Tropical Cyclone Intensity Prediction Research Articles

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.

Comparison of different global ensemble prediction systems for tropical cyclone intensity forecasting

AbstractMany meteorological centers have operationally implemented global model‐based ensemble prediction systems (GEPSs), making tropical cyclone (TC) forecasts from these systems available. The relatively low resolution of these GEPSs means that limits previous studies primarily focused on TC track forecasting. However, recent GEPS upgrades mean that TC intensity predictions from GEPSs are now also becoming of interest. This study focuses on the verification and comparison of the latest generation of GEPSs for TC intensity forecasts, particularly during the rapid intensification (RI) period over the western North Pacific (WP), eastern North Pacific (EP), and North Atlantic (NA) basins in 2021–2022. On average, the National Centers for Environmental Prediction (NCEP) GEPS performed best in predicting both TC intensity and RI across all three basins. Nevertheless, the exact timing of RI remains highly uncertain for these GEPS, indicating significant limitations in using GEPSs to forecast RI.

Operational tropical cyclone intensity prediction in the bay of bengal - An updated empirical technique

Given its role in preventing cyclone-related property damage and fatalities, effective weather forecasting is of utmost importance. Numerous methods for forecasting the weather have been developed via extensive research. To improve precise and trustworthy weather forecast techniques, however, there is a constant need. This study suggests an improved empirical methodology for forecasting tropical cyclone (TC) intensity, particularly in the Bay of Bengal (BoB) region. The study is based on an analysis of 32 cyclones that occurred between 2003 and 2019. The model predicts that TC has significantly strengthened. This article's methodology has the potential to raise forecast precision at 12-hour intervals. However, the forecast's precision decreases over time.

A modified vertical eddy diffusivity parameterization in the HWRF model based on large eddy simulations and its impact on the prediction of two landfalling hurricanes

Vertical eddy diffusivity (VED) in the planetary boundary layer (PBL) has a significant impact on forecasts of tropical cyclone (TC) structure and intensity. VED uncertainties in PBL parameterizations can be partly attributed to the model’s inability to represent roll vortices (RV). In this study, RV effects on turbulent fluxes derived from a large eddy simulation (LES) by Li et al. (Geophys. Res. Lett., 2021, 48, e2020GL090703) are added to the VED parameterization of the PBL scheme within the operational Hurricane Weather Research and Forecasting (HWRF) model. RV contribution to VED is parameterized through a coefficient and varies with the RV intensity and velocity scale. A modification over land has also been implemented. This modified VED parameterization is compared with the original wind-speed-dependent VED scheme in HWRF. Retrospective HWRF forecasts of Hurricanes Florence (2018) and Laura (2020) are analyzed to evaluate the impacts of the modified VED scheme on landfalling hurricane forecasts. Results show that the modified PBL scheme with the RV effect leads to an improvement in 10-m maximum wind speed forecasts of 14%–31%, with a neutral to positive improvement for track forecasts. Improved wind structure and precipitation forecasts against observations are also noted with the modified PBL scheme. Further diagnoses indicate that the revised PBL scheme enhances moist entropy in the boundary layer over land, leading to improved TC intensity prediction compared to the original scheme.

User-Responsive Diagnostic Forecast Evaluation Approaches: Application to Tropical Cyclone Predictions

