Tropical Cyclone Maximum Wind Research Articles

Global expansion of tropical cyclone precipitation footprint

Precipitation from tropical cyclones (TCs) can cause massive damage from inland floods and is becoming more intense under a warming climate. However, knowledge gaps still exist in changes of spatial patterns in heavy TC precipitation. Here we define a metric, DIST30, as the mean radial distance from centers of clustered heavy rainfall cells (> 30 mm/3 h) to TC center, representing the footprint of heavy TC precipitation. There is significant global increase in DIST30 at a rate of 0.34 km/year. Increases of DIST30 cover 59.87% of total TC impact areas, with growth especially strong in the Western North Pacific, Northern Atlantic, and Southern Pacific. The XGBoost machine learning model showed that monthly DIST30 variability is majorly controlled by TC maximum wind speed, location, sea surface temperature, vertical wind shear, and total water column vapor. TC poleward migration in the Northern Hemisphere contributes substantially to the DIST30 upward trend globally.

Improvement of Global Forecast of Tropical Cyclone Intensity by Spray Heat Flux and Surface Roughness

AbstractIn global forecasting systems, tropical cyclone (TC) intensity is usually underestimated, which is one of the major challenges for TC forecasting. One possible reason is the deficiency of physical parameterizations associated with ocean surface waves. To improve the TC intensity forecast, effects of two powerful wave‐related processes, the spray‐mediated heat flux and surface roughness, are considered in a global coupled ocean‐atmosphere‐wave system (CFSv2.0‐WW3). Serial 24 hr forecasting experiments for all TCs from May to October 2019 are conducted, and comparisons are made against the International Best Track TC data. The results show that for strong TCs the underestimation of TC intensity in CFSv2.0‐WW3 is significantly improved by the modified surface roughness parameterization and the accelerated spray heat flux parameterization based on Gaussian Quadrature. For major hurricanes with initial wind speeds above 30 m/s, the error of TC maximum wind speed decreases by about 34.4%. To understand the associated dynamic and thermodynamic processes, a series of 120 hr simulations for major hurricane Hagibis is conducted. The effect of spray‐mediated heat flux accounts for 91.7% of the TC intensity improvement. The increased heat fluxes offset the negative contributions of vertical advection and ventilation, leading to enhanced entropy. The low‐level pressure is then decreased, resulting in enhanced convergence of absolute vorticity, which overpowers the negative effects of frictional dissipation and vertical momentum advection. The study indicates that the application of these two wave‐related parameterizations can potentially improve the global forecast and reanalysis of TC intensity.

Global increase in tropical cyclone ocean surface waves

The long-term changes of ocean surface waves associated with tropical cyclones (TCs) are poorly observed and understood. Here, we present the global trend analysis of TC waves for 1979–2022 based on the ERA5 wave reanalysis. The maximum height and the area of the TC wave footprint in the six h reanalysis have increased globally by about 3%/decade and 6%/decade, respectively. The TC wave energy transferred at the interface from the atmosphere to the ocean has increased globally by about 9%/decade, which is three times larger than that reported for all waves. The global energy changes are mostly driven by the growing area of the wave footprint. Our study shows that the TC-associated wave hazard has increased significantly and these changes are larger than those of the TC maximum wind speed. This suggests that the wave hazard should be a concern in the future.

A Comparison of the Impacts of Inner-Core, In-Vortex, and Environmental Dropsondes on Tropical Cyclone Forecasts during the 2017–20 Hurricane Seasons

Abstract This study conducts the first large-sample comparison of the impact of dropsondes in the tropical cyclone (TC) inner core, vortex, and environment on NWP-model TC forecasts. We analyze six observing-system experiments, focusing on four sensitivity experiments that denied dropsonde observations within annuli corresponding with natural breakpoints in reconnaissance sampling. These are evaluated against two other experiments detailed in a recent parallel study: one that assimilated and another that denied dropsonde observations. Experiments used a basin-scale, multistorm configuration of the Hurricane Weather Research and Forecasting (HWRF) Model and covered active periods of the 2017–20 North Atlantic hurricane seasons. Analysis focused on forecasts initialized with dropsondes that used mesoscale error covariance derived from a cycled HWRF ensemble, as these forecasts were where dropsondes had the greatest benefits in the parallel study. Some results generally support findings of previous research, while others are novel. Most notable was that removing dropsondes anywhere, particularly from the vortex, substantially degraded forecasts of maximum sustained winds. Removing in-vortex dropsondes also degraded outer-wind-radii forecasts in many instances. As such, in-vortex dropsondes contribute to a majority of the overall impacts of the dropsonde observing system. Additionally, track forecasts of weak TCs benefited more from environmental sampling, while track forecasts of strong TCs benefited more from in-vortex sampling. Finally, inner-core-only sampling strategies should be avoided, supporting a change made to the U.S. Air Force Reserve’s sampling strategy in 2018 that added dropsondes outside of the inner core. Significance Statement This study uses a regional hurricane model to conduct the most comprehensive assessment to date of the impact of dropsondes at different distances away from the center of a tropical cyclone (TC) on TC forecasts. The main finding is that in-vortex dropsondes are most important for intensity and outer-wind-radii forecasts. Particularly notable is the impact of dropsondes on TC maximum wind speed forecasts, as reducing sampling anywhere would degrade those forecasts.

