Tropical Cyclone Surface Wind Research Articles

Estimating tropical cyclone surface winds: Current status, emerging technologies, historical evolution, and a look to the future

This article provides a review of tropical cyclone (TC) surface wind estimation from an operational forecasting perspective. First, we provide a summary of operational forecast center practices and historical databases. Next, we discuss current and emerging objective estimates of TC surface winds, including algorithms, archive datasets, and individual algorithm strengths and weaknesses as applied to operational TC surface wind forecast parameters. Our review leads to recommendations about required surface coverage – an area covering at least 1100 km from center of TC at a 2-km resolution in the inner-core, and at a frequency of at least once every 6 h. This is enough coverage to support a complete analysis of the TC surface wind field from center to the extent of the 34-kt (17 m s−1) winds at 6-h intervals. We also suggest future designs of TC surface wind capabilities include funding to ensure near real-time data delivery to operators so that operational evaluation and use are feasible within proposed budgets. Finally, we suggest that users of archived operational wind radii datasets contact operational organizations to ensure these datasets are appropriate for their needs as the datasets vary in quality through time and space, even from a single organisation.

Estimating Met-Ocean Parameters during a Tropical Cyclone for Marine Science and Engineering

During a tropical cyclone (TC) worldwide, the Regional and Mesoscale Meteorological Branch (RAMMB) of NOAA/NESDIS (http://rammb.cira.colostate.edu/) issues real-time TC surface wind analyses. The purpose of this article is to provide value-added estimations of several meteorological and oceanographic (met-ocean) parameters including overwater friction velocity and turbulence intensity, variation of the wind speed with height, significant wave height, peak or dominant wave period, wind-driven currents and wind-stress tides. Since these proposed value-added parameters are also validated by other independent methods available from the literature, these met-ocean parameters may be used for marine science and engineering including offshore energy (such as oil, gas and wind power) research, development, operation and maintenance.

Physics-augmented Deep Learning to Improve Tropical Cyclone Intensity and Size Estimation from Satellite Imagery

AbstractA deep learning-based method augmented by prior knowledge of tropical cyclones (TCs), called DeepTCNet, is introduced to estimate TC intensity and wind radii from infrared (IR) imagery over the North Atlantic Ocean. While standard deep learning practices have many advantages over conventional analysis approaches and can produce reliable estimates of TCs, the data-driven models informed by machine-readable physical knowledge of TCs could achieve higher performance. To this end, two approaches are explored to develop the physics-augmented DeepTCNet: (i) infusing the auxiliary physical information of TCs into models for single-task learning; (ii) learning auxiliary physical tasks for multi-task learning. More specifically, augmented by auxiliary information of TC fullness (a measure of the radial decay of the TC wind field), the DeepTCNet yielded a 12% improvement in estimating TC intensity over the non-augmented one. By learning TC wind radii and auxiliary TC intensity task simultaneously, the model’s wind radii estimation skill is improved by 6% over only learning four wind radii tasks, and by 9% over separately learning a single wind radii task. The evaluation results showed that the DeepTCNet is in-line with the Satellite Consensus technique (SATCON) but systematically outperforms the Advanced Dvorak Technique (ADT) at all intensity scales with an averaged 39% enhancement in TC intensity estimation. The DeepTCNet also surpasses the Multi-platform Tropical Cyclone Surface Wind Analysis technique (MTCSWA) with an average improvement of 32% in wind radii estimation.

Potential Role of Irreversible Moist Processes in Modulating Tropical Cyclone Surface Wind Structure

AbstractTropical cyclone (TC) wind structure is important for its intensity change and induced damage, but its modulating factors remain to be explored. A heat-engine-based surface wind structure parameter α, reflecting TC’s relative compactness, is introduced and derived based on an entropy budget framework. We found that α is modulated by three key parameters: the thermodynamic efficiency ϵPI in potential intensity theory, the Carnot efficiency ϵC of the system, and the degree of irreversibility αirr of the system. A higher αirr contributes to a larger α and a lower heat engine efficiency. An expression linking TC intensity and compactness also emerges under this framework. Idealized simulations of a typical moist TC (CTL), a dry (DRY) TC, and a moist reversible TC (REV; in which hydrometeors do not fall out) evinced that the significantly higher αirr in CTL, due to irreversible entropy productions from precipitation dissipation, water vapor diffusion, and irreversible phase changes, contributes to its much larger compactness compared to DRY and REV. The study illustrates the importance of irreversible entropy production processes in modulating TC surface wind field. Simple estimate suggests that α will increase due to a hypothesized increased αirr with warming because of increased water content. This indicates that TCs will become more compact in a warmer climate.