Abstract The Hurricane Forecast Improvement Project (HFIP; renamed the “Hurricane Forecast Improvement Program” in 2017) was established by the U.S. National Oceanic and Atmospheric Administration (NOAA) in 2007 with a goal of improving tropical cyclone (TC) track and intensity predictions. A major focus of HFIP has been to increase the quality of guidance products for these parameters that are available to forecasters at the National Weather Service National Hurricane Center (NWS/NHC). One HFIP effort involved the demonstration of an operational decision process, named Stream 1.5, in which promising experimental versions of numerical weather prediction models were selected for TC forecast guidance. The selection occurred every year from 2010 to 2014 in the period preceding the hurricane season (defined as August–October), and was based on an extensive verification exercise of retrospective TC forecasts from candidate experimental models run over previous hurricane seasons. As part of this process, user-responsive verification questions were identified via discussions between NHC staff and forecast verification experts, with additional questions considered each year. A suite of statistically meaningful verification approaches consisting of traditional and innovative methods was developed to respond to these questions. Two examples of the application of the Stream 1.5 evaluations are presented, and the benefits of this approach are discussed. These benefits include the ability to provide information to forecasters and others that is relevant for their decision-making processes, via the selection of models that meet forecast quality standards and are meaningful for demonstration to forecasters in the subsequent hurricane season; clarification of user-responsive strengths and weaknesses of the selected models; and identification of paths to model improvement. Significance Statement The Hurricane Forecast Improvement Project (HFIP) tropical cyclone (TC) forecast evaluation effort led to innovations in TC predictions as well as new capabilities to provide more meaningful and comprehensive information about model performance to forecast users. Such an effort—to clearly specify the needs of forecasters and clarify how forecast improvements should be measured in a “user-oriented” framework—is rare. This project provides a template for one approach to achieving that goal.

Effect of Storm Size on Sea Surface Cooling and Tropical Cyclone Intensification in the Western North Pacific

Abstract The effect of tropical cyclone (TC) size on TC-induced sea surface temperature (SST) cooling and subsequent TC intensification is an intriguing issue without much exploration. Via compositing satellite-observed SST over the western North Pacific during 2004–19, this study systematically examined the effect of storm size on the magnitude, spatial extension, and temporal evolution of TC-induced SST anomalies (SSTA). Consequential influence on TC intensification is also explored. Among the various TC wind radii, SSTA are found to be most sensitive to the 34-kt wind radius (R34) (1 kt ≈ 0.51 m s−1). Generally, large TCs generate stronger and more widespread SSTA than small TCs (for category 1–2 TCs, R34: ∼270 vs 160 km; SSTA: −1.7° vs −0.9°C). Despite the same effect on prolonging residence time of TC winds, the effect of doubling R34 on SSTA is more profound than halving translation speed, due to more wind energy input into the upper ocean. Also differing from translation speed, storm size has a rather modest effect on the rightward shift and timing of maximum cooling. This study further demonstrates that storm size regulates TC intensification through an oceanic pathway: large TCs tend to induce stronger SST cooling and are exposed to the cooling for a longer time, both of which reduce the ocean’s enthalpy supply and thereby diminish TC intensification. For larger TCs experiencing stronger SST cooling, the probability of rapid intensification is half of smaller TCs. The presented results suggest that accurately specifying storm size should lead to improved cooling effect estimation and TC intensity prediction. Significance Statement Storm size has long been speculated to play a crucial role in modulating the TC self-induced sea surface temperature (SST) cooling and thus potentially influence TC intensification through ocean negative feedback. Nevertheless, systematic analysis is lacking. Here we show that larger TCs tend to generate stronger SST cooling and have longer exposure to the cooling effect, both of which enhance the strength of the negative feedback. Consequently, larger TCs undergo weaker intensification and are less likely to experience rapid intensification than smaller TCs. These results demonstrate that storm size can influence TC intensification not only from the atmospheric pathway, but also via the oceanic pathway. Accurate characterization of this oceanic pathway in coupled models is important to accurately forecast TC intensity.

A tropical cyclone intensity prediction model using conditional generative adversarial network

This study proposes a model for predicting tropical cyclone (TC) intensity evolution by utilizing the conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP) for TC-related risk assessment. The TC intensity evolution is treated as a random variable, which is the output of a nonlinear system conditioned on a series of input random variables that characterize both the TC state (e.g., translation speed and potential intensity) and environment (e.g., vertical wind shear and ocean mixed layer depth). To represent the high-dimensional non-Gaussian probabilistic characteristics, the system is modeled by the CWGAN-GP and calibrated concerning the dataset of 1010 historical TCs from the western North Pacific basin. The over-ocean and overland branches of the model are calibrated separately and then merged. The suitability of the established model is validated by comparing the predicted TC intensity to the observations and the results of existing models such as those based on linear regression. Numerical results indicate that the proposed model successfully replicates the probabilistic properties such as the non-Gaussian marginal distribution of the intensity change and the input-output joint distribution and moments between the TC intensity and predictors, which the linear regression model cannot provide. Further application examples are also carried out by using the model to simulate the evolution of historical TC events and the extreme TC intensities in Southern China, demonstrating its potential role in the TC hazard assessment.

