Hurricane Center Research Articles

Tropical Cyclone Center Fix Using CYGNSS Winds

AbstractA new method is described for determining the center location of a tropical cyclone (TC) using wind speed measurements by the NASA Cyclone Global Navigation Satellite System (CYGNSS). CYGNSS measurements made during TC overpasses are used to constrain a parametric wind speed model in which storm center location is varied. The “MTrack” storm center location is selected to minimize the residual difference between model and measurement. Results of the MTrack center fix are compared to the National Hurricane Center (NHC) Best Track, the Automated Rotational Center Hurricane Eye Retrieval (ARCHER), and aircraft reconnaissance fixes for category 1–category 3 TCs during the 2017 and 2018 hurricane seasons. MTrack produces storm center locations at intermediate times between NHC fixes with a factor of 5.6 overall reduction in sensitivity to uncertainties in the NHC fixes between which it interpolates. The MTrack uncertainty is found to be larger in the cross-track direction than the along-track direction, although this behavior and the absolute accuracy of position estimates require further investigation.

Impact of Atmospheric Retrievals on Hurricane Florence/Michael Forecasts in a Regional NWP Model

AbstractAtmospheric temperature and water vapor profiles from the Infrared Atmospheric Sounding Interferometer and the Cross‐track Infrared Sounder with acceptable qualities under clear condition and above clouds are derived using the Dual‐Regression algorithm based on the Principle Component‐based Radiative Transfer Model. The application of the derived atmospheric retrievals with high temporal and spatial resolutions in a regional weather model is studied for two hurricane cases by assimilating the retrievals in an hourly update cycle. Improvements on hurricane forecast are obtained by assimilating satellite retrievals as compared to both conventional operational data and radiance assimilation. Position of the predicted hurricane center, which is especially critical for landfall position, is corrected with a maximum improvement of 45 km compared to conventional assimilation. Predictions of heavy precipitation produced by hurricanes are improved with smaller bias and standard deviation. Precipitation scores used for the validation of predictions also show great improvements in heavy precipitation forecast against conventional data and radiance assimilation. Hurricane structure representation is substantially improved. Specifically, more symmetric structures, stronger warm cores, deeper convections, and more accurate winds lead to the improvements in precipitation forecasts. Potential applications of such approaches can be applied to assimilating the retrieved information from the geostationary satellite instruments by adding higher temporal and horizontal resolutions to the polar satellite hyperspectral sounding data.

Impact of Gulfstream-IV Dropsondes on Tropical Cyclone Prediction in a Regional OSSE System

Abstract Aircraft reconnaissance missions remain the primary means of collecting direct measurements of marine atmospheric conditions affecting tropical cyclone formation and evolution. The National Hurricane Center tasks the NOAA G-IV aircraft to sample environmental conditions that may impact the development of a tropical cyclone threatening to make landfall in the United States or its territories. These aircraft data are assimilated into deterministic models and used to produce real-time analyses and forecasts for a given tropical cyclone. Existing targeting techniques aim to optimize the use of reconnaissance observations and partially rely on regions of highest uncertainty in the Global Ensemble Forecast System. Evaluating the potential impact of various trade-offs in the targeting process is valuable for determining the ideal aircraft flight track for a prospective mission. AOML’s Hurricane Research Division has developed a system for performing regional observing system simulation experiments (OSSEs) to assess the potential impact of proposed observing systems on hurricane track and intensity forecasting. This study focuses on improving existing targeting methods by investigating the impact of proposed aircraft observing system designs through various sensitivity studies. G-IV dropsonde retrievals were simulated from a regional nature run, covering the life cycle of a rapidly intensifying Atlantic hurricane. Results from sensitivity studies provide insight into improvements for real-time operational synoptic surveillance targeting for hurricanes and tropical storms, where dropsondes released closer to the vortex–environment interface provide the largest impact on the track forecast. All dropsonde configurations provide a positive 2-day impact on intensity forecasts by improving the environmental conditions known to impact tropical cyclone intensity.