Evaluation of the Surface Wind Field over Land in WRF Simulations of Hurricane Wilma (2005). Part I: Model Initialization and Simulation Validation

AbstractAlthough global and regional dynamical models are used to predict the tracks and intensities of hurricanes over the ocean, these models are not currently used to predict the wind field and other impacts over land. This two-part study performs detailed evaluations of the near-surface, overland wind fields produced in simulations of Hurricane Wilma (2005) as it traveled across South Florida. This first part describes the production of two high-resolution simulations using the Weather Research and Forecasting (WRF) Model, using different boundary layer parameterizations available in WRF: the Mellor–Yamada–Janjić (MYJ) scheme and the Yonsei University (YSU) scheme. Initial conditions from the Global Forecasting System are manipulated with a vortex-bogusing technique to modify the initial intensity, size, and location of the cyclone. It is found possible through trial and error to successfully produce simulations using both the YSU and MYJ schemes that closely reproduce the track, intensity, and size of Wilma at landfall. For both schemes the storm size and structure also show good agreement with the wind fields diagnosed by H*WIND and the Tropical Cyclone Surface Wind Analysis. Both over water and over land, the YSU scheme has stronger winds over larger areas than does the MYJ, but the surface winds are more reduced in areas of greater surface roughness, particularly in urban areas. Both schemes produced very similar inflow angles over land and water. The overland wind fields are examined in more detail in the second part of this study.

Estimating Tropical Cyclone Wind Structure and Intensity From Spaceborne Radiometer and Synthetic Aperture Radar

We present a relatively simple method to estimate tropical cyclone (TC) surface wind structure (34-, 50-, and 64-kt wind radii) and intensity [maximum wind speed (MWS)] from wind fields acquired from the L-band SMAP radiometer and C-band Sentinel-1A/B and RADARSAT-2 synthetic aperture radar (SAR) between 2015 and 2020. The radiometer and SAR-derived wind radii and MWS are systematically compared with the best-track estimates. The root-mean-square errors (RMSEs) of R34, R50, and R64 are 31.2, 21.8, and 17.0 nmi (1 nmi = 1.852 km) for radiometer, and 21.7, 16.5, and 16.3 nmi for SAR, respectively. These error values are smaller than the averaged best-track uncertainty estimates for the three wind radii. Compared with the best-track reports, the bias and RMSE for the MWS estimates are -0.2 m/s and 5.8 m/s for radiometer, and 4.4 m/s and 9.1 m/s for SAR, respectively. These results are for the wind speeds in the range of 17-80 m/s. For the two typical TCs (Lionrock and Noru) in the Northwest Pacific Ocean, our results show that a combination of the radiometer and SAR wind data acquired within a very short time interval has the potential to simultaneously obtain reasonable measurements of the wind radii and intensity parameters. Moreover, for a TC with a long lifecycle, such as Typhoon Noru, we demonstrate that the high-resolution and multitemporal synergistic observations from SAR and radiometer are valuable for studying fine-scale features of the wind field and characteristics of wind asymmetry associated with intensity change, as well as the evolution of TC surface wind structure and intensity.

Examination of Surface Wind Asymmetry in Tropical Cyclones over the Northwest Pacific Ocean Using SMAP Observations

Tropical cyclone (TC) surface wind asymmetry is investigated by using wind data acquired from an L-band passive microwave radiometer onboard the NASA Soil Moisture Active Passive (SMAP) satellite between 2015 and 2017 over the Northwest Pacific (NWP) Ocean. The azimuthal asymmetry degree is defined as the factor by which the maximum surface wind speed is greater than the mean wind speed at the radius of the maximum wind (RMW). We examined storm motion and environmental wind shear effects on the degree of TC surface wind asymmetry under different intensity conditions. Results show that the surface wind asymmetry degree significantly decreases with increasing TC intensity, but increases with increasing TC translation speed, for tropical storm and super typhoon strength TCs; whereas no such relationship is found for typhoon and severe typhoon strength TCs. However, the degree of surface wind asymmetry increases with increasing wind shear magnitude for all TC intensity categories. The relative strength between the storm translation speed and the wind shear magnitude has the potential to affect the location of the maximum wind speed. Moreover, the maximum degree of wind asymmetry is found when the direction of the TC motion is nearly equal to the direction of the wind shear.