Deep Learning Experiments for Tropical Cyclone Intensity Forecasts

AbstractReducing tropical cyclone (TC) intensity forecast errors is a challenging task that has interested the operational forecasting and research community for decades. To address this, we developed a deep learning (DL)-based Multilayer Perceptron (MLP) TC intensity prediction model. The model was trained using the global Statistical Hurricane Intensity Prediction Scheme (SHIPS) predictors to forecast the change in TC maximum wind speed for the Atlantic Basin. In the first experiment, a 24-hour forecast period was considered. To overcome sample size limitations, we adopted a Leave One Year Out (LOYO) testing scheme, where a model is trained using data from all years except one and then evaluated on the year that is left out. When tested on 2010–2018 operational data using the LOYO scheme, the MLP outperformed other statistical-dynamical models by 9-20%. Additional independent tests in 2019 and 2020 were conducted to simulate real-time operational forecasts, where the MLP model again outperformed the statistical-dynamical models by 5-22% and achieved comparable results as HWFI. The MLP model also correctly predicted more rapid intensification events than all the four operational TC intensity models compared. In the second experiment, we developed a lightweight MLP for 6-hour intensity predictions. When coupled with a synthetic TC track model, the lightweight MLP generated realistic TC intensity distribution in the Atlantic Basin. Therefore, the MLP-based approach has the potential to improve operational TC intensity forecasts, and will also be a viable option for generating synthetic TCs for climate studies.

Performance Assessment of Simulated CYGNSS Measurements in the Tropical Cyclone Environment

The capability of the cyclone global navigation satellite system (CYGNSS) to observe winds within tropical cyclones (TCs) is assessed by using simulated CYGNSS observations over 43 cyclones from 2010 to 2011. The CYGNSS end-to-end simulator (E2ES) is utilized to generate delay-Doppler maps from which wind speeds are then retrieved. These wind speeds are first compared to the high-resolution model winds input into the E2ES. For range corrected gain (RCG) values greater than 20, the CYGNSS winds have a standard deviation of 0.57 m/s relative to these model winds. An error probability lookup table is developed to improve performance for RCG values lower than 20, as well as minimizing data loss (up to 4.4%). Since actual TCs are utilized in this study, the CYGNSS winds are also compared to winds from the advanced SCATterometer (ASCAT), the Oceansat-II scatterometer, GPS dropsondes, the stepped frequency microwave radiometer, and the H*wind model analysis. The CYGNSS winds compared best to the ASCAT winds with an overall bias around $-$ 0.4 m/s, and a standard deviation less than 1.54 m/s. GPS dropsonde winds compared less favorably with a standard deviation around 7.36 m/s, which can be partially attributed to spatial and temporal sampling differences. The CYGNSS ability to retrieve the TC maximum wind is also evaluated using the national hurricane center best track maximum winds. Although the CYGNSS overall bias varied from season to season (from $-$ 4.7 m/s in the Atlantic basin-2011 to $-$ 13.0 m/s in the Atlantic basin-2010), the standard deviation remained fairly consistent.

