Tropical Cyclone Prediction Research Articles

ENSO influence on Bay of Bengal cyclogenesis confined to low latitudes

The low-latitudinal cyclones (LLCs, originating between 5°N–10°N) constitute ≈40% of tropical cyclones (TCs) formed in the Bay of Bengal (BoB). We investigate the interannual variability of post-monsoonal (October to December) BoB LLCs and their teleconnection with El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). It is found that the years with the fewer number of BoB LLCs are associated with anomalous equatorial easterlies that are largely connected with the El Niño and positive IOD. Likewise, equatorial westerly phases, often associated with the La Niña and negative IOD years, favour the LLC formation by providing the initial spin-up required for cyclogenesis. This teleconnection between ENSO/IOD and BoB TC frequency is confined in the low-latitudinal region with little influence for cyclogenesis north of 10°N during ENSO and IOD except during negative IOD. These results may help extend the lead time and improve the seasonal prediction of BoB TCs.

A Statistical Intraseasonal Prediction Model of Extended Boreal Summer Western North Pacific Tropical Cyclone Genesis

Abstract An L2 regularized logistic regression model is developed in this study to predict weekly tropical cyclone (TC) genesis over the western North Pacific (WNP) Ocean and subregions of the WNP including the South China Sea (SCS), the western WNP (WWNP), and the eastern WNP (EWNP). The potential predictors for the TC genesis model include a time-varying TC genesis climatology, the Madden–Julian oscillation (MJO), the quasi-biweekly oscillation (QBWO), and ENSO. The relative importance of the predictors in a constructed L2 regression model is justified by a forward stepwise selection procedure for each region from a 0-week to a 7-week lead. Cross-validated hindcasts are then generated for the corresponding prediction schemes out to a 7-week lead. The TC genesis climatology generally improves the regional model skill, and the importance of intraseasonal oscillations and ENSO is regionally dependent. Over the WNP, there is increased model skill over the time-varying climatology in predicting weekly TC genesis out to a 4-week lead by including the MJO and QBWO, whereas ENSO has a limited impact. On a regional scale, ENSO and then either the MJO or QBWO are the two most important predictors over the EWNP and WWNP after the TC genesis climatology. The MJO is found to be the most important predictor over the SCS. The logistic regression model is shown to have comparable reliability and forecast skill scores to the ECMWF dynamical model on intraseasonal time scales. Significance Statement Skillful forecasts of tropical cyclone activity on time scales from short-range to seasonal are now issued operationally. Although there has been great progress in the understanding of physical mechanisms driving tropical cyclone (TC) activity, intraseasonal prediction of TCs remains a significant scientific challenge. This study develops a statistically based intraseasonal model to predict weekly TC genesis over the western North Pacific Ocean basin. The intraseasonal prediction model developed here for TC genesis over the western North Pacific basin shows skill extending out to four weeks. We discuss the regional dependence of the model skill on ENSO and other subseasonal climate oscillations. This approach provides skillful intraseasonal forecasting of TCs over the western North Pacific basin.

Assessing the Sensitivity of the Tropical Cyclone Boundary Layer to the Parameterization of Momentum Flux in the Community Earth System Model

Abstract Recent studies have demonstrated that high-resolution (∼25 km) Earth System Models (ESMs) have the potential to skillfully predict tropical cyclone (TC) occurrence and intensity. However, biases in ESM TCs still exist, largely due to the need to parameterize processes such as boundary layer (PBL) turbulence. Building on past studies, we hypothesize that the depiction of the TC PBL in ESMs is sensitive to the configuration of the PBL parameterization scheme, and that the targeted perturbation of tunable parameters can reduce biases. The Morris one-at-a-time (MOAT) method is implemented to assess the sensitivity of the TC PBL to tunable parameters in the PBL scheme in an idealized configuration of the Community Atmosphere Model, version 6 (CAM6). The MOAT method objectively identifies several parameters in an experimental version of the Cloud Layers Unified by Binormals (CLUBB) scheme that appreciably influence the structure of the TC PBL. We then perturb the parameters identified by the MOAT method within a suite of CAM6 ensemble simulations and find a reduction in model biases compared to observations and a high-resolution, cloud-resolving model. We demonstrate that the high-sensitivity parameters are tied to PBL processes that reduce turbulent mixing and effective eddy diffusivity, and that in CAM6 these parameters alter the TC PBL in a manner consistent with past modeling studies. In this way, we provide an initial identification of process-based input parameters that, when altered, have the potential to improve TC predictions by ESMs.

