Tropical Cyclone Wind Speed Research Articles

Tropical Cyclone Wind Speeds from WindSat, AMSR and SMAP: Algorithm Development and Testing

The measurement of ocean surface wind speeds in precipitation from satellite microwave radiometers is a challenging task. Rain attenuates the signal that is emitted from the ocean surface. Moreover, the rain and wind signals are very similar, which makes it difficult to distinguish wind from rain. The rain contamination can be mitigated for radiometers that operate simultaneously at C-band and X-band channels, such as WindSat, AMSR-E and AMSR2. The basic principle is to use combinations between C-band and X-band channels that are sensitive to wind speed but relatively insensitive to rain. Based on this principle, we have developed algorithms for retrieving wind speeds in rain from the WindSat and AMSR sensors. These algorithms are statistical regressions and are trained specifically under tropical cyclone conditions. We lay out the steps of the algorithm development, training, and testing. The major source for training the algorithm is provided by wind speeds from the SMAP L-band radiometer, which have been proven to provide reliable wind speeds in strong storms and are not affected by rain. We show that the WindSat and AMSR tropical cyclone wind algorithms perform well under precipitation where standard passive wind speed retrievals fail. We examine the possibility of extending the C/X-band tropical cyclone wind algorithm to X/K-band channels and discuss how it can be broadened from tropical cyclone conditions to global winds in rain retrievals.

Spatial variations of North Atlantic landfalling tropical cyclone wind speed decay over the continental United States

AbstractUnderstanding tropical cyclone wind speed decay during the post-landfall stage is critical for inland hazard preparation. This paper examines the spatial variation of wind speed decay of tropical cyclones over the continental United States. We find that tropical cyclones making landfall over the Gulf Coast decay faster within the first 24 hours after landfall than those making landfall over the Atlantic East Coast. The variation of the decay rate over the Gulf Coast remains larger than that over the Atlantic East Coast for tropical cyclones that had made landfall more than 24 hours prior. Besides an average weaker tropical cyclone landfall intensity, the near-parallel trajectory and the proximity of storms to the coastline also help to explain the slower post-landfall wind speed decay for Atlantic East Coast landfalling tropical cyclones. Tropical cyclones crossing the Florida peninsula only slowly weaken after landfall, with an average of less than 20% post-landfall wind speed drop while transiting the state. The existence of these spatial variations also brings into question the utility of a uniform wind decay model. While weak intensity decay over the Florida peninsula is well estimated by the uniform wind decay model, the error from the uniform wind decay model increases with tropical cyclones making direct landfall more parallel to the Atlantic East Coast. The underestimation of inland wind speed by the uniform wind decay model found over the western Gulf Coast brings attention to the role of land-air interactions in the decay of inland tropical cyclones.

Early Season Hurricane Risk Assessment: Climate-Conditioned HITS Simulation of North Atlantic Tropical Storm Tracks

AbstractWe present a hurricane risk assessment model that simulates North Atlantic Ocean tropical cyclone (TC) tracks and intensity, conditioned on the early season large-scale climate state. The model, Cluster-Based Climate-Conditioned Hurricane Intensity and Track Simulator (C3-HITS), extends a previous version of HITS. HITS is a nonparametric, spatial semi-Markov, stochastic model that generates TC tracks by conditionally simulating segments of randomly varying lengths from the TC tracks contained in NOAA’s Best Track Data, version 2, dataset. The distance to neighboring tracks, track direction, TC wind speed, and age are used as conditioning variables. C3-HITS adds conditioning on two early season, large-scale climate covariates to condition the track simulation: the Niño-3.4 index, representing the eastern equatorial Pacific Ocean sea surface temperature (SST) departure from climatology, and main development region, representing tropical North Atlantic SST departure from climatology in the North Atlantic TC main development region. A track clustering procedure is used to identify track families, and a Poisson regression model is used to model the probabilistic number of storms formed in each cluster, conditional on the two climate covariates. The HITS algorithm is then applied to evolve these tracks forward in time. The output of this two-step, climate-conditioned simulator is compared with an unconditional HITS application to illustrate its prognostic efficacy in simulating tracks during the subsequent season. As in the HITS model, each track retains information on velocity and other attributes that can be used for predictive coastal risk modeling for the upcoming TC season.