Examination of Surface Wind Asymmetries in Tropical Cyclones. Part I: General Structure and Wind Shear Impacts

Because surface wind speeds within tropical cyclones are important for operational and research interests, it is vital to understand surface wind structure in relation to various storm and environmental influences. In this study, global rain-corrected scatterometer winds are used to quantify and evaluate characteristics of tropical cyclone surface wind asymmetries using a modified version of a proven aircraft-based low-wavenumber analysis tool. The globally expanded surface wind dataset provides an avenue for a robust statistical analysis of the changes in structure due to tropical cyclone intensity, deep-layer vertical wind shear, and wind shear’s relationship with forward storm motion. A presentation of the quantified asymmetry indicates that wind shear has a significant influence on tropical storms at all radii but only for areas away from the radius of maximum wind in both nonmajor and major hurricanes. Evaluation of a shear’s directional relation to motion indicates that a cyclonic rotation of the surface wind field asymmetry from downshear left to upshear left occurs in conjunction with an anticyclonic rotation of the directional relationship (i.e., from shear direction to the left, same, right, or opposite of the motion direction). It was discovered that in tropical cyclones experiencing effects from wind shear, an increase in absolute angular momentum transport occurs downshear and often downshear right. The surface wind speed low-wavenumber maximum in turn forms downwind of this momentum transport.

Determining Tropical Cyclone Surface Wind Speed Structure and Intensity with the CYGNSS Satellite Constellation

AbstractThe Cyclone Global Navigation Satellite System (CYGNSS) consists of a constellation of eight microsatellites that provide observations of surface wind speed in all precipitating conditions. A method for estimating tropical cyclone (TC) metrics—maximum surface wind speed VMAX, radius of maximum surface wind speed RMAX, and wind radii (R64, R50, and R34)—from CYGNSS observations is developed and tested using simulated CYGNSS observations with realistic measurement errors. Using two inputs, 1) CYGNSS observations and 2) the storm center location, estimates of TC metrics are possible through the use of a parametric wind model algorithm that effectively interpolates between the available observations as a constraint on the assumed wind speed distribution. This methodology has a promising performance as evaluated from the simulations presented. In particular, after quality-control filters based on sampling properties are applied to the population of test cases, the standard deviation of retrieval error for VMAX is 4.3 m s−1 (where 1 m s−1 = 1.94 kt), for RMAX is 17.4 km, for R64 is 16.8 km, for R50 is 21.6 km, and for R34 is 41.3 km (where 1 km = 0.54 n mi). These TC data products will be available for the 2017 Atlantic Ocean hurricane season using on-orbit CYGNSS observations, but near-real-time operations are the subject of future work. Future work will also include calibration and validation of the algorithm once real CYGNSS data are available.

Wind Structure Discrepancies between Two Best Track Datasets for Western North Pacific Tropical Cyclones

Abstract Symmetric and wavenumber-1 asymmetric characteristics of western North Pacific tropical cyclone (TC) outer wind structures are compared between best tracks from the Joint Typhoon Warning Center (JTWC) and the Japan Meteorological Agency (JMA) from 2004 to 2014 as well as the Multiplatform Tropical Cyclone Surface Wind Analysis (MTCSWA) product from 2007 to 2014. Significant linear relationships of averaged wind radii are obtained among datasets, in which both gale-force and storm-force wind radii are generally estimated slightly smaller (much larger) by JTWC (JMA) than by MTCSWA. These correlations are strongly related to TC intensity relationships discussed in earlier work. Moreover, JTWC (JMA) on average represents a smaller (greater) derived shape parameter than MTCSWA does, implying that JTWC (JMA) typically assesses a more compact (less compact) storm than MTCSWA. For the wavenumber-1 asymmetry, large differences among datasets are found regardless of the magnitude or the direction of the longest radius. JTWC estimates more asymmetric storms than JMA, and it provides greater wavenumber-1 asymmetry magnitudes on average. Asymmetric storms are most frequently oriented toward the east, northeast, and north in JTWC and MTCSWA, whereas they are most frequently oriented toward the southeast, east, and northeast in JMA. The direction of the longest gale-force (storm force) wind radius in JTWC is statistically rotated 18° (32°) clockwise to that in JMA. Although the wind radii in JTWC are of higher quality than those in JMA when using MTCSWA as a baseline, there remains a need to provide a consistent and reliable wind radii estimating process among operational centers.