A GIS Study of the Influences of Warm Ocean Eddies on the Intensity Variations of Tropical Cyclones in the South China Sea

This study presented the spatial distribution patterns of tropical cyclones (TCs) in the South China Sea (SCS) and discussed the possible influences of average sea surface temperature (SST) and the size of warm ocean eddies on changes in the intensity of TCs passing over them. Between 1993 and 2013, the SCS has experienced 233 TCs, of which 134 have interacted with warm ocean eddies. The results of fuzzy c-means (FCM) clustering showed that these TCs are mainly located in the northern portion of the SCS. After interacting with warm ocean eddies, TCs may intensify, remain at the same intensity, or weaken. For intensifying TCs, the enhancements range from 0 to 3 m/s only; however, this level of TC intensity enhancement is statistically significant at p<0.05. Further statistical analyses show that warm ocean eddies with a higher-than-average SST and a larger ratio between the size of the warm ocean eddies and the radius of the TC maximum wind may help intensify passing TCs.

Hurricane Wind–Pressure Relationship and Eyewall Replacement Cycles

Abstract The relationship between minimum central surface pressure and the maximum sustained surface wind in tropical cyclones has been studied for many years, motivated by the fact that minimum pressure is generally easier to measure, but maximum wind is a much more relevant metric when considering tropical cyclone risk and potential impacts. It is well understood that tropical cyclone wind is closely related to the radial gradient of pressure through gradient or cyclostrophic balance assumptions, and not to a single point value of the minimum pressure near the storm center. But it is often the case that the maximum wind must be inferred from this single value. To accomplish this, a number of statistical relationships have been documented, such as those used in the Dvorak technique for estimating tropical cyclone intensity from satellite imagery. Here, the relationship between tropical cyclone maximum wind and minimum pressure is explored during eyewall replacement cycles (ERCs) that have been observed in North Atlantic hurricanes. It is shown that the wind–pressure relationship (WPR) can vary substantially during an ERC and generally moves away from the statistically fitted WPR used by the Dvorak technique in that basin. The changes in WPR during an ERC can be quite different depending on the intensity of the hurricane at the start of the ERC.

Estimation of the Upper-Layer Rotation and Maximum Wind Speed of Tropical Cyclones via Satellite Imagery

Abstract The movement of convective rainbands embedded in a tropical cyclone (TC) is usually derived from satellite images via the atmospheric motion vector (AMV) method or through the calculation of a radar’s echo track. In estimating the rotation speed of a TC rainband, however, the land-based radar can only detect approaching tropical cyclones within the vicinity. The AMV method is unable to fully account for the TC eyewall movement, thus making it difficult to estimate the TC intensity. The widely used method in estimating the TC maximum wind speed is the Dvorak technique in which the cloud pattern is extracted from only one image. In this study, the rainband rotation speeds are computed via satellite imagery and further applied in estimating the TC maximum wind speed. In contrast to previous research, this study adopts an innovative method by using two subsequent geostationary satellite images. The TC spin rates observed by weather satellites could often be seen to be positively related to the TC intensity. Analyses of the relationship between the typhoon wind intensity and estimated rotation speed at the 130–260-km ring via infrared channels are conducted for major typhoon cases that occurred during 2000–05 in the northwestern Pacific Ocean. Results show that the correlation between the wind intensity and estimated rotation speeds is strong for most of the cases. The highest R2 value from the individual cases could reach 0.93, and on an annual basis it could attain a value of 0.67. The mean R2 value for the 2000–05 dataset was roughly 0.53. The correlation between the wind intensity and estimated rotation speeds is further improved by factoring in the previous 6-h average rotation speeds. A regression equation is derived from the chosen typhoon cases between 2000 and 2005, which is utilized in verifying the major typhoon occurrences during 2006–08. The mean absolute error (MAE) of the hourly and 6-h average intensity estimates during 2000–08 was 20 and 18.7 kt, respectively (1 kt ≃ 0.5 m s−1). The best verification result occurred during 2008, for which the R2 value and MAE could reach 0.7 and 15.6, respectively. These research results demonstrate the suitability of using geostationary satellite image data in estimating the maximum wind speed. Nevertheless, the drawback of this study is that sometimes the rotation speeds will become slower when tropical cyclones mature because of the strong outflow of the secondary circulation. It is assumed that the relationship between the estimated rotation speeds and wind intensity can be further improved if the outflow speed of the tropical cyclones is also considered.