The Role of Ocean–Atmosphere Interactions in Tropical Cyclone Intensity Predictability

Abstract The negative feedback between tropical cyclone (TC) intensity and sea surface temperature (SST) plays an important role in TC development. In this study, ocean–atmosphere coupled and uncoupled ensemble forecasts are conducted to investigate the dynamics of error growth and predictability of TC intensity in an ocean–atmosphere coupled system. For the TC–ocean coupled system, the TC intensity–SST negative feedback is the essential mechanism to reduce the error growth of TC intensity by two routes, and thereby improves the TC intensity predictability. For the first route (atmosphere-limited route), the TC-induced SST cooling slows the intensification rate of the TC and weakens the final TC intensity, thereby reducing the error growth of TC intensity. In this route, the TC intensity spread is limited by the magnitude of TC intensity, while SST can be regarded as an environmental forcing. For the second route (atmosphere–ocean mutually influenced route), the interaction between the TC intensity spread and SST spread is dominant. The increasing TC intensity spread could lead to an increase in SST cooling spread, and then reduce the TC intensity spread through the negative feedback. In other words, the more (less) intense TC produces stronger (weaker) SST cooling, and thereby limits (enhances) further TC intensification in an ensemble forecast. In the second route, initial ocean temperature uncertainty could suppress the TC intensity spread reduction. Significance Statement Tropical cyclones force the sea surface and can lead to its cooling. This cooled sea surface can then suppress tropical cyclone intensification. The purpose of this study is to better examine the influence of such an interaction between a tropical cyclone and the ocean on tropical cyclone forecasts. We explore how accurately representing the interaction can improve the capacity to forecast tropical cyclone intensity. Given that many weather forecasting centers have considered this interaction in their models, this study should help them to understand and improve their forecasts.

Evaluation of a Mesoscale Coupled Ocean‐Atmosphere Configuration for Tropical Cyclone Forecasting in the South West Indian Ocean Basin

AbstractThe performance in term of tropical cyclone track and intensity prediction of the new coupled ocean‐atmosphere system based on the operational atmospheric model AROME‐Indian Ocean and the ocean model NEMO is assessed against that of the current operational configuration in the case of seven recent tropical cyclones. Five different configurations of the forecast system are evaluated: two with the coupled system, two with an ocean mixed layer parameterization and one with a constant sea surface temperature. For each ocean‐atmosphere coupling option, one is initialized directly with the MERCATOR‐Ocean PSY4 product as in the current operational configuration and the other with the ocean state that is cycled in the AROME‐NEMO coupled suite since a few days before the cyclone intensification. The results show that the coupling with NEMO generally improves the intensity of cyclones in AROME‐IO, reducing the mean intensity bias of the 72 hr forecast of about 10 hPa. However, the impact is especially significant when the TCs encounter a slow propagation phase. For short‐term forecasts (less than 36 hr), the presence of a cooling in the initial state that has been triggered by the AROME high‐resolution cyclonic winds in a previous coupled forecast already improves the tropical cyclone intensity bias of 2–3 hPa for both coupled or uncoupled configurations.