Improvements to the Operational Tropical Cyclone Wind Speed Probability Model

Abstract The National Hurricane Center Hurricane Probability Program, which estimated the probability of a tropical cyclone passing within a specific distance of a selected set of coastal stations, was replaced by the more general Tropical Cyclone Surface Wind Speed Probabilities in 2006. A Monte Carlo (MC) method is used to estimate the probabilities of 34-, 50-, and 64-kt (1 kt = 0.51 m s−1) winds at multiple time periods through 120 h. Versions of the MC model are available for the Atlantic, the combined eastern and central North Pacific, and the western North Pacific. This paper presents a verification of the operational runs of the MC model for the period 2008–11 and describes model improvements since 2007. The most significant change occurred in 2010 with the inclusion of a method to take into account the uncertainty of the track forecasts on a case-by-case basis, which is estimated from the spread of a dynamical model ensemble and other parameters. The previous version represented the track uncertainty from the error distributions from the previous 5 yr of forecasts from the operational centers, with no case-to-case variability. Results show the MC model provides robust estimates of the wind speed probabilities using a number of standard verification metrics, and that the inclusion of the case-by-case measure of track uncertainty improved the probability estimates. Beginning in 2008, an older operational wind speed probability table product was modified to include information from the MC model. This development and a verification of the new version of the table are described.

An Automated, Objective, Multiple-Satellite-Platform Tropical Cyclone Surface Wind Analysis

AbstractA method to estimate objectively the surface wind fields associated with tropical cyclones using only data from multiple satellite platforms and satellite-based wind retrieval techniques is described. The analyses are computed on a polar grid using a variational data-fitting method that allows for the application of variable data weights to input data. The combination of gross quality control and the weighted variational analysis also produces wind estimates that have generally smaller errors than do the raw input data. The resulting surface winds compare well to the NOAA Hurricane Research Division H*Wind aircraft reconnaissance–based surface wind analyses, and operationally important wind radii estimated from these wind fields are shown to be generally more accurate than those based on climatological data. Most important, the analysis system produces global tropical cyclone surface wind analyses and related products every 6 h—without aircraft reconnaissance data. Also, the analysis and products are available in time for consideration by forecasters at the Joint Typhoon Warning Center, the Central Pacific Hurricane Center, and the National Hurricane Center in preparing their forecasts and advisories. This Multiplatform Tropical Cyclone Surface Wind Analysis (MTCSWA) product is slated to become an operationally supported product at the National Environmental Satellite Data and Information Service (NESDIS). The input data, analysis method, products, and verification statistics associated with the MTCSWA are discussed within.

Development and Testing of Tropical Cyclone Parametric Wind Models Tailored for Midlatitude Application—Preliminary Results

Abstract Over the years, researchers have developed parametric wind models to depict the surface winds within a tropical cyclone (TC). Most models were developed using data from aircraft flights into low-latitude (south of 30°N) TCs in the Atlantic Ocean, Gulf of Mexico, and Caribbean Sea. Such models may not adequately reproduce the midlatitude TC wind field where synoptic interaction and acceleration are more pronounced. To tailor these models for midlatitude application, latitude-dependent angular size and shape details were added by using new techniques to set values for model input parameters and by incorporating additional field-shaping procedures. A method to assess the different techniques and field-shaping procedures was developed in which qualitative and quantitative assessment was performed using five parametric models and samples of buoy and 2D surface wind data. Contingency tables and statistical scores such as mean absolute error and bias were used to select the techniques and procedures that create the most realistic depiction of low- and midlatitude TC surface wind fields.

Tropical Cyclone Wind Retrievals from the Advanced Microwave Sounding Unit: Application to Surface Wind Analysis

Abstract Horizontal winds at 850 hPa from tropical cyclones retrieved using the nonlinear balance equation, where the mass field was determined from Advanced Microwave Sounding Unit (AMSU) temperature soundings, are compared with the surface wind fields derived from NASA's Quick Scatterometer (QuikSCAT) and Hurricane Research Division H*Wind analyses. It was found that the AMSU-derived wind speeds at 850 hPa have linear relations with the surface wind speeds from QuikSCAT or H*Wind. There are also characteristic biases of wind direction between AMSU and QuikSCAT or H*Wind. Using this information to adjust the speed and correct for the directional bias, a new algorithm was developed for estimation of the tropical cyclone surface wind field from the AMSU-derived 850-hPa winds. The algorithm was evaluated in two independent cases from Hurricanes Floyd (1999) and Michelle (2001), which were observed simultaneously by AMSU, QuikSCAT, and H*Wind. In this evaluation the AMSU adjustment algorithm for wind speed worked well. Results also showed that the bias correction algorithm for wind direction has room for improvement.