Influence of environmental moisture on TRMM‐derived tropical cyclone precipitation over land and ocean

The rainfall climatology and persistence model (R‐CLIPER) used operationally in the Atlantic Ocean basin mainly utilizes tropical cyclone (TC) intensity to predict TC rainfall. However, the rain production by TCs is also influenced by environmental parameters such as total moisture availability, horizontal moisture convergence, vertical wind shear, and sea surface temperature (SST). Previous TC case studies have used environmental moisture parameters to diagnose TC rainfall. In this study, we composite over 3000 snapshots of 3‐hourly TRMM 3B42 rainfall fields for Atlantic landfalling tropical cyclones between 1998–2006 to analyze the rainfall distribution and storm total volumetric rain as a function of total precipitable water (TPW), horizontal moisture convergence (HMC), and ocean surface flux (OSF) over land and over ocean. For over ocean conditions, higher TPW, HMC, or OSF values are associated with higher azimuthally averaged rain rates. Over land, this is still the case but less obvious. Computing the linear correlation coefficients between total volumetric rain and moisture parameters shows this fact much more clearly. These coefficients are generally higher for over ocean conditions than those for over land conditions. To test if moisture parameters can provide additional information other than TC intensity to help TC rainfall forecasts, a multiple linear regression is performed between TC volumetric rain and several variables including TC maximum wind speed, TPW, HMC, and OSF. By adding moisture parameters as additional variables, TC volumetric rain will be better predicted than using TC intensity (maximum wind speed) only. The correlation coefficient between volumetric rain and maximum wind speed can increase from 0.52 (0.51) to 0.67 (0.65) for over ocean (land) conditions by adding TPW, HMC and OSF. By adding TPW only, the correlation coefficient increases to 0.59 and 0.64 for over ocean and land, respectively.

Hurricane “Rainfall Potential” Derived from Satellite Observations Aids Overland Rainfall Prediction

Abstract The Tropical Rainfall Measuring Mission–based National Aeronautics and Space Administration Goddard Multisatellite Precipitation Analysis (MPA) product is used to quantify the rainfall distribution in tropical cyclones that made landfall in the United States during 1998–2004. A total of 37 tropical cyclones (TC) are examined, including 2680 three-hourly MPA precipitation observations. Rainfall distributions for overland and overocean observations are compared. It is found that the TC rainfall over ocean bears a strong relationship with the TC maximum wind, whereas the relationship for overland conditions is much weaker. The rainfall potential is defined by using the satellite-derived rain rate, the satellite-derived storm size, and the storm translation speed. This study examines the capability of the overocean rainfall potential to predict a storm’s likelihood of producing heavy rain over land. High correlations between rain potentials before landfall and the maximum storm total rain over land are found using the dataset of the 37 landfalling TCs. Correlations are higher with the average rain potential on the day prior to landfall than with averages over any other time period. A TC overland rainfall index is introduced based on the rainfall potential study. This index can be used to predict the storm peak rainfall accumulation over land. Six landfalling storms during the 2005 Atlantic Ocean hurricane season are examined to verify the capability of using this index to forecast the maximum storm total rain over land in the United States. The range of the maximum storm overland rain forecast error for these six storms is between 2.5% and 24.8%.

Estimation of Tropical Cyclone's Intensity Using TRMM/TMI Brightness Temperature Data

A new method for the estimation of tropical cyclone (TC) intensity utilizing 10, 19, 21, 37 and 85 GHz channel TRMM Microwave Imager (TMI) data from 1999 to 2003 is developed. As a first step, we investigated. the relationship between the TRMM/TMI brightness temperature (TB) parameters, which are computed in concentric circles, or annuli of different radius in different TMI frequencies, and the TC maximum wind speed from the TC best track data, and/or observed by microwave scatterometers (QuikSCAT and SeaWinds). In contrast to the previous studies, we found that the parameters with lower frequency channels of 10 or 19 GHz give higher correlation. This would be because that TBs of lower frequencies, that have less sensitivity to rain than those of higher frequencies, reflect the speed of sea surface wind more directly in the TC case. The highest correlation coefficient obtained is 0.7, and the root mean square error (RMSE) of the regression between a parameter of highest correlation case is found to be 6 ms -1 . We developed a TC intensity estimation method, based on the multiple regression equations using a few parameters. After choosing 3 parameters out of all possible combinations, we computed the regression coefficients and chose 10 regression equations, sorted by the lower RMSEs. Finally, we evaluated our estimation method using independent verification data during 2004. The RMSEs are found to be about 8 ms -1 in the entire basin for the best track data, and about 6 ms -1 for the best track data in the northwestern Pacific. Whereas, for the microwave scatterometer data in all basins RMSE is found to be about 7 ms -1 . We also found that the temporal TC intensity change in our method shows good agreement with the TC best track data.