Hurricane Track Research Articles

Objective Detection of a Tropical Cyclone’s Center Using Satellite Image Sequences in the Northwest Pacific

A tropical cyclone (TC) is one of the most destructive natural disasters that can cause heavy loss of life and property. Determining a TC’s center is crucial to TC forecasting. It is difficult to locate the center of a TC during its formation stage and dissipation period. To address this problem, a novel objective algorithm called cloud motion wind (CMW) was proposed for detecting a TC’s center using infrared (IR) image sequences from a geostationary meteorology satellite. First, the optical flow model with weighted median filtering was utilized to build a cloud motion wind field. Second, the density matrix method was used to calculate the center of the TC. FY-2E (Fengyun-2E geostationary meteorological satellites) IR images of three TCs in the Northwest Pacific, Halong, Rammasun and Haiyan were analyzed using the proposed algorithm. The present algorithm estimated the track with an averaged track error of around 41 km. Experimental results compared with the observed track that was given by the China Meteorological Administration (CMA) show that the proposed method provided accurate estimates of the cyclone center. The present algorithm has the potential to be employed to assist forecasters to detect the track of imminent TC.

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.

Testing Vertical Wind Shear and Nonlinear MJO–ENSO Interactions as Predictors for Subseasonal Atlantic Tropical Cyclone Forecasts

Abstract Hansen et al. found patterns of vertical wind shear, relative humidity (RH), and nonlinear interactions between the Madden–Julian oscillation and El Niño–Southern Oscillation that impact subseasonal Atlantic TC activity. We test whether these patterns can be used to improve subseasonal predictions. To do this we build a statistical–dynamical hybrid model using Navy-ESPC reforecasts as a part of the SUBX project. By adding and removing Navy-ESPC reforecasted values of predictors from a logistic regression model, we assess the contribution of skill from each predictor. We find that Atlantic SSTs and the MJO are the most important factors governing subseasonal Atlantic TC activity. RH contributes little to subseasonal TC predictions; however, shear predictors improve forecast skill at 5–10-day lead times, before forecast shear errors become too large. Nonlinear MJO–ENSO interactions did not improve skill compared to separate linear considerations of these factors but did improve the reliability of predictions for high-probability active TC periods. Both nonlinear MJO–ENSO interactions and the subseasonal shear signal appear linked to PV streamer activity. This study suggests that correcting model shear biases and improving representation of Rossby wave breaking is the most efficient way to improve subseasonal Atlantic TC forecasts.

Data Science and Machine Learning for hurricane forecasting

Data Science and Machine Learning for hurricane forecasting

Statistical investigation of wind duration using a refined hurricane track model

A complete characterization of the atmospheric boundary-layer winds essentially involves speed, direction and duration. While extensive statistical analyses have been carried out to determine wind speed and directionality for appropriate wind design of buildings, there is a lack of similar research effort for wind duration. Although wind duration may have limited effects on linear, elastic structural response, it can present significant importance in the consideration of nonlinear, inelastic response due to damage accumulation and cyclic deterioration of strength and stiffness. Recent advances of performance-based wind design methodology allow the controlled nonlinear, inelastic deformation of buildings under strong winds, and hence place a demand on statistical investigation of wind duration. In this study, the wind data used for probabilistic duration analysis result from a refined hurricane track model. Specifically, both environmental flow and Coriolis force (the beta effect) are integrated into the translation module for improved modeling of hurricane movement. Based on the numerically generated wind data, the wind duration measured with the over-threshold method (i.e., uniform duration) is obtained. Furthermore, a statistical analysis framework is developed to jointly consider wind duration, speed and direction. A case study is conducted to demonstrate the efficacy of the proposed statistical analysis framework for characterization and quantification of duration in wind climate consideration. Finally, structural wind performance of a simplified single-degree-of freedom system is examined to highlight the importance of incorporating duration consideration into the performance-based wind design.