Identification of tropical cyclones via deep convolutional neural network based on satellite cloud images

Abstract. Tropical cyclones (TCs) are one of the most destructive natural disasters. For the prevention and mitigation of TC-induced disasters, real-time monitoring and prediction of TCs is essential. At present, satellite cloud images (SCIs) are utilized widely as a basic data source for such studies. Although great achievements have been made in this field, there is a lack of concern about on the identification of TC fingerprints from SCIs, which is usually involved as a prerequisite step for follow-up analyses. This paper presents a methodology which identifies TC fingerprints via deep convolutional neural network (DCNN) techniques based on SCIs of more than 200 TCs over the northwestern Pacific basin. In total, two DCNN models have been proposed and validated, which are able to identify the TCs from not only single TC-featured SCIs but also multiple TC-featured SCIs. Results show that both models can reach 96 % of identification accuracy. As the TC intensity strengthens, the accuracy becomes better. To explore how these models work, heat maps are further extracted and analyzed. Results show that all the fingerprint features are focused on clouds during the testing process. For the majority of the TC images, the cloud features in TC's main parts, i.e., eye, eyewall, and primary rainbands, are most emphasized, reflecting a consistent pattern with the subjective method.

Impact of data assimilation on a calibrated WRF model for the prediction of tropical cyclones over the Bay of Bengal

Impact of data assimilation on a calibrated WRF model for the prediction of tropical cyclones over the Bay of Bengal

Seasonal Prediction of Tropical Cyclones over the North Atlantic and Western North Pacific

AbstractIn this study, tropical cyclones (TCs) over the western North Pacific (WNP) and North Atlantic (NA) basins are analyzed in seasonal forecasting models from five European modeling centers. Most models are able to capture the observed seasonal cycle of TC frequencies over both basins; however, large differences for numbers and spatial track densities are found. In agreement with previous studies, TC numbers are often underestimated, which is likely related to coarse model resolutions. Besides shortcomings in TC characteristics, significant positive skill (deterministic and probabilistic) in predicting TC numbers and accumulated cyclone energy is found over both basins. Whereas the predictions of TC numbers over the WNP basin are mostly unreliable, most seasonal forecast provide reliable predictions for the NA basin. Besides positive skill over the entire NA basin, all seasonal forecasting models are skillful in predicting the interannual TC variability over a region covering the Caribbean and North American coastline, suggesting that the models carry useful information, including for adaptation and mitigation purposes ahead of the upcoming TC season. However, skill in all forecast models over a smaller region centered along the Asian coastline is smaller compared to their skill in the entire WNP basin.

Recent progress on the seasonal tropical cyclone predictions over the western North Pacific from 2014 to 2020

This study summarized the procedure for the seasonal predictions of tropical cyclones (TCs) over the western North Pacific (WNP), which is currently operating at the Korea Meteorological Administration (KMA), Republic of Korea. The methodology was briefly described, and its prediction accuracy was verified. Seasonal predictions were produced by synthesizing spatiotemporal evolutions of various climate factors such as El Niño–Southern Oscillation (ENSO), monsoon activity, and Madden–Julian Oscillation (MJO), using four models: a statistical, a dynamical, and two statistical–dynamical models. The KMA forecaster predicted the number of TCs over the WNP based on the results of the four models and season to season climate variations. The seasonal prediction of TCs is announced through the press twice a year, for the summer on May and fall on August. The present results showed low accuracy during the period 2014–2020. To advance forecast skill, a set of recommendations are suggested.