Assessing the Influence of Large-Scale Environmental Conditions on the Rainfall Structure of Atlantic Tropical Cyclones: An Observational Study

AbstractUnderstanding the mechanisms related to the variations in the rainfall structure of tropical cyclones (TCs) is crucial in improving forecasting systems of TC rainfall and its impact. Using satellite precipitation and reanalysis data, we examined the influence of along-track large-scale environmental conditions on inner-core rainfall strength (RS) and total rainfall area (RA) for Atlantic TCs during the TC season (July–November) from 1998 to 2019. Factor analysis revealed three major factors associated with variations in RS and RA: large-scale low and high pressure systems [factor 1 (F1)]; environmental flows, sea surface temperature, and humidity [factor 2 (F2)]; and maximum wind speed of TCs [factor 3 (F3)]. Results from our study indicate that RS increases with an increase in the inherent primary circulation of TCs (i.e., F3) but is less affected by large-scale environmental conditions (i.e., F1 and F2), whereas RA is primarily influenced by large-scale low and high pressure systems (i.e., F1) over the entire North Atlantic and partially influenced by environmental flows, sea surface temperature, humidity, and maximum wind speed (i.e., F2 and F3). A multivariable regression model based on the three factors accounted for the variations of RS and RA across the entire basin. In addition, regional distributions of mean RS and RA from the model significantly resembled those from observations. Therefore, our study suggests that large-scale environmental conditions over the North Atlantic Ocean are important predictors for TC rainfall forecasts, particularly with regard to RA.

Multi-Parameter Neural Network for Altimeter Tropical Cyclone Wind Speed Estimation

The ability of satellite altimeter to estimate wind speed in tropical cyclone condition has been investigated. In the extreme condition with higher spatio-temporal variation, the ocean-atmosphere interaction is very complex and makes the existing algorithm become an ill-posed solution. In such condition, the developed algorithm from single frequency backscatter and significant waves height were insufficient. Besides, wind speed estimates become saturated at high regimes and the reflected backscatter was contaminated by rain. Therefore, other simultaneously observed parameters are needed to comprehensively account for this condition and is expected to improve the accuracy of wind speed retrieval. Aside from altimeter instrument, the microwave radiometer onboard Jason-2 concurrently records the brightness temperature and the rain information. To accommodate related multiple parameters for wind speed derivation, the neural network approach is proposed. Its unique advantage is relationship among multi-parameters can be easily established without prior knowledge on their physical attributes. Therefore, this study intended to determine the multi-parameter neural network (MPNN) model in estimating altimeter wind speed during the tropical cyclone condition. The results proved that the MPNN technique has potential in reducing the root mean square error by 30% in comparison between tropical cyclone wind speed estimate by the existing algorithm.

From reanalysis to satellite observations: gap-filling with imbalanced learning

Increasing the spatial coverage and temporal resolution of Earth surface monitoring can significantly improve forecasting or monitoring capabilities in the context of smart city, such as extreme weather forecasting, ecosystem monitoring and anthropogenic impact monitoring. As an essential data source for Earth’s surface monitoring, most satellite observations exist data gaps due to various factors like the limitations of measuring equipment, the interferences of environments, and the delay or loss of data updates. Although many efforts have been conducted to fill the gaps in the last decade, the existing techniques cannot efficiently address the problem. In this paper, we extensively study the gap-filling problem of satellite observations using imbalanced learning. Specifically, we propose a framework called Reanalysis to Satellite (R2S) to simulate satellite observations with reanalysis data. In the R2S framework, we propose a generic method called Spatial Temporal Match (STM), matching reanalysis data and satellite observations to construct the Reanalysis-Satellite (R-S) dataset used to train the model. Based on the R-S dataset, we propose a novel method called Semi-imbalanced (SIMBA) to handle the imbalance problem of gap-filling by taking advantages of traditional machine learning and imbalanced learning. We construct a hybrid model in the R2S framework for the Soil Moisture Active Passive (SMAP) satellite observations of the tropical cyclone wind speed. Extensive experiments demonstrate the hybrid model outperforms the traditional machine learning model and closely approximates in situ observations.