A Novel Tropical Cyclone Size Estimation Model Based on a Convolutional Neural Network Using Geostationary Satellite Imagery

A novel tropical cyclone (TC) size estimation model (TC-SEM) in the western North Pacific was developed based on a convolutional neural network (CNN) using geostationary satellite infrared (IR) images. The proposed TC-SEM was tested using three CNN schemes: a single-task regression model that separately estimated the radius of maximum wind (RMW) and the radius of 34 kt wind (R34) of the TC, a multi-task regression model that estimated the RMW and R34 simultaneously, and a multi-task regression model using best-track TC intensity information. For model training, validation, and testing, 29,730, 2505, and 11,624 geostationary satellite images of the region around the center of the TC, respectively, were used, each containing four IR bands: short-wavelength IR (3.7 µm), water vapor (6.7 µm), IR1 (10.8 µm), and IR2 (12.0 µm). The results showed that the multi-task model performed better than the single-task model due to knowledge sharing and its ability to solve multiple interrelated tasks simultaneously. The inclusion of TC intensity information in the multi-task model further improved the performance of the RMW and R34 estimations, with correlations (mean absolute errors) of 0.95 (2.05 nmi) and 0.93 (9.77 nmi), respectively, which represent significant improvements over the performance of existing linear regression statistical methods. The results suggested that this CNN model using geostationary satellite images may be a powerful tool for estimating TC sizes in operational TC forecasts.

The Use of Composite GOES-R Satellite Imagery to Evaluate a TC Intensity and Vortex Structure Forecast by an FV3GFS-Based Hurricane Forecast Model

The HAFS model is an effort under the NGGPS and UFS initiatives to create the next generation of hurricane prediction and analysis system based on FV3-GFS. It has been validated extensively using traditional verification indicators such as tracker error and biases, intensity error and biases, and the radii of gale, damaging and hurricane strength winds. While satellite images have been used to verify hurricane model forecasts, they have not been used on HAFS. The community radiative transfer model CRTM is used to generate model synthetic satellite images from HAFS model forecast state variables. The 24 forecast snapshots in the mature stage of hurricane Dorian in 2019 are used to generate a composite model synthetic GOES-R infrared brightness image. The composite synthetic image is compared to the corresponding composite image generated from the observed GOES-R data, to evaluate the model forecast TC vortex intensity, size, and asymmetric structure. Results show that the HAFS forecast TC Dorian agrees reasonably well with the observation, but the forecast intensity is weaker, its overall vortex size smaller, and the radii of its eye and maximum winds larger than the observed. The evaluation results can be used to further improve the model. While these results are consistent with those obtained by traditional verification methods, evaluations based on composite satellite images provide an additional benefit with richer information because they have near-real-times spatially and temporally continuous high-resolution data with global coverage. Composite satellite infrared images could be used routinely to supplement traditional verification methods in the HAFS and other hurricane model evaluations. Note since this study only evaluated one hurricane, the above conclusions are only applicable to the model behavior of the mature stage of hurricane Dorian in 2019, and caution is needed to extend these conclusions to expect model biases in predicting other TCs. Nevertheless, the consistency between the evaluation using composite satellite images and the traditional metrics, of hurricane Dorian, shows that this method has the potential to be applied to other storms in future studies.

Tropical Cyclone Forecast Using Multitask Deep Learning Framework

A tropical cyclone is a robust weather system that affects human daily life. Accurate and rapid tropical cyclone forecast can guide human disaster prevention and mitigation work against tropical cyclones. The mainstream tropical cyclone forecasting method is numerical forecasting, which requires abundant prior knowledge and luxurious calculation. Nowadays, machine learning methods have received increasing attention for which they can overcome these disadvantages. However, existing machine learning methods usually ignored some potential factors since they mainly concentrated on one aspect of the tropical cyclone forecast. This letter proposes a multitask machine learning framework to forecast tropical cyclone path and intensity, which possesses two modules: one is the prediction module and the other is the estimate module. We use an improved generative adversarial network as the prediction module to predict the tropical cyclone spatial data at a certain moment in the future. Then, we use two different deep neural networks as the estimation module to extract the position and intensity from the generated prediction data. The method we propose is a general and relatively accurate tropical cyclone forecast method. We reach a 24-h path forecast error of 116 km and a 24-h intensity forecast error of 13.06 kt.