Predicting Hurricane Trajectories Using a Recurrent Neural Network

Hurricanes are cyclones circulating about a defined center whose closed wind speeds exceed 75 mph originating over tropical and subtropical waters. At landfall, hurricanes can result in severe disasters. The accuracy of predicting their trajectory paths is critical to reduce economic loss and save human lives. Given the complexity and nonlinearity of weather data, a recurrent neural network (RNN) could be beneficial in modeling hurricane behavior. We propose the application of a fully connected RNN to predict the trajectory of hurricanes. We employed the RNN over a fine grid to reduce typical truncation errors. We utilized their latitude, longitude, wind speed, and pressure publicly provided by the National Hurricane Center (NHC) to predict the trajectory of a hurricane at 6-hour intervals. Results show that this proposed technique is competitive to methods currently employed by the NHC and can predict up to approximately 120 hours of hurricane path.

The Effect of the Surface Wind Field Representation in the Operational Storm Surge Model of the National Hurricane Center

The Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model is the operational storm surge model of the National Hurricane Center (NHC). Previous studies have found that the SLOSH model estimates storm surges with an accuracy of ±20%. In this study, through hindcasts of historical storms, we assess the accuracy of the SLOSH model for four coastal regions in the Northeastern United States. We investigate the potential to improve this accuracy through modification of the wind field representation. We modify the surface background wind field, the parametric wind profile, and the maximum wind speed based on empirical, physical, and observational data. We find that on average the SLOSH model underestimates maximum storm surge heights by 22%. The modifications to the surface background wind field and the parametric wind profile have minor impacts; however, the effect of the modification to maximum wind speed is significant—it increases the variance in the SLOSH model estimates of maximum storm surges, but improves its accuracy overall. We recommend that observed values of maximum wind speed be used in SLOSH model simulations when possible.

Understanding Hurricane Storm Surge Generation and Propagation Using a Forecasting Model, Forecast Advisories and Best Track in a Wind Model, and Observed Data—Case Study Hurricane Rita

Meteorological forcing is the primary driving force and primary source of errors for storm surge forecasting. The objective of this study was to learn how forecasted meteorological forcing influences storm surge generation and propagation during a hurricane so that storm surge models can be reliably used to forecast actual events. Hindcasts and forecasts of Hurricane Rita (2005) storm surge was used as a case study. Meteorological forcing or surface wind/pressure fields for Hurricane Rita were generated using both the Weather Research and Forecasting (WRF) full-scale forecasting model along with archived hurricane advisories ingested into a sophisticated parametric wind model, namely Generalized Asymmetric Holland Model (GAHM). These wind fields were used to forecast Rita storm surges. Observation based wind fields from the OceanWeather Inc. (OWI) Interactive Objective Kinematic Analysis (IOKA) model, and Best track wind data ingested into the GAHM model were used to generate wind fields for comparison purposes. These wind fields were all used to hindcast Rita storm surges with the ADvanced CIRCulation (ADCIRC) model coupled with the Simulating Waves Nearshore (SWAN) model in a tightly coupled storm surge-wave model referred to as ADCIRC+SWAN. The surge results were compared against a quality-controlled database of observed data to assess the performance of these wind fields on storm surge generation and propagation. The surge hindcast produced by the OWI wind field performed the best, although some high water mark (HWM) locations were overpredicted. Although somewhat underpredicted, the WRF wind fields forecasted wider surge extent and wetted most HWM locations. The hindcast using the Best track parameters in the GAHM and the forecast using forecast/advisories from the National Hurricane Center (NHC) in the GAHM produced strong and narrow wind fields causing localized high surges, which resulted in overprediction near landfall while many HWM locations away from wind bands remained dry.

Impacts of particulate matter on Gulf of Mexico tropical cyclones

ABSTRACTThis research analyzes the relationship between tropical cyclones and fine particulate matter (PM2.5) for landfalling Atlantic tropical cyclones from 2000 to 2015. Daily mean PM2.5 concentrations were collected from the United States Environmental Protection Agency. Tropical cyclone data were acquired from Tropical Prediction Center Best Track Reanalysis in Unisys weather. GRIdded Binary (GRIB formatted) data were downloaded from the Data Support Section of the Computational and Information Systems Laboratory at the National Center for Atmospheric Research (NCAR). Tracks of tropical cyclones were overlaid with the interpolated daily mean PM2.5 concentration value. Results suggest that, in general, tracks are distant from areas with the largest PM2.5 concentrations. To examine the cause-effect nature of this relationship, simulation using the Weather Research and Forecasting (WRF) model suggests that the intensity of Hurricane Lili was weakened only after passing the most PM2.5-polluted area in Louisiana. This result suggests that aerosol loading may weaken the intensity of tropical cyclones, at least in some cases.