High-Definition Hurricanes: Improving Forecasts with Storm-Following Nests

Abstract To forecast tropical cyclone (TC) intensity and structure changes with fidelity, numerical weather prediction models must be “high definition,” i.e., horizontal grid spacing ≤ 3 km, so that they permit clouds and convection and resolve sharp gradients of momentum and moisture in the eyewall and rainbands. Storm-following nests are computationally efficient at fine resolutions, providing a practical approach to improve TC intensity forecasts. Under the Hurricane Forecast Improvement Project, the operational Hurricane Weather Research and Forecasting (HWRF) system was developed to include telescopic, storm-following nests for a single TC per model integration. Subsequently, HWRF evolved into a state-of-the-art tool for TC predictions around the globe, although its single-storm nesting approach does not adequately simulate TC–TC interactions as they are observed. Basin-scale HWRF (HWRF-B) was developed later with a multistorm nesting approach to improve the simulation of TC–TC interactions by producing high-resolution forecasts for multiple TCs simultaneously. In this study, the multistorm nesting approach in HWRF-B was compared with a single-storm nesting approach using an otherwise identical model configuration. The multistorm approach demonstrated TC intensity forecast improvements, including more realistic TC–TC interactions. Storm-following nests developed in HWRF and HWRF-B will be foundational to NOAA’s next-generation hurricane application in the Unified Forecast System.

Accomplishments of NOAA’s Airborne Hurricane Field Program and a Broader Future Approach to Forecast Improvement

Abstract Since 2005, NOAA has conducted the annual Intensity Forecasting Experiment (IFEX), led by scientists from the Hurricane Research Division at NOAA’s Atlantic Oceanographic and Meteorological Laboratory. They partner with NOAA’s Aircraft Operations Center, who maintain and operate the WP-3D and Gulfstream IV-SP (G-IV) Hurricane Hunter aircraft, and NCEP’s National Hurricane Center and Environmental Modeling Center, who task airborne missions to gather data used by forecasters for analysis and forecasting and for ingest into operational numerical weather prediction models. The goal of IFEX is to improve tropical cyclone (TC) forecasts using an integrated approach of analyzing observations from aircraft, initializing and evaluating forecast models with those observations, and developing new airborne instrumentation and observing strategies targeted at filling observing gaps and maximizing the data’s impact in model forecasts. This summary article not only highlights recent IFEX contributions toward improved TC understanding and prediction, but also reflects more broadly on the accomplishments of the program during the 16 years of its existence. It describes how IFEX addresses high-priority forecast challenges, summarizes recent collaborations, describes advancements in observing systems monitoring structure and intensity, as well as in assimilation of aircraft data into operational models, and emphasizes key advances in understanding of TC processes, particularly those that lead to rapid intensification. The article concludes by laying the foundation for the next generation of IFEX as it broadens its scope to all TC hazards, particularly rainfall, storm-surge inundation, and tornadoes, that have gained notoriety during the last few years after several devastating landfalling TCs.

Factors Controlling Tropical Cyclone Intensification Over the Marginal Seas of China

Tropical cyclone (TC) intensification over marginal seas, especially rapid intensification (RI), often poses great threat to lives and properties in coastal regions and is subject to large forecast errors. It is thus important to understand the characteristics of TC intensification and the involved key factors affecting TC intensification over marginal seas. In this study, the 6-hourly TC best-track data from Shanghai Typhoon Institute of China Meteorological Administration, ERA-Interim reanalysis data, and TRMM satellite rainfall products are used to analyze and compare the climatological characteristics and key factors of different intensification stratifications over the marginal seas of China (MSC) and the western North Pacific (WNP) during 1980–2018. The statistical results show that TC intensification over the MSC is more likely to occur when TCs experience relatively large intensities, weak vertical wind shear, small translation perpendicular to the coastline, relatively high fullness, strong upper-level divergence, low-level relative vorticity, and high inner-core precipitation rate. The box difference index method is used to quantify the relative contributions of these factors to TC RI. Results show that the initial (relative) intensity contributes the most to TC RI over both the MSC and the WNP. The inner-core precipitation rate and translation perpendicular to the coastline are of second importance to TC RI over the MSC, while both vertical wind shear and TC fullness are crucial to TC RI over the WNP. These findings may help understand TC activity over the MSC and provide a basis for improving intensity prediction of TCs in the MSC.