北西太平洋の台風通過に伴って生じる気象庁準リアルタイム版全球海面水温解析のバイアス

The near-real-time merged satellite and in-situ data global daily sea surface temperature (SST) of the Japan Meteorological Agency (hereinafter abbreviated as R-MGD) is subjected to filtering out short-time-scale fluctuations from observations prior to the analysis time. Therefore, the rapid SST change due to the passage of tropical cyclones (TCs) is thought to cause biases. Here, the biases in the R-MGD with respect to in-situ observations were quantified along the passage of TCs in the western North Pacific. First, we examined a case study on the approach of three successive TCs in August–September 2020. The R-MGD had positive biases of > 2°C just after the passage of three TCs, and negative biases were observed after one week of the last TC's passage. The comparison of the R-MGD with a moored buoy indicates that the biases can be explained by short-term fluctuations filtered out and the SST prior to the analysis time in R-MGD analysis. Second, the composite analysis from May 2015–October 2020 indicates that the statistically significant biases at the observation points ranged between −1 days and +4 days for positive biases and between +7 days and +14 days for negative biases relative to the time of the closest approach of a TC within 500 km. The positive SST bias is largely associated with cold subsurface water and intense TCs, being pronounced in the mid-latitude, except around the Kuroshio and Kuroshio extension regions. The assimilation of in-situ observations recorded within 72 h prior to the R-MGD analysis time through additional optimal interpolation alleviates these biases because this process redeems short-time-scale fluctuations. The impact on TC forecasts and the validity of the optimal interpolation experiment against the independent observations were also investigated.

Improved Tropical Cyclone Forecasts with Increased Vertical Resolution in the TRAMS Model

Improved Tropical Cyclone Forecasts with Increased Vertical Resolution in the TRAMS Model

Performance of 2020 Real-Time Atlantic Hurricane Forecasts from High-Resolution Global-Nested Hurricane Models: HAFS-globalnest and GFDL T-SHiELD

Abstract The global-nested Hurricane Analysis and Forecast System (HAFS-globalnest) is one piece of NOAA’s Unified Forecast System (UFS) application for hurricanes. In this study, results are analyzed from 2020 real-time forecasts by HAFS-globalnest and a similar global-nested model, the Tropical Atlantic version of GFDL’s System for High‐resolution prediction on Earth‐to‐Local Domains (T-SHiELD). HAFS-globalnest produced the highest track forecast skill compared to several operational and experimental models, while T-SHiELD showed promising track skills as well. The intensity forecasts from HAFS-globalnest generally had a positive bias at longer lead times primarily due to the lack of ocean coupling, while T-SHiELD had a much smaller intensity bias particularly at longer forecast lead times. With the introduction of a modified planetary boundary layer scheme and an increased number of vertical levels, particularly in the boundary layer, HAFS forecasts of storm size had a smaller positive bias than occurred in the 2019 version of HAFS-globalnest. Despite track forecasts that were comparable to the operational GFS and HWRF, both HAFS-globalnest and T-SHiELD suffered from a persistent right-of-track bias in several cases at the 4–5-day forecast lead times. The reasons for this bias were related to the strength of the subtropical ridge over the western North Atlantic and are continuing to be investigated and diagnosed. A few key case studies from this very active hurricane season, including Hurricanes Laura and Delta, were examined.

Regional CubeSat Constellation Design to Monitor Hurricanes

State-of-the-art weather forecasting systems depend on a variety of data collected by airborne, orbiting, and ground sensors. Regional CubeSat constellations have the potential to improve hurricane forecasting by collecting sensor data over data-starved oceanic regions. Even in regions where strong terrestrial sensor networks exist, constellation sensor data can help reduce forecasting model errors. To this end, the article considers the problem of designing a low-earth orbit CubeSat constellation that meets given resolution requirements over a region of interest. We propose a novel optimization framework that uses the concept of satellite coverage maps to determine the number of satellites and constellation pattern. Numerical simulations are presented for asymmetric constellation design that can provide sensor data over important geographical regions within a specified repeated time window.