Tropical Cyclone intensity prediction based on hybrid learning techniques

Tropical Cyclone intensity prediction based on hybrid learning techniques

Tropical cyclone intensity prediction by inter- and intra-pattern fusion based on multi-source data

Tropical cyclones (TCs) are one of the most destructive natural disasters, which can bring huge life and economic losses to the global coastal areas. Accurate TC intensity prediction is critical for disaster prevention and loss reduction, but the dynamic processes involved in TCs are complicated and not adequately understood, which make the intensity prediction is still a challenging task. In recent years, several deep-learning (DL)-based methods have been developed for TC prediction by mining TC intensity series or related environmental factors. However, information hidden between the two different data sources is generally ignored. Here, a novel DL-based TC intensity prediction network named Pre_3D is proposed, which aimed to mine of inter- and intra-patterns of TC intensity and related external factors independently by separate feature extraction sub-networks. An MLP network is adopted to achieve adaptive fusion of the two patterns for accurate TCs intensity prediction. TC records from several agencies were used to evaluate generalizability of the proposed framework and extensive experiments were conducted validate its effectiveness. The experimental results demonstrate that the models based on the Pre_3D framework achieved considerable performance. ConvGRU-based Pre_3D yields a significant improvement of over 15% in prediction accuracy in 24 h prediction relative to official agencies.

Tropical Cyclone Intensity and Track Prediction in the Bay of Bengal Using LSTM-CSO Method

Tropical Cyclone Intensity and Track Prediction in the Bay of Bengal Using LSTM-CSO Method

A Machine Learning (ML)-Based Approach to Improve Tropical Cyclone Intensity Prediction of NCMRWF Ensemble Prediction System

A Machine Learning (ML)-Based Approach to Improve Tropical Cyclone Intensity Prediction of NCMRWF Ensemble Prediction System

New parameterization of air-sea exchange coefficients and its impact on intensity prediction under major tropical cyclones

Understanding and quantifying air-sea exchanges of enthalpy and momentum fluxes are crucial for the advanced prediction of tropical cyclone (TC) intensity. Here, we present a new parameterization of air-sea fluxes at extreme wind speeds from 40 m s−1 to 75 m s−1, which covers the range of major TCs. Our approach assumes that the TC can reach its maximum potential intensity (MPI) if there are no influences of external forces such as vertical wind shear or other environmental constraints.This method can estimate the ratio of the enthalpy and momentum exchange coefficient (Ck/Cd) under the most intense TCs without direct flux measurements. The estimation showed that Ck/Cd increases with wind speed at extreme winds above 40 m s−1. Two types of surface layer schemes of the Hurricane Weather and Research Forecast (HWRF) were designed based on the wind speed dependency of the Ck/Cd found at high winds: (i) an increase of Ck/Cd based on decreasing Cd (Cd_DC) and (ii) an increase of Ck/Cd based on increasing Ck (Ck_IC). The modified surface layer schemes were compared to the original HWRF scheme (using nearly fixed Cd and Ck at extreme winds; CTRL) through idealized experiments and real-case predictions. The idealized experiments showed that Cd_DC reduced frictional dissipation in the air-sea interface as well as significantly reduced sea surface cooling, making the TC stronger than other schemes. As a result, Cd_DC reduced the mean absolute error and negative bias by 15.0% (21.0%) and 19.1% (32.0%), respectively, for all lead times of Hurricane Irma in 2017 (Typhoon Mangkhut in 2018) compared to CTRL. This result suggests that new parameterization of Ck/Cd with decreasing Cd at high winds can help improve TC intensity prediction, which currently suffers from underestimating the intensity of the strongest TCs.

Characteristics and Controlling Factors of Rapid Weakening of Tropical Cyclones After Reaching Their Intensity Peaks Over the Western North Pacific

AbstractThe sudden transition to rapid weakening immediately after a tropical cyclone (TC) reaches its intensity peak (hereafter TP‐RW) is a challenge to operational TC intensity forecasts, but the characteristics and the controlling factors of the TP‐RW have not been investigated so far. In this study, the main characteristics of the TP‐RW events over the western North Pacific (WNP) and the corresponding key controlling factors and their relative importance are analyzed and quantified. Results show that about 2.5 TP‐RW events occurred each year, which corresponds to about 10% of total TCs over the WNP during 1982–2018. The TP‐RW events occurred in all months but peaked in fall and were the most likely to occur for TCs with intensities between 90 and 145 kt. The sea surface temperature, the TC maximum potential intensity and translational speed, the environmental vertical wind shear and relative humidity are key to the TP‐RW. Four distinct clusters of the TP‐RW events are identified based on their tracks using the K‐means clustering algorithm. The relative contribution of each of the identified key factors to each cluster is quantified using the growth rate from the simplified dynamical system for TC intensity prediction developed by DeMaria. The reversal of the growth rate from positive to negative values is shown to be a good indicator of the TP‐RW processes. Different clusters are predominantly controlled by different key factors. The findings from this study can help better understand TC intensity changes and improve TC intensity forecasts for such kind of RW.