An Evaluation of the Advanced Dvorak Technique (9.0) for the tropical cyclones over the North Indian Ocean

The Advanced Dvorak Technique (ADT) is used by tropical cyclone prediction centres around the world to accurately evaluate the intensity of tropical cyclones (TCs) from meteorological operational satellites. The algorithm development team has introduced new improvements to the objective ADT to further extend its capabilities and accuracy. A study has therefore undergone to evaluate the new edition of ADT (9.0) based on all the North Indian Ocean Tropical cyclones during 2018, 2019 and 2020 (Total 15 No.). It is found that ADT (9.0) performed well with the conformity of IMD’s best track T. No estimates. ADT is reasonably good in estimating the intensity for T ≥ 4.0 (VSCS to SuCS) and overestimate the intensity for T ≤ 3.5(CS/SCS).

Numerical prediction of tropical cyclones : A review of research at Andhra University

Numerical prediction of tropical cyclones : A review of research at Andhra University

The Spatial Characteristics of Hourly Rainfall Induced by Tropical Cyclones Along the South China Coast

AbstractAccurate prediction of tropical cyclone (TC) induced rainfall is of great importance to disaster prevention but quite challenging to operational forecasters. This study quantifies the spatial characteristics of the hourly rainfall caused by TCs at several individual weather stations over the south China coast. We use the historical TC datasets from 2004 to 2018 and the hourly rainfall data from six weather stations to investigate the stations' rainfall distribution characteristics induced by TCs within a distance of 700 km to the weather stations. The hourly rainfall data during the study period from six coastal stations: Zhuzilin (ZZL), Shekou Ferry Terminal (SFT), and Wutong Village (WTV) in Shenzhen, and Hong Kong Observatory (HKO), Sha Tin (SHA), and Tai Mo Shan (TMS) in Hong Kong are used to investigate the statistical relationship. Results show that the most hazardous areas for TC‐induced rainfall are TCs within a distance of 200 km relative to the stations. High intensity TCs in the southwest quadrant may bring torrential rainfall to stations within 500 km distance, and low intensity TCs are likely to induce torrential rainfall to stations within 300 km distance when they are in the northeast and northwest quadrants. If TCs are over the region with the azimuth of 60–150° and distance out of 200 km to the six stations, there is almost no rainfall influence on those stations. The findings of this study are valuable to provide information for the local operational forecasters when predicting the potential rainfall along the south China coast due to future TCs.

Machine learning in calibrating tropical cyclone intensity forecast of ECMWF EPS

AbstractIntensity prediction of tropical cyclones (TC) has been one of the major challenges for the operational forecast and warning service, as well as consequential assessment of impacts including high winds, storm surge and heavy rainfall caused by TC. With the advances in global numerical weather prediction (NWP) modelling systems, TC track and intensity forecasts for medium range are available every 6 or 12 h, and ensemble prediction system (EPS) outputs provide various scenarios for producing probabilistic forecasts. The TC intensity forecast from the EPS of the European Centre for Medium‐Range Weather Forecasts (ECMWF) has shown systematic negative biases, although the performance is better than other global models in general. A machine learning model based on XGBoost, a decision‐tree‐based machine learning algorithm, is introduced in this paper to post‐process ECMWF EPS outputs and generate an improved forecast of TC intensity. The predictors such as selected percentiles of ensemble members in maximum wind and minimum pressure of previous TC cases were applied in the XGBoost model to generate a calibrated forecast for the maximum wind of TCs. Verification of the XGBoost model was made using TCs over the western North Pacific during 2016–2019. It is found that the negative biases of the intensity forecast from ECMWF EPS and HRES can be reduced with improvement in the overall accuracy.