Effects of the Uncertainty of Hurricane Tracks on Coastal Hazards and Evacuations

Effects of the Uncertainty of Hurricane Tracks on Coastal Hazards and Evacuations

Integrating storm surge modeling with traffic data analysis to evaluate the effectiveness of hurricane evacuation

An integrated storm surge modeling and traffic analysis were conducted in this study to assess the effectiveness of hurricane evacuations through a case study of Hurricane Irma. The Category 5 hurricane in 2017 caused a record evacuation with an estimated 6.8 million people relocating statewide in Florida. The Advanced Circulation (ADCIRC) model was applied to simulate storm tides during the hurricane event. Model validations indicated that simulated pressures, winds, and storm surge compared well with observations. Model simulated storm tides and winds were used to estimate the area affected by Hurricane Irma. Results showed that the storm surge and strong wind mainly affected coastal counties in south-west Florida. Only moderate storm tides (maximum about 2.5 m) and maximum wind speed about 115 mph were shown in both model simulations and Federal Emergency Management Agency (FEMA) post-hurricane assessment near the area of hurricane landfall. Storm surges did not rise to the 100-year flood elevation level. The maximum wind was much below the design wind speed of 150–170 mph (Category 5) as defined in Florida Building Code (FBC) for south Florida coastal areas. Compared with the total population of about 2.25 million in the six coastal counties affected by storm surge and Category 1–3 wind, the statewide evacuation of approximately 6.8 million people was found to be an over-evacuation due mainly to the uncertainty of hurricane path, which shifted from south-east to south-west Florida. The uncertainty of hurricane tracks made it difficult to predict the appropriate storm surge inundation zone for evacuation. Traffic data were used to analyze the evacuation traffic patterns. In south-east Florida, evacuation traffic started 4 days before the hurricane’s arrival. However, the hurricane path shifted and eventually landed in south-west Florida, which caused a high level of evacuation traffic in south-west Florida. Over-evacuation caused Evacuation Traffic Index (ETI) to increase to 200% above normal conditions in some sections of highways, which reduced the effectiveness of evacuation. Results from this study show that evacuation efficiency can be improved in the future by more accurate hurricane forecasting, better public awareness of real-time storm surge and wind as well as integrated storm surge and evacuation modeling for quick response to the uncertainty of hurricane forecasting.

Optimization of physical schemes in WRF model on cyclone simulations over Bay of Bengal using one-way ANOVA and Tukey\u2019s test

Evaluation of appropriate physics parameterization schemes for the Weather Research and Forecasting (WRF) model is vital for accurately forecasting tropical cyclones. Three cyclones Nargis, Titli and Fani have been chosen to investigate the combination of five cloud microphysics (MP), three cumulus convection (CC), and two planetary boundary layer (PBL) schemes of the WRF model (ver. 4.0) with ARW core with respect to track and intensity to determine an optimal combination of these physical schemes. The initial and boundary conditions for sensitivity experiments are drawn from the National Centers for Environmental Prediction (NCEP) global forecasting system (GFS) data. Simulated track and intensity of three cyclonic cases are compared with the India Meteorological Department (IMD) observations. One-way analysis of variance (ANOVA) is applied to check the significance of the data obtained from the model. Further, Tukey’s test is applied for post-hoc analysis in order to identify the cluster of treatments close to IMD observations for all three cyclones. Results are obtained through the statistical analysis; average root means square error (RMSE) of intensity throughout the cyclone period and time error at landfall with the step-by-step elimination method. Through the elimination method, the optimal scheme combination is obtained. The YSU planetary boundary layer with Kain–Fritsch cumulus convection and Ferrier microphysics scheme combination is identified as an optimal combination in this study for the forecasting of tropical cyclones over the Bay of Bengal.

Uncrewed Ocean Gliders and Saildrones Support Hurricane Forecasting and Research

Uncrewed Ocean Gliders and Saildrones Support Hurricane Forecasting and Research

Long-term forecasting of tropical cyclones over Bay of Bengal using linear and non-linear statistical models

Long-term forecasting of tropical cyclones over Bay of Bengal using linear and non-linear statistical models

Climatological analysis of tropical cyclone impacts on hydrological extremes in the Mid-Atlantic region of the United States

Research efforts related to landfalling tropical cyclones (TCs) and their hydrological impacts have focused mostly on the continental or regional scales, whereas many coastal management and infrastructure decisions are made at much finer spatial scales. In this context, this study aims to provide local-scale understandings of the climatological characteristics and hydrological impacts of TCs (from 1950 to 2019) over the Mid-Atlantic region defined as the Delaware River Basin (DRB) and Susquehanna River Basin (SRB). The climatological analysis is based on analyzing long-term, spatially distributed observational datasets of hurricane tracks, precipitation, and streamflows. Results suggest that, despite limited contribution of TCs to regional precipitation (<9%), TC is the dominant driver for extreme floods in the southern part of DRB (e.g. tributaries of the Christina River and lower Schuylkill River) and the southwestern portions of SRB (e.g. tributaries of the Lower Susquehanna and Junita River), where TC’s effect on drought alleviation is also comparatively higher. At the basin level, SRB is more susceptible to flooding associated with TCs and prone to drought relative to DRB; however, strong spatial variability of TC’s impact on hydrological extremes is observed within and across the basins. While the TC effect on flood/drought is negligible for the high-elevation, northern part of the region, TC increases the magnitude of the 100 year flood by up to 19.6% in DRB and 53.0% in SRB; the duration of short-term extreme hydrological drought is reduced by TC by up to 25.0% in SRB and 24.7% in DRB, respectively.

Communicating Probability Information in Hurricane Forecasts: Assessing Statements that Forecasters Use on Social Media and Implications for Public Assessments of Reliability

Forecasters are responsible for predicting the weather and communicating risk with stakeholders and members of the public. This study investigates the statements that forecasters use to communicate probability information in hurricane forecasts and the impact these statements may have on how members of the public evaluate forecast reliability. We use messages on Twitter to descriptively analyze probability statements in forecasts leading up to Hurricanes Harvey, Irma, Maria, and Florence from forecasters in three different groups: the National Hurricane Center, local Weather Forecast Offices, and in the television broadcast community. We then use data from a representative survey of United States adults to assess how members of the public wish to receive probability information and the impact of information format on assessments of forecast reliability. Results from the descriptive analysis indicate forecasters overwhelmingly use words and phrases in place of numbers to communicate probability information. In addition, the words and phrases forecasters use are generally vague in nature -- they seldom include rank adjectives (e.g., “low” or “high”) to qualify blanket expressions of uncertainty (e.g., “there is a chance of flooding”). Results from the survey show members of the public generally prefer both words/phrases and numbers when receiving forecast information. They also show information format affects public judgments of forecast reliability; on average, people believe forecasts are more reliable when they include numeric probability information.

Forecasting of tropical cyclone using global and regional ensemble prediction systems of NCMRWF : A review

NCMRWF global ensemble prediction system (NEPS-G) is being used for operational forecasting of tropical cyclones (TC) since the year 2016. The NEPS-G was upgraded and its horizontal resolution was increased from 33 km to 12 km in the year 2018. The upgraded NEPS-G has shown improved performance in forecasting tropical cyclones over the North Indian Ocean (NIO). The average 120 hours track forecast errors of NEPS-G for six TCs of 2019 and four TCs of 2020 are about 250 km and 220 km, respectively. The regional ensemble prediction system of NCMWRF (NEPS-R) has 4 km horizontal resolution. Average track forecast error of NEPS-R at 72 hours forecast lead time is less than that of NEPS-G. The strike probability forecast of NEPS-G shows good reliability and the area under the relative operating characteristic curve is greater than 0.5 for four TCs of 2020. The forecasting of peak TC intensity is better in NEPS-R. It could also predict the rapid intensifications of the TCs ‘Phani’ and ‘Amphan’. NEPS-G was unable to predict rapid intensifications. The prediction of time of peak intensity is still an issue with the models. Both NEPS-G and NEPS-R overestimate peak intensity of TC ‘Nisarga’. The overestimation is more in NEPS-R forecast.