Improving Analysis and Prediction of Tropical Cyclones by Assimilating Radar and GNSS-R Wind Observations: Ensemble Data Assimilation and Observing System Simulation Experiments Using a Coupled Atmosphere–Ocean Model

Abstract This study investigates the impact of assimilating ground-based radar reflectivity and wind data on tropical cyclone (TC) intensity prediction. The effect on a high-impact TC in the western North Pacific region that penetrated the Bashi Channel is examined. A multiscale correction based on the successive covariance localization (SCL) method is adopted to improve the analysis and forecast performance. In addition, GNSS-R wind speed is assimilated jointly in the rapid update assimilation framework to complement the TC boundary layer where radar data are limited. Model experiments are conducted and evaluated using the observing system simulation experiments (OSSEs) framework with a coupled atmosphere–ocean model nature run. Taking the experiment without data assimilation as the baseline, assimilating the radar data with the standard localization and SCL methods reduces the wind speed analysis error by 12% and 44%, respectively. The SCL method dominates the improvement in TC intensity prediction with a lead time longer than 2 days and the TC’s peak intensity forecast is improved by 18 hPa. The additional assimilation of the GNSS-R wind speed observation further reduces the wind speed error in the low-level analysis by 12% and 5% under the standard and SCL radar assimilation framework, respectively. GNSS-R wind assimilation leads to a further 6-hPa improvement in TC’s peak intensity. However, the sampling error introduced by the SCL method restrains the effect of GNSS-R assimilation. Sensitivity experiments with different GNSS-R data arrangements show that better GNSS-R wind coverage and additional wind direction information can further improve the TC analysis. Significance Statement Tropical cyclone (TC) intensity prediction over the western North Pacific (WNP) region remains a significant challenge due to limited observations. This study aims to improve the TC intensity prediction in WNP by assimilating the ground-based radar data using a multiscale correction framework and incorporating with the satellite ocean surface wind speed observation. We particularly focus on a high-impact TC like Typhoon Hato (2017), which penetrated the Bashi Channel and later made landfall in China, causing great damage. Our results showed that the assimilation strategy improved the TC intensity prediction for a lead time longer than 2 days. These results demonstrate the great potential of these observations and can provide guidance for future applications in operation centers.

An Upper Ocean Thermal Field Metrics Dataset

The upper ocean provides a source of thermal energy for tropical cyclone development and maintenance through a series of complex interactions. In this work, we develop a seventeen-year dataset of upper ocean thermal field metrics for use in tropical cyclone studies and development of tropical cyclone intensity prediction models. These metrics include the surface temperature, two different measures of vertically integrated heat content, and four different measures of vertically averaged temperature. Some metrics have been used to study upper-ocean energy response to tropical cyclone passage, while others have been employed to improve operational tropical cyclone intensity prediction models. The vertically integrated ocean heat content has been used to improve tropical cyclone intensity forecasts at U.S. tropical cyclone forecast centers and is an integral part of several operational intensity forecast models. A static 2005–2021 dataset that includes all twelve metrics described within is available on the Naval Research Laboratory web server, and a subset of six metrics have been produced in real-time at Fleet Numerical Meteorology and Oceanography Center and provided to the public via the GODAE server since 2021.