New Applications of Satellite-Measured Tropical Cyclone Wind Speeds

New Applications of Satellite-Measured Tropical Cyclone Wind Speeds

Tropical Cyclone Wind Speed Retrieval from Dual-Polarization Sentinel-1 EW Mode Products

AbstractRecent studies indicate that the cross-polarization synthetic aperture radar (SAR) images have the ability of retrieving high wind speed on ocean surface without wind direction input. This study presents a new approach for tropical cyclone (TC) wind speed retrieval utilizing thermal-noise-removed Sentinel-1 dual-polarization (VV + VH) Extra-Wide Swath (EW) Mode products. Based on 20 images of 9 TCs observed in the 2016 and 2018 and SAR-collocated European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5) data and the National Oceanic and Atmospheric Administration (NOAA) Hurricane Research Division’s (HRD) Real-time Hurricane Wind Analysis System (H*Wind) data, a subswath-based geophysical model function (GMF) Sentinel-1 EW Mode Wind Speed Retrieval Model after Noise Removal (S1EW.NR) is developed and validated statistically. TC wind speed is retrieved by using the proposed GMF and the C-band model 5.N (CMOD5.N). The results show that the wind speeds retrieved by the S1EW.NR model are in good agreement with wind references up to 31 m s−1. The correlation coefficient, bias, and standard deviation between the retrieval results and reference wind speeds are 0.74, −0.11, and 3.54 m s−1, respectively. Comparison of the wind speeds retrieved from both channels suggests that the cross-polarized signal is more suitable for high–wind speed retrieval, indicating the promising capability of cross-polarization SAR for TC monitoring.

Estimating Typhoon Waves based on the Modified ECMWF ERA-5 Wind Data

ERA-5 is a recently released reanalysis meteorological dataset provided by European Centre for Medium-Range Weather Forecasts (ECMWF). This study evaluates the representation of tropical cyclones (TC) in ERA-5 and the widely used ECMWF ERA-Interim (ERA-I) reanalysis data in the northwest Pacific region during year 2013 to 2015. Although ERA-5 wind data has a great improvement of precision during TC period than ERA-I data, it is still not accurate enough for the description of TCs especially when it is applied to estimate typhoon waves. To effectively correct this data, an empirical correlation is established between the maximum wind speed of TCs in ERA-5 and Best-track data. Then the ERA-5 data can be improved using a directly modified method without the requirement of observation TC track data. The modified ERA-5 wind data, the original ERA-5 data and the ERA-I data are applied to drive the FVCOM-SWAVE wave model to estimate the typhoon waves. The typhoon waves driven by the modified ERA-5 data show a better performance than that driven by other two wind datasets. The method introduced in this study for modification of ERA-5 wind data provides an alternative for the correction of this data during TC period when the observation TC track is unavailable.

Statistical significance of tropical cyclone forward speed on storm surge generation: retrospective analysis of best track and tidal data in Japan

ABSTRACT In this study, 42 years of tidal records and landfall TC best tracks in Japan were used to demonstrate that TC pre-landfall forward speed is significantly correlated with maximum storm surge height. Coastal morphology was the determining factor for the correlation between storm surge and TC forward speed. Fast-moving TCs tended to amplify the storm surge along open coastlines (Pearson correlation coefficient, R = 0.62), but reduce it in semi-enclosed bays (R = −0.52). The negative correlation contrasts with the general perception that the coincidence of TC wind speed and forward speed vectors generates a larger storm surge. The influence of coastal morphology was most prominent for TCs with a central pressure lower than 956 hPa. Tropical cyclone (TC) operational forecasts are continuously improving; however, there is still scope to improve the precision of storm surge predictions. These findings could contribute to the improvement of storm surge forecasting and provide emergency management personnel with more precise early warnings of dangerous storm surges.