The Hurricane Decision Simulator: A Tool for Marine Forces in New Orleans to Practice Operations Management in Advance of a Hurricane

The U.S. Marine Forces Reserve (MFR) in New Orleans is frequently threatened by hurricanes. To protect the safety of personnel and their families while maintaining mission capability, the Commander must make timely decisions to set up an alternate headquarters and allow for an orderly evacuation. The MFR relies on forecasts from the National Hurricane Center, but these forecasts are uncertain, are updated frequently, and can be difficult to interpret in the context of the MFR’s decision timeline. In addition, there are few opportunities to learn from experience. We developed the Hurricane Decision Simulator (HDS) to allow MFR personnel to practice making preparation decisions in the context of many realistic simulated storms and forecasts, and to develop a better understanding of the decision sequence, the forecast products, and their relationship. The HDS has improved the MFR’s training and readiness for hurricane preparation operations and decision making by enabling more and better focused training and is being extended to other facilities.

LESSONS LEARNED FROM RAPID DEPLOYMENT OF WAVE GAGES AND CAMERAS DURING HURRICANE IRMA

Hurricane Irma was a category 5 hurricane on the Saffir-Simpson hurricane wind scale. Irma developed from a tropical wave around the Cape Verde Islands. The National Hurricane Center started monitoring it on August 26, and it was classified as a tropical storm named Irma on August 30. Moving across the Atlantic Ocean, Irma increased in strength. On September 5, Irma was classified as a category 5 hurricane with wind speeds up to 175 mph (280 km/h). Irma made landfall in the U.S. on Cudjoe Key (near Big Pine and Summerland Keys) in the morning of September 10, still being a category 4 hurricane, and made a second landfall on Marco Island, south of Naples, on the same day as a category 3 hurricane. This paper describes the lessons learned by the authors when deploying wave gages and cameras to observe the wave run-up.

The changing validity of tropical cyclone warnings under global warming

Hurricanes and typhoons are a regular threat to large populations across the globe. Facing the potential of a storm disaster the warning process and associated administrative activities across the western North Pacific are confounded by various tropical cyclone classifications. Here, we show that current storm warning categories have suffered from the warming environment over the past decades. Warning now at an average of one out of four storms, the category of ‘‘Super typhoon’’ from U.S. Joint Typhoon Warning Center is seriously influenced by the warming environment. The categories of ‘‘Very strong typhoon’’ from Japan Meteorological Agency, ‘‘Hurricane category 4’’ from U.S. National Hurricane Center and the higher now occur as often as one per nearly every three storms. The cumulative proportions of storms falling into all warning categories are enlarging, which is interpreted as an increasing threat but the frequency of warnings is potentially making the warnings less effective.

A New Hurricane Wind Direction Retrieval Method for SAR Images without Hurricane Eye

AbstractThis study presents a new approach for hurricane wind direction retrieval utilizing rainband streaks contained in synthetic aperture radar (SAR) images without hurricane eye information, based on the hurricane inflow angle. To calculate the wind direction field, a method for estimating the location of the hurricane center is given. In this paper, four Sentinel-1A (S-1A) images with a hurricane eye are used to clarify the center estimation method. Three S-1A SAR images without a hurricane eye are studied to evaluate the accuracy of the new method. The estimated locations of hurricane centers show good agreement with hurricane track data provided by the National Oceanic and Atmospheric Administration (NOAA)’s Atlantic Oceanographic and Meteorological Laboratory (AOML) Hurricane Research Division (HRD), HurricaneCity, and the National Institute of Informatics (NII). To validate the estimated wind directions, the NOAA HRD dropwindsonde observations for Tropical Storm Karl are collected and compared. The wind directions retrieved by our approach are more consistent with visual inspection than the fast Fourier transform (FFT) method in subimages. Moreover, the retrieved wind speeds utilizing C-band model 5.N (CMOD5.N) are compared with wind speed estimations observed by Stepped Frequency Microwave Radiometer (SFMR). The results suggest that the proposed method has good potential to retrieve hurricane wind direction from SAR images without a hurricane eye and external data.