Tropical Cyclone Intensity Prediction Using Deep Convolutional Neural Network

In this study, deep convolutional neural network (CNN) models of stimulated tropical cyclone intensity (TCI), minimum central pressure (MCP), and maximum 2 min mean wind speed at near center (MWS) were constructed based on ocean and atmospheric reanalysis, as well Best Track of tropical hurricane data over 2014–2018. In order to explore the interpretability of the model structure, sensitivity experiments were designed with various combinations of predictors. The model test results show that simplified VGG-16 (VGG-16 s) outperforms the other two general models (LeNet-5 and AlexNet). The results of the sensitivity experiments display good consistency with the hypothesis and perceptions, which verifies the validity and reliability of the model. Furthermore, the results also suggest that the importance of predictors varies in different targets. The top three factors that are highly related to TCI are sea surface temperature (SST), temperature at 500 hPa (TEM_500), and the differences in wind speed between 850 hPa and 500 hPa (vertical wind shear speed, VWSS). VWSS, relative humidity (RH), and SST are more significant than MCP. For MWS and SST, TEM_500, and temperature at 850 hPa (TEM_850) outweigh the other variables. This conclusion also implies that deep learning could be an alternative way to conduct intensive and quantitative research.

An Advanced Artificial Intelligence System for Identifying the Near-Core Impact Features to Tropical Cyclone Rapid Intensification from the ERA-Interim Data

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, which is accumulated based on expert expertise in past studies of TC intensity changes and RI. Are there any more important variables in TC intensity predictions that were not identified in past studies? A systematic and comprehensive search for those variables from vast amounts of gridded data, satellite images, and other historically collected data could be helpful for answering the above question. Artificial intelligence (AI) has the capabilities to distill features in large array data, and it is helpful in identifying new features related to TC intensity changes in general and RI in particular. Here, we leverage the local linear embedding (LLE) dimension reduction techniques to the European Centre for Medium-Range Weather Forecasts ERA-Interim reanalysis data for identifying new variables related to RI. In addition to the well-known features in the SHIPS (statistical hurricane intensity prediction scheme) database, we identified other significant features, such as 400 and 450 hPa meridional wind, 1000 hPa potential vorticity, and vertical pressure speed, that could help the understanding and prediction of RI occurrences. Furthermore, our AI system outperforms our baseline model with SHIPS data only by 26.6% and 8.4% in kappa and PSS (Peirce’s skill score), respectively.

Tropical cyclone track and intensity prediction skill of GFS model over NIO during 2019 & 2020

The Tropical Cyclone (TC) track prediction using different NWP models and its verification is the critical task to provide prior knowledge about the model errors, which is beneficial for giving the model guidance-based real-time cyclone warning advisories. This study has attempted to verify the Global Forecast System (GFS) model forecasted tropical cyclone track and intensity over the North Indian Ocean (NIO) for the years 2019 and 2020. GFS is one of the operational models in the India Meteorological Department (IMD), which provides the medium-range weather forecast up to 10 days. The forecasted tracks from the GFS forecast are obtained using a vortex tracker developed by Geophysical Fluid Dynamics Laboratory (GFDL). A total of 13 tropical cyclones formed over the North Indian Ocean, eight during 2019 and five in 2020 have been considered in this study. The accuracy of the model predicted tracks and intensity is verified for five days forecasts (120 h) at 6-h intervals; the track errors are verified in terms of Direct Position Error (DPE), Along Track Error (ATE) and Cross-Track Error (CTE). The annual mean DPE over NIO during 2019 (51–331 km) is lower than 2020 (82–359 km), and the DPE is less than 150 km up to 66 h during 2019 and 48 h during 2020. The positive ATE (76–332 km) indicates the predicted track movement is faster than the observed track during both years. The positive CTE values for most forecast lead times suggest that the predicted track is towards the right side of the observed track during both years. The cyclone Intensity forecast for the maximum sustained wind speed (MaxWS) and central mean sea level pressure (MSLP) are verified in terms of mean error (ME) and root mean square error (RMSE). The errors are lead time independent. However, most of the time model under-predicted the cyclone intensity during both years. Finally, there is a significant variance in track and intensity errors from the cyclone to cyclone and Bay of Bengal basin to the Arabian Sea basin.