Design Tropical Cyclone Wind Speed when Considering Climate Change

AbstractThis paper investigated the evolution of tropical cyclone (TC) wind threat from the past into the future and discussed its implications for the building code specifications for two vulnerab...

Deepti: Deep-Learning-Based Tropical Cyclone Intensity Estimation System

Tropical cyclones are one of the costliest natural disasters globally because of the wide range of associated hazards. Thus, an accurate diagnostic model for tropical cyclone intensity can save lives and property. There are a number of existing techniques and approaches that diagnose tropical cyclone wind speed using satellite data at a given time with varying success. This article presents a deep-learning-based objective, diagnostic estimate of tropical cyclone intensity from infrared satellite imagery with 13.24-kn root mean squared error. In addition, a visualization portal in a production system is presented that displays deep learning output and contextual information for end users, one of the first of its kind.

Advancing global storm surge modelling using the new ERA5 climate reanalysis

This study examines the implications of recent advances in global climate modelling for simulating storm surges. Following the ERA-Interim (0.75° × 0.75°) global climate reanalysis, in 2018 the European Centre for Medium-range Weather Forecasts released its successor, the ERA5 (0.25° × 0.25°) reanalysis. Using the Global Tide and Surge Model, we analyse eight historical storm surge events driven by tropical—and extra-tropical cyclones. For these events we extract wind fields from the two reanalysis datasets and compare these against satellite-based wind field observations from the Advanced SCATterometer. The root mean squared errors in tropical cyclone wind speed reduce by 58% in ERA5, compared to ERA-Interim, indicating that the mean sea-level pressure and corresponding strong 10-m winds in tropical cyclones greatly improved from ERA-Interim to ERA5. For four of the eight historical events we validate the modelled storm surge heights with tide gauge observations. For Hurricane Irma, the modelled surge height increases from 0.88 m with ERA-Interim to 2.68 m with ERA5, compared to an observed surge height of 2.64 m. We also examine how future advances in climate modelling can potentially further improve global storm surge modelling by comparing the results for ERA-Interim and ERA5 against the operational Integrated Forecasting System (0.125° × 0.125°). We find that a further increase in model resolution results in a better representation of the wind fields and associated storm surges, especially for small size tropical cyclones. Overall, our results show that recent advances in global climate modelling have the potential to increase the accuracy of early-warning systems and coastal flood hazard assessments at the global scale.

SFMR Surface Wind Undersampling over the Tropical Cyclone Life Cycle

Surface wind speeds in tropical cyclones are important for defining current intensity and intensification. Traditionally, airborne observations provide the best information about the surface wind speeds, with the Stepped Frequency Microwave Radiometer (SFMR) providing a key role in obtaining such data. However, the flight patterns conducted by hurricane hunter aircraft are limited in their azimuthal coverage of the surface wind field, resulting in an undersampling of the wind field and consequent underestimation of the peak 10-m wind speed. A previous study provided quantitative estimates of the average underestimate for a very strong hurricane. However, no broader guidance on applying a correction based on undersampling has been presented in detail. To accomplish this task, a modified observing system simulation experiment with five hurricane simulations is used to perform a statistical evaluation of the peak wind speed underestimate over different stages of the tropical cyclone life cycle. Analysis of numerous simulated flights highlights prominent relationships between wind speed undersampling and storm size, where size is defined by the radius of maximum wind speed (RMW). For example, an intense hurricane with small RMW needs negligible correction, while a large-RMW tropical storm requires a 16%–19% change. A lookup table of undersampling correction factors as a function of peak SFMR wind speed and RMW is provided to assist the tropical cyclone operations community. Implications for hurricane best track intensity estimates are also discussed using real data from past Atlantic hurricane seasons.