A review of the development and implementation of a tropical cyclone prediction system for North Indian Ocean in a multi-model ensemble framework

The seasonal genesis parameters for tropical cyclogenesis developed by W. M. Gray, is widely used for the climatological and seasonal monitoring of cyclogenesis over the tropical oceans. Over the North Indian Ocean (NIO), cyclogenesis and evolution is monitored and predicted in the short, medium and extended ranges by India Meteorological Department with the implementation of different deterministic and probabilistic forecasting techniques. This paper provides a review of an in-house developed tropical cyclone prediction system involving an improved storm evolution index and an objective tracking algorithm for detecting cyclogenesis, evolution and storm tracks from post-processed Multi-model ensemble (MME) outputs from the Climate Forecast System-based Grand Ensemble Prediction System (CGEPS) implemented for operational extended range prediction. In the first part, the reliability of cyclogenesis prediction when more than one storm systems develop simultaneously is discussed using a case study. Prominent cyclogenesis indices and constituent parameters are used to analyse the atmospheric and oceanic features which affected the evolution two consecutive storms over NIO by using ERA-Interim daily averaged datasets. The performance of indices from MME outputs is also analysed. Further the reliability of objective track prediction system is discussed by using ERA-5 and ERA-Interim datasets. Finally, the performance of the CGEPS-MME in predicting the recent tropical cyclones, Amphan and Nisarga are discussed in detail. Real-time implementation of this prediction system has proven to be critical in providing early guidance on the formation of storms, enabling the cyclone warning community to be on alert thereby providing enough lead time for better planning and mitigation strategies.

Global Ensemble Forecast System (GEFS T1534) evaluation for tropical cyclone prediction over the North Indian Ocean

The high-resolution global ensemble prediction system was implemented at IITM (Indian Institute of Tropical Meteorology) in June 2018 and handed over to India Meteorological Department (IMD) for operational running. During the Tropical Cyclone (TC) season over the North Indian Ocean, real time forecasts of ensemble tracks, intensity and strike probability of TCs are provided to IMD. This paper evaluates the skill of the model in predicting TCs during 2018, 2019 and two cases in 2020. A total of 13 cases are considered and various skill scores are calculated for the TC predictions. This study evaluates and highlights the importance of the Ensemble Prediction System for Tropical Cyclone forecasting. The key finding from this analysis is that, the higher skill of ensemble prediction system based on GEFS at longer lead time compared with the deterministic prediction. This is particularly beneficial for operational forecaster for issuing early warnings.

Effect of horizontal resolution on the simulation of tropical cyclones in the Chinese Academy of Sciences FGOALS-f3 climate system model