Application of Surface-Adjusted GOES Low-Level Cloud-Drift Winds in the Environment of Atlantic Tropical Cyclones. Part I: Methodology and Validation

Abstract Beginning with the 1997 hurricane season, the Cooperative Institute for Meteorological Satellite Studies at the University of Wisconsin—Madison began demonstrating the derivation of real-time Geostationary Operational Environmental Satellite (GOES) low-level cloud-drift winds in the vicinity of Atlantic tropical cyclones. The winds are derived from tracking low-level clouds in sequential, high-resolution GOES visible channel imagery. Since then, these data have been provided to the National Oceanic and Atmospheric Administration (NOAA) Hurricane Research Division (HRD) for evaluation in their real-time tropical cyclone surface wind objective analyses (H*Wind) that are disseminated to forecasters at the NOAA National Hurricane Center on an experimental basis. These wind analyses are proving useful as guidance to support forecasters's tropical cyclone advisories and warnings. The GOES satellite wind observations often provide essential near-surface coverage in the outer radii of the tropical cyclon...

Application of Surface-Adjusted GOES Low-Level Cloud-Drift Winds in the Environment of Atlantic Tropical Cyclones. Part II: Integration into Surface Wind Analyses

The Cooperative Institute for Meteorological Satellite Studies at the University of Wisconsin—Madison recently (1997 season) began providing real-time Geostationary Operational Environmental Satellite (GOES) low-level cloud-drift winds in the vicinity of tropical cyclones on an experimental basis to the National Oceanic and Atmospheric Administration's (NOAA) Hurricane Research Division (HRD). The cloud-drift winds are derived from sequential high-resolution GOES visible channel imagery. These data were included in many of HRD's real-time tropical cyclone surface wind objective analyses, which were sent to NOAA's National Hurricane Center and the Central Pacific Hurricane Center on an experimental basis during the 1997–2001 hurricane seasons. These wind analyses were used to support the forecasters' tropical cyclone advisories and warnings. The satellite wind observations provide essential low-level coverage in the periphery of the tropical cyclone circulation where conventional in situ observations (e.g., ships, buoys, and Coastal-Marine Automated Network stations) are often widely spaced or nonexistent and reconnaissance aircraft do not normally fly. Though winds derived from microwave channels on polar orbiting satellites provide valuable surface wind data for HRD surface wind analyses, their swath coverage and orbital passes are limited spatially and temporally. GOES low-level visible (GLLV) winds offer nearly continuous spatial and temporal coverage in the western Atlantic and eastern Pacific basins. The GLLV winds were extrapolated to the surface using a planetary boundary layer model developed at HRD. These surface-adjusted satellite data were used in real-time surface wind analyses of 1998 Hurricane Georges, as well as in poststorm analyses of 1996 Hurricane Lili and 1997 Tropical Storm Claudette. The satellite observations often helped to define the spatial extent of the 17.5 m s−1 (34 kt) surface wind radii and also redefined the 25.7 m s−1 (50 kt) wind radius for one case. Examples of the impact of these data on real-time hurricane surface wind fields provided to the NHC will be discussed.

NUMERICAL MODELLING OF TROPICAL CYCLONE STORM SURGE

Details are presented of a general numerical hydrodynamic model for the generation and propagation of tropical cyclone or hurricane storm surge. The model, known as SURGE, solves the two-dimensional depth-integrated form of the Long Wave Equations using an explicit finite difference procedure, with tropical cyclone surface wind and pressure forcing estimated from an adaption of available models based on U.S. hurricanes. Variations in tropical cyclone parameters as well as the physical characteristics of a coastal location such as bathymetry and details of capes, bays, reefs and islands are accommodated by the model. The accuracy and stability of the numerical solution have been confirmed by a comprehensive wave deformation analysis including quasi-non-linear effects and the open boundary problem has been overcome by the use of a Bathystrophic Storm Tide approximation to boundary water levels. A detailed sensitivity analysis has identified the principal surge generating parameters and the model has been checked against an historical tropical cyclone storm surge. SURGE has been used extensively in the northern Australian region and examples are presented.