PHURIE: hurricane intensity estimation from infrared satellite imagery using machine learning

Automated prediction of hurricane intensity from satellite infrared imagery is a challenging problem with implications in weather forecasting and disaster planning. In this work, a novel machine learning-based method for estimation of intensity or maximum sustained wind speed of tropical cyclones over their life cycle is presented. The approach is based on a support vector regression model over novel statistical features of infrared images of a hurricane. Specifically, the features characterize the degree of uniformity in various temperature bands of a hurricane. Performance of several machine learning methods such as ordinary least squares regression, backpropagation neural networks and XGBoost regression has been compared using these features under different experimental setups for the task. Kernelized support vector regression resulted in the lowest prediction error between true and predicted hurricane intensities (approximately 10 knots or 18.5 km/h), which is better than previously proposed techniques and comparable to SATCON consensus. The performance of the proposed scheme has also been analyzed with respect to errors in annotation of center of the hurricane and aircraft reconnaissance data. The source code and webserver implementation of the proposed method called PHURIE (PIEAS HURricane Intensity Estimator) is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#PHURIE.

Using Simulated Dropsondes to Understand Extreme Updrafts and Wind Speeds in Tropical Cyclones

AbstractExtreme updrafts (≥10 m s−1) and wind gusts (≥90 m s−1) are ubiquitous within the low-level eyewall of intense tropical cyclones (TCs). Previous studies suggest that both of these features are associated with coherent subkilometer-scale vortices. Here, over 100 000 “virtual” dropsonde trajectories are examined within a large-eddy simulation (31.25-m horizontal grid spacing) of a category 5 hurricane in order to gain insight into the nature of these features and to better understand and interpret dropsonde observations. At such a high resolution, profiles of wind speed and vertical velocity from the virtual sondes are difficult to distinguish from those of real dropsondes. PDFs of the strength of updrafts and wind gusts compare well between the simulated and observed dropsondes, as do the respective range of heights over which these features are found. Individual simulated updrafts can be tracked for periods of up to several minutes, revealing structures that are both coherent and rapidly evolving. It appears that the updrafts are closely associated with vortices and wind speed maxima, consistent with previous studies. The peak instantaneous wind gusts in the simulations (up to 150 m s−1) are substantially stronger than have ever been observed. Using the virtual sondes, it is demonstrated that the probability of sampling such extremes is vanishingly small, and it is argued that actual intense TCs might also be characterized by gusts of these magnitudes.

Global composites of surface wind speeds in tropical cyclones based on a 12 year scatterometer database

AbstractA 12 year global database of rain‐corrected satellite scatterometer surface winds for tropical cyclones (TCs) is used to produce composites of TC surface wind speed distributions relative to vertical wind shear and storm motion directions in each TC‐prone basin and various TC intensity stages. These composites corroborate ideas presented in earlier studies, where maxima are located right of motion in the Earth‐relative framework. The entire TC surface wind asymmetry is down motion left for all basins and for lower strength TCs after removing the motion vector. Relative to the shear direction, the motion‐removed composites indicate that the surface wind asymmetry is located down shear left for the outer region of all TCs, but for the inner‐core region it varies from left of shear to down shear right for different basin and TC intensity groups. Quantification of the surface wind asymmetric structure in further stratifications is a necessary next step for this scatterometer data set.

Spline-based modelling of near-surface wind speeds in tropical cyclones

A tropical cyclone (TC) making landfall at a populated coastal area can cause both loss of life and damage to infrastructure and the natural environment. Wind field models which accurately describe the spatial distribution of TC wind speeds can assist in reducing some of these risks, by identifying the most dangerous locations with respect to damaging winds. Existing models either have limitations in describing the skewness of the wind field or do not have the flexibility to incorporate multiple wind observations. We developed a new mathematical model to represent the temporal-spatial distribution of near-surface wind speeds in a TC over an open ocean that captures asymmetries in both the eyewall and tail of the wind profile. The model formulation is flexible, with options for a generic fit or inclusion of extra observations to achieve a fit with higher accuracy and has tuning parameters for calibration to wind observations. Utilising several covariates that could be obtained from satellite remote sensing data, we derive anchor points that the wind profile should pass through. Next, polar coordinate based cubic splines that minimise modified discontinuity energy and preserve specified shape constraints are used to model the wind speeds profile, which is formulated as solving several linear programming problems. Appropriate formulation adjustments are made for clockwise and counterclockwise rotations of air around a TC centre in the Southern and Northern Hemispheres, respectively. Model performance of the generic fit is tested using near-surface wind observations at automatic weather stations. Comparison with an earlier developed wind model shows that the spline model produces more accurate wind estimates near the radius of maximum winds. Although the spline model describes TC wind speeds over water well, a limitation is its inability to capture reduction in wind speeds caused by terrain friction. Overall, the spline model can be used successfully to describe TC wind speeds over waters.

Typhoon wind hazard estimation for China using an empirical track model

A probabilistic model for simulating tropical cyclone (TC) track from the genesis to lysis is developed based on historical best-track dataset for western North Pacific basin. The spatially varying model coefficients are determined through regression analysis. Tracks are simulated using the developed probabilistic model; statistics of the key parameters defining the TC track: the translation velocity, storm heading and, relative intensity that is a function of central pressure difference, are estimated at sites along the coastline using the simulated tracks. The statistics of the key parameters are compared with those estimated using historical tracks, indicating the adequacy of the developed empirical track model. The developed track model together with a well-established planetary boundary layer wind field model are subsequently used to assess the TC or typhoon wind hazard, in terms of the return period value of the annual maximum TC wind speed, for coastal region of the mainland China. The estimated wind hazard at selected sites is compared to that based on the circular sub-regional method. Comparison is also extended to the values recommended in Chinese design code at many sites, and the results from the surface wind observations at four sites. It is shown that the estimated typhoon wind hazard is consistent with that derived based on surface wind observations, and the mapped typhoon wind hazard near the coastal line is in good agreement with that presented in current design code.

The Impact of Spray-Mediated Enhanced Enthalpy and Reduced Drag Coefficients in the Modelling of Tropical Cyclones

The impact of new parametrizations for drag and air–sea enthalpy exchange on modelling the intensity of tropical cyclones with a numerical weather prediction (NWP) model is examined. These parametrizations follow from a model for the marine atmospheric boundary layer for high wind-speed conditions in the presence of spray droplets that originate from breaking wave crests. This model accounts for the direct impact of these droplets on the air–sea momentum flux through action of a spray force, which originates from the interaction of the ‘rain’ of spray droplets with the vertical wind shear and is expressed in terms of the spray generation function (SGF). The SGF is cubic in the wind speed up to 50 m s\(^{-1}\) beyond which its value increases less strongly. The drag coefficient (\(C_\mathrm{D}\)) decreases from a wind speed of approximately 30 m s\(^{-1}\), in agreement with the available measurements in these conditions. The enthalpy exchange coefficient (\(C_k\)) increases with increasing wind speed and slowly decreases beyond a wind speed of about 40 m s\(^{-1}\) due to the strong decrease in \(C_\mathrm{D}\). The value for \(C_k/C_\mathrm{D}\) is in agreement with observational data for wind speeds up to 30 m s\(^{-1}\); for higher wind speeds the value is in the range 1.2–1.5 in agreement with a well-established theory. The parametrization is tested in an NWP model. The tropical cyclones Ivan (2004) and Katrina (2005) in the Gulf of Mexico are simulated. To the sea surface temperatures (SSTs) from the European Centre archive that were prescribed to the NWP model, a parametrized cooling (based on estimations from theoretical studies and measurements) was applied during the model forecasts, as the NWP model does not resolve locally rather strong induced reductions in SSTs. The simulations show that realistic tropical cyclone wind speeds and central pressure can be obtained with the proposed drag and enthalpy parametrizations. The results indicate that the value for \(C_k/C_\mathrm{D}\) at very high wind speeds is in the correct range. Moreover, the results motivate the application of the parametrizations in atmosphere–ocean coupled models.