Abstract. The effects of horizontal resolution on the simulation of tropical cyclones were studied using the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System Finite-Volume version 3 (FGOALS-f3) climate system model from the High-Resolution Model Intercomparison Project (HighResMIP) for the Coupled Model Intercomparison Project phase 6 (CMIP6). Both the low-resolution (about 100 km resolution) FGOALS-f3 model (FGOALS-f3-L) and the high-resolution (about 25 km resolution) FGOALS-f3 (FGOALS-f3-H) models were used to achieve the standard Tier 1 experiment required by HighResMIP. FGOALS-f3-L and FGOALS-f3-H have the same model parameterizations with the exactly the same parameters. The only differences between the two models are the horizontal resolution and the time step. The performance of FGOALS-f3-H and FGOALS-f3-L in simulating tropical cyclones was evaluated using observations. FGOALS-f3-H (25 km resolution) simulated more realistic distributions of the formation, movement and intensity of the climatology of tropical cyclones than FGOALS-f3-L at 100 km resolution. Although the number of tropical cyclones increased by about 50 % at the higher resolution and better matched the observed values in the peak month, both FGOALS-f3-L and FGOALS-f3-H appear to replicate the timing of the seasonal cycle of tropical cyclones. The simulated average and interannual variabilities of the number of tropical cyclones and the accumulated cyclone energy were both significantly improved from FGOALS-f3-L to FGOALS-f3-H over most of the ocean basins. The characteristics of tropical cyclones (e.g., the average lifetime, the wind–pressure relationship and the horizontal structure) were more realistic in the simulation using the high-resolution model. The possible physical linkage between the performance of the tropical cyclone simulation and the horizontal resolution were revealed by further analyses. The improvement in the response between the El Niño–Southern Oscillation and the number of tropical cyclones and the accumulated cyclone energy in FGOALS-f3 contributed to the realistic simulation of tropical cyclones. The genesis potential index and the vorticity, relative humidity, maximum potential intensity and the wind shear terms were used to diagnose the effects of resolution. We discuss the current insufficiencies and future directions of improvement for the simulation of tropical cyclones and the potential applications of the FGOALS-f3-H model in the subseasonal to seasonal prediction of tropical cyclones.

Tropical Cyclone Tornadoes: A Review of Knowledge in Research and Prediction

The scientific documentation and investigation of tropical cyclone (TC) tornadoes has spanned portions of ten decades, but has been missing a documentary overview of topical knowledge accumulated to any given point in that time span. This review article summarizes the evolution of TC tornado-related literature from the perspectives of crucial historic tornadoes, climatology, distribution patterns, applied research into their environments, remote and environmental observations, forecasting practices, and numerical simulations at various scales. Discussion of the future of TC tornado research and prediction includes several testable hypotheses, along with potentially beneficial tools soon to be available to operational forecasters.

Dynamical Seasonal Prediction of Tropical Cyclone Activity Using the FGOALS-f2 Ensemble Prediction System

AbstractThere is a distinct gap between tropical cyclone (TC) prediction skill and the societal demand for accurate predictions, especially in the western Pacific (WP) and North Atlantic (NA) basins, where densely populated areas are frequently affected by intense TC events. In this study, seasonal prediction skill for TC activity in the WP and NA of the fully coupled FGOALS-f2 V1.0 dynamical prediction system is evaluated. In total, 36 years of monthly hindcasts from 1981 to 2016 were completed with 24 ensemble members. The FGOALS-f2 V1.0 system has been used for real-time predictions since June 2017 with 35 ensemble members, and has been operationally used in the two operational prediction centers of China. Our evaluation indicates that FGOALS-f2 V1.0 can reasonably reproduce the density of TC genesis locations and tracks in the WP and NA. The model shows significant skill in terms of the TC number correlation in the WP (0.60) and the NA (0.61) from 1981 to 2015; however, the model underestimates accumulated cyclone energy. When the number of ensemble members was increased from 2 to 24, the correlation coefficients clearly increased (from 0.21 to 0.60 in the WP, and from 0.18 to 0.61 in the NA). FGOALS-f2 V1.0 also successfully reproduces the genesis potential index pattern and the relationship between El Niño–Southern Oscillation and TC activity, which is one of the dominant contributors to TC seasonal prediction skill. However, the biases in large-scale factors are barriers to the improvement of the seasonal prediction skill, e.g., larger wind shear, higher relative humidity, and weaker potential intensity of TCs. For real-time predictions in the WP, FGOALS-f2 V1.0 demonstrates a skillful prediction for track density in terms of landfalling TCs, and the model successfully forecasts the correct sign of seasonal anomalies of landfalling TCs for various regions in China.

Impact assessment of Indian Ocean Dipole on the North Indian Ocean tropical cyclone prediction using a Statistical model

Impact assessment of Indian Ocean Dipole on the North Indian Ocean tropical cyclone prediction using a Statistical model