Climatology and Variability of Tropical Cyclones Affecting Charleston, South Carolina

ABSTRACT Lindner, B.L. and Neuhauser, A., 2018. Climatology and variability of tropical cyclones affecting Charleston, South Carolina. Communities benefit from site-specific examinations of tropical cyclone climatology. The impact data from a tropical cyclone climatology are useful for planning purposes for emergency managers, medical facilities, building and infrastructure codes, utility and communications design, insurance, and wildlife and resource management. To that end, per an analysis of National Hurricane Center data for the years 1851 to 2015, a chronological list of tropical cyclones that passed within 139 km (75 nautical miles) of Charleston, South Carolina was compiled. Date, intensity, approach angle, and translational velocity were catalogued for each system upon closest approach. The return period is approximately 1.5 years for tropical storms and hurricanes, 3.8 years for hurricanes, and 28 years for major hurricanes. The frequency of zero-strike and multistrike seasons is well reproduced ...

Visualizing Uncertain Tropical Cyclone Predictions using Representative Samples from Ensembles of Forecast Tracks.

A common approach to sampling the space of a prediction is the generation of an ensemble of potential outcomes, where the ensemble's distribution reveals the statistical structure of the prediction space. For example, the US National Hurricane Center generates multiple day predictions for a storm's path, size, and wind speed, and then uses a Monte Carlo approach to sample this prediction into a large ensemble of potential storm outcomes. Various forms of summary visualizations are generated from such an ensemble, often using spatial spread to indicate its statistical characteristics. However, studies have shown that changes in the size of such summary glyphs, representing changes in the uncertainty of the prediction, are frequently confounded with other attributes of the phenomenon, such as its size or strength. In addition, simulation ensembles typically encode multivariate information, which can be difficult or confusing to include in a summary display. This problem can be overcome by directly displaying the ensemble as a set of annotated trajectories, however this solution will not be effective if ensembles are densely overdrawn or structurally disorganized. We propose to overcome these difficulties by selectively sampling the original ensemble, constructing a smaller representative and spatially well organized ensemble. This can be drawn directly as a set of paths that implicitly reveals the underlying spatial uncertainty distribution of the prediction. Since this approach does not use a visual channel to encode uncertainty, additional information can more easily be encoded in the display without leading to visual confusion. To demonstrate our argument, we describe the development of a visualization for ensembles of tropical cyclone forecast tracks, explaining how their spatial and temporal predictions, as well as other crucial storm characteristics such as size and intensity, can be clearly revealed. We verify the effectiveness of this visualization approach through a cognitive study exploring how storm damage estimates are affected by the density of tracks drawn, and by the presence or absence of annotating information on storm size and intensity.

A semi-empirical model for mean wind velocity profile of landfalling hurricane boundary layers

The existence of the super-gradient-wind region, where the tangential winds are larger than the gradient wind, has been widely observed inside the hurricane boundary layer. Hence, the extensively used log-law or power-law wind profiles under near-neutral conditions may be inappropriate to characterize the boundary layer winds associated with hurricanes. Recent development in the wind measurement techniques overland together with the abundance of data over ocean enabled a further investigation on the boundary layer wind structure of hurricanes before/after landfall. In this study, a semi-empirical model for mean wind velocity profile of landfalling hurricanes has been developed based on the data from the Weather Surveillance Radar-1988 Doppler (WSR-88D) network operated by the National Weather Service and the Global Positioning System (GPS) dropsondes collected by the National Hurricane Center and Hurricane Research Division. The proposed mathematical representation of engineering wind profile consists of a logarithmic function of the height z normalized by surface roughness z0 (z/z0) and an empirical function of z normalized by the height of maximum wind δ (z/δ). In addition, the consideration of wind direction in terms of the inflow angle is integrated in the boundary layer wind profile. Field-measurement wind data for both overland and over-ocean conditions have been employed to demonstrate the accuracy of simulation and convenience in use of the developed semi-empirical model for mean wind velocity profile of landfalling hurricanes.

A Numerical Study on the Extreme Intensification of Hurricane Patricia (2015)

Abstract In October 2015 Hurricane Patricia stormed through the eastern Pacific, taking its place as the strongest hurricane in recorded history when its intensity reached a record breaking 185 kt (1 kt = 0.51 m s−1). Operational models and the National Hurricane Center’s official forecast failed to predict Patricia’s unprecedented intensification, provoking questions as to whether such an extreme event can actually be forecast. This study reports on the successful simulation of Patricia using a state-of-the-art high-resolution numerical weather prediction model. It was found that high model resolution (Δx ≤ 1 km), vortex initialization, and the parameterization of dissipative heating were key factors in realistically simulating Patricia’s intensity evolution. The simulation was used to investigate Patricia’s environment in terms of sea surface temperature, vertical wind shear, and humidity, under the premise that a simulation able to capture Patricia’s peak intensity would also accurately represent Patricia’s environment. Compared with a climatology derived from the Statistical Hurricane Intensity Prediction Scheme dataset, sea surface temperature ranked in the 99th percentile and environmental vertical wind shear in the 83rd percentile (ordered from high to low). However, humidity ranked more moderately. Ensemble forecasts indicate that Patricia had relatively high predictability in comparison to other well-studied rapid intensification cases such as 2010’s Hurricane Earl. The results from this study imply that high-resolution models are in principle able to predict the intensity of extreme hurricanes like Patricia.

Weathering US–Cuba Political Storms: José Rubiera PhD

Full disclosure: chief weather forecaster is not his official title, but rather one affectionally conferred on Dr Rubiera by the Cuban people, who look to him not only in times of peril, but also to learn about the science of meteorology. Anyone who has taken a taxi in Cuba during hurricane season (June 1 to November 30), and bothered to ask the driver, will receive a clear explanation about how hurricanes are formed, what the Saffir-Simpson scale is all about, and how the season is shaping up—all courtesy of Dr Rubiera’s talent for communication during nightly weather forecasts and special broadcasts. It’s no exaggeration to say that he is something of an icon in Cuba, a man people trust. Now, he is retired as chief of forecasting at Cuba’s Meteorology Institute, but he stays on as an advisor, and since 1989 represents Cuba’s Meteorological Service as the Vice Chairperson of the World Meteorological Organization’s Hurricane Committee for Region IV (North America, Central America and the Caribbean). He also keeps a spot on nightly news and hosts two TV shows of his own: Global Weather and Weather in the Caribbean. And his PhD in meteorology serves him well as full professor at the University of Havana in—what else?— meteorology communications. Finally, he has been a driving force in Cuba for collaboration with Miami’s National Hurricane Center (NHC) and other US meteorologists (in fact, the Chairperson of the Region IV Committee is the head of the NHC). But, as he says over a cup of rich Cuban coffee, it all started with a two-year-old in a yellow sweater.

Probabilistic Prediction of Tropical Cyclone Intensity with an Analog Ensemble

An analog ensemble (AnEn) technique is applied to the prediction of tropical cyclone (TC) intensity (i.e., maximum 1-min averaged 10-m wind speed). The AnEn is an inexpensive, naturally calibrated ensemble prediction of TC intensity derived from a training dataset of deterministic Hurricane Weather Research and Forecasting (HWRF; 2015 version) Model forecasts. In this implementation of the AnEn, a set of analog forecasts is generated by searching an HWRF archive for forecasts sharing key features with the current HWRF forecast. The forecast training period spans 2011–15. The similarity of a current forecast with past forecasts is estimated using predictors derived from the HWRF reforecasts that capture thermodynamic and kinematic properties of a TC’s environment and its inner core. Additionally, the value of adding a multimodel intensity consensus forecast as an AnEn predictor is examined. Once analogs are identified, the verifying intensity observations corresponding to each analog HWRF forecast are used to produce the AnEn intensity prediction. In this work, the AnEn is developed for both the eastern Pacific and Atlantic Ocean basins. The AnEn’s performance with respect to mean absolute error (MAE) is compared with the raw HWRF output, the official National Hurricane Center (NHC) forecast, and other top-performing NHC models. Also, probabilistic intensity forecasts are compared with a quantile mapping model based on the HWRF’s intensity forecast. In terms of MAE, the AnEn outperforms HWRF in the eastern Pacific at all lead times examined and up to 24-h lead time in the Atlantic. Also, unlike traditional dynamical ensembles, the AnEn produces an excellent spread–skill relationship.

Disaster early warning and damage assessment analysis using social media data and geo-location information

Societies are confronted with destructive natural disasters of increasing frequency. Social networks are playing an increasingly important role as early warning systems, aiding with rapid disaster assessment and post-disaster recovery. There is a need for both the public and disaster-relief agencies to better understand how social media can be utilized to assess and respond to natural disasters. However, existing research on the role of social media in society's response to natural disasters is neither holistic nor systematic. In this study, we conduct a hierarchical multiscale analysis based on multiple data resources, combining social media data, economic losses, and geo-information. We verify the role played by social media before, during, and after a natural disaster. We investigate whether the combination of social media and geo-location information can contribute to a more efficient early warning system and help with disaster assessment. This paper draws attention to the fact that during a disaster, citizens turn to social media and the majority of tweets contain information about the hurricane and/or its impact with negative sentiment. We demonstrate that the severity of damage in one area is positively correlated with the intensity of disaster-related activity. Meanwhile, the coastal areas and areas with close proximity to Hurricane center tend to suffer from higher losses during a disaster. Our findings explore the role played by social media from individuals in affected populations and how they respond to unfolding natural disasters. Results hold significance with regard to providing timely assistance for both official institutions and netizens.

Numerical study of sediment dynamics during hurricane Gustav

In this study, the coupled ocean-atmosphere-wave-and-sediment transport (COAWST) modeling system was employed to explore sediment dynamics in the northern Gulf of Mexico during hurricane Gustav in 2008. The performance of the model was evaluated quantitatively and qualitatively against in-situ and remote sensing measurements, respectively. After Gustav's landfall in coastal Louisiana, the maximum significant wave heights reached more than 8 m offshore and they decreased quickly as it moved toward the inner shelf, where the vertical stratification was largely destroyed. Alongshore currents were dominant westward on the eastern sector of the hurricane track, and offshoreward currents prevailed on the western sector. High suspended sediment concentrations (>1000 mg/l) were confined to the inner shelf at surface layers and the simulated high concentrations at the bottom layer extended to the 200 m isobaths. The stratification was restored one week after landfall, although not fully. The asymmetric hurricane winds induced stronger hydrodynamics in the eastern sector, which led to severe erosion. The calculated suspended sediment flux (SSF) was convergent to the hurricane center and the maximum SSF was simulated near the south and southeast of the Mississippi river delta. The averaged post-hurricane deposition over the Louisiana shelf was 4.0 cm, which was 3.2–26 times higher than the annual accumulation rate under normal weather conditions.

Simulating Storm Surge Impacts with a Coupled Atmosphere-Inundation Model with Varying Meteorological Forcing

Storm surge events have the potential to cause devastating damage to coastal communities. The magnitude of their impacts highlights the need for increased accuracy and real-time forecasting and predictability of storm surge. In this study, we assess two meteorological forcing configurations to hindcast the storm surge of Hurricane Sandy, and ultimately support the improvement of storm surge forecasts. The Weather Research and Forecasting (WRF) model is coupled to the ADvanced CIRCulation Model (ADCIRC) to determine water elevations. We perform four coupled simulations and compare storm surge estimates resulting from the use of a parametric vortex model and a full-physics atmospheric model. One simulation is forced with track-based meteorological data calculated from WRF, while three simulations are forced with the full wind and pressure field outputs from WRF simulations of varying resolutions. Experiments were compared to an ADCIRC simulation forced by National Hurricane Center best track data, as well as to station observations. Our results indicated that given accurate meteorological best track data, a parametric vortex model can accurately forecast maximum water elevations, improving upon the use of a full-physics coupled atmospheric-surge model. In the absence of a best track, atmospheric forcing in the form of full wind and pressure field from a high-resolution atmospheric model simulation prove reliable for storm surge forecasting.