

Tropical cyclone activities in the Western North Pacific in 2022

Based on the best-track dataset from the Shanghai Typhoon Institute/China Meteorological Administration, the paper provides a comprehensive summary and analysis of tropical cyclone (TC) activities in the Western North Pacific (WNP) and the South China Sea (SCS) for 2022. Using the historical climatology from 1951 to 2020, the anomalous conditions during 2022 in TC frequency, origin locations, tracks, intensity, and duration for the entire ocean basin as well as landfall events in China are examined. Results show that the overall TC frequency is slightly lower than normal, but the multiple TC events have a very high frequency of occurrence. Origin locations of TCs, which mark the starting points of their paths, show a large westward and northward deviation from climatology. Around 40% of the named TCs exhibit a shift in their direction of movement from westerly to easterly. Additionally, comparisons of the means, medians, upper and lower quartiles all indicate that the intensity of TCs in 2022 is generally lower than the climatology, with the duration of TCs at tropical storm intensity or above being shorter than usual. A notable observation is the fewer incidence of TC landfalls in China, but with a geographical concentration in Guangdong Province. These anomalous annual TC activities are influenced by related atmospheric and oceanic environmental conditions modulated by multi-scale climate variability. The findings provide useful information for enhancing disaster mitigation strategies in the Asia-Pacific region.

Predicting the Intensity of Tropical Cyclones over the Western North Pacific Using a Dual-Branch Spatiotemporal Attention Convolutional Network

Abstract This paper proposes a spatiotemporal attention convolutional network (STAC-Pred) that leverages deep learning techniques to model the spatiotemporal features of tropical cyclones (TCs) and enable real-time prediction of their intensity. The proposed model employs dual branches to concurrently extract and integrate features from intensity heatmaps and satellite cloud imagery. Additionally, a residual attention (RA) module is integrated into the three-channel cloud imagery convolution process to automatically respond to high wind speed regions. TC’s longitude, latitude, and radius of winds are injected into the multi-timepoint prediction model to assist in the prediction task. Furthermore, a rolling mechanism (RM) is employed to smooth the fluctuation of losses, achieving accurate prediction of TC intensity. We use several TC records to evaluate and validate the universality and effectiveness of the model. The results indicate that STAC-Pred achieves satisfactory performance. Specifically, the STAC-Pred model improves prediction performance by 47.69% and 28.26% compared to the baseline (official institutions) at 3- and 6-h intervals, respectively. Significance Statement Tropical cyclones are one of the most deadly and damaging natural disasters in coastal areas worldwide. Early prediction can significantly reduce casualties and property losses. This study innovatively conducts dimensionality augmentation on one-dimensional intensity numerical sequences and proposes a new network model for rolling forecast of their future intensity. The proposed prototype model (not yet incorporating any atmospheric conditions) shows promising results for 3- and 6-h advance forecasts, providing valuable guidance for forecasters regarding real-time operational predictions of short-term tropical cyclone intensity.

A Global Increase in Nearshore Tropical Cyclone Intensification

AbstractTropical Cyclones (TCs) inflict substantial coastal damages, making it pertinent to understand changing storm characteristics in the important nearshore region. Past work examined several aspects of TCs relevant for impacts in coastal regions. However, few studies explored nearshore storm intensification and its response to climate change at the global scale. Here, we address this using a suite of observations and numerical model simulations. Over the historical period 1979–2020, observations reveal a global mean TC intensification rate increase of about 3 kt per 24‐hr in regions close to the coast. Analysis of the observed large‐scale environment shows that stronger decreases in vertical wind shear and larger increases in relative humidity relative to the open oceans are responsible. Further, high‐resolution climate model simulations suggest that nearshore TC intensification will continue to rise under global warming. Idealized numerical experiments with an intermediate complexity model reveal that decreasing shear near coastlines, driven by amplified warming in the upper troposphere and changes in heating patterns, is the major pathway for these projected increases in nearshore TC intensification.

Improvement of Global Forecast of Tropical Cyclone Intensity by Spray Heat Flux and Surface Roughness

AbstractIn global forecasting systems, tropical cyclone (TC) intensity is usually underestimated, which is one of the major challenges for TC forecasting. One possible reason is the deficiency of physical parameterizations associated with ocean surface waves. To improve the TC intensity forecast, effects of two powerful wave‐related processes, the spray‐mediated heat flux and surface roughness, are considered in a global coupled ocean‐atmosphere‐wave system (CFSv2.0‐WW3). Serial 24 hr forecasting experiments for all TCs from May to October 2019 are conducted, and comparisons are made against the International Best Track TC data. The results show that for strong TCs the underestimation of TC intensity in CFSv2.0‐WW3 is significantly improved by the modified surface roughness parameterization and the accelerated spray heat flux parameterization based on Gaussian Quadrature. For major hurricanes with initial wind speeds above 30 m/s, the error of TC maximum wind speed decreases by about 34.4%. To understand the associated dynamic and thermodynamic processes, a series of 120 hr simulations for major hurricane Hagibis is conducted. The effect of spray‐mediated heat flux accounts for 91.7% of the TC intensity improvement. The increased heat fluxes offset the negative contributions of vertical advection and ventilation, leading to enhanced entropy. The low‐level pressure is then decreased, resulting in enhanced convergence of absolute vorticity, which overpowers the negative effects of frictional dissipation and vertical momentum advection. The study indicates that the application of these two wave‐related parameterizations can potentially improve the global forecast and reanalysis of TC intensity.

Spatio-temporal patterns of intense tropical cyclones in the Western North Pacific over the past 1600 years

There are concerns that intense tropical cyclones (TCs) are expected to become more frequent and powerful in warming climates. However, the long-term trend of TC activities, the spatiotemporal variability of such trends across different latitudes of the Western North Pacific (WNP), and the drives of the variability remain unclear. Here, we present a reconstruction of a 1600-year paleo-TCs activity using a sediment core taken in Li’an Lagoon located in southeastern Hainan Island, South China Sea. We used muti-dating methods (210Pb and AMS 14C dating) for age control and muti-proxy analysis (XRF geochemical element scanning and grain size analysis) for identifying periods of frequent intense TC activities. Based on an updated compilation of basin-wide paleo-TC records, we confirm that there exists a seesaw pattern of intense TC frequency between low and middle latitude in the WNP. Comparing with global and regional paleoclimate proxies, we propose that the basin-wide latitudinal TC activity variation in the WNP can be linked to the migration of Western Pacific Subtropical High (WPSH) and its associated high-latitude forcings (e.g., NAO) and low-latitude internal variability forcings (i.e., El Niño Southern Oscillation, sea surface temperature (SST)). More intense TC will occur at low latitudes in the future, though with less frequency.

Numerical Study of Effects of Warm Ocean Eddies on Tropical Cyclones Intensity in Northwest Pacific

This study investigates the impact of warm core eddies (WCEs) on the ocean response and intensity of tropical cyclones (TCs) in the Northwest Pacific, focusing on three typhoons in 2018: Jebi, Trami, and Kong-rey. The research uses the Hurricane Weather Research and Forecast (HWRF) model coupled with the MPIPOM-TC ocean model. Idealized WCEs are embedded into the ocean model ahead of each TC. The impacts of WCEs are evaluated by comparing simulations with and without their presence. Uncoupled experiments with the fixed sea surface temperature (SST) serve as a reference for TC maximum potential intensity. To quantitatively assess the impact of WCEs on the SST, enthalpy fluxes, and TC intensity, a Maximum WCE Potential Index (MWPI) is introduced. Our findings indicate that for a WCE with a 200 km radius, the potential to reduce SST cooling ranges from 34 to 37%, while the potential to increase enthalpy fluxes varies between 25 and 39%. The influence of WCEs on TC intensity, as measured by minimum pressure, shows a larger variation from 27% to 48%, depending on the oceanic and atmospheric environmental conditions in each storm. Additional experiments reveal the sensitivity of the MWPI to WCE size, with TC Trami showing less sensitivity due to its slower translational speed. This study underscores the significant role of oceanic thermal conditions, particularly WCEs, in modulating TC intensity.

Why Was the Weakening of Typhoon In-fa (2021) to the East of Taiwan Island Not Forecasted in the GFS?

Abstract Typhoon In-fa (2021) experienced a weakening process on 22–23 July in a large-scale environment favorable for tropical cyclone (TC) intensification. All operational forecasts and the Global Forecast System (GFS) forecasts predicted a continuous intensification, which deviated significantly from the observation. The analysis of the GFS analysis product shows a coalescence process of Typhoon In-fa with an intraseasonal monsoon gyre during the period, resulting in an increased outer size of In-fa and well-organized convection to the east, which prevented transporting the mass and moisture into the inner-core area of In-fa, thus leading to the weakening. Nevertheless, this essential coalescence process was not captured in the GFS forecasts due to the poor prediction of the monsoon gyre. The analysis shows that the forecasted monsoon gyre on 20–22 July had an eastward location at 72- and 96-h lead times and a weaker intensity and outer circulation at 24- and 48-h lead times, leading to the forecasted TC always moving in its north and west, in agreement with numerical simulation results that the monsoon gyre with a weaker outer circulation is not conducive to the coalescence. Thus, the deep convection to the east of In-fa preventing the inward transportation of mass and moisture did not develop in the GFS forecasts. As a result, the GFS forecasted that In-fa would continue intensifying in a favorable environment on 22–23 July. The findings of this study would prompt forecasters to pay attention to the prediction of the monsoon gyre and its influence on the TC intensity in forecast